主动配电网、互联微电网与电动汽车的协同运行：一种多智能体PPO优化方法

This paper proposes a multi-agent cooperative op-eration optimization strategy for regional power grids consider-ing the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not only improves the economy of networked microgrid(NMG)scheduling but also reduces the impact on active distribution network(ADN).EV condition matrix and model of the adjustable charge-and-discharge capacity of the EV may be built up by simulating the trip rule of an EV using the driving behavior of the vehicle model.In the day-ahead stage,by taking into account NMG operating cost,distribution network loss,and EV owners' payment cost,a multi-objective optimal scheduling model is developed,and the day-ahead scheduling contract for EV is obtained.Generative Adversarial Network(GAN)generates a significant number of intraday scenarios of photovoltaic(PV),load,and EV based on historical scheduling data as training data for the intra-day scheduling model multi-agent PPO(MAPPO).In the intra-day scheduling stage,intra-day ultra-short-term forecast data is input into the intra-day scheduling model,and the trained multi-agent model realizes NMG distributed real-time optimal scheduling.Finally,the economy and effectiveness of the proposed strategy are verified by Day-after optimal scheduling results.