A Safe Economic Dispatch for Microgrid with Ladder-Type Carbon Trading

The energy scheduling strategy for microgrids based on reinforcement learning plays a very crucial role in realizing low-carbon and economic energy utilization. Considering the traditional reinforcement learning is difficult to meet the operational constraints of microgrids, this paper proposes a safety reinforcement learning method based on proximal policy optimization. Firstly, ladder-type carbon trading is introduced to strictly constrain the system carbon emission. Then, a safety proximal policy optimization method is proposed that incorporates a safety network to decouple the economic and safety factors in the traditional reward composition. Finally, experiments are conducted on real-world datasets to verify the effectiveness of the proposed algorithm. The experimental results illustrate that the proposed safety reinforcement learning method is able to minimize the economic cost and carbon emission of microgrid scheduling while strict guaranteeing safety compared to existing methods.