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

Yizheng Wang, Enhao Shi, Yang Xu, Jiahua Hu and Changsen Feng ()
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Yizheng Wang: Economic Research Institute of State Grid, Zhejiang Electric Power Company, Hangzhou 310000, China
Enhao Shi: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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

Energies, 2024, vol. 17, issue 21, 1-23

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.

Keywords: electricity futures; price risk mitigation; power producer; multi-agent deep reinforcement learning; portfolio strategies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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