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model.energy: online optimisation of energy systems

This is the code for the online optimisation of zero-direct-emission electricity systems with wind, solar and storage (using batteries and electrolysed hydrogen gas) to provide a baseload electricity demand, using the cost and other assumptions of your choice. 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/

Requirements

Software

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

Automatic preparation

After installing the dependencies above, run the following line of code:

python prepare.py

This helps you:

Fetch the pre-processed wind and solar data for the globe (around 6.5 GB per weather year specified in config.yaml)
Create folders for results
Fetch static files not included in this repository

Now you are ready to run the server locally.

Generating wind and solar data yourself

The script prepare.py will download everything you need to get started, including the pre-processed wind and solar data for the globe. If you want to build this data from scratch from wind and solar data, follow these instructions. Be warned that the global datasets take space of 444 GB per weather year.

For the wind and solar generation time series, we use the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset.

First you need to download the weather data (e.g. wind speeds, direct and diffuse solar radiation) as cutouts, then you need to convert them to power system data for particular wind turbines and solar panels. The weather data is in a 0.25 by 0.25 degree spatial resolution grid for the whole globe, but to save space, we downscale it to 0.5 by 0.5 degrees.

Data is downloaded from the European Climate Data Store (CDS) using the atlite library using the script:

python build_cutouts.py

Note that you need to register an account on the CDS first in order to get a CDS API key.

As of 19.03.2023 the atlite master cannot cope with such large cutouts, so you need to use the monthly retrieval branch of atlite. If you have shapely 2.0 you will need to backport this bug fix by hand in the code.

Set the weather_years you want to download in config.yaml. For each year it will download 4 quadrants cutouts (4 slices of 90 degrees of longitude) to cover the whole globe. Each quadrant takes up 111 GB, so you will need 444 GB per year.

To build the power system data, i.e. wind and solar generation time series for each point on the globe, run the script:

python convert_and_downscale_cutout.py

Each quadrant is split into two octants, one for the northern half of the quadrant with solar panels facing south, and the other for the southern half with solar panels facing north (with a slope of 35 degrees against the horizontal in both cases). The script downscales the spatial resolution to 0.5 by 0.5 degrees to save disk space. Each octant takes up 820 MB for both technologies (solar and onshore wind), so in total for a year we have 820 MB times 8 octants, i.e. 6.5 GB.

Run without server

See the regular WHOBS repository.

Run server locally on your own computer

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:

http://127.0.0.1:5002/

In the second terminal start Redis:

rq worker whobs

where whobs 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.

Deploy on a publicly-accessible server

Use nginx, gunicorn for the Python server, rq, and manage with supervisor.

See nginx server configuration.

License

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.