Q-learning algorithm based multi-agent coordinated control method for microgrids

Abstract

This paper proposes a Q-learning algorithm (Q-LA) based multi-agent coordinated control method for microgrids. By the method, Q-LA is adopted to calculate the power to be regulated, which is called the microgrid regulation error (MRE), in secondary control for real-time operation. And the generation schedule of distributed generators (DGs) as well as batteries is modified in real time with the MRE by the fuzzy theory and particle swarm optimization method, taking the economy and environmental benefits into consideration together. The simulation platform of Q-LA based multi-agent hybrid energy management system for microgrid (HEMS-MG) is established in C++ Builder. The simulation results verify the effectiveness and feasibility of the proposed method.