Microgrid Energy Management Strategy Base on UCB-A3C Learning

Abstract

The uncertainty of renewable energy and demand response brings many challenges to the microgrid energy management. Driven by the recent advances and applications of deep reinforcement learning a microgrid energy management strategy, i.e., upper confidence bound based advantage actor-critic (A3C), is proposed to utilize a novel action exploration mechanism to learn the power output of wind power generation, the price of electricity trading and power load. The simulation results indicate that the UCB-A3C learning based energy management strategy is better than conventional PPO, actor critical and A3C algorithm.