Genetic Based Reinforcement Learning Load Control for Smart Grids

As the improvement of smart grids, the customer participation has reinvigorated interest in demand-side features such as load control for domestic users. A genetic based reinforcement learning (RL) load controller is proposed. The genetic is used to adjust the parameters of the controller. The RL algorithm, which is independent of the mathematic model, shows the particular superiority in load control. 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.