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

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 pa...

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Bibliographic Details
Published in:Energies (Basel) Vol. 12; no. 15; p. 2891
Main Authors: Wang, Ning, Xu, Weisheng, Shao, Weihui, Xu, Zhiyu
Format: Journal Article
Language:English
Published: Basel MDPI AG 26.07.2019
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ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary: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 bidding 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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en12152891