Metropolis Criterion Based Fuzzy Q-Learning Energy Management for Smart Grids

Xin Li, Chuanzhi Zang, Wenwei Liu, Peng Zeng, Haibin Yu


For the energy management problems for demand response in electricity grid, a Metropolis Criterion based fuzzy Q-learning consumer energy management controller (CEMC) is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for the consumer behavior in electricity grid. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference and Metropolis Criterion are introduced in order to facilitate generalization in large state space and balance exploration and exploitation in action selection in Q-learning individually. Simulation results show that the proposed controller can learn to take the best action to regulate consumer behavior with the features of low average end-user financial costs and high consumer satisfaction.



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