A posteriori time series aggregation for energy system planning models with storage

Summary

This repository contains data, model files and example code for the paper Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage.

Entry point

A single bash script runs all simulations in the paper, post-processes the results and generates the figures. To run it on Linux or Mac OS, call

sh scripts/main.sh

from a command line in the main directory (not in the scripts/ directory). This, in turn, runs three scripts:

• run_validation.sh: the validation experiment
• run_example.sh: the example experiment
• make_figures.sh: collate and clean the data, and create the figures. These appear in the directory outputs/plots_post/.

In this repo, this code is structured to run all simulations in series. However, each of the 40 replications can also be run in parallel -- you can do this for your machine by running each REPLICATION separately (this variable appears in the .sh files).

Contains

• models/: power system model generating files, for Calliope (see acknowledgements)
• data/: demand and weather time series data
• model_files/: power system model generating files, for Calliope (see acknowledgements)
• models/: python code to run the models
• outputs/: where simulation outputs and figures are stored
• scripts/: bash shell to run experiments and create figures
• various .py functions to run the simulations and create figures

Requirements & Installation

Running the code in this repo requires two things: some python packages and a solver for the optimisation problem. For a very quick way to install these, follow the Requirements & installation instructions for this repo, as it has the same dependencies. Otherwise, install the following:

• Python modules:
  • Calliope 0.6.10: A (fully open-source) energy system model generator. See this link for installation. If the conda install takes a long time, you can also use pip install calliope.
  • numpy (pip install numpy)
  • pandas (pip install pandas)
  • matplotlib (pip install matplotlib)
  • yaml (pip install pyyaml)
  • sklearn (pip install scikit-learn)
• Other:
  • cbc: open-source optimiser: see this link for installation. Other solvers (e.g. gurobi) are also possible -- the solver can be specified in model_files/model.yaml.

How to cite

If you use this repository for further research, please cite the following papers:

• AP Hilbers, DJ Brayshaw, A Gandy (2023). Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage. Applied Energy, 334, 120624.

Contact

Adriaan Hilbers. Department of Mathematics, Imperial College London. [email protected].

Acknowledgements

Models are constructed in the modelling framework Calliope, created by Stefan Pfenninger and Bryn Pickering. See callio.pe or the following paper for details:

• Pfenninger, S. and Pickering, B. (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825, doi:10.21105/joss.00825.

The demand and wind dataset is based on work by Hannah Bloomfield et al. Details can be found in the following paper and dataset:

• Bloomfield, H. C., Brayshaw, D. J. and Charlton-Perez, A. (2019) Characterising the winter meteorological drivers of the European electricity system using Targeted Circulation Types. Meteorological Applications. ISSN 1469-8080. doi:10.1002/met.1858

• HC Bloomfield, DJ Brayshaw, A Charlton-Perez (2020). MERRA2 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation. University of Reading. Dataset. doi:10.17864/1947.239