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A Reinforcement Learning–Based Approach With Downside‐Risk Protection for Battery Dispatch in Day‐Ahead Markets.

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Title: A Reinforcement Learning–Based Approach With Downside‐Risk Protection for Battery Dispatch in Day‐Ahead Markets.
Authors: Jiang, Xiayu1,2 (AUTHOR), Tang, Fei1,2,3 (AUTHOR) tangfei@whu.edu.cn, Chen, Mo1,2 (AUTHOR), Li, Bincheng1,2 (AUTHOR), Yu, Yixin1,2 (AUTHOR), Ding, Jinxiu1,2 (AUTHOR), Li, Xiao1,2 (AUTHOR), Yang, Nan (AUTHOR) ynyyayy@ctgu.edu.cn
Source: International Transactions on Electrical Energy Systems. 10/23/2025, Vol. 2025, p1-10. 10p.
Subject Terms: *Reinforcement learning, *Energy storage, *Electricity markets, *Risk management in business
Geographic Terms: Germany
Abstract: In day‐ahead electricity markets with high renewable penetration, price prediction errors are prevalent. These errors significantly increase the downside risk of energy storage arbitrage, potentially diminishing profits or even causing sustained losses. To address the lack of effective downside protection for energy storage systems operating in highly uncertain environments, this paper proposes a reinforcement learning–based battery‐dispatch method. The method is enhanced by three mechanisms to improve policy robustness and risk management capabilities. Residual injection disturbs predictive inputs to simulate various bias scenarios, guiding agents toward more conservative decision‐making. Action hard projection maps outputs in real time onto feasible regions, ensuring physical feasibility and training stability. Teacher model behaviour cloning incorporates low‐risk demonstrations based on actual prices, accelerating convergence and avoiding high‐risk actions. The approach underwent long‐term empirical validation using highly volatile data from the Germany–Luxembourg market for 2020–2024. Results indicate that, although the proposed method yields slightly lower average returns compared to the traditional prediction‐and‐optimization baseline, it significantly reduces maximum drawdowns, loss probability and profit volatility, thereby demonstrating robust downside‐risk protection. This study validates reinforcement learning's capacity for effective risk control in energy storage dispatch and provides a viable pathway for robust asset management in highly volatile electricity markets. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
An: 188860019
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A Reinforcement Learning–Based Approach With Downside‐Risk Protection for Battery Dispatch in Day‐Ahead Markets.
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  Data: <searchLink fieldCode="JN" term="%22International+Transactions+on+Electrical+Energy+Systems%22">International Transactions on Electrical Energy Systems</searchLink>. 10/23/2025, Vol. 2025, p1-10. 10p.
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  Data: *<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Electricity+markets%22">Electricity markets</searchLink><br />*<searchLink fieldCode="DE" term="%22Risk+management+in+business%22">Risk management in business</searchLink>
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In day‐ahead electricity markets with high renewable penetration, price prediction errors are prevalent. These errors significantly increase the downside risk of energy storage arbitrage, potentially diminishing profits or even causing sustained losses. To address the lack of effective downside protection for energy storage systems operating in highly uncertain environments, this paper proposes a reinforcement learning–based battery‐dispatch method. The method is enhanced by three mechanisms to improve policy robustness and risk management capabilities. Residual injection disturbs predictive inputs to simulate various bias scenarios, guiding agents toward more conservative decision‐making. Action hard projection maps outputs in real time onto feasible regions, ensuring physical feasibility and training stability. Teacher model behaviour cloning incorporates low‐risk demonstrations based on actual prices, accelerating convergence and avoiding high‐risk actions. The approach underwent long‐term empirical validation using highly volatile data from the Germany–Luxembourg market for 2020–2024. Results indicate that, although the proposed method yields slightly lower average returns compared to the traditional prediction‐and‐optimization baseline, it significantly reduces maximum drawdowns, loss probability and profit volatility, thereby demonstrating robust downside‐risk protection. This study validates reinforcement learning's capacity for effective risk control in energy storage dispatch and provides a viable pathway for robust asset management in highly volatile electricity markets. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.1155/etep/7939775
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        Text: English
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      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Electricity markets
        Type: general
      – SubjectFull: Risk management in business
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      – SubjectFull: Germany
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              Text: 10/23/2025
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