Expert Systems for Microgrids
K.T.M.U. Hemapala, MK Perera · 2022
The introduction of agent-based control for microgrids is an emerging concept. In a novel approach, these agents can be improved by introducing smart decision-making ability such that they follow machine learning algorithms. Reinforcement learning is a branch of machine learning in which agents learn through interaction with the environment and find the optimal operational policy. This chapter introduces the fundamental concepts related to the reinforcement learning approach, together with an analysis of recent applications of single-agent and multi-agent reinforcement learning on microgrids. In addition, the chapter presents a detailed discussion on the formation of the optimization problem in reinforcement learning for microgrid energy management systems. The chapter concludes with a case study based on the performance evaluation of single and multi-agent reinforcement learning approaches for a proposed microgrid.