Introduction

This repository contains the code for reproducing the results presented in Enabling agency: trade-offs between regional and integrated energy systems design flexibility (under review). In the paper, we develop a novel decomposition of the near-optimal space of energy system optimisation models into regional components. This allows us to study trade-offs in investment and design flexibility between different regions in Europe.

The present codebase is an evolution of the code presented here (and part of this paper), with the main changes being:

• The use of the sector-coupled model pypsa-eur-sec instead of pypsa-eur;
• Enabling the intersection of near-optimal spaces coming not only from different weather years but also other scenarios with different technology costs are land-use restrictions;
• The introduction of country clustering (i.e. merging two or more countries into a single model node) in order to save on spatial resolution;
• A clustering scheme where specified numbers of nodes are allocated to a chosen focus region, the group of countries bordering the focus region and all other countries;
• The implementation of a net self-sufficiency constraint for the sector coupled model;
• A script for generating many model solutions located on the boundary of an intersection of near-optimal spaces in order to investigate details of many examples of robust solutions.

Installation and usage

The model is built using a snakemake workflow, and PyPSA-Eur and PyPSA-Eur-Sec are included as git submodules and snakemake modules. While these two frameworks have recently been merged, the present workflow includes versions of PyPSA-Eur and PyPSA-Eur-Sec from before the merge.

1. Clone the git repository, making sure to also bring in the pypsa-eur and pypsa-eur-sec submodules with --recurse-submodules:

   git clone --recurse-submodules [email protected]:koen-vg/enabling-agency.git

2. Install a patched version of snakemake which deals properly with nested modules. First install conda or mamba, then build a conda environment containing the correct version of snakemake:

   mamba env create -f workflow/envs/snakemake.yaml

   Now activate the environment with conda activate snakemake_patched.

3. Place ERA5 cutouts produced by atlite in the workflow/modules/pypsa-eur/cutouts directory, following the naming scheme europe-era5_{year}.

4. Execute a testing model by running the following:

   snakemake --configfile config/config-testing.yaml --use-conda -j all -- compute_all_intersections

   Before starting to run a big task, it's a good idea to check how snakemake plans to execute the various rules by running snakemake with the -n (--dry-run) argument.

Organisation

We structure our implementation as a snakemake workflow with PyPSA-Eur and PyPSA-Eur-Sec as a snakemake submodules. This image represents the workflow:

A summary of the most important rules follows:

1. The rule compute_optimum is analogous to solve_network in PyPSA-Eur. It solves the energy system with the given configuration such that the total system costs are minimised.

2. The rule mga is the first step used to explore the geometry of a near-optimal feasible space in cardinal directions. Allowing a total system cost increase within some chosen eps slack, it minimises and maximises the decision variables defined in the configuration file.

3. The rule compute_near_opt computes an approximation of the near-optimal space of the given network. It does so by solving the network repeatedly with different objectives in order to find extreme points of the near-optimal space.

4. The rule compute_intersection computes the intersection of the given near-optimal feasible spaces from step 3. Additionally, it computes the Chebyshev centre of the intersection.

5. The rule compute_robust_networks computes a number of robust networks located on the boundary of an intersection of near-optimal spaces.

Computational requirements

The Snakemake workflow specification includes resource estimations, so running any workflow with the -n (dry-run) flag will show how much time and memory the task is expected to take. To reproduce the results presented in the paper, expect the computations to take approximately a couple of weeks on a small computing cluster.

Configuration

A configuration file needs to have a name (corresponding to the file name of the form config-{name}.yaml) and furthermore the following:

• a "near_opt_approx" section with values for directions, directions_angle_separation, num_parallel_solvers, iterations, conv_method, conv_epsilon and conv_iterations;
• a "projection" section specifying variables and coefficients to aggregate to each dimension of the reduced near-optimal space.
• a "pypsa-eur" section which updates changes in the default pypsa-eur config (including the choice of countries, at which date to start a weather year, the CO_2 limit, the extendable and conventional carriers to be used, as well as options on atlite, the solver and load data).
• a "pypsa-eur-sec" section which updates changes in the defaul pypsa-eur-sec config (including choices about the gas network and which conventional generators to carry over from PyPSA-Eur).
• a "scenario" or "intersection_scenarios" section specifying the behaviour of *_all_* rules.
• a "robust_networks" sections specifying the options to sample robust networks (in full resolution) on the boundary of the intersection.

Data

We are using data from the same sources that PyPSA-Eur and PyPSA-Eur-Sec do, however with some slight variations:

Cost data

We use cost data as in PyPSA-Eur from the technology data repository. In the expensive_wind and expensive_solar scenarios we use the upper limits of 2050 cost data of the Danish Energy Agency.

Weather and renewable resource data

We use reanalysis data from ERA5 for the time period between 1980 and 2020, which covers the studied region in an hourly resolution. This is processed by atlite to generate capacity factor time series for solar PV, on- and offshore wind. Although the ERA5 data may be retrieved through the Copernicus API using Atlite (using the build_cutouts config option), this takes a long time. Therefore we have also make the cutouts available at DOI: 10.11582/2022.00034. These cutouts must be downloaded manually.

Load data

We use an artificial electricity demand dataset (v.0.1.0) through an regression on ENTSO-E load data and temperature influence on it (per country) DOI: 10.5281/zenodo.7070438 developed by Martha Frysztacki, Lieke van der Most, and Fabian Neumann. When the retrieve_artificial_load_data setting is activated in PyPSA-Eur (as in our configuration file), it is used automatically.