Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

• Published in: IEEE transactions on sustainable energy Vol. 13; no. 3; pp. 1861 - 1864
• Main Authors: Wei, Xiangyu, Xiang, Yue, Li, Junlong, Zhang, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-07-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Deep learning; deep reinforcement learning; distributed prioritized experience replay; Economics; electricity market; Electricity pricing; energy storage system; Entropy; Learning; Maximum entropy; Profitability; Real-time systems; Reinforcement; Reinforcement learning; Renewable energy; Training; Uncertainty; Wind farm; Wind farms; Wind power; Wind power generation
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2022.3156426

The uncertainty of wind power and electricityprice restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness.