A distributed real-time pricing strategy based on reinforcement learning approach for smart grid

Abstract

The real-time pricing (RTP) plays an important role in demand side management of smart grid. In this paper, a distributed RTP strategy which takes the interests of both supply and demand sides into consideration is studied. A holistic model focusing on the interactions between users and the power supplier is proposed in the framework of Markov decision process (MDP). The MDP presentation of smart appliances’ operational processes well embodies their characteristics and the energy correlation of adjacent time slots. Different from existing ones, a novel distributed online algorithm based on a reinforcement learning approach is proposed to solve the MDP model without acquisition of the transition probabilities. Through information exchange between users and the power supplier, the real-time electricity price is decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. The specific information of the utility function for each user does not need to be disclosed to the supplier and other users. Simulation results show that the proposed model and algorithm balance energy supply and demand well, and have a good performance in peak shaving and valley filling.