Genetic based fuzzy Q-learning energy management for smart grid

For the energy management problems for demand response in electricity grid, a genetic 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 has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. 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.