RE 100
This repository contains a cost optimization model for dispatch and investment in 100% renewable electricity systems. The model was developed for the following scientific article:
- Ruhnau, O., Qvist, S., 2021. Storage requirements in a 100% renewable electricity system: Extreme events and inter-annual variability. http://hdl.handle.net/10419/236723
License:
- Copyright (c) 2021: Oliver Ruhnau
- This file is licensed under the MIT license (https://opensource.org/licenses/MIT)
- Attribution should be given to the above-mentioned article
- Feedback welcome: ruhnau@hertie-school.org
Key model features:
- Investment optimization based on 35 consecutive years of hourly data (from ENTSO-E)
- Technologies:
- Investable renewables: solar PV, onshore and offshore wind
- Investable storages: batteries (inverters and packs) and hydrogen (electrolyzers, salt caverns, and combined cycle turbines)
- Existing renewables: bioenergy, hydro run-of-river, and dispatchable hydro (reservoirs and pumped hydro)
- Cost assumptions reflect 2050
- In the above-mentioned article, the model was applied to Germany, but it may easily be used for other countries
For details on the model, please refer to the article.
Implementation and workflow:
- The main model is included in the file RE100.gms, using the modeling software GAMS
- The model needs about 2 hours to solve with the CPLEX solver
- The model can be run directly from the RE100.gms file or from the separate call.gms and call.py files, where the sensitivities used in the article are specified
- The results are analyzed and figures are created using the evaluation.ipynb, using python/jupyter notebook