Coordinated operation of active distribution network, networked microgrids, and electric vehicle: A multi-agent PPO optimization method

This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of new energy output and the flexibility of electric vehicle (EV) scheduling, which not only improves the economy of the networked microgrids (NMG) scheduling, but also reduces the impact on active distribution network (ADN). The EV condition matrix and the 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 the NMG operating cost, distribution network loss, and EV owners' payment cost, a multi-objective optimal scheduling model was developed, and the day-ahead scheduling contract for EV was obtained. The 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, the intra-day ultra-short term forecast data is input into the intra-day scheduling model, and the trained multi-agent model realizes the NMG distributed real-time optimal scheduling. Finally, the economy and effectiveness of the proposed strategy are verified by the Day-after optimal scheduling results.