Dynamic pricing for smart grid with reinforcement learning

In the smart grid system, dynamic pricing can be an efficient tool for the service provider which enables efficient and automated management of the grid. However, in practice, the lack of information about the customers' time-varying load demand and energy consumption patterns and the volatility of electricity price in the wholesale market make the implementation of dynamic pricing highly challenging. In this paper, we study a dynamic pricing problem in the smart grid system where the service provider decides the electricity price in the retail market. In order to overcome the challenges in implementing dynamic pricing, we develop a reinforcement learning algorithm. To resolve the drawbacks of the conventional reinforcement learning algorithm such as high computational complexity and low convergence speed, we propose an approximate state definition and adopt virtual experience. Numerical results show that the proposed reinforcement learning algorithm can effectively work without a priori information of the system dynamics.