Hierarchical Simulated Annealing-Reinforcement Learning Energy Management for Smart Grids

Abstract

For energy management problems in smart grid, a hybrid intelligent hierarchical controller based on simulated annealing (SA) and reinforcement learning (RL) is proposed. The SA is used to adjust the parameters of the controller. The RL algorithm shows the particular superiority, which is independent of the mathematic model and just needs simple fuzzy information obtained through trial-and-error and interaction with the environment. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.