PtX-BOA: PtX Business Opportunity Analyser

PtX-BOA is a tool that aims to promote the export of a wide range of PtX molecules, including amongst others, green ammonia, e-methanol and synthetic fuels. Users can calculate the delivered cost of PtX molecules from an export country to an import country, with a detailed cost breakdown comparison highlighting the competitive edge of one country against another.

Development

Setup

After cloning the repository, create a virtual python environment in a subdirectory .env and activate it:

python -m venv .\.env
.env\Scripts\activate.bat

Install the necessary dependencies:

python -m pip install --upgrade pip
pip install -r requirements-dev.txt

The code is autoformatted and checked with pre-commit. If you make changes to the code that you want to commit back to the repository, please install pre-commit with:

pre-commit install

If you have pre-commit installed, every file in a commit is checked to match a certain style and the commit is stopped if any rules are violated. Before committing, you can also check your staged files manually by running:

pre-commit run

In order to run the tests locally run pytest in the root directory:

pytest

Download optimization cache for local development

cd ptxboa\cache
scp -r ptxboa2:ptx-boa_offline_optimization/optimization_cache/* .

Release Procedure

• merge all relevant branches into develop
• create a relase branch
• change and commit CHANGELOG.md with description of changes
• update version (bumpversion patch|minor|major). This creates automatically a commit
• create pull requests to merge release into main
• merging this will automatically (via git action) create and publish the new docker image wingechr/ptx-boa:<VERSION>-<BRANCH>
• merge main back into develop

Update docker image in production

# connect to server
ssh ptxboa

# set variables
VERSION=2.2.6
APP=app
PORT=9000

# is preview?
IS_PREVIEW=1
if [[ "$IS_PREVIEW" == "1" ]]; then
  PORT=9001
  APP=$APP-preview
  VERSION=$VERSION-preview
fi

# pull latest image from dockerhub
docker pull wingechr/ptx-boa:$VERSION
# stop and delete the currently running container "app"
docker stop $APP
docker rm $APP
# start the latest image as "app"
docker run -d -p $PORT:80 -v /home/ptxboa/ptx-boa_offline_optimization/optimization_cache:/mnt/cache --name $APP --restart unless-stopped wingechr/ptx-boa:$VERSION

# see logs
docker logs --follow $APP

Cleanup docker images from old versions

# check which docker images are downloaded
docker image ls
# show running containers
docker ps
# delete all unused objects
docker system prune -a

Testing / code coverage

pytest --cov=ptxboa --cov-report=term-missing --cov-report=html:htmlcov

Internal documentation

This section contains internal documenation on data flows, structure of the code base etc.

flowchart LR
    subgraph app [PtX-BOA WebApp]
        streamlit[ptxboa_streamlit.py]
    end

    subgraph script [PtX-BOA WebApp]
        offline_optimization[offline_optimization_script.py]
    end

    subgraph api [PtX-BOA Calculation Api]
        PtxboaAPI[ptxboa/api.py:PtxboaAPI]
        PtxCalc[ptxboa/api_calc.py:PtxCalc]
        DataHandler[ptxboa/api_data.py:DataHandler]
        PtxOpt[ptxboa/api_optimize.py:PtxOpt]
    end

    subgraph opt [FLH Optimizer]
        api_opt[flh_opt/api_opt.py:api_opt]
    end

    subgraph files [Files]
        profiles[flh_opt/renewable_profiles/*.csv]
        cache[ptxboa/cache/XX/XX/*.pickle]
        data[ptxboa/data/*.csv]
    end

    streamlit <-->|user data, settings| PtxboaAPI
    profiles --> api_opt
    cache <-->|cache| PtxOpt
    data --> DataHandler
    PtxOpt --> api_opt
    PtxboaAPI <--> PtxCalc
    DataHandler <--> PtxOpt
    PtxboaAPI <-->|user data| DataHandler
    offline_optimization --> cache
    offline_optimization <--> api_opt

Loading

Structure of input data

• csv file(s) with scalar data
• RE profiles (flh and weighting coefficients)

Caching optimization results

• location of cached files
• hashing

Creating renewable generation profiles

• atlite
• methodology

The PyPSA model

The pypsa optimization model is created and solved via the flh_opt.api_opt.optimize() function

ptx-boa/flh_opt/api_opt.py

Line 148 in 61a5915

def optimize(

.

Function parameters are a dictionary with all required parameters, and the path to the folder with the renewable profiles data.

Function output is a dictionary with the results of the optimization, and the pypsa network object that contains the solved model.

Example input dict

{
  "SOURCE_REGION_CODE": "GYE",
  "RES": [
    {
      "CAPEX_A": 0.826,
      "OPEX_F": 0.209,
      "OPEX_O": 0.025,
      "PROCESS_CODE": "PV-FIX"
    }
  ],
  "ELY": {
    "EFF": 0.834,
    "CAPEX_A": 0.52,
    "OPEX_F": 0.131,
    "OPEX_O": 0.2,
    "CONV": {
      "H2O-L": 0.677
    }
  },
  "DERIV": {
    "EFF": 0.717,
    "CAPEX_A": 0.367,
    "OPEX_F": 0.082,
    "OPEX_O": 0.132,
    "PROCESS_CODE": "CH4SYN",
    "CONV": {
      "CO2-G": 0.2,
      "HEAT": -0.2,
      "H2O-L": -0.15
    }
  },
  "H2O": {
    "CAPEX_A": 0.07726085034488815,
    "OPEX_F": 0.0356900588308774,
    "OPEX_O": 0,
    "CONV": {
      "EL": 0.003
    }
  },
  "CO2": {
    "CAPEX_A": 0.07726085034488815,
    "OPEX_F": 0.0356900588308774,
    "OPEX_O": 0,
    "CONV": {
      "EL": 0.4515,
      "HEAT": 1.743,
      "H2O-L": -1.4
    }
  },
  "EL_STR": {
    "EFF": 0.544,
    "CAPEX_A": 0.385,
    "OPEX_F": 0.835,
    "OPEX_O": 0.501
  },
  "H2_STR": {
    "EFF": 0.478,
    "CAPEX_A": 0.342,
    "OPEX_F": 0.764,
    "OPEX_O": 0.167
  },
  "SPECCOST": {
    "H2O-L": 0.658,
    "CO2-G": 1.0
  }
}

Example output dict

{
  "RES": [
    {
      "SHARE_FACTOR": 0.519,
      "FLH": 0.907,
      "PROCESS_CODE": "PV-FIX"
    }
  ],
  "ELY": {
    "FLH": 0.548
  },
  "DERIV": {
    "FLH": 0.548
  },
  "EL_STR": {
    "CAP_F": 0.112
  },
  "H2_STR": {
    "CAP_F": 0.698
  }
}

Example flowchart

This flowchart shows an example model as being created by the optimize function: