A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Ning Wang, Weisheng Xu, Weihui Shao, Zhiyu Xu
2019 Energies  
Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both
more » ... ing price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids' profits.
doi:10.3390/en12152891 fatcat:z3adfrb7ivgiljavsm7puxsbie