Reinforcement Learning Method Based Load shifting strategy with Demand Response

With the development of modern information and communication technology, it has made possible the application of demand response (DR) in smart grid systems. In this paper, a trial-and-error based reinforcement learning load control method is proposed. In this method, the customer's electricity demand can be represented as a decision matrix at discrete moments, and during the peak consumption period, by shifting or curtailing the flexible load of users to achieve the goal of flattening the load demand curve and improving grid stability. Based on the idea of reinforcement learning algorithms, which formulate the dynamic electricity consumption behavior of users as a discrete finite Markov decision process, the intelligent body (Agent) interacts with the power environment by simulating user behavior and can adaptively determine the amount of electricity consumption decisions at each moment. It reduces the complexity of solving the problem under flexible electricity price.