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A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids
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
doi:10.3390/en12152891
fatcat:z3adfrb7ivgiljavsm7puxsbie