电力工程技术2025，Vol.44Issue(3)：30-42,13.DOI:10.12158/j.2096-3203.2025.03.003
深度强化学习驱动的风储系统参与能量-调频市场竞价策略
Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets
摘要
Abstract
In the power market environment,participation of wind-storage system in both the energy market and the frequency regulation market is essential to enhance economic efficiency and support grid frequency regulation and peak shaving.However,key issues such as formulating bidding strategies for wind-storage systems in energy-frequency regulation dual markets need to be addressed.A bidding model driven by deep reinforcement learning is proposed in this paper to formulate bidding strategies in an incomplete information market environment.Firstly,a framework for wind-storage systems participating in the energy and frequency regulation markets is established to clarify the bidding operation strategies of each market entity.Then,a real-time frequency regulation performance scoring model is introduced to address the differences in frequency regulation response capabilities among various resources.Based on this,a bidding model for wind-storage systems is developed.Finally,a multi-agent deep reinforcement learning method with strong model-free learning capabilities is employed to solve the stochastic game problem in an incomplete information market environment and to handle the multi-agent bidding game relationship.Simulation results indicate that the proposed method can effectively formulate bidding strategies for wind-storage systems participating in the energy and frequency regulation markets.The method achieves high returns while ensuring high convergence stability.As a result,the economic efficiency of wind-storage systems is enhanced,and grid frequency regulation and peak shaving are effectively supported.关键词
风储系统/能量-调频市场/深度强化学习/实时调频性能得分/演员-评论家/多主体竞价博弈Key words
wind-storage system/energy and frequency regulation market/deep reinforcement learning/real-time frequency regulation performance scoring/actor-critic/multi-entity bidding game分类
动力与电气工程引用本文复制引用
李钟平,向月..深度强化学习驱动的风储系统参与能量-调频市场竞价策略[J].电力工程技术,2025,44(3):30-42,13.基金项目
国家自然科学基金资助项目(U2166211) （U2166211）
