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https://github.com/PyPSA/whobs-server

Online optimisation tool for wind+solar+storage systems
https://github.com/PyPSA/whobs-server

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Online optimisation tool for wind+solar+storage systems

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README 

        

# 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)](https://github.com/PyPSA/PyPSA) for the optimisation

framework, the European Centre for Medium-Range Weather Forecasts

(ECMWF) [ERA5

dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels)

for the open weather data, the [atlite

library](https://github.com/FRESNA/atlite) for converting weather data

to generation profiles, [Clp](https://projects.coin-or.org/Clp) for

the solver, [D3.js](https://d3js.org/) for graphics,

[Mapbox](https://www.mapbox.com/), [Leaflet](http://leafletjs.com/)

and [Natural Earth](https://www.naturalearthdata.com/) for maps, and

free software for the server infrastructure (GNU/Linux, nginx, Flask,

gunicorn, Redis).



You can find a live version at:



## 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](https://docs.conda.io/en/latest/miniconda.html) in combination with [mamba](https://github.com/QuantStack/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:



1. Fetch the pre-processed wind and solar data for the globe (around 6.5 GB per weather year specified in `config.yaml`)

1. Create folders for results

1. Fetch static files not included in this repository



Now you are ready to [run the server locally](#run-server-locally-on-your-own-computer).



### 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](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels).



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)](https://cds.climate.copernicus.eu/) using the [atlite

library](https://github.com/FRESNA/atlite) 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](https://github.com/PyPSA/atlite/tree/feat/era5-monthly-retrieveal)

of atlite. If you have shapely 2.0 you will need to backport [this bug

fix](https://github.com/PyPSA/atlite/blob/ad6c9f5a076054e2b953666076447729e33c2fb0/atlite/gis.py#L150)

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](https://github.com/PyPSA/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](nginx-configuration.txt).



## License



Copyright 2018-2023 Tom Brown 



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](LICENSE.txt), 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](LICENSE.txt) for more details.