diff --git a/CHANGELOG.md b/CHANGELOG.md index ac5cb82..e3df255 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,13 +12,15 @@ Here is a template for new release sections ### Added - ### Changed -- +- ### Removed - ``` ## [Unreleased] ### Added +- oemof workshop by @smartie2076 (from https://github.com/smartie2076/oemof_workshop) to rli oemof workshop repro into new folder 3-day-workshop +- csv file with overview over workshops in oemof folder ### Changed @@ -50,4 +52,4 @@ Here is a template for new release sections - CHANGELOG.md - requirements.txt ### Changed -- README.md \ No newline at end of file +- README.md diff --git a/oemof/01_Simple_dispatch_store_results.ipynb b/oemof/1_day_workshop/01_Simple_dispatch_store_results.ipynb similarity index 100% rename from oemof/01_Simple_dispatch_store_results.ipynb rename to oemof/1_day_workshop/01_Simple_dispatch_store_results.ipynb diff --git a/oemof/1_Beispiel_und_Uebung/1_Beispiel.html b/oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Beispiel.html similarity index 100% rename from oemof/1_Beispiel_und_Uebung/1_Beispiel.html rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Beispiel.html diff --git a/oemof/1_Beispiel_und_Uebung/1_Beispiel.ipynb b/oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Beispiel.ipynb similarity index 100% rename from oemof/1_Beispiel_und_Uebung/1_Beispiel.ipynb rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Beispiel.ipynb diff --git a/oemof/1_Beispiel_und_Uebung/1_Uebung_dispatch.py b/oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Uebung_dispatch.py similarity index 100% rename from oemof/1_Beispiel_und_Uebung/1_Uebung_dispatch.py rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Uebung_dispatch.py diff --git a/oemof/1_Beispiel_und_Uebung/1_Uebung_dispatch_loesung.py b/oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Uebung_dispatch_loesung.py similarity index 100% rename from oemof/1_Beispiel_und_Uebung/1_Uebung_dispatch_loesung.py rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/1_Uebung_dispatch_loesung.py diff --git a/oemof/1_Beispiel_und_Uebung/Energiesystem.png b/oemof/1_day_workshop/1_Beispiel_und_Uebung/Energiesystem.png similarity index 100% rename from oemof/1_Beispiel_und_Uebung/Energiesystem.png rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/Energiesystem.png diff --git a/oemof/1_Beispiel_und_Uebung/input_data.csv b/oemof/1_day_workshop/1_Beispiel_und_Uebung/input_data.csv similarity index 100% rename from oemof/1_Beispiel_und_Uebung/input_data.csv rename to oemof/1_day_workshop/1_Beispiel_und_Uebung/input_data.csv diff --git a/oemof/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.html b/oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.html similarity index 100% rename from oemof/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.html rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.html diff --git a/oemof/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.ipynb b/oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.ipynb similarity index 100% rename from oemof/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.ipynb rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Postprocessing_und_Plots.ipynb diff --git a/oemof/2_Beispiel_und_Uebung/2_Uebung_investment.py b/oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_investment.py similarity index 100% rename from oemof/2_Beispiel_und_Uebung/2_Uebung_investment.py rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_investment.py diff --git a/oemof/2_Beispiel_und_Uebung/2_Uebung_investment_loesung.py b/oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_investment_loesung.py similarity index 100% rename from oemof/2_Beispiel_und_Uebung/2_Uebung_investment_loesung.py rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_investment_loesung.py diff --git a/oemof/2_Beispiel_und_Uebung/2_Uebung_postprocessing.py b/oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_postprocessing.py similarity index 100% rename from oemof/2_Beispiel_und_Uebung/2_Uebung_postprocessing.py rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/2_Uebung_postprocessing.py diff --git a/oemof/2_Beispiel_und_Uebung/3_Investoptimierung.html b/oemof/1_day_workshop/2_Beispiel_und_Uebung/3_Investoptimierung.html similarity index 100% rename from oemof/2_Beispiel_und_Uebung/3_Investoptimierung.html rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/3_Investoptimierung.html diff --git a/oemof/2_Beispiel_und_Uebung/3_Investoptimierung.ipynb b/oemof/1_day_workshop/2_Beispiel_und_Uebung/3_Investoptimierung.ipynb similarity index 100% rename from oemof/2_Beispiel_und_Uebung/3_Investoptimierung.ipynb rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/3_Investoptimierung.ipynb diff --git a/oemof/2_Beispiel_und_Uebung/input_data.csv b/oemof/1_day_workshop/2_Beispiel_und_Uebung/input_data.csv similarity index 100% rename from oemof/2_Beispiel_und_Uebung/input_data.csv rename to oemof/1_day_workshop/2_Beispiel_und_Uebung/input_data.csv diff --git a/oemof/3_Weitere_Funktionen/4_Weitere_Funktionen.html b/oemof/1_day_workshop/3_Weitere_Funktionen/4_Weitere_Funktionen.html similarity index 100% rename from oemof/3_Weitere_Funktionen/4_Weitere_Funktionen.html rename to oemof/1_day_workshop/3_Weitere_Funktionen/4_Weitere_Funktionen.html diff --git a/oemof/3_Weitere_Funktionen/4_Weitere_Funktionen.ipynb b/oemof/1_day_workshop/3_Weitere_Funktionen/4_Weitere_Funktionen.ipynb similarity index 100% rename from oemof/3_Weitere_Funktionen/4_Weitere_Funktionen.ipynb rename to oemof/1_day_workshop/3_Weitere_Funktionen/4_Weitere_Funktionen.ipynb diff --git a/oemof/3_Weitere_Funktionen/input_data.csv b/oemof/1_day_workshop/3_Weitere_Funktionen/input_data.csv similarity index 100% rename from oemof/3_Weitere_Funktionen/input_data.csv rename to oemof/1_day_workshop/3_Weitere_Funktionen/input_data.csv diff --git a/oemof/scripts/dispatch_exercise.py b/oemof/1_day_workshop/scripts/dispatch_exercise.py similarity index 100% rename from oemof/scripts/dispatch_exercise.py rename to oemof/1_day_workshop/scripts/dispatch_exercise.py diff --git a/oemof/scripts/dispatch_solution.py b/oemof/1_day_workshop/scripts/dispatch_solution.py similarity index 100% rename from oemof/scripts/dispatch_solution.py rename to oemof/1_day_workshop/scripts/dispatch_solution.py diff --git a/oemof/scripts/dispatch_workshop_results.py b/oemof/1_day_workshop/scripts/dispatch_workshop_results.py similarity index 100% rename from oemof/scripts/dispatch_workshop_results.py rename to oemof/1_day_workshop/scripts/dispatch_workshop_results.py diff --git a/oemof/scripts/invest_exercise.py b/oemof/1_day_workshop/scripts/invest_exercise.py similarity index 100% rename from oemof/scripts/invest_exercise.py rename to oemof/1_day_workshop/scripts/invest_exercise.py diff --git a/oemof/scripts/invest_solution.py b/oemof/1_day_workshop/scripts/invest_solution.py similarity index 100% rename from oemof/scripts/invest_solution.py rename to oemof/1_day_workshop/scripts/invest_solution.py diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/1a_tutorial_dispatch.ipynb b/oemof/3_day_workshop/Day_1_Oemof_Basics/1a_tutorial_dispatch.ipynb new file mode 100644 index 0000000..4f6ff06 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/1a_tutorial_dispatch.ipynb @@ -0,0 +1,772 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Energy system optimisation with oemof" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An energy system should be modelled that satisfies a certain heat and electricity demand using gas, coal, PV and wind plants. The capacities of the plants is known and a dispatch optimization is performed.\n", + "\n", + "The example was edited based on https://github.com/rl-institut/workshop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from oemof.solph import Sink, Source, Transformer, Bus, Flow, EnergySystem, Model\n", + "from oemof.solph.components import GenericStorage\n", + "import oemof.outputlib as outputlib\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an energy system and load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The oemof energy system model has to be initalized with a pandas datetimeindex:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "datetimeindex = pd.date_range('1/1/2016', periods=24*365, freq='H')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00',\n", + " '2016-01-01 02:00:00', '2016-01-01 03:00:00',\n", + " '2016-01-01 04:00:00', '2016-01-01 05:00:00',\n", + " '2016-01-01 06:00:00', '2016-01-01 07:00:00',\n", + " '2016-01-01 08:00:00', '2016-01-01 09:00:00',\n", + " ...\n", + " '2016-12-30 14:00:00', '2016-12-30 15:00:00',\n", + " '2016-12-30 16:00:00', '2016-12-30 17:00:00',\n", + " '2016-12-30 18:00:00', '2016-12-30 19:00:00',\n", + " '2016-12-30 20:00:00', '2016-12-30 21:00:00',\n", + " '2016-12-30 22:00:00', '2016-12-30 23:00:00'],\n", + " dtype='datetime64[ns]', length=8760, freq='H')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energysystem = EnergySystem(timeindex=datetimeindex)\n", + "datetimeindex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading input data, ie. the demands to be supplied (here: thermal and electric demand) and renewable sources." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0demand_thdemand_elwindpv
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\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 demand_th demand_el wind pv\n", + "0 0 0.146945 0.613761 0.236754 0.0\n", + "1 1 0.150044 0.613761 0.240720 0.0\n", + "2 2 0.156926 0.557561 0.327421 0.0\n", + "3 3 0.174050 0.531738 0.402503 0.0\n", + "4 4 0.217514 0.519880 0.428876 0.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filename = 'input_data.csv'\n", + "data = pd.read_csv(filename, sep=\",\")\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get an impression of your demand profiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.index = datetimeindex\n", + "data['demand_th'].plot()\n", + "data['demand_el'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize your energy system\n", + "Use the generic components possible in oemof: Busses, sources, sinks, transformers and storages." + ] + }, + { + "attachments": { + "energysystem.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![energysystem.png](attachment:energysystem.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Buses" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# resource buses\n", + "bus_gas = Bus(label='gas')\n", + "bus_coal = Bus(label='coal')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# electricity and heat buses\n", + "bus_el = Bus(label='electricity')\n", + "bus_th = Bus(label='heat')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create components" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define your energy carrier sources, including their per-unit costs:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "source_gas = Source(label='source_gas', outputs={bus_gas: Flow(variable_costs=0.035*1e6)}) # EUR/GWh\n", + "source_coal = Source(label='source_coal', outputs={bus_coal: Flow(variable_costs=0.02*1e6)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define your renewable sources. They are non-dispatchable (\"fixed=True\") and their effective renewable feedin is calculated by multiplying the timeseries \"actual_value\" by the \"nominal_value\":" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "wind = Source(label='wind', outputs={bus_el: Flow(actual_value=data['wind'],\n", + " nominal_value=60,\n", + " fixed=True)})\n", + "\n", + "pv = Source(label='pv', outputs={bus_el: Flow(actual_value=data['pv'],\n", + " nominal_value=43,\n", + " fixed=True)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define your sinks for electricity and heat demand. Again, as the demand is non-dispatchable, use setting \"fixed=True\"." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "demand_el = Sink(label='demand_el',\n", + " inputs={bus_el: Flow(nominal_value=85,\n", + " actual_value=data['demand_el'],\n", + " fixed=True)})\n", + "\n", + "demand_th = Sink(label='demand_th',\n", + " inputs={bus_th: Flow(nominal_value=40,\n", + " actual_value=data['demand_th'],\n", + " fixed=True)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, introduce the coal and gas plant. They are realized through a transformer object connecting two busses with a certain efficiency, ie. conversion factor. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# coal power plant\n", + "pp_coal = Transformer(label='pp_coal',\n", + " inputs={bus_coal: Flow()},\n", + " outputs={bus_el: Flow(nominal_value=50)},\n", + " conversion_factors={bus_el: 0.39})" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# combined heat and power plant (chp)\n", + "chp_gas = Transformer(label='chp_gas',\n", + " inputs={bus_gas: Flow()},\n", + " outputs={bus_el: Flow(nominal_value=40), # does not have any effect and can be omitted\n", + " bus_th: Flow(nominal_value=40)}, # this constraint binds\n", + " conversion_factors={bus_el: 0.3, bus_th: 0.4})" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "storage_el = GenericStorage(label='storage_el',\n", + " nominal_storage_capacity=1000,\n", + " inputs={bus_el: Flow(nominal_value=9)},\n", + " outputs={bus_el: Flow(nominal_value=9)},\n", + " loss_rate=0.01,\n", + " initial_storage_level=0,\n", + " max_storage_level=0.9,\n", + " inflow_conversion_factor=0.9,\n", + " outflow_conversion_factor=0.9)\n", + "\n", + "storage_th = GenericStorage(label='storage_th',\n", + " nominal_storage_capacity=1000,\n", + " inputs={bus_th: Flow(nominal_value=20)},\n", + " outputs={bus_th: Flow(nominal_value=20)},\n", + " loss_rate=0.01,\n", + " initial_storage_level=0,\n", + " max_storage_level=0.9,\n", + " inflow_conversion_factor=0.9,\n", + " outflow_conversion_factor=0.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid infeasible problems, an excess sink and shortage source can be defined. However, take care that using the shortage source is not cheaper than supplying the electricity from the rest of the system!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "excess_el = Sink(label='excess_el', inputs={bus_el: Flow()})\n", + "\n", + "shortage_el = Source(label='shortage_el',\n", + " outputs={bus_el: Flow(variable_costs=1e15)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add all to the energysystem\n", + "The fuction calls above only created the components, but now they have to be added to the oemof model:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "energysystem.add(bus_coal, bus_gas, bus_el, bus_th,\n", + " source_gas, source_coal,\n", + " wind, pv, demand_el, demand_th,\n", + " pp_coal, chp_gas, \n", + " storage_el, storage_th,\n", + " excess_el, shortage_el)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Optimization Model and solve it" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Problem': [{'Name': 'unknown', 'Lower bound': 1.482357068e+16, 'Upper bound': 1.482357068e+16, 'Number of objectives': 1, 'Number of constraints': 78843, 'Number of variables': 131401, 'Number of nonzeros': 25938, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 2.35, 'Wallclock time': 2.4, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': None, 'Number of created subproblems': None}, 'Black box': {'Number of iterations': 54120}}, 'Error rc': 0, 'Time': 2.412694215774536}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create optimization model based on energy_system\n", + "optimization_model = Model(energysystem=energysystem)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver='cbc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "results_main = outputlib.processing.results(optimization_model)\n", + "results_meta = outputlib.processing.meta_results(optimization_model)\n", + "params = outputlib.processing.parameter_as_dict(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Have a look at some results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'objective': 1.4823570812975064e+16,\n", + " 'problem': {'Name': 'unknown',\n", + " 'Lower bound': 1.482357068e+16,\n", + " 'Upper bound': 1.482357068e+16,\n", + " 'Number of objectives': 1,\n", + " 'Number of constraints': 78843,\n", + " 'Number of variables': 131401,\n", + " 'Number of nonzeros': 25938,\n", + " 'Sense': EnumValue(, 1, 'minimize')},\n", + " 'solver': {'Status': EnumValue(, 0, 'ok'),\n", + " 'User time': -1.0,\n", + " 'System time': 2.35,\n", + " 'Wallclock time': 2.4,\n", + " 'Termination condition': EnumValue(, 8, 'optimal'),\n", + " 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.',\n", + " 'Statistics': {'Branch and bound': {'Number of bounded subproblems': None, 'Number of created subproblems': None}, 'Black box': {'Number of iterations': 54120}},\n", + " 'Error rc': 0,\n", + " 'Time': 2.412694215774536}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_meta" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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variable_nameflow
2016-01-01 00:00:0014.694523
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" + ], + "text/plain": [ + "variable_name flow\n", + "2016-01-01 00:00:00 14.694523\n", + "2016-01-01 01:00:00 15.004446\n", + "2016-01-01 02:00:00 15.692587\n", + "2016-01-01 03:00:00 17.405038\n", + "2016-01-01 04:00:00 21.751368" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_main[bus_gas, chp_gas]['sequences'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot electricity flows in system:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "flows_el=pd.DataFrame(index=datetimeindex)\n", + "flows_el['PV'] = results_main[pv, bus_el]['sequences']\n", + "flows_el['Wind'] = results_main[wind, bus_el]['sequences']\n", + "flows_el['Coal plant'] = results_main[pp_coal, bus_el]['sequences']\n", + "flows_el['CHP'] = results_main[chp_gas, bus_el]['sequences']\n", + "flows_el['Excess'] = results_main[bus_el, excess_el]['sequences']\n", + "flows_el['Shortage'] = results_main[shortage_el, bus_el]['sequences']\n", + "flows_el['Total'] = flows_el.sum(axis=1)\n", + "\n", + "flows_el_percentage=pd.DataFrame(index=datetimeindex)\n", + "for column in flows_el.columns:\n", + " if column != 'Total':\n", + " flows_el_percentage[column]=flows_el[column]/flows_el['Total']\n", + " \n", + "flows_el_percentage.plot.area()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pass results to energysystem.results object before saving" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Problem', 'Solver', 'Solution', 'Main', 'Meta']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energysystem.results['main'] = results_main\n", + "energysystem.results['meta'] = results_meta\n", + "energysystem.results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "energysystem.params = params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save results - Dump the energysystem\n", + "Specify path and filename if you do not want to overwrite" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Attributes dumped to: ./energy.oemof'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energysystem.dump(dpath='./', filename='energy.oemof')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/1b_task_dispatch.py b/oemof/3_day_workshop/Day_1_Oemof_Basics/1b_task_dispatch.py new file mode 100644 index 0000000..66db861 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/1b_task_dispatch.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python + +""" +Edited based on https://github.com/rl-institut/workshop + +This script includes the basic sections necessary for optimizing the operation of an energy system in oemof. Tasks: + +1) Load input data with pandas from input_data.csv +2) To complete the model, + a) create all necessary components of the energy system (compare file energysystem.png) + b) parameterize the components + c) add the components to the energy system +""" + + +import pandas as pd +import matplotlib.pyplot as plt + +from oemof.solph import Sink, Source, Transformer, Bus, Flow, EnergySystem, Model +from oemof.solph.components import GenericStorage +import oemof.outputlib as outputlib + +# ## Specify solver +solver = 'cbc' + +# ## Create an energy system and load data +datetimeindex = pd.date_range('1/1/2016', periods=24*365, freq='H') +energysystem = EnergySystem(timeindex=datetimeindex) + +filename = '' +data = pd.read_csv(filename, sep=",") +# ## Create Buses + + +# ## Create components + + +# ## Add all to the energysystem +energysystem.add() + + +# ## Create an Optimization Model and solve it +# create optimization model based on energy_system +optimization_model = Model(energysystem=energysystem) + +# solve problem +optimization_model.solve(solver=solver) + +# ## Get results +results_main = outputlib.processing.results(optimization_model) +results_meta = outputlib.processing.meta_results(optimization_model) +params = outputlib.processing.parameter_as_dict(energysystem) + +# ## Pass results to energysystem.results object before saving +energysystem.results['main'] = results_main +energysystem.results['meta'] = results_meta +energysystem.params = params + +# ## Save results - Dump the energysystem (to ~/home/user/.oemof by default) +# Specify path and filename if you do not want to overwrite +energysystem.dump(dpath=None, filename=None) + +print(results_meta) + +sequences_el = outputlib.views.node(results_main, 'electricity')['sequences'] +print(sequences_el.head()) diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/1c_task_dispatch_solution.py b/oemof/3_day_workshop/Day_1_Oemof_Basics/1c_task_dispatch_solution.py new file mode 100644 index 0000000..9f62981 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/1c_task_dispatch_solution.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python + +import pandas as pd +import matplotlib.pyplot as plt + +from oemof.solph import Sink, Source, Transformer, Bus, Flow, EnergySystem, Model, Investment +from oemof.solph.components import GenericStorage +import oemof.outputlib as outputlib + +# ## Specify solver +solver = 'cbc' + + +# ## Create an energy system and load data +datetimeindex = pd.date_range('1/1/2016', periods=24*365, freq='H') +energysystem = EnergySystem(timeindex=datetimeindex) + +filename = 'input_data.csv' +data = pd.read_csv(filename, sep=",") +data = data.dropna(axis=1) +print(data.head()) + +# ## Create Buses + +# resource buses +bus_gas = Bus(label='gas') +bus_coal = Bus(label='coal') + +# electricity and heat buses +bus_el = Bus(label='electricity') +bus_th = Bus(label='heat') + +# ## Create components +source_gas = Source(label='source_gas', outputs={bus_gas: Flow(variable_costs=30)}) +source_coal = Source(label='source_coal', outputs={bus_coal: Flow(variable_costs=30)}) + +# Renewable feedin +wind = Source(label='wind', outputs={bus_el: Flow( + actual_value=data['wind'], nominal_value=66.3, fixed=True)}) + +pv = Source(label='pv', outputs={bus_el: Flow( + actual_value=data['pv'], nominal_value=65.3, fixed=True)}) + +# Electricity demand +demand_el = Sink(label='demand_el', inputs={bus_el: Flow( + nominal_value=85, actual_value=data['demand_el'], fixed=True)}) + + +# power plants +pp_coal = Transformer(label='pp_coal', + inputs={bus_coal: Flow()}, + outputs={bus_el: Flow(nominal_value=40, + emission_factor=0.335)}, + conversion_factors={bus_el: 0.39}) + +storage_el = GenericStorage(label='storage_el', + invest=Investment(ep_cost=412), + inputs={bus_el: Flow(nominal_value=200)}, + outputs={bus_el: Flow(nominal_value=200)}, + loss_rate=0.01, + initial_storage_level=0, + max_storage_level=0.9, + inflow_conversion_factor=0.9, + outflow_conversion_factor=0.9) + +# an excess and a shortage variable can help to avoid infeasible problems +excess_el = Sink(label='excess_el', inputs={bus_el: Flow()}) + +shortage_el = Source(label='shortage_el', + outputs={bus_el: Flow(variable_costs=100000)}) + + +# ## Add all to the energysystem +energysystem.add(bus_coal, bus_gas, bus_el, + source_gas, source_coal, + wind, pv, demand_el, + pp_coal, storage_el, + excess_el, shortage_el) + + +# ## Create an Optimization Model and solve it +# create optimization model based on energy_system +optimization_model = Model(energysystem=energysystem) + +# solve problem +optimization_model.solve(solver=solver) + +# ## Get results +results_main = outputlib.processing.results(optimization_model) +results_meta = outputlib.processing.meta_results(optimization_model) +params = outputlib.processing.parameter_as_dict(energysystem) + +# ## Pass results to energysystem.results object before saving +energysystem.results['main'] = results_main +energysystem.results['meta'] = results_meta +energysystem.params = params + +# ## Save results - Dump the energysystem (to ~/home/user/.oemof by default) +# Specify path and filename if you do not want to overwrite +energysystem.dump(dpath=None, filename=None) + +print(results_meta) + +sequences_el = outputlib.views.node(results_main, 'electricity')['sequences'] +print(sequences_el.head()) \ No newline at end of file diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/2a_tutorial_investment_optimization.ipynb b/oemof/3_day_workshop/Day_1_Oemof_Basics/2a_tutorial_investment_optimization.ipynb new file mode 100644 index 0000000..c5d9f33 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/2a_tutorial_investment_optimization.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Investment optimization with oemof" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the optimal capacities of the energy system that was only dispatch optimized earlier will be determined.\n", + "\n", + "Loading all necessary packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from oemof.solph import (Sink, Source, Transformer, Bus, Flow, Model,\n", + " EnergySystem, Investment)\n", + "\n", + "import oemof.outputlib as outputlib\n", + "import oemof.solph as solph\n", + "from oemof.tools import economics\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reading all data and initializing the EnergySystem object:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "datetimeindex = pd.date_range('1/1/2016', periods=240, freq='H')\n", + "filename = 'input_data.csv'\n", + "data = pd.read_csv(filename, sep=',')\n", + "energysystem = EnergySystem(timeindex=datetimeindex)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define all components that are not to be capacity, ie. investment optimized as before." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# buses\n", + "bcoal = Bus(label='coal')\n", + "bgas = Bus(label='gas')\n", + "bel = Bus(label='electricity')\n", + "energysystem.add(bcoal, bgas, bel)\n", + "\n", + "# sources\n", + "energysystem.add(Source(label='coal_source', outputs={bcoal: Flow(variable_costs=25*0.39)}))\n", + "\n", + "energysystem.add(Source(label='gas_source', outputs={bgas: Flow(variable_costs=40*0.5)}))\n", + "\n", + "# sources\n", + "energysystem.add(Source(label='wind', outputs={bel: Flow(actual_value=data['wind'], \n", + " nominal_value=66.3, \n", + " fixed=True)}))\n", + "\n", + "energysystem.add(Source(label='pv', outputs={bel: Flow(actual_value=data['pv'], \n", + " nominal_value=65.3, \n", + " fixed=True)}))\n", + "\n", + "# excess and shortage to avoid infeasibilies\n", + "energysystem.add(Sink(label='excess_el', inputs={bel: Flow()}))\n", + "energysystem.add(Source(label='shortage_el',\n", + " outputs={bel: Flow(variable_costs=100000000)}))\n", + "\n", + "# demands (electricity/heat)\n", + "energysystem.add(Sink(label='demand_el', \n", + " inputs={bel: Flow(nominal_value=85, \n", + " actual_value=data['demand_el'], \n", + " fixed=True)}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate annuity of investments into coal and gas power plant:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "epc_coal = economics.annuity(capex=1500000, n=50, wacc=0.05)\n", + "epc_gas = economics.annuity(capex=900000, n=20, wacc=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Introduce investment-objects to components that are to be optimized:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# power plants\n", + "energysystem.add(Transformer(\n", + " label='pp_coal',\n", + " inputs={bcoal: Flow()},\n", + " outputs={bel: Flow(investment=Investment(ep_costs=epc_coal,maximum=5e9, existing=0))},\n", + " conversion_factors={bel: 0.39}))\n", + "\n", + "energysystem.add(Transformer(\n", + " label='pp_gas',\n", + " inputs={bgas: Flow()},\n", + " outputs={bel: Flow(investment=Investment(ep_costs=epc_gas,maximum=5e9, existing=0))},\n", + " conversion_factors={bel: 0.50}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optimize energy system:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Problem': [{'Name': 'unknown', 'Lower bound': 5959590.209, 'Upper bound': 5959590.209, 'Number of objectives': 1, 'Number of constraints': 1681, 'Number of variables': 1923, 'Number of nonzeros': 704, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 0.01, 'Wallclock time': 0.01, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': None, 'Number of created subproblems': None}, 'Black box': {'Number of iterations': 469}}, 'Error rc': 0, 'Time': 0.019908905029296875}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create optimization model based on energy_system\n", + "optimization_model = Model(energysystem=energysystem)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver='cbc',\n", + " solve_kwargs={'tee': False, 'keepfiles': False})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot results:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = outputlib.processing.results(optimization_model)\n", + "\n", + "results_el = outputlib.views.node(results, 'electricity')\n", + "\n", + "el_sequences = results_el['sequences']\n", + "\n", + "to_el = {key[0][0]: key for key in el_sequences.keys() if key[0][1] == 'electricity' and key[1] == 'flow'}\n", + "to_el = [to_el.pop('pv'), to_el.pop('wind')] + list(to_el.values())\n", + "el_prod = el_sequences[to_el]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity', 'demand_el'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "el_sequences[(('electricity', 'excess_el'), 'flow')].plot(ax=ax, linewidth=3)\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Capacity coal: 0.0 \n", + "Capacity gas: 76.56623\n" + ] + } + ], + "source": [ + "cap_coal = results_el['scalars'][(('pp_coal', 'electricity'), 'invest')]\n", + "cap_gas = results_el['scalars'][(('pp_gas', 'electricity'), 'invest')]\n", + "print('Capacity coal: ' + str(cap_coal) + ' \\nCapacity gas: ' + str(cap_gas))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((electricity, demand_el), flow) 15091.323571\n", + "((electricity, excess_el), flow) 43.203459\n", + "((pp_coal, electricity), flow) 0.000000\n", + "((pp_gas, electricity), flow) 10752.625856\n", + "((pv, electricity), flow) 809.758253\n", + "((shortage_el, electricity), flow) 0.000000\n", + "((wind, electricity), flow) 3572.142914\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "el_sequences.sum(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/2b_task_investment.py b/oemof/3_day_workshop/Day_1_Oemof_Basics/2b_task_investment.py new file mode 100644 index 0000000..c536ae7 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/2b_task_investment.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python + +""" +Edited based on https://github.com/rl-institut/workshop + +This script provides the basic structure for perfoming an investment optimization with oemof. Tasks + +1) Read input data with pandas +2) Complete the model by +a) Modifying installed capacities manually while avoiding shortage +b) Inluding an invest-object to each fossil fuelled power plant to optimize their capacity + +""" + +import pandas as pd +import matplotlib.pyplot as plt + +from oemof.solph import (Sink, Source, Transformer, Bus, Flow, Model, + EnergySystem, Investment, NonConvex) +import oemof.outputlib as outputlib +from oemof.tools import economics + +solver = 'cbc' + +# initialize energysytem +datetimeindex = pd.date_range('1/1/2016', periods=24*365, freq='H') +energysystem = EnergySystem(timeindex=datetimeindex) + +# load data +filename = '' +data = pd.read_csv() +print(data.head()) + +# create buses +bus_coal = Bus(label='coal', balanced=False) +bus_gas = Bus(label='gas', balanced=False) +bus_el = Bus(label='electricity') + +# create sources +wind = Source(label='wind', + outputs={bus_el: Flow(actual_value=data['wind'], + nominal_value=1, + fixed=True)}) + +pv = Source(label='pv', + outputs={bus_el: Flow(actual_value=data['pv'], + nominal_value=1, + fixed=True)}) + +# create excess and shortage to avoid infeasibilies +excess = Sink(label='excess_el', + inputs={bus_el: Flow()}) + +shortage = Source(label='shortage_el', + outputs={bus_el: Flow(variable_costs=1e12)}) + +# create demand +demand_el = Sink(label='demand_el', + inputs={bus_el: Flow(nominal_value=1, + actual_value=data['demand_el'], + fixed=True)}) + +epc_coal = economics.annuity(capex=1500000, n=50, wacc=0.05) +epc_gas = economics.annuity(capex=900000, n=20, wacc=0.05) + +# create power plants +pp_coal = Transformer(label='pp_coal', + inputs={bus_coal: Flow()}, + outputs={bus_el: Flow(nominal_value=5000, + variable_costs=25)}, + conversion_factors={bus_el: 0.39}) + +pp_gas = Transformer(label='pp_gas', + inputs={bus_gas: Flow()}, + outputs={bus_el: Flow(nominal_value=2000, + variable_costs=40)}, + conversion_factors={bus_el: 0.50}) + +# add all components to energysystem +energysystem.add(bus_coal, bus_gas, bus_el, + wind, pv, excess, shortage, demand_el, + pp_coal, pp_gas) + + +# create optimization model based on energy_system +optimization_model = Model(energysystem=energysystem) + +# solve problem +optimization_model.solve(solver=solver, + solve_kwargs={'tee': False, 'keepfiles': False}) + + +# postprocessing +results = outputlib.processing.results(optimization_model) +string_results = outputlib.processing.convert_keys_to_strings(results) + +results_investment = pd.DataFrame({key: value['scalars'] for key, value in string_results.items() if hasattr(value['scalars'], 'invest')}) +print(results_investment) + +el_sequences = outputlib.views.node(results, 'electricity')['sequences'] + +sorted_sequences = pd.DataFrame() +for column in el_sequences.columns: + sorted_sequences[column]=sorted(el_sequences[column], reverse=True) + +fig, ax = plt.subplots(figsize=(8,3)) +sorted_sequences.plot(ax=ax, linewidth=3) +ax.set_ylabel('Power in kW') +ax.set_xlabel('Sorted hours') +ax.set_title('Electricity: Annual load duration curve') +legend = plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # place legend outside of plot +plt.tight_layout() +plt.show() \ No newline at end of file diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/2c_task_investment_solution.py b/oemof/3_day_workshop/Day_1_Oemof_Basics/2c_task_investment_solution.py new file mode 100644 index 0000000..c3001dc --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/2c_task_investment_solution.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python + +import pandas as pd +import matplotlib.pyplot as plt + +from oemof.solph import (Sink, Source, Transformer, Bus, Flow, Model, + EnergySystem, Investment) +import oemof.outputlib as outputlib +from oemof.tools import economics + +solver = 'cbc' + +# initialize energysytem +datetimeindex = pd.date_range('1/1/2016', periods=24*365, freq='H') +energysystem = EnergySystem(timeindex=datetimeindex) + +# load data +filename = '' +data = pd.read_csv(filename, sep=';', decimal=',') +print(data.head()) + +# create buses +bus_coal = Bus(label='coal', balanced=False) +bus_gas = Bus(label='gas', balanced=False) +bus_el = Bus(label='electricity') + +# create sources +wind = Source(label='wind', + outputs={bus_el: Flow(actual_value=data['wind'], + nominal_value=1, + fixed=True)}) + +pv = Source(label='pv', + outputs={bus_el: Flow(actual_value=data['pv'], + nominal_value=1, + fixed=True)}) + +# create excess and shortage to avoid infeasibilies +excess = Sink(label='excess_el', + inputs={bus_el: Flow()}) + +shortage = Source(label='shortage_el', + outputs={bus_el: Flow(variable_costs=1e12)}) + +# create demand +demand_el = Sink(label='demand_el', + inputs={bus_el: Flow(nominal_value=1, + actual_value=data['demand_el'], + fixed=True)}) + +epc_coal = economics.annuity(capex=1500000, n=50, wacc=0.05) +epc_gas = economics.annuity(capex=900000, n=20, wacc=0.05) + +# create power plants +pp_coal = Transformer(label='pp_coal', + inputs={bus_coal: Flow()}, + outputs={bus_el: Flow(investment=Investment(ep_costs=epc_coal, + maximum=5e9, + existing=0), + variable_costs=25)}, + conversion_factors={bus_el: 0.39}) + +pp_gas = Transformer(label='pp_gas', + inputs={bus_gas: Flow()}, + outputs={bus_el: Flow(investment=Investment(ep_costs=epc_gas, + maximum=5e9, + existing=0), + variable_costs=40)}, + conversion_factors={bus_el: 0.50}) + +# add all components to energysystem +energysystem.add(bus_coal, bus_gas, bus_el, + wind, pv, excess, shortage, demand_el, + pp_coal, pp_gas) + + +# create optimization model based on energy_system +optimization_model = Model(energysystem=energysystem) + +# solve problem +optimization_model.solve(solver=solver, + solve_kwargs={'tee': False, 'keepfiles': False}) + + +# postprocessing +results = outputlib.processing.results(optimization_model) +string_results = outputlib.processing.convert_keys_to_strings(results) + +results_investment = pd.DataFrame({key: value['scalars'] for key, value in string_results.items() if hasattr(value['scalars'], 'invest')}) +print(results_investment) + +el_sequences = outputlib.views.node(results, 'electricity')['sequences'] + +sorted_sequences = pd.DataFrame() +for column in el_sequences.columns: + sorted_sequences[column]=sorted(el_sequences[column], reverse=True) + +fig, ax = plt.subplots(figsize=(8,3)) +sorted_sequences.plot(ax=ax, linewidth=3) +ax.set_ylabel('Power in kW') +ax.set_xlabel('Sorted hours') +ax.set_title('Electricity: Annual load duration curve') +legend = plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # place legend outside of plot +plt.tight_layout() +plt.show() \ No newline at end of file diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/3a_Postprocessing_und_Plots.ipynb b/oemof/3_day_workshop/Day_1_Oemof_Basics/3a_Postprocessing_und_Plots.ipynb new file mode 100644 index 0000000..17527cf --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/3a_Postprocessing_und_Plots.ipynb @@ -0,0 +1,1560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Postprocessing and plots\n", + "\n", + "The outputlib module\n", + "https://oemof.readthedocs.io/en/stable/api/oemof.outputlib.html\n", + "\n", + "Copied from: https://github.com/rl-institut/workshop\n", + "Not discussed in oemof workshop." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports pandas, matplotlib and oemof" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rcParams\n", + "\n", + "import oemof.solph as solph\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools.economics import annuity" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "rcParams['figure.figsize'] = [20.0, 7.0]\n", + "rcParams['font.size'] = 25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restore the energysystem with results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Attributes restored from: ./energy.oemof'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energysystem = solph.EnergySystem()\n", + "energysystem.restore(dpath='./', filename='energy.oemof')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter the dictionary using outputlib\n", + "### Get all the flows into and out of the electricity bus\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['scalars', 'sequences'])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labeled_results = outputlib.processing.convert_keys_to_strings(energysystem.results['main'])\n", + "labeled_results[('chp_gas', 'electricity')].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "results_bus_el = outputlib.views.node(energysystem.results['main'], 'electricity')['sequences']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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(chp_gas, electricity)(electricity, demand_el)(electricity, excess_el)(electricity, storage_el)(pp_coal, electricity)(pv, electricity)(shortage_el, electricity)(storage_el, electricity)(wind, electricity)
timeindex
2016-01-01 00:00:004.40835752.1696530.00.033.5560640.00.00.014.205231
2016-01-01 01:00:004.50133452.1696530.00.033.2250890.00.00.014.443230
2016-01-01 02:00:004.70777647.3926440.00.023.0396200.00.00.019.645248
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\n", + "
" + ], + "text/plain": [ + " (chp_gas, electricity) (electricity, demand_el) \\\n", + "timeindex \n", + "2016-01-01 00:00:00 4.408357 52.169653 \n", + "2016-01-01 01:00:00 4.501334 52.169653 \n", + "2016-01-01 02:00:00 4.707776 47.392644 \n", + "2016-01-01 03:00:00 5.221512 45.197772 \n", + "2016-01-01 04:00:00 6.525410 44.189789 \n", + "\n", + " (electricity, excess_el) (electricity, storage_el) \\\n", + "timeindex \n", + "2016-01-01 00:00:00 0.0 0.0 \n", + "2016-01-01 01:00:00 0.0 0.0 \n", + "2016-01-01 02:00:00 0.0 0.0 \n", + "2016-01-01 03:00:00 0.0 0.0 \n", + "2016-01-01 04:00:00 0.0 0.0 \n", + "\n", + " (pp_coal, electricity) (pv, electricity) \\\n", + "timeindex \n", + "2016-01-01 00:00:00 33.556064 0.0 \n", + "2016-01-01 01:00:00 33.225089 0.0 \n", + "2016-01-01 02:00:00 23.039620 0.0 \n", + "2016-01-01 03:00:00 15.826051 0.0 \n", + "2016-01-01 04:00:00 11.931793 0.0 \n", + "\n", + " (shortage_el, electricity) (storage_el, electricity) \\\n", + "timeindex \n", + "2016-01-01 00:00:00 0.0 0.0 \n", + "2016-01-01 01:00:00 0.0 0.0 \n", + "2016-01-01 02:00:00 0.0 0.0 \n", + "2016-01-01 03:00:00 0.0 0.0 \n", + "2016-01-01 04:00:00 0.0 0.0 \n", + "\n", + " (wind, electricity) \n", + "timeindex \n", + "2016-01-01 00:00:00 14.205231 \n", + "2016-01-01 01:00:00 14.443230 \n", + "2016-01-01 02:00:00 19.645248 \n", + "2016-01-01 03:00:00 24.150209 \n", + "2016-01-01 04:00:00 25.732585 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# results_bus_th = outputlib.views.node(energysystem.results['main'], 'heat')['sequences']\n", + "results_bus_el.columns = [col[0] for col in results_bus_el.columns]\n", + "results_bus_el.index.name = 'timeindex'\n", + "results_bus_el.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare input parameters for postprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys([(\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", \"\"), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None), (\"\", None)])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = energysystem.params\n", + "params.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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fixedfrommaxminnegative_gradient_costspositive_gradient_coststovariable_costsnominal_valuebalanced...conversion_factors_electricityconversion_factors_gasconversion_factors_heatinflow_conversion_factorinitial_storage_levelloss_ratemax_storage_levelmin_storage_levelnominal_storage_capacityoutflow_conversion_factor
label
(coal, pp_coal)Falsecoal1.00.00.00.0pp_coal0.0NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(gas, chp_gas)Falsegas1.00.00.00.0chp_gas0.0NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(electricity, demand_el)Trueelectricity1.00.00.00.0demand_el0.085.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(electricity, storage_el)Falseelectricity1.00.00.00.0storage_el0.09.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(electricity, excess_el)Falseelectricity1.00.00.00.0excess_el0.0NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " fixed from max min \\\n", + "label \n", + "(coal, pp_coal) False coal 1.0 0.0 \n", + "(gas, chp_gas) False gas 1.0 0.0 \n", + "(electricity, demand_el) True electricity 1.0 0.0 \n", + "(electricity, storage_el) False electricity 1.0 0.0 \n", + "(electricity, excess_el) False electricity 1.0 0.0 \n", + "\n", + " negative_gradient_costs positive_gradient_costs \\\n", + "label \n", + "(coal, pp_coal) 0.0 0.0 \n", + "(gas, chp_gas) 0.0 0.0 \n", + "(electricity, demand_el) 0.0 0.0 \n", + "(electricity, storage_el) 0.0 0.0 \n", + "(electricity, excess_el) 0.0 0.0 \n", + "\n", + " to variable_costs nominal_value \\\n", + "label \n", + "(coal, pp_coal) pp_coal 0.0 NaN \n", + "(gas, chp_gas) chp_gas 0.0 NaN \n", + "(electricity, demand_el) demand_el 0.0 85.0 \n", + "(electricity, storage_el) storage_el 0.0 9.0 \n", + "(electricity, excess_el) excess_el 0.0 NaN \n", + "\n", + " balanced ... conversion_factors_electricity \\\n", + "label ... \n", + "(coal, pp_coal) NaN ... NaN \n", + "(gas, chp_gas) NaN ... NaN \n", + "(electricity, demand_el) NaN ... NaN \n", + "(electricity, storage_el) NaN ... NaN \n", + "(electricity, excess_el) NaN ... NaN \n", + "\n", + " conversion_factors_gas conversion_factors_heat \\\n", + "label \n", + "(coal, pp_coal) NaN NaN \n", + "(gas, chp_gas) NaN NaN \n", + "(electricity, demand_el) NaN NaN \n", + "(electricity, storage_el) NaN NaN \n", + "(electricity, excess_el) NaN NaN \n", + "\n", + " inflow_conversion_factor initial_storage_level \\\n", + "label \n", + "(coal, pp_coal) NaN NaN \n", + "(gas, chp_gas) NaN NaN \n", + "(electricity, demand_el) NaN NaN \n", + "(electricity, storage_el) NaN NaN \n", + "(electricity, excess_el) NaN NaN \n", + "\n", + " loss_rate max_storage_level min_storage_level \\\n", + "label \n", + "(coal, pp_coal) NaN NaN NaN \n", + "(gas, chp_gas) NaN NaN NaN \n", + "(electricity, demand_el) NaN NaN NaN \n", + "(electricity, storage_el) NaN NaN NaN \n", + "(electricity, excess_el) NaN NaN NaN \n", + "\n", + " nominal_storage_capacity outflow_conversion_factor \n", + "label \n", + "(coal, pp_coal) NaN NaN \n", + "(gas, chp_gas) NaN NaN \n", + "(electricity, demand_el) NaN NaN \n", + "(electricity, storage_el) NaN NaN \n", + "(electricity, excess_el) NaN NaN \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_params_to_dataframe(params):\n", + " r\"\"\"\n", + " Collect all parameters in one pd.DataFrame\n", + " \"\"\" \n", + " param_df = pd.DataFrame()\n", + " for key in params.keys():\n", + " new_row = params[key]['scalars']\n", + " label = tuple(map(str, key)) if isinstance(key, tuple) else str(key)\n", + " new_row['label'] = label\n", + " new_row['from'] = label[0]\n", + " new_row['to'] = label[1]\n", + " param_df = param_df.append(new_row, ignore_index=True)\n", + " \n", + " param_df['fixed'] = param_df['fixed'].astype('bool')\n", + " param_df = param_df[param_df.columns.drop('label').insert(0, 'label')]\n", + " param_df = param_df.set_index('label')\n", + " return param_df\n", + "\n", + "params_df = convert_params_to_dataframe(params)\n", + "params_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define color dictionary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use color palette generators to generate suitable colors, e.g.: \n", + "http://javier.xyz/cohesive-colors/ \n", + "https://colourco.de/ \n", + "http://seaborn.pydata.org/tutorial/color_palettes.html " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "cdict = {('chp_gas', 'electricity'): '#eeac7e',\n", + " ('pp_coal', 'electricity'): '#0f2e2e',\n", + " ('pv', 'electricity'): '#ffde32',\n", + " ('wind', 'electricity'): '#4ca7c3',\n", + " ('electricity', 'demand_el'): '#000000',\n", + " ('electricity', 'storage_el'): '#E04644',\n", + " ('storage_el', 'electricity'): '#B7D968',\n", + " ('electricity', 'excess_el'): '#C748E2',\n", + " ('shortage_el', 'electricity'): '#B576AD'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot renewable feedin and demand" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seq_re = results_bus_el[[('pv', 'electricity'), ('wind', 'electricity')]]\n", + "seq_demand = results_bus_el[('electricity', 'demand_el')]\n", + "\n", + "range_low = 1\n", + "range_high = 1000\n", + "\n", + "fig, ax = plt.subplots()\n", + "color = [cdict[column] for column in seq_re.columns]\n", + "seq_demand[range_low:range_high].plot(ax=ax, linewidth=3, color='k')\n", + "seq_re[range_low:range_high].plot.area(ax=ax, color=color)\n", + "ax.set_ylim(-90, 130)\n", + "ax.set_ylabel('Power in GW')\n", + "ax.set_xlabel('Time')\n", + "ax.set_title('Electricity demand and renewable feedin')\n", + "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # place legend outside of plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot all dispatch" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "produced_el = results_bus_el.drop(('electricity', 'demand_el'), axis=1)\n", + "produced_el = produced_el.clip(lower=0)\n", + "produced_el[('electricity', 'storage_el')] *= -1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_columns = [('electricity', 'storage_el'),\n", + " ('storage_el', 'electricity'),\n", + " ('wind', 'electricity'),\n", + " ('pv', 'electricity'),\n", + " ('pp_coal', 'electricity'),\n", + " ('chp_gas', 'electricity'),\n", + " ('electricity', 'excess_el'),\n", + " ('shortage_el', 'electricity')]\n", + "\n", + "produced_el = produced_el[sorted_columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "color = [cdict[column] for column in produced_el.columns]\n", + "produced_el[range_low:range_high].plot.area(ax=ax, color=color)\n", + "seq_demand[range_low:range_high].plot(c='k', linewidth=3)\n", + "ax.set_ylim(-90, 130)\n", + "ax.set_ylabel('Power in GW')\n", + "ax.set_xlabel('Time')\n", + "ax.set_title('Electricity demand and generation')\n", + "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # place legend outside of plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Jahresdauerlinie" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_sequences = pd.DataFrame()\n", + "for column in results_bus_el.columns:\n", + " sorted_sequences[column]=sorted(results_bus_el[column], reverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "color = [cdict[column] for column in sorted_sequences.columns]\n", + "sorted_sequences.plot(ax=ax, color=color, linewidth=5)\n", + "ax.set_ylabel('Power in GW')\n", + "ax.set_xlabel('Sorted hours')\n", + "ax.set_title('Electricity: Annual load duration curve')\n", + "legend = plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # place legend outside of plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate metrics and indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installierte Leistung für Technologie i\n", + "$$P_{inst,i}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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fixedfrommaxminnegative_gradient_costspositive_gradient_coststovariable_costsnominal_valuebalanced...conversion_factors_electricityconversion_factors_gasconversion_factors_heatinflow_conversion_factorinitial_storage_levelloss_ratemax_storage_levelmin_storage_levelnominal_storage_capacityoutflow_conversion_factor
label
(wind, electricity)Truewind1.00.00.00.0electricity0.000000e+0060.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(pv, electricity)Truepv1.00.00.00.0electricity0.000000e+0043.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(pp_coal, electricity)Falsepp_coal1.00.00.00.0electricity0.000000e+0050.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(chp_gas, electricity)Falsechp_gas1.00.00.00.0electricity0.000000e+0040.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(storage_el, electricity)Falsestorage_el1.00.00.00.0electricity0.000000e+009.0NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(shortage_el, electricity)Falseshortage_el1.00.00.00.0electricity1.000000e+15NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

6 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " fixed from max min \\\n", + "label \n", + "(wind, electricity) True wind 1.0 0.0 \n", + "(pv, electricity) True pv 1.0 0.0 \n", + "(pp_coal, electricity) False pp_coal 1.0 0.0 \n", + "(chp_gas, electricity) False chp_gas 1.0 0.0 \n", + "(storage_el, electricity) False storage_el 1.0 0.0 \n", + "(shortage_el, electricity) False shortage_el 1.0 0.0 \n", + "\n", + " negative_gradient_costs positive_gradient_costs \\\n", + "label \n", + "(wind, electricity) 0.0 0.0 \n", + "(pv, electricity) 0.0 0.0 \n", + "(pp_coal, electricity) 0.0 0.0 \n", + "(chp_gas, electricity) 0.0 0.0 \n", + "(storage_el, electricity) 0.0 0.0 \n", + "(shortage_el, electricity) 0.0 0.0 \n", + "\n", + " to variable_costs nominal_value \\\n", + "label \n", + "(wind, electricity) electricity 0.000000e+00 60.0 \n", + "(pv, electricity) electricity 0.000000e+00 43.0 \n", + "(pp_coal, electricity) electricity 0.000000e+00 50.0 \n", + "(chp_gas, electricity) electricity 0.000000e+00 40.0 \n", + "(storage_el, electricity) electricity 0.000000e+00 9.0 \n", + "(shortage_el, electricity) electricity 1.000000e+15 NaN \n", + "\n", + " balanced ... conversion_factors_electricity \\\n", + "label ... \n", + "(wind, electricity) NaN ... NaN \n", + "(pv, electricity) NaN ... NaN \n", + "(pp_coal, electricity) NaN ... NaN \n", + "(chp_gas, electricity) NaN ... NaN \n", + "(storage_el, electricity) NaN ... NaN \n", + "(shortage_el, electricity) NaN ... NaN \n", + "\n", + " conversion_factors_gas conversion_factors_heat \\\n", + "label \n", + "(wind, electricity) NaN NaN \n", + "(pv, electricity) NaN NaN \n", + "(pp_coal, electricity) NaN NaN \n", + "(chp_gas, electricity) NaN NaN \n", + "(storage_el, electricity) NaN NaN \n", + "(shortage_el, electricity) NaN NaN \n", + "\n", + " inflow_conversion_factor initial_storage_level \\\n", + "label \n", + "(wind, electricity) NaN NaN \n", + "(pv, electricity) NaN NaN \n", + "(pp_coal, electricity) NaN NaN \n", + "(chp_gas, electricity) NaN NaN \n", + "(storage_el, electricity) NaN NaN \n", + "(shortage_el, electricity) NaN NaN \n", + "\n", + " loss_rate max_storage_level min_storage_level \\\n", + "label \n", + "(wind, electricity) NaN NaN NaN \n", + "(pv, electricity) NaN NaN NaN \n", + "(pp_coal, electricity) NaN NaN NaN \n", + "(chp_gas, electricity) NaN NaN NaN \n", + "(storage_el, electricity) NaN NaN NaN \n", + "(shortage_el, electricity) NaN NaN NaN \n", + "\n", + " nominal_storage_capacity \\\n", + "label \n", + "(wind, electricity) NaN \n", + "(pv, electricity) NaN \n", + "(pp_coal, electricity) NaN \n", + "(chp_gas, electricity) NaN \n", + "(storage_el, electricity) NaN \n", + "(shortage_el, electricity) NaN \n", + "\n", + " outflow_conversion_factor \n", + "label \n", + "(wind, electricity) NaN \n", + "(pv, electricity) NaN \n", + "(pp_coal, electricity) NaN \n", + "(chp_gas, electricity) NaN \n", + "(storage_el, electricity) NaN \n", + "(shortage_el, electricity) NaN \n", + "\n", + "[6 rows x 21 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "to_bus_el = [i[1]=='electricity' for i in params_df.index]\n", + "params_df_to_el = params_df.loc[to_bus_el]\n", + "params_df_to_el" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "label\n", + "(chp_gas, electricity) 40.0\n", + "(pp_coal, electricity) 50.0\n", + "(pv, electricity) 43.0\n", + "(storage_el, electricity) 9.0\n", + "(wind, electricity) 60.0\n", + "Name: installed_capacity, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_inst = params_df_to_el['nominal_value'].loc[params_df_to_el['nominal_value']>0]\n", + "p_inst = p_inst.sort_index()\n", + "p_inst = p_inst.rename('installed_capacity')\n", + "p_inst" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,3))\n", + "color = [cdict[label] for label in p_inst.index]\n", + "x = [label[0] for label in p_inst.index]\n", + "height = p_inst.values\n", + "plt.bar(x=x, height=height, color=color)\n", + "ax.set_ylabel('Power in GW')\n", + "ax.set_xlabel('Technology')\n", + "ax.set_title('Electricity: Installed capacity')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jährliche Energiemenge für Technologie i\n", + "$$E_{ges,i} = \\sum_t P_{t,i} \\cdot \\Delta t$$" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(chp_gas, electricity) 36016.100699\n", + "(pp_coal, electricity) 321693.382017\n", + "(pv, electricity) 46995.301771\n", + "(shortage_el, electricity) 14.823550\n", + "(storage_el, electricity) 9450.284847\n", + "(wind, electricity) 134168.601611\n", + "Name: yearly_energy, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "to_bus_el = [column for column in results_bus_el.columns if column[1]=='electricity']\n", + "yearly_energy = results_bus_el[to_bus_el].sum()\n", + "yearly_energy = yearly_energy.sort_index()\n", + "yearly_energy = yearly_energy.rename('yearly_energy')\n", + "yearly_energy" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,3))\n", + "x = [label[0] for label in yearly_energy.index]\n", + "height = yearly_energy.values\n", + "color = [cdict[label] for label in yearly_energy.index]\n", + "plt.bar(x=x, height=height, color=color)\n", + "ax.set_ylabel('Energy in GWh')\n", + "ax.set_xlabel('Technology')\n", + "ax.set_title('Electricity: yearly energy')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Volllaststunden von Technologie i\n", + "$$t_{Volllast, i} = \\frac{E_{ges,i}}{P_{inst,i}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(chp_gas, electricity) 900.402517\n", + "(pp_coal, electricity) 6433.867640\n", + "(pv, electricity) 1092.913995\n", + "(shortage_el, electricity) NaN\n", + "(storage_el, electricity) 1050.031650\n", + "(wind, electricity) 2236.143360\n", + "Name: full_load_hours, dtype: float64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_load_hours = yearly_energy * 1/p_inst\n", + "full_load_hours = full_load_hours.rename('full_load_hours')\n", + "full_load_hours" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deckungsgrad von Technologie i\n", + "$$\\frac{E_{ges,i}}{\\sum_i E_{ges,i}}, \\ for \\ i \\ \\neq \\ storage $$" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(chp_gas, electricity) 0.066834\n", + "(pp_coal, electricity) 0.596958\n", + "(pv, electricity) 0.087208\n", + "(shortage_el, electricity) 0.000028\n", + "(wind, electricity) 0.248973\n", + "Name: coverage_ratio, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_production = yearly_energy.drop(('storage_el', 'electricity'))\n", + "coverage_ratio = (yearly_production * 1/(yearly_production.sum()))\n", + "coverage_ratio = coverage_ratio.rename('coverage_ratio')\n", + "coverage_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Speicherzyklen\n", + "https://github.com/oemof/cydets\n", + "\n", + "Dambrowski, Jonny; Pichlmaier, Simon & Jossen, Andreas. Mathematical methods for classification of state-of-charge time series for cycle lifetime prediction. Advanced Automotive Battery Conference. Mainz, Germany. 2012." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'cydets'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcydets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdetect_cycles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mstate_of_charge_el\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstring_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'storage_el'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'None'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sequences'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'capacity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cydets'" + ] + } + ], + "source": [ + "from cydets.algorithm import detect_cycles\n", + "\n", + "\n", + "state_of_charge_el = string_results[('storage_el', 'None')]['sequences']['capacity']\n", + "\n", + "cycles = detect_cycles(state_of_charge_el)\n", + "print(len(cycles))\n", + "cycles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "range_low = 1\n", + "range_high = 3000\n", + "\n", + "fig, ax = plt.subplots()\n", + "charge_el = charge_el.clip(lower=0)\n", + "charge_el[range_low:range_high].plot.area(ax=ax)\n", + "for timepoint in cycles['t_start']:\n", + " ax.axvline(x=timepoint, c='r', alpha=0.3)\n", + "ax.set_ylabel('State of Charge in GWh')\n", + "ax.set_xlabel('Time')\n", + "ax.set_title('Charging profile of electricity storage and detected cycles')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gesamte Treibhausgasemissionen\n", + "\n", + "Zur Verteilung von Emissionen auf Strom und Wärme siehe diesen Vergleich verschiedener Allokationsmethoden:\n", + "\n", + "https://www.ffe.de/download/wissen/334_Allokationsmethoden_CO2/ET_Allokationsmethoden_CO2.pdf\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emission_factors = params_df['emission_factor'].loc[params_df['emission_factor']>0]\n", + "emission_factors # kgCO2/kWh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "string_results = outputlib.processing.convert_keys_to_strings(energysystem.results['main'])\n", + "flows_with_emissions = pd.concat([string_results[i]['sequences']['flow'].rename(i) for i in emission_factors.index], axis=1)\n", + "flows_with_emissions.sum() # GWh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emissions = emission_factors * flows_with_emissions.sum()\n", + "emissions = emissions.rename('emissions')\n", + "emissions # kgCO2/kWh * GWh = 1e6 t CO2\n", + "# 0.8 Gt CO2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gesamtkosten Betrieb\n", + "$$\\sum_{i} (C_{FOM,i} + \\sum_t P_{t, i} C_{VOM})$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "variable_costs = params_df['variable_costs'][params_df['variable_costs']>0]\n", + "variable_costs # Eur/GWh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flows_with_variable_costs = pd.concat([string_results[i]['sequences']['flow'].rename(i) for i in variable_costs.index], axis=1)\n", + "summed_variable_costs = variable_costs * flows_with_variable_costs.sum()\n", + "summed_variable_costs = summed_variable_costs.rename('summed_variable_costs') \n", + "summed_variable_costs # Eur" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gesamtkosten Investition\n", + "$$\\sum_{i} C_{capital,i} P_{inst,i}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "overnight_costs = pd.Series({('chp_gas', 'electricity'): 888.5, # EUR/kWh\n", + " ('pp_coal', 'electricity'): 1554.8,\n", + " ('pv', 'electricity'): 1000,\n", + " ('storage_el', 'electricity'): 552,\n", + " ('wind', 'electricity'): 1377.1}).rename('overnight_cost')\n", + "\n", + "overnight_costs = overnight_costs * 1e6 # kwh -> GWh\n", + "\n", + "investment_periods = pd.Series({('chp_gas', 'electricity'): 30,\n", + " ('pp_coal', 'electricity'): 40,\n", + " ('pv', 'electricity'): 25,\n", + " ('storage_el', 'electricity'): 10,\n", + " ('wind', 'electricity'): 25}).rename('investment_period')\n", + "\n", + "wacc = 0.05\n", + "def get_annuity(df):\n", + " return annuity(df['overnight_cost'], df['investment_period'], df['wacc'])\n", + "\n", + "annuity_calculation = pd.concat([overnight_costs, investment_periods], axis=1)\n", + "annuity_calculation['wacc'] = wacc\n", + "annuity_calculation['annuity'] = annuity_calculation.apply(get_annuity, axis=1)\n", + "annuity_calculation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "investment_costs = annuity_calculation['annuity'] * p_inst\n", + "investment_costs = investment_costs.rename('investment_costs')\n", + "investment_costs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gesamtübersicht" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.concat([p_inst,\n", + " yearly_energy,\n", + " full_load_hours,\n", + " coverage_ratio,\n", + " emissions,\n", + " summed_variable_costs,\n", + " investment_costs],\n", + " sort=True,\n", + " axis=1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/3b_task_postprocessing.py b/oemof/3_day_workshop/Day_1_Oemof_Basics/3b_task_postprocessing.py new file mode 100644 index 0000000..cdcd400 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/3b_task_postprocessing.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python + +# Postprocessing and plots + +""" +Edited from: https://github.com/rl-institut/workshop + +This script presents the basic structure for a dispatch and investment optimization with oemof. Tasks: + +1) Load the stored energy system +2) Filter the results to display + a) Dispatch timeseries of dispatchable plants + b) Timeseries for storage charge and discharge + c) Timeseries of stored capacity (SOC) + +3) Filter the parameters to display + a) installed capacities + b) variable costs + +4) Calculate annual sums of all electricity generating units + +5) Plot + a) The energy fed in by each plant + b) The energy fed in by each plant within a week + c) A barplot of the installed capacities. + + +""" + +import pandas as pd +import matplotlib.pyplot as plt + +import oemof.solph as solph +import oemof.outputlib as outputlib + +# ## Restore the energysystem with results +energysystem = solph.EnergySystem() +energysystem.restore(dpath=None, filename=None) +results = energysystem.results['main'] + +# ### Get all the flows into and out of the electricity bus +results_bus_el = outputlib.views.node(results, 'electricity')['sequences'] +results_bus_el.columns = [col[0] for col in results_bus_el.columns] +results_bus_el.index.name = 'timeindex' + +# ### Prepare input parameters for postprocessing +params = energysystem.params + +# ## Define color dictionary +cdict = {('chp_gas', 'electricity'): '#eeac7e', + ('pp_coal', 'electricity'): '#0f2e2e', + ('pv', 'electricity'): '#ffde32', + ('wind', 'electricity'): '#4ca7c3', + ('electricity', 'demand_el'): '#000000', + ('electricity', 'storage_el'): '#E04644', + ('storage_el', 'electricity'): '#B7D968', + ('electricity', 'excess_el'): '#C748E2', + ('shortage_el', 'electricity'): '#B576AD'} diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/4a_other_functions.ipynb b/oemof/3_day_workshop/Day_1_Oemof_Basics/4a_other_functions.ipynb new file mode 100644 index 0000000..88ec901 --- /dev/null +++ b/oemof/3_day_workshop/Day_1_Oemof_Basics/4a_other_functions.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More features\n", + "\n", + "Copied from: https://github.com/rl-institut/workshop\n", + "Not discussed in oemof workshop. Includes advanced network graphing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import pyomo.environ as po\n", + "\n", + "from oemof.solph import (Sink, Source, Transformer, Bus, Flow, Model,\n", + " EnergySystem, Investment, NonConvex)\n", + "import oemof.outputlib as outputlib\n", + "import oemof.solph as solph\n", + "import oemof.graph as graph\n", + "from oemof.tools import economics\n", + "\n", + "solver = 'cbc'\n", + "\n", + "%matplotlib inline\n", + "matplotlib.rcParams['figure.figsize'] = [8.0, 6.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "color_dict ={\n", + " 'coal': '#755d5d',\n", + " 'gas': '#c76c56',\n", + " 'oil': '#494a19',\n", + " 'lignite': '#56201d',\n", + " 'wind': '#4ca7c3',\n", + " 'pv': '#ffde32',\n", + " 'excess_el': '#9a9da1',\n", + " 'pp_coal': '#755d5d',\n", + " 'pp_gas': '#c76c56',\n", + " 'pp_chp': '#eeac7e',\n", + " 'b_heat_source': '#cd3333',\n", + " 'heat_source': '#cd3333',\n", + " 'heat_pump': '#42c77a',\n", + " 'electricity': '#0079ff',\n", + " 'demand_el': '#0079ff',\n", + " 'shortage_el': '#ff2626',\n", + " 'excess_el': '#ff2626',\n", + " 'biomass': '#01b42e',\n", + " 'pp_biomass': '#01b42e'}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_graph(energysystem, edge_labels=True, node_color='#eeac7e',\n", + " edge_color='#eeac7e', plot=True, node_size=5500,\n", + " with_labels=True, arrows=True, layout='dot'):\n", + " \n", + " grph = graph.create_nx_graph(energysystem)\n", + " \n", + " if type(node_color) is dict:\n", + " node_color = [node_color.get(g, '#AFAFAF') for g in grph.nodes()]\n", + "\n", + " # set drawing options\n", + " options = {\n", + " 'prog': 'dot',\n", + " 'with_labels': with_labels,\n", + " 'node_color': node_color,\n", + " 'edge_color': edge_color,\n", + " 'node_size': node_size,\n", + " 'arrows': arrows,\n", + " 'font_size': 12,\n", + " 'font_color': 'w'\n", + " }\n", + "\n", + " # draw graph\n", + " pos = nx.drawing.nx_agraph.graphviz_layout(grph, prog=layout)\n", + "\n", + " nx.draw(grph, pos=pos, **options)\n", + "\n", + " # add edge labels for all edges\n", + " if edge_labels is True and plt:\n", + " labels = nx.get_edge_attributes(grph, 'weight')\n", + " nx.draw_networkx_edge_labels(grph, pos=pos, edge_labels=labels)\n", + "\n", + " # show output\n", + " if plot is True:\n", + " plt.show()\n", + "\n", + "def initialize_basic_energysystem(debug=False):\n", + " if debug:\n", + " periods = 2\n", + " else:\n", + " periods = 240\n", + " # initialize and provide data\n", + " datetimeindex = pd.date_range('1/1/2016', periods=periods, freq='H')\n", + " filename = 'input_data.csv'\n", + " data = pd.read_csv(filename, sep=',')\n", + " energysystem = EnergySystem(timeindex=datetimeindex)\n", + "\n", + " # buses\n", + " bcoal = Bus(label='coal', balanced=False)\n", + " bgas = Bus(label='gas', balanced=False)\n", + " bel = Bus(label='electricity')\n", + " energysystem.add(bcoal, bgas, bel)\n", + "\n", + " # sources\n", + " energysystem.add(Source(label='wind', outputs={bel: Flow(\n", + " actual_value=data['wind'], nominal_value=66.3, fixed=True)}))\n", + "\n", + " energysystem.add(Source(label='pv', outputs={bel: Flow(\n", + " actual_value=data['pv'], nominal_value=65.3, fixed=True)}))\n", + "\n", + " # excess and shortage to avoid infeasibilies\n", + " energysystem.add(Sink(label='excess_el', inputs={bel: Flow()}))\n", + " energysystem.add(Source(label='shortage_el',\n", + " outputs={bel: Flow(variable_costs=100000)}))\n", + "\n", + " # demands (electricity/heat)\n", + " energysystem.add(Sink(label='demand_el', inputs={bel: Flow(\n", + " nominal_value=85, actual_value=data['demand_el'], fixed=True)}))\n", + " \n", + " return bcoal, bgas, bel, energysystem\n", + "\n", + "\n", + "def postprocess_and_plot(optimization_model):\n", + " results = outputlib.processing.results(optimization_model)\n", + "\n", + " results_el = outputlib.views.node(results, 'electricity')\n", + "\n", + " el_sequences = results_el['sequences']\n", + "\n", + " to_el = {key[0][0]: key for key in el_sequences.keys() if key[0][1] == 'electricity' and key[1] == 'flow'}\n", + " to_el = [to_el.pop('pv'), to_el.pop('wind')] + list(to_el.values())\n", + " el_prod = el_sequences[to_el]\n", + "\n", + " fig, ax = plt.subplots(figsize=(14, 3))\n", + " for key in el_sequences.keys():\n", + " color_dict[key] = color_dict[key[0][0]]\n", + " c=[color_dict.get(x, '#333333') for x in el_prod.columns]\n", + " el_prod.plot.area(ax=ax, color=c)\n", + " el_sequences[(('electricity', 'demand_el'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + " legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Class Nonconvex: Minimum load" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem()\n", + "\n", + "pp_gas = solph.Transformer(label='pp_gas',\n", + " inputs={bgas: Flow()},\n", + " outputs={bel: Flow(nominal_value=80,\n", + " nonconvex=NonConvex(),\n", + " min=0.5)},\n", + " conversion_factors={bel: 0.3})\n", + "\n", + "energysystem.add(pp_gas)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "('requires pygraphviz ', 'http://pygraphviz.github.io/')", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/media/mh/Daten/PycharmProjects/oemof_workshop/venv_ubuntu/lib/python3.6/site-packages/networkx/drawing/nx_agraph.py\u001b[0m in \u001b[0;36mpygraphviz_layout\u001b[0;34m(G, prog, root, args)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpygraphviz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 284\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pygraphviz'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdraw_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menergysystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mdraw_graph\u001b[0;34m(energysystem, edge_labels, node_color, edge_color, plot, node_size, with_labels, arrows, layout)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;31m# draw graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnx_agraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphviz_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrph\u001b[0m\u001b[0;34m,\u001b[0m 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create optimization model based on energy_system\n", + "optimization_model = Model(energysystem=energysystem)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver=solver,\n", + " solve_kwargs={'tee': False, 'keepfiles': False},\n", + " tcmdline_options={'AllowableGap': 0.01})\n", + "\n", + "postprocess_and_plot(optimization_model)\n", + "\n", + "results = outputlib.processing.convert_keys_to_strings(outputlib.processing.results(optimization_model))\n", + "\n", + "fig2, ax2 = plt.subplots(figsize=(14, 3))\n", + "results[('pp_gas','electricity')]['sequences']['flow'].plot(ax=ax2, label=(('pp_gas','electricity'),'flow'))\n", + "legend = ax2.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Startup costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem()\n", + "\n", + "pp_gas = Source(label='pp_gas',\n", + " outputs={bel: solph.Flow(nominal_value=30,\n", + " variable_costs=15)})\n", + "\n", + "# shutdown costs only work in combination with a minimum load\n", + "# since otherwise the status variable is \"allowed\" to be active i.e.\n", + "# it permanently has a value of one which does not allow to set the shutdown\n", + "# variable which is set to one if the status variable changes from one to zero\n", + "\n", + "pp_coal = Source(label='pp_coal',\n", + " outputs={bel: solph.Flow(nominal_value=60, \n", + " min=0.5, \n", + " max=1.0, \n", + " variable_costs=10,\n", + " nonconvex=solph.NonConvex(startup_costs=80,\n", + " shutdown_costs=80))})\n", + "\n", + "energysystem.add(pp_coal, pp_gas)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create optimization model based on energy_system\n", + "optimization_model = Model(energysystem=energysystem)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver=solver,\n", + " solve_kwargs={'tee': False, 'keepfiles': False})\n", + "\n", + "postprocess_and_plot(optimization_model)\n", + "\n", + "results = outputlib.processing.convert_keys_to_strings(outputlib.processing.results(optimization_model))\n", + "\n", + "fig2, ax2 = plt.subplots(figsize=(14, 3))\n", + "results[('pp_coal','electricity')]['sequences']['flow'].plot(ax=ax2, label=(('pp_coal','electricity'),'flow'))\n", + "legend = ax2.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Minimum up and down times" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem()\n", + "\n", + "pp_coal = Source(label='pp_coal',\n", + " outputs={bel: solph.Flow(nominal_value=60,\n", + " min=0.5,\n", + " max=1.0,\n", + " variable_costs=10,\n", + " nonconvex=solph.NonConvex(minimum_downtime=1,\n", + " initial_status=0))})\n", + "\n", + "pp_gas = Source(label='pp_gas',\n", + " outputs={bel: solph.Flow(nominal_value=60,\n", + " min=0.5,\n", + " max=1.0,\n", + " variable_costs=10,\n", + " nonconvex=solph.NonConvex(minimum_uptime=1,\n", + " initial_status=1))})\n", + "\n", + "energysystem.add(pp_coal, pp_gas)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create optimization model based on energy_system\n", + "optimization_model = Model(energysystem=energysystem)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver=solver,\n", + " solve_kwargs={'tee': False, 'keepfiles': False})\n", + "\n", + "postprocess_and_plot(optimization_model)\n", + "\n", + "results = outputlib.processing.convert_keys_to_strings(outputlib.processing.results(optimization_model))\n", + "\n", + "fig2, axes = plt.subplots(2, 1, figsize=(14, 5))\n", + "results[('pp_coal','electricity')]['sequences']['flow'].plot(ax=axes[0], label=(('pp_coal','electricity'),'flow'))\n", + "results[('pp_gas','electricity')]['sequences']['flow'].plot(ax=axes[1], label=(('pp_gas','electricity'),'flow'))\n", + "for i in [0, 1]:\n", + " axes[i].legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional constraints\n", + "### Emission constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem()\n", + "\n", + "# power plants\n", + "energysystem.add(Transformer(\n", + " label='pp_coal',\n", + " inputs={bcoal: Flow()},\n", + " outputs={bel: Flow(nominal_value=20.2, variable_costs=25)},\n", + " conversion_factors={bel: 0.39}))\n", + "\n", + "pp_gas = Transformer(\n", + " label='pp_gas',\n", + " inputs={bgas: Flow()},\n", + " outputs={bel: Flow(nominal_value=41, variable_costs=40)},\n", + " conversion_factors={bel: 0.50})\n", + "energysystem.add(pp_gas)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_graph(energysystem, node_color=color_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the model\n", + "optimization_model = Model(energysystem)\n", + "\n", + "emission_limit = 8000\n", + "emission_factor = {}\n", + "emission_factor['gas'] = 0.27 # tCO2/MWh\n", + "emission_factor['coal'] = 0.39 # tCO2/MWh\n", + "\n", + "# add specific emission values to flow objects if source is a commodity bus\n", + "for s, t in optimization_model.flows.keys():\n", + " if s is bgas:\n", + " optimization_model.flows[s, t].emission_factor = emission_factor['gas']\n", + " if s is bcoal:\n", + " optimization_model.flows[s, t].emission_factor = emission_factor['coal']\n", + "\n", + "# Add a new pyomo Block\n", + "myblock = po.Block()\n", + "\n", + "# pyomo does not need a po.Set, we can use a simple list as well\n", + "myblock.COMMODITYFLOWS = [k for (k, v) in optimization_model.flows.items()\n", + " if hasattr(v, 'emission_factor')]\n", + "\n", + "# add emission constraint\n", + "myblock.emission_constr = po.Constraint(expr=(\n", + " sum(optimization_model.flow[i, o, t] *\n", + " optimization_model.flows[i, o].emission_factor\n", + " for (i, o) in myblock.COMMODITYFLOWS\n", + " for t in optimization_model.TIMESTEPS) <= emission_limit))\n", + "\n", + "# add the sub-model to the oemof Model instance\n", + "optimization_model.add_component('MyBlock', myblock)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# solve problem\n", + "optimization_model.solve(solver=solver,\n", + " solve_kwargs={'tee': False, 'keepfiles': False})\n", + "\n", + "postprocess_and_plot(optimization_model)\n", + "\n", + "results = outputlib.processing.convert_keys_to_strings(outputlib.processing.results(optimization_model))\n", + "emissions = {k[0]: v['sequences']['flow'].sum() * emission_factor[k[0]] for k, v in results.items() if (k[0] == 'gas') or (k[0] == 'coal')}\n", + "\n", + "print('Emissions: ', emissions)\n", + "print('Total emissions: ', sum(emissions.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myblock.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for v in optimization_model.component_objects(po.Constraint, descend_into=True):\n", + "# print(\"FOUND VAR:\" + v.name)\n", + "# print('\\n ')\n", + "# v.pprint()\n", + "# print('\\n ')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overall investment constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem()\n", + "\n", + "investment_max = 90000\n", + "\n", + "epc_c = economics.annuity(capex=10000, n=20, wacc=0.05)\n", + "epc_g = economics.annuity(capex=20000, n=20, wacc=0.05)\n", + "\n", + "# power plants\n", + "energysystem.add(Transformer(\n", + " label='pp_coal',\n", + " inputs={bcoal: Flow()},\n", + " outputs={bel: Flow(investment=solph.Investment(ep_costs=epc_c, maximum=30),\n", + " variable_costs=25)},\n", + " conversion_factors={bel: 0.39}))\n", + "\n", + "pp_gas = Transformer(\n", + " label='pp_gas',\n", + " inputs={bgas: Flow()},\n", + " outputs={bel: Flow(investment=solph.Investment(ep_costs=epc_g),\n", + " variable_costs=40)},\n", + " conversion_factors={bel: 0.50})\n", + "energysystem.add(pp_gas)\n", + "\n", + "# create the model\n", + "optimization_model = Model(energysystem)\n", + "\n", + "solph.constraints.investment_limit(optimization_model, investment_max)\n", + "\n", + "# solve problem\n", + "optimization_model.solve(solver=solver,\n", + " solve_kwargs={'tee': False, 'keepfiles': False})\n", + "\n", + "postprocess_and_plot(optimization_model)\n", + "\n", + "results = outputlib.processing.convert_keys_to_strings(outputlib.processing.results(optimization_model))\n", + "new_capacity = {k[0]: v['scalars'][0] for k, v in results.items() if (k[0] == 'pp_gas') or (k[0] == 'pp_coal')}\n", + "\n", + "costs = {}\n", + "costs['pp_coal'] = new_capacity['pp_coal'] * epc_c\n", + "costs['pp_gas'] = new_capacity['pp_gas'] * epc_g\n", + "print('new capacity: ')\n", + "for k, v in new_capacity.items():\n", + " print(' ', k, v)\n", + "print('\\nannualized costs :')\n", + "for k, v in costs.items():\n", + " print(' ', k, v)\n", + "print(' sum: ', sum(costs.values()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Debugging" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import oemof.tools.helpers\n", + "\n", + "bcoal, bgas, bel, energysystem = initialize_basic_energysystem(debug=True)\n", + "\n", + "pp_gas = solph.Transformer(label='pp_gas',\n", + " inputs={bgas: Flow()},\n", + " outputs={bel: Flow(nominal_value=80,\n", + " nonconvex=NonConvex(),\n", + " min=0.5)},\n", + " conversion_factors={bel: 0.3})\n", + "\n", + "energysystem.add(pp_gas)\n", + "\n", + "# create the model\n", + "optimization_model = Model(energysystem)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filename = os.path.join(oemof.tools.helpers.extend_basic_path('lp_files'),\n", + " 'lp-file.lp')\n", + "filename" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "optimization_model.write(filename, io_options={'symbolic_solver_labels': True})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/energy.oemof b/oemof/3_day_workshop/Day_1_Oemof_Basics/energy.oemof new file mode 100644 index 0000000..55e2057 Binary files /dev/null and b/oemof/3_day_workshop/Day_1_Oemof_Basics/energy.oemof differ diff --git a/oemof/3_day_workshop/Day_1_Oemof_Basics/energysystem.png b/oemof/3_day_workshop/Day_1_Oemof_Basics/energysystem.png new file mode 100644 index 0000000..13c0c11 Binary files /dev/null and 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+++ b/oemof/3_day_workshop/Day_2_Components_Oemof/1_oemof_basic_component.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Oemof Component Models\n", + "\n", + "**Please note, that this tutorial refers to v:0.3.1 Buggy Battery and its licenced by GPL v3.0. The concept was created by @srhbrnds in Sep 2019**\n", + "\n", + "Component Models in omeof are categorized in three different categories: \n", + "* Basic (oemof.solph.network)\n", + "* Components (oemof.solph.components)\n", + "* Custom (oemof.solph.custom)\n", + "\n", + "![Oemof components classification](./graphics/oemof_component_classification_inkscape.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Components\n", + "\n", + "* **Sink:**\n", + "object with n-inputs / \n", + "examples: demand, excess electricity\n", + "![Sink Component](./graphics/components_basic_sink.svg)\n", + "\n", + "### Lets build a Sink component model together in oemof! \n", + "We model the electricity demand (demand_el) as a Sink and assign a fixed output timeseries to it.\n", + "\n", + "To do so, we need to do the initialization first!\n", + "\n", + "**1)** we collect our necessary tools (import packages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import pandas as pd\n", + "import oemof.solph as solph\n", + "from oemof.tools import economics\n", + "import oemof.outputlib as outputlib\n", + "import logging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2)** we initiaize the EnergySystem in oemof " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize a timeindex with the help of pandas.date_range() for one year (2017) \n", + "# with hourly time increments\n", + "\n", + "timeindex = pd.date_range('1/1/2017', periods=8760, freq='H')\n", + "\n", + "# Initialize the energy system and hand over the timeindex\n", + "es = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# Initialize a first node, the electricity bus (bus_el)\n", + "bel=solph.Bus(label='bus_el')\n", + "\n", + "es.add(bel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3)** read in a data set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# cost dictionary\n", + "costs = {'pp_pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)},\n", + " 'pp_diesel': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05),\n", + " 'var': 0}}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#read in a dataset as pandas DataFrame\n", + "data=pd.read_csv('./1_timeseries.csv')\n", + "data.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4)** we create a Sink obejct and assign the variable name and specify the label as *demand_el*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Create Sink object for demand_el\n", + "demand=solph.Sink(label='demand_el', inputs={bel:solph.Flow()})\n", + "\n", + "if isinstance(demand,solph.Sink):\n", + " print(\"Congrats you created a Sink object! Add a timeseries to the input Flow object\") \n", + "else:\n", + " print(\"Something went wrong! Try it again\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Create Sink object for demand_el with parameterization in the Flow object\n", + "demand=solph.Sink(label='demand_el', inputs={bel:solph.Flow(actual_value=data['demand_el'],\n", + " nominal_value=500, \n", + " fixed=True)})\n", + "\n", + "# add demand to the EnergySystem \n", + "es.add(demand)\n", + "\n", + "# Create Sink object for excess electricty\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "es.add(excess_sink)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Source:**\n", + "object with m-outputs / examples: fuel commodities, PV systems\n", + "\n", + "![Source Component](./graphics/components_basic_source.svg)\n", + "\n", + "### Lets build a Source component model together in oemof! \n", + "We model the pv output (pv) as a Source and assign a fixed output timeseries to it. \n", + "\n", + "**Hint!** You can download RE time series from here: https://www.renewables.ninja/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pv_plant= solph.Source(label='pp_pv',\n", + " outputs={bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=data['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pp_pv']['epc']))})\n", + "\n", + "es.add(pv_plant)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "* **Transformer:** \n", + "object with n-inputs and m-outputs and an input-output relation\n", + "\n", + "![Transformer Component](./graphics/components_basic_transformer_io_relation.svg)\n", + "\n", + "### Lets build a Transformer component model together in oemof! \n", + "We model a diesel generator as a transformer. The generator runs on diesel from a fuel commodity and transfers it with a constant conversion factor of 33%. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#add a gas Bus as input for the transformer\n", + "bfuel=solph.Bus(label='bus_diesel')\n", + "\n", + "# create a Source for the fuel source as input for the diesel genset\n", + "fuel_source=solph.Source(label='diesel',outputs={bfuel:solph.Flow()})\n", + "\n", + "#create a Transformer for the diesel_genset\n", + "genset=solph.Transformer(label=\"diesel_genset\",\n", + " inputs={bfuel: solph.Flow(variable_costs=0.03)},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['pp_diesel']['var'],\n", + " investment=solph.Investment(maximum=300,\n", + " ep_costs=costs['pp_diesel']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "#Add multiple components to the EnergySystem\n", + "es.add(genset, fuel_source, bfuel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the energy system model\n", + "\n", + "Now we created a simple microgrid that will be translated into a operational model (m) in the next step and than solved by the open source solver cbc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('\\n')\n", + "logging.info('Initializing model')\n", + "m = solph.Model(es)\n", + "\n", + "# m.write(filename, io_options={'symbolic_solver_labels': True})\n", + "\n", + "logging.info('Starting oemof-optimization of capacities')\n", + "m.solve(solver='cbc', solve_kwargs={'tee': False})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Postprocessing the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = outputlib.processing.results(m)\n", + "\n", + "el_bus = outputlib.views.node(results, 'bus_el')\n", + "cap_pv = el_bus['scalars'][(('pp_pv', 'bus_el'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('diesel_genset', 'bus_el'), 'invest')]\n", + "\n", + "print('PV capacity in [kW]:' + str(cap_pv))\n", + "print('Diesel genset capacity in [kW]:' + str(cap_genset))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "outputlib.views.node(results, 'bus_el')['sequences'].plot(drawstyle='steps')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + 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b/oemof/3_day_workshop/Day_2_Components_Oemof/2a_task_micro_grid.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Capacity and dispatch optimization of a micro grid - Task" + ] + }, + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optimize the capacities and the dispatch of a micro grid system that can be described with the following design:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)\n", + "\n", + "Its electricity demand and renewable potential is described in \"2_timeseries.csv\". Assume following parameters:\n", + "* project lifetime of 20 a\n", + "* wacc of 0.05\n", + "* PV plant lifetime of 20a, with 750 Euro/kWp\n", + "* Wind plant lifetime of 20a, with 1000 Euro/kWp\n", + "* Storage with 5a lifetime, and 300 Euro/kWh\n", + "* Diesel generator with 15a lifetime, 500 Euro/kW, efficiency 33%, fuel costs of 0.05 Euro/kWh(el)\n", + "\n", + "Tasks:\n", + "* Download the renewable feedin of a location of your choosing from renewables ninjas (take care of time zone changes - renewables ninjas uses a germany-centric timestap - and that PV generation actually takes place during the day.)\n", + "* Build the energy model shown above. Optimize the energy system for two the locations described by the demand profiles \"2_micro_grid_demand_large.csv\" and \"2_micro_grid_demand_medium.csv\", using the renewable feedin that you downloaded. For that:\n", + " * Create a csv with wind potential, renewable feedin and demand \n", + " * Create and run a model to optimize only the dispatch of the micro grid by guessing the components capacities (for that, plot the demand timeseries)\n", + " * Create and run a model to optimize the capacities of the micro grid's components and their dispatch.\n", + " \n", + "* Add a grid connection (feed-in with 0.08 Euro/kWh and consumption of 0.20 Euro/kWh) and optimize your energy systems.\n", + "* Optinal task: Add a generator with minimal loading and try to optimize its capacities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_2_Components_Oemof/2b_task_micro_grid_solution.ipynb b/oemof/3_day_workshop/Day_2_Components_Oemof/2b_task_micro_grid_solution.ipynb new file mode 100644 index 0000000..dd3253b --- /dev/null +++ b/oemof/3_day_workshop/Day_2_Components_Oemof/2b_task_micro_grid_solution.ipynb @@ -0,0 +1,405 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Capacity and dispatch optimization of a micro grid - Solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Task: Optimize the capacities and the dispatch of a micro grid that can be described with the timeseries file \"2_timeseries.csv\" and below design. " + ] + }, + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import all necessary packages (here: including logging)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00:31:55-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "00:31:55-INFO-Used oemof version: 0.3.1\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from oemof.tools import logger\n", + "import logging\n", + "\n", + "logger.define_logging(screen_level=logging.INFO) #screen_level=logging.DEBUG\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.outputlib as outputlib\n", + "import oemof.solph as solph\n", + "from oemof.tools import economics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize your energy system:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "duration_hours = 24*1\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read data and process economic values:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00:31:57-INFO-Loading timeseries\n", + "00:31:57-INFO-Defining costs\n" + ] + } + ], + "source": [ + "logging.info('Loading timeseries')\n", + "full_filename = '1_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create oemof model:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "00:31:57-INFO-DEFINITION OF OEMOF MODEL:\n", + "00:31:57-INFO-Electricity bus\n", + "00:31:57-INFO-Demand, fixed timeseries\n", + "00:31:57-INFO-Excess sink\n", + "00:31:57-INFO-Wind plant with fixed feed-in timeseries\n", + "00:31:57-INFO-PV plant with fixed feed-in timeseries\n", + "00:31:57-INFO-Diesel fuel bus, source and transformer\n", + "00:31:57-INFO-Battery storage\n" + ] + } + ], + "source": [ + "print('\\n')\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh,\n", + " )}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, # or None\n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve model:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "00:31:57-INFO-Initializing model\n", + "00:31:57-INFO-Starting oemof-optimization of capacities\n", + "00:31:57-INFO-Optimization successful...\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Name': 'unknown', 'Lower bound': 764.0731071, 'Upper bound': 764.0731071, 'Number of objectives': 1, 'Number of constraints': 293, 'Number of variables': 224, 'Number of nonzeros': 28, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 0.0, 'Wallclock time': 0.0, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': None, 'Number of created subproblems': None}, 'Black box': {'Number of iterations': 85}}, 'Error rc': 0, 'Time': 0.013462066650390625}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('\\n')\n", + "logging.info('Initializing model')\n", + "m = solph.Model(energysystem)\n", + "\n", + "# m.write(filename, io_options={'symbolic_solver_labels': True})\n", + "\n", + "logging.info('Starting oemof-optimization of capacities')\n", + "m.solve(solver='cbc', solve_kwargs={'tee': False})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process results - optimal capacities:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00:31:57-INFO-Processing results\n", + "00:31:57-INFO-Get optimized capacities\n", + "00:31:57-INFO-Capacities optimized: Storage (303.44179), Wind (1128.5236), PV (613.21074), Genset (66.607433).\n" + ] + } + ], + "source": [ + "logging.info('Processing results')\n", + "results = outputlib.processing.results(m)\n", + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "\n", + "el_storage = outputlib.views.node(results, 'storage')\n", + "cap_storage = el_storage['scalars'][(('storage', 'None'), 'invest')] # Divided by c-rate charge\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process results - energy flows:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00:31:57-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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0yHRMoVDgb3/7GzZt2gSplDvbDCUGgwFHjhzBtm3bsGvXLrS1tV3XZtSoUVi/fj0efvhhjBo1ygxVUl81abTY81YWakubEPNwCAKmu5u7pB4xACDqOwYAw0D55XpkHi5EXkYFBEGA/3Q3TI7xg9p3ZO6NTEREg++rr77C448/jtraWtOx0NBQ7NixA6GhoWasjPpDTU0NvvjiC2zbtg0ZGRnXnRcEAdHR0diwYQNWrFgBGxsbM1RJt0pT2Yzv38hEc4MOix+dCL8QV3OX1CsGAER9xwBgGKmvakFWQhEupJRCrzXAJ9gZk2P84DfBxeLmcBER0fBQW1uLJ554Al988YXpmCAI2Lx5M1588UVYWVmZsToaCJmZmdi2bRs+/fTTLoFPJycnJzzwwAPYsGEDpk6dys8gFqrqagO+fzMLRoMRS58Mg8cYR3OXdEMMAIj6jgHAMKRt1uF8cgnOJBShSdMGZ087TI7xRdCdHhazjQsREQ19hw4dwvr161FcXGw6Nnr0aHzyyScIDw83Y2U0GFpbW/H9999j27ZtOHjwILr7TBkaGooNGzbgwQcfhEqlMkOV1J2SnFrE/fsMFDYyLHt6Mlw8h8ZOHAwAiPqOAcAwZtAbkZtejtOHi1B9tRE2DgqERvlgYoQ3rO3l5i6PiIiGqObmZvzxj3/EW2+91eX4hg0b8K9//QsODg5mqozMpaioCNu3b8dHH32E/Pz8687L5XLcfffd2LBhAxYsWMD1IMzoclYlDnxwHg6u1lj29GQoXYbO2lEMAIj6jgHACCCKIq5m1yLzcCEKz9dAJpcgeLYnwqJ94eRma+7yiIhoCElPT8fatWuRnZ1tOqZWq/H+++/j7rvvNmNlZAmMRiOSk5Oxbds2fP311112gujk7e2NdevWYf369fD39zdDlSPXTydKcfTTbKj9lFj6ZChs7BXmLumWMAAg6jsGACNMdXEjMo8U4dIPZTAaRIwNU2NyjC88xjlyjh4REfVIr9fj73//O1566SXo9XrT8eXLl+P999+Hm5ubGasjS6TRaPCf//wH27Ztw8mTJ7ttExERgQ0bNuDee++Fnd3QGIY+VGUcvILUnXnwHe+MRY9OGpLbRzMAIOo7BgAjVJNGi7PHruJcYjG0zXq4j3HA5Bg/jJ2ihkTCIICIiH526dIlPPTQQ0hLSzMds7e3x+uvv44NGzYwQKYbunDhAj766CN88sknqKiouO68vb097r//fmzYsAEzZ87kz1Q/EkURJ3bmIfNQIfynuSFmXciQXROKAQBR3zEAGOF0WgOyU0uReaQI9ZUtcFBZIyzaF8GzPIdkMkxERP1HFEW8++67eO6559Dc3Gw6PmfOHHzyyScYO3asGaujoUin0yE+Ph7btm1DXFwcDAbDdW28vLywZMkSxMbGIjo6GkoltzW+XUaDEUc/zUZ2ahkmRnoj/L7AIX2hhwEAUd8xACAAgNEooiCrCqcPFaIsXwMrWxnCon0xZYEfZHIu1ENkiYxGI5qamlBfX4+GhgY0NjbC09MTXl5evHpGfVZSUoLf/OY32L9/v+mYXC7Hiy++iM2bN3MRN+qzsrIy7NixAx9++CEuXrzYbRu5XI7IyEjExsZiyZIlCAwMHOQqhy59mwEHPjiPgjNVmL50DKbHjh7yfxsYABD1HQMAuk5ZvgYZB67gclYVHN1sELk6CL7jXcxdFtGw8MtOe09fezt3bZvuqFQqhIWFISwsDJMnT0ZYWBjGjx8PuZy7f9DN+frrr/HYY4+hpqbGdGzixInYsWMHJk+ebMbKaDgSRREnT57Ehx9+iF27dnX5ufslf39/0+iAiIgIWFsPnRXsB5O2WYe4LWdQmqdBxH2BmBTlY+6S+gUDAKK+YwBAPSq6UIPELy5CU9mCgOnumHOvP+wcrcxdFpHFEEURFy5cQGpqKiorK2+q097Y2NjtXtkDTaFQICQkxBQIdN5cXBju0c/q6urw5JNP4rPPPjMdEwQBzz77LF5++WV2tmjA6fV6pKWlIT4+HnFxccjKyuqxra2tLWJiYkyjA3x8hkcnt6+aNFrseSsLtaVNiHk4BAHT3c1dUr9hAEDUdwwAqFd6nQEZ+6/g1IErkMmlmHn3WEyI8B7S88eIbpfRaMTZs2eRmJiIxMREJCUloaqqyqw12dnZwcHBAUqlEjY2NsjPz+9xZEB3fH19TaFA59exY8dCIhmaC0TR7UtISMC6detw9epV0zE/Pz9s374dUVFR5iuMRrSrV69i3759iIuLw+HDh9HU1NRj29DQUFMYMHPmTMhkI28tI01lM75/IxPNDTosfnQi/EJczV1Sv2IAQNR3DADoptSVNyPxi4u4ml0Lt1FKRD0YDLUfF+Wh4c1gMCAzM9PU4U9OTkZtbW2fX9fOzg5KpdLUcb/dr/b29tfNwzYajbh8+TKysrKQlZWFzMxMZGVl4cqVKzddn729PUJDQ7uEApMmTYKtrW2f/9vJ8rS0tODPf/4zXn/99S7H161bhzfeeAOOjo5mqoyoK61Wi6SkJMTFxSEuLg65ubk9tnV2dsaiRYuwZMkSLFq0CCqVahArNY/KogbseSsLRoMRS58Mg8eY4fe7ywCAqO8YANBNE0URuekVSP46B60NbZgU5YMZy8dCYTPyEnYannQ6HTIyMkwd/uPHj6O+vr7X57i6uiI8PByBgYE31XHvrtM+GGpra3HmzJkuocC5c+fQ1tZ2U8+XSCQICAi4brSAp6fnkF9UaiTLyMjA2rVrceHCBdMxV1dXvPfee1i1apUZKyO6sZycHMTFxSE+Ph6JiYk9/nsmCAJmzJhhGh0wZcqUYffvVklOLeL+fQYKGxmWPT0ZLp525i5pQDAAIOo7BgB0y7TNOqR9l4+zScWwdVBg7q8C4H+H27D7Y0rDX1tbG3788UdThz8lJaXX4aUA4ObmhsjISNMtJCRkyA6X1+l0uHjxYpdQIDMzE5WVlTf9GiqV6rpQIDg4mAsOWji9Xo//+Z//wV//+lfo9XrT8SVLluDDDz+Eh4eHGasjunWNjY04cuSIKRAoLi7usa2npyeWLFmCJUuW4K677rKobQZFUURTUxM0Gg00Gg2kUikCAwN7/YyVn1mJgx+ch4PKGsuengyly/Bdq4MBAFHfMQCg21ZeUI/Ezy+isrABfiEuiFgdCEc1hwiT5WptbUVaWpqpw5+amoqWlpZen+Pl5dWlwx8UFDSswy5RFFFWVmYKBDpDgUuXLsFoNN7UaygUCvj4+MDNzQ3u7u5wc3Mz3a597O7uDhcXlyEboAxVubm5eOihh5Cammo6Zmdnh9deew0bN24c1j/fNDKIoogzZ86YpgqcPHmyx3+/5HI5wsPDERsbi9jY2Bt2tnuj1+tRX1+P+vp6Uwdeo9H0+ri7c7+sdeLEidi8eTPuv/9+KBSKLud+OlGCozuyoR7lgKVPhsLGvuv54YYBAFHfMQCgPjEaRZxLvIqT3+XDqBdxx+JRmLpgFKRyfqAn82tubsbJkydNHf6TJ09Cq9X2+hw/P78uHf5x48axQ4T27+W5c+e6jBY4c+bMLS042B2pVAqVSnVdMNDTfa5Cf/tEUcR7772HP/zhD11GusyaNQuffPIJ/P39zVgd0cCprq7GgQMHEB8fj3379vW6zeDYsWMRGxuLefPmQRTFG3bgr318oxFkfeXt7Y1NmzZh48aNcHBwQMbBK0jdmQff8c5Y9OgkKKyH/5RMBgBEfccAgPpFU50Wx7/JQW56BZzcbRG5OhA+wdxejAZXY2MjTpw4Yerw//DDD9DpdL0+Z+zYsV06/KNHjx6cYoeBaxccvHbEwK0sOHirlErlDUOCzvvOzs4jenSBKIqorKxEYWEhCgsLsW3bNsTFxZnOy2Qy/Pd//zeef/75EblaOo1MBoOhyzaDmZmZ5i7pOjY2NnB0dISjoyOuXr16XbDg6OiIZfPvx3i7aNwROR4xD4dAKhsZ/9YxACDquxsGAIIgWANIAmAFQAbgG1EU/0sQhDEAvgTgCuAUgLWiKLYJgmAF4BMAdwCoBnCfKIoFvb0HA4Dho/B8NRK/vIT6yhYE3umOOfcGwNZheA9HI/Opr6/H8ePHTR3+U6dOdZnP3J3AwEBTZz8iIgK+vr6DVO3IUV9fj7KyMlRUVKCiogLl5eU93q+rqxuwOmQyGTw8PODt7Q0vLy94e3ubbtc+tqT5v7dCq9Xi6tWrKCwsxJUrV0wd/Wvvt7a2dvvckJAQ7NixA1OnTh3kqoksS3FxsWmbwUOHDvXpKr4gCHBwcICjo6Ppa+ett8e/vH/tGiq1tbV455138Oabb6K8vLzL+8llcqx9aC02b96M4ODg2657KGEAQNR3NxMACADsRFFsFARBDuA4gN8DeBbATlEUvxQE4V0AWaIoviMIwu8AhIqi+JggCPcDWCmK4n29vQcDgOFF32bAqf1XkHHwCmRyKWatGIuQcG9IJBxGTX1XUlKC3bt3Y9euXTh69CgMBkOv7UNCQrp0+D09PQepUroZbW1tpkDgRmFBRUXFDQOe26FUKm8YEnh4eAzqVXJRFFFbW9tr576srAy3M4rvmWeewd///nfY2NgMQOVEQ5dWq0VycjLi4uJw/vx52Nra3lIn3t7efsBGHbW2tuLjj7bjpb/+HSUVhdedv/vuu/H8889j9uzZA/L+loIBAFHf3dIUAEEQbNEeADwOIA6AhyiKekEQZgH4qyiKCwVBONBxP1UQBBmAMgBqsZc3YgAwPNWWNSHxi0sovlgLt9EOiHogCGq/oXmljcwrNzcXu3btwq5du7osXNad0NDQLh1+tVo9SFXSQBNFEXV1daYw4EZhwY22cLwVgiDA3d29S0DQXWjg6Oh4U2tG6HQ6lJSU9Ni5LywsRGNjY5/rdnR0xKhRo+Dn54dRo0Zh9erVmDNnTp9fl4gGl7ZZh7gtZ1CcUwODTwG+it+GkydPXtduzpw5eP7557F06dJhOQWKAQBR391UACAIghTtw/z9AfwbwP8COCmKon/HeV8A+0RRnCgIwjkAi0RRvNpxLg/ADFEUq37xmr8F8FsA8PPzu2Mg54yS+YiiiEs/lCPlmxy0NuoQOs8Xdy4fMyIWqqHb17mC865du7Bz506cPXu2x7ZTpkxBVFQUIiMjER4eDhcXrj1B7VpaWlBaWori4mIUFxejpKTEdP/axzdaGPJW2NradgkFvLy84O7ujqqqqi4d/ZKSkpveVaEnEokEXl5epg5+Zye/86uvry8cHR376b+MiMylSaPFnjezUFvWhJj1IQiY5g5RFJGSkoJXXnkFe/bsue45wcHBeO6557BmzRpYWVmZoeqBwQCAqO9udQSAE4BdAP4fgI/7EgBciyMAhr/WJh3SvsvHueRi2DkoMPfXgRg3Vc3V1cnEaDTi5MmT2LlzJ3bt2oX8/Pxu20mlUkRFRWHVqlW4++674e3tPciV0nAiiiJqamp6DQiKi4tRUVEx6LXZ2dl127nvvO/l5dVlrjARDT+aymZ8/0Ymmht0WPzoRPiFuF7X5sKFC/jnP/+JTz/99LqFbz08PPDMM8/g0UcfhZOT02CVPWAYABD13S3vAiAIwl8AtAB4AZwCQLeo/HI9jn2ejaqiRvhNcEXE/YFwVHMe6kil0+lw9OhR7Nq1C7t370ZZWVm37aytrbFgwQKsWrUKS5cuhavr9R+AiAZSW1sbysrKegwIOm/Nzc03/ZoeHh69dvCdnZ0ZkhKNYMUXa3Hgw/MQDSJinwyFx5jeR/QUFxfjjTfewNatW6+bAqVUKvHb3/4WzzzzDHx8fAay7AHFAICo725mEUA1AJ0oinWCINgAOAjgfwCsA/DtNYsAnhFFcYsgCE8AmHTNIoCrRFH8dW/vwQBgZDEajDh7rBhp3+fDaBQxbfFoTLnLD1L58JurRtdrbm7GgQMHsGvXLuzZs6fHVeAdHBywdOlSrFy5EosWLYK9vf0gV0p0a0RRRH19/XXhQHl5OVxdXbt07n18fIbVsFwi6j9NGi1OfJuLSz+Uw0FljdgnwuDiaXfTz9doNHjvvffw+uuvo6SkpMs5mUyGBx98EM899xwmTpzY36UPOAYARH13MwFAKIDtAKQAJAD+I4rii4IgjEX7NvKRFZMAACAASURBVIAuAE4DWCOKorZj28AdAKYAqAFwvyiK3Y/l7cAAYGRqrNXi+Nc5yMuogLOHLSJXB8E7yNncZdEAqKurw969e7Fz507s378fLS0t3bZTq9VYsWIFVq5cifnz57ODREREI4bRKOJcYjHSvsuDXmfElAV+uGPxaMgV0tt6Pa1Wi88//xz//Oc/ceHChevOx8bGYvPmzYiIiBgyo40YABD13S1PARgIDABGtivnqpH05UXUV7UiaIYHZt/jD1sHhbnLoj4qKyvDd999h507dyIhIaHH7dtGjRqFlStXYtWqVZg9ezak0tv7oENERDRUlV3WIPHzi6gqaoRPsDMi7g+Es8fNX/XvjdFoRHx8PF555RUkJydfd/7OO+/E888/jxUrVlj832AGAER9xwCALIK+zYBT+68g48AVyK2kmLVyHELmeEGQDI1Emtrl5+ebtus7ceJEj3uUh4SEmDr9U6ZMGTJXHoiIiPpTa6MOqd/l4cLxEtg6KDD3VwHwv8NtwP4upqam4n//93+xe/fu6/5G+/v747nnnsNDDz0EGxvLXJ+JAQBR3zEAIItSW9aExM8vovhSHdzHOCDqwSCofJTmLot6IIoizp07Z9quLysrq8e206dPx6pVq7By5UoEBQUNYpVERESWRTSK+Cm1FKk786Bt0SN0ng/uXDoGCpvB2Sb50qVLePXVV7F9+/brtkJ1c3PD008/jccff9zittZlAEDUdwwAyOKIoohLaWVI+TYXrU16hM73wYxlYyG3suxhaSOFXq/Hjz/+iN27d2Pnzp3Izc3ttp1EIkFERARWrVqFFStWwNfXd5ArJSIisjxVVxuQ+PkllOVr4DHWEZEPBEHlY56FbsvKyvDWW29hy5Yt1y3Ka2dnh0ceeQSbNm3CqFGjzFLfLzEAIOo7BgBksVqbdEjdnYcLySVQuloj6oEg+E3g9m+DzWg04vz580hISMCRI0eQmJh43fZCnRQKBRYsWICVK1di2bJlUKvVg1wtERGRZWpr0eOHPZdx5thVWNnKMHvVOATP9LSI6Y4NDQ348MMP8dprr6GoqKjLOalUivvuuw8PPPAA5s+fb9bpAQwAiPqOAQBZvJKcOhz7LBu1Zc0InOGOufcGwEbJRQIHiiiKyM/Px5EjR5CQkICEhARUVlb22N7e3h6xsbFYuXIlFi9eDAcHh0GsloiIyLKJoojc9Aoc/yYHzfVtmDDXCzNXjIO1ndzcpV1Hp9Phq6++wiuvvIKzZ89ed97Gxgbz58/H0qVLERsbO+ij+xgAEPUdAwAaEgw6I9L3FyBj/xUorGWY+yt/BM7w4OJx/aS4uBhHjx41dfoLCwt7be/t7Y2FCxdi1apViI6OhrW19SBVSkRENHTUljUh6ctLuJpdC5WvPSIfCILHGEdzl3VDoiji4MGDeOWVV5CQkNBju0mTJpnCgJkzZw74LgIMAIj6ziICALlcLi5fvhzh4eGIiIhAWFiYxW9DQuZRXdKIY59moyy/Hr7jnRH5QDAc1Za5Uq0lq66uxrFjx0zD+i9evNhre1dXV8yfPx/z589HdHQ0/P39Gb4QERH1QNdmwKl9BTh9sBAyhRQz7x6LCRHekFjAcP9bderUKXz55ZfYu3cvsrOze2zn6uqKRYsWYenSpVi4cCGcnZ37vRYGAER9ZxEBgCAIXYpQKpWYM2eOKRCYPn06rKyszFUeWRjRKOJcUjFSd+dBNIi4c9lYhEX7QCKVmLs0i9XY2Ijk5GTTFf7MzMwet+gD2of1R0ZGIjo6GvPnz8ekSZMgkfD7S0REdCOXz1Qh+atLaKhuReAMd8xe5Q87x+HxOTY/Px9xcXHYu3cvjh07hra2tm7bSaVSzJkzB7GxsYiNjUVISEi/XDhgAEDUdxYZAPySlZUVZsyYgYiICISHh2PWrFlQKrk13EjXWNuKxC8uoeBMFdR+SsxbEwy1H38uAECr1SI1NdV0hf+HH36AXq/vsb2VlRXmzJljuso/bdo0yOWWNzeRiIjIUtVXtSD5PzkoOFMFZ087RN4fCO+g/r8KbikaGxtx+PBhxMXFIS4uDqWlpT22HT16NGJjY7F06VJERUXd9tRBBgBEfWcRAcCECRPEp556CklJSUhKSkJxcXGv7aVSKaZMmWIKBObOnQuVSjVI1ZIlEUUReRmVSP7qEloadQiL9sWdy8ZArhhZU0j0ej0yMjJMV/iPHz+O1tbWHttLpVJMnz7ddIV/1qxZZl3Vl4iIaKgy6Iw4fbgQp+ILAAGYHjsGYdG+kMpGzsg5o9GIzMxM7N27F3Fxcfjhhx96bGtra4uYmBjT6ABvb++bfh8GAER9ZxEBwLWLAIqiiIKCAiQnJ5sCgZycnBu+RkhIiCkQCA8P557jI0xrkw6pu/Jw4XgJHFTWiHogGL4hLuYua8CIoohz587d1NZ8ncLCwkxX+CMiIrhaPxERUR8VZdcg6YtLqCtvxtgpasz9VQCULlwYt7y8HPv27cPevXtx8OBBNDQ09Nh28uTJpoUEp0+f3us6YAwAiPrO4gKA7pSVlSE5OdkUCpw5c6bX+ctA+1CjzkAgIiICAQEBXLRsBCi+VItjn11EXXkzgmZ6YM69/rCxHx5bBhYVFWH//v2mq/y9bc0HAAEBAaZF+6KioqBWqwepUiIiouGtqU6LlG9ykJNeAQeVNSLuD8Koia7mLssitbW14fjx46bRAZcuXeqxrVqtxuLFixEbG4uFCxfC0bHrjgkMAIj6bkgEAL9UV1eHlJQUJCUlITk5GT/++GOv85sBwN3d3TQ6ICIiApMmTeJOA8OUXmdAenwBTh8ohMJWhrm/CkDgne5DLgDS6XRISUnBvn37EB8fj3PnzvXa3tvb2zSkf/78+RwFQ0RE1M+MBiPOHitG2p58GPUipi70w9SFoyAbYVMP+yInJ8e0kGBSUhJ0Ol237WQyGebOnWsaHRAUFASJRMIAgKiPhmQA8EvNzc1IS0szBQKpqalobm7u9TmOjo6YM2eOaZTAtGnToFAMjyvF1K66uBFHP81G+eV6+IW4IPKBIDioLHuee0lJianDf/jw4V6H9bu6umLevHmmTj9HuRAREQ2c0jwNEr+4iOqrjfALcUH4/YFwcrM1d1lDWn19PQ4dOmRaSLCioqLHtuPGjUNeXh4DAKI+GhYBwC/pdDpkZGSYAoHk5GTU1dX1+hxra2vMmjXLNGSaq6APD0ajiHOJV3Fydz5EUcSM5WMROs9ytgzU6/VITU01dfqzsrJ6bKtQKBAZGYmFCxciJiaGW/MRERENgpbGNqTuysNPKaWwc7JC+K8DMHaKmqF7PzMajTh16pRpqsCpU6e6a8YAgKiPhmUA8EtGoxHnzp0zrSGQnJzc61YlAKBUKhEREcF90IeJhppWJH1xEQVnq9u3DFwbDLWvebYMLCsrw/79+xEfH49Dhw71Gk6NGjUKS5YsweLFizF//nzY2dkNYqVEREQjl2gUcSGlBKm786BrMSAs2hfTYkdDYS0zd2kjQmlpKeLj4xEXF4eDBw+iqakJYABA1GcjIgD4JVEUkZeX1yUQyMvL6/U5KpXKNNw6Ojoa48aNY/I7xIiiiNxTFUj+6hJam/SYHOOL6UsHfstAg8GAtLQ001X+jIyMHtvK5XJERERg8eLFWLJkCYKDg/lzRkRENMgqCxuQ+MVFlF+uh1eAEyJWB8LVy97cZY1YWq0WSUlJWLBgAQMAoj4akQFAd4qLi3H06FHTtmqFhYW9tvf19e2y4Nqt7GFK5tXapMOJb3Px04lSOKhtEPVgEHyD+3fLwIqKChw4cADx8fE4ePAgampqemzr4+NjusofHR0NpdI8IxOIiIhGGlEU0VirRXVxY/vtaiOqiptQW9YEG3s55tzjj8AZHgzjLQR3ASDqOwYA3egcIdAZBiQkJKCqqqrX5wQFBZlGB0RFRcHFZfjuQT9cXL1Yi2OfZkNT2YLgmR6Yc28ArO1vb90Hg8GA9PR0xMfHY9++fUhPT+9xq8rOVW07r/JPmDCBHyyIiIgGWFurHjWlTai+2ojq4iZTp1/b/PNOUkoXa7j62MNtlBKh83xgZcv1oCwJAwCivmMAcBM61xDoDAQSExPR0NDQY3tBEDBlyhTTgoLh4eGcu22h9G0G/BhfgMyDhbCyk2HurwMQMO3mtgysqqrCwYMHER8fjwMHDvQaEnl6emLJkiVYsmQJoqOjr9vXloiIiPqHaBShqWoxXdGvLmnv9GuqWoCOj71yKylcve3h6mMPVy+79q/e9rCy4fx+S8YAgKjvGADcBr1ej/T0dNPogJSUFGi12h7by+VyzJgxwzRlYObMmdxy0MJUXW3E0R0/oeJKA/wmuCLygUA4uHbdMtBoNCIjI8N0lT8tLa3Hq/xSqRSzZ882XeUPDQ3lVX4iIqJ+1tqkQ01JI6qu/nxFv7qkCXqtob2BADi52cLV2669w+9tD5WPPZQu1hAk/Ls81DAAIOo7BgD9oKWlBSdOnDCNEPjxxx9hNBp7bG9ra4vw8HDTCIHJkydDKh3YhehGura2NjQ3N6O5uRlNTU3d3m9oaEReVgkunS6GVt8K1Shr2LpK0NzcjPr6epw4caLX/Wnd3d1NHf677roLTk5Og/hfSERENHwZDUbUlbdf1a+6Zr5+Y+3PF2Cs7GRQdVzJ77y5eNkN+GK/NHgYABD1HQOAAaDRaJCUlGQaIXD27Nle2zs7OyMqKsoUCAz3ld9FUYROp4NWq73u1traet2xlpaWXjvuN3Nfr9ffuLBbJJFIMHPmTFOnf/LkydwqkoiIqI+a69s6FuNrNF3VryltglHf/plVIhHg7Gn7c0ffxx4qb3vYOiqG9ecnYgBA1B8YAAyC8vJyHDt2DEeOHMGRI0eQn5/fa3uFQgG5XA65XA6ZTNav92+lrdFovKkO+u0ct4Sfu9uhVquxaNEiLF68GAsWLICrq6u5SyIiIhqSOufqVxY2oKqoEVVFDagsakBLg87Uxs5RYZqf33lz9rCFVMbAfSTqLgA4deqUm0wm+wDARAD8waCRzgjgnF6vf+SOO+7odugyAwAzKCgoQEJCgmnKQFlZmblLGvZkMhns7Oxga2sLW1vbm74vl1qh+Hw9qq9oMWrUaGzYfDd8gtjpJxJFEUaNBvqqqo5bNfRVlTBUVUFfVwf7yEgoY2J4NY6IALQP4a8ta0ZlUYOpw19Z1ABda/tcfYlUgIuXHVS+Sqg6F+fztoONPddMop91FwBkZWV97+HhMV6tVtdLJBLzd2yIzMhoNAqVlZWOZWVlF8LCwpZ314YBgJmJooiffvrJFAYcO3YMdXV15i5rwMlkMlhZWZlu1tbWXR5fe+vskN9Kx/2X9+Xyvm3jU5Rdg2OfZqO+qhUTI70xa8U4KLhSMA0zoijC2NQMQ1Ul9NXV0Fd2du4rYejyuAr66mpAp7vuNQS5HIKNDYz19bAKGQ/1U0/BPiqKQQDRCKJvM6C6uKm9s1/UgKrCBlQXN8Ggb18fSaaQQOVjD5WvEmo/JdS+Srh42kEq58Vb6l0PAUD+pEmTatn5J2pnNBqFs2fPOoeFhY3t7jwDAAsjiiK0Wi10Oh10Oh30en2Xr325f6vPk0gkN9VJv5Xj1tbWUCgUQ3LRQ53WgLTv8pF1tAj2TlaIejAYoyZyNABZPqNW235lvvNWWQV9dft9g+lxNfRVVRBbWq5/AYkEUlcXyFRqyFQqyFxdIVOrIFOpIFWpIHNVmR5LHBwAgwGavXtR9e8t0BUVwTo0FOqnnoLd3DkMAoiGGW2LHlVFHVf0C9s7/LVlzRCN7Z8vrWxl7R19X3uo/ZRQ+Srh5G4LCVfgp9vQQwBQEBYW1vNezEQjUFZWliosLGx0d+d4CdPCCIIAa2trWFtbm7sU+gW5lRRzfx0A/2luSNiRjb1vZyFohgfm/ioA1vZ9G2FA1Fei0Yi6b7+F9lIODNVVXa7WGxsaun2O1MkJMrUKUlcVbMLC2jv3ahWkrq7tnf3OTr6TE4RbCe1kMjitWAHH2FhovvsOVVveQdHGjbCZOhXqp5+C7YwZDAKIhqDm+jbTPP3KwvYh/PWVP4eGto4KqH2VGDtZDZWvPdS+Sihdrfn7TkRkQTgCgOg2GHRGpO8rQMb+K7CykyH8vkD43+HGDzlkFsamJhS/8AIaDx+BxM6uvdOuVv18xV7l+vPVepW6/bGLCwTF4MytFdvaULdzF6refRf6sjLYTp8O9e+fhu00LuRMZIlEUURDTavpqn5Vx7z9Jk2bqY2DyhpqXyVUHUP4Vb72sHO0MmPVNBJY6giAF154wePbb791lUgkokQiwZYtW67Mnz+/6cUXX3TbtGlTlVKp7Hl/cDO75557Ri9dulSzfv36WnPXQv2HIwCI+plULsGM5WMxbqobEj75CQc/OI+cH8sRuToIdk78AESDR1daiqLfPQHtxYtw//Of4Lx2rcUFUYJCAef774PjyhWo+/obVG/diitr1sJu9iyonnoKtlOmmLtEohGtvroF5fn1piH8lUUN0Da1b58rCICThx28g52h9v25s29ly5FvRABw+PBhuwMHDjidPXv2go2NjVhaWirTarUCAGzdutV948aNNbcSAOj1eshkQ6OLZjQaIYrikJzaO5INjZ8uIgul8rHHvS/cgcwjRfhhz2V8/t9pmHOvP8bP9rS4ThgNPy1nzqDoiScgNrfA9913YB8RYe6SeiWxsoLLmgfhdO89qP3yS1S//wGurH4AdhHhUD/1NGwmTTR3iUTDnmgUUVPahNLcOpTkalCaW4fGWi0AQCIT4Oplj3GT1ab5+q4+9pAr+OGeqCfFxcVyFxcXvY2NjQgAnp6eegB4+eWX3SoqKuSRkZGBzs7O+rS0tEtbt251efXVVz1EURRiYmLq3nnnnWIAsLW1nfLggw9WJiUlObz55puFhw4dUu7fv99Jq9VKpk2b1vjZZ59dkUgkSExMtN24ceNoiUSCyMjI+oSEBMecnJzzer0eTzzxhE9KSoqyra1N2LhxY8XmzZuvGxXx9ttvu7755pvugiBg/PjxLbt3774MAImJifZvvvmme2Vlpfyll166un79+lqNRiNZtGiRv0ajker1euEvf/lLyZo1a+ouXryoWLhwYeCUKVMaz549axcfH58TFxfn8MYbb3golUrDhAkTmhUKhfjJJ58UlpSUyNavXz+quLhYAQCvvfZa4YIFC5oG7/8OdYdTAIj6SV15M45+mo2SnDr4BDtj3ppgOKhszF0WDVP1+/ah5I9/gkylgu+778AqIMDcJd0yY3Mzaj77DDUffAiDRgP7+fOhfupJWI8fb+7SiIYNg86Iiiv1KM3ToCS3DmV5Gmib26/u2zoq4OXvBE9/R3iOc4KLtx2kUq7ET5brRlMANn+T5XuprMG2P98z0EPZ/L/3hhX1dF6j0UhmzJgR3NraKpk7d2796tWra2JjYxsBwNvbe1J6evpPnp6e+oKCAvmsWbOCT5069ZNardaHh4cHPvHEExVr166tEwThjvfffz//kUceqQWA8vJyqbu7uwEAVqxYMebXv/51zQMPPKAJCAiY8M477xTExMQ0/e53v/M+dOiQU05Ozvl//vOfqoqKCvkrr7xS2tLSIkyfPj34m2++yQsODjbN20lPT7e+9957/VNTU7M9PT31ne9xzz33jG5ubpbs3bs3PzMz03rlypX+hYWF53Q6HRoaGiQuLi7G0tJS2YwZM4ILCgrO5eTkKMaPHz/p0KFD2dHR0U0FBQXyOXPmBGdkZFxwcnIyzp49O3DChAktn3zySeGyZcvGPPnkk5ULFy5szMnJUSxcuDAgPz//fH/+/6HucQoA0SBwcrfFik1TcP54CU7szMUXL6Zh5t3jMGmeD1c7pn4jiiKqtmxB1Vtvw2bqVPi8/RZkLi7mLuu2SGxtodq4Ec6rV6N2xw5Uf/QxLq9cBeWCBVA9+QSsAwPNXSLRkKNt0aMsX4PSnDqU5NahoqDBtP2ek7stxk1Rw9PfCZ7+TnBQcYE+or5ydHQ0njt37sL+/fuVR44cUa5bt27cX/7yl6tPP/109bXtjh8/bjdz5swGLy8vPQDcd999NYmJifZr166tk0qlePjhh01z8Pft26d87bXXPFpbWyV1dXWykJCQlqqqqsampiZJTExMEwCsW7eu5tChQ04AcPjwYYfs7Gzb77//3hkAGhoapBcuXLC+NgA4cOCAw7Jly2o7Ryh0BgwAsHz58jqpVIo77rijtbq6Wg60byX3zDPP+Jw8edJeIpGgoqJCcfXqVRkAeHp6tkVHRzcBQHJyst2MGTMaOl9v5cqVtZcuXbIGgJSUFIecnBzT1bDGxkapRqORODo6WuyaCCMBAwCifiRIBEyM8Maoia449tlFHP86B7mnyjFvzXi4eNmZuzwa4oytrSj9P/8X9XFxcLz7bni89CIkg7SQ30CS2ttD9fjjcH7wQdR8vB0127ej4dAhOCxeDNWTT8BqbLfb2BIRgKY6LUpy61Caq0FpXh2qrzZCFNv/Hql97TExyhte49qv8tsoh/6/F0S96e1K/UCSyWRYunRpw9KlSxtCQ0NbduzY4frLAKA3CoXC2Dnvv7m5WfjDH/4wKi0t7YK/v7/u2Wef9Wptbe11aI4oisKrr75aeM8999TfTv3W1tamIeGdo8O3bt3qUl1dLTt79uxPVlZWore396SWlhYJANja2t5UB14URWRkZPxka2tr/iHnZMJxXkQDQOlijaVPhiJmfQjqylvw1d9/QHr8ZRgMDDzp9ugrK3Fl3TrUx8VB/eyz8Pz//jEsOv/Xkjo4QP30U/A/chiuGzei4dgx5C9dhpIXXkDblSvmLo/I7ERRRG1ZEy4cL8Hhjy9gx/89gY//mIKDH5zHTydKYG0nx7TYMVj+zGRs/FcEfvWn6Zh7bwDGTlGz8080QLKysqzOnj1rWgH69OnTNj4+Pm0AYGdnZ9BoNBIACA8Pb0pLS1OWlpbK9Ho9vv76a5eoqKjGX75ec3OzBAA8PDz0Go1GsmfPHmcAUKlUBjs7O2NCQoIdAOzYscM0/O+uu+7SvPPOO+rOxQfPnDljVV9f36Wft3Dhwvo9e/Y4l5WVSYH2aQa9/XdpNBqpSqXSWVlZiXv27FGWlJR0+4/I3Llzm9LS0pSVlZVSnU6H7777zvmac/X/+Mc/3DofnzhxgnNjLQBHABANEEEQEDTDA77jXZD81SWkfX8ZuacqMf+hYLiNcjB3eTSEtGZno+jx38FQVwfvt96Ew113mbukASV1coLbs5vg8vA6VH/wIWo//xyavXFwXHE3VI//Dgofb3OXSDQoDAYjqooaUXrNFf6WBh0AwEYph+c4J0yK8oGnvxNUvvacv09kBvX19dKnn37ar76+XiqVSsXRo0drt2/ffgUA1q1bV7Vo0aJAd3f3trS0tEv/9V//VRwZGRnYuQjgmjVr6n75eiqVyvDggw9Wjh8/foJardaHhYWZFs3bunVrwWOPPTZKIpFg1qxZDUql0gAAmzZtqiooKLCaNGnSeFEUBRcXF118fHzeta87bdq01j/84Q+l4eHhwRKJRJw4cWLzt99+W9DTf9cjjzxSs3jxYv/AwMCQ0NDQ5jFjxrR2127MmDG6TZs2lU6bNm28o6Oj3t/fv9XR0dEAAO+9917RI4884hcYGBhiMBiEGTNmNMyePbvwtr7R1G+4CCDRIMnPrETiFxfR0qDDlLt8MT12DGRcWZluoCEhAcXPbYZUqYTvO1tgHRJi7pIGnb6yElXvv4+6L7+CaDTC6Z57oHrsUcg9Pc1dGlG/amvVo7ygHqU5dSjN06AsXwN9W/vIMQe1DbzGOcIzwAme4xzh5G7L+fs04txoEcDh7tr583/+8589SktL5R999JFZpj10V5dOp8PChQv9H3744aqHHnrounCDBg8XASSyAGMnq+Ed6ISUb3ORcaAQeacrMX9tMLwCnG/8ZBpxRFFEzbZtqPjnq7CeMAE+//435O5uN37iMCRTq+Hx5z/DdcMGVL/3Hmq//gaanTvh9Otfw/XR30LuNjK/LzT06bQGFP1Ug5KcOpTm1qGyqBGiUYQgAK4+9hg/x6t9lf5xjrBzsrrxCxLRsPaf//zH8dVXX/U0GAyCt7e39vPPPy8wd00AsHnzZq+kpCQHrVYrREZG1nc3soEsB0cAEJlBUXYNjn2ajfqqVkyM9MasFeOgsGEeR+3EtjaU/vW/odm5E8pFi+D1j79DYsNpc510xcWoencr6nbtgiCVwvn+++G68RHIVCpzl0Z0QwadEVfOVyM3vRyXz1RB32aEVC6B+2gHePo7wsvfCR5jHfk3gagbI30EANHN6m0EAAMAIjPRaQ1I+y4fWUeLYO9khagHgzFqoqu5yyIz09fWovipp9Gcng7V7x6H6sknIUg4r7c7bUVFqNryDjTffQfBygouDz4Al9/8BjJnjqohy2I0GHH1Yi1y0iuQf7oSbS16WNvJMe4ON/jf4QbPsY6Qyvl7TnQjDACIbg4DACILVpavQcInP6G2rBlBMzww91cBsLaXm7ssMgNtXh6KHnsc+vJyeP7tb3BcttTcJQ0J2suXUbXlHdTv3QuJjQ2cH1oL1/XrIXV0NHdpNIKJRhGleRrkpJcjL6MCLQ06KKylGDtZjYDp7vAOduaifUS3iAEA0c1hAEBk4Qw6I9L3FSBj/xVY2ckQcX8Qxk1Vc4GnEaTxeAqKN22CoFDA5+23YDtlirlLGnK0ubmo/Pe/0bBvPyT29nB5+GG4/mYDp0/QoBFFEZWFDcj5sRy5pyrQWKuFTC7B6FAVAqa5w2+iC2RyLv5KdLsYABDdHC4CSGThpHIJZiwfi3FT1Uj4JBsH3j+HMWEqRD4QBDtHLvw03NV8+hnK//EPWI0bB993tkDuzW3uboeVvz98/vUvtD72GKrefhtVb7+NlrNn4PvvYnxJAQAAIABJREFUf0OQ8c8dDZzqkkbkplcg58dyaCpbIJEK8Jvgilkrx2F0qAoKa/78ERGRZeDYMyILovJR4t4X7sCsVeNQeKEGn/81DRdSSmAJI3Wo/4l6PcpefBHlL78M+/BwjPr8c3b++4F1UBB83noLHn/9LzQlJqHsxZf4O0T9TlPZgvR9BfjypTR8+eIPOLWvAEpXa8xbG4z1r8xF7O9CEXinBzv/RCPACy+84OHv7z8hMDAwJDg4OCQhIcEOAF588UW3hoYGi+xvPfvss15/+ctf3AHgmWee8dq9e7fyVp5/8eJFRUBAwISBqY4GEv8qEVkYiVSCqQtGYWyYGkc/zcbRHdnI+bEc89YEw0HFoczDhaG+HsWbnkVTSgpcNmyA2x+ehSDl0OD+5Hz//dAVl6D6/fch9/GB6rcbzV0SDXGNtVrknipHTnoFKgrqAQCe4xwRfl8gxk1Vc8QW0Qh0+PBhuwMHDjidPXv2go2NjVhaWirTarUCAGzdutV948aNNUql0nizr6fX6yEb5FFrr7/+esmgviEAnU4HuZxrXpmDRSZSRAQ4udtixaYpiHwgCOUF9fjixTRkJRTBaOSVzKGurbAQBfevRlNaGjxffgnuz29m53+AqDc9A4fYWFS+9ho0e/aauxwagloa23AuqRi7Xs3A9j+nIOWbXIhGEbNWjcPav83Cqs13IHSeDzv/RCNUcXGx3MXFRW9jYyMCgKenp3706NG6l19+2a2iokIeGRkZOGPGjEAA2Lp1q0tgYGBIQEDAhMcff9w05M/W1nbKxo0bfYKCgkKOHDli/9xzz3lOnDhxfEBAwITVq1ePMhrb84PExETbzlEGjz76/7N333FR138cwF/fm4w71rH33kMUUFFcqVk/N66cleXKballWmmaphZZruyn5ciFqblzzxygoogooAiy990xjhvf3x+AP9wL/IK+n4+HD+++9x2vA4X7vL+fMdK+5g68RqPByJEj7f39/X08PT19Fy5c+Mh1cadNm2bt7Ozs36xZM6+kpKR7P7QiIyOd16xZYwoAY8aMsXNzc/Pz9PT0HTFihD0ApKenCzp16uTm5eXl6+Xl5Xvw4EFDANBqtRgwYICTu7u7X6tWrTyUSiUDAIsXLzb39/f38fLy8n377bfdanpBREZGOg8cONAxMDDQe/To0faZmZmC8PBwD3d3d7/+/fs72draBmRlZQkAYNmyZWYBAQE+3t7evgMHDnTSaDR1/J17c1EPAEIaMIbHwL+NHZz8ZTi24QZObUlCysVcdPrQD1IzPa7jkRdQduEC7o4bD7AsHFf/F4ZhYVxHeq0xPB5svpsHTW4uMr/4AgILCxi2aM51LNLAqco1uH05D0kxOUi/XgRWx8LU2gBhXV3g3swSptaGXEckhDzKjk8ckJtgUKfntPQtQ8+l6Y97uWfPnvLvvvvO1tnZ2b9169by9957r/A///mP8ssvv8xdvny51fHjx2/a2NhoUlNThV9//bVdbGzsdQsLC01ERITnunXrTIYMGVJcXl7Oa968eemqVavuAkCTJk3KFy1alFV9fpdNmzYZDxw4sOSjjz5yWb58eWrHjh1Lx4wZc6+AEBUVZW5sbKyNj4+/Xl5ezoSGhnp369ZN7u3tXVmzz8mTJw22b99udvXq1QS1Wo0mTZr4BgcHl9V+L9nZ2fy9e/ea3rp1K57H4yE/P58PAKNGjXKMiIhQzJo1K0Wj0aCkpISfn5/PT0tL01u/fv2t8PDwO++++67r2rVrTceMGVM4aNCgoilTpuQDwPjx422XLFliPmPGjFwAyMrKEl28eDFRIBBg6NChjm3btlV899132dHR0UZbtmwxB4CLFy/qRUdHm8XExCSKxWJ28ODBjitWrJCNHTu2oO6+sW8uKgAQ0ghIzfTQdWwgbpzNxolNN7H52/NoO9ALHiFWXEcjz6F421/I+vpriBwc4LB8GUROTlxHeiPwqldWSB04CHfHjYPznxsg9vDgOhZpYNSVWqReyUdyTC7uxBdAq9FBKtNDcCdHeIRaQmYnoZVZCCEPMTY21sXHxyfs379fevjwYemwYcPcZs2adXf8+PH3NVZPnTpl2KJFC4Wtra0GAPr37194/PhxyZAhQ4r5fD7ef//9opp99+3bJ/3hhx+sKyoqeMXFxQJfX9/y/Px8ZWlpKa9jx46lADBs2LDCgwcPmgDAoUOHjBITEw3+/vtvUwBQKBT8hIQEvdoFgKNHj0refffd4prhCJ07dy5+8L3IZDKtWCzW9e/f37lr167F/fv3LwGAM2fOSKOjo28DgEAggEwm0+bn5/Pt7OxU4eHh5QAQHBxclpqaKgaA2NhY/VmzZtkpFAp+aWkpv23btiU11+jdu3dRzRCH8+fPS3bs2JEMAH369JEbGRlpAWD//v3S+Ph4g6CgIB8AqKio4FlaWlIXgDpCBQBCGgmGYeDd0gY27sY4uDoB//x2DWkJhYjo50GTTDVwrFaL3MU/oHD1ahiGh8Mu6kfwjYy4jvVG4Rsbw/HXlbg9YADSRo6E86ZNEFpach2LcEyr0SEtoRBJF3Jw+0o+NCotDIxE8GtjC48QK1i5GFGjn5DG5Al36uuTQCBA165dFV27dlUEBgaWr1u3TvZgAeBJRCKRrqZRXFZWxkyZMsXp3LlzCe7u7urJkyfbVlRUPHHYNsuyzOLFi9MiIyPlL/M+hEIhLl++fP3vv/82io6ONl2+fLnl2bNnbz4h971xqXw+ny0vL+cBwIgRI1yio6OTW7ZsWb5kyRLZ8ePH700wKJFInjofAsuyTN++fQuWLl2a8TLvhzwazQFASCNjbGGAXp82RbN3nJD4bxa2zLuA3Dsv9fOe1CNdaSnujhuPwtWrYTrwPTj8upIa/xwR2tnBYcUKaItLkD5qFLTKUq4jEY7I88txdH0i1kw9hb3LriAtoQCeYVboMSkYw+a3QkQ/T1i7GlPjnxDyVHFxceKrV6/eG09/6dIlfXt7+0oAMDQ01JaUlPAAICIiovTcuXPSrKwsgUajwdatW83atWunfPB8ZWVlPACwtrbWlJSU8Hbt2mUKAObm5lpDQ0NdzQoD69atM6s5plOnTiXLly+3qJl88MqVK2K5XH5fO69Dhw7KvXv3miiVSqaoqIhX03ugtpKSEl5hYSG/f//+JStWrEhPTEw0AIBWrVopFi5caAFUzTdQUFDwxImLysrKeI6OjmqVSsVs2rTJ7HH7hYaGKmvex19//WUkl8v5ANClSxf57t27TTMyMgQAkJOTw79586boSdckz45uGxLSCPH5PLTo4QYHHzMcWpOAbQti0byHK4I7OYLh0QfWhkKdmYn00WOgSk6G1cwvYTZoENeR3nj6fn6wj/oR6aPHIGPSJDgsXwbmFc+2TLijrtTi4v47uPRPGhgGcG1qAY8QKzj4mIEvoHsihJDnJ5fL+ePHj3eUy+V8Pp/POjs7q/744487ADBs2LD8Ll26eFpZWVWeO3fu5ldffZXRtm1bT5ZlmY4dOxYPHjz4oW745ubm2kGDBuX5+Pj4WVhYaIKCgu5Vq1euXJk6atQoJx6Ph5YtWyqkUqkWACZNmpSfmpoqDggI8GFZljEzM1Pv3bs3pfZ5W7duXdarV69Cf39/P5lMpg4MDHyoCl5cXMzv2rWre00hYc6cOekAsHz58rT333/fydPT05zH4+GXX3654+DgoH7c12T69OmZYWFhPmZmZpqmTZsqlUrlIwsG8+fPz+zTp4+rh4eHrFmzZkpzc3O1iYmJ1sbGRvPll19mvPXWW546nQ5CoZBdsmRJmqenZ+WjzkOeD9MQ1kYOCQlhY2JiuI5BSKNUUarGsQ2JSLmYBzsvU3R83xcSU5qNmmvlly8jfew4sBUVsIuKgqR1K64jkVqKNm9B9ldfwaRvH1jPnk13el9zLMsi5WIeTkcnQVmkgkeoFcJ7u0FiSpOpEtKYMAwTy7JsSO1tcXFxqUFBQflcZXqVSkpKeMbGxjoA+OKLL6yzsrKEa9as4WTYQ10oLy9nBAIBKxQKcejQIcOxY8c6JSYmJnCd63UQFxdnHhQU5Pyo1+i2ByGNnJ6hEG9/7I/rZ7JwcvNNbPr2HDoM9oFrsAXX0d5YJbv3IOuLLyCwsoLDH79D7ObGdSTyANP+/aDOzETBypUQ2tnBfNQoriORelKQocTJzTeRcbMY5g4SdPrQD7YeD/V8JYSQBm/Lli3GixcvttFqtYydnZ3qzz//TOU608tITk4W9evXz63mLv/KlStTuc70JqAeAIS8RoqyS3FwdQLy0hTwi7BFq74eEIpofflXhdXpkP/LUuQvWwaDkBDY/bwEAlNTrmORx2BZFpnTpkH+9y7Yfr8Axt27cx2J1KGKUjXO/30L8ScyIDYQonkPV/i2tgWPhkkR0mi96T0ACHlW1AOAkDeEqbUhIqc2w7m/b+HSP2nITCpGp+F+sHCQPv1g8lJ05eXI/OILKPbth3Hv3rD5+iswIpqvpiFjGAa2334LTU4uMmd8CYGlJQxbtOA6FnlJOh2LhFOZOLfzFlRlavi3tUdYNxfoGQq5jkYIIYRw7qkz3jAM48AwzFGGYRIYhrnGMMyE6u1mDMMcZBgmqfpv0+rtDMMwSxiGSWYY5grDME3r+00QQv6PL+AhvLc7uk9oAlW5BtELYnD5UBpYHfe9fV5X6txc3Bk6DIr9B2D52aewmfstNf4bCUYkgv3PSyB2dsLdseNQcfOxqx2RRiAzqRhbv7uA43/egMzOEP1mhKHNAE9q/BNCCCHVnmXKWw2AKSzL+gJoAeAThmF8AUwHcJhlWQ8Ah6ufA8A7ADyq/4wAsLzOUxNCnsrBxwwDZobByU+G09HJ2PVLHEpLVFzHeu1oi4txZ8B7UKWkwP6XnyEbPpwmlGtk+EZGcFi5Ejx9faSPGAl1Tg7XkchzUhZV4J//XsP2xRdRoVTj7Y/90WNSMMztJVxHI4QQQhqUpxYAWJbNYln2YvVjBYDrAOwA9ADwR/VufwDoWf24B4C1bJWzAEwYhrGp8+SEkKfSl4jwzqgAtB3ohaykYmz+9jxSr9IwubrCsiyyvvkG6txcOK1ZDelbb3Edibwgoa0tHFaugE4uR/rIUdAqH1odiTRAGrUWMftSseGrs7h1KQ8h7zpj4Nct4N7MkgpxhBBCyCM816K3DMM4AwgGcA6AFcuyWdUvZQOwqn5sB6D2chR3q7cRQjjAMAz829ih7+ehMDAWY8/SKzix6SY0lVquozV68l27oNi3HxbjxkE/KIjrOOQl6fn6wu6nKKiSkpAxcSJY9WOXOCYcY1kWt+PysPGbczi38xYcfWUY+HVzNO/uCqGYJj4lhLx66enpgm7durnY29sH+Pn5+TRp0sR77dq1nCw5snv3bunBgwcNubg2afieuQDAMIwEwDYAE1mWldd+ja1aSuC5BhgzDDOCYZgYhmFi8vLynudQQsgLMLM1RJ9pzRDUwQFXj93F1vkxKMhQch2r0VJnZiJ79hzoN20K2UfDuY5D6ogkIgI233yN0lOnkPX112gIK+WQ+xVll2L3z3HYu/wq+AIeuk9ogndGBcDIXJ/raISQN5ROp0O3bt3cIyIilHfv3r167dq161u2bLmVnp5ebxMCqZ9QpD5y5Ij05MmTzzUG6knnI6+XZ1oFgGEYIaoa/xtYlv2renMOwzA2LMtmVXfxz63engHAodbh9tXb7sOy7K8AfgWqlgF8wfyEkOcgEPLRup8HHPzMcPj3BGz9Lgbhke4IaGdH3WWfA6vTIXP654BOB9vvF4Dh0x3H14lJnz5QZ2Yif9lyCO3sYDFmDNeRCIDKcg0u7E3FlcPpEIj5aN3XA/7t7MDnP1dnRkIIqXO7du2SCoVCdurUqffuanp6elbOmDEjV6PR4JNPPrE/ffq0tLKykvn4449zP/vss/zdu3dLZ8+ebWtmZqa+ceOGfkBAQNmOHTtu83g8nDx50mDy5MkOZWVlPFNTU82GDRtSnZyc1GFhYV7+/v5l58+fl0RGRhZ6eXlVzJ8/30atVvNMTU01mzdvvlVWVsZbu3atBY/HY7ds2SKLiopKc3FxqRw2bJhzYWGhQCaTadauXZvq4eFRGRkZ6SwWi3Xx8fEGYWFhyt9+++0ul19H8mo8tQDAVLUK/gvgOsuyP9R66W8AwwDMr/57Z63tYxmG2QSgOYCSWkMFCCENgJOfDANmNsfhP67j5OabSEsoQIchPjAwopnrn0Xhmt9Rdv48bObOhcjenus4pB6YjxsHdUYG8pf8DKGNLUx69Xz6QaResDoWN85l48z2FJQrKuEbboPmPdzo5xUh5CEzT890SC5KNqjLc7qbupfNaTUn/Un7XL16VT8wMLDsUa9FRUWZGxsba+Pj46+Xl5czoaGh3t26dZMDwPXr1/UvX758y9nZWd2sWTPvgwcPStq1a1c6fvx4xz179iTb2tpqVq1aZfrpp5/abd26NRUAKisrmfj4+OsAkJeXxx8wYEAij8fDDz/8YD579mzrVatW3R06dGieRCLRzp49OwcAOnTo4D5o0KCCcePGFURFRclGjx7tcOjQoRQAyMrKEl28eDFRIKDV4d8Uz/KdbgVgCICrDMNcrt72Baoa/lsYhhkO4A6AftWv7QXwLoBkAGUAPqjTxISQOmFgJELXsYG4euwuzmxLwaZvz6PjMB84+sm4jtagVdy4gbyoKEg6vgXj3r24jkPqCcMwsJkzB+rcXGTNnAmhlSUMw8O5jvXGyUmV4+Tmm8i5LYeVixH+MyYQVs5GXMcihJAnGjJkiOP58+clQqGQtbe3VyUmJhr8/fffpgCgUCj4CQkJeiKRiA0ICCh1c3NTA4Cfn19ZSkqKyMzMTJOUlKTfoUMHT6BqeIGFhcW9/vnvvfdeYc3j27dvi3r27Gmfl5cnrKys5Dk4ODxyuadLly4Z7tu3LwUARo8eXfjNN9/cu3vRu3fvImr8v1me+t1mWfYUgMf1DX5oyuvq+QA+eclchJBXgGEYBLZ3gK2HKf757zXs+jkOQR0d0LKHG/hC6lb7IJ1KhczPpoJnbAyb2bNp2MRrjhGJYL9kCe4MHIS748bD6c8N0PPy4jrWG6FMXol/d6Qg8UwWDIxEeOt9H3iFWYPh0f85QsjjPe1OfX0JCAgo37lzp2nN83Xr1qVlZWUJQkJCfOzs7CoXL16cFhkZed8cart375aKxeJ7w6D5fD40Gg3Dsizj7u5efvny5cRHXUsqlepqHo8dO9ZxwoQJ2YMGDSqpGVLwvNklEonu6XuR1wl9wieEwNxegn6fhyCgrR3iDqUj+vsYFGbRMmgPyov6CaqbN2E791sIzMy4jkNeAb5UCodfV4JnaIj0ESOhzs7mOtJrTavV4fKhNGyY9S9unstGcCdHDPqmBbxb2FDjnxDSYHXr1k2hUqmYBQsWWNRsUyqVPADo1KlTyfLlyy1UKhUDAFeuXBHL5fLHtsECAwMrCgsLBYcOHTIEAJVKxcTExOg9al+FQsF3dHRUA8Dvv/9+rwunVCrVKhSKexMUBQcHl/7222+mALBy5UqzkJAQmgX6DUYFAEIIAEAg4qPNe154d0wglEUqbJ13AfEnMmgW9GqlZ8+h8PffYfLeAEjatuU6DnmFhDY2cPh1JXRKJdJHjoJWSZ+b6kNaQgE2zzmP09HJsHYzxoCZYQiPdIdIn7qmEkIaNh6Ph127dqWcPHlSamdnFxAQEOAzePBg56+//vrupEmT8r29vSsCAgJ8PDw8/D7++GMntVr92Iqmnp4eu2nTppTp06fbe3l5+fr5+fkeP378kTP6z5gxI/O9995z8/Pz85HJZJqa7ZGRkcV79uwx8fb29t2/f79kxYoVaevWrTP39PT03bhxo2zZsmWc9JQgDQPTED7ch4SEsDExMVzHIIRUKy1R4fDvCUi/XgSXIHN0GOIDPYmQ61ic0crluNWjJ3giEVy2/wWeQZ3OL0QaCeWp00gfNQqGYaFwWLkSjPDN/T9Rl0ryynE6Ogm34/JhZKGPiL4ecAqQ0RAbQshDGIaJZVk2pPa2uLi41KCgoHyuMhHSEMXFxZkHBQU5P+o16gFACHmIobEY3cY1QXikO+7EF2DTnHO4m1j49ANfU9lzvoUmNxe2C7+nxv8bTNK6FWy++QalZ/5F1qyvqHfMS1KrtDi7MwUbvzmH9MQitOjpioGzmsM50Jwa/4QQQkg9oX51hJBHYngMgjs5wt6raoLAnT9dRtPOjgjr5gq+4M2pHcr37oV81y6YjxsL/cBAruMQjplE9oY6MxP5S5dCaGcHi7E05+3zYlkWybG5OLMtGcoiFTzDrNCylzskpmKuoxFCCCGvPSoAEEKeyMJRin5fhOLU1iRcPJCG9OtF6DzcDyZWr/+dcHV2NrK+/gZ6QYEwHzmS6zikgTAf+0lVEeCXXyC0sYFJZG+uIzUapSUq/PPbNWQmFcPcQYJOw/1g627CdSxCCCHkjUEFAELIUwnFfLQf7A1HPzMcXZeIzfMuIKKfB3zCbV7brrqsTofMzz8Hq1bDbsECMLRGLqnGMAxsZn8DTU4Osr76CgIrK0hat+I6VoOnLKrAzqjLUBar0HagF3xb24JHM/sTQgghr9Sb04+XEPLS3IItMWBmGKycpTi6LhH7VlxFuaKS61j1omj9epT9exZW06dD5OzMdRzSwDBCIeyW/ASxmxsyJkxAReIjl2sm1eT55di++CJKS1ToPi4I/m3sqPFPCCGEcIAKAISQ5yIx1UOPCcFVEwReK8DGOeeRevX1mnxXlZSE3EWLIWnXDib9+nIdhzRQfIkEDr+uBE8qRfqIkVBnZXEdqUEqzinD9sUXoSrToMfEYNhQl39CCCGEM1QAIIQ8t5oJAvtOD4W+RIg9S6/g2J83oFZpuY720tjKSmRMnQaeRAKbb+e8tkMcSN0QWlnBYeVK6MrKkD5iJLQKBdeRGpTCzFJsX3wRGrUOPScHw8rZiOtIhBBS5/h8fjNvb29fDw8Pv3feecdVoVDwmjdv7rlt27b7fujNnj3bctCgQY5c5SQEoAIAIeQlmNtL0PfzEAR1dMC1ExnYMu8CclLlXMd6KXk//wLV9euw+XYOBObmXMchjYCelyfsl/wE1e3buDt+PNjK13NYzPPKv6vAjh8vAgB6TW4Kc3spx4kIIaR+iMViXWJiYkJSUtI1oVDILl682KJv376FGzduNKu937Zt28wGDx785q6rTBoEKgAQQl6KQMhH6z4e6DGxCTSVWmz7PhYX9tyGTqvjOtpzK4uJQcFvv8Gkbx9IO3TgOg5pRAzDw2EzZw7K/j2LrJmzwLIs15E4lXtHjh0/XAJfwEOvKU1hZmvIdSRCCHklWrdurUxOThYPGTKk6MiRI8YVFRUMANy4cUOUm5srfPvtt5VcZyRvNprWmhBSJ+y9zdD/yzCc2HQT53fdxp34AnT60BfGFo1juUCtUonMqdMgtLeH1fTpXMchjZBJr55QZ2Uif8nPENrZwmL8eK4jcSL7Vgl2LbkMsaEQPScFw8hcn+tIhJA3ROYXMxxUSUl1+sFD7OFRZjtvbvqz7KtWq3HgwAGjzp07y62srLRBQUGl0dHRxoMHDy7+448/zLp161bE49H9V8It+hdICKkzeoZCdB7uh07DfVGUXYZN315AwqnMRnE3NGfuPKizs2H7/QLwDOluJXkx5qNHwziyN/KXLUdxdDTXcV65jJtF2PnTZegbidBrSlNq/BNC3ggqlYrn7e3tGxAQ4Gtvb185YcKEfADo169f4ebNm00B4K+//jIbMmQIdf8nnKMeAISQOucZag0bNxMc/iMBR9cn4vaVfLQf7A0DIxHX0R5J/s8/KNm+HbLRo2AQHMx1HNKIMQwDm6+/hiYnF1lffQ2BlTUkEa25jvVKpCUUYN/yq5DK9NBjUjAMjcVcRyKEvGGe9U59XauZA+DB7QMHDiyeMWOGw6lTpwwqKip4ERERZVzkI6Q26gFACKkXUrOq5QJb9XFHWkIBNs051yCXC1Tn5iJ71lfQ8/ODxZgxXMchrwFGKIRdVBTEnp7ImDABFQkPfSZ87aReyceeZVdgbGmAnpObUuOfEEIAGBsb61q2bKn46KOPnHv16kV3/0mDQAUAQki9YXgMmnR0RL/PQ2FgJK5aLnBDYoNZLpBlWWTN+BK6igrYLvwejFDIdSTymuBLDOGwYgV4xsZIHzkK6uxsriPVm5SLudi34irM7SToOTm4wfb0IYQQLgwYMKDwxo0b+kOHDqUCAGkQqABACKl3MjsJ+k4PQXAnR1w7lYnNc88j5zb3ywUWbdyI0pMnYfnZpxC7unIdh7xmhFaWcFi5AlqFArkLF3Edp17cPJ+NA79dg6WzEbpPDIaeIRXRCCFvnrKyskuPe23IkCHFLMvGBgcHV7zKTIQ8DhUACCGvBF/IQ3ikO3pODIZWrcO2hbE4v5u75QJVt24j9/uFMIyIgOnAgZxkIK8/PU9PmL0/DPI9e1B+9SrXcerU9TOZOLgmAbbuxug2PghifZpWiBBCCGnoqABACHml7LxMMWBmGDxCLHFh9238tegiinNe7Zw4rFqNzKlTwROLYTP3WzAM80qvT94sso8+Bl8mQ+6C7xvFihjPIv74XRxZmwgHHzP8Z2wQRHrU+CeEEEIaAyoAEEJeObGBEJ0+9EPnj/xQnFOGzXPP49rJjFfWOMpfvhwV8fGwnj0bQkvLV3JN8ubiSwxhMfYTlMXEQHnkCNdxXtrlQ2k4vvEmnAPN8e7oAAhFfK4jEUIIIeQZUQGAEMIZjxArDJgZBmtXYxzbcAN7l11BmbyyXq9ZdukS8leshHHPnjB6u3O9XouQGiZ9+kDk6orcRYvBqtVcx3lhsftTcTo6GW7BFugywh8CITX+CSGEkMaECgCEEE5JTPXQfXwTtO7rgfTrRdg05xxuX6mf5QIh39WOAAAgAElEQVR1paXInDYdQmtrWH05o16uQcijMEIhLD+dgsrbt1G0dSvXcZ4by7I4t+sWzu64BY9QK3T+yA98AX2EIIQQQhob+u1NCOEcw2MQ9JYD+n4eAgNjMfYuu4Kj6xNRWaGp0+vkzJ8PdXo6bL9fAL5EUqfnJuRpJO3bwyA0FPm/LIVWqeQ6zjNjWRb/bk9BzJ5U+ITboOMHvuDx6eMDIYQQ0hjRb3BCSIMhs5Og77QQBHd2RMLpTGyZewHZt0rq5NyKI0dQvDUaso+GwyAkpE7OScjzYBgGllOnQltYiIJVv3Ed55mwLItTW5Jw6Z80+LexQ/vB3uDxaNJMQgipbfjw4Q6zZ8++N6lQ69atPfr37+9U8/zjjz+2nz59unWXLl2ea83hJUuWyIYOHepYl1kJoQIAIaRB4Qt5CO/tjp6TgqHV6vDXoos4t+sWtC+xXKAmPx9ZX86E2McHFuPG1WFaQp6PfoA/jLp2ReHvv0Odnc11nCdidSyO/XkDV47eRVBHB7R5zxMMNf4JIeQhrVu3Vp49e1YCAFqtFkVFRYIbN27o17x+4cIFSadOnRT79++/xV1KQqpQAYAQ0iDZeZpiwMzm8Ay1QsyeVPz1fewLLRfIsiyyvpwJnVIJu+8XgBGJ6iEtIc/OYuJEgGWRF/UT11EeS6fV4fDa60g4mYlmXZzQKtKdlsskhJDHaN++vfLixYsSAIiNjdX38vIqNzQ01Obl5fHLy8uZlJQUPQsLC42Hh4cfUHVnv3Pnzm4REREeTk5O/qNGjbKvOddPP/0kc3Z29g8ICPA5c+YMjVckdY4W7iWENFhifQE6fuAL50BzHNuQiM1zz6NVHw/4Rdg+c2OkeMtWKI8dg9Xn0yH28KjnxIQ8ncjeDqZDBqNw9RqYDRsKPR8friPdR6vV4dCaBCTH5KJ5dxeEvOvCdSRCCHkmh9dedyjMUBrU5TnN7CRlbw31SX/SPs7Ozmo+n88mJSWJjh8/btiiRYvSjIwM4ZEjRySmpqYaT0/PcrFYfN9axwkJCQZxcXEJ+vr6Ond3d/9PP/00RygUYv78+baxsbHXzczMtOHh4V7+/v7Pf/eDkCegHgCEkAbPvZklBsxsDhs3Yxz/8wb2PONygZV37iBn/nwYtGwB0yFDXkFSQp6N+ciR4BsZIef778Gy7NMPeEW0ah0O/BqP5JhchPd2p8Y/IYQ8o2bNmimPHj1q+O+//0oiIiKU4eHhpadPnzY8efKkpHnz5g/N/Nq6dWu5TCbTGhgYsO7u7hUpKSniEydOGLZo0UJha2ur0dPTY3v37l3IxXshrzfqAUAIaRQkpmJ0G9cEV47dxb/bU7Bx9jm0H+wN1yYWj9yf1WiQMXUqGKEQtt99B4ZH9U7ScPCNjGD+ySfImTcPpSdPQtKmDdeRoKnUYt/KeKRdK0BEf08Etrd/+kGEENKAPO1OfX0KDw9XnjlzRpKYmKgfGhpa7urqWhkVFWUlkUi077///kPrG4tEonvVXz6fz6rVahpnRV4J+kRMCGk0GB6DoA4O6Pd5KCSmYuxbcRWb557H0fWJuHYyA3lpinuTBeavXImKuCuw/moWhNbWHCcn5GGmA/pD6OSI3IULwWrqdsnL56VWabFn2RWkJRSg3SAvavwTQshzatOmjfLQoUMmJiYmWoFAACsrK61cLudfunRJ0qFDh9JnPEfpuXPnpNnZ2XyVSsVs377dtL5zkzcP9QAghDQ6ZraG6DMtBHGH05F+vRApF3ORcCoTAMAX8GAm40F8KReWb4+EVXA76HQsLV1GGhxGJILl5CnImDABxdu3w7RvX05yVJZrsHtpHLJTStBxmA+8WthwkoMQQhqzsLCw8uLiYkHv3r0LarZ5e3uXl5aW8m1sbDRyufypsxA7OTmpp02bltmiRQsfqVSqpfH/pD4wDWHsYUhICBsTE8N1DEJII8WyLEryypF3R4Gc5EKkHTgPucgKWl7V71qhmA8LRyksnKSwcjKChZMUxhb6NKs54RzLsrgzcBDUd+/Cbf8+8AwNX+n1VWVq7Po5Dnl3FOj4oS88Qqxe6fUJIeR5MAwTy7JsSO1tcXFxqUFBQQ91sSfkTRYXF2ceFBTk/KjXqAcAIaTRYxgGJpYGMLE0gGTvCsjOboL96jVQOwcg944cuXcUyL0jR/yxDMRpqoYHig0EsHCUwtLZCJZOUlg6GUFiKqaiAHmlGIaB5dTPcOe9gShY8zssxn7yyq5drqzE3z9dRmFWKd4e4f/Y+TQIIYQQ8vqgAgAh5LWhPH4cxRs3wez99yENbwGgariAd8uqLs1arQ6FGaX3FQUu/5MGna6qJ5S+keheMaDmbwOjp/bYI+SlGAQHQ/r22yhYvRom/fpCaGlZ79csk1diZ9QllOSV493RgXDyk9X7NQkhhBDCPSoAEEJeC5qiImTO+BJiT09YTJr4yH34fF7VUABHKfwiqo+r1CI/Q4ncVAXy7siRc0eBO/EFQPXoKImp+L5eAhaOUugZCl/RuyJvCsvJk6A4cgT5P/8Cmzmz6/VayiIVdkZdgrKoAl0/CYS9t1m9Xo8QQgghDQcVAAghjR7LssieNQu6khLY/vc38MTiZz5WIOLD2sUY1i7G97ZVVmiQn66o6iWQWtVb4NalvHuvG1voVxUEqgsD5g5SiPToxyl5cSInJ5gNfA+F69bDbOgQiD086uU68oJy7PzxEsqVanQb3wS27ib1ch1CCCGENEz0iZUQ0uiV/LUdioOHYPnZZ9Dz8nrp84n0BLD1MIWtx/9X36koVSMvTXFv+EBWSgmSYnKrXmQAkZgPvpAHgZAPgYhX/Zj3/23CWttEfAgEPPBFVc8FQv4D+1dvEz3hHEIezVfwmpGNGoXiv7YjZ9EiOK5cWefnL84tw86oS1BXaNFjQjCsXIzq/BqEEEIIadioAEAIadQq09ORM3cuDEJDYfb+sHq7jp6hEA4+ZnDw+X936TJ5JXLvyJGXpoCqVAONWguNWgetWgeNWgdNpRZajQ5l5ZXV2+9/XavWvVSmh4oGIj5ktoaw9TCBjYcJzKwNwdDyh42GwNQU5qNGIXfhQpT++y8MW7ass3PfvVGEg6uvQadh0WNiMCwcpXV2bkIIIYQ0HlQAIIQ0WqxWi8xp0wEeD7bzvwPD57/S6xsYieAcYA7nAPMXOp5lWWg1OmgqaxUN1Nr/FwgqdQ8VFbTV+9Q+pqawUFmhRWZS8b2eCXqGQti4G8PWwwS2HiYwt5eAx+fV5ZeA1DHTwYNQtGEDcr5fCJdt0WB4L/f9qqzQ4N+/UhB/IgPGlvp4Z3wAZHaSOkpLCCEEAPh8fjMPD4/ymue9e/cunDdvXjaXmQh5HCoAEEIarYLf/ovyixdh+/0CCO3suI7z3BiGqe7aX3eFC5ZlIc+vQGZSMTKTi5F5swi346qWRxbq8WHjVlMQMIWlkxR8ARUEGhKeWAyLyZOR+emnKPn7b5j07PnC57qbWIgj6xKhKKxAUEcHNO/uCqHo1RbJCCHkTSAWi3WJiYkJXOcg5FlQAYAQ0iipbt1C3i+/QNqlC4y6deM6ToPBMAyMLfRhbKEPn/Cq5Q+VRRVVxYCkEmQmFePsjlsAqoYQWLsawda9qoeAlasxNRAbAKN330Hh778jL+onGHXpAp6e3nMdX1mhwZm/UnCt+q5/7ylNYUOT/RFCyCtVUFDAb9asmc/OnTuTgoKCVN26dXNp166dYsqUKfnR0dFGs2bNstNqtYyZmZnm33//vSmXy3nDhw93TExM1NdoNMyMGTMyBw8eXBwTE6P3wQcfuKjVakan02Hbtm0pTk5O6u7du7tmZWWJdDodM3Xq1MyPP/64iOv3TBoHKgAQQhodlmWRPXsOePr6sP5yBk2G9xQSUz14hlrDM9QaAFCuqERWcsm9XgIxe1PBsgCPz8DSSVo9AaIJrN2MIdanXxOvGsPjwXLqZ0gbOgyFf6yF+cgRz3xsemIhjq5NhKKI7voTQt48B5ZHOeSn3zGoy3OaOziVvT16YvqT9lGpVDxvb2/fmudTpkzJ+vjjj4t+/PHHtGHDhrmMGTMmp7i4WDBlypT8zMxMwdixY52PHTuW6O3tXZmTk8MHgC+++MKmffv28q1bt6bm5+fzQ0JCfLp37y7/+eefLcaMGZMzevTowoqKCkaj0SA6OtrY2tpafezYsWSgqthQl++ZvN7okx0hpNGR796DsrNnYf3VLAjMX2z8/ZtMXyqCa7AFXIMtAACqcg2ykouRlVyMzKRiXD6YhosH7oBhAHMH6b0eAjYextCXiDhO/2YwDAuD5K23UPDrrzDpEwmBTPbE/SsrNDizLRnXTmbCxMoAvT9tBhs34yceQwghpG48bghAr1695Fu2bDGdOnWqU2xs7DUAOHbsmGFYWJjC29u7EgCsrKy01duNDhw4YLJkyRJrAFCpVExycrKoZcuWpYsWLbK5e/euaMCAAUUBAQGqpk2bls+YMcNh9OjRdj169Cjp0qWL8lW+X9K4UQGAENKoaBUK5CxYAL2AAJj068d1nNeCWF9w32SG6kotcm79v4dA/MkMxB2puvlhalO1yoCdhwls3E0gMRVzGf21ZjllCm5164b8pctgPWvmY/dLv16II+uuQ1mkQpNOjmjezQUCuutPCHkDPe1O/aum1Wpx8+ZNPT09PV1BQYHAzc1N/bh9WZZFdHR0clBQkKr29qZNm1ZERESUbt++3bhr164eP//8853u3bsrLl68mLBt2zbjmTNn2h06dEi+aNGirPp/R+R1QAUAQkijkhf1E7SFhXBYseKVz/r/phCK+LD3NoO9d9WSh1q1Drl35NXzCBTj5vlsXDuRAQAwstCvmlSwupeAkbkeDcmoI2JXF5j274eizZthOngwxK4u971eWa7B6b+SkVB91z/ys2awdqW7/oQQ0lDMnj3bytPTs2Lu3LkZH374oXNsbGxiu3btSidPnuyUmJgoqhkCYGVlpW3fvr188eLFVr///nsaj8fD6dOn9Vu1alWekJAg8vHxUfn5+eWmpaWJLl++rB8YGFhhaWmpGTNmTKGpqan2v//9L3WHJM+MCgCEkEajPP4aijZuhOl770Hf34/rOG8MvpAHG/eqO/7NugA6rQ75d5VVPQSSinE7Lg+JZ6puPBiaiGHrYQJHXzM4+cugL6UhAy/D/JNPULLzb+T+sBgOv/xyb3t6QiGOrL+OUrrrTwghnHtwDoAOHTqUjBw5Mn/dunXmsbGx101NTXXR0dGK6dOn2/z444+ZS5YsSe3Vq5e7TqeDTCZTnzlzJmn+/PmZI0aMcPT29vbV6XSMg4OD6ujRo8nr168327Jli0wgELAWFhbqOXPmZJ06dcrw888/t+fxeBAIBOyyZcvucPn+SePCsCzLdQaEhISwMTExXMcghDRgrFaL1AHvQZ2VBbe9e8A3MuI6EqnG6lgUZpci82bVkIGMm8Uol1cCDGDtYgznQBmcA81hZmNIvQNeQP6KlciLioLT+nUQ+DXB6W3JSDhVddf/rWE+dNefEPLGYBgmlmXZkNrb4uLiUoOCgvK5ykRIQxQXF2ceFBTk/KjXqAcAIaRRKN66FRVXr8J24UJq/DcwDI+BzFYCma0EAe3swepY5KUrkHolH6lXC3B2xy2c3XELRuZ6cAowh0uAOWw9TcAX8LiO3iiYDRuKoo0bEf/Dn7jupEJpsQrBnR0R1pXu+hNCCCHk+VABgBDS4Gny85H7w48waNECRl3/w3Uc8hQMj4GlkxEsnYwQ1s0VyiIVUq/mI/VqPhJOZeLq0bsQ6vHh6GsG50DzqqECtLrAY6khRErH6UjJEMFIXY7eU0Nh7UJ3/QkhhBDy/KgAQAhp8HIXLoKuvBzWs2ZSF/JGSGIqhn8bO/i3sYO6Uou7iUXVvQPykXIxDwwDWLsaVxUDAmQ0VKCWtGsFOLo+EaXFIriUxsIt+RAs7XZxHYsQQgghjRQVAAghDVrp+fMo2bkTspEjIXZ15ToOeUlCER8ugeZwCTS/N1Tg9pV8pF7Jx7/bU/Dv9hQYmetVLUsYaA5bjzdzqICqXIPT0Um4fjoLptYG6D21GaRZekj7cDWK1m+A7MMPuI5ICCGEkEaICgCEkAaLraxE9uzZENrZwXzUSK7jkDpWe6hA826uUBZVIPVqAVKv5OPayUxcOXoXIj0+HHxlcAmUwcnfHHoSIdex692dawU4tj4RpcUqNH3bCaFdnSEQ8gGXcBi2iUD+ihUw6d0LfBMTrqMSQgghpJGhAgAhpMEq+OMPVCanwH75MvD09bmOQ+qZxFTv/0MFVFrcTSy8N5FgysXcqqECbsZVvQMCzGFqY/BaDRVQlalxOjoZ189kwdTGEJFTA2Dlcv+El5afforbPXshf/kKWH0+naOkhBBCCGmsqABACGmQ1BkZyF+2HJKOb0Havj3XccgrJhTz4RJkAZcgC7A6FrlpinvzBtw3VCCweqiAe+MeKnAnvmqsf1nJA3f9H6Dn6QmTyN4o/PNPmA4aCJGjIwdpCSGEENJYPfXTEsMwqxmGyWUYJr7WNjOGYQ4yDJNU/bdp9XaGYZglDMMkMwxzhWGYpvUZnhDy+sqe9x0AwPqLLzhOQrjG8BhYORuheXdX9J8RhqHzwtH2PU+YWhvi2olM/B11Gas/PYkDq+Jx41w2KpRqriM/M1WZGofXXsfuX+Ig0hcgcloIWvZye2Tjv4b5uHFgBALk/vjjK0xKCCHkcZRKJRMaGuql0WgAAHfu3BG2b9/e/UnH3LhxQ+Th4eH3ItdbsmSJLDU19bFj4iZOnGi7Y8cO6eNeP3HihMH777/vAAC7d++WHjx40PBFcoSFhXmdOHHC4EWOfREGBgbBr+paT7JkyRLZ0KFDHQFg3rx5FlFRUbKnHVNeXs6Eh4d7ent7+65atcq0Lr52Xbt2db169ar4eY97lh4AvwP4BcDaWtumAzjMsux8hmGmVz+fBuAdAB7Vf5oDWF79NyGEPDPFkaNQHj4My0+nQGhry3Uc0sBIzfTg39Ye/m3toVZpkX69sHqZwQIkx94/VEBmL4HUTA9SmR6Eosc3qrmQejUfxzbcQJm8Es26OCH0Py7gC5/ei0FoaQnZhx8if+lSlA8bBv0mTV5BWkIIIY/z888/m3fv3r1IIKhqWs2bN89q+PDh+fV1vfXr15s3adKk3NnZ+aGKt0ajQVRUVOaTjm/Tpk1ZmzZtygDgyJEjUolEou3UqVNpfeV9nY0bN64gLCzMe+LEiQVP2u/MmTMGAJCYmJgAAKtWrbJ82WuPHj06d+7cudabNm268zzHPfWTBsuyJwAUPrC5B4A/qh//AaBnre1r2SpnAZgwDGPzPIEIIW82XXk5cubOhcjdDWZDh3IdhzRwQjEfrk0s0GGIDz6Y3wp9poWg2TvOqKzQ4t/tKdj9cxw2fnMOv44/jtWfncTW7y5g/8qrOB2dhCtH03E7Lg/5d5VQlWteWWZVmRqH/0jAnqVXIDYQoM+0ZmjR0+2ZGv81ZB9+AL6FOXK+XwiWZesxLSGENB4MwzSrrz9Puu6WLVtk/fr1K655vmfPHtPIyMgSoKpBPnLkSHt/f38fT09P34ULF5o/ePyT9pkxY4a1p6enr5eXl++YMWPs1qxZYxofH28wdOhQV29vb1+lUsnY2dkFjB492s7X19dn9erVppGRkc5r1qwxBYDjx48bBAcHe3t5efkGBAT4FBUV8Xbv3i1t3769+40bN0Rr1661WLFihZW3t7fv/v37JXZ2dgEqlYoBgMLCQl7t54+yZs0ambe3t6+Hh4ff0aNHDQBg8uTJtrNmzbKq2cfDw8Pvxo0bIrlczmvXrp27l5eXr4eHh9+qVatMn/R1TUxMFDVp0sTb09PTd/z48ffdEZo5c6ZVzddr0qRJtkBVrwoXFxe/yMhIZ2dnZ//u3bu77NixQ9q0aVNvJycn/5p8R48eNWjSpIm3j4+Pb3BwsHdcXJwYqLqz37lzZ7eIiAgPJycn/1GjRtnXXO+nn36SOTs7+wcEBPicOXNGUrNdKpXq7O3tVTXnfpSMjAzBBx984HL16lUDb29v32vXrt13137lypVmnp6evh4eHn6jR4+2A4DVq1ebfvTRR/YAMGfOHEt7e/sAAEhISBA1bdrUGwC6dOmiPHnypJFa/Xw9H190DgArlmWzqh9nA6j5BtsBSK+1393qbVkghJBnkL9iJdQZGXBatxaMSMR1HNKIMDwGVi5GsHKpGi5QJq9ESW4ZFIUVkBdUQFFYAWVBBQoyS5EaXwCtWnff8SJ9AaQyvXs9BoxqPZaa6UFPInzpSQdTr+bj2PpElCnUaPaOE0Lffba7/g/iGRrCYtw4ZM/6CoqDB2HUufNL5SKEEPJiKioqmPT0dLGXl1clUNVoNTY21ujr67MAEBUVZW5sbKyNj4+/Xl5ezoSGhnp369ZNXvv3yeP2uXLlit7evXtNYmNjE6VSqS4nJ4dvZWWlXb58ueWiRYvSa+7iA4BMJtMkJCRcB4ADBw4Y12QbNGiQ24YNG1Latm1bVlhYyJNIJPd++Xl5eVUOHTo0TyKRaGfPnp0DAC1btlRs2bLFeMiQIcWrV682e/fdd4vEYvFjK83l5eW8xMTEhH379klGjBjhkpSUdO1x+/71119G1tbW6mPHjiUDQEFBwRO75o0ZM8bxo48+yhs7dmzBd999Z1H7PMnJyXpXrly5zrIsOnbs6L5v3z6Jq6trZXp6ut7mzZtvNWvWLDUwMNBnw4YNspiYmMQ///zTZO7cuTbt27dPCQoKqrhw4UKiUCjEjh07pFOnTrU/cOBACgAkJCQYxMXFJejr6+vc3d39P/300xyhUIj58+fbxsbGXjczM9OGh4d7+fv73/vaN23atPTYsWPS9u3blz3qfdjZ2WmWLVt2Z/HixVZHjx5Nrv1aamqq8Ouvv7aLjY29bmFhoYmIiPBct26dSceOHRU//PCDNQCcPn1aYmJiorl9+7bw8OHD0pYtWyoAgM/nw8nJqeLs2bMGERERj7z2o7z0JIAsy7IMwzz37QeGYUYAGAEAjjSJESEEgColBQWrV8O4Rw8YhIZyHYc0cgZGIhgYifCobmgsy6JcoYaiujCgKKiAoqC8+nE5Mm4WQV2hve8YgYj3/4KATB9SM3F1oUAfUjM9GBiJwPAeXSCoKFXj9NYkJJ7NhpmtId4dEwhLJ6NH7vusTHr3RtG6dchdvBjSdu2oYEYIIRzIzs4WSKXSe93I0tPThWZmZveeHzp0yCgxMdHg77//NgUAhULBT0hI0PPz86t42j4HDx40Gjx4cL5UKtUBgJWV1f2/mGoZOnRo0YPbrly5omdpaalu27ZtGQCYmZnpHj7yfiNGjMhbsGCB9ZAhQ4rXr19vvmrVqtQn7T9w4MBCAHjnnXeUSqWSl5+f/9hGfdOmTctnzJjhMHr0aLsePXqUdOnSRfmkc1+8eFGyb9++FAAYOXJkwZw5c+wBYP/+/UYnTpww8vX19QWAsrIyXmJiop6rq2ulnZ2dKiwsrBwAPD09yzt06CDn8Xho2rRp2bfffmsLAIWFhfz+/fu7pKam6jEMw6rV6nu/vFu3bi2XyWRaAHB3d69ISUkR5+bmClq0aKGwtbXVAEDv3r0Lb968qVdzjKWlpSYxMfHe8+dx6tQpw9rn7t+/f+Hx48clQ4YMKS4rK+MVFRXxMjMzRX379i34559/pKdOnZL07t37Xm8Tc3NzTXp6+nOtkfyiBYAchmFsWJbNqu7in1u9PQOAQ6397Ku3PYRl2V8B/AoAISEh1H+RkDccy7LInj0HPH19WE79jOs45DXHMMy9AsGDS+0BVf8eVWWaWsWBWoWCwgrkpipQUXp/lzuegIHUVO++XgRSmR7AAmd3pKBMoUbIu84Iecf5he76P/QeBAJYfvop0keOQtHmLTAbMvilz0kIIY0Zy7Kxr/qahoaGusrKyns/1A0MDHQqlerec5ZlmcWLF6dFRkbKax9348YN0dP22bdv3zNXimuKBC+rc+fOpePGjRPv3r1bqtVqmdDQ0Ion7f9gzziGYSAQCFid7v9xaoYQBAYGqi5evJiwbds245kzZ9odOnRIvmjRoif2FOfxeA+1E1mWxcSJE7M+++yz++ZZuHHjhkgkErG1joWenh4LVN0t12q1DABMmzbNrm3btoqDBw+m3LhxQ9ShQwevmmNqH8/n8+8rDjxORUUFT19fv06+/rWFhIQoly5dau7m5lbRvn175a+//moeGxsrWbZs2d2afVQqFc/AwOC5rv2in0D+BjCs+vEwADtrbR9avRpACwAltYYKEELIY8l370bZuXOwnDwJAtlTJ1MlpF4xDAM9QyEsHKRwbWKBoLcc0LqvB94ZFYB+X4Ri+OIIfBzVBgNmheE/nwSizQBPBHVwgIWTFGqVFnfiC3B+120c/v06Dv9xHXoSIfpMa4bm3V3rpPFfw7BNGxi0bIH8pUuhlcuffgAhhJA6ZWFhodVqtUxZWRkDAAEBAaqMjIx7jftOnTqVLF++3KKmEXzlyhWxXC6/7xfB4/Z5++235evXrzdXKBQ8AMjJyeEDgEQi0ZaUlDx1ZtvAwMCK3Nxc4fHjxw0AoKioiPfgeHGpVKpVKBT3nWvAgAEFH374ocvgwYOfOpHhxo0bTQHgwIEDEqlUqpXJZFpnZ2fV5cuXDQHg1KlTBhkZGWKgqru7VCrVjRkzpnDy5MnZly9fNgCATz75xG7t2rUmD567adOmylWrVpkBwKpVq+59OHznnXfk69atMy8pKeEBwO3bt4UZGRnPfGNbLpfz7e3tKwFg5cqVD83J8KA2bdqUnjt3Tpqdnc1XqVTM9u3b75u74ObNm2J/f/9yoGpVgHnz5lk8+kwPi4iIKD137pw0KytLoNFosHXrVrN27dopAaB169bKpUuXWkVERCjDw8PLzpw5IxWJRLqaHo1dAUgAACAASURBVArV713ctGnT8me9HvAMPQAYhtkIoB0Ac4Zh7gL4CsB8AFsYhhkO4A6AftW77wXwLoBkAGUAPnieMISQN5NWLkfOgu+hFxgIk759uY5DyDMR6Qkgs5VAZit55OsatRbKQhXKFZWwdDYCX1B3Df8aDMPAaupU3O4diYJVq2A5ZUqdX4MQQsiTtWnTpuSff/6R9OzZU2FkZKRzdHRUxcfHi/39/VWTJk3KT01NFQcEBPiwLMuYmZmp9+7dm1L7+Mft06dPH/nFixcNmjRp4iMUCtmOHTuW/PLLLxlDhw7NHzdunNNnn32mi4mJuf64XHp6euyGDRtSxo8f71hRUcHT09PTnThx4mbtfSIjI4v79Onjtm/fPpOoqKi0Ll26KIcPH16wYMECu+HDhz84Efwjr+Hj4+Or0WiYX3/99TZQNRxhw4YNMnd3d7/g4OBSJyenCgCIjY3V//zzz+15PB4EAgG7bNmyOwCQkJCg36tXr+IHz71s2bK0AQMGuEZFRVl36dLl3uu9e/eWX7t2TS80NNQbqOp1sWHDhtsCgeCZepVPmzYt+6OPPnJZsGCBbadOnR667oOcnJzU06ZNy2zRooWPVCrV1h7/DwAXLlyQLFiwIBMAEhMT9Vu1avXEoQ0Pnvurr77KaNu2rSfLskzHjh2LBw8eXAwAb731lnLChAmijh07KgQCAWxsbCo9PDzu9chIT08XiMVi1tHR8blmMmYawuzBISEhbExMDNcxCCEcyZ49B0WbNsF56xbo+73QkriEvNEyp02HfN8+uO3bC6GdHddxCCGkXjAME8uybEjtbXFxcalBQUH1tuTeszh16pTBokWLrHbs2HEbANauXWsSExNjsGTJkicux9dQrVmzxnTnzp0mNe+nvrVu3drj1KlTSa/iWnXt9OnT+gsXLrSu+Vq1b9/efd++fSk1Qw/q0zfffGNpZGSkmzRp0kP//uPi4syDgoKcH3XcS08CSAghL6M8/hqKNm6E6aBB1Pgn5AVZTJwA+f79yP3pJ9h9/z3XcR4v9RSwZwpg6gx4vg14vA0YU8GCENK4tW7duiwmJkau0WggEAgwdOjQ4vz8/EbZzho2bJjD0aNHjXfv3v3KGuSNtfEPALm5ucIFCxbcm/PuwVn+65OJiYl2zJgxBc97HPUAIIRwhtVqkdp/ANQ52XDbuxd8qZTrSIQ0Wrk//IiCX3+Fc3Q09P0bYDEt9TSwoQ9gaAGABYrTqrZbBwCeXar+2DYFeHU/VIIQ8npoqD0AXndDhgxxvHDhwn3j3UaPHp0zYcKE5258kleDegAQQhqk4i1bUBEfD9tFi6jxT8hLko34GMXR0cj9/ns4/vH7QzMzc+rOGWBDX8DYAXh/d1URIO8GcHM/cPMAcHIxcGIhYGAOeHSu6h3g1gHQe7mlEgkhbwSdTqdjHjVbPKkb69atS+M6A3l2Op2OAfDYlQGoAEAI4YQmPx+5P/wIg5YtYPSfd7mOQ0ijx5dIYP7JGOTM+RbKY8cgbd+e60hV7pwB1vep6uo/bBcgsazabuld9af1RKCsEEg5UlUQuLEXiPsT4An/x959x1VZvnEc/5zD3iIIAiKK4p64ZzY0Tc1cWaZpmfWrzMxVqZUzTVNLW2bT3Ktybyv33gsUVIYyZM+znt8fj9nSUg/wHOB6v16+zoGzLgQOz/197vu6IaTlzdkBj4JPFW2/DiGErTqdlJRUq1y5cukSAojSzmKx6JKSkryA03e6jywBEEJoIv7NN0nfsJHQn3/GKbSy1uUIUSIoRiNRXR8HvZ7QNT+js9c457+yDxb2vDn4Xwce/v/9GLMJYg/+MTsg6bz6eZ8wNQio9ihUbAF2DoVbuxDC5txuCcCRI0f87O3tvwLqcP9bnAtRUliA0yaT6YVGjRol3u4OEgAIIYpc9oGDXB0wAJ//vYTfsGFalyNEiZK5fTuxrw6h/PjxeD/VR7tCru5XB/8eAeq0f4/y9/c8KdEQuUUNAy7vArMBnLyg6kPq7ICq7cHN57+fRwhR7N0uABBC3BsJAIQQRUoxGIjq3gMlP5/QdWvROztrXZIQJYqiKFzp3x9D9GWqbN6Mnbtb0Rdxa/BfHgauv//B/9/lZ0HUL+rsgMgtkJUA6CC46c3ZAR3BrxbYUv8DIUSBkQBACOuVmB4AZpMJQ24Ohtwc8nP+evlfn/v9ekBYdR4bMgJHF1etvxwhSqwb332P4dIlKnzxuQz+hSgEOp0O/9GjufxkH1K++ZpyQ4cWbQFXD/wx+B9gxZn/23Fyh5pd1H8WC1w7rs4MiNgE2yeq/7yCbzYS7AiV24CDS8G9vhBCCFHM2cQMgPDwcGXnpo03B+LZGHJzyc/NwZDzx8d/DNSzb17mYsjNvjmIz8VkyP/P19Hp9Ti5uOLo6nrr0tFF/Wfv4MDZXTvxD61Kj7cn4OIuHcmFKGjGuDgude6Ce5vWVJg7V+tyhCjR4oaPIHPHDqps3oSD/12svS8IMQfhhx5qo7+B68EzoGheFyDz+h9LBS7tBGM22LtAaLs/egd4BhZdPUKIAiczAISwnk0EAMFlyyjD2re+4+06nR4n178O2J1uXr/dgP62n3N1xd7R6V+3Rbp4aD/rPppG2cAK9Bw7Cbcy3oXx5QpRasW88irZ+/ZRZf06HALlQFyIwmSIjSWq02N4Pt6VwClTCv8FYw7BD93BvdzNwb+Gv+PGPLiy+4/ZAWk3d7AqX+/mrgIdIbAh6KVfmBDFiQQAQljPJgKAWmFVleXzv1AH7C6uOLq64OTqdmswb+/07wP3gnTl5HF++nASHmV96TVuMp6+5YrkdYUo6TJ37CD2lVfxGzUSn0GDtC5HiFIhYfoMUr79lso//Yhz9eqF90Kxh9XBv6uPOvj3Ciq817pXiqLuJBCxWf0Xsx8UC7iVg9rdod5TEBQufQOEKAYkABDCejYRANhaE8C4C+f4cdp4nNzc6DVuMt7l5UylENaw5OQQ1aUrejdXKq9ejc5Btu8SoiiY09O52OFRXOrUoeLXXxXOi9jy4P92clLg4nY4vxYubAJzPvhUVYOAek+Cd4jWFQohbiPu/Fkq1KwtAYAQVpK5b7cRVL0mvd+ZgjEvj2XvvUny1ctalyREsZb8xTyM8fGUf+89GfwLUYTsvLwo98rLZO/ZQ9au3QX/ArFHbg7+y6pb/dn64B/UWuv1hicXwMgI6DoH3MvDzsnwcT34piMc/hZyU7WuVAhxU15WFuvnztC6DCFKBAkA7sA/tCp9xk9Dp9OxbMLbXL8UqXVJQhRL+ZcucePbb/F64glcG0toL0RR8376aRyCg0mcMQPFbC64J4778+B/PXhVKLjnLiouZaDRAHhuPQw7BQ+9Azk3YN0w+LAaLOsP59eDyaB1pUKUWoqisOXLOWSnpmhdihAlggQA/8KnQkX6TJiOo4srKyaNIfbcaa1LEqJYURSF6xMmond1xW/0KK3LEaJU0jk64jdiOPkREaR8913BPGncUVjQXR1AD1hXPAf/f1emIrQdCa8ehME7ofHzcGUvLO0LM6vBuuHqLgc2sHRSiNLk1I7NRB7YS6s+/bUuRYgSQXoA3IXMlGRWThpHRnIS3UaMoVKDRlqXJESxkL5mDfGj36T8+PF4P9VH63KEKLUupV0i+tWXCTgWy9bhLUmobkWD29wUuPQL2DlClQfB0a3A6rQ5igUyEyD1MmTEgWIGR3e1T0CZSuDkrnWFBUqHDp1Ohw4dep3+1nWdToce/W1v0+v0ty7RgR69eh1u3e9Oj73dbW4ObrQPaY+7Y8n6vxX350bsVRa+/QaB1WvSa8xE9HZ20gNACCtJAHCXctLTWPn+u9yIuUqX10cT1qyl1iUJYdPMGRlc6vQYDkFBVFq6BJ1styVEkUvJS+Gz45+xMmIlniYHJn6Th2uuhVmvBpLuZX/vT2g2QOY10NmBR3nQ38dzFFeKBYw5kJ8FpjxAAXtnNRBwdANd8X+PU1BQFAULFhRFva6gYFEst73t1vXfb1MsWLCAwl9uu1ceDh70qdGHfjX74ePiUwhfqSgOTAYDi8YOJzstlWenz8Xdu6zsAiBEAZAA4B7kZWexetp4rl+MoOPLw6jV9iGtSxKiaFjMkHQB4g6rHb+vnYAKTeChceoU4Nu4PnEiqUuXUXnlCpxr1SrigoUo3fLN+fxw9ge+OvUVeaY8elfrzcsNXsYtNoXoJ/vgHBZGyA8L0Dk63v2Txh+HBd3AyVNt+Feau+Wnx8KpFXBiGSSdA70DVHsU6vVRL+2dtK7QZvwlIPhTOGBRLP+4TVEUYjJj+Pb0t2y9shVHO0e6V+3OwDoDCXIvBg0mRYHa/s0XHN+8ju5vvUdowyaAbAMoREGQAOAeGfJy+XnGJK6ePsnDg16hQYfHtC5JiIKXmXBzsH9IHfDHHwdDpnqbsxf41YKYA+DqC4++D3V7/WUP7dxTp7n85JN49+tH+bFjNPoihCh9LIqFjdEb+fjox1zLvka74HYMbzScyl6Vb90nY9Mm4oa9gXffvpR/9527e+Jbg38PteFfaR78/5miwPWTcHK5GghkJajvkbW7q9sKVmz+l/dGcfcup1/muzPf8fOln1EUhY6VOzKoziDCvMO0Lk0UgYuHD/DzjEk06tyNds8OvvV5CQCEsJ4EAPfBZDCw9qNpRB05SNtnnqPJ4z21LkmI+2fMVQ/ufz+7H3cE0mPU2/T24F8HKjSGoMbqZdkqoNerswDWDoP4oxDaDjrPAp8qKGYzl5/sgzExgSobNmDn4aHlVydEqXEk4QgfHvqQ0zdOU7NsTUY2HknTgKa3vW/CB9NJ+fZbAj+Yhle3bv/+xNdOwPeP3xz8rwPvSgVffElgNkH0L+qsgPPr1OUCZULUWQH1+oBvVa0rLJYSshP44ewPLI9YTq4plwcqPMALdV+ggV8DrUsThSQzJZkFo17D09ePpyd/iP2ftg+WAEAI60kAcJ/MJhMbP5nJhX27aN6jDy2f7IdOUn5h6ywWSLn0x5n9uMOQcAYsJvV2r4pQodEfg/2A+uDg8i/PZ4Yj38K2iWDKhTYjSIkJJGHKVAJnfohX585F83UJUYpdybjC7COz2X51O/6u/rwe/jqdQzvfasR2O4rJxNXnnif31CkqLV2Cc40at7/jtZOw4HFwcFMH/2Ur3/5+4q/ys9QQ4MRSiP5V7R8Q1EidFVCnJ7jJuvZ7lZ6fzpLzS1h0bhFp+WmE+4XzQt0XaB3UWo6/ShCLxczKSeO4fimSftM+pmzgX5d+SAAghPUkALCCxWJm65efcnrnFsI7PU67AYPlj5CwLdk3/nRm/+bZ/bx09TZHDwhq+MdgP6gxePjf3+tkJsDmMZgOrebSxvI416xOxaU/yu+DEIUoLS+NL05+wbLzy3C0c2RQ3UH0r9UfF/t/Ce3+xJScTHSPnuicnKi8cgV2Xl5/vcP1U/B9Vxn8Wyvjmro84OQySDitzqyq2h7q94FqncDBWesKi5UcYw6rI1fz3ZnvSMhJoLp3dQbVHUT7kPbYl6amlCXU/tXL2LPsBx59eRh12j3yj9slABDCehIAWElRFH5Z8BVHN/xMnQc70P7FV9Hr7bQuS5RGpnz1gP33wX7sYUiNVm/T6dV1+3+eyu9bDQr4ZzXu5WfJ/PUQlTsm4NSqB3SYcv+hghDitgxmA0vOL2HeyXlkG7PpGdaTVxq8gq+L7z0/V87RY1wZMAD3li2p8Plnf+zWcf2UOu3fweXm4D+0gL+KUur6aTUIOLVC3U3ByRNqPwHNXwG/mlpXV6wYzUbWR6/nm9PfEJ0eTbBHMANrD6Rb1W442UkTxuIo7sI5lo1/k+ot2vDYayNvexJBAgAhrCcBQAFQFIW9Kxazf9USqrdoQ6chw7Gzd/jvBwpxvxRFHdzHHlGn88cdVg/YzQb1do/Av03lb1Do+1Vn7z/A1YED8XnxBfwaGmD3LLB3gUfehUbPq30DhBD3TVEUNl/ZzEdHPiIuK47WQa0Z0WgEVb2tW1uesmgRCZMm4zv0Ncq98oo6SP2+q7rF3XPrZfBfGCxmiP5NDQPO/qz2C6jRBdqOhMCGWldXrFgUCzuv7uSrU19x+sZpfF186V+rP09WexJ3x8L9uycKTl52FgtGv4Zer6f/B3NwcnW77f0kABDCehIAFKBDa1fz28JvCA1vQpc33sLBURJoUYAUBY79AOfWqlP5c26on3dwVQ8Ygxr9cYbfq2i3S1IMBqKe6I5iMBC6bi16Z2dIjoT1w9WD3KDG0GU2BNQr0rqEKCmOJx7nw8MfciLpBNW8qzGi8QhaBrYskOdWFIX4N98kY+06gj8Yg/v5d8HOST3z71OlQF5D/IucFNj/ORycpy7RqvKwGgSEFMz3t7RQFIWD1w/y1amv2H9tPx6OHjxV/SmeqfkMPi7Sc8GWKYrCuo8+4OKhfTw1YToBYdXveF8JAISwngQABezE1o1s+/ozgmvV5YnR7+DofHdrMYX4V4oCW8bBvk/ApyoEN1cH+xUaQ7maYKftusfkeV+SNHs2wfO+wP2BB/64QVHUqa6bx6iBRbOX4cG31W7iQoj/FJMZw8dHP2bz5c2UcynHaw1f4/Eqj2NXwMt3LLm5XO7VHWPMZSp3B8eh62XwX9TyMuDQV7DvU8hJhootoe0INRCQfir35EzyGb4+/TXbrmzD0c6R7lW7M7DOQILcizYct0aeKY+o9ChMFhP1ypXs8Pzk9s1s/XIurZ8eQLMnev/rfSUAEMJ6EgAUgrO7drLps9mUrxJGj7cm4OwuU9CEFSxmWPu6eva/6UvQcZpNTac3xMYR1aUL7m3aUGHunNvfKTcVtk+Ew9+CRwB0+gBqdpWDWiHuID0/nfkn57P4/GLs9fYMrD2QgbUH4urgWjgvmHAWw9yuRK9xwKFSVSotX6nO5BFFz5Cjvt/v+Rgy4tQlXG1GqEsEbOi9vziITo/muzPfsebSGhRFoVPlTjxf53nCvMO0Lu0Wk8VETGYMkamRXEy7eOvyauZVLIoFgBfrvciQBkNKZGPdG7ExLHx7GIHVatBr7KQ/+pDcgQQAQlhPAoBCEnlwL+s/nk7ZoGB6jZ2Eq1cZrUsSxZHJAKsHw9mfoO1oeHCMzQ2aY15+hewDB6iyfh0OAQH/cedDsO4NSDgF1TpCp+ngHVI0hQpRDBjNRpZdWMYXJ78gIz+DJ6o+wZCGQ/Bz9Su8F008B991Ab09mWHvEfvmJLy6dyfg/SklcsBRbJgMcHIp7J4NKVFQrga0Hq5uI6jxrK/i5nr2dRacXcDKiJXkmnJpV6Edg+oOooFfgyKrQVEUEnISiEyNJDItkoupF4lMiyQqLQqDRe3fo0NHRc+KhJUJo6p3VcLKhLE7bjc/XvyR/rX6M6rxqBL1O2kyGFg8djhZqSk8O30u7mX/e6mGBABCWE8CgEJ0+cRRfv5wCh6+5eg9bjIePvfeoVmUYoYcWN4fLm5Tu+m3HKJ1Rf+QuWMHsa+8it+oUfgMev7uHmQ2wYEvYOf76t7Y7d6EFkPAThpnitJLURR2XN3BrCOzuJp5leYBzRnZeCTVy955LWyBSDwP33cBnZ265t83jKQ5c0j+7HPKT5iAd58nC/f1xX8zm9QQeNdMSDwL3pWg1TBo0BfspdfQvUjLS2PJ+SUsOr+I9Px0Gvs3ZlDdQbQKbFWgA+v0/HQiUiO4mHbx1kD/YupFMo2Zt+7j5+pHWJkwwrzDqFqmKmHeYYR6heJs/9eZN4qiMP3QdBaeW0ivar0Y12xcgS8B0sqOb+dxbNNaur/5HqHhTe7qMRIACGE9CQAKWez5M/w4bQLO7h70HjeZMuX/4wypEKA2glrcB2IOQNePIfxZrSv6B0tODpe6dMHOzZ3Kq1ehc7jHAXx6LGx8E86vU/sYdJkNIS0Kp1ghbNjp5NPMODSDo4lHqeJVheGNh9MmqE3hn+m7NfjXw8D14KtOi1bMZmJe+h85Bw4QsmghLvVK9vrjYsNigYiN8NuHEH9U3e2l5WvQaAA43r5juri9HGMOqyJX8f2Z70nISaBG2RoMqjOI9iHt72lwnWvKJSotisi0yL9M4U/KTbp1Hw9Hj1sD/d/P7FctUxUvJ6+7fh1FUZh7bC7zT82nc2hnJreajL2+eM8CuXTkAD9Nn0R4p8d5cOCLd/04CQCEsJ4EAEUgIeoiK99/Fzt7e3qPm4xPhYpalyRsWVYSLOyhTsvtOR9qd9e6ottKnDmLG/PnE7JoIa6NGt3/E13YCBtGQXoMNOwP7SeCa9mCK1QIGxWfFc/HRz9mQ/QGyjqX5dUGr9IjrEfRHNgnXVCn/et0MGAdlKv2l5tNqalc7tkLRVGovGol9mXld9JmKApE7YTfZsKV3eDqA81fgaaDwfnuB5VCXXKzLmod35z+hssZlwn2COa5Os/RrUo3HO0cb93PZDFxNeMqEWkR6hn9m4P9mMwYFNTjaCc7J0K9Qm8N9H8/s+/n6ldgYd78k/OZc2wOD1d8mOltp/+lxuIkMyWZBaOH4uHjS9/JM7G/hxMIEgAIYT0JAIpIcswVVk4eh9lspteYifiHWrdvsyih0mNhwRPqZZ+FEPaI1hXdVv7Fi0Q90R2vrl0JnPq+9U9oyIZfP1C7Xzt5QofJ6vTWErTWUYjfZRoy+frU1/xw9gd0Oh3P1nqWQXUH4eZQRGdxkyLgu87q9YHr/zH4/13umTNcebovLuHhVPxqPjr74n3GsUS6ul9dGhC5RX3vbDpYDQPcZMnhvTBbzOyM2clXp77izI0zlHMpR+fQziTlJnEx9SJR6VEYLUYA9Do9IZ4ht6bt/z7Yr+BeoUim5i86t4hpB6fRKqgVH7X76B9LBmydxWJm5eR3uHbxAv2mfoRPUPA9PV4CACGsJwFAEUq9Hs/KyePIy8qi+1vvUaFGba1LErbkxiVY0E2d/t93uc1OhzenpXH1pZcwXL5ClY0bCvbMYMIZWDccYvZDSCvoPAv8ahTc8wuhIZPFxMqIlXx+4nNS8lLoGtqVoeFDKe9WvuiKSIpQp/0rlpuD/3/vMZC2ajXXxo7FZ/Bg/EYML6IixT27dkINAs6uAQcXaDRQXR7gGah1ZcWKoigcuH6Ar099zf5r+ynvVv4vDfnCvMOo7FUZJzttey+sjlzN+L3jaVy+MXMfmlt04WEBOPDjcnYvXUCH/w2l7oMd7vnxEgAIYT0JAIpYRnISKyePI/NGMt1GjaNSvYZalyRswfXT8EN3UMzQbzUEFl1n4nuRe/IkccPewJiURNCM6Xh27FjwL2KxwPGFsPVdyM+ElkOh7ShwLKTtz4QoZIqi8Fvsb8w8MpPo9GialG/CyMYjqeVTq2gLSY5Uz/wrFnXa/12Ga9fefY+05csJmjsHz/btC7lIYZWkC+quASeXg95OnUnVahiUrax1ZcWO0WzEwYab026I2sCY3WOo7VObzx757J56CmglPuIcS997k2rNW9N56P3taCABgBDWkwBAA9lpqaya8g4p8bF0HvYmYU1s80yvKCIxB2FRL3B0h/4/3XE6rpYURSF10WISPvgAh3LlCPpoduE3BstOhi3vwInFUCYEHvsQqt372QIhtBSTGcOEfRM4cO0AlTwrMbzRcNoFtyv6rbyu7IUVz4HFpHb796t51w+1GAxceaYfhqgoKq1YgVOoDCZtXupl2DMHji1Uv+d1e6lbCMqMqhJlx9UdjPx1JKFeocxrPw8fl//eRk8redlZ/PDmUEDHs9Pn4OR6f7MWJAAQwnoSAGgkNyuTH6eO53pUJJ1eeYOabR7UuiShhUs7YWlf8CgPz/4MZWyvQaQ5K5tr74wjc+Mm3B94gMAPpmFXpkzRFRC9C9YPh+QIqNUNOk6Taa3C5imKwk8Xf2LawWnY6ewY0nAIvav3xkFfxGcUTQb4dZp6VrhMCDy95J4G/78zxscT3bMXdj5lqbxsGXq34jPluFTLvA5758Lhb8GYDTW7QpsRECizD0uKPXF7GLZzGAHuAcxvPx9/N3+tS/oHRVFY//F0Ig7s4akJ0wmsdv9BlAQAQlhPAgANGXJz+Gn6JGLOneaRQa9Qv30nrUsSRencWlj5PPhWU6f9e9jeH+28CxeIe30YhpgYyg17HZ9Bg9Dp9UVfiMkAe+fAbzNAbw8PjYMmg8FOmpIJ25Oal8r4vePZEbODJuWbMKXVFALcNdgCNikCVg+Ga8fVHTY6TgUnj/t+uux9+7g66AU8Oz5K4MyZRT+LQdy/nBTY/zkcnKf2manyMLQdCSEtta5MFIDD1w8zZMcQvJ28+erRrwhyD9K6pL84tXMLW76YQ+unnqVZ9yetei4JAISwngQAGjMa8lk3expRRw/Rtt/zNOnaQ+uSRFE4vhh+fhWCGsMzy8HFW+uK/iFt1WquT5yInacnQbNm4tqkidYlQUo0bBgJF7dBQH3oMhuCrNiCUIgCtit2F+/ufZf0/HSGNhzKs7WfRa8r4tBMUeDw17B5nNoQ7vE56pnfApD85XySZs3C/+23KDtgQIE8pyhCeRlw6Ct1x5WcZKjYEtqOUAMBCXSKtVNJp/jftv/hYu/CVx2+opJXJa1LAuBGXAwL3x5GYFh1eo6dhN7KnRIkABDCehIA2ACzyciGT2YRsW8XzXs+TcvefeXMSkm2/wvY9CaEtoM+i8DJXeuK/sKSm8v1SZNJX70a1+bNCfpwBva+NrSllKLA2Z9g41uQlQCNn4OH3gFX2adcaCfXlMvMwzNZdmEZVctUZVqbaVQv++8d9gtFViL8PAQiN0OVh6DbZ+BZcLMPFEUh9rXXyNr5CyHffWsbwaC4d4YcOPYD7PkYMuIgoIE6I6BGFwkCirELKRd4ceuL6NDxZYcvqeatbU8hk8HA4nEjyEy5wYDpc3Eva32PAgkAhLCeBAA2wmIxs/XLTzi9cys1W7ej6RO98Q0O0bosUZAURZ3CvnOKepDV4eutPwAAIABJREFU6xuw13Yrob/Lj44m7vVh5EdG4vvy//B99VV0doW/r/F9ycuAne/DwS/Vac0PjYPGz6udr4UoQmdunOGt397icsZl+tfqz+vhr2uzTdiFjergPz8TOkxSl8kUwpIdc2Yml3s/iTkri8qrVuHg71fgryGKiMkAJ5eqPSJSoiC4GTw6FSrIzKriKio9isFbBpNnymNe+3nU8a2jWS07v/uSoxvX8MTod6nSqGmBPKcEAEJYTwIAG6JYLOxZvpAj637CZDRQsW4Dwjt1JbRhE23WXYuCoyiwZRzs+wTq94XH59rc+vWMjRu5NnYcOkdHAmdMx71NG61LujsJZ2HjaLi8C/zrQqcPoFIrrasSpYDZYubr01/z+fHPKetSlimtp9A8oHnRF2LIhs1j4ci36u9Az/n31ejvXuRHRhL9ZB+ca9Qg5Pvv0Dk6FurriUJmNqk7rmyfBNmJUO8peOQ9abhaTMVmxvLClhdIy0/js4c/I9w/vMhruHTkID9Nn0jDTl15aOBLBfa8EgAIYT0JAGxQTkY6p7Zv5viW9WSl3KCMfwANO3ahdrv2OLnKXujFjsUMa19Xp1s2fUntYm9DgY5iMJAwfQapCxfi0qABQbNn4RCgQcMyaygKnP1ZDVnSY6BOL2g/EbxsqxGSKDliM2MZs3sMxxKP0bFSR8Y1H6fNPtxxR9VGfzcuQcvX1JkwRTSzKH39euJHjMS7f3/Kjx1TJK8pCll+JuyapfYI0NtBq2Hqz5WjHHsUN9ezrzN4y2CuZ1/n44c+pmVg0TV8zEq5wYLRr+Fe1oe+k2diX4ABoQQAQlhPAgAbZjaZiDy4l2Mb1xIfcQ4HZxfqtHuEhh274B0gA5tiwWRQD87P/gQPvAnt3rap9ZXGuDhi3xhO3smTlB0wAL8Rw4v3mTxDDuz5CHZ/pO4W0HYEtBhic0stRPH15+399Do9Y5uPpXPlzkXft8Viht2z4Jdp4O4P3b+Aym2LtgYgYepUUr5fQOCMGXh17VLkry8KSeoV2PYenPkRPIPgkfFqsGpD4bX4bzdyb/Di1heJTo9mVrtZtAtuV+ivabGYWTXlHeIjL9Bv6kf4BAUX6PNLACCE9SQAKCauX4zg6Ka1XNi7C4vFTGjDxjTs2JWQeg2lYaCtMuTA8v5qx/oOU6DlEK0r+ovMX34h/s23wGwm4P0peHbooHVJBSf1sjol+vw68K6szrqo3lHrqkQxl5qXysR9E9l2dRuN/RszpfUUAt01mCKdehlWvwQx+6FOT+g8U7OdRBSjkSsDnyPvzBkqLVuKc3UNGh+KwnNlL2x6W91KMqix+l4aLI0fi5P0/HRe3vYy526cY2qbqXSsXLh/Cw/8tILdS76nw0tDqftQwR9XSAAghPUkAChmslJTOLltIye2biQnPY2yQcGEd+pKrTYP4eDsrHV54nd56bC4D8QcgK4fQ/izWld0i2IykfTxHG7Mn49TzZpU+Gg2jiEltOHkpR2w8U1IjoCwDmpzK9+qWlcliqE9cXsYt2ccaflp6vZ+tZ7FrqgbTioKnFgKG0apM4k6z4S6vTWfVWRMTCS6Z0/0rq5UXrECO09PTesRBcxiURsFbpsAWdfVn7lHxoNXBa0rE3cpy5DFkB1DOJpwlAktJ9A9rHuhvE58xHmWvjeasGat6PL66EI5QSUBgBDWkwCgmDIZjVzY+xtHN64hMfoSTm5u1H3oURp06IyXn7/W5ZVuWUmwsAcknlObcdUunD+098OYmEj8iJHkHDpEmd698R87Bn1JD47MRjgwT50qbcqDFq+q2105eWhdmSgGck25zD4ymyXnl1C1TFWmtplKjbI1ir6QnBRY94a6nCiklTrlv0zFoq/jDnKOHOHKgIG4t2lDhU8/kca1JVF+lrrEau9cQAethkKr18HRTevKxF3INeUybOcw9sbv5e2mb9O3Zt8Cff78nGwWjB4KKPT/YA7OboWzxbEEAEJYTwKAYk5RFOIvnOPoprVEHtgDClRp3Izwxx6nQs06sjygqKXHwoIn1Ms+CyHsEa0ruiV7/wHiRo7Ekp1NwPj38OrWTeuSilZmAmyfAMcXgUeA2iTQBs6eCtt19sZZ3tr1FtHp0fSr2Y9hjYZps71f1C/w48tqd/YHx6qDLhvc7jJlwQ8kvP8+5Ya9ju///qd1OaKwpF2FbePh9Cr1vfTh96BeH+kPUAwYzAZG/TqKHTE7GBY+jEF1BxXI8yqKwvqPpxNxYA99xn9AUPXC24VEAgAhrCcBQAmSkZzEiS3rObl9M3lZmZQLqUzDTl2p2apdgXZgFXdw4xIs6KZO/++7HEJaaF0RoG4veWPePJLmfoJjpUpU+PgjnMLCtC5LO7GH1SnU8UchuDk8Nh0C6mtdlbAhZouZb898y6fHPqWsS1kmt5pMi0ANfp+NebBjkrp9qG816DEfAhsUfR13SVEU4keOImPDBoLnz8e9tWzHWaJdPQCb34a4IxDYUO0PUFGDbTDFPTFajIzbPY4N0Rt4sd6LDGkwxOqTRad3bmXzFx/Tqk9/mvfoU0CV3p4EAEJYTwKAEshoyOfcrl84tnENyTFXcPHwpN4jnajfoRMeZX21Lq9kun4KfuiurtHtv9pmBpSm1FTiR79J9q5deHbpQsCE8ejdZLomFgscX6iuac25AY2fg4feAdeyWlcmNBabGcvY3WM5mniURys9yjvN39Fme7+EM7BqMCSegSYvQPtJxWIrNktODpf7PIUpMZFKq1bhWEF2rCnRLBY4tUKdEZAZD7V7QPsJNrU8RfyT2WJm0v5JrIpcRf9a/RnVeNR9hwAp8bH88NbrBFSpRq93JqMv5NlJEgAIYT0JAEowRVGIOXOSoxvXcunIAfR6PWHNWhHeqSsBYTVkeUBBuXoAFvcGR3d49mfwtY2z6znHjhE3fATm5GT8x46hTJ8+8j3/u9w0tTfAwS/VngAPjYNGz4GdvdaViSKmKAprLq1h6sGp6NAxptkYuoR20WB7Pwsc+EIdUDl7QrfPoFrx2qHDcPky0b164xgSQsjiReidZBvOEs+QDXvmwJ6PQbGou960fkN6rdgwRVGYfmg6C88tpGdYT95p/s49NzY1GY0sHjeCzOQknp0xt0hOMkkAIIT1JAAoJdISrnN881pO7diKITeH8lXCCO/0ONVatMbO3kHr8oqvSztg6TPqOshnf7KJsx6KopC6YAEJMz7EoXx5gj76CJc6tbUuy7YlnoONoyH6N/CvC50+gEoyfbm0SMtLY+L+iWy9spVG/o14v/X72mzvlxEPP72srvmv/hh0nQPu5Yq+jgKQuX07sa8OwatXTwInT9a6HFFU0mPVmVWnloO7Pzz8LtTvK/0BbJSiKMw9Npf5p+bTObQzk1tNxl5/9wH4zu/nc3TDz3Qb9Q5VGzcrxEr/IAGAENaTAKCUMeTlcubX7RzbuJbUa3G4lfGmfvvHqPdIR9zKaLOPdLF1bi2sfF5dm9v/R3D307oizJmZXBs7jswtW3B/+GEC35+CnZcG05eLI0WBc2tg81hIj1H3V28/CbxkCnNJtiduD+/seYfU/FRea/gaA2oNKPrt/QDO/ARrXwezATpOhfABxb5BZeLsj7gxbx7lJ03Eu3dvrcsRRSn2MGx6C2IPqUviHp0qoaoNm39yPnOOzeHhig8zve10HO3+u29U1NFD/PjBBBo82oWHny+6pp8SAAhhPQkASinFYuHyiaMc3bSWy8ePYGdvT41WD9CwY1f8Q2Wf9P90fDH8/CoENYZnloOL9uFJ3rlzxA4bhjE2Dr/hwyn7/HMy5f9+GHLUaax7PgKdXt0ysMUQsJdpzCVJnimP2Udms/j8Yqp4VWFa22nabO+Xl6EOlI4vgsBwtdGfb8l4D1bMZmIGDybn0GFCFi/CpW5drUsSRUlR1J0Ctr4HGbFQq5u6+4p3Ja0rE7ex6Nwiph2cRqugVnzU7iOc7e+8RXBWagoLRg3Bzbssz0yZVaSNpiUAEMJ6EgAIbsTFcHzzOs78sh1jfh5BNWrRsOPjVG3SHDt7WQv9D/u/gE1vQmg76LMInApnr9u7pSgKaStXkjBpMnbe3gTNnoVreLimNZUIqVdgy1h1pod3ZbXDdbVHi/1ZWQHnbpzjrV1vEZUeRb+a/Xg9/PV/PdgtNFf3w+oX1RknbUbCA6PBrmQtyTKlphLdsycAlVetwt5b+7BUFDFDjrqTxe7ZYDFB81egzQi1x4WwKasjVzN+73ga+Tfik4c/wc3hn02DFYuFlVPeIT7iPP2mzsanQtEufZQAQAjrSQAgbsnLzuL0zq0c37yO9MQEdHo97mV98PT1w7OcH17l/PD483WfcqVre0FFgd9mwM4pUKML9PpG87PClpwcrk+YQPrPa3Br2ZLAD2dgX1Y62ReoSztg41uQfAGqtleDgBJyhra0ubW93/FPKetUlkmtJ9EysKUGhRjh1w9g10zwClbP+lcsmvWzWsg9dZorffvi2qQxwfPno7PTYImF0F5GPGyfCCeWgFs5deeVhv1AiyU34o42RG1gzO4x1PapzWePfPaPXVAO/rySXYu/o/2LQ6j3cMcir08CACGsJwGA+AeLxUz0scNcvxhBelIiGUmJZCQnknXjBopi+ct93cp43woIPMv5qdf9/G59ztHZRaOvooApCmwZp57FqN8XHp+reaf4/Kgo4l5/nfyLl/B99VV8X/6fHFgXFrNR3Sngl2lgzIUWr0DbUdLhuhiJy4pjzK4xHE08SoeQDrzb4l1ttvdLvgirB0P8UWjQT13vXwrOhKauWMH1d97F56WX8HtjmNblCC3FHYFNb0PMAbXpasf3oXJbrasSf7Lj6g5G/jqSUK9Q5rWfh4+LDwDXIi+w9L3RVG3cnC5vvKXJMkMJAISwXqEEADqdriPwMWAHfKUoyrR/u78EAMWD2WQiK+UGGck3Q4GbwcDvl5nJSZhNpr88xtnDE0/fcn+dRXAzKPAq54+Tm5s222wZsiA/84/L/AzI//PnMm5+/ubn0mPg6j5o+pJ6Bljjjsbp69Zz7d130Ts5EfjhDNxbSXOlIpGVqHa4Pr4Q3MtDh0lQt7csC0BdimI2GjEZDJiMBvR2dtg7OmLv6Fjo+0L/V11ro9by/oH3ARjbbKw22/spChz5DjaPATtH6Pox1H6iaGvQWPy4caSvXEWFTz/B4+GHtS5HaElR4MyPan+A9KvqrLoOk6BsqNaViZv2xO1h2M5hBLgHML/9fMro3PnhzaFYLBae/WAuzu7aLH+UAEAI6xV4AKDT6eyACKA9EAscAp5WFOXsnR4jAUDJoFgsZKel3goF0pMSybwVECSRnpSAKT//L49xdHH55wyCP1139SqjHqgrirrP8F8G6L8P3u/xc4asu/uC7JzU9f1OHuDoAXV7Qqthmg72LAYDCVOnkrZkKS7h4QTNnoWDv79m9ZRasYdhwyj1LG5wc3hsutrp2oYoFgsmgwGjIV8dlBsMmP583Xjzen7+rduMt7vf79eNd/j8reczqL+nt6G3s8fe0REHJ6eboYDTrXDg1nWHO3z+X6473OHzejt7dDod6fnpTNw3kS1XthDuF877bd4nyF2DXR2yk2HNa3Bhg9o75InPwVODbQY1ZsnP50rfZzBcvoxrs2boHBzu/M/R8W8f/8t9b/7DwQH9nx7Hrdsc1cfb26OT7ehsizEX9n0Ku2apO2A0f1ltvOosu9fYgsPXDzNkxxC8Hb15/mpzYg4eoc/4aQTVqKVZTRIACGG9wggAWgDjFUV59ObHbwMoijL1To+p6eqifFuzUoHWIWyPApj0evId7MmzdyDfwZ58e3vyb17Ps7fH/Lcp7HqLBTuL5fZPWErpFMh30JHnKGedf6fpCXhFQf3p1v77oQP0Fh16sw475f7rMestmPUKZjsFy81Ls17BcvPSbGf528f/vJ/OAna/12LRYWfW37zUob95+cft+r99rN6uu8//UwW1FpNeQdGBq6LgotFqNwVAsaADcnQu5FK6d5PQKxZcDHnoUNChgPL7b476DdLd+j4V5jdMhxW/HqKQ6ED9mRA25fe/cEZ7eyreSCE4NVXTelocPS8BgBBWKoxFzEFAzJ8+jgX+0d1Ip9O9CLwIULmMC1cqO2NBna55p0xCr9Oh06mXeh3obn2sXtfr7vdwUWjBCTNOmIE/ZgWYFR0m7DAqeozYYdTZYbH//YyN7uZI7++X/PFxAf4EKEC+yYzBaEGnA3s72zhzlFrGhTQvDTqW2yBFgWyDCbNFwV6vw9PFAU9nBxzsivCdwGKB3FS1u7UtsOPWIB07UOz441L/p+t2yq3bsANFj/oXQc/ffo102KGjKCbxWwALCqabA0MUwAw6s3r5l+sW3a3r/7xdvc3eoqOy3g1vfdF21jeZFVJzDWTkGlEs4OxkT5pbKHl2nqX+b5QC5NzlfXWKAhYLOsWCTlHUS8ufr9+8VCw373eb+9x8Dr1iAUX5222Wws0ZxD2zVwy4mLOwhW+MyaxgURQc7fW4OJTu/joGxUIumTj7GUn213bnI45q+/JClASadTFTFOVL4EtQlwD0WfnHEoBcg5nEzDwSM/NJyMgjMSOfhMw8km5eJmaon8/I++cBt4OdjnLuTvh5OuPn4YT/ny7LeTrh7+GMn6cTZV0d0etL+6GYuJPt5xJ49+czxKXl8nTTYN7sWIMyrqVox4NixGi2sP1cIksPXWVlRBLkQeuqvjzVpCLta/njaG8bwY0o+c7GZzB/VxRrT8SjuELX5gEMbhtK7UCZzixEcZNnNDNl/Tl+2H+F2oGezH26IaHlNB78Clgsx+5CWMsmlgDcbw+APKOZxIx8EjPzSLh5+XtokPR7eJCZT1qO8R+PtdfrKOfx56DAiYdq+PFQDVlPXZpdT89jwtozbDx9nTA/d97vUZcmlWRbveIiLi2XFYdjWH4ohvj0PHzcHOnZqAJ9mgRTRQ7cRCFQFIW9l27wxa+X2BWZjKujHU83rcjzrSsTVKaE7IIiRCm25cx1Rq86icFkYfzjtendqIIm3e+FSnoACGG9wggA7FGbAD4MxKE2AeyrKMqZOz2msJsA5hnNJGXeDAgy/ggGfg8NkjLziU/LJTPfxEd9GtCtgQZNooSmzBaFH/Zd5sMtERjNFoY+HMbgNqFy9riYMlsUfotMYtnBGLadS8BkUWhaqSxPNQ2mU50AXBxL93ROYT2T2cKG09eZ9+slzsRn4OvuxHOtKtGvWQherkW75EAIUbiupefyxrLj7I9KoUu9AKZ0r4uXi/yea0ECACGsV1jbAD4GfIS6uvQbRVGm/Nv9bWEXgDyjmYHfHuTQ5VQ+eyacR2uX17QeUXROx6Uz5sdTnIxNp02YL5OfqEOIj5vWZYkCkpiZx+qjcSw7FEN0cjYezvZ0bxhEnybBMjVb3LMcg4llh2L4enc0sam5hPq68WLbUJ5oGIRzKV8nLERJZrYofPHrJWZtjaC8pzNznm5AoxCZIVjUJAAQwnqFEgDcK1sIAACy8k30//oAZ+IymD+gMQ9UK6d1SaIQZeebmL01gm/2RFPWzZF3utTi8fqBMrWvhFIUhf1RKSw7dJUNp69jMFmoV8GLp5pUpGv9ADyc5WyOuLPkrHy+33uZH/ZfIS3HSOMQb15sG8ojNf2ln4wQpcjRq6kMXXKMa+l5DHs4jFcerIqdvAcUGQkAhLCeBAB/k55rpO/8/VxMzOL755vSPNRH65JEIdh2NoF3fz5NfHoeTzetyFsda8i03VIkLcfAj8fiWHowhgsJmbg62tGlXgBPNa1Iw+AyEgKJW6KTs5m/K4pVR2IxmC20r+nPSw+Eypk/IUqxjDwj4348zZoT8TSrXJaPnmpAgJf0/CgKEgAIYT0JAG4jJdtAn3n7iE/LZeELzWhY0VvrkkQBuZaey/g1Z9h8JoFq/u68370ujaXJX6mlKArHY9JYdiiGNSfiyTGYqe7vQZ8mwfQID5KdH0qxY1dTmfdrFJvPXsfBTk/P8CBeaBMqzSSFEID692PV0Tje/fk0jvZ6PuhZT5aPFgEJAISwngQAd5CYkUfveftIzTaw5MXmsla4mDNbFBbsu8yHmy9gsii8/kgYL7SWJn/iD1n5JtaeiGfpoRhOxKThaK+nY+3yPNU0mOaVfWSadylgsSjsOJ/Il79FcfByCp7O9vRvEcKAlpXw83DWujwhhA2KSspi6NJjnI7L4JlmFXmnSy3pB1KIJAAQwnoSAPyL2NQcnvxiH/kmC8teak5VPw+tSxL34c9N/tpWK8fkbnWo6OOqdVnChp27lsGyQzGsPhpLRp6JEB9X+jQJplejCjIQLIHyTWZ+PhbPl7uiuJiYRVAZF55vXZk+TYJxd7LXujwhhI0zmCzM2Hye+buiqebvztynw6leXo4ZC4MEAEJYTwKA/xCdnM2T8/ah18Hyl1pId/hiJDvfxKytEXy7J5qybk6827UWXesFyPpucdfyjGY2nb7OkoNXORCdgp1ex8M1/Hi6aUXaVisnjZ+KufRcI4sPXOXbPdEkZuZTM8CT/z0QymN1A3Cwk9lBQoh782tEEiOWn1B7BHSuSf/mIXLMUcAkABDCehIA3IUL1zN56st9uDras+J/LQgsI41ebN2WM9cZv+YM8el5PNOsIqM71pA9e4VVopKyWHY4hlVHYknOMhDg5UzvxsE82bgCFbxlRklxci09l292R7PkYAxZ+SZaV/XlpQdCaV3VVw7WhRBWSc7KZ8TyE/wakcQjNf2Z0ase3m7ST6agSAAghPUkALhLp2LT6Tt/P+U8nFj2UgvKeThpXZK4jfg0tcnflrMJVPf34P0edaRbtyhQBpOF7ecSWHooht8ikwBoG1aON9pXo0FwGY2rE//m/PUMvvwtijXH41GALvUCGNwmlDpB0uNFCFFwLBaFb/ZE88Gm85R1c2R2nwa0rOKrdVklggQAQlhPAoB7cORKCv2/PkiwtytLX2wuia4NMVsUvt97mZlbLmBWFF5/uBovtKks03hFoYpNzWHF4ViWHLxKSraBtzrVYFDrynIW2YYoisK+qBt8+VsUv1xIwtXRjj5Ngnm+VWWCy8rMDSFE4Tkdl87QpceITs7mlXZVGPZINTkusZIEAEJYTwKAe7T3YjIDvztEdX8PFg1uhqezTCvX2qlYtcnfqbh0HqhWjknS5E8UsfQcIyNXnmDr2QQ61PJnRq/6eLnKe4OWFEVh0+nrfPbLJU7FpePr7sjAlpXo1zxEtncUQhSZHIOJCWvOsuxwDA2CyzD36YYSPlpBAgAhrCcBwH3YcT6Bl344Qv0KZVgwqCmujtIlWgtZ+SZmbrnA93sv4+PuxHtda9G5rjT5E9pQFIWvd0czbeN5yns582nfcOrLkgBNJGXm8/bqk2w7l0iorxsvtAmlR3iQbM0lhNDMupPxvL36FCgwuXsdujUI0rqkYkkCACGsJwHAfdpw6hpDFh+lZRVfvhrQWA4si9jmm03+rmeoTf5GPSpN/oRtOHo1ldcWHyMxM4+xj9VkQMtKEkoVoU2nrzHmx9Nk55t4s2MNBrSsJLs1CCFsQkxKDsOWHefIlVR6NarAhMdr4yZbjd4TCQCEsJ4EAFZYdSSWEStO8EhNPz7v10jWdRWB+LRc3ltzhq1nE6hR3oMp3evSKMRb67KE+Iu0HAMjlp9g+/lEOtUpzwe96slyoUKWnmtkwpozrD4WR90gL2b3qU9VP9mHWwhhW0xmC3N2XOSTHZGE+Lgx56mG1K0gjUjvlgQAQlhPAgArLdx/hXE/naZzvQDmPNVQzjQVEpPZwvf7rjBzywUsisKwR6oxqLU0+RO2y2JRmL8riumbLxBUxoXPngmXbvOFZHdkMqNWniAxM5/XHqrKqw9WlfcGIYRNOxB1g2HLjpOclc+oR6vzQutQ9HIM+Z8kABDCehIAFID5v0UxZcM5ejWqwPSe9eQNvICdjE1jzI+nOB2XQbvqapM/aaAjiosjV1IYsvgYN7IMvNOlJv2ah8iSgAKSazDzwabzfLf3MlXKuTG7TwPqVZC+C0KI4iEtx8Cbq06y+UwCbcJ8mflkffw8nLUuy6ZJACCE9SQAKCAfb4tk9rYI+jcPYWK32nKAXwASM/OYu/0iiw5cwcfdifFda/NY3fLyfyuKnZRsA8OXH+eXC0l0rhfAtB518ZAlAVY5HpPG8OXHiUrK5vlWlRndsbr0YhFCFDuKorD44FUmrj2Lh7M9M3rX58HqflqXZbMkABDCehIAFBBFUZi26Tzzfo3ipbahvNWphgxU71NajoF5v0Xx7Z5ojGaFZ5pVZOSj1WUNtSjWLBaFL367xMwtEQR7u/DpM+HUDpQlAffKaLYwd3skn/5yifKezszoVY+WVX21LksIIawSkZDJ0CXHOH89k0Gt1VDTyV5Czb+TAEAI60kAUIAUReG9NWdYsO8KbzxSjdcfCdO6pGIlK9/Et7uj+fK3KLIMJh6vH8iwR6pR2ddN69KEKDAHo1N4bclRUnOMvNe1Fn2bVpSw8C5FJmTyxvLjnI7LoFejCrzbtZYEg0KIEiPPaGbqhnN8v+8KtQM9mfN0Q6qUc9e6LJsiAYAQ1pMAoIBZLAqjV51k5ZFYxj5Wk8FtQ7UuyeblGc0s3H+Fz365REq2gfa1/BnRoRo1yntqXZoQheJGVj5vLD/BbxFJPF4/kPd71MVdtoK6I4tF4Zs90UzffAEPJ3ve71GXR2uX17osIYQoFFvPJjB65QnyjBbe6VKLJxtXwF4amwISAAhRECQAKARmi8LQpcdYf/Iak56oQ//mIVqXZJOMZgsrDscyd0ck19LzaF3VlxEdqtGwomzrJ0o+i0Xhs18uMmtrBJV83Pj0mXBqBkjo9XcxKTmMXHGCA9EptK/lz9QedfF1d9K6LCGEKFTX0/MYvvw4ey/dINDLmWeah/BUk2B8Svn7nwQAQlhPAoBPcsB6AAAS6klEQVRCYjRbeHnhEbadS2Rm7/r0bFRB65JshtmisPZEPLO3RXDlRg4NK5ZhVIfqso5XlEr7o24wdMkxdR/7x2vTp0mwLAlAXVK14nAsE9edBWD847XpGR4k/zdCiFLDYlHYdi6BBfuusPtiMo52errUD2BAi0rUDy6dO55IACCE9SQAKER5RjMvfH+YvZeS+aRvOI/VDdC6JE0pisKWswnM2hLBhYRMapT3YNSj1Xmohp8c1ItSLSkznzeWHWf3xWS6Nwxi8hN1cCvFSwKSMvN5e/VJtp1LpEWoDzN616OCt2z9KYQovS4mZvLDviusPBJLtsFM/eAyDGgRQud6AaWqWaAEAEJYTwKAQpZjMDHgm4Mcu5rGl8824qEa/lqXVOQURWH3xWQ+3BLBiZg0Kvu6Mbx9NTrXDUCvl4G/EKDOjPlkx0U+2h5BqK8bnz3TiOrlPbQuq8htOn2NMT+eJjvfxOiONXiuZSV5nxBCiJsy84z8eCyO7/de5lJSNj5ujjzVNJhnmoUQWMZF6/IKnQQAQlhPAoAikJFnpN9XBzh/PZPvBjYpVVPdj1xJYcbmC+yPSiHQy5lhj1SjR3iQNLMR4g72Xkxm6NLjZOUbmdStDr0bB2tdUpFIzzUyYc0ZVh+Lo26QF7P71KeqX+kLQIQQ4m4oisKeizf4ft9ltp9LQKfT0b6mPwNaVqJ5aNkSO7NSAgAhrCcBQBFJzTbw1Jf7iUnN4YdBTWkUUlbrkgrVmfh0Zm6JYMf5RHzdHXn1war0bVaxVE1TE+J+JWbm8fqS4+yLukHP8ApMeqI2ro4ld0nA7shkRq08QWJmPkMerMqQh6riICGhEELclZiUHBYduMrSQ1dJyzFSzd+dZ1tUonvDoBK3nEwCACGsJwFAEUrKzKfPvH0kZeazeHBz6lbw0rqkAncpKYtZWyNYf/Ians72vPRAFZ5rValED16EKAxmi8LH2yOZuyOSquXc+bxfeIk7I55rMPPBpvN8t/cyVcq5MevJBqW2sZUQQlgrz2hm7Yl4vt93mdNxGXg42dOrcQX6Nw8htJy71uUVCAkAhLCeBABFLD4tl95f7CPbYGLZiy1KzBrf2NQc5myPZOWRWJwd7Hi+VWUGtw3Fy8VB69KEKNZ2RSYxbOlxcgxmpnSvQ4/wkrGjyPGYNIYvO05UcjbPt6rM6I7VcXaQGUJCCGEtRVE4ejWNBfsus+HUNYxmhbbVyjGgRQjtqvthV4z7qkgAIIT1JADQwJUb2Tw5bx9mC6z4Xwsq+7ppXdJ9S8zM47Odl1h84CoA/ZqH8MqDVWSfbiEKUEJGHq8tOcbB6BT6NA5mQrfaxXawbDRbmLs9kk9/uYS/hxMf9q5fqvqiCCFEUUrMzGPpwRgWHbhCQkY+Fcu60r95CL0bV6CMq6PW5d0zCQCEsJ4EABq5mJhJn3n7cbLXs/x/LYrdFldpOQbm/RbFd3suYzBb6N2oAq89HEZQKehAK4QWTGYLH22L5JOdF6lR3oNPnwmnSjGb0hmZkMkby49zOi6DnuEVeO/xWng6yywhIYQobEazhS1nEvh+32UORqfg7KDniQZBPNuiErUCPbUu765JACCE9SQA0NDZ+Aye+nIf3m6OLH+pBf6ezlqX9J+y8k18uzuaL3+LIstgomu9QN5oX61Yz2IQojj5NSKJN5YdJ89oZmqPunRrEKR1Sf/JYlH4Zk800zdfwMPJnvd71OXR2uW1LksIIUqlc9cyWLDvCj8eiyXPaKFJJW+ebVGJjnXK23wDVgkAhLCeBAAaO3Y1lX5fHSCgjAvLXmyOj41Onc8zmlm4/wqf/3KJG9kGHqnpz4gO1agZUHxSYyFKimvpuQxdcoxDl1N5umlF3utay2aXBMSk5DByxQkORKfQvpY/U3vUlSVCQghhA9JzjKw4EsOCfVe4mpKDn4cTfZtVpG/TivjZ6EkpCQCEsJ4EADZgf9QNBn57kFBfd5a82NymGucZzRZWHollzvZIrqXn0aqqDyM6VCe8orfWpQlRqhnNFmZuieCLXy9RM8CTz54Jt6mZOIqisOJwLBPXnQXgva616NWoQondm1oIIYori0Xh14gkvt93mV8uJGGv19GpbgADW4YQXtHbpt63JQAQwnoSANiIXyOSeOH7Q9QJ8uKHQc1w13jfVotFYe3JeGZtjeDKjRwaVizDqA7VpVmXEDZmx/kEhi8/gdFkYVrPenStH6h1SSRl5vP26pNsO5dI8/+3d/exclTnHce/P2xwq4YX04oXAy1QKGBHhBCLphVt1FCBTSuTqk0CbVVCqCoR0YRIqJAiRQlSqkZQUrVNiBKgoi0kIQlJqBJE+AOUiPJmzHuMwbxj3kR5sZsUg/HTP+a4XDu75hrv3l3f/X6k0d17ds7cM/Nozt559syZg/fkwg++a4eb50SSJtGjL/yU/7jlca5a/iTrXt3AogW7cepvHciyoxaMxUgzEwDS9jMBMEauu/9ZPnbFCgBG/YSWjdU9h/zwfXbl7OMP47gj9hqrDLCkN615+X/56ytXsOKJl9l5zujP0w0bi53n7MQ5Sw7ntN8+kJ1G3aFJkrbJz17bwHfvfJrL/+sxVj23jp3CWDw+cPXf/YEJAGk7mQAYMzetfoGbVr8w6mYAsGjB7ix95z7+8y7tAF5/YyNX3voEz619ddRNYc5O4aSjFnDIXruOuimSpO1QVdz26Iv8+KEX2DgG1wznLD3CBIC0nUwASJIkSRp73gIgbb/xftaHJEmSJEkaCBMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNABMAkiRJkiRNgFTVqNtAknXAqlG3Qz9nd+CVUTdCP8e4jCfjMp6My3gyLuPHmIwn47K5w6pq11E3QtqRzR11A5pVVbV41I3Q5pJ8par+atTt0OaMy3gyLuPJuIwn4zJ+jMl4Mi6bS7J81G2QdnTeAqCt+c9RN0A9GZfxZFzGk3EZT8Zl/BiT8WRcJA3UuNwCsNwRAJIkSZL68ZpB2n7jMgLgK6NugCRJkqSx5jWDtJ3GYgSAJEmSJEkarnEZAaAhS7Ikyaokq5Oc28ouTXJ3knuSfCvJO/rU/VSrtyrJCVvbprZNn7gkyeeSPJhkZZKP96l7apKH2nLqlPL3JLm3bfOfkmSm9me26BOX9ydZkeS+JJcn6TmJqnEZniSXJXk+yX1Tyi5I8kDrx76TZI8+dXv2V0kOSnJrK/9Gkl1mYl9miz4x+UySNUnuasuJfeoakyHpE5ejktzSYrI8yTF96tqHDUmSA5LckOQnSe5P8olW/sH2+8YkfYe3e85IGoiqGvgCLKF7rN9q4NxWdhBwayv7BrBLn7qfauusAk7Y2jZdph2POcDDwMHALsDdwEJgtynrXNTruLb17gbmtRg+3LbXc5uj3tcdadlKXE4D/g3Yqa23V4+6ewKPtJ/z2+v57b3bgPcCAa4Flo56X3ekZStxeRL4jbbO+cDpxmXGY/O7wNHAfVPKjgfmttefBz4/3Zi2964CTm6vvwycMer93JGWPjH5DHD2W9QzJjMflx9u6neAE4Ebe9SzDxtuXPYFjm6vdwUebJ8vRwCHATcCi/vU9ZxxcXEZyDLwEQBJ5gBfBJa2Tu2UJAvp/jH7QlUdArwEnN6j7kLgZGAR3QX/l5LM2co2NT3HAKur6pGqeg34OnBSVa2F7htn4BeBXveDnAR8varWV9WjdAmYY/ptcwb2ZTbpdwzPAM6vqo0AVfV8j7onANdX1YtV9RJwPbAkyb50iZ1bqqroEgkfmImdmUV6xeWPgdeq6sG2zvWtbEvGZYiq6kfAi1uU/bCqNrRfbwH271G157nW+r73A99q612OcdkmvWIyTcZkiPrEpYDd2uvdgad7VLUPG6KqeqaqVrTX64CVwH5VtbKqVr1Fdc8ZSQMxjFsA+l3UTKdz8mJzOPaj+/Zyk6daGUn+FXgWOBz451a2LMn5b1G37zY1bf2O4a8DH25DNK9NcihAksVJLnmLuvu111uWa/p6Hdt9gLlThmb+CXAAGJcx81G6byZJsiDJD1p5v7j8MvDylASCcRmcM9ttGZclmQ/GZAycBVyQ5EngQro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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_sink.svg b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_sink.svg new file mode 100644 index 0000000..d75105e --- /dev/null +++ b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_sink.svg @@ -0,0 +1,190 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + Sink + + + + + input(s) + + diff --git a/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_source.svg b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_source.svg new file mode 100644 index 0000000..f6f581a --- /dev/null +++ b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_source.svg @@ -0,0 +1,188 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + Source + + + + + output(s) + + diff --git a/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_transformer_io_relation.svg b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_transformer_io_relation.svg new file mode 100644 index 0000000..ebbe385 --- /dev/null +++ b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/components_basic_transformer_io_relation.svg @@ -0,0 +1,299 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + Transformer + + input(s) + + output(s) + linear input-ouput relation + + + out + in + + + + diff --git a/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/oemof_component_classification_inkscape.svg b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/oemof_component_classification_inkscape.svg new file mode 100644 index 0000000..b263d20 --- /dev/null +++ b/oemof/3_day_workshop/Day_2_Components_Oemof/graphics/oemof_component_classification_inkscape.svg @@ -0,0 +1,478 @@ + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + • + + + g + + + eneric + + + • + + + For + + + developers + + + • + + + Reduced to + + + a common + + + model + + + concept + + + + + • + + + specific + + + • + + + For users + + + • + + + Concrete + + + for a + + + specific use + + + case + + + + + basic + + + + + + custom + + + + + + components + + + + o + + + wn elaboration, © Reiner + + + Lemoine + + + Institut | CC BY 4.0 + + + diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_LP_general_example.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_LP_general_example.ipynb new file mode 100644 index 0000000..53d3f93 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_LP_general_example.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Oemof Workshop Week: \n", + "## Introduction to Linear Programming and Constraints\n", + "\n", + "**The concept was created by @srhbrnds in Sep 2019**\n", + "\n", + "Welcome to this Notebook dealing with the basics of linear programming. We will calculate an example and interactively learn the basics of LP.\n", + "\n", + "### The example: \n", + "**LuckyBerlinCitizen has 20sqm of rooftop space. She wants to maximize her happiness (maxhp), and figured out two options to use the space. 1) If she would invest in a beautiful rooftop-terace (rt), furniture and plants, she rated her choice a 40 HappyPoints (hp) per sqm rooftop-terace. 2) Though if she would instead invest in a PV system (pv) she had a great feeling of activiely supporting the energy transformation from button-up, and rated that choice a 30 HappyPoints per sqm. Investment per sqm in the rooftop terace costs her 100 € per sqm and investment in a PV plant costs 60 €. She only has 1500 € total investment **" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1) Every LP has a target funtion\n", + "\n", + "From the example we derive: \n", + "\n", + "maxhp=40 x rt + 30 x pv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2) Every LP has a set of constraints / inequalities\n", + "\n", + "From the example we derive: \n", + "\n", + "a) Both options can never be negative \n", + "\n", + "rt ≥ 0 \n", + "pv ≥ 0\n", + "\n", + "b) She has limited space on her rooftop\n", + "\n", + "rt + pv ≤ 20\n", + "\n", + "c) She has a limited amount of money, and know that one option is more expensive than the other\n", + "\n", + "100 x rt + 60 pv ≤ 1500" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3) This reuslts in a feasible solution space \n", + "\n", + "![](./graphics/feasible_space.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4) Solve the optimization problem\n", + "\n", + "**NOTE!** In LP the solution space is infinite, as you can see. But LP obtained, that the optimal solutions are to be found in the vertices of the problem (the edges)\n", + "\n", + "So in our example we only have to look at 4 points. These are:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " rt pv\n", + "0 15.0 0.0\n", + "1 0.0 20.0\n", + "2 0.0 0.0\n", + "3 7.5 12.5" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "edges=pd.read_csv('1_edges_pv_rt.csv')\n", + "edges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can calculate the objective function for each pair of point (rt,pv)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtpvmaxHP
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" + ], + "text/plain": [ + " rt pv maxHP\n", + "0 15.0 0.0 600.0\n", + "1 0.0 20.0 600.0\n", + "2 0.0 0.0 0.0\n", + "3 7.5 12.5 675.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edges['maxHP']= 40* edges['rt']+30*edges['pv']\n", + "edges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we look for the maximum value of maxHP!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtpvmaxHP
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" + ], + "text/plain": [ + " rt pv maxHP\n", + "3 7.5 12.5 675.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = edges.loc [ edges [ 'maxHP' ] == edges [ 'maxHP' ].max() ]\n", + "y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Congrats you solved the LP problem :) **\n", + "\n", + "LuckyBerlinCitizen should invest in 7.5 sqm roff-top terace and 12.5 sqm of pv plant to maximize her happiness!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_edges_pv_rt.csv b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_edges_pv_rt.csv new file mode 100644 index 0000000..8d77a3f --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/1_edges_pv_rt.csv @@ -0,0 +1,5 @@ +rt,pv +15,0 +0,20 +0,0 +7.5,12.5 diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2_timeseries.csv b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2_timeseries.csv new file mode 100644 index 0000000..ac303e4 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2_timeseries.csv @@ -0,0 +1,8761 @@ +demand_el,pv,wind +0.5590619824,0,0.315569 +0.5336064859,0,0.311572 +0.5060587569,0,0.304005 +0.504140877,0,0.282872 +0.5071048732,0,0.25397 +0.5113765147,0,0.224077 +0.5418010636,0,0.19358 +0.5692616162,0.065722,0.159925 +0.6029988667,0.206962,0.127115 +0.6290645977,0.330638,0.117491 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a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2a_tutorial_micro_grid_basic.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2a_tutorial_micro_grid_basic.ipynb new file mode 100644 index 0000000..1937fc7 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2a_tutorial_micro_grid_basic.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even when adding a customized constraint to an oemof model, the initial model building is identical:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:47-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "09:53:47-INFO-Used oemof version: 0.3.1\n", + "09:53:47-INFO-Loading timeseries\n", + "09:53:47-INFO-Defining costs\n", + "\n", + "\n", + "09:53:47-INFO-DEFINITION OF OEMOF MODEL:\n", + "09:53:47-INFO-Electricity bus\n", + "09:53:47-INFO-Demand, fixed timeseries\n", + "09:53:47-INFO-Excess sink\n", + "09:53:47-INFO-Wind plant with fixed feed-in timeseries\n", + "09:53:47-INFO-PV plant with fixed feed-in timeseries\n", + "09:53:47-INFO-Diesel fuel bus, source and transformer\n", + "09:53:47-INFO-Battery storage\n", + "\n", + "\n", + "09:53:47-INFO-Generating linear equation system describing defined energy system\n" + ] + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 24*1\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh,\n", + " )}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we write the generated linear equation system to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_basic.lp', 139776358791824)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_micro_grid_basic.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Starting oemof-optimization of capacities\n", + "09:53:48-INFO-Optimization successful...\n", + "09:53:48-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Get optimized capacities\n", + "09:53:48-INFO-Capacities optimized: Storage (303.44179), Wind (1128.5236), PV (613.21074), Genset (66.607433).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2b_tutorial_micro_grid_inbuilt_bounds.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2b_tutorial_micro_grid_inbuilt_bounds.ipynb new file mode 100644 index 0000000..5d3fa30 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2b_tutorial_micro_grid_inbuilt_bounds.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even when adding a customized constraint to an oemof model, the initial model building is identical:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:38:45-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "02:38:45-INFO-Used oemof version: 0.3.1\n", + "02:38:45-INFO-Loading timeseries\n", + "02:38:45-INFO-Defining costs\n", + "\n", + "\n", + "02:38:45-INFO-DEFINITION OF OEMOF MODEL:\n", + "02:38:45-INFO-Electricity bus\n", + "02:38:45-INFO-Demand, fixed timeseries\n", + "02:38:45-INFO-Excess sink\n", + "02:38:45-INFO-Wind plant with fixed feed-in timeseries\n", + "02:38:45-INFO-PV plant with fixed feed-in timeseries\n", + "02:38:45-INFO-Diesel fuel bus, source and transformer\n", + "02:38:45-INFO-Battery storage\n", + "\n", + "\n", + "02:38:45-INFO-Generating linear equation system describing defined energy system\n" + ] + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 5\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc'], maximum=500))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc'], maximum=800))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(variable_costs=fuel_price_kWh)}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " min_storage_level = 0.2,\n", + " max_storage_level = 0.8,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we write the generated linear equation system to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:38:45-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_inbuilt_bounds.lp', 140004795674352)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_micro_grid_inbuilt_bounds.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:38:45-INFO-Starting oemof-optimization of capacities\n", + "02:38:45-INFO-Optimization successful...\n", + "02:38:45-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:38:45-INFO-Get optimized capacities\n", + "02:38:45-INFO-Capacities optimized: Storage (0.0), Wind (500.0), PV (0.0), Genset (126.56744).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:38:46-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2c_tutorial_micro_grid_inbuild_limits.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2c_tutorial_micro_grid_inbuild_limits.ipynb new file mode 100644 index 0000000..2dc75e9 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2c_tutorial_micro_grid_inbuild_limits.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even when adding a customized constraint to an oemof model, the initial model building is identical:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:43:01-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "02:43:01-INFO-Used oemof version: 0.3.1\n", + "02:43:01-INFO-Loading timeseries\n", + "02:43:01-INFO-Defining costs\n", + "\n", + "\n", + "02:43:01-INFO-DEFINITION OF OEMOF MODEL:\n", + "02:43:01-INFO-Electricity bus\n", + "02:43:01-INFO-Demand, fixed timeseries\n", + "02:43:01-INFO-Excess sink\n", + "02:43:01-INFO-Wind plant with fixed feed-in timeseries\n", + "02:43:01-INFO-PV plant with fixed feed-in timeseries\n", + "02:43:01-INFO-Diesel fuel bus, source and transformer\n", + "02:43:01-INFO-Battery storage\n", + "\n", + "\n", + "02:43:01-INFO-Generating linear equation system describing defined energy system\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "from oemof.solph import constraints\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 5\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh,\n", + " summed_max=0.5*timeseries['demand_el'].max()/0.33)}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " emission_factor = 0.6,\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)\n", + "constraints.emission_limit(model, limit=duration_hours*0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we write the generated linear equation system to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:43:01-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_inbuilt_limits.lp', 140628510109648)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_micro_grid_inbuilt_limits.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:43:01-INFO-Starting oemof-optimization of capacities\n", + "02:43:02-INFO-Optimization successful...\n", + "02:43:02-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:43:02-INFO-Get optimized capacities\n", + "02:43:02-INFO-Capacities optimized: Storage (61.27848), Wind (895.64832), PV (0.0), Genset (0.0).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02:43:02-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2d_tutorial_micro_grid_custom_constraint_summed_limit.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2d_tutorial_micro_grid_custom_constraint_summed_limit.ipynb new file mode 100644 index 0000000..55634be --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2d_tutorial_micro_grid_custom_constraint_summed_limit.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even when adding a customized constraint to an oemof model, the initial model building is identical:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "10:28:48-INFO-Used oemof version: 0.3.1\n", + "10:28:48-INFO-Loading timeseries\n", + "10:28:48-INFO-Defining costs\n", + "\n", + "\n", + "10:28:48-INFO-DEFINITION OF OEMOF MODEL:\n", + "10:28:48-INFO-Electricity bus\n", + "10:28:48-INFO-Demand, fixed timeseries\n", + "10:28:48-INFO-Excess sink\n", + "10:28:48-INFO-Wind plant with fixed feed-in timeseries\n", + "10:28:48-INFO-PV plant with fixed feed-in timeseries\n", + "10:28:48-INFO-Diesel fuel bus, source and transformer\n", + "10:28:48-INFO-Battery storage\n", + "\n", + "\n", + "10:28:48-INFO-Generating linear equation system describing defined energy system\n" + ] + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "from oemof.solph import constraints\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 24\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=400, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh)}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we now add our renewable share constraint. For that, we have to define a function describing the renewable share criterion:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as po\n", + "\n", + "def renewable_share_criterion(model, genset, pv_plant, wind_plant, bel, min_renewable_share):\n", + " def renewable_share_rule(model): \n", + " total_renewable_generation = sum(model.flow[pv_plant, bel, :])\n", + " total_renewable_generation += sum(model.flow[wind_plant, bel, :])\n", + " total_generation = total_renewable_generation \n", + " total_generation = total_generation + sum(model.flow[genset, bel, :])\n", + " \n", + " expr = total_renewable_generation - total_generation * min_renewable_share\n", + " logging.info('Expression generated: %s', expr)\n", + " return expr >= 0\n", + "\n", + " model.renewable_share_constraint = po.Constraint(rule=renewable_share_rule)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Now, we have to call the previously defined function renewable_share_constraint. \n", + "With \"po.Constraint\" the constraint is added to the linear equation system generated by pyomo.\n", + "Save to lp file to check constraint:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Expression generated: flow[pv,electricity_bus,0] + flow[pv,electricity_bus,1] + flow[pv,electricity_bus,2] + flow[pv,electricity_bus,3] + flow[pv,electricity_bus,4] + flow[pv,electricity_bus,5] + flow[pv,electricity_bus,6] + flow[pv,electricity_bus,7] + flow[pv,electricity_bus,8] + flow[pv,electricity_bus,9] + flow[pv,electricity_bus,10] + flow[pv,electricity_bus,11] + flow[pv,electricity_bus,12] + flow[pv,electricity_bus,13] + flow[pv,electricity_bus,14] + flow[pv,electricity_bus,15] + flow[pv,electricity_bus,16] + flow[pv,electricity_bus,17] + flow[pv,electricity_bus,18] + flow[pv,electricity_bus,19] + flow[pv,electricity_bus,20] + flow[pv,electricity_bus,21] + flow[pv,electricity_bus,22] + flow[pv,electricity_bus,23] + flow[wind,electricity_bus,0] + flow[wind,electricity_bus,1] + flow[wind,electricity_bus,2] + flow[wind,electricity_bus,3] + flow[wind,electricity_bus,4] + flow[wind,electricity_bus,5] + flow[wind,electricity_bus,6] + flow[wind,electricity_bus,7] + flow[wind,electricity_bus,8] + flow[wind,electricity_bus,9] + flow[wind,electricity_bus,10] + flow[wind,electricity_bus,11] + flow[wind,electricity_bus,12] + flow[wind,electricity_bus,13] + flow[wind,electricity_bus,14] + flow[wind,electricity_bus,15] + flow[wind,electricity_bus,16] + flow[wind,electricity_bus,17] + flow[wind,electricity_bus,18] + flow[wind,electricity_bus,19] + flow[wind,electricity_bus,20] + flow[wind,electricity_bus,21] + flow[wind,electricity_bus,22] + flow[wind,electricity_bus,23] - (flow[pv,electricity_bus,0] + flow[pv,electricity_bus,1] + flow[pv,electricity_bus,2] + flow[pv,electricity_bus,3] + flow[pv,electricity_bus,4] + flow[pv,electricity_bus,5] + flow[pv,electricity_bus,6] + flow[pv,electricity_bus,7] + flow[pv,electricity_bus,8] + flow[pv,electricity_bus,9] + flow[pv,electricity_bus,10] + flow[pv,electricity_bus,11] + flow[pv,electricity_bus,12] + flow[pv,electricity_bus,13] + flow[pv,electricity_bus,14] + flow[pv,electricity_bus,15] + flow[pv,electricity_bus,16] + flow[pv,electricity_bus,17] + flow[pv,electricity_bus,18] + flow[pv,electricity_bus,19] + flow[pv,electricity_bus,20] + flow[pv,electricity_bus,21] + flow[pv,electricity_bus,22] + flow[pv,electricity_bus,23] + flow[wind,electricity_bus,0] + flow[wind,electricity_bus,1] + flow[wind,electricity_bus,2] + flow[wind,electricity_bus,3] + flow[wind,electricity_bus,4] + flow[wind,electricity_bus,5] + flow[wind,electricity_bus,6] + flow[wind,electricity_bus,7] + flow[wind,electricity_bus,8] + flow[wind,electricity_bus,9] + flow[wind,electricity_bus,10] + flow[wind,electricity_bus,11] + flow[wind,electricity_bus,12] + flow[wind,electricity_bus,13] + flow[wind,electricity_bus,14] + flow[wind,electricity_bus,15] + flow[wind,electricity_bus,16] + flow[wind,electricity_bus,17] + flow[wind,electricity_bus,18] + flow[wind,electricity_bus,19] + flow[wind,electricity_bus,20] + flow[wind,electricity_bus,21] + flow[wind,electricity_bus,22] + flow[wind,electricity_bus,23] + flow[genset,electricity_bus,0] + flow[genset,electricity_bus,1] + flow[genset,electricity_bus,2] + flow[genset,electricity_bus,3] + flow[genset,electricity_bus,4] + flow[genset,electricity_bus,5] + flow[genset,electricity_bus,6] + flow[genset,electricity_bus,7] + flow[genset,electricity_bus,8] + flow[genset,electricity_bus,9] + flow[genset,electricity_bus,10] + flow[genset,electricity_bus,11] + flow[genset,electricity_bus,12] + flow[genset,electricity_bus,13] + flow[genset,electricity_bus,14] + flow[genset,electricity_bus,15] + flow[genset,electricity_bus,16] + flow[genset,electricity_bus,17] + flow[genset,electricity_bus,18] + flow[genset,electricity_bus,19] + flow[genset,electricity_bus,20] + flow[genset,electricity_bus,21] + flow[genset,electricity_bus,22] + flow[genset,electricity_bus,23])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_renewable_share = 1\n", + "renewable_share_criterion(model, genset, pv_plant, wind_plant, bel, min_renewable_share)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_custom_summed_limit.lp', 139741797388528)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_micro_grid_custom_summed_limit.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Starting oemof-optimization of capacities\n", + "10:28:48-INFO-Optimization successful...\n", + "10:28:48-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Get optimized capacities\n", + "10:28:48-INFO-Capacities optimized: Storage (512.49327), Wind (1367.2112), PV (488.26773), Genset (0.0).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10:28:48-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check whether constraint is fullfilled by calculating renewable share:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constraint fullfilled, renewable share at 100.0%.\n" + ] + } + ], + "source": [ + "renewable_generation = sum(el_bus['sequences'][(('wind', 'electricity_bus'), 'flow')])\n", + "renewable_generation += sum(el_bus['sequences'][(('pv', 'electricity_bus'), 'flow')]) \n", + "total_generation = renewable_generation + sum(el_bus['sequences'][(('genset', 'electricity_bus'), 'flow')]) \n", + "\n", + "renewable_share = renewable_generation/total_generation\n", + "\n", + "if renewable_share >= min_renewable_share:\n", + " print('Constraint fullfilled, renewable share at ' + str(round(renewable_share*100, 1)) + '%.')\n", + "else:\n", + " print('Constraint failed, renewable share at ' + str(round(renewable_share*100, 1)) + '%.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2e_tutorial_micro_grid_custom_constraint_flows.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2e_tutorial_micro_grid_custom_constraint_flows.ipynb new file mode 100644 index 0000000..ca4118e --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2e_tutorial_micro_grid_custom_constraint_flows.ipynb @@ -0,0 +1,590 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even when adding a customized constraint to an oemof model, the initial model building is identical:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "16:03:39-INFO-Used oemof version: 0.3.1\n", + "16:03:39-INFO-Loading timeseries\n", + "16:03:39-INFO-Defining costs\n", + "\n", + "\n", + "16:03:39-INFO-DEFINITION OF OEMOF MODEL:\n", + "16:03:39-INFO-Electricity bus\n", + "16:03:39-INFO-Demand, fixed timeseries\n", + "16:03:39-INFO-Excess sink\n", + "16:03:39-INFO-Wind plant with fixed feed-in timeseries\n", + "16:03:39-INFO-PV plant with fixed feed-in timeseries\n", + "16:03:39-INFO-Diesel fuel bus, source and transformer\n", + "16:03:39-INFO-Battery storage\n", + "\n", + "\n", + "16:03:39-INFO-Generating linear equation system describing defined energy system\n" + ] + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "from oemof.solph import constraints\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 24\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh)}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " min_storage_level = 0.2,\n", + " max_storage_level=0.9,\n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1/5,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we now add our renewable share constraint. For that, we have to define a function describing the renewable share criterion:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as po\n", + "\n", + "def stability_criterion(model, storage, demand_sink, genset, bel, stability_limit, optimize_storage):\n", + " '''\n", + " The stability constraint can be divided into two sub-problems:\n", + "\n", + " * A minimal stored electricity per timestep, in order to have enough energy stored to fullfill the needs\n", + " * A minimal storage discharge power, to allow sufficiently high storage discharge\n", + " '''\n", + " def stability_rule_capacity(model, t):\n", + " # The first is dependent on the installed capacity and specific technical storage parameters:\n", + " expr = 0\n", + " ## ------- Get demand at t ------- #\n", + " demand = model.flows[bel, demand_sink].actual_value[t] \\\n", + " * model.flows[bel, demand_sink].nominal_value\n", + " expr += - stability_limit * demand\n", + "\n", + " ## ------- Genset generation at t ------- #\n", + " expr += model.flow[genset, bel, t]\n", + " \n", + " ## ------- Get stored capacity storage at t------- #\n", + " print(storage)\n", + " stored_electricity = 0\n", + " if optimize_storage == True: \n", + " stored_electricity += model.GenericInvestmentStorageBlock.capacity[storage, t] \n", + " stored_electricity -= storage.min_storage_level[t] * model.GenericInvestmentStorageBlock.invest[storage]\n", + " stored_electricity = stored_electricity * storage.outflow_conversion_factor[t]\n", + " else: \n", + " stored_electricity += model.GenericStorageBlock.capacity[storage, t] \n", + " stored_electricity -= storage.min_storage_level[t] * storage.nominal_capacity\n", + " stored_electricity = stored_electricity * storage.invest_relation_output_capacity * storage.outflow_conversion_factor[t] \n", + " \n", + " expr += stored_electricity \n", + " logging.info('Expression generated (stability_rule_capacity): %s', expr)\n", + " return (expr >= 0)\n", + " \n", + " def stability_rule_power(model, t):\n", + " #The second can be defined solely on the flows of the energy system:\n", + " expr = 0\n", + " ## ------- Get demand at t ------- #\n", + " demand = model.flows[bel, demand_sink].actual_value[t] \\\n", + " * model.flows[bel, demand_sink].nominal_value\n", + " \n", + " expr += - stability_limit * demand\n", + " expr += model.flow[genset, bel, t]\n", + " \n", + " ## ------- Get output power of storage ------- #\n", + " storage_power = 0\n", + " if optimize_storage==True: \n", + " storage_power += model.InvestmentFlow.invest[storage, bel]\n", + " else: \n", + " storage_power = model.flows[storage, bel].nominal_value \n", + "\n", + " expr += storage_power \n", + " logging.info('Expression generated (stability_rule_power): %s', expr)\n", + " return (expr >= 0)\n", + " \n", + " #Both constraints are processed with Pyomo to generate a set of linear equations:\n", + " model.stability_constraint_capacity = po.Constraint(model.TIMESTEPS, rule=stability_rule_capacity)\n", + " model.stability_constraint_power = po.Constraint(model.TIMESTEPS, rule=stability_rule_power)\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -111.81239648000002 + flow[genset,electricity_bus,0] + (GenericInvestmentStorageBlock.capacity[storage,0] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -106.72129718000002 + flow[genset,electricity_bus,1] + (GenericInvestmentStorageBlock.capacity[storage,1] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -101.21175138000001 + flow[genset,electricity_bus,2] + (GenericInvestmentStorageBlock.capacity[storage,2] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -100.8281754 + flow[genset,electricity_bus,3] + (GenericInvestmentStorageBlock.capacity[storage,3] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -101.42097464 + flow[genset,electricity_bus,4] + (GenericInvestmentStorageBlock.capacity[storage,4] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -102.27530294000002 + flow[genset,electricity_bus,5] + (GenericInvestmentStorageBlock.capacity[storage,5] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -108.36021272 + flow[genset,electricity_bus,6] + (GenericInvestmentStorageBlock.capacity[storage,6] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -113.85232323999999 + flow[genset,electricity_bus,7] + (GenericInvestmentStorageBlock.capacity[storage,7] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -120.59977334000001 + flow[genset,electricity_bus,8] + (GenericInvestmentStorageBlock.capacity[storage,8] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -125.81291954000001 + flow[genset,electricity_bus,9] + (GenericInvestmentStorageBlock.capacity[storage,9] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -126.92877692 + flow[genset,electricity_bus,10] + (GenericInvestmentStorageBlock.capacity[storage,10] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -123.00584081999999 + flow[genset,electricity_bus,11] + (GenericInvestmentStorageBlock.capacity[storage,11] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -121.15770204 + flow[genset,electricity_bus,12] + (GenericInvestmentStorageBlock.capacity[storage,12] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -118.94342254000001 + flow[genset,electricity_bus,13] + (GenericInvestmentStorageBlock.capacity[storage,13] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -121.45410164 + flow[genset,electricity_bus,14] + (GenericInvestmentStorageBlock.capacity[storage,14] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -136.13460030000002 + flow[genset,electricity_bus,15] + (GenericInvestmentStorageBlock.capacity[storage,15] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -141.76619300000002 + flow[genset,electricity_bus,16] + (GenericInvestmentStorageBlock.capacity[storage,16] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -141.40005230000003 + flow[genset,electricity_bus,17] + (GenericInvestmentStorageBlock.capacity[storage,17] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -136.23921192 + flow[genset,electricity_bus,18] + (GenericInvestmentStorageBlock.capacity[storage,18] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -128.13181066 + flow[genset,electricity_bus,19] + (GenericInvestmentStorageBlock.capacity[storage,19] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -121.95972452 + flow[genset,electricity_bus,20] + (GenericInvestmentStorageBlock.capacity[storage,20] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -123.18019354 + flow[genset,electricity_bus,21] + (GenericInvestmentStorageBlock.capacity[storage,21] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -113.43387673999999 + flow[genset,electricity_bus,22] + (GenericInvestmentStorageBlock.capacity[storage,22] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "storage\n", + "16:03:39-INFO-Expression generated (stability_rule_capacity): -107.19204951999998 + flow[genset,electricity_bus,23] + (GenericInvestmentStorageBlock.capacity[storage,23] - 0.2*GenericInvestmentStorageBlock.invest[storage])*0.95\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -111.81239648000002 + flow[genset,electricity_bus,0] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -106.72129718000002 + flow[genset,electricity_bus,1] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -101.21175138000001 + flow[genset,electricity_bus,2] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -100.8281754 + flow[genset,electricity_bus,3] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -101.42097464 + flow[genset,electricity_bus,4] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -102.27530294000002 + flow[genset,electricity_bus,5] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -108.36021272 + flow[genset,electricity_bus,6] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -113.85232323999999 + flow[genset,electricity_bus,7] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -120.59977334000001 + flow[genset,electricity_bus,8] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -125.81291954000001 + flow[genset,electricity_bus,9] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -126.92877692 + flow[genset,electricity_bus,10] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -123.00584081999999 + flow[genset,electricity_bus,11] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -121.15770204 + flow[genset,electricity_bus,12] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -118.94342254000001 + flow[genset,electricity_bus,13] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -121.45410164 + flow[genset,electricity_bus,14] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -136.13460030000002 + flow[genset,electricity_bus,15] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -141.76619300000002 + flow[genset,electricity_bus,16] + InvestmentFlow.invest[storage,electricity_bus]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Expression generated (stability_rule_power): -141.40005230000003 + flow[genset,electricity_bus,17] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -136.23921192 + flow[genset,electricity_bus,18] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -128.13181066 + flow[genset,electricity_bus,19] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -121.95972452 + flow[genset,electricity_bus,20] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -123.18019354 + flow[genset,electricity_bus,21] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -113.43387673999999 + flow[genset,electricity_bus,22] + InvestmentFlow.invest[storage,electricity_bus]\n", + "16:03:39-INFO-Expression generated (stability_rule_power): -107.19204951999998 + flow[genset,electricity_bus,23] + InvestmentFlow.invest[storage,electricity_bus]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stability_limit = 0.4\n", + "stability_criterion(model, storage, demand_sink, genset, bel, stability_limit, optimize_storage=True)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Now, we have to call the previously defined function renewable_share_constraint. \n", + "With \"po.Constraint\" the constraint is added to the linear equation system generated by pyomo.\n", + "Save to lp file to check constraint:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_custom_constraint_flows.lp', 140000044503448)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_micro_grid_custom_constraint_flows.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Starting oemof-optimization of capacities\n", + "16:03:39-INFO-Optimization successful...\n", + "16:03:39-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Get optimized capacities\n", + "16:03:39-INFO-Capacities optimized: Storage (615.0292), Wind (1061.5153), PV (656.25015), Genset (68.044965).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "stored_electricity =storage_bus['sequences'][(('storage','None'), 'capacity')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:39-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check whether constraint is fullfilled by calculating renewable share:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "boolean_test = [el_prod['Generator'][t] + (stored_electricity[t] - cap_storage * 0.2) * 1/5 * 0.9 >= stability_limit * el_sequences[(('electricity_bus', 'demand'), 'flow')][t] \n", + " for t in range(0, len(el_prod.index))] " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16:03:40-INFO-Stability criterion is not fullfilled.\n", + "16:03:40-WARNING-ATTENTION: Stability criterion NOT fullfilled!\n", + "16:03:40-WARNING-Number of timesteps not meeting criteria: 1\n", + "16:03:40-WARNING-Deviation from stability criterion: -0.13719797571672585(mean) / -0.25941626954742303(max).\n" + ] + } + ], + "source": [ + "if all(boolean_test) == True:\n", + " logging.info(\"Stability criterion is fullfilled.\")\n", + "else:\n", + " logging.info(\"Stability criterion is not fullfilled.\")\n", + " peak_demand = el_sequences[(('electricity_bus', 'demand'), 'flow')].max()\n", + " ratio = pd.Series([\n", + " (el_prod['Generator'][t]\n", + " + (stored_electricity[t] - cap_storage * 0.2) *1/5 * 0.9\n", + " - stability_limit * el_sequences[(('electricity_bus', 'demand'), 'flow')][t])\n", + " / peak_demand\n", + " for t in range(0, len(el_prod.index))], index=el_prod.index)\n", + " ratio_below_zero = ratio.clip(upper=0)\n", + " if abs(ratio_below_zero.values.min()) < 10 ** (-6):\n", + " logging.warning(\"Stability criterion is strictly not fullfilled, but deviation is less then e6.\")\n", + " else:\n", + " logging.warning(\"ATTENTION: Stability criterion NOT fullfilled!\")\n", + " logging.warning('Number of timesteps not meeting criteria: ' + str(sum(boolean_test)))\n", + " logging.warning('Deviation from stability criterion: ' + str(\n", + " ratio_below_zero.values.mean()) + '(mean) / ' + str(\n", + " ratio_below_zero.values.min()) + '(max).') " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2f_task_micro_grid_customize_constraints.ipynb b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2f_task_micro_grid_customize_constraints.ipynb new file mode 100644 index 0000000..13814ff --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/2f_task_micro_grid_customize_constraints.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "attachments": { + "2_micro_grid_system.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a micro grid system with a renewable share constraint\n", + "\n", + "The energy system model was generated in yesterdays tutorial \"2_tutorial_micro_grid.ipynb\". It can be described as following:\n", + "\n", + "![2_micro_grid_system.png](attachment:2_micro_grid_system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You also created an own version of this energy system - you can also use it. Please do following tasks:\n", + "\n", + "* Test out the in-build bounds \"Investment(maximum=...)\" and \"min_storage_level\", check the generated lp-file for \n", + "* Add a renewable minimal share constraint to your system. Validate it by trying out different minimal shares.\n", + "* Discuss a potential stability constraint within the group, then implement it - additionally to the renewable constraint - to your system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:47-INFO-Path for logging: /home/mh/.oemof/log_files/oemof.log\n", + "09:53:47-INFO-Used oemof version: 0.3.1\n", + "09:53:47-INFO-Loading timeseries\n", + "09:53:47-INFO-Defining costs\n", + "\n", + "\n", + "09:53:47-INFO-DEFINITION OF OEMOF MODEL:\n", + "09:53:47-INFO-Electricity bus\n", + "09:53:47-INFO-Demand, fixed timeseries\n", + "09:53:47-INFO-Excess sink\n", + "09:53:47-INFO-Wind plant with fixed feed-in timeseries\n", + "09:53:47-INFO-PV plant with fixed feed-in timeseries\n", + "09:53:47-INFO-Diesel fuel bus, source and transformer\n", + "09:53:47-INFO-Battery storage\n", + "\n", + "\n", + "09:53:47-INFO-Generating linear equation system describing defined energy system\n" + ] + } + ], + "source": [ + "# importing packages\n", + "import os\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import oemof.solph as solph\n", + "import oemof.outputlib as outputlib\n", + "from oemof.tools import economics\n", + "from oemof.tools import logger #logger to document progress\n", + "import logging\n", + "\n", + "# Define screen level of logger\n", + "logger.define_logging(screen_level=logging.INFO)\n", + "\n", + "# initialize energy system\n", + "duration_hours = 24*1\n", + "cost_ratio_timeinterval = duration_hours/(365*24)\n", + "timeindex = pd.date_range('1/1/2017', periods=duration_hours, freq='H')\n", + "energysystem = solph.EnergySystem(timeindex=timeindex)\n", + "\n", + "# loading input data\n", + "logging.info('Loading timeseries')\n", + "full_filename = '2_timeseries.csv'\n", + "timeseries = pd.read_csv(full_filename, sep=',')\n", + "\n", + "# Defining fix parameters\n", + "logging.info('Defining costs')\n", + "\n", + "fuel_price_kWh = 0.6/9.41 # fuel price in currency/kWh\n", + "\n", + "costs = {'wind': {\n", + " 'epc': economics.annuity(capex=2000, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'pv': {\n", + " 'epc': economics.annuity(capex=750, n=20, wacc=0.05)*cost_ratio_timeinterval},\n", + " 'genset': {\n", + " 'epc': economics.annuity(capex=300, n=10, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0},\n", + " 'storage': {\n", + " 'epc': economics.annuity(capex=300, n=5, wacc=0.05)*cost_ratio_timeinterval,\n", + " 'var': 0}}\n", + "\n", + "print('\\n')\n", + "\n", + "# Creating all oemof components\n", + "logging.info('DEFINITION OF OEMOF MODEL:')\n", + "\n", + "logging.info('Electricity bus')\n", + "bel = solph.Bus(label='electricity_bus')\n", + "energysystem.add(bel)\n", + "\n", + "logging.info('Demand, fixed timeseries')\n", + "demand_sink = solph.Sink(label='demand',\n", + " inputs={bel: solph.Flow(actual_value=timeseries['demand_el'],\n", + " fixed=True,\n", + " nominal_value=500)})\n", + "energysystem.add(demand_sink)\n", + "\n", + "logging.info('Excess sink')\n", + "excess_sink = solph.Sink(label='excess',\n", + " inputs={bel: solph.Flow()})\n", + "energysystem.add(excess_sink)\n", + "\n", + "logging.info('Wind plant with fixed feed-in timeseries')\n", + "wind_plant = solph.Source(label='wind',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['wind'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['wind']['epc']))})\n", + "energysystem.add(wind_plant)\n", + "\n", + "logging.info('PV plant with fixed feed-in timeseries')\n", + "pv_plant = solph.Source(label='pv',\n", + " outputs={\n", + " bel: solph.Flow(nominal_value=None,\n", + " fixed=True,\n", + " actual_value=timeseries['pv'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['pv']['epc']))})\n", + "\n", + "energysystem.add(pv_plant)\n", + "\n", + "logging.info('Diesel fuel bus, source and transformer')\n", + "bfuel = solph.Bus(label='fuel_bus')\n", + "\n", + "fuel_source = solph.Source(label='diesel',\n", + " outputs={\n", + " bfuel: solph.Flow(nominal_value=None,\n", + " variable_costs=fuel_price_kWh,\n", + " )}\n", + " )\n", + "\n", + "genset = solph.Transformer(label=\"genset\",\n", + " inputs={bfuel: solph.Flow()},\n", + " outputs={bel: solph.Flow(\n", + " variable_costs=costs['genset']['var'],\n", + " investment=solph.Investment(\n", + " ep_costs=costs['genset']['epc']))},\n", + " conversion_factors={bel: 0.33}\n", + " )\n", + "\n", + "energysystem.add(bfuel, fuel_source, genset)\n", + "\n", + "logging.info('Battery storage')\n", + "storage = solph.components.GenericStorage(\n", + " label='storage',\n", + " inputs={\n", + " bel: solph.Flow()},\n", + " outputs={\n", + " bel: solph.Flow()},\n", + " loss_rate=0.00,\n", + " initial_storage_level=0.5, \n", + " invest_relation_input_capacity=1/5,\n", + " invest_relation_output_capacity=1,\n", + " inflow_conversion_factor=0.95,\n", + " outflow_conversion_factor=0.95,\n", + " investment=solph.Investment(ep_costs=costs['storage']['epc']))\n", + "\n", + "energysystem.add(storage)\n", + "\n", + "print('\\n')\n", + "logging.info('Generating linear equation system describing defined energy system')\n", + "model = solph.Model(energysystem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of directly processing and solving the linear equation system with the solver, we write the generated linear equation system to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Saving linear equation system to file.\n" + ] + }, + { + "data": { + "text/plain": [ + "('./output_lp_files/2_micro_grid_basic.lp', 139776358791824)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info('Saving linear equation system to file.')\n", + "model.write('./output_lp_files/2_task_micro_grid.lp', io_options={'symbolic_solver_labels': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we optimize and post-process the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Starting oemof-optimization of capacities\n", + "09:53:48-INFO-Optimization successful...\n", + "09:53:48-INFO-Processing results\n" + ] + } + ], + "source": [ + "logging.info('Starting oemof-optimization of capacities')\n", + "model.solve(solver='cbc', solve_kwargs={'tee': False})\n", + "\n", + "logging.info('Processing results')\n", + "results = outputlib.processing.results(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Get optimized capacities\n", + "09:53:48-INFO-Capacities optimized: Storage (303.44179), Wind (1128.5236), PV (613.21074), Genset (66.607433).\n" + ] + } + ], + "source": [ + "el_bus = outputlib.views.node(results, 'electricity_bus')\n", + "logging.info('Get optimized capacities')\n", + "cap_wind = el_bus['scalars'][(('wind', 'electricity_bus'), 'invest')]\n", + "cap_pv = el_bus['scalars'][(('pv', 'electricity_bus'), 'invest')]\n", + "cap_genset = el_bus['scalars'][(('genset', 'electricity_bus'), 'invest')]\n", + "\n", + "storage_bus = outputlib.views.node(results, 'storage')\n", + "cap_storage = storage_bus['scalars'][(('storage','None'), 'invest')]\n", + "\n", + "logging.info('Capacities optimized: Storage (' + str(cap_storage)\n", + " + '), Wind (' + str(cap_wind)\n", + " + '), PV (' + str(cap_pv)\n", + " + '), Genset (' + str(cap_genset) + ').')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "09:53:48-INFO-Plot flows on electricity bus\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logging.info('Plot flows on electricity bus')\n", + "el_sequences = el_bus['sequences']\n", + "\n", + "el_prod = pd.DataFrame(index=timeindex)\n", + "el_prod['Storage charge'] = - el_sequences[(('electricity_bus', 'storage'), 'flow')].clip(lower=0)\n", + "el_prod['Storage discharge'] = el_sequences[(('storage', 'electricity_bus'), 'flow')].clip(lower=0)\n", + "el_prod['Generator']=el_sequences[(('genset', 'electricity_bus'), 'flow')]\n", + "el_prod['PV']=el_sequences[(('pv', 'electricity_bus'), 'flow')]\n", + "el_prod['Wind']=el_sequences[(('wind', 'electricity_bus'), 'flow')]\n", + "el_prod['Excess']=el_sequences[(('electricity_bus', 'excess'), 'flow')]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "# line plot\n", + "el_prod.plot(ax=ax)\n", + "# area plot\n", + "#el_prod.plot.area(ax=ax)\n", + "el_sequences[(('electricity_bus', 'demand'), 'flow')].plot(ax=ax, linewidth=3, c='k')\n", + "legend = ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) # legend outside of plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/graphics/2_micro_grid_system.png b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/graphics/2_micro_grid_system.png new file mode 100644 index 0000000..252c82e Binary files /dev/null and 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a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_basic.lp b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_basic.lp new file mode 100644 index 0000000..834e45d --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_basic.lp @@ -0,0 +1,1852 @@ +\* Source Pyomo model name=Model *\ + +min +objective: ++0.18984229983145307 GenericInvestmentStorageBlock_invest(storage) ++0.10644211640996434 InvestmentFlow_invest(genset_electricity_bus) ++0.1648820284740232 InvestmentFlow_invest(pv_electricity_bus) ++0.43968540926406191 InvestmentFlow_invest(wind_electricity_bus) ++0.063761955366631234 flow(diesel_fuel_bus_0) ++0.063761955366631234 flow(diesel_fuel_bus_1) ++0.063761955366631234 flow(diesel_fuel_bus_10) ++0.063761955366631234 flow(diesel_fuel_bus_11) ++0.063761955366631234 flow(diesel_fuel_bus_12) ++0.063761955366631234 flow(diesel_fuel_bus_13) ++0.063761955366631234 flow(diesel_fuel_bus_14) ++0.063761955366631234 flow(diesel_fuel_bus_15) ++0.063761955366631234 flow(diesel_fuel_bus_16) ++0.063761955366631234 flow(diesel_fuel_bus_17) ++0.063761955366631234 flow(diesel_fuel_bus_18) ++0.063761955366631234 flow(diesel_fuel_bus_19) ++0.063761955366631234 flow(diesel_fuel_bus_2) ++0.063761955366631234 flow(diesel_fuel_bus_20) ++0.063761955366631234 flow(diesel_fuel_bus_21) ++0.063761955366631234 flow(diesel_fuel_bus_22) ++0.063761955366631234 flow(diesel_fuel_bus_23) ++0.063761955366631234 flow(diesel_fuel_bus_3) ++0.063761955366631234 flow(diesel_fuel_bus_4) ++0.063761955366631234 flow(diesel_fuel_bus_5) ++0.063761955366631234 flow(diesel_fuel_bus_6) ++0.063761955366631234 flow(diesel_fuel_bus_7) ++0.063761955366631234 flow(diesel_fuel_bus_8) ++0.063761955366631234 flow(diesel_fuel_bus_9) + +s.t. + +c_e_Bus_balance(electricity_bus_0)_: +-1 flow(electricity_bus_excess_0) +-1 flow(electricity_bus_storage_0) ++1 flow(genset_electricity_bus_0) ++1 flow(pv_electricity_bus_0) ++1 flow(storage_electricity_bus_0) ++1 flow(wind_electricity_bus_0) += 279.53099120000002 + +c_e_Bus_balance(electricity_bus_1)_: +-1 flow(electricity_bus_excess_1) +-1 flow(electricity_bus_storage_1) ++1 flow(genset_electricity_bus_1) ++1 flow(pv_electricity_bus_1) ++1 flow(storage_electricity_bus_1) ++1 flow(wind_electricity_bus_1) += 266.80324295000003 + +c_e_Bus_balance(electricity_bus_2)_: +-1 flow(electricity_bus_excess_2) +-1 flow(electricity_bus_storage_2) ++1 flow(genset_electricity_bus_2) ++1 flow(pv_electricity_bus_2) ++1 flow(storage_electricity_bus_2) ++1 flow(wind_electricity_bus_2) += 253.02937845 + +c_e_Bus_balance(electricity_bus_3)_: +-1 flow(electricity_bus_excess_3) +-1 flow(electricity_bus_storage_3) ++1 flow(genset_electricity_bus_3) ++1 flow(pv_electricity_bus_3) ++1 flow(storage_electricity_bus_3) ++1 flow(wind_electricity_bus_3) += 252.07043849999999 + +c_e_Bus_balance(electricity_bus_4)_: +-1 flow(electricity_bus_excess_4) +-1 flow(electricity_bus_storage_4) ++1 flow(genset_electricity_bus_4) ++1 flow(pv_electricity_bus_4) ++1 flow(storage_electricity_bus_4) ++1 flow(wind_electricity_bus_4) += 253.55243659999999 + +c_e_Bus_balance(electricity_bus_5)_: +-1 flow(electricity_bus_excess_5) +-1 flow(electricity_bus_storage_5) ++1 flow(genset_electricity_bus_5) ++1 flow(pv_electricity_bus_5) ++1 flow(storage_electricity_bus_5) ++1 flow(wind_electricity_bus_5) += 255.68825735000001 + +c_e_Bus_balance(electricity_bus_6)_: +-1 flow(electricity_bus_excess_6) +-1 flow(electricity_bus_storage_6) ++1 flow(genset_electricity_bus_6) ++1 flow(pv_electricity_bus_6) ++1 flow(storage_electricity_bus_6) ++1 flow(wind_electricity_bus_6) += 270.90053180000001 + +c_e_Bus_balance(electricity_bus_7)_: +-1 flow(electricity_bus_excess_7) +-1 flow(electricity_bus_storage_7) ++1 flow(genset_electricity_bus_7) ++1 flow(pv_electricity_bus_7) ++1 flow(storage_electricity_bus_7) ++1 flow(wind_electricity_bus_7) += 284.63080809999997 + +c_e_Bus_balance(electricity_bus_8)_: +-1 flow(electricity_bus_excess_8) +-1 flow(electricity_bus_storage_8) ++1 flow(genset_electricity_bus_8) ++1 flow(pv_electricity_bus_8) ++1 flow(storage_electricity_bus_8) ++1 flow(wind_electricity_bus_8) += 301.49943335 + +c_e_Bus_balance(electricity_bus_9)_: +-1 flow(electricity_bus_excess_9) +-1 flow(electricity_bus_storage_9) ++1 flow(genset_electricity_bus_9) ++1 flow(pv_electricity_bus_9) ++1 flow(storage_electricity_bus_9) ++1 flow(wind_electricity_bus_9) += 314.53229885000002 + +c_e_Bus_balance(electricity_bus_10)_: +-1 flow(electricity_bus_excess_10) +-1 flow(electricity_bus_storage_10) ++1 flow(genset_electricity_bus_10) ++1 flow(pv_electricity_bus_10) ++1 flow(storage_electricity_bus_10) ++1 flow(wind_electricity_bus_10) += 317.32194229999999 + +c_e_Bus_balance(electricity_bus_11)_: +-1 flow(electricity_bus_excess_11) +-1 flow(electricity_bus_storage_11) ++1 flow(genset_electricity_bus_11) ++1 flow(pv_electricity_bus_11) ++1 flow(storage_electricity_bus_11) ++1 flow(wind_electricity_bus_11) += 307.51460204999995 + +c_e_Bus_balance(electricity_bus_12)_: +-1 flow(electricity_bus_excess_12) +-1 flow(electricity_bus_storage_12) ++1 flow(genset_electricity_bus_12) ++1 flow(pv_electricity_bus_12) ++1 flow(storage_electricity_bus_12) ++1 flow(wind_electricity_bus_12) += 302.89425510000001 + +c_e_Bus_balance(electricity_bus_13)_: +-1 flow(electricity_bus_excess_13) +-1 flow(electricity_bus_storage_13) ++1 flow(genset_electricity_bus_13) ++1 flow(pv_electricity_bus_13) ++1 flow(storage_electricity_bus_13) ++1 flow(wind_electricity_bus_13) += 297.35855635000001 + +c_e_Bus_balance(electricity_bus_14)_: +-1 flow(electricity_bus_excess_14) +-1 flow(electricity_bus_storage_14) ++1 flow(genset_electricity_bus_14) ++1 flow(pv_electricity_bus_14) ++1 flow(storage_electricity_bus_14) ++1 flow(wind_electricity_bus_14) += 303.6352541 + +c_e_Bus_balance(electricity_bus_15)_: +-1 flow(electricity_bus_excess_15) +-1 flow(electricity_bus_storage_15) ++1 flow(genset_electricity_bus_15) ++1 flow(pv_electricity_bus_15) ++1 flow(storage_electricity_bus_15) ++1 flow(wind_electricity_bus_15) += 340.33650075000003 + +c_e_Bus_balance(electricity_bus_16)_: +-1 flow(electricity_bus_excess_16) +-1 flow(electricity_bus_storage_16) ++1 flow(genset_electricity_bus_16) ++1 flow(pv_electricity_bus_16) ++1 flow(storage_electricity_bus_16) ++1 flow(wind_electricity_bus_16) += 354.4154825 + +c_e_Bus_balance(electricity_bus_17)_: +-1 flow(electricity_bus_excess_17) +-1 flow(electricity_bus_storage_17) ++1 flow(genset_electricity_bus_17) ++1 flow(pv_electricity_bus_17) ++1 flow(storage_electricity_bus_17) ++1 flow(wind_electricity_bus_17) += 353.50013075000004 + +c_e_Bus_balance(electricity_bus_18)_: +-1 flow(electricity_bus_excess_18) +-1 flow(electricity_bus_storage_18) ++1 flow(genset_electricity_bus_18) ++1 flow(pv_electricity_bus_18) ++1 flow(storage_electricity_bus_18) ++1 flow(wind_electricity_bus_18) += 340.59802980000001 + +c_e_Bus_balance(electricity_bus_19)_: +-1 flow(electricity_bus_excess_19) +-1 flow(electricity_bus_storage_19) ++1 flow(genset_electricity_bus_19) ++1 flow(pv_electricity_bus_19) ++1 flow(storage_electricity_bus_19) ++1 flow(wind_electricity_bus_19) += 320.32952664999999 + +c_e_Bus_balance(electricity_bus_20)_: +-1 flow(electricity_bus_excess_20) +-1 flow(electricity_bus_storage_20) ++1 flow(genset_electricity_bus_20) ++1 flow(pv_electricity_bus_20) ++1 flow(storage_electricity_bus_20) ++1 flow(wind_electricity_bus_20) += 304.89931129999997 + +c_e_Bus_balance(electricity_bus_21)_: +-1 flow(electricity_bus_excess_21) +-1 flow(electricity_bus_storage_21) ++1 flow(genset_electricity_bus_21) ++1 flow(pv_electricity_bus_21) ++1 flow(storage_electricity_bus_21) ++1 flow(wind_electricity_bus_21) += 307.95048385000001 + +c_e_Bus_balance(electricity_bus_22)_: +-1 flow(electricity_bus_excess_22) +-1 flow(electricity_bus_storage_22) ++1 flow(genset_electricity_bus_22) ++1 flow(pv_electricity_bus_22) ++1 flow(storage_electricity_bus_22) ++1 flow(wind_electricity_bus_22) += 283.58469184999996 + +c_e_Bus_balance(electricity_bus_23)_: +-1 flow(electricity_bus_excess_23) +-1 flow(electricity_bus_storage_23) ++1 flow(genset_electricity_bus_23) ++1 flow(pv_electricity_bus_23) ++1 flow(storage_electricity_bus_23) ++1 flow(wind_electricity_bus_23) += 267.98012379999994 + +c_e_Bus_balance(fuel_bus_0)_: ++1 flow(diesel_fuel_bus_0) +-1 flow(fuel_bus_genset_0) += 0 + +c_e_Bus_balance(fuel_bus_1)_: ++1 flow(diesel_fuel_bus_1) +-1 flow(fuel_bus_genset_1) += 0 + +c_e_Bus_balance(fuel_bus_2)_: ++1 flow(diesel_fuel_bus_2) +-1 flow(fuel_bus_genset_2) += 0 + +c_e_Bus_balance(fuel_bus_3)_: ++1 flow(diesel_fuel_bus_3) +-1 flow(fuel_bus_genset_3) += 0 + +c_e_Bus_balance(fuel_bus_4)_: ++1 flow(diesel_fuel_bus_4) +-1 flow(fuel_bus_genset_4) += 0 + +c_e_Bus_balance(fuel_bus_5)_: ++1 flow(diesel_fuel_bus_5) +-1 flow(fuel_bus_genset_5) += 0 + +c_e_Bus_balance(fuel_bus_6)_: ++1 flow(diesel_fuel_bus_6) +-1 flow(fuel_bus_genset_6) += 0 + +c_e_Bus_balance(fuel_bus_7)_: ++1 flow(diesel_fuel_bus_7) +-1 flow(fuel_bus_genset_7) += 0 + +c_e_Bus_balance(fuel_bus_8)_: ++1 flow(diesel_fuel_bus_8) +-1 flow(fuel_bus_genset_8) += 0 + +c_e_Bus_balance(fuel_bus_9)_: ++1 flow(diesel_fuel_bus_9) +-1 flow(fuel_bus_genset_9) += 0 + +c_e_Bus_balance(fuel_bus_10)_: ++1 flow(diesel_fuel_bus_10) +-1 flow(fuel_bus_genset_10) += 0 + +c_e_Bus_balance(fuel_bus_11)_: ++1 flow(diesel_fuel_bus_11) +-1 flow(fuel_bus_genset_11) += 0 + +c_e_Bus_balance(fuel_bus_12)_: ++1 flow(diesel_fuel_bus_12) +-1 flow(fuel_bus_genset_12) += 0 + +c_e_Bus_balance(fuel_bus_13)_: ++1 flow(diesel_fuel_bus_13) +-1 flow(fuel_bus_genset_13) += 0 + +c_e_Bus_balance(fuel_bus_14)_: ++1 flow(diesel_fuel_bus_14) +-1 flow(fuel_bus_genset_14) += 0 + +c_e_Bus_balance(fuel_bus_15)_: ++1 flow(diesel_fuel_bus_15) +-1 flow(fuel_bus_genset_15) += 0 + +c_e_Bus_balance(fuel_bus_16)_: ++1 flow(diesel_fuel_bus_16) +-1 flow(fuel_bus_genset_16) += 0 + +c_e_Bus_balance(fuel_bus_17)_: ++1 flow(diesel_fuel_bus_17) +-1 flow(fuel_bus_genset_17) += 0 + +c_e_Bus_balance(fuel_bus_18)_: ++1 flow(diesel_fuel_bus_18) +-1 flow(fuel_bus_genset_18) += 0 + +c_e_Bus_balance(fuel_bus_19)_: ++1 flow(diesel_fuel_bus_19) +-1 flow(fuel_bus_genset_19) += 0 + +c_e_Bus_balance(fuel_bus_20)_: ++1 flow(diesel_fuel_bus_20) +-1 flow(fuel_bus_genset_20) += 0 + +c_e_Bus_balance(fuel_bus_21)_: ++1 flow(diesel_fuel_bus_21) +-1 flow(fuel_bus_genset_21) += 0 + +c_e_Bus_balance(fuel_bus_22)_: ++1 flow(diesel_fuel_bus_22) +-1 flow(fuel_bus_genset_22) += 0 + +c_e_Bus_balance(fuel_bus_23)_: ++1 flow(diesel_fuel_bus_23) +-1 flow(fuel_bus_genset_23) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_0)_: ++1 flow(fuel_bus_genset_0) +-3.0303030303030303 flow(genset_electricity_bus_0) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_1)_: ++1 flow(fuel_bus_genset_1) +-3.0303030303030303 flow(genset_electricity_bus_1) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_2)_: ++1 flow(fuel_bus_genset_2) +-3.0303030303030303 flow(genset_electricity_bus_2) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_3)_: ++1 flow(fuel_bus_genset_3) +-3.0303030303030303 flow(genset_electricity_bus_3) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_4)_: ++1 flow(fuel_bus_genset_4) +-3.0303030303030303 flow(genset_electricity_bus_4) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_5)_: ++1 flow(fuel_bus_genset_5) +-3.0303030303030303 flow(genset_electricity_bus_5) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_6)_: ++1 flow(fuel_bus_genset_6) +-3.0303030303030303 flow(genset_electricity_bus_6) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_7)_: ++1 flow(fuel_bus_genset_7) +-3.0303030303030303 flow(genset_electricity_bus_7) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_8)_: ++1 flow(fuel_bus_genset_8) +-3.0303030303030303 flow(genset_electricity_bus_8) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_9)_: ++1 flow(fuel_bus_genset_9) +-3.0303030303030303 flow(genset_electricity_bus_9) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_10)_: ++1 flow(fuel_bus_genset_10) +-3.0303030303030303 flow(genset_electricity_bus_10) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_11)_: ++1 flow(fuel_bus_genset_11) +-3.0303030303030303 flow(genset_electricity_bus_11) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_12)_: ++1 flow(fuel_bus_genset_12) +-3.0303030303030303 flow(genset_electricity_bus_12) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_13)_: ++1 flow(fuel_bus_genset_13) +-3.0303030303030303 flow(genset_electricity_bus_13) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_14)_: ++1 flow(fuel_bus_genset_14) +-3.0303030303030303 flow(genset_electricity_bus_14) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_15)_: ++1 flow(fuel_bus_genset_15) +-3.0303030303030303 flow(genset_electricity_bus_15) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_16)_: ++1 flow(fuel_bus_genset_16) +-3.0303030303030303 flow(genset_electricity_bus_16) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_17)_: ++1 flow(fuel_bus_genset_17) +-3.0303030303030303 flow(genset_electricity_bus_17) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_18)_: ++1 flow(fuel_bus_genset_18) +-3.0303030303030303 flow(genset_electricity_bus_18) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_19)_: ++1 flow(fuel_bus_genset_19) +-3.0303030303030303 flow(genset_electricity_bus_19) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_20)_: ++1 flow(fuel_bus_genset_20) +-3.0303030303030303 flow(genset_electricity_bus_20) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_21)_: ++1 flow(fuel_bus_genset_21) +-3.0303030303030303 flow(genset_electricity_bus_21) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_22)_: ++1 flow(fuel_bus_genset_22) +-3.0303030303030303 flow(genset_electricity_bus_22) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_23)_: ++1 flow(fuel_bus_genset_23) +-3.0303030303030303 flow(genset_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_0)_: ++1 flow(pv_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_1)_: ++1 flow(pv_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_2)_: ++1 flow(pv_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_3)_: ++1 flow(pv_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_4)_: ++1 flow(pv_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_5)_: ++1 flow(pv_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_6)_: ++1 flow(pv_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_7)_: +-0.065722000000000003 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_8)_: +-0.20696199999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_9)_: +-0.33063799999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_10)_: +-0.40313100000000002 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_11)_: +-0.411138 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_12)_: +-0.367118 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_13)_: +-0.26857700000000001 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_14)_: +-0.11243399999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_15)_: +-0.0020409999999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_16)_: ++1 flow(pv_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_17)_: ++1 flow(pv_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_18)_: ++1 flow(pv_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_19)_: ++1 flow(pv_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_20)_: ++1 flow(pv_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_21)_: ++1 flow(pv_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_22)_: ++1 flow(pv_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_23)_: ++1 flow(pv_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_0)_: +-0.31556899999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_1)_: +-0.31157199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_2)_: +-0.30400500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_3)_: +-0.28287199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_4)_: +-0.25396999999999997 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_5)_: +-0.224077 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_6)_: +-0.19358 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_7)_: +-0.15992500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_8)_: +-0.12711500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_9)_: +-0.11749100000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_10)_: +-0.115909 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_11)_: +-0.10452400000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_12)_: +-0.090434 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_13)_: +-0.084694000000000005 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_14)_: +-0.11590999999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_15)_: +-0.16127900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_16)_: +-0.18877099999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_17)_: +-0.20496599999999998 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_18)_: +-0.21605700000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_19)_: +-0.22511900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_20)_: +-0.23353400000000002 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_21)_: +-0.26206299999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_22)_: +-0.30506500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_23)_: +-0.355796 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) += 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_0)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_0) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_1)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_1) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_2)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_2) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_3)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_3) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_4)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_4) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_5)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_5) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_6)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_6) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_7)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_7) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_8)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_8) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_9)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_9) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_10)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_10) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_11)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_11) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_12)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_12) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_13)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_13) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_14)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_14) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_15)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_15) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_16)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_16) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_17)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_17) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_18)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_18) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_19)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_19) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_20)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_20) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_21)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_21) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_22)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_22) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_23)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_23) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_0)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_1)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_2)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_3)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_4)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_5)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_6)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_7)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_8)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_9)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_10)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_11)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_12)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_13)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_14)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_15)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_16)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_17)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_18)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_19)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_20)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_21)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_22)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_23)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_0)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_1)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_2)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_3)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_4)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_5)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_6)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_7)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_8)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_9)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_10)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_11)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_12)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_13)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_14)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_15)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_16)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_17)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_18)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_19)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_20)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_21)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_22)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_23)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_0)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_1)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_2)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_3)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_4)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_5)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_6)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_7)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_8)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_9)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_10)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_11)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_12)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_13)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_14)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_15)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_16)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_17)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_18)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_19)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_20)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_21)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_22)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_23)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_0)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_1)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_2)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_3)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_4)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_5)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_6)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_7)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_8)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_9)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_10)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_11)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_12)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_13)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_14)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_15)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_16)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_17)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_18)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_19)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_20)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_21)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_22)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_23)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) +<= 0 + +c_e_GenericInvestmentStorageBlock_init_cap_fix(storage)_: ++1 GenericInvestmentStorageBlock_init_cap(storage) +-0.5 GenericInvestmentStorageBlock_invest(storage) += 0 + +c_e_GenericInvestmentStorageBlock_balance_first(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_init_cap(storage) +-0.94999999999999996 flow(electricity_bus_storage_0) ++1.0526315789473684 flow(storage_electricity_bus_0) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.94999999999999996 flow(electricity_bus_storage_1) ++1.0526315789473684 flow(storage_electricity_bus_1) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.94999999999999996 flow(electricity_bus_storage_2) ++1.0526315789473684 flow(storage_electricity_bus_2) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.94999999999999996 flow(electricity_bus_storage_3) ++1.0526315789473684 flow(storage_electricity_bus_3) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.94999999999999996 flow(electricity_bus_storage_4) ++1.0526315789473684 flow(storage_electricity_bus_4) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_5)_: +-1 GenericInvestmentStorageBlock_capacity(storage_4) ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-0.94999999999999996 flow(electricity_bus_storage_5) ++1.0526315789473684 flow(storage_electricity_bus_5) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_6)_: +-1 GenericInvestmentStorageBlock_capacity(storage_5) ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-0.94999999999999996 flow(electricity_bus_storage_6) ++1.0526315789473684 flow(storage_electricity_bus_6) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_7)_: +-1 GenericInvestmentStorageBlock_capacity(storage_6) ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-0.94999999999999996 flow(electricity_bus_storage_7) ++1.0526315789473684 flow(storage_electricity_bus_7) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_8)_: +-1 GenericInvestmentStorageBlock_capacity(storage_7) ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-0.94999999999999996 flow(electricity_bus_storage_8) ++1.0526315789473684 flow(storage_electricity_bus_8) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_9)_: +-1 GenericInvestmentStorageBlock_capacity(storage_8) ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_9) ++1.0526315789473684 flow(storage_electricity_bus_9) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_10) ++1.0526315789473684 flow(storage_electricity_bus_10) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_11)_: +-1 GenericInvestmentStorageBlock_capacity(storage_10) ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-0.94999999999999996 flow(electricity_bus_storage_11) ++1.0526315789473684 flow(storage_electricity_bus_11) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_12)_: +-1 GenericInvestmentStorageBlock_capacity(storage_11) ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-0.94999999999999996 flow(electricity_bus_storage_12) ++1.0526315789473684 flow(storage_electricity_bus_12) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_13)_: +-1 GenericInvestmentStorageBlock_capacity(storage_12) ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-0.94999999999999996 flow(electricity_bus_storage_13) ++1.0526315789473684 flow(storage_electricity_bus_13) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_14)_: +-1 GenericInvestmentStorageBlock_capacity(storage_13) ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-0.94999999999999996 flow(electricity_bus_storage_14) ++1.0526315789473684 flow(storage_electricity_bus_14) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_15)_: +-1 GenericInvestmentStorageBlock_capacity(storage_14) ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-0.94999999999999996 flow(electricity_bus_storage_15) ++1.0526315789473684 flow(storage_electricity_bus_15) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_16)_: +-1 GenericInvestmentStorageBlock_capacity(storage_15) ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-0.94999999999999996 flow(electricity_bus_storage_16) ++1.0526315789473684 flow(storage_electricity_bus_16) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_17)_: +-1 GenericInvestmentStorageBlock_capacity(storage_16) ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-0.94999999999999996 flow(electricity_bus_storage_17) ++1.0526315789473684 flow(storage_electricity_bus_17) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_18)_: +-1 GenericInvestmentStorageBlock_capacity(storage_17) ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-0.94999999999999996 flow(electricity_bus_storage_18) ++1.0526315789473684 flow(storage_electricity_bus_18) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_19)_: +-1 GenericInvestmentStorageBlock_capacity(storage_18) ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-0.94999999999999996 flow(electricity_bus_storage_19) ++1.0526315789473684 flow(storage_electricity_bus_19) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_20)_: +-1 GenericInvestmentStorageBlock_capacity(storage_19) ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-0.94999999999999996 flow(electricity_bus_storage_20) ++1.0526315789473684 flow(storage_electricity_bus_20) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_21)_: +-1 GenericInvestmentStorageBlock_capacity(storage_20) ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-0.94999999999999996 flow(electricity_bus_storage_21) ++1.0526315789473684 flow(storage_electricity_bus_21) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_22)_: +-1 GenericInvestmentStorageBlock_capacity(storage_21) ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-0.94999999999999996 flow(electricity_bus_storage_22) ++1.0526315789473684 flow(storage_electricity_bus_22) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_23)_: +-1 GenericInvestmentStorageBlock_capacity(storage_22) ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-0.94999999999999996 flow(electricity_bus_storage_23) ++1.0526315789473684 flow(storage_electricity_bus_23) += 0 + +c_e_GenericInvestmentStorageBlock_balanced_cstr(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-1 GenericInvestmentStorageBlock_init_cap(storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_inflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(electricity_bus_storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_outflow(storage)_: +-1 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(storage_electricity_bus) += 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_0)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_1)_: ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_2)_: ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_3)_: ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_4)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_5)_: ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_6)_: ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_7)_: ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_8)_: ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_9)_: ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_11)_: ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_12)_: ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_13)_: ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_14)_: ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_15)_: ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_16)_: ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_17)_: ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_18)_: ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_19)_: ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_20)_: ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_21)_: ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_22)_: ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_23)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_e_ONE_VAR_CONSTANT: +ONE_VAR_CONSTANT = 1.0 + +bounds + 0 <= flow(diesel_fuel_bus_0) <= +inf + 0 <= flow(diesel_fuel_bus_1) <= +inf + 0 <= flow(diesel_fuel_bus_2) <= +inf + 0 <= flow(diesel_fuel_bus_3) <= +inf + 0 <= flow(diesel_fuel_bus_4) <= +inf + 0 <= flow(diesel_fuel_bus_5) <= +inf + 0 <= flow(diesel_fuel_bus_6) <= +inf + 0 <= flow(diesel_fuel_bus_7) <= +inf + 0 <= flow(diesel_fuel_bus_8) <= +inf + 0 <= flow(diesel_fuel_bus_9) <= +inf + 0 <= flow(diesel_fuel_bus_10) <= +inf + 0 <= flow(diesel_fuel_bus_11) <= +inf + 0 <= flow(diesel_fuel_bus_12) <= +inf + 0 <= flow(diesel_fuel_bus_13) <= +inf + 0 <= flow(diesel_fuel_bus_14) <= +inf + 0 <= flow(diesel_fuel_bus_15) <= +inf + 0 <= flow(diesel_fuel_bus_16) <= +inf + 0 <= flow(diesel_fuel_bus_17) <= +inf + 0 <= flow(diesel_fuel_bus_18) <= +inf + 0 <= flow(diesel_fuel_bus_19) <= +inf + 0 <= flow(diesel_fuel_bus_20) <= +inf + 0 <= flow(diesel_fuel_bus_21) <= +inf + 0 <= flow(diesel_fuel_bus_22) <= +inf + 0 <= flow(diesel_fuel_bus_23) <= +inf + 0 <= flow(electricity_bus_excess_0) <= +inf + 0 <= flow(electricity_bus_excess_1) <= +inf + 0 <= flow(electricity_bus_excess_2) <= +inf + 0 <= flow(electricity_bus_excess_3) <= +inf + 0 <= flow(electricity_bus_excess_4) <= +inf + 0 <= flow(electricity_bus_excess_5) <= +inf + 0 <= flow(electricity_bus_excess_6) <= +inf + 0 <= flow(electricity_bus_excess_7) <= +inf + 0 <= flow(electricity_bus_excess_8) <= +inf + 0 <= flow(electricity_bus_excess_9) <= +inf + 0 <= flow(electricity_bus_excess_10) <= +inf + 0 <= flow(electricity_bus_excess_11) <= +inf + 0 <= flow(electricity_bus_excess_12) <= +inf + 0 <= flow(electricity_bus_excess_13) <= +inf + 0 <= flow(electricity_bus_excess_14) <= +inf + 0 <= flow(electricity_bus_excess_15) <= +inf + 0 <= flow(electricity_bus_excess_16) <= +inf + 0 <= flow(electricity_bus_excess_17) <= +inf + 0 <= flow(electricity_bus_excess_18) <= +inf + 0 <= flow(electricity_bus_excess_19) <= +inf + 0 <= flow(electricity_bus_excess_20) <= +inf + 0 <= flow(electricity_bus_excess_21) <= +inf + 0 <= flow(electricity_bus_excess_22) <= +inf + 0 <= flow(electricity_bus_excess_23) <= +inf + 0 <= flow(electricity_bus_storage_0) <= +inf + 0 <= flow(electricity_bus_storage_1) <= +inf + 0 <= flow(electricity_bus_storage_2) <= +inf + 0 <= flow(electricity_bus_storage_3) <= +inf + 0 <= flow(electricity_bus_storage_4) <= +inf + 0 <= flow(electricity_bus_storage_5) <= +inf + 0 <= flow(electricity_bus_storage_6) <= +inf + 0 <= flow(electricity_bus_storage_7) <= +inf + 0 <= flow(electricity_bus_storage_8) <= +inf + 0 <= flow(electricity_bus_storage_9) <= +inf + 0 <= flow(electricity_bus_storage_10) <= +inf + 0 <= flow(electricity_bus_storage_11) <= +inf + 0 <= flow(electricity_bus_storage_12) <= +inf + 0 <= flow(electricity_bus_storage_13) <= +inf + 0 <= flow(electricity_bus_storage_14) <= +inf + 0 <= flow(electricity_bus_storage_15) <= +inf + 0 <= flow(electricity_bus_storage_16) <= +inf + 0 <= flow(electricity_bus_storage_17) <= +inf + 0 <= flow(electricity_bus_storage_18) <= +inf + 0 <= flow(electricity_bus_storage_19) <= +inf + 0 <= flow(electricity_bus_storage_20) <= +inf + 0 <= flow(electricity_bus_storage_21) <= +inf + 0 <= flow(electricity_bus_storage_22) <= +inf + 0 <= flow(electricity_bus_storage_23) <= +inf + 0 <= flow(fuel_bus_genset_0) <= +inf + 0 <= flow(fuel_bus_genset_1) <= +inf + 0 <= flow(fuel_bus_genset_2) <= +inf + 0 <= flow(fuel_bus_genset_3) <= +inf + 0 <= flow(fuel_bus_genset_4) <= +inf + 0 <= flow(fuel_bus_genset_5) <= +inf + 0 <= flow(fuel_bus_genset_6) <= +inf + 0 <= flow(fuel_bus_genset_7) <= +inf + 0 <= flow(fuel_bus_genset_8) <= +inf + 0 <= flow(fuel_bus_genset_9) <= +inf + 0 <= flow(fuel_bus_genset_10) <= +inf + 0 <= flow(fuel_bus_genset_11) <= +inf + 0 <= flow(fuel_bus_genset_12) <= +inf + 0 <= flow(fuel_bus_genset_13) <= +inf + 0 <= flow(fuel_bus_genset_14) <= +inf + 0 <= flow(fuel_bus_genset_15) <= +inf + 0 <= flow(fuel_bus_genset_16) <= +inf + 0 <= flow(fuel_bus_genset_17) <= +inf + 0 <= flow(fuel_bus_genset_18) <= +inf + 0 <= flow(fuel_bus_genset_19) <= +inf + 0 <= flow(fuel_bus_genset_20) <= +inf + 0 <= flow(fuel_bus_genset_21) <= +inf + 0 <= flow(fuel_bus_genset_22) <= +inf + 0 <= flow(fuel_bus_genset_23) <= +inf + 0 <= flow(genset_electricity_bus_0) <= +inf + 0 <= flow(genset_electricity_bus_1) <= +inf + 0 <= flow(genset_electricity_bus_2) <= +inf + 0 <= flow(genset_electricity_bus_3) <= +inf + 0 <= flow(genset_electricity_bus_4) <= +inf + 0 <= flow(genset_electricity_bus_5) <= +inf + 0 <= flow(genset_electricity_bus_6) <= +inf + 0 <= flow(genset_electricity_bus_7) <= +inf + 0 <= flow(genset_electricity_bus_8) <= +inf + 0 <= flow(genset_electricity_bus_9) <= +inf + 0 <= flow(genset_electricity_bus_10) <= +inf + 0 <= flow(genset_electricity_bus_11) <= +inf + 0 <= flow(genset_electricity_bus_12) <= +inf + 0 <= flow(genset_electricity_bus_13) <= +inf + 0 <= flow(genset_electricity_bus_14) <= +inf + 0 <= flow(genset_electricity_bus_15) <= +inf + 0 <= flow(genset_electricity_bus_16) <= +inf + 0 <= flow(genset_electricity_bus_17) <= +inf + 0 <= flow(genset_electricity_bus_18) <= +inf + 0 <= flow(genset_electricity_bus_19) <= +inf + 0 <= flow(genset_electricity_bus_20) <= +inf + 0 <= flow(genset_electricity_bus_21) <= +inf + 0 <= flow(genset_electricity_bus_22) <= +inf + 0 <= flow(genset_electricity_bus_23) <= +inf + 0 <= flow(pv_electricity_bus_0) <= +inf + 0 <= flow(pv_electricity_bus_1) <= +inf + 0 <= flow(pv_electricity_bus_2) <= +inf + 0 <= flow(pv_electricity_bus_3) <= +inf + 0 <= flow(pv_electricity_bus_4) <= +inf + 0 <= flow(pv_electricity_bus_5) <= +inf + 0 <= flow(pv_electricity_bus_6) <= +inf + 0 <= flow(pv_electricity_bus_7) <= +inf + 0 <= flow(pv_electricity_bus_8) <= +inf + 0 <= flow(pv_electricity_bus_9) <= +inf + 0 <= flow(pv_electricity_bus_10) <= +inf + 0 <= flow(pv_electricity_bus_11) <= +inf + 0 <= flow(pv_electricity_bus_12) <= +inf + 0 <= flow(pv_electricity_bus_13) <= +inf + 0 <= flow(pv_electricity_bus_14) <= +inf + 0 <= flow(pv_electricity_bus_15) <= +inf + 0 <= flow(pv_electricity_bus_16) <= +inf + 0 <= flow(pv_electricity_bus_17) <= +inf + 0 <= flow(pv_electricity_bus_18) <= +inf + 0 <= flow(pv_electricity_bus_19) <= +inf + 0 <= flow(pv_electricity_bus_20) <= +inf + 0 <= flow(pv_electricity_bus_21) <= +inf + 0 <= flow(pv_electricity_bus_22) <= +inf + 0 <= flow(pv_electricity_bus_23) <= +inf + 0 <= flow(storage_electricity_bus_0) <= +inf + 0 <= flow(storage_electricity_bus_1) <= +inf + 0 <= flow(storage_electricity_bus_2) <= +inf + 0 <= flow(storage_electricity_bus_3) <= +inf + 0 <= flow(storage_electricity_bus_4) <= +inf + 0 <= flow(storage_electricity_bus_5) <= +inf + 0 <= flow(storage_electricity_bus_6) <= +inf + 0 <= flow(storage_electricity_bus_7) <= +inf + 0 <= flow(storage_electricity_bus_8) <= +inf + 0 <= flow(storage_electricity_bus_9) <= +inf + 0 <= flow(storage_electricity_bus_10) <= +inf + 0 <= flow(storage_electricity_bus_11) <= +inf + 0 <= flow(storage_electricity_bus_12) <= +inf + 0 <= flow(storage_electricity_bus_13) <= +inf + 0 <= flow(storage_electricity_bus_14) <= +inf + 0 <= flow(storage_electricity_bus_15) <= +inf + 0 <= flow(storage_electricity_bus_16) <= +inf + 0 <= flow(storage_electricity_bus_17) <= +inf + 0 <= flow(storage_electricity_bus_18) <= +inf + 0 <= flow(storage_electricity_bus_19) <= +inf + 0 <= flow(storage_electricity_bus_20) <= +inf + 0 <= flow(storage_electricity_bus_21) <= +inf + 0 <= flow(storage_electricity_bus_22) <= +inf + 0 <= flow(storage_electricity_bus_23) <= +inf + 0 <= flow(wind_electricity_bus_0) <= +inf + 0 <= flow(wind_electricity_bus_1) <= +inf + 0 <= flow(wind_electricity_bus_2) <= +inf + 0 <= flow(wind_electricity_bus_3) <= +inf + 0 <= flow(wind_electricity_bus_4) <= +inf + 0 <= flow(wind_electricity_bus_5) <= +inf + 0 <= flow(wind_electricity_bus_6) <= +inf + 0 <= flow(wind_electricity_bus_7) <= +inf + 0 <= flow(wind_electricity_bus_8) <= +inf + 0 <= flow(wind_electricity_bus_9) <= +inf + 0 <= flow(wind_electricity_bus_10) <= +inf + 0 <= flow(wind_electricity_bus_11) <= +inf + 0 <= flow(wind_electricity_bus_12) <= +inf + 0 <= flow(wind_electricity_bus_13) <= +inf + 0 <= flow(wind_electricity_bus_14) <= +inf + 0 <= flow(wind_electricity_bus_15) <= +inf + 0 <= flow(wind_electricity_bus_16) <= +inf + 0 <= flow(wind_electricity_bus_17) <= +inf + 0 <= flow(wind_electricity_bus_18) <= +inf + 0 <= flow(wind_electricity_bus_19) <= +inf + 0 <= flow(wind_electricity_bus_20) <= +inf + 0 <= flow(wind_electricity_bus_21) <= +inf + 0 <= flow(wind_electricity_bus_22) <= +inf + 0 <= flow(wind_electricity_bus_23) <= +inf + 0 <= InvestmentFlow_invest(electricity_bus_storage) <= +inf + 0 <= InvestmentFlow_invest(genset_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(pv_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(storage_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(wind_electricity_bus) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_0) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_1) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_2) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_3) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_4) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_5) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_6) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_7) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_8) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_9) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_10) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_11) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_12) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_13) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_14) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_15) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_16) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_17) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_18) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_19) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_20) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_21) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_22) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_23) <= +inf + 0 <= GenericInvestmentStorageBlock_invest(storage) <= +inf + 0 <= GenericInvestmentStorageBlock_init_cap(storage) <= +inf +end diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_constraint_flows.lp b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_constraint_flows.lp new file mode 100644 index 0000000..31df9b9 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_constraint_flows.lp @@ -0,0 +1,2236 @@ +\* Source Pyomo model name=Model *\ + +min +objective: ++0.18984229983145307 GenericInvestmentStorageBlock_invest(storage) ++0.10644211640996434 InvestmentFlow_invest(genset_electricity_bus) ++0.1648820284740232 InvestmentFlow_invest(pv_electricity_bus) ++0.43968540926406191 InvestmentFlow_invest(wind_electricity_bus) ++0.063761955366631234 flow(diesel_fuel_bus_0) ++0.063761955366631234 flow(diesel_fuel_bus_1) ++0.063761955366631234 flow(diesel_fuel_bus_10) ++0.063761955366631234 flow(diesel_fuel_bus_11) ++0.063761955366631234 flow(diesel_fuel_bus_12) ++0.063761955366631234 flow(diesel_fuel_bus_13) ++0.063761955366631234 flow(diesel_fuel_bus_14) ++0.063761955366631234 flow(diesel_fuel_bus_15) ++0.063761955366631234 flow(diesel_fuel_bus_16) ++0.063761955366631234 flow(diesel_fuel_bus_17) ++0.063761955366631234 flow(diesel_fuel_bus_18) ++0.063761955366631234 flow(diesel_fuel_bus_19) ++0.063761955366631234 flow(diesel_fuel_bus_2) ++0.063761955366631234 flow(diesel_fuel_bus_20) ++0.063761955366631234 flow(diesel_fuel_bus_21) ++0.063761955366631234 flow(diesel_fuel_bus_22) ++0.063761955366631234 flow(diesel_fuel_bus_23) ++0.063761955366631234 flow(diesel_fuel_bus_3) ++0.063761955366631234 flow(diesel_fuel_bus_4) ++0.063761955366631234 flow(diesel_fuel_bus_5) ++0.063761955366631234 flow(diesel_fuel_bus_6) ++0.063761955366631234 flow(diesel_fuel_bus_7) ++0.063761955366631234 flow(diesel_fuel_bus_8) ++0.063761955366631234 flow(diesel_fuel_bus_9) + +s.t. + +c_l_stability_constraint_capacity(0)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_0) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_0) +>= 111.81239648000002 + +c_l_stability_constraint_capacity(1)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_1) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_1) +>= 106.72129718000002 + +c_l_stability_constraint_capacity(2)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_2) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_2) +>= 101.21175138000001 + +c_l_stability_constraint_capacity(3)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_3) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_3) +>= 100.82817540000001 + +c_l_stability_constraint_capacity(4)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_4) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_4) +>= 101.42097464 + +c_l_stability_constraint_capacity(5)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_5) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_5) +>= 102.27530294000002 + +c_l_stability_constraint_capacity(6)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_6) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_6) +>= 108.36021272000001 + +c_l_stability_constraint_capacity(7)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_7) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_7) +>= 113.85232323999999 + +c_l_stability_constraint_capacity(8)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_8) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_8) +>= 120.59977334000001 + +c_l_stability_constraint_capacity(9)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_9) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_9) +>= 125.81291954000001 + +c_l_stability_constraint_capacity(10)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_10) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_10) +>= 126.92877692 + +c_l_stability_constraint_capacity(11)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_11) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_11) +>= 123.00584081999999 + +c_l_stability_constraint_capacity(12)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_12) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_12) +>= 121.15770204 + +c_l_stability_constraint_capacity(13)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_13) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_13) +>= 118.94342254000001 + +c_l_stability_constraint_capacity(14)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_14) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_14) +>= 121.45410164 + +c_l_stability_constraint_capacity(15)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_15) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_15) +>= 136.13460030000002 + +c_l_stability_constraint_capacity(16)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_16) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_16) +>= 141.76619300000002 + +c_l_stability_constraint_capacity(17)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_17) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_17) +>= 141.40005230000003 + +c_l_stability_constraint_capacity(18)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_18) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_18) +>= 136.23921192 + +c_l_stability_constraint_capacity(19)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_19) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_19) +>= 128.13181066000001 + +c_l_stability_constraint_capacity(20)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_20) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_20) +>= 121.95972451999999 + +c_l_stability_constraint_capacity(21)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_21) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_21) +>= 123.18019354 + +c_l_stability_constraint_capacity(22)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_22) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_22) +>= 113.43387673999999 + +c_l_stability_constraint_capacity(23)_: ++0.94999999999999996 GenericInvestmentStorageBlock_capacity(storage_23) +-0.19 GenericInvestmentStorageBlock_invest(storage) ++1 flow(genset_electricity_bus_23) +>= 107.19204951999998 + +c_l_stability_constraint_power(0)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_0) +>= 111.81239648000002 + +c_l_stability_constraint_power(1)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_1) +>= 106.72129718000002 + +c_l_stability_constraint_power(2)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_2) +>= 101.21175138000001 + +c_l_stability_constraint_power(3)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_3) +>= 100.82817540000001 + +c_l_stability_constraint_power(4)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_4) +>= 101.42097464 + +c_l_stability_constraint_power(5)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_5) +>= 102.27530294000002 + +c_l_stability_constraint_power(6)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_6) +>= 108.36021272000001 + +c_l_stability_constraint_power(7)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_7) +>= 113.85232323999999 + +c_l_stability_constraint_power(8)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_8) +>= 120.59977334000001 + +c_l_stability_constraint_power(9)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_9) +>= 125.81291954000001 + +c_l_stability_constraint_power(10)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_10) +>= 126.92877692 + +c_l_stability_constraint_power(11)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_11) +>= 123.00584081999999 + +c_l_stability_constraint_power(12)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_12) +>= 121.15770204 + +c_l_stability_constraint_power(13)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_13) +>= 118.94342254000001 + +c_l_stability_constraint_power(14)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_14) +>= 121.45410164 + +c_l_stability_constraint_power(15)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_15) +>= 136.13460030000002 + +c_l_stability_constraint_power(16)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_16) +>= 141.76619300000002 + +c_l_stability_constraint_power(17)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_17) +>= 141.40005230000003 + +c_l_stability_constraint_power(18)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_18) +>= 136.23921192 + +c_l_stability_constraint_power(19)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_19) +>= 128.13181066000001 + +c_l_stability_constraint_power(20)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_20) +>= 121.95972451999999 + +c_l_stability_constraint_power(21)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_21) +>= 123.18019354 + +c_l_stability_constraint_power(22)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_22) +>= 113.43387673999999 + +c_l_stability_constraint_power(23)_: ++1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(genset_electricity_bus_23) +>= 107.19204951999998 + +c_e_Bus_balance(electricity_bus_0)_: +-1 flow(electricity_bus_excess_0) +-1 flow(electricity_bus_storage_0) ++1 flow(genset_electricity_bus_0) ++1 flow(pv_electricity_bus_0) ++1 flow(storage_electricity_bus_0) ++1 flow(wind_electricity_bus_0) += 279.53099120000002 + +c_e_Bus_balance(electricity_bus_1)_: +-1 flow(electricity_bus_excess_1) +-1 flow(electricity_bus_storage_1) ++1 flow(genset_electricity_bus_1) ++1 flow(pv_electricity_bus_1) ++1 flow(storage_electricity_bus_1) ++1 flow(wind_electricity_bus_1) += 266.80324295000003 + +c_e_Bus_balance(electricity_bus_2)_: +-1 flow(electricity_bus_excess_2) +-1 flow(electricity_bus_storage_2) ++1 flow(genset_electricity_bus_2) ++1 flow(pv_electricity_bus_2) ++1 flow(storage_electricity_bus_2) ++1 flow(wind_electricity_bus_2) += 253.02937845 + +c_e_Bus_balance(electricity_bus_3)_: +-1 flow(electricity_bus_excess_3) +-1 flow(electricity_bus_storage_3) ++1 flow(genset_electricity_bus_3) ++1 flow(pv_electricity_bus_3) ++1 flow(storage_electricity_bus_3) ++1 flow(wind_electricity_bus_3) += 252.07043849999999 + +c_e_Bus_balance(electricity_bus_4)_: +-1 flow(electricity_bus_excess_4) +-1 flow(electricity_bus_storage_4) ++1 flow(genset_electricity_bus_4) ++1 flow(pv_electricity_bus_4) ++1 flow(storage_electricity_bus_4) ++1 flow(wind_electricity_bus_4) += 253.55243659999999 + +c_e_Bus_balance(electricity_bus_5)_: +-1 flow(electricity_bus_excess_5) +-1 flow(electricity_bus_storage_5) ++1 flow(genset_electricity_bus_5) ++1 flow(pv_electricity_bus_5) ++1 flow(storage_electricity_bus_5) ++1 flow(wind_electricity_bus_5) += 255.68825735000001 + +c_e_Bus_balance(electricity_bus_6)_: +-1 flow(electricity_bus_excess_6) +-1 flow(electricity_bus_storage_6) ++1 flow(genset_electricity_bus_6) ++1 flow(pv_electricity_bus_6) ++1 flow(storage_electricity_bus_6) ++1 flow(wind_electricity_bus_6) += 270.90053180000001 + +c_e_Bus_balance(electricity_bus_7)_: +-1 flow(electricity_bus_excess_7) +-1 flow(electricity_bus_storage_7) ++1 flow(genset_electricity_bus_7) ++1 flow(pv_electricity_bus_7) ++1 flow(storage_electricity_bus_7) ++1 flow(wind_electricity_bus_7) += 284.63080809999997 + +c_e_Bus_balance(electricity_bus_8)_: +-1 flow(electricity_bus_excess_8) +-1 flow(electricity_bus_storage_8) ++1 flow(genset_electricity_bus_8) ++1 flow(pv_electricity_bus_8) ++1 flow(storage_electricity_bus_8) ++1 flow(wind_electricity_bus_8) += 301.49943335 + +c_e_Bus_balance(electricity_bus_9)_: +-1 flow(electricity_bus_excess_9) +-1 flow(electricity_bus_storage_9) ++1 flow(genset_electricity_bus_9) ++1 flow(pv_electricity_bus_9) ++1 flow(storage_electricity_bus_9) ++1 flow(wind_electricity_bus_9) += 314.53229885000002 + +c_e_Bus_balance(electricity_bus_10)_: +-1 flow(electricity_bus_excess_10) +-1 flow(electricity_bus_storage_10) ++1 flow(genset_electricity_bus_10) ++1 flow(pv_electricity_bus_10) ++1 flow(storage_electricity_bus_10) ++1 flow(wind_electricity_bus_10) += 317.32194229999999 + +c_e_Bus_balance(electricity_bus_11)_: +-1 flow(electricity_bus_excess_11) +-1 flow(electricity_bus_storage_11) ++1 flow(genset_electricity_bus_11) ++1 flow(pv_electricity_bus_11) ++1 flow(storage_electricity_bus_11) ++1 flow(wind_electricity_bus_11) += 307.51460204999995 + +c_e_Bus_balance(electricity_bus_12)_: +-1 flow(electricity_bus_excess_12) +-1 flow(electricity_bus_storage_12) ++1 flow(genset_electricity_bus_12) ++1 flow(pv_electricity_bus_12) ++1 flow(storage_electricity_bus_12) ++1 flow(wind_electricity_bus_12) += 302.89425510000001 + +c_e_Bus_balance(electricity_bus_13)_: +-1 flow(electricity_bus_excess_13) +-1 flow(electricity_bus_storage_13) ++1 flow(genset_electricity_bus_13) ++1 flow(pv_electricity_bus_13) ++1 flow(storage_electricity_bus_13) ++1 flow(wind_electricity_bus_13) += 297.35855635000001 + +c_e_Bus_balance(electricity_bus_14)_: +-1 flow(electricity_bus_excess_14) +-1 flow(electricity_bus_storage_14) ++1 flow(genset_electricity_bus_14) ++1 flow(pv_electricity_bus_14) ++1 flow(storage_electricity_bus_14) ++1 flow(wind_electricity_bus_14) += 303.6352541 + +c_e_Bus_balance(electricity_bus_15)_: +-1 flow(electricity_bus_excess_15) +-1 flow(electricity_bus_storage_15) ++1 flow(genset_electricity_bus_15) ++1 flow(pv_electricity_bus_15) ++1 flow(storage_electricity_bus_15) ++1 flow(wind_electricity_bus_15) += 340.33650075000003 + +c_e_Bus_balance(electricity_bus_16)_: +-1 flow(electricity_bus_excess_16) +-1 flow(electricity_bus_storage_16) ++1 flow(genset_electricity_bus_16) ++1 flow(pv_electricity_bus_16) ++1 flow(storage_electricity_bus_16) ++1 flow(wind_electricity_bus_16) += 354.4154825 + +c_e_Bus_balance(electricity_bus_17)_: +-1 flow(electricity_bus_excess_17) +-1 flow(electricity_bus_storage_17) ++1 flow(genset_electricity_bus_17) ++1 flow(pv_electricity_bus_17) ++1 flow(storage_electricity_bus_17) ++1 flow(wind_electricity_bus_17) += 353.50013075000004 + +c_e_Bus_balance(electricity_bus_18)_: +-1 flow(electricity_bus_excess_18) +-1 flow(electricity_bus_storage_18) ++1 flow(genset_electricity_bus_18) ++1 flow(pv_electricity_bus_18) ++1 flow(storage_electricity_bus_18) ++1 flow(wind_electricity_bus_18) += 340.59802980000001 + +c_e_Bus_balance(electricity_bus_19)_: +-1 flow(electricity_bus_excess_19) +-1 flow(electricity_bus_storage_19) ++1 flow(genset_electricity_bus_19) ++1 flow(pv_electricity_bus_19) ++1 flow(storage_electricity_bus_19) ++1 flow(wind_electricity_bus_19) += 320.32952664999999 + +c_e_Bus_balance(electricity_bus_20)_: +-1 flow(electricity_bus_excess_20) +-1 flow(electricity_bus_storage_20) ++1 flow(genset_electricity_bus_20) ++1 flow(pv_electricity_bus_20) ++1 flow(storage_electricity_bus_20) ++1 flow(wind_electricity_bus_20) += 304.89931129999997 + +c_e_Bus_balance(electricity_bus_21)_: +-1 flow(electricity_bus_excess_21) +-1 flow(electricity_bus_storage_21) ++1 flow(genset_electricity_bus_21) ++1 flow(pv_electricity_bus_21) ++1 flow(storage_electricity_bus_21) ++1 flow(wind_electricity_bus_21) += 307.95048385000001 + +c_e_Bus_balance(electricity_bus_22)_: +-1 flow(electricity_bus_excess_22) +-1 flow(electricity_bus_storage_22) ++1 flow(genset_electricity_bus_22) ++1 flow(pv_electricity_bus_22) ++1 flow(storage_electricity_bus_22) ++1 flow(wind_electricity_bus_22) += 283.58469184999996 + +c_e_Bus_balance(electricity_bus_23)_: +-1 flow(electricity_bus_excess_23) +-1 flow(electricity_bus_storage_23) ++1 flow(genset_electricity_bus_23) ++1 flow(pv_electricity_bus_23) ++1 flow(storage_electricity_bus_23) ++1 flow(wind_electricity_bus_23) += 267.98012379999994 + +c_e_Bus_balance(fuel_bus_0)_: ++1 flow(diesel_fuel_bus_0) +-1 flow(fuel_bus_genset_0) += 0 + +c_e_Bus_balance(fuel_bus_1)_: ++1 flow(diesel_fuel_bus_1) +-1 flow(fuel_bus_genset_1) += 0 + +c_e_Bus_balance(fuel_bus_2)_: ++1 flow(diesel_fuel_bus_2) +-1 flow(fuel_bus_genset_2) += 0 + +c_e_Bus_balance(fuel_bus_3)_: ++1 flow(diesel_fuel_bus_3) +-1 flow(fuel_bus_genset_3) += 0 + +c_e_Bus_balance(fuel_bus_4)_: ++1 flow(diesel_fuel_bus_4) +-1 flow(fuel_bus_genset_4) += 0 + +c_e_Bus_balance(fuel_bus_5)_: ++1 flow(diesel_fuel_bus_5) +-1 flow(fuel_bus_genset_5) += 0 + +c_e_Bus_balance(fuel_bus_6)_: ++1 flow(diesel_fuel_bus_6) +-1 flow(fuel_bus_genset_6) += 0 + +c_e_Bus_balance(fuel_bus_7)_: ++1 flow(diesel_fuel_bus_7) +-1 flow(fuel_bus_genset_7) += 0 + +c_e_Bus_balance(fuel_bus_8)_: ++1 flow(diesel_fuel_bus_8) +-1 flow(fuel_bus_genset_8) += 0 + +c_e_Bus_balance(fuel_bus_9)_: ++1 flow(diesel_fuel_bus_9) +-1 flow(fuel_bus_genset_9) += 0 + +c_e_Bus_balance(fuel_bus_10)_: ++1 flow(diesel_fuel_bus_10) +-1 flow(fuel_bus_genset_10) += 0 + +c_e_Bus_balance(fuel_bus_11)_: ++1 flow(diesel_fuel_bus_11) +-1 flow(fuel_bus_genset_11) += 0 + +c_e_Bus_balance(fuel_bus_12)_: ++1 flow(diesel_fuel_bus_12) +-1 flow(fuel_bus_genset_12) += 0 + +c_e_Bus_balance(fuel_bus_13)_: ++1 flow(diesel_fuel_bus_13) +-1 flow(fuel_bus_genset_13) += 0 + +c_e_Bus_balance(fuel_bus_14)_: ++1 flow(diesel_fuel_bus_14) +-1 flow(fuel_bus_genset_14) += 0 + +c_e_Bus_balance(fuel_bus_15)_: ++1 flow(diesel_fuel_bus_15) +-1 flow(fuel_bus_genset_15) += 0 + +c_e_Bus_balance(fuel_bus_16)_: ++1 flow(diesel_fuel_bus_16) +-1 flow(fuel_bus_genset_16) += 0 + +c_e_Bus_balance(fuel_bus_17)_: ++1 flow(diesel_fuel_bus_17) +-1 flow(fuel_bus_genset_17) += 0 + +c_e_Bus_balance(fuel_bus_18)_: ++1 flow(diesel_fuel_bus_18) +-1 flow(fuel_bus_genset_18) += 0 + +c_e_Bus_balance(fuel_bus_19)_: ++1 flow(diesel_fuel_bus_19) +-1 flow(fuel_bus_genset_19) += 0 + +c_e_Bus_balance(fuel_bus_20)_: ++1 flow(diesel_fuel_bus_20) +-1 flow(fuel_bus_genset_20) += 0 + +c_e_Bus_balance(fuel_bus_21)_: ++1 flow(diesel_fuel_bus_21) +-1 flow(fuel_bus_genset_21) += 0 + +c_e_Bus_balance(fuel_bus_22)_: ++1 flow(diesel_fuel_bus_22) +-1 flow(fuel_bus_genset_22) += 0 + +c_e_Bus_balance(fuel_bus_23)_: ++1 flow(diesel_fuel_bus_23) +-1 flow(fuel_bus_genset_23) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_0)_: ++1 flow(fuel_bus_genset_0) +-3.0303030303030303 flow(genset_electricity_bus_0) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_1)_: ++1 flow(fuel_bus_genset_1) +-3.0303030303030303 flow(genset_electricity_bus_1) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_2)_: ++1 flow(fuel_bus_genset_2) +-3.0303030303030303 flow(genset_electricity_bus_2) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_3)_: ++1 flow(fuel_bus_genset_3) +-3.0303030303030303 flow(genset_electricity_bus_3) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_4)_: ++1 flow(fuel_bus_genset_4) +-3.0303030303030303 flow(genset_electricity_bus_4) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_5)_: ++1 flow(fuel_bus_genset_5) +-3.0303030303030303 flow(genset_electricity_bus_5) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_6)_: ++1 flow(fuel_bus_genset_6) +-3.0303030303030303 flow(genset_electricity_bus_6) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_7)_: ++1 flow(fuel_bus_genset_7) +-3.0303030303030303 flow(genset_electricity_bus_7) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_8)_: ++1 flow(fuel_bus_genset_8) +-3.0303030303030303 flow(genset_electricity_bus_8) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_9)_: ++1 flow(fuel_bus_genset_9) +-3.0303030303030303 flow(genset_electricity_bus_9) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_10)_: ++1 flow(fuel_bus_genset_10) +-3.0303030303030303 flow(genset_electricity_bus_10) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_11)_: ++1 flow(fuel_bus_genset_11) +-3.0303030303030303 flow(genset_electricity_bus_11) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_12)_: ++1 flow(fuel_bus_genset_12) +-3.0303030303030303 flow(genset_electricity_bus_12) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_13)_: ++1 flow(fuel_bus_genset_13) +-3.0303030303030303 flow(genset_electricity_bus_13) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_14)_: ++1 flow(fuel_bus_genset_14) +-3.0303030303030303 flow(genset_electricity_bus_14) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_15)_: ++1 flow(fuel_bus_genset_15) +-3.0303030303030303 flow(genset_electricity_bus_15) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_16)_: ++1 flow(fuel_bus_genset_16) +-3.0303030303030303 flow(genset_electricity_bus_16) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_17)_: ++1 flow(fuel_bus_genset_17) +-3.0303030303030303 flow(genset_electricity_bus_17) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_18)_: ++1 flow(fuel_bus_genset_18) +-3.0303030303030303 flow(genset_electricity_bus_18) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_19)_: ++1 flow(fuel_bus_genset_19) +-3.0303030303030303 flow(genset_electricity_bus_19) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_20)_: ++1 flow(fuel_bus_genset_20) +-3.0303030303030303 flow(genset_electricity_bus_20) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_21)_: ++1 flow(fuel_bus_genset_21) +-3.0303030303030303 flow(genset_electricity_bus_21) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_22)_: ++1 flow(fuel_bus_genset_22) +-3.0303030303030303 flow(genset_electricity_bus_22) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_23)_: ++1 flow(fuel_bus_genset_23) +-3.0303030303030303 flow(genset_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_0)_: ++1 flow(pv_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_1)_: ++1 flow(pv_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_2)_: ++1 flow(pv_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_3)_: ++1 flow(pv_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_4)_: ++1 flow(pv_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_5)_: ++1 flow(pv_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_6)_: ++1 flow(pv_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_7)_: +-0.065722000000000003 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_8)_: +-0.20696199999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_9)_: +-0.33063799999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_10)_: +-0.40313100000000002 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_11)_: +-0.411138 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_12)_: +-0.367118 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_13)_: +-0.26857700000000001 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_14)_: +-0.11243399999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_15)_: +-0.0020409999999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_16)_: ++1 flow(pv_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_17)_: ++1 flow(pv_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_18)_: ++1 flow(pv_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_19)_: ++1 flow(pv_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_20)_: ++1 flow(pv_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_21)_: ++1 flow(pv_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_22)_: ++1 flow(pv_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_23)_: ++1 flow(pv_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_0)_: +-0.31556899999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_1)_: +-0.31157199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_2)_: +-0.30400500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_3)_: +-0.28287199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_4)_: +-0.25396999999999997 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_5)_: +-0.224077 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_6)_: +-0.19358 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_7)_: +-0.15992500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_8)_: +-0.12711500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_9)_: +-0.11749100000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_10)_: +-0.115909 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_11)_: +-0.10452400000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_12)_: +-0.090434 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_13)_: +-0.084694000000000005 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_14)_: +-0.11590999999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_15)_: +-0.16127900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_16)_: +-0.18877099999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_17)_: +-0.20496599999999998 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_18)_: +-0.21605700000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_19)_: +-0.22511900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_20)_: +-0.23353400000000002 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_21)_: +-0.26206299999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_22)_: +-0.30506500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_23)_: +-0.355796 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) += 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_0)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_0) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_1)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_1) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_2)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_2) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_3)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_3) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_4)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_4) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_5)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_5) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_6)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_6) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_7)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_7) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_8)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_8) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_9)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_9) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_10)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_10) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_11)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_11) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_12)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_12) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_13)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_13) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_14)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_14) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_15)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_15) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_16)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_16) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_17)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_17) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_18)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_18) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_19)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_19) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_20)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_20) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_21)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_21) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_22)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_22) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_23)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_23) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_0)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_1)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_2)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_3)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_4)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_5)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_6)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_7)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_8)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_9)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_10)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_11)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_12)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_13)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_14)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_15)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_16)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_17)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_18)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_19)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_20)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_21)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_22)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_23)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_0)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_1)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_2)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_3)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_4)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_5)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_6)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_7)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_8)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_9)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_10)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_11)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_12)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_13)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_14)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_15)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_16)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_17)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_18)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_19)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_20)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_21)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_22)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_23)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_0)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_1)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_2)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_3)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_4)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_5)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_6)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_7)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_8)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_9)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_10)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_11)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_12)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_13)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_14)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_15)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_16)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_17)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_18)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_19)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_20)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_21)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_22)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_23)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_0)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_1)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_2)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_3)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_4)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_5)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_6)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_7)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_8)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_9)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_10)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_11)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_12)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_13)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_14)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_15)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_16)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_17)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_18)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_19)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_20)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_21)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_22)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_23)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) +<= 0 + +c_e_GenericInvestmentStorageBlock_init_cap_fix(storage)_: ++1 GenericInvestmentStorageBlock_init_cap(storage) +-0.5 GenericInvestmentStorageBlock_invest(storage) += 0 + +c_e_GenericInvestmentStorageBlock_balance_first(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_init_cap(storage) +-0.94999999999999996 flow(electricity_bus_storage_0) ++1.0526315789473684 flow(storage_electricity_bus_0) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.94999999999999996 flow(electricity_bus_storage_1) ++1.0526315789473684 flow(storage_electricity_bus_1) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.94999999999999996 flow(electricity_bus_storage_2) ++1.0526315789473684 flow(storage_electricity_bus_2) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.94999999999999996 flow(electricity_bus_storage_3) ++1.0526315789473684 flow(storage_electricity_bus_3) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.94999999999999996 flow(electricity_bus_storage_4) ++1.0526315789473684 flow(storage_electricity_bus_4) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_5)_: +-1 GenericInvestmentStorageBlock_capacity(storage_4) ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-0.94999999999999996 flow(electricity_bus_storage_5) ++1.0526315789473684 flow(storage_electricity_bus_5) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_6)_: +-1 GenericInvestmentStorageBlock_capacity(storage_5) ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-0.94999999999999996 flow(electricity_bus_storage_6) ++1.0526315789473684 flow(storage_electricity_bus_6) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_7)_: +-1 GenericInvestmentStorageBlock_capacity(storage_6) ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-0.94999999999999996 flow(electricity_bus_storage_7) ++1.0526315789473684 flow(storage_electricity_bus_7) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_8)_: +-1 GenericInvestmentStorageBlock_capacity(storage_7) ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-0.94999999999999996 flow(electricity_bus_storage_8) ++1.0526315789473684 flow(storage_electricity_bus_8) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_9)_: +-1 GenericInvestmentStorageBlock_capacity(storage_8) ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_9) ++1.0526315789473684 flow(storage_electricity_bus_9) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_10) ++1.0526315789473684 flow(storage_electricity_bus_10) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_11)_: +-1 GenericInvestmentStorageBlock_capacity(storage_10) ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-0.94999999999999996 flow(electricity_bus_storage_11) ++1.0526315789473684 flow(storage_electricity_bus_11) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_12)_: +-1 GenericInvestmentStorageBlock_capacity(storage_11) ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-0.94999999999999996 flow(electricity_bus_storage_12) ++1.0526315789473684 flow(storage_electricity_bus_12) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_13)_: +-1 GenericInvestmentStorageBlock_capacity(storage_12) ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-0.94999999999999996 flow(electricity_bus_storage_13) ++1.0526315789473684 flow(storage_electricity_bus_13) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_14)_: +-1 GenericInvestmentStorageBlock_capacity(storage_13) ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-0.94999999999999996 flow(electricity_bus_storage_14) ++1.0526315789473684 flow(storage_electricity_bus_14) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_15)_: +-1 GenericInvestmentStorageBlock_capacity(storage_14) ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-0.94999999999999996 flow(electricity_bus_storage_15) ++1.0526315789473684 flow(storage_electricity_bus_15) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_16)_: +-1 GenericInvestmentStorageBlock_capacity(storage_15) ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-0.94999999999999996 flow(electricity_bus_storage_16) ++1.0526315789473684 flow(storage_electricity_bus_16) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_17)_: +-1 GenericInvestmentStorageBlock_capacity(storage_16) ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-0.94999999999999996 flow(electricity_bus_storage_17) ++1.0526315789473684 flow(storage_electricity_bus_17) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_18)_: +-1 GenericInvestmentStorageBlock_capacity(storage_17) ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-0.94999999999999996 flow(electricity_bus_storage_18) ++1.0526315789473684 flow(storage_electricity_bus_18) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_19)_: +-1 GenericInvestmentStorageBlock_capacity(storage_18) ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-0.94999999999999996 flow(electricity_bus_storage_19) ++1.0526315789473684 flow(storage_electricity_bus_19) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_20)_: +-1 GenericInvestmentStorageBlock_capacity(storage_19) ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-0.94999999999999996 flow(electricity_bus_storage_20) ++1.0526315789473684 flow(storage_electricity_bus_20) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_21)_: +-1 GenericInvestmentStorageBlock_capacity(storage_20) ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-0.94999999999999996 flow(electricity_bus_storage_21) ++1.0526315789473684 flow(storage_electricity_bus_21) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_22)_: +-1 GenericInvestmentStorageBlock_capacity(storage_21) ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-0.94999999999999996 flow(electricity_bus_storage_22) ++1.0526315789473684 flow(storage_electricity_bus_22) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_23)_: +-1 GenericInvestmentStorageBlock_capacity(storage_22) ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-0.94999999999999996 flow(electricity_bus_storage_23) ++1.0526315789473684 flow(storage_electricity_bus_23) += 0 + +c_e_GenericInvestmentStorageBlock_balanced_cstr(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-1 GenericInvestmentStorageBlock_init_cap(storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_inflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(electricity_bus_storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_outflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(storage_electricity_bus) += 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_0)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_1)_: ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_2)_: ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_3)_: ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_4)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_5)_: ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_6)_: ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_7)_: ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_8)_: ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_9)_: ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_11)_: ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_12)_: ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_13)_: ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_14)_: ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_15)_: ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_16)_: ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_17)_: ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_18)_: ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_19)_: ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_20)_: ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_21)_: ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_22)_: ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_23)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-0.90000000000000002 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_0)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_4) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_5)_: +-1 GenericInvestmentStorageBlock_capacity(storage_5) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_6)_: +-1 GenericInvestmentStorageBlock_capacity(storage_6) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_7)_: +-1 GenericInvestmentStorageBlock_capacity(storage_7) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_8)_: +-1 GenericInvestmentStorageBlock_capacity(storage_8) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_9)_: +-1 GenericInvestmentStorageBlock_capacity(storage_9) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_10)_: +-1 GenericInvestmentStorageBlock_capacity(storage_10) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_11)_: +-1 GenericInvestmentStorageBlock_capacity(storage_11) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_12)_: +-1 GenericInvestmentStorageBlock_capacity(storage_12) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_13)_: +-1 GenericInvestmentStorageBlock_capacity(storage_13) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_14)_: +-1 GenericInvestmentStorageBlock_capacity(storage_14) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_15)_: +-1 GenericInvestmentStorageBlock_capacity(storage_15) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_16)_: +-1 GenericInvestmentStorageBlock_capacity(storage_16) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_17)_: +-1 GenericInvestmentStorageBlock_capacity(storage_17) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_18)_: +-1 GenericInvestmentStorageBlock_capacity(storage_18) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_19)_: +-1 GenericInvestmentStorageBlock_capacity(storage_19) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_20)_: +-1 GenericInvestmentStorageBlock_capacity(storage_20) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_21)_: +-1 GenericInvestmentStorageBlock_capacity(storage_21) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_22)_: +-1 GenericInvestmentStorageBlock_capacity(storage_22) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_23)_: +-1 GenericInvestmentStorageBlock_capacity(storage_23) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_e_ONE_VAR_CONSTANT: +ONE_VAR_CONSTANT = 1.0 + +bounds + 0 <= flow(diesel_fuel_bus_0) <= +inf + 0 <= flow(diesel_fuel_bus_1) <= +inf + 0 <= flow(diesel_fuel_bus_2) <= +inf + 0 <= flow(diesel_fuel_bus_3) <= +inf + 0 <= flow(diesel_fuel_bus_4) <= +inf + 0 <= flow(diesel_fuel_bus_5) <= +inf + 0 <= flow(diesel_fuel_bus_6) <= +inf + 0 <= flow(diesel_fuel_bus_7) <= +inf + 0 <= flow(diesel_fuel_bus_8) <= +inf + 0 <= flow(diesel_fuel_bus_9) <= +inf + 0 <= flow(diesel_fuel_bus_10) <= +inf + 0 <= flow(diesel_fuel_bus_11) <= +inf + 0 <= flow(diesel_fuel_bus_12) <= +inf + 0 <= flow(diesel_fuel_bus_13) <= +inf + 0 <= flow(diesel_fuel_bus_14) <= +inf + 0 <= flow(diesel_fuel_bus_15) <= +inf + 0 <= flow(diesel_fuel_bus_16) <= +inf + 0 <= flow(diesel_fuel_bus_17) <= +inf + 0 <= flow(diesel_fuel_bus_18) <= +inf + 0 <= flow(diesel_fuel_bus_19) <= +inf + 0 <= flow(diesel_fuel_bus_20) <= +inf + 0 <= flow(diesel_fuel_bus_21) <= +inf + 0 <= flow(diesel_fuel_bus_22) <= +inf + 0 <= flow(diesel_fuel_bus_23) <= +inf + 0 <= flow(electricity_bus_excess_0) <= +inf + 0 <= flow(electricity_bus_excess_1) <= +inf + 0 <= flow(electricity_bus_excess_2) <= +inf + 0 <= flow(electricity_bus_excess_3) <= +inf + 0 <= flow(electricity_bus_excess_4) <= +inf + 0 <= flow(electricity_bus_excess_5) <= +inf + 0 <= flow(electricity_bus_excess_6) <= +inf + 0 <= flow(electricity_bus_excess_7) <= +inf + 0 <= flow(electricity_bus_excess_8) <= +inf + 0 <= flow(electricity_bus_excess_9) <= +inf + 0 <= flow(electricity_bus_excess_10) <= +inf + 0 <= flow(electricity_bus_excess_11) <= +inf + 0 <= flow(electricity_bus_excess_12) <= +inf + 0 <= flow(electricity_bus_excess_13) <= +inf + 0 <= flow(electricity_bus_excess_14) <= +inf + 0 <= flow(electricity_bus_excess_15) <= +inf + 0 <= flow(electricity_bus_excess_16) <= +inf + 0 <= flow(electricity_bus_excess_17) <= +inf + 0 <= flow(electricity_bus_excess_18) <= +inf + 0 <= flow(electricity_bus_excess_19) <= +inf + 0 <= flow(electricity_bus_excess_20) <= +inf + 0 <= flow(electricity_bus_excess_21) <= +inf + 0 <= flow(electricity_bus_excess_22) <= +inf + 0 <= flow(electricity_bus_excess_23) <= +inf + 0 <= flow(electricity_bus_storage_0) <= +inf + 0 <= flow(electricity_bus_storage_1) <= +inf + 0 <= flow(electricity_bus_storage_2) <= +inf + 0 <= flow(electricity_bus_storage_3) <= +inf + 0 <= flow(electricity_bus_storage_4) <= +inf + 0 <= flow(electricity_bus_storage_5) <= +inf + 0 <= flow(electricity_bus_storage_6) <= +inf + 0 <= flow(electricity_bus_storage_7) <= +inf + 0 <= flow(electricity_bus_storage_8) <= +inf + 0 <= flow(electricity_bus_storage_9) <= +inf + 0 <= flow(electricity_bus_storage_10) <= +inf + 0 <= flow(electricity_bus_storage_11) <= +inf + 0 <= flow(electricity_bus_storage_12) <= +inf + 0 <= flow(electricity_bus_storage_13) <= +inf + 0 <= flow(electricity_bus_storage_14) <= +inf + 0 <= flow(electricity_bus_storage_15) <= +inf + 0 <= flow(electricity_bus_storage_16) <= +inf + 0 <= flow(electricity_bus_storage_17) <= +inf + 0 <= flow(electricity_bus_storage_18) <= +inf + 0 <= flow(electricity_bus_storage_19) <= +inf + 0 <= flow(electricity_bus_storage_20) <= +inf + 0 <= flow(electricity_bus_storage_21) <= +inf + 0 <= flow(electricity_bus_storage_22) <= +inf + 0 <= flow(electricity_bus_storage_23) <= +inf + 0 <= flow(fuel_bus_genset_0) <= +inf + 0 <= flow(fuel_bus_genset_1) <= +inf + 0 <= flow(fuel_bus_genset_2) <= +inf + 0 <= flow(fuel_bus_genset_3) <= +inf + 0 <= flow(fuel_bus_genset_4) <= +inf + 0 <= flow(fuel_bus_genset_5) <= +inf + 0 <= flow(fuel_bus_genset_6) <= +inf + 0 <= flow(fuel_bus_genset_7) <= +inf + 0 <= flow(fuel_bus_genset_8) <= +inf + 0 <= flow(fuel_bus_genset_9) <= +inf + 0 <= flow(fuel_bus_genset_10) <= +inf + 0 <= flow(fuel_bus_genset_11) <= +inf + 0 <= flow(fuel_bus_genset_12) <= +inf + 0 <= flow(fuel_bus_genset_13) <= +inf + 0 <= flow(fuel_bus_genset_14) <= +inf + 0 <= flow(fuel_bus_genset_15) <= +inf + 0 <= flow(fuel_bus_genset_16) <= +inf + 0 <= flow(fuel_bus_genset_17) <= +inf + 0 <= flow(fuel_bus_genset_18) <= +inf + 0 <= flow(fuel_bus_genset_19) <= +inf + 0 <= flow(fuel_bus_genset_20) <= +inf + 0 <= flow(fuel_bus_genset_21) <= +inf + 0 <= flow(fuel_bus_genset_22) <= +inf + 0 <= flow(fuel_bus_genset_23) <= +inf + 0 <= flow(genset_electricity_bus_0) <= +inf + 0 <= flow(genset_electricity_bus_1) <= +inf + 0 <= flow(genset_electricity_bus_2) <= +inf + 0 <= flow(genset_electricity_bus_3) <= +inf + 0 <= flow(genset_electricity_bus_4) <= +inf + 0 <= flow(genset_electricity_bus_5) <= +inf + 0 <= flow(genset_electricity_bus_6) <= +inf + 0 <= flow(genset_electricity_bus_7) <= +inf + 0 <= flow(genset_electricity_bus_8) <= +inf + 0 <= flow(genset_electricity_bus_9) <= +inf + 0 <= flow(genset_electricity_bus_10) <= +inf + 0 <= flow(genset_electricity_bus_11) <= +inf + 0 <= flow(genset_electricity_bus_12) <= +inf + 0 <= flow(genset_electricity_bus_13) <= +inf + 0 <= flow(genset_electricity_bus_14) <= +inf + 0 <= flow(genset_electricity_bus_15) <= +inf + 0 <= flow(genset_electricity_bus_16) <= +inf + 0 <= flow(genset_electricity_bus_17) <= +inf + 0 <= flow(genset_electricity_bus_18) <= +inf + 0 <= flow(genset_electricity_bus_19) <= +inf + 0 <= flow(genset_electricity_bus_20) <= +inf + 0 <= flow(genset_electricity_bus_21) <= +inf + 0 <= flow(genset_electricity_bus_22) <= +inf + 0 <= flow(genset_electricity_bus_23) <= +inf + 0 <= flow(pv_electricity_bus_0) <= +inf + 0 <= flow(pv_electricity_bus_1) <= +inf + 0 <= flow(pv_electricity_bus_2) <= +inf + 0 <= flow(pv_electricity_bus_3) <= +inf + 0 <= flow(pv_electricity_bus_4) <= +inf + 0 <= flow(pv_electricity_bus_5) <= +inf + 0 <= flow(pv_electricity_bus_6) <= +inf + 0 <= flow(pv_electricity_bus_7) <= +inf + 0 <= flow(pv_electricity_bus_8) <= +inf + 0 <= flow(pv_electricity_bus_9) <= +inf + 0 <= flow(pv_electricity_bus_10) <= +inf + 0 <= flow(pv_electricity_bus_11) <= +inf + 0 <= flow(pv_electricity_bus_12) <= +inf + 0 <= flow(pv_electricity_bus_13) <= +inf + 0 <= flow(pv_electricity_bus_14) <= +inf + 0 <= flow(pv_electricity_bus_15) <= +inf + 0 <= flow(pv_electricity_bus_16) <= +inf + 0 <= flow(pv_electricity_bus_17) <= +inf + 0 <= flow(pv_electricity_bus_18) <= +inf + 0 <= flow(pv_electricity_bus_19) <= +inf + 0 <= flow(pv_electricity_bus_20) <= +inf + 0 <= flow(pv_electricity_bus_21) <= +inf + 0 <= flow(pv_electricity_bus_22) <= +inf + 0 <= flow(pv_electricity_bus_23) <= +inf + 0 <= flow(storage_electricity_bus_0) <= +inf + 0 <= flow(storage_electricity_bus_1) <= +inf + 0 <= flow(storage_electricity_bus_2) <= +inf + 0 <= flow(storage_electricity_bus_3) <= +inf + 0 <= flow(storage_electricity_bus_4) <= +inf + 0 <= flow(storage_electricity_bus_5) <= +inf + 0 <= flow(storage_electricity_bus_6) <= +inf + 0 <= flow(storage_electricity_bus_7) <= +inf + 0 <= flow(storage_electricity_bus_8) <= +inf + 0 <= flow(storage_electricity_bus_9) <= +inf + 0 <= flow(storage_electricity_bus_10) <= +inf + 0 <= flow(storage_electricity_bus_11) <= +inf + 0 <= flow(storage_electricity_bus_12) <= +inf + 0 <= flow(storage_electricity_bus_13) <= +inf + 0 <= flow(storage_electricity_bus_14) <= +inf + 0 <= flow(storage_electricity_bus_15) <= +inf + 0 <= flow(storage_electricity_bus_16) <= +inf + 0 <= flow(storage_electricity_bus_17) <= +inf + 0 <= flow(storage_electricity_bus_18) <= +inf + 0 <= flow(storage_electricity_bus_19) <= +inf + 0 <= flow(storage_electricity_bus_20) <= +inf + 0 <= flow(storage_electricity_bus_21) <= +inf + 0 <= flow(storage_electricity_bus_22) <= +inf + 0 <= flow(storage_electricity_bus_23) <= +inf + 0 <= flow(wind_electricity_bus_0) <= +inf + 0 <= flow(wind_electricity_bus_1) <= +inf + 0 <= flow(wind_electricity_bus_2) <= +inf + 0 <= flow(wind_electricity_bus_3) <= +inf + 0 <= flow(wind_electricity_bus_4) <= +inf + 0 <= flow(wind_electricity_bus_5) <= +inf + 0 <= flow(wind_electricity_bus_6) <= +inf + 0 <= flow(wind_electricity_bus_7) <= +inf + 0 <= flow(wind_electricity_bus_8) <= +inf + 0 <= flow(wind_electricity_bus_9) <= +inf + 0 <= flow(wind_electricity_bus_10) <= +inf + 0 <= flow(wind_electricity_bus_11) <= +inf + 0 <= flow(wind_electricity_bus_12) <= +inf + 0 <= flow(wind_electricity_bus_13) <= +inf + 0 <= flow(wind_electricity_bus_14) <= +inf + 0 <= flow(wind_electricity_bus_15) <= +inf + 0 <= flow(wind_electricity_bus_16) <= +inf + 0 <= flow(wind_electricity_bus_17) <= +inf + 0 <= flow(wind_electricity_bus_18) <= +inf + 0 <= flow(wind_electricity_bus_19) <= +inf + 0 <= flow(wind_electricity_bus_20) <= +inf + 0 <= flow(wind_electricity_bus_21) <= +inf + 0 <= flow(wind_electricity_bus_22) <= +inf + 0 <= flow(wind_electricity_bus_23) <= +inf + 0 <= InvestmentFlow_invest(electricity_bus_storage) <= +inf + 0 <= InvestmentFlow_invest(genset_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(pv_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(storage_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(wind_electricity_bus) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_0) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_1) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_2) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_3) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_4) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_5) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_6) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_7) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_8) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_9) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_10) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_11) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_12) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_13) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_14) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_15) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_16) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_17) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_18) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_19) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_20) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_21) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_22) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_23) <= +inf + 0 <= GenericInvestmentStorageBlock_invest(storage) <= +inf + 0 <= GenericInvestmentStorageBlock_init_cap(storage) <= +inf +end diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_summed_limit.lp b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_summed_limit.lp new file mode 100644 index 0000000..e4b0423 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_custom_summed_limit.lp @@ -0,0 +1,1879 @@ +\* Source Pyomo model name=Model *\ + +min +objective: ++0.25312306644193744 GenericInvestmentStorageBlock_invest(storage) ++0.10644211640996434 InvestmentFlow_invest(genset_electricity_bus) ++0.1648820284740232 InvestmentFlow_invest(pv_electricity_bus) ++0.43968540926406191 InvestmentFlow_invest(wind_electricity_bus) ++0.063761955366631234 flow(diesel_fuel_bus_0) ++0.063761955366631234 flow(diesel_fuel_bus_1) ++0.063761955366631234 flow(diesel_fuel_bus_10) ++0.063761955366631234 flow(diesel_fuel_bus_11) ++0.063761955366631234 flow(diesel_fuel_bus_12) ++0.063761955366631234 flow(diesel_fuel_bus_13) ++0.063761955366631234 flow(diesel_fuel_bus_14) ++0.063761955366631234 flow(diesel_fuel_bus_15) ++0.063761955366631234 flow(diesel_fuel_bus_16) ++0.063761955366631234 flow(diesel_fuel_bus_17) ++0.063761955366631234 flow(diesel_fuel_bus_18) ++0.063761955366631234 flow(diesel_fuel_bus_19) ++0.063761955366631234 flow(diesel_fuel_bus_2) ++0.063761955366631234 flow(diesel_fuel_bus_20) ++0.063761955366631234 flow(diesel_fuel_bus_21) ++0.063761955366631234 flow(diesel_fuel_bus_22) ++0.063761955366631234 flow(diesel_fuel_bus_23) ++0.063761955366631234 flow(diesel_fuel_bus_3) ++0.063761955366631234 flow(diesel_fuel_bus_4) ++0.063761955366631234 flow(diesel_fuel_bus_5) ++0.063761955366631234 flow(diesel_fuel_bus_6) ++0.063761955366631234 flow(diesel_fuel_bus_7) ++0.063761955366631234 flow(diesel_fuel_bus_8) ++0.063761955366631234 flow(diesel_fuel_bus_9) + +s.t. + +c_l_renewable_share_constraint_: +-1 flow(genset_electricity_bus_0) +-1 flow(genset_electricity_bus_1) +-1 flow(genset_electricity_bus_10) +-1 flow(genset_electricity_bus_11) +-1 flow(genset_electricity_bus_12) +-1 flow(genset_electricity_bus_13) +-1 flow(genset_electricity_bus_14) +-1 flow(genset_electricity_bus_15) +-1 flow(genset_electricity_bus_16) +-1 flow(genset_electricity_bus_17) +-1 flow(genset_electricity_bus_18) +-1 flow(genset_electricity_bus_19) +-1 flow(genset_electricity_bus_2) +-1 flow(genset_electricity_bus_20) +-1 flow(genset_electricity_bus_21) +-1 flow(genset_electricity_bus_22) +-1 flow(genset_electricity_bus_23) +-1 flow(genset_electricity_bus_3) +-1 flow(genset_electricity_bus_4) +-1 flow(genset_electricity_bus_5) +-1 flow(genset_electricity_bus_6) +-1 flow(genset_electricity_bus_7) +-1 flow(genset_electricity_bus_8) +-1 flow(genset_electricity_bus_9) +>= 0 + +c_e_Bus_balance(electricity_bus_0)_: +-1 flow(electricity_bus_excess_0) +-1 flow(electricity_bus_storage_0) ++1 flow(genset_electricity_bus_0) ++1 flow(pv_electricity_bus_0) ++1 flow(storage_electricity_bus_0) ++1 flow(wind_electricity_bus_0) += 279.53099120000002 + +c_e_Bus_balance(electricity_bus_1)_: +-1 flow(electricity_bus_excess_1) +-1 flow(electricity_bus_storage_1) ++1 flow(genset_electricity_bus_1) ++1 flow(pv_electricity_bus_1) ++1 flow(storage_electricity_bus_1) ++1 flow(wind_electricity_bus_1) += 266.80324295000003 + +c_e_Bus_balance(electricity_bus_2)_: +-1 flow(electricity_bus_excess_2) +-1 flow(electricity_bus_storage_2) ++1 flow(genset_electricity_bus_2) ++1 flow(pv_electricity_bus_2) ++1 flow(storage_electricity_bus_2) ++1 flow(wind_electricity_bus_2) += 253.02937845 + +c_e_Bus_balance(electricity_bus_3)_: +-1 flow(electricity_bus_excess_3) +-1 flow(electricity_bus_storage_3) ++1 flow(genset_electricity_bus_3) ++1 flow(pv_electricity_bus_3) ++1 flow(storage_electricity_bus_3) ++1 flow(wind_electricity_bus_3) += 252.07043849999999 + +c_e_Bus_balance(electricity_bus_4)_: +-1 flow(electricity_bus_excess_4) +-1 flow(electricity_bus_storage_4) ++1 flow(genset_electricity_bus_4) ++1 flow(pv_electricity_bus_4) ++1 flow(storage_electricity_bus_4) ++1 flow(wind_electricity_bus_4) += 253.55243659999999 + +c_e_Bus_balance(electricity_bus_5)_: +-1 flow(electricity_bus_excess_5) +-1 flow(electricity_bus_storage_5) ++1 flow(genset_electricity_bus_5) ++1 flow(pv_electricity_bus_5) ++1 flow(storage_electricity_bus_5) ++1 flow(wind_electricity_bus_5) += 255.68825735000001 + +c_e_Bus_balance(electricity_bus_6)_: +-1 flow(electricity_bus_excess_6) +-1 flow(electricity_bus_storage_6) ++1 flow(genset_electricity_bus_6) ++1 flow(pv_electricity_bus_6) ++1 flow(storage_electricity_bus_6) ++1 flow(wind_electricity_bus_6) += 270.90053180000001 + +c_e_Bus_balance(electricity_bus_7)_: +-1 flow(electricity_bus_excess_7) +-1 flow(electricity_bus_storage_7) ++1 flow(genset_electricity_bus_7) ++1 flow(pv_electricity_bus_7) ++1 flow(storage_electricity_bus_7) ++1 flow(wind_electricity_bus_7) += 284.63080809999997 + +c_e_Bus_balance(electricity_bus_8)_: +-1 flow(electricity_bus_excess_8) +-1 flow(electricity_bus_storage_8) ++1 flow(genset_electricity_bus_8) ++1 flow(pv_electricity_bus_8) ++1 flow(storage_electricity_bus_8) ++1 flow(wind_electricity_bus_8) += 301.49943335 + +c_e_Bus_balance(electricity_bus_9)_: +-1 flow(electricity_bus_excess_9) +-1 flow(electricity_bus_storage_9) ++1 flow(genset_electricity_bus_9) ++1 flow(pv_electricity_bus_9) ++1 flow(storage_electricity_bus_9) ++1 flow(wind_electricity_bus_9) += 314.53229885000002 + +c_e_Bus_balance(electricity_bus_10)_: +-1 flow(electricity_bus_excess_10) +-1 flow(electricity_bus_storage_10) ++1 flow(genset_electricity_bus_10) ++1 flow(pv_electricity_bus_10) ++1 flow(storage_electricity_bus_10) ++1 flow(wind_electricity_bus_10) += 317.32194229999999 + +c_e_Bus_balance(electricity_bus_11)_: +-1 flow(electricity_bus_excess_11) +-1 flow(electricity_bus_storage_11) ++1 flow(genset_electricity_bus_11) ++1 flow(pv_electricity_bus_11) ++1 flow(storage_electricity_bus_11) ++1 flow(wind_electricity_bus_11) += 307.51460204999995 + +c_e_Bus_balance(electricity_bus_12)_: +-1 flow(electricity_bus_excess_12) +-1 flow(electricity_bus_storage_12) ++1 flow(genset_electricity_bus_12) ++1 flow(pv_electricity_bus_12) ++1 flow(storage_electricity_bus_12) ++1 flow(wind_electricity_bus_12) += 302.89425510000001 + +c_e_Bus_balance(electricity_bus_13)_: +-1 flow(electricity_bus_excess_13) +-1 flow(electricity_bus_storage_13) ++1 flow(genset_electricity_bus_13) ++1 flow(pv_electricity_bus_13) ++1 flow(storage_electricity_bus_13) ++1 flow(wind_electricity_bus_13) += 297.35855635000001 + +c_e_Bus_balance(electricity_bus_14)_: +-1 flow(electricity_bus_excess_14) +-1 flow(electricity_bus_storage_14) ++1 flow(genset_electricity_bus_14) ++1 flow(pv_electricity_bus_14) ++1 flow(storage_electricity_bus_14) ++1 flow(wind_electricity_bus_14) += 303.6352541 + +c_e_Bus_balance(electricity_bus_15)_: +-1 flow(electricity_bus_excess_15) +-1 flow(electricity_bus_storage_15) ++1 flow(genset_electricity_bus_15) ++1 flow(pv_electricity_bus_15) ++1 flow(storage_electricity_bus_15) ++1 flow(wind_electricity_bus_15) += 340.33650075000003 + +c_e_Bus_balance(electricity_bus_16)_: +-1 flow(electricity_bus_excess_16) +-1 flow(electricity_bus_storage_16) ++1 flow(genset_electricity_bus_16) ++1 flow(pv_electricity_bus_16) ++1 flow(storage_electricity_bus_16) ++1 flow(wind_electricity_bus_16) += 354.4154825 + +c_e_Bus_balance(electricity_bus_17)_: +-1 flow(electricity_bus_excess_17) +-1 flow(electricity_bus_storage_17) ++1 flow(genset_electricity_bus_17) ++1 flow(pv_electricity_bus_17) ++1 flow(storage_electricity_bus_17) ++1 flow(wind_electricity_bus_17) += 353.50013075000004 + +c_e_Bus_balance(electricity_bus_18)_: +-1 flow(electricity_bus_excess_18) +-1 flow(electricity_bus_storage_18) ++1 flow(genset_electricity_bus_18) ++1 flow(pv_electricity_bus_18) ++1 flow(storage_electricity_bus_18) ++1 flow(wind_electricity_bus_18) += 340.59802980000001 + +c_e_Bus_balance(electricity_bus_19)_: +-1 flow(electricity_bus_excess_19) +-1 flow(electricity_bus_storage_19) ++1 flow(genset_electricity_bus_19) ++1 flow(pv_electricity_bus_19) ++1 flow(storage_electricity_bus_19) ++1 flow(wind_electricity_bus_19) += 320.32952664999999 + +c_e_Bus_balance(electricity_bus_20)_: +-1 flow(electricity_bus_excess_20) +-1 flow(electricity_bus_storage_20) ++1 flow(genset_electricity_bus_20) ++1 flow(pv_electricity_bus_20) ++1 flow(storage_electricity_bus_20) ++1 flow(wind_electricity_bus_20) += 304.89931129999997 + +c_e_Bus_balance(electricity_bus_21)_: +-1 flow(electricity_bus_excess_21) +-1 flow(electricity_bus_storage_21) ++1 flow(genset_electricity_bus_21) ++1 flow(pv_electricity_bus_21) ++1 flow(storage_electricity_bus_21) ++1 flow(wind_electricity_bus_21) += 307.95048385000001 + +c_e_Bus_balance(electricity_bus_22)_: +-1 flow(electricity_bus_excess_22) +-1 flow(electricity_bus_storage_22) ++1 flow(genset_electricity_bus_22) ++1 flow(pv_electricity_bus_22) ++1 flow(storage_electricity_bus_22) ++1 flow(wind_electricity_bus_22) += 283.58469184999996 + +c_e_Bus_balance(electricity_bus_23)_: +-1 flow(electricity_bus_excess_23) +-1 flow(electricity_bus_storage_23) ++1 flow(genset_electricity_bus_23) ++1 flow(pv_electricity_bus_23) ++1 flow(storage_electricity_bus_23) ++1 flow(wind_electricity_bus_23) += 267.98012379999994 + +c_e_Bus_balance(fuel_bus_0)_: ++1 flow(diesel_fuel_bus_0) +-1 flow(fuel_bus_genset_0) += 0 + +c_e_Bus_balance(fuel_bus_1)_: ++1 flow(diesel_fuel_bus_1) +-1 flow(fuel_bus_genset_1) += 0 + +c_e_Bus_balance(fuel_bus_2)_: ++1 flow(diesel_fuel_bus_2) +-1 flow(fuel_bus_genset_2) += 0 + +c_e_Bus_balance(fuel_bus_3)_: ++1 flow(diesel_fuel_bus_3) +-1 flow(fuel_bus_genset_3) += 0 + +c_e_Bus_balance(fuel_bus_4)_: ++1 flow(diesel_fuel_bus_4) +-1 flow(fuel_bus_genset_4) += 0 + +c_e_Bus_balance(fuel_bus_5)_: ++1 flow(diesel_fuel_bus_5) +-1 flow(fuel_bus_genset_5) += 0 + +c_e_Bus_balance(fuel_bus_6)_: ++1 flow(diesel_fuel_bus_6) +-1 flow(fuel_bus_genset_6) += 0 + +c_e_Bus_balance(fuel_bus_7)_: ++1 flow(diesel_fuel_bus_7) +-1 flow(fuel_bus_genset_7) += 0 + +c_e_Bus_balance(fuel_bus_8)_: ++1 flow(diesel_fuel_bus_8) +-1 flow(fuel_bus_genset_8) += 0 + +c_e_Bus_balance(fuel_bus_9)_: ++1 flow(diesel_fuel_bus_9) +-1 flow(fuel_bus_genset_9) += 0 + +c_e_Bus_balance(fuel_bus_10)_: ++1 flow(diesel_fuel_bus_10) +-1 flow(fuel_bus_genset_10) += 0 + +c_e_Bus_balance(fuel_bus_11)_: ++1 flow(diesel_fuel_bus_11) +-1 flow(fuel_bus_genset_11) += 0 + +c_e_Bus_balance(fuel_bus_12)_: ++1 flow(diesel_fuel_bus_12) +-1 flow(fuel_bus_genset_12) += 0 + +c_e_Bus_balance(fuel_bus_13)_: ++1 flow(diesel_fuel_bus_13) +-1 flow(fuel_bus_genset_13) += 0 + +c_e_Bus_balance(fuel_bus_14)_: ++1 flow(diesel_fuel_bus_14) +-1 flow(fuel_bus_genset_14) += 0 + +c_e_Bus_balance(fuel_bus_15)_: ++1 flow(diesel_fuel_bus_15) +-1 flow(fuel_bus_genset_15) += 0 + +c_e_Bus_balance(fuel_bus_16)_: ++1 flow(diesel_fuel_bus_16) +-1 flow(fuel_bus_genset_16) += 0 + +c_e_Bus_balance(fuel_bus_17)_: ++1 flow(diesel_fuel_bus_17) +-1 flow(fuel_bus_genset_17) += 0 + +c_e_Bus_balance(fuel_bus_18)_: ++1 flow(diesel_fuel_bus_18) +-1 flow(fuel_bus_genset_18) += 0 + +c_e_Bus_balance(fuel_bus_19)_: ++1 flow(diesel_fuel_bus_19) +-1 flow(fuel_bus_genset_19) += 0 + +c_e_Bus_balance(fuel_bus_20)_: ++1 flow(diesel_fuel_bus_20) +-1 flow(fuel_bus_genset_20) += 0 + +c_e_Bus_balance(fuel_bus_21)_: ++1 flow(diesel_fuel_bus_21) +-1 flow(fuel_bus_genset_21) += 0 + +c_e_Bus_balance(fuel_bus_22)_: ++1 flow(diesel_fuel_bus_22) +-1 flow(fuel_bus_genset_22) += 0 + +c_e_Bus_balance(fuel_bus_23)_: ++1 flow(diesel_fuel_bus_23) +-1 flow(fuel_bus_genset_23) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_0)_: ++1 flow(fuel_bus_genset_0) +-3.0303030303030303 flow(genset_electricity_bus_0) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_1)_: ++1 flow(fuel_bus_genset_1) +-3.0303030303030303 flow(genset_electricity_bus_1) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_2)_: ++1 flow(fuel_bus_genset_2) +-3.0303030303030303 flow(genset_electricity_bus_2) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_3)_: ++1 flow(fuel_bus_genset_3) +-3.0303030303030303 flow(genset_electricity_bus_3) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_4)_: ++1 flow(fuel_bus_genset_4) +-3.0303030303030303 flow(genset_electricity_bus_4) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_5)_: ++1 flow(fuel_bus_genset_5) +-3.0303030303030303 flow(genset_electricity_bus_5) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_6)_: ++1 flow(fuel_bus_genset_6) +-3.0303030303030303 flow(genset_electricity_bus_6) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_7)_: ++1 flow(fuel_bus_genset_7) +-3.0303030303030303 flow(genset_electricity_bus_7) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_8)_: ++1 flow(fuel_bus_genset_8) +-3.0303030303030303 flow(genset_electricity_bus_8) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_9)_: ++1 flow(fuel_bus_genset_9) +-3.0303030303030303 flow(genset_electricity_bus_9) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_10)_: ++1 flow(fuel_bus_genset_10) +-3.0303030303030303 flow(genset_electricity_bus_10) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_11)_: ++1 flow(fuel_bus_genset_11) +-3.0303030303030303 flow(genset_electricity_bus_11) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_12)_: ++1 flow(fuel_bus_genset_12) +-3.0303030303030303 flow(genset_electricity_bus_12) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_13)_: ++1 flow(fuel_bus_genset_13) +-3.0303030303030303 flow(genset_electricity_bus_13) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_14)_: ++1 flow(fuel_bus_genset_14) +-3.0303030303030303 flow(genset_electricity_bus_14) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_15)_: ++1 flow(fuel_bus_genset_15) +-3.0303030303030303 flow(genset_electricity_bus_15) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_16)_: ++1 flow(fuel_bus_genset_16) +-3.0303030303030303 flow(genset_electricity_bus_16) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_17)_: ++1 flow(fuel_bus_genset_17) +-3.0303030303030303 flow(genset_electricity_bus_17) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_18)_: ++1 flow(fuel_bus_genset_18) +-3.0303030303030303 flow(genset_electricity_bus_18) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_19)_: ++1 flow(fuel_bus_genset_19) +-3.0303030303030303 flow(genset_electricity_bus_19) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_20)_: ++1 flow(fuel_bus_genset_20) +-3.0303030303030303 flow(genset_electricity_bus_20) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_21)_: ++1 flow(fuel_bus_genset_21) +-3.0303030303030303 flow(genset_electricity_bus_21) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_22)_: ++1 flow(fuel_bus_genset_22) +-3.0303030303030303 flow(genset_electricity_bus_22) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_23)_: ++1 flow(fuel_bus_genset_23) +-3.0303030303030303 flow(genset_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_0)_: ++1 flow(pv_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_1)_: ++1 flow(pv_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_2)_: ++1 flow(pv_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_3)_: ++1 flow(pv_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_4)_: ++1 flow(pv_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_5)_: ++1 flow(pv_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_6)_: ++1 flow(pv_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_7)_: +-0.065722000000000003 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_8)_: +-0.20696199999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_9)_: +-0.33063799999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_10)_: +-0.40313100000000002 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_11)_: +-0.411138 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_12)_: +-0.367118 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_13)_: +-0.26857700000000001 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_14)_: +-0.11243399999999999 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_15)_: +-0.0020409999999999998 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_16)_: ++1 flow(pv_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_17)_: ++1 flow(pv_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_18)_: ++1 flow(pv_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_19)_: ++1 flow(pv_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_20)_: ++1 flow(pv_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_21)_: ++1 flow(pv_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_22)_: ++1 flow(pv_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_23)_: ++1 flow(pv_electricity_bus_23) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_0)_: +-0.31556899999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_1)_: +-0.31157199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_2)_: +-0.30400500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_3)_: +-0.28287199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_4)_: +-0.25396999999999997 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_5)_: +-0.224077 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_6)_: +-0.19358 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_7)_: +-0.15992500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_8)_: +-0.12711500000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_9)_: +-0.11749100000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_10)_: +-0.115909 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_11)_: +-0.10452400000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_12)_: +-0.090434 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_13)_: +-0.084694000000000005 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_14)_: +-0.11590999999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_15)_: +-0.16127900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_16)_: +-0.18877099999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_17)_: +-0.20496599999999998 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_18)_: +-0.21605700000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_19)_: +-0.22511900000000001 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_20)_: +-0.23353400000000002 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_21)_: +-0.26206299999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_22)_: +-0.30506500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_23)_: +-0.355796 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) += 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_0)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_0) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_1)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_1) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_2)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_2) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_3)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_3) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_4)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_4) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_5)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_5) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_6)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_6) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_7)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_7) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_8)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_8) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_9)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_9) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_10)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_10) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_11)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_11) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_12)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_12) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_13)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_13) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_14)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_14) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_15)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_15) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_16)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_16) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_17)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_17) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_18)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_18) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_19)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_19) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_20)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_20) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_21)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_21) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_22)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_22) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_23)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_23) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_0)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_1)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_2)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_3)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_4)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_5)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_6)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_7)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_8)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_9)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_10)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_11)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_12)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_13)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_14)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_15)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_16)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_17)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_18)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_19)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_20)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_21)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_22)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_23)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_0)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_1)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_2)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_3)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_4)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_5)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_6)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_7)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_8)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_9)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_10)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_11)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_12)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_13)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_14)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_15)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_16)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_17)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_18)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_19)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_20)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_21)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_22)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_23)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_0)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_1)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_2)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_3)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_4)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_5)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_6)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_7)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_8)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_9)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_10)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_11)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_12)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_13)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_14)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_15)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_16)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_17)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_18)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_19)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_20)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_21)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_22)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_23)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_23) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_0)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_1)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_2)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_3)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_4)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_5)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_5) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_6)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_6) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_7)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_7) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_8)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_8) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_9)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_9) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_10)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_10) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_11)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_11) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_12)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_12) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_13)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_13) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_14)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_14) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_15)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_15) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_16)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_16) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_17)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_17) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_18)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_18) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_19)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_19) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_20)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_20) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_21)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_21) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_22)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_22) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_23)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_23) +<= 0 + +c_e_GenericInvestmentStorageBlock_init_cap_fix(storage)_: ++1 GenericInvestmentStorageBlock_init_cap(storage) +-0.5 GenericInvestmentStorageBlock_invest(storage) += 0 + +c_e_GenericInvestmentStorageBlock_balance_first(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_init_cap(storage) +-0.94999999999999996 flow(electricity_bus_storage_0) ++1.0526315789473684 flow(storage_electricity_bus_0) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.94999999999999996 flow(electricity_bus_storage_1) ++1.0526315789473684 flow(storage_electricity_bus_1) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.94999999999999996 flow(electricity_bus_storage_2) ++1.0526315789473684 flow(storage_electricity_bus_2) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.94999999999999996 flow(electricity_bus_storage_3) ++1.0526315789473684 flow(storage_electricity_bus_3) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.94999999999999996 flow(electricity_bus_storage_4) ++1.0526315789473684 flow(storage_electricity_bus_4) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_5)_: +-1 GenericInvestmentStorageBlock_capacity(storage_4) ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-0.94999999999999996 flow(electricity_bus_storage_5) ++1.0526315789473684 flow(storage_electricity_bus_5) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_6)_: +-1 GenericInvestmentStorageBlock_capacity(storage_5) ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-0.94999999999999996 flow(electricity_bus_storage_6) ++1.0526315789473684 flow(storage_electricity_bus_6) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_7)_: +-1 GenericInvestmentStorageBlock_capacity(storage_6) ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-0.94999999999999996 flow(electricity_bus_storage_7) ++1.0526315789473684 flow(storage_electricity_bus_7) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_8)_: +-1 GenericInvestmentStorageBlock_capacity(storage_7) ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-0.94999999999999996 flow(electricity_bus_storage_8) ++1.0526315789473684 flow(storage_electricity_bus_8) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_9)_: +-1 GenericInvestmentStorageBlock_capacity(storage_8) ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_9) ++1.0526315789473684 flow(storage_electricity_bus_9) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-1 GenericInvestmentStorageBlock_capacity(storage_9) +-0.94999999999999996 flow(electricity_bus_storage_10) ++1.0526315789473684 flow(storage_electricity_bus_10) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_11)_: +-1 GenericInvestmentStorageBlock_capacity(storage_10) ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-0.94999999999999996 flow(electricity_bus_storage_11) ++1.0526315789473684 flow(storage_electricity_bus_11) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_12)_: +-1 GenericInvestmentStorageBlock_capacity(storage_11) ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-0.94999999999999996 flow(electricity_bus_storage_12) ++1.0526315789473684 flow(storage_electricity_bus_12) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_13)_: +-1 GenericInvestmentStorageBlock_capacity(storage_12) ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-0.94999999999999996 flow(electricity_bus_storage_13) ++1.0526315789473684 flow(storage_electricity_bus_13) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_14)_: +-1 GenericInvestmentStorageBlock_capacity(storage_13) ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-0.94999999999999996 flow(electricity_bus_storage_14) ++1.0526315789473684 flow(storage_electricity_bus_14) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_15)_: +-1 GenericInvestmentStorageBlock_capacity(storage_14) ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-0.94999999999999996 flow(electricity_bus_storage_15) ++1.0526315789473684 flow(storage_electricity_bus_15) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_16)_: +-1 GenericInvestmentStorageBlock_capacity(storage_15) ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-0.94999999999999996 flow(electricity_bus_storage_16) ++1.0526315789473684 flow(storage_electricity_bus_16) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_17)_: +-1 GenericInvestmentStorageBlock_capacity(storage_16) ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-0.94999999999999996 flow(electricity_bus_storage_17) ++1.0526315789473684 flow(storage_electricity_bus_17) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_18)_: +-1 GenericInvestmentStorageBlock_capacity(storage_17) ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-0.94999999999999996 flow(electricity_bus_storage_18) ++1.0526315789473684 flow(storage_electricity_bus_18) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_19)_: +-1 GenericInvestmentStorageBlock_capacity(storage_18) ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-0.94999999999999996 flow(electricity_bus_storage_19) ++1.0526315789473684 flow(storage_electricity_bus_19) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_20)_: +-1 GenericInvestmentStorageBlock_capacity(storage_19) ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-0.94999999999999996 flow(electricity_bus_storage_20) ++1.0526315789473684 flow(storage_electricity_bus_20) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_21)_: +-1 GenericInvestmentStorageBlock_capacity(storage_20) ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-0.94999999999999996 flow(electricity_bus_storage_21) ++1.0526315789473684 flow(storage_electricity_bus_21) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_22)_: +-1 GenericInvestmentStorageBlock_capacity(storage_21) ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-0.94999999999999996 flow(electricity_bus_storage_22) ++1.0526315789473684 flow(storage_electricity_bus_22) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_23)_: +-1 GenericInvestmentStorageBlock_capacity(storage_22) ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-0.94999999999999996 flow(electricity_bus_storage_23) ++1.0526315789473684 flow(storage_electricity_bus_23) += 0 + +c_e_GenericInvestmentStorageBlock_balanced_cstr(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-1 GenericInvestmentStorageBlock_init_cap(storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_inflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(electricity_bus_storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_outflow(storage)_: +-1 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(storage_electricity_bus) += 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_0)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_1)_: ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_2)_: ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_3)_: ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_4)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_5)_: ++1 GenericInvestmentStorageBlock_capacity(storage_5) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_6)_: ++1 GenericInvestmentStorageBlock_capacity(storage_6) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_7)_: ++1 GenericInvestmentStorageBlock_capacity(storage_7) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_8)_: ++1 GenericInvestmentStorageBlock_capacity(storage_8) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_9)_: ++1 GenericInvestmentStorageBlock_capacity(storage_9) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_10)_: ++1 GenericInvestmentStorageBlock_capacity(storage_10) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_11)_: ++1 GenericInvestmentStorageBlock_capacity(storage_11) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_12)_: ++1 GenericInvestmentStorageBlock_capacity(storage_12) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_13)_: ++1 GenericInvestmentStorageBlock_capacity(storage_13) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_14)_: ++1 GenericInvestmentStorageBlock_capacity(storage_14) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_15)_: ++1 GenericInvestmentStorageBlock_capacity(storage_15) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_16)_: ++1 GenericInvestmentStorageBlock_capacity(storage_16) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_17)_: ++1 GenericInvestmentStorageBlock_capacity(storage_17) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_18)_: ++1 GenericInvestmentStorageBlock_capacity(storage_18) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_19)_: ++1 GenericInvestmentStorageBlock_capacity(storage_19) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_20)_: ++1 GenericInvestmentStorageBlock_capacity(storage_20) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_21)_: ++1 GenericInvestmentStorageBlock_capacity(storage_21) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_22)_: ++1 GenericInvestmentStorageBlock_capacity(storage_22) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_23)_: ++1 GenericInvestmentStorageBlock_capacity(storage_23) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_e_ONE_VAR_CONSTANT: +ONE_VAR_CONSTANT = 1.0 + +bounds + 0 <= flow(diesel_fuel_bus_0) <= +inf + 0 <= flow(diesel_fuel_bus_1) <= +inf + 0 <= flow(diesel_fuel_bus_2) <= +inf + 0 <= flow(diesel_fuel_bus_3) <= +inf + 0 <= flow(diesel_fuel_bus_4) <= +inf + 0 <= flow(diesel_fuel_bus_5) <= +inf + 0 <= flow(diesel_fuel_bus_6) <= +inf + 0 <= flow(diesel_fuel_bus_7) <= +inf + 0 <= flow(diesel_fuel_bus_8) <= +inf + 0 <= flow(diesel_fuel_bus_9) <= +inf + 0 <= flow(diesel_fuel_bus_10) <= +inf + 0 <= flow(diesel_fuel_bus_11) <= +inf + 0 <= flow(diesel_fuel_bus_12) <= +inf + 0 <= flow(diesel_fuel_bus_13) <= +inf + 0 <= flow(diesel_fuel_bus_14) <= +inf + 0 <= flow(diesel_fuel_bus_15) <= +inf + 0 <= flow(diesel_fuel_bus_16) <= +inf + 0 <= flow(diesel_fuel_bus_17) <= +inf + 0 <= flow(diesel_fuel_bus_18) <= +inf + 0 <= flow(diesel_fuel_bus_19) <= +inf + 0 <= flow(diesel_fuel_bus_20) <= +inf + 0 <= flow(diesel_fuel_bus_21) <= +inf + 0 <= flow(diesel_fuel_bus_22) <= +inf + 0 <= flow(diesel_fuel_bus_23) <= +inf + 0 <= flow(electricity_bus_excess_0) <= +inf + 0 <= flow(electricity_bus_excess_1) <= +inf + 0 <= flow(electricity_bus_excess_2) <= +inf + 0 <= flow(electricity_bus_excess_3) <= +inf + 0 <= flow(electricity_bus_excess_4) <= +inf + 0 <= flow(electricity_bus_excess_5) <= +inf + 0 <= flow(electricity_bus_excess_6) <= +inf + 0 <= flow(electricity_bus_excess_7) <= +inf + 0 <= flow(electricity_bus_excess_8) <= +inf + 0 <= flow(electricity_bus_excess_9) <= +inf + 0 <= flow(electricity_bus_excess_10) <= +inf + 0 <= flow(electricity_bus_excess_11) <= +inf + 0 <= flow(electricity_bus_excess_12) <= +inf + 0 <= flow(electricity_bus_excess_13) <= +inf + 0 <= flow(electricity_bus_excess_14) <= +inf + 0 <= flow(electricity_bus_excess_15) <= +inf + 0 <= flow(electricity_bus_excess_16) <= +inf + 0 <= flow(electricity_bus_excess_17) <= +inf + 0 <= flow(electricity_bus_excess_18) <= +inf + 0 <= flow(electricity_bus_excess_19) <= +inf + 0 <= flow(electricity_bus_excess_20) <= +inf + 0 <= flow(electricity_bus_excess_21) <= +inf + 0 <= flow(electricity_bus_excess_22) <= +inf + 0 <= flow(electricity_bus_excess_23) <= +inf + 0 <= flow(electricity_bus_storage_0) <= +inf + 0 <= flow(electricity_bus_storage_1) <= +inf + 0 <= flow(electricity_bus_storage_2) <= +inf + 0 <= flow(electricity_bus_storage_3) <= +inf + 0 <= flow(electricity_bus_storage_4) <= +inf + 0 <= flow(electricity_bus_storage_5) <= +inf + 0 <= flow(electricity_bus_storage_6) <= +inf + 0 <= flow(electricity_bus_storage_7) <= +inf + 0 <= flow(electricity_bus_storage_8) <= +inf + 0 <= flow(electricity_bus_storage_9) <= +inf + 0 <= flow(electricity_bus_storage_10) <= +inf + 0 <= flow(electricity_bus_storage_11) <= +inf + 0 <= flow(electricity_bus_storage_12) <= +inf + 0 <= flow(electricity_bus_storage_13) <= +inf + 0 <= flow(electricity_bus_storage_14) <= +inf + 0 <= flow(electricity_bus_storage_15) <= +inf + 0 <= flow(electricity_bus_storage_16) <= +inf + 0 <= flow(electricity_bus_storage_17) <= +inf + 0 <= flow(electricity_bus_storage_18) <= +inf + 0 <= flow(electricity_bus_storage_19) <= +inf + 0 <= flow(electricity_bus_storage_20) <= +inf + 0 <= flow(electricity_bus_storage_21) <= +inf + 0 <= flow(electricity_bus_storage_22) <= +inf + 0 <= flow(electricity_bus_storage_23) <= +inf + 0 <= flow(fuel_bus_genset_0) <= +inf + 0 <= flow(fuel_bus_genset_1) <= +inf + 0 <= flow(fuel_bus_genset_2) <= +inf + 0 <= flow(fuel_bus_genset_3) <= +inf + 0 <= flow(fuel_bus_genset_4) <= +inf + 0 <= flow(fuel_bus_genset_5) <= +inf + 0 <= flow(fuel_bus_genset_6) <= +inf + 0 <= flow(fuel_bus_genset_7) <= +inf + 0 <= flow(fuel_bus_genset_8) <= +inf + 0 <= flow(fuel_bus_genset_9) <= +inf + 0 <= flow(fuel_bus_genset_10) <= +inf + 0 <= flow(fuel_bus_genset_11) <= +inf + 0 <= flow(fuel_bus_genset_12) <= +inf + 0 <= flow(fuel_bus_genset_13) <= +inf + 0 <= flow(fuel_bus_genset_14) <= +inf + 0 <= flow(fuel_bus_genset_15) <= +inf + 0 <= flow(fuel_bus_genset_16) <= +inf + 0 <= flow(fuel_bus_genset_17) <= +inf + 0 <= flow(fuel_bus_genset_18) <= +inf + 0 <= flow(fuel_bus_genset_19) <= +inf + 0 <= flow(fuel_bus_genset_20) <= +inf + 0 <= flow(fuel_bus_genset_21) <= +inf + 0 <= flow(fuel_bus_genset_22) <= +inf + 0 <= flow(fuel_bus_genset_23) <= +inf + 0 <= flow(genset_electricity_bus_0) <= +inf + 0 <= flow(genset_electricity_bus_1) <= +inf + 0 <= flow(genset_electricity_bus_2) <= +inf + 0 <= flow(genset_electricity_bus_3) <= +inf + 0 <= flow(genset_electricity_bus_4) <= +inf + 0 <= flow(genset_electricity_bus_5) <= +inf + 0 <= flow(genset_electricity_bus_6) <= +inf + 0 <= flow(genset_electricity_bus_7) <= +inf + 0 <= flow(genset_electricity_bus_8) <= +inf + 0 <= flow(genset_electricity_bus_9) <= +inf + 0 <= flow(genset_electricity_bus_10) <= +inf + 0 <= flow(genset_electricity_bus_11) <= +inf + 0 <= flow(genset_electricity_bus_12) <= +inf + 0 <= flow(genset_electricity_bus_13) <= +inf + 0 <= flow(genset_electricity_bus_14) <= +inf + 0 <= flow(genset_electricity_bus_15) <= +inf + 0 <= flow(genset_electricity_bus_16) <= +inf + 0 <= flow(genset_electricity_bus_17) <= +inf + 0 <= flow(genset_electricity_bus_18) <= +inf + 0 <= flow(genset_electricity_bus_19) <= +inf + 0 <= flow(genset_electricity_bus_20) <= +inf + 0 <= flow(genset_electricity_bus_21) <= +inf + 0 <= flow(genset_electricity_bus_22) <= +inf + 0 <= flow(genset_electricity_bus_23) <= +inf + 0 <= flow(pv_electricity_bus_0) <= +inf + 0 <= flow(pv_electricity_bus_1) <= +inf + 0 <= flow(pv_electricity_bus_2) <= +inf + 0 <= flow(pv_electricity_bus_3) <= +inf + 0 <= flow(pv_electricity_bus_4) <= +inf + 0 <= flow(pv_electricity_bus_5) <= +inf + 0 <= flow(pv_electricity_bus_6) <= +inf + 0 <= flow(pv_electricity_bus_7) <= +inf + 0 <= flow(pv_electricity_bus_8) <= +inf + 0 <= flow(pv_electricity_bus_9) <= +inf + 0 <= flow(pv_electricity_bus_10) <= +inf + 0 <= flow(pv_electricity_bus_11) <= +inf + 0 <= flow(pv_electricity_bus_12) <= +inf + 0 <= flow(pv_electricity_bus_13) <= +inf + 0 <= flow(pv_electricity_bus_14) <= +inf + 0 <= flow(pv_electricity_bus_15) <= +inf + 0 <= flow(pv_electricity_bus_16) <= +inf + 0 <= flow(pv_electricity_bus_17) <= +inf + 0 <= flow(pv_electricity_bus_18) <= +inf + 0 <= flow(pv_electricity_bus_19) <= +inf + 0 <= flow(pv_electricity_bus_20) <= +inf + 0 <= flow(pv_electricity_bus_21) <= +inf + 0 <= flow(pv_electricity_bus_22) <= +inf + 0 <= flow(pv_electricity_bus_23) <= +inf + 0 <= flow(storage_electricity_bus_0) <= +inf + 0 <= flow(storage_electricity_bus_1) <= +inf + 0 <= flow(storage_electricity_bus_2) <= +inf + 0 <= flow(storage_electricity_bus_3) <= +inf + 0 <= flow(storage_electricity_bus_4) <= +inf + 0 <= flow(storage_electricity_bus_5) <= +inf + 0 <= flow(storage_electricity_bus_6) <= +inf + 0 <= flow(storage_electricity_bus_7) <= +inf + 0 <= flow(storage_electricity_bus_8) <= +inf + 0 <= flow(storage_electricity_bus_9) <= +inf + 0 <= flow(storage_electricity_bus_10) <= +inf + 0 <= flow(storage_electricity_bus_11) <= +inf + 0 <= flow(storage_electricity_bus_12) <= +inf + 0 <= flow(storage_electricity_bus_13) <= +inf + 0 <= flow(storage_electricity_bus_14) <= +inf + 0 <= flow(storage_electricity_bus_15) <= +inf + 0 <= flow(storage_electricity_bus_16) <= +inf + 0 <= flow(storage_electricity_bus_17) <= +inf + 0 <= flow(storage_electricity_bus_18) <= +inf + 0 <= flow(storage_electricity_bus_19) <= +inf + 0 <= flow(storage_electricity_bus_20) <= +inf + 0 <= flow(storage_electricity_bus_21) <= +inf + 0 <= flow(storage_electricity_bus_22) <= +inf + 0 <= flow(storage_electricity_bus_23) <= +inf + 0 <= flow(wind_electricity_bus_0) <= +inf + 0 <= flow(wind_electricity_bus_1) <= +inf + 0 <= flow(wind_electricity_bus_2) <= +inf + 0 <= flow(wind_electricity_bus_3) <= +inf + 0 <= flow(wind_electricity_bus_4) <= +inf + 0 <= flow(wind_electricity_bus_5) <= +inf + 0 <= flow(wind_electricity_bus_6) <= +inf + 0 <= flow(wind_electricity_bus_7) <= +inf + 0 <= flow(wind_electricity_bus_8) <= +inf + 0 <= flow(wind_electricity_bus_9) <= +inf + 0 <= flow(wind_electricity_bus_10) <= +inf + 0 <= flow(wind_electricity_bus_11) <= +inf + 0 <= flow(wind_electricity_bus_12) <= +inf + 0 <= flow(wind_electricity_bus_13) <= +inf + 0 <= flow(wind_electricity_bus_14) <= +inf + 0 <= flow(wind_electricity_bus_15) <= +inf + 0 <= flow(wind_electricity_bus_16) <= +inf + 0 <= flow(wind_electricity_bus_17) <= +inf + 0 <= flow(wind_electricity_bus_18) <= +inf + 0 <= flow(wind_electricity_bus_19) <= +inf + 0 <= flow(wind_electricity_bus_20) <= +inf + 0 <= flow(wind_electricity_bus_21) <= +inf + 0 <= flow(wind_electricity_bus_22) <= +inf + 0 <= flow(wind_electricity_bus_23) <= +inf + 0 <= InvestmentFlow_invest(electricity_bus_storage) <= +inf + 0 <= InvestmentFlow_invest(genset_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(pv_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(storage_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(wind_electricity_bus) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_0) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_1) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_2) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_3) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_4) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_5) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_6) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_7) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_8) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_9) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_10) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_11) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_12) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_13) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_14) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_15) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_16) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_17) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_18) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_19) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_20) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_21) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_22) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_23) <= +inf + 0 <= GenericInvestmentStorageBlock_invest(storage) <= +inf + 0 <= GenericInvestmentStorageBlock_init_cap(storage) <= +inf +end diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_bounds.lp b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_bounds.lp new file mode 100644 index 0000000..1f050a9 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_bounds.lp @@ -0,0 +1,443 @@ +\* Source Pyomo model name=Model *\ + +min +objective: ++0.03955047913155272 GenericInvestmentStorageBlock_invest(storage) ++0.022175440918742569 InvestmentFlow_invest(genset_electricity_bus) ++0.034350422598754829 InvestmentFlow_invest(pv_electricity_bus) ++0.091601126930012891 InvestmentFlow_invest(wind_electricity_bus) ++0.063761955366631234 flow(diesel_fuel_bus_0) ++0.063761955366631234 flow(diesel_fuel_bus_1) ++0.063761955366631234 flow(diesel_fuel_bus_2) ++0.063761955366631234 flow(diesel_fuel_bus_3) ++0.063761955366631234 flow(diesel_fuel_bus_4) + +s.t. + +c_e_Bus_balance(electricity_bus_0)_: +-1 flow(electricity_bus_excess_0) +-1 flow(electricity_bus_storage_0) ++1 flow(genset_electricity_bus_0) ++1 flow(pv_electricity_bus_0) ++1 flow(storage_electricity_bus_0) ++1 flow(wind_electricity_bus_0) += 279.53099120000002 + +c_e_Bus_balance(electricity_bus_1)_: +-1 flow(electricity_bus_excess_1) +-1 flow(electricity_bus_storage_1) ++1 flow(genset_electricity_bus_1) ++1 flow(pv_electricity_bus_1) ++1 flow(storage_electricity_bus_1) ++1 flow(wind_electricity_bus_1) += 266.80324295000003 + +c_e_Bus_balance(electricity_bus_2)_: +-1 flow(electricity_bus_excess_2) +-1 flow(electricity_bus_storage_2) ++1 flow(genset_electricity_bus_2) ++1 flow(pv_electricity_bus_2) ++1 flow(storage_electricity_bus_2) ++1 flow(wind_electricity_bus_2) += 253.02937845 + +c_e_Bus_balance(electricity_bus_3)_: +-1 flow(electricity_bus_excess_3) +-1 flow(electricity_bus_storage_3) ++1 flow(genset_electricity_bus_3) ++1 flow(pv_electricity_bus_3) ++1 flow(storage_electricity_bus_3) ++1 flow(wind_electricity_bus_3) += 252.07043849999999 + +c_e_Bus_balance(electricity_bus_4)_: +-1 flow(electricity_bus_excess_4) +-1 flow(electricity_bus_storage_4) ++1 flow(genset_electricity_bus_4) ++1 flow(pv_electricity_bus_4) ++1 flow(storage_electricity_bus_4) ++1 flow(wind_electricity_bus_4) += 253.55243659999999 + +c_e_Bus_balance(fuel_bus_0)_: ++1 flow(diesel_fuel_bus_0) +-1 flow(fuel_bus_genset_0) += 0 + +c_e_Bus_balance(fuel_bus_1)_: ++1 flow(diesel_fuel_bus_1) +-1 flow(fuel_bus_genset_1) += 0 + +c_e_Bus_balance(fuel_bus_2)_: ++1 flow(diesel_fuel_bus_2) +-1 flow(fuel_bus_genset_2) += 0 + +c_e_Bus_balance(fuel_bus_3)_: ++1 flow(diesel_fuel_bus_3) +-1 flow(fuel_bus_genset_3) += 0 + +c_e_Bus_balance(fuel_bus_4)_: ++1 flow(diesel_fuel_bus_4) +-1 flow(fuel_bus_genset_4) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_0)_: ++1 flow(fuel_bus_genset_0) +-3.0303030303030303 flow(genset_electricity_bus_0) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_1)_: ++1 flow(fuel_bus_genset_1) +-3.0303030303030303 flow(genset_electricity_bus_1) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_2)_: ++1 flow(fuel_bus_genset_2) +-3.0303030303030303 flow(genset_electricity_bus_2) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_3)_: ++1 flow(fuel_bus_genset_3) +-3.0303030303030303 flow(genset_electricity_bus_3) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_4)_: ++1 flow(fuel_bus_genset_4) +-3.0303030303030303 flow(genset_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_0)_: ++1 flow(pv_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_1)_: ++1 flow(pv_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_2)_: ++1 flow(pv_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_3)_: ++1 flow(pv_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_4)_: ++1 flow(pv_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_0)_: +-0.31556899999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_1)_: +-0.31157199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_2)_: +-0.30400500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_3)_: +-0.28287199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_4)_: +-0.25396999999999997 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) += 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_0)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_0) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_1)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_1) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_2)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_2) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_3)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_3) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_4)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_4) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_0)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_1)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_2)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_3)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_4)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_0)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_1)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_2)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_3)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_4)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_0)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_1)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_2)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_3)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_4)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_0)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_1)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_2)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_3)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_4)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) +<= 0 + +c_e_GenericInvestmentStorageBlock_init_cap_fix(storage)_: ++1 GenericInvestmentStorageBlock_init_cap(storage) +-0.5 GenericInvestmentStorageBlock_invest(storage) += 0 + +c_e_GenericInvestmentStorageBlock_balance_first(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_init_cap(storage) +-0.94999999999999996 flow(electricity_bus_storage_0) ++1.0526315789473684 flow(storage_electricity_bus_0) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.94999999999999996 flow(electricity_bus_storage_1) ++1.0526315789473684 flow(storage_electricity_bus_1) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.94999999999999996 flow(electricity_bus_storage_2) ++1.0526315789473684 flow(storage_electricity_bus_2) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.94999999999999996 flow(electricity_bus_storage_3) ++1.0526315789473684 flow(storage_electricity_bus_3) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.94999999999999996 flow(electricity_bus_storage_4) ++1.0526315789473684 flow(storage_electricity_bus_4) += 0 + +c_e_GenericInvestmentStorageBlock_balanced_cstr(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-1 GenericInvestmentStorageBlock_init_cap(storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_inflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(electricity_bus_storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_outflow(storage)_: +-1 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(storage_electricity_bus) += 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_0)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-0.80000000000000004 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_1)_: ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.80000000000000004 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_2)_: ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.80000000000000004 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_3)_: ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.80000000000000004 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_4)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.80000000000000004 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_0)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_min_capacity(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_4) ++0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_e_ONE_VAR_CONSTANT: +ONE_VAR_CONSTANT = 1.0 + +bounds + 0 <= flow(diesel_fuel_bus_0) <= +inf + 0 <= flow(diesel_fuel_bus_1) <= +inf + 0 <= flow(diesel_fuel_bus_2) <= +inf + 0 <= flow(diesel_fuel_bus_3) <= +inf + 0 <= flow(diesel_fuel_bus_4) <= +inf + 0 <= flow(electricity_bus_excess_0) <= +inf + 0 <= flow(electricity_bus_excess_1) <= +inf + 0 <= flow(electricity_bus_excess_2) <= +inf + 0 <= flow(electricity_bus_excess_3) <= +inf + 0 <= flow(electricity_bus_excess_4) <= +inf + 0 <= flow(electricity_bus_storage_0) <= +inf + 0 <= flow(electricity_bus_storage_1) <= +inf + 0 <= flow(electricity_bus_storage_2) <= +inf + 0 <= flow(electricity_bus_storage_3) <= +inf + 0 <= flow(electricity_bus_storage_4) <= +inf + 0 <= flow(fuel_bus_genset_0) <= +inf + 0 <= flow(fuel_bus_genset_1) <= +inf + 0 <= flow(fuel_bus_genset_2) <= +inf + 0 <= flow(fuel_bus_genset_3) <= +inf + 0 <= flow(fuel_bus_genset_4) <= +inf + 0 <= flow(genset_electricity_bus_0) <= +inf + 0 <= flow(genset_electricity_bus_1) <= +inf + 0 <= flow(genset_electricity_bus_2) <= +inf + 0 <= flow(genset_electricity_bus_3) <= +inf + 0 <= flow(genset_electricity_bus_4) <= +inf + 0 <= flow(pv_electricity_bus_0) <= +inf + 0 <= flow(pv_electricity_bus_1) <= +inf + 0 <= flow(pv_electricity_bus_2) <= +inf + 0 <= flow(pv_electricity_bus_3) <= +inf + 0 <= flow(pv_electricity_bus_4) <= +inf + 0 <= flow(storage_electricity_bus_0) <= +inf + 0 <= flow(storage_electricity_bus_1) <= +inf + 0 <= flow(storage_electricity_bus_2) <= +inf + 0 <= flow(storage_electricity_bus_3) <= +inf + 0 <= flow(storage_electricity_bus_4) <= +inf + 0 <= flow(wind_electricity_bus_0) <= +inf + 0 <= flow(wind_electricity_bus_1) <= +inf + 0 <= flow(wind_electricity_bus_2) <= +inf + 0 <= flow(wind_electricity_bus_3) <= +inf + 0 <= flow(wind_electricity_bus_4) <= +inf + 0 <= InvestmentFlow_invest(electricity_bus_storage) <= +inf + 0 <= InvestmentFlow_invest(genset_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(pv_electricity_bus) <= 800 + 0 <= InvestmentFlow_invest(storage_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(wind_electricity_bus) <= 500 + 0 <= GenericInvestmentStorageBlock_capacity(storage_0) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_1) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_2) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_3) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_4) <= +inf + 0 <= GenericInvestmentStorageBlock_invest(storage) <= +inf + 0 <= GenericInvestmentStorageBlock_init_cap(storage) <= +inf +end diff --git a/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_limits.lp b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_limits.lp new file mode 100644 index 0000000..f5c9282 --- /dev/null +++ b/oemof/3_day_workshop/Day_3_Custom_Constraints_for_Oemof/output_lp_files/2_micro_grid_inbuilt_limits.lp @@ -0,0 +1,426 @@ +\* Source Pyomo model name=Model *\ + +min +objective: ++0.03955047913155272 GenericInvestmentStorageBlock_invest(storage) ++0.022175440918742569 InvestmentFlow_invest(genset_electricity_bus) ++0.034350422598754829 InvestmentFlow_invest(pv_electricity_bus) ++0.091601126930012891 InvestmentFlow_invest(wind_electricity_bus) ++0.063761955366631234 flow(diesel_fuel_bus_0) ++0.063761955366631234 flow(diesel_fuel_bus_1) ++0.063761955366631234 flow(diesel_fuel_bus_2) ++0.063761955366631234 flow(diesel_fuel_bus_3) ++0.063761955366631234 flow(diesel_fuel_bus_4) + +s.t. + +c_u_emission_limit_: ++0.59999999999999998 flow(genset_electricity_bus_0) ++0.59999999999999998 flow(genset_electricity_bus_1) ++0.59999999999999998 flow(genset_electricity_bus_2) ++0.59999999999999998 flow(genset_electricity_bus_3) ++0.59999999999999998 flow(genset_electricity_bus_4) +<= 1.5 + +c_e_Bus_balance(electricity_bus_0)_: +-1 flow(electricity_bus_excess_0) +-1 flow(electricity_bus_storage_0) ++1 flow(genset_electricity_bus_0) ++1 flow(pv_electricity_bus_0) ++1 flow(storage_electricity_bus_0) ++1 flow(wind_electricity_bus_0) += 279.53099120000002 + +c_e_Bus_balance(electricity_bus_1)_: +-1 flow(electricity_bus_excess_1) +-1 flow(electricity_bus_storage_1) ++1 flow(genset_electricity_bus_1) ++1 flow(pv_electricity_bus_1) ++1 flow(storage_electricity_bus_1) ++1 flow(wind_electricity_bus_1) += 266.80324295000003 + +c_e_Bus_balance(electricity_bus_2)_: +-1 flow(electricity_bus_excess_2) +-1 flow(electricity_bus_storage_2) ++1 flow(genset_electricity_bus_2) ++1 flow(pv_electricity_bus_2) ++1 flow(storage_electricity_bus_2) ++1 flow(wind_electricity_bus_2) += 253.02937845 + +c_e_Bus_balance(electricity_bus_3)_: +-1 flow(electricity_bus_excess_3) +-1 flow(electricity_bus_storage_3) ++1 flow(genset_electricity_bus_3) ++1 flow(pv_electricity_bus_3) ++1 flow(storage_electricity_bus_3) ++1 flow(wind_electricity_bus_3) += 252.07043849999999 + +c_e_Bus_balance(electricity_bus_4)_: +-1 flow(electricity_bus_excess_4) +-1 flow(electricity_bus_storage_4) ++1 flow(genset_electricity_bus_4) ++1 flow(pv_electricity_bus_4) ++1 flow(storage_electricity_bus_4) ++1 flow(wind_electricity_bus_4) += 253.55243659999999 + +c_e_Bus_balance(fuel_bus_0)_: ++1 flow(diesel_fuel_bus_0) +-1 flow(fuel_bus_genset_0) += 0 + +c_e_Bus_balance(fuel_bus_1)_: ++1 flow(diesel_fuel_bus_1) +-1 flow(fuel_bus_genset_1) += 0 + +c_e_Bus_balance(fuel_bus_2)_: ++1 flow(diesel_fuel_bus_2) +-1 flow(fuel_bus_genset_2) += 0 + +c_e_Bus_balance(fuel_bus_3)_: ++1 flow(diesel_fuel_bus_3) +-1 flow(fuel_bus_genset_3) += 0 + +c_e_Bus_balance(fuel_bus_4)_: ++1 flow(diesel_fuel_bus_4) +-1 flow(fuel_bus_genset_4) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_0)_: ++1 flow(fuel_bus_genset_0) +-3.0303030303030303 flow(genset_electricity_bus_0) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_1)_: ++1 flow(fuel_bus_genset_1) +-3.0303030303030303 flow(genset_electricity_bus_1) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_2)_: ++1 flow(fuel_bus_genset_2) +-3.0303030303030303 flow(genset_electricity_bus_2) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_3)_: ++1 flow(fuel_bus_genset_3) +-3.0303030303030303 flow(genset_electricity_bus_3) += 0 + +c_e_Transformer_relation(genset_fuel_bus_electricity_bus_4)_: ++1 flow(fuel_bus_genset_4) +-3.0303030303030303 flow(genset_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_0)_: ++1 flow(pv_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_1)_: ++1 flow(pv_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_2)_: ++1 flow(pv_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_3)_: ++1 flow(pv_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(pv_electricity_bus_4)_: ++1 flow(pv_electricity_bus_4) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_0)_: +-0.31556899999999999 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_1)_: +-0.31157199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_2)_: +-0.30400500000000003 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_3)_: +-0.28287199999999996 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) += 0 + +c_e_InvestmentFlow_fixed(wind_electricity_bus_4)_: +-0.25396999999999997 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) += 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_0)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_0) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_1)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_1) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_2)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_2) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_3)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_3) +<= 0 + +c_u_InvestmentFlow_max(electricity_bus_storage_4)_: +-1 InvestmentFlow_invest(electricity_bus_storage) ++1 flow(electricity_bus_storage_4) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_0)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_1)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_2)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_3)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(genset_electricity_bus_4)_: +-1 InvestmentFlow_invest(genset_electricity_bus) ++1 flow(genset_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_0)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_1)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_2)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_3)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(pv_electricity_bus_4)_: +-1 InvestmentFlow_invest(pv_electricity_bus) ++1 flow(pv_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_0)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_1)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_2)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_3)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(storage_electricity_bus_4)_: +-1 InvestmentFlow_invest(storage_electricity_bus) ++1 flow(storage_electricity_bus_4) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_0)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_0) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_1)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_1) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_2)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_2) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_3)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_3) +<= 0 + +c_u_InvestmentFlow_max(wind_electricity_bus_4)_: +-1 InvestmentFlow_invest(wind_electricity_bus) ++1 flow(wind_electricity_bus_4) +<= 0 + +c_e_GenericInvestmentStorageBlock_init_cap_fix(storage)_: ++1 GenericInvestmentStorageBlock_init_cap(storage) +-0.5 GenericInvestmentStorageBlock_invest(storage) += 0 + +c_e_GenericInvestmentStorageBlock_balance_first(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_init_cap(storage) +-0.94999999999999996 flow(electricity_bus_storage_0) ++1.0526315789473684 flow(storage_electricity_bus_0) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_1)_: +-1 GenericInvestmentStorageBlock_capacity(storage_0) ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-0.94999999999999996 flow(electricity_bus_storage_1) ++1.0526315789473684 flow(storage_electricity_bus_1) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_2)_: +-1 GenericInvestmentStorageBlock_capacity(storage_1) ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-0.94999999999999996 flow(electricity_bus_storage_2) ++1.0526315789473684 flow(storage_electricity_bus_2) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_3)_: +-1 GenericInvestmentStorageBlock_capacity(storage_2) ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-0.94999999999999996 flow(electricity_bus_storage_3) ++1.0526315789473684 flow(storage_electricity_bus_3) += 0 + +c_e_GenericInvestmentStorageBlock_balance(storage_4)_: +-1 GenericInvestmentStorageBlock_capacity(storage_3) ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-0.94999999999999996 flow(electricity_bus_storage_4) ++1.0526315789473684 flow(storage_electricity_bus_4) += 0 + +c_e_GenericInvestmentStorageBlock_balanced_cstr(storage)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-1 GenericInvestmentStorageBlock_init_cap(storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_inflow(storage)_: +-0.20000000000000001 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(electricity_bus_storage) += 0 + +c_e_GenericInvestmentStorageBlock_storage_capacity_outflow(storage)_: +-1 GenericInvestmentStorageBlock_invest(storage) ++1 InvestmentFlow_invest(storage_electricity_bus) += 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_0)_: ++1 GenericInvestmentStorageBlock_capacity(storage_0) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_1)_: ++1 GenericInvestmentStorageBlock_capacity(storage_1) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_2)_: ++1 GenericInvestmentStorageBlock_capacity(storage_2) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_3)_: ++1 GenericInvestmentStorageBlock_capacity(storage_3) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_u_GenericInvestmentStorageBlock_max_capacity(storage_4)_: ++1 GenericInvestmentStorageBlock_capacity(storage_4) +-1 GenericInvestmentStorageBlock_invest(storage) +<= 0 + +c_e_ONE_VAR_CONSTANT: +ONE_VAR_CONSTANT = 1.0 + +bounds + 0 <= flow(diesel_fuel_bus_0) <= +inf + 0 <= flow(diesel_fuel_bus_1) <= +inf + 0 <= flow(diesel_fuel_bus_2) <= +inf + 0 <= flow(diesel_fuel_bus_3) <= +inf + 0 <= flow(diesel_fuel_bus_4) <= +inf + 0 <= flow(electricity_bus_excess_0) <= +inf + 0 <= flow(electricity_bus_excess_1) <= +inf + 0 <= flow(electricity_bus_excess_2) <= +inf + 0 <= flow(electricity_bus_excess_3) <= +inf + 0 <= flow(electricity_bus_excess_4) <= +inf + 0 <= flow(electricity_bus_storage_0) <= +inf + 0 <= flow(electricity_bus_storage_1) <= +inf + 0 <= flow(electricity_bus_storage_2) <= +inf + 0 <= flow(electricity_bus_storage_3) <= +inf + 0 <= flow(electricity_bus_storage_4) <= +inf + 0 <= flow(fuel_bus_genset_0) <= +inf + 0 <= flow(fuel_bus_genset_1) <= +inf + 0 <= flow(fuel_bus_genset_2) <= +inf + 0 <= flow(fuel_bus_genset_3) <= +inf + 0 <= flow(fuel_bus_genset_4) <= +inf + 0 <= flow(genset_electricity_bus_0) <= +inf + 0 <= flow(genset_electricity_bus_1) <= +inf + 0 <= flow(genset_electricity_bus_2) <= +inf + 0 <= flow(genset_electricity_bus_3) <= +inf + 0 <= flow(genset_electricity_bus_4) <= +inf + 0 <= flow(pv_electricity_bus_0) <= +inf + 0 <= flow(pv_electricity_bus_1) <= +inf + 0 <= flow(pv_electricity_bus_2) <= +inf + 0 <= flow(pv_electricity_bus_3) <= +inf + 0 <= flow(pv_electricity_bus_4) <= +inf + 0 <= flow(storage_electricity_bus_0) <= +inf + 0 <= flow(storage_electricity_bus_1) <= +inf + 0 <= flow(storage_electricity_bus_2) <= +inf + 0 <= flow(storage_electricity_bus_3) <= +inf + 0 <= flow(storage_electricity_bus_4) <= +inf + 0 <= flow(wind_electricity_bus_0) <= +inf + 0 <= flow(wind_electricity_bus_1) <= +inf + 0 <= flow(wind_electricity_bus_2) <= +inf + 0 <= flow(wind_electricity_bus_3) <= +inf + 0 <= flow(wind_electricity_bus_4) <= +inf + 0 <= InvestmentFlow_invest(electricity_bus_storage) <= +inf + 0 <= InvestmentFlow_invest(genset_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(pv_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(storage_electricity_bus) <= +inf + 0 <= InvestmentFlow_invest(wind_electricity_bus) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_0) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_1) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_2) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_3) <= +inf + 0 <= GenericInvestmentStorageBlock_capacity(storage_4) <= +inf + 0 <= GenericInvestmentStorageBlock_invest(storage) <= +inf + 0 <= GenericInvestmentStorageBlock_init_cap(storage) <= +inf +end diff --git a/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pdf b/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pdf new file mode 100644 index 0000000..f5df17f Binary files /dev/null and b/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pdf differ diff --git a/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pptx b/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pptx new file mode 100644 index 0000000..08ebf25 Binary files /dev/null and b/oemof/3_day_workshop/Oemof_Workshop_01_Kickoff_Monday.pptx differ diff --git a/oemof/3_day_workshop/Oemof_Workshop_02_Installation.pdf 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b/oemof/3_day_workshop/Oemof_Workshop_07_Constraints.pptx new file mode 100644 index 0000000..4efa25e Binary files /dev/null and b/oemof/3_day_workshop/Oemof_Workshop_07_Constraints.pptx differ diff --git a/oemof/3_day_workshop/Oemof_workshop_programme.pdf b/oemof/3_day_workshop/Oemof_workshop_programme.pdf new file mode 100644 index 0000000..936f461 Binary files /dev/null and b/oemof/3_day_workshop/Oemof_workshop_programme.pdf differ diff --git a/oemof/3_day_workshop/README.md b/oemof/3_day_workshop/README.md new file mode 100644 index 0000000..ec0eb01 --- /dev/null +++ b/oemof/3_day_workshop/README.md @@ -0,0 +1,22 @@ +## Oemof workshop @ RLI +In this repository all contents of the oemof workshop week at RLI, hold from 16 to 20.9.2019 will be collected. +Programme: + +* Monday: Introducing oemof +* Tuesday: Oemof component models +* Wednesday: Custom constraints for oemof +* Thursday: Case example: The MVS of the E-Land toolbox +* Friday: Gathering of ideas + +# File structure + +All presentations are presented in order in the main folder. Coding examples used during the sessions and for training are included in the subfolders + +* Day_1_Oemof_Basics: + * Workshop tutorials taken from: git\jann... +* Day_2_Components_Oemof: +* Day_3_Custom_Constraints_for_Oemof: + * Exemplary file with all linear equations generated by oemof for a micro grid system ("mg_24h.lp") + * Exemplary file with linear equations of three timesteps of oemof micro grid model, with commentary ("mg_3h.lp") + * Generic oemof constraint: Emission limit ("emission_constraint.py") + \ No newline at end of file diff --git a/oemof/3_day_workshop/requirements.txt b/oemof/3_day_workshop/requirements.txt new file mode 100644 index 0000000..29fc980 --- /dev/null +++ b/oemof/3_day_workshop/requirements.txt @@ -0,0 +1,4 @@ +jupyter==1.0.0 +matplotlib==3.1.1 +oemof==0.3.1 +pandas==0.24.2 \ No newline at end of file diff --git a/oemof/workshop_overview.csv b/oemof/workshop_overview.csv new file mode 100644 index 0000000..7e79618 --- /dev/null +++ b/oemof/workshop_overview.csv @@ -0,0 +1,2 @@ +Folder/Name,Workshop duration,Number of participants,topics,material,creator +All files in 3_day_workshop,3 days,up to 20 (presentations) or 10 (sessions),basics/model building/components/constraints,presentations/jupyter presentations/jupyter tasks,@smartie2076 \ No newline at end of file