Proceedings of the International Renewable Energy Storage Conference (IRES 2022)

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Automating Storage Arbitrage in German Electricity Market

Authors
Mariia Bilousova1, 2, 3, *, Anton Motornenko1, Fabian Hofmann1
1Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438, Frankfurt am Main, Germany
2efl - the Data Science Institute, House of Finance Theodor-W.-Adorno-Platz 3, 60323, Frankfurt am Main, Germany
3Goethe-Universität Frankfurt Fachbereich Wirtschaftswissenschaften, Theodor-W.-Adorno-Platz 4, 60323, Frankfurt am Main, Germany
*Corresponding author.
Corresponding Author
Mariia Bilousova
Available Online 25 May 2023.
DOI
10.2991/978-94-6463-156-2_6How to use a DOI?
Keywords
Energy storage arbitrage; Artificial intelligence; Reinforcement Learning; Expert System; Pypsa
Abstract

This study examines the potential of energy arbitrage in the German electricity market as a way to increase the return on investment of battery storage technologies. The main goal is to develop and estimate the performance of automated arbitrage strategies for households using Tesla Powerwall energy storage. Based on historical prices of the German intraday electricity market, artificial intelligence algorithm is developed to find feasible charging and discharging strategies for battery storage. This is done by employing a Deep Q-Learning approach of Reinforcement Learning. As a baseline, a simple Expert System algorithm is suggested, that is based on buy/sell at fixed price approach. The maximal possible return from the arbitrage is explored by a linear optimization of the system under perfect price foresight. The Reinforcement Learning algorithm is found to achieve only ∼ 35 % of the maximal return which is only 5 % more than the simplistic Expert system. Finally, the performance of both algorithms is compared to the already available results at other electricity markets.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Renewable Energy Storage Conference (IRES 2022)
Series
Atlantis Highlights in Engineering
Publication Date
25 May 2023
ISBN
10.2991/978-94-6463-156-2_6
ISSN
2589-4943
DOI
10.2991/978-94-6463-156-2_6How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mariia Bilousova
AU  - Anton Motornenko
AU  - Fabian Hofmann
PY  - 2023
DA  - 2023/05/25
TI  - Automating Storage Arbitrage in German Electricity Market
BT  - Proceedings of the International Renewable Energy Storage  Conference (IRES 2022)
PB  - Atlantis Press
SP  - 63
EP  - 75
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-156-2_6
DO  - 10.2991/978-94-6463-156-2_6
ID  - Bilousova2023
ER  -