This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering 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.