This is the code for the online optimisation of hydrogen-based e-product delivery from wind and solar.
It uses only free software and open data, including Python for Power System Analysis (PyPSA) for the optimisation framework, the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset for the open weather data, the atlite library for converting weather data to generation profiles, Clp for the solver, D3.js for graphics, Mapbox, Leaflet and Natural Earth for maps, and free software for the server infrastructure (GNU/Linux, nginx, Flask, gunicorn, Redis).
You can find a live version at:
https://model.energy/products/
The tool is a simplified version of the model behind the research paper Import options for chemical energy carriers from renewable sources to Germany by Johannes Hampp, Michael Düren and Tom Brown in PLOS ONE, 2023.
This software has only been tested on the Ubuntu distribution of GNU/Linux.
Ubuntu packages:
sudo apt install coinor-clp coinor-cbc redis-server
To install, we recommend using miniconda in combination with mamba.
conda install -c conda-forge mamba
mamba env create -f environment.yaml
For (optional) server deployment:
sudo apt install nginx
mamba install gunicorn
This uses the same data as https://model.energy/, see the WHOBS-server repository.
The basic assumptions in defaults-initial.csv are added to by the script generate_defaults.py, which adds assumptions from the trace repository and technology-data repository (which is in turn largely based on the Danish Energy Agency's Technology Data). The CSV defaults.csv is an output of this script and should not be modified. All modifications should be make in defaults-initial.csv.
To run the script do:
python generate_defaults.py
To run locally you need to start the Python Flask server in one terminal, and redis in another:
Start the Flask server in one terminal with:
python server.py
This will serve to local address:
In the second terminal start Redis:
rq worker efuels
where efuels is the name of the queue. No jobs will be solved until
this is run. You can run multiple workers to process jobs in parallel.
Use nginx, gunicorn for the Python server, rq, and manage with supervisor.
Copyright 2022-3 Tom Brown https://nworbmot.org/
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.