behnam-zakeri / pypsa-eur-curtail-emulator

PyPSA-Eur Curtailment Emulator

This repository introduces a linear representation of curtailment outputs in PyPSA-Eur. The representation is particularly relevant for models lacking subannual resolution to represent renewable curtailment. The parameterization is currently limited to the following aspects:

• Wind and solar mix
• Deployment of short-duration battery storage
• Deployment of long-duration energy storage

For the PyPSA-Eur framework, please refer to the master branch. However, for clarity, relevant scripts adjusted for this project are included in this repository. These adjustments comprise:

• Equality constraints on renewable resources (solve_networks.py). Find the function add_renewable_potential_target.
• LDES technology, which has the properties of an H2 storage with electrolyzers and fuel cells but is only connected to the electricity bus to function as a pure electricity storage (prepare_sector_network.py)
• LDES technology cost assumptions (costs_2030.csv)
• Removal of existing capacities of hydropower to make it a greenfield capacity optimization (except transmission lines) with the intention to enhance generalizability (prepare_sector_network.py).

Dependencies

To run this framework, the following modules need to be installed:

• pandas
• numpy
• openpyxl
• pycel

How to Run the Full Framework

1. Fetch PyPSA scenarios (Zenodo) or calculate from scratch using the modifications documented in the folder PyPSA-Eur/.

2. Run scripts/metrics_from_pypsa.py:

   • Calculate metrics such as renewable curtailment, backup capacity, system cost, etc. This script requires access to PyPSA-Eur network files.

Note that steps 1 and 2 have already been performed, with resulting files located in the subfolder calculated_metrics/. However, if additional parameters or metrics are desired, it's possible with access to the PyPSA-Eur network files. This allows the framework to run without necessarily requiring a new PyPSA-Eur execution.

3. Run PyPSA_emulator.ipynb:

   • This script creates an emulator based on metrics calculated in step 2. It then evaluates curtailment for a user-specified range of wind and solar penetration combined with a given level of short-duration (Li-ion battery) and long-duration (H2) energy storage deployment.

4. MESSAGE_implementation.py:

   • As a use-case of the curtailment emulator, we apply the tool to represent curtailment in the Integrated Assessment Model MESSAGEix-GLOBIOM based on scenarios obtained in PyPSA-Eur. This with the aim to capture the synergies between wind and solar PV with technological deployment, as well as the impact of electrifying other energy-consuming sectors.

5. Run MESSAGEix-GLOBIOM/make_diagnostics_w_PyPSA.ipynb:

   • Runs the diagnostics of the energy and capacity mix of the scenario that includes curtailment parameters derived from PyPSA-Eur.