Adding 3 day oemof workshop

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
+- README.md

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())

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())

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()