Modeling Participation of Storage Units in Electricity Markets using Multi-Agent Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Modeling Participation of Storage Units in Electricity Markets using Multi-Agent Deep Reinforcement Learning

Published:2023-06-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th ACM International Conference on Future Energy Systems
language:
Short-container-title:

Author:

Harder Nick¹^ORCID,Weidlich Anke¹^ORCID,Staudt Philipp²^ORCID

Affiliation:

1. Department of Sustainable Systems Engineering, University of Freiburg, Germany

2. Department of Computing Science, University of Oldenburg, Germany

Funder

Bundesministerium für Wirtschaft und Klimaschutz

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3575813.3597351

Reference47 articles.

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2. Muhammad Anwar Changlong Wang Frits de Nijs and Hao Wang. 2022. Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets. In 2022 IEEE Power & Energy Society General Meeting (PESGM). 1-5. https://doi.org/10.1109/PESGM48719.2022.9917082

3. Agent-based modeling: Methods and techniques for simulating human systems

4. Tom Brown , Jonas Hörsch , and David Schlachtberger . 2018. PyPSA: Python for Power System Analysis. Journal of Open Research Software 6 ( 2018 ). https://doi.org/10.5334/jors.188 10.5334/jors.188 Tom Brown, Jonas Hörsch, and David Schlachtberger. 2018. PyPSA: Python for Power System Analysis. Journal of Open Research Software 6 (2018). https://doi.org/10.5334/jors.188

5. Bundesnetzagentur. 2021. BNetzA List of Power Plants. Bundesnetzagentur. 2021. BNetzA List of Power Plants.

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