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Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

Author

Listed:
  • Yizheng Wang

    (Economic Research Institute of State Grid, Zhejiang Electric Power Company, Hangzhou 310000, China
    These authors contributed equally to this work.)

  • Enhao Shi

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    These authors contributed equally to this work.)

  • Yang Xu

    (State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China)

  • Jiahua Hu

    (State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China)

  • Changsen Feng

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability.

Suggested Citation

  • Yizheng Wang & Enhao Shi & Yang Xu & Jiahua Hu & Changsen Feng, 2024. "Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 17(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5350-:d:1508027
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    References listed on IDEAS

    as
    1. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    2. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    3. Yucekaya, A., 2022. "Electricity trading for coal-fired power plants in Turkish power market considering uncertainty in spot, derivatives and bilateral contract market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    4. Xiao, Jihong & Wang, Yudong, 2022. "Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression," Energy, Elsevier, vol. 241(C).
    5. Jaeck, Edouard & Lautier, Delphine, 2016. "Volatility in electricity derivative markets: The Samuelson effect revisited," Energy Economics, Elsevier, vol. 59(C), pages 300-313.
    Full references (including those not matched with items on IDEAS)

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