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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| Published in | Energies (Basel) Vol. 12; no. 15; p. 2891 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
26.07.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1996-1073 1996-1073 |
| DOI | 10.3390/en12152891 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Wang, Ning Shao, Weihui Xu, Zhiyu Xu, Weisheng |
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| SubjectTerms | Algorithms Alternative energy sources battery energy storage system, Q-cube framework Behavior bidding strategy Blockchain continuous double auction Decision making Electric power Electricity distribution Electricity generation Energy industry Energy management International conferences microgrids Peer to peer computing Q-learning algorithm |
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| Title | A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids |
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