Microgrid Energy Management Strategy Base on UCB-A3C Learning - Yang, Yanhong; Li, Haitao; Shen, Baochen; Pei, Wei; Peng, Dajian (Frontiers in Energy Research, 2022)
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.