Day-Ahead Optimization Scheduling Strategy for Photovoltaic-Storage-Load Virtual Power Plant Considering Electric Vehicles

Under the dual carbon goals and the increasing penetration of renewable energy, the volatility of photovoltaic (PV) output generation poses significant challenges to the stable operation of the power systems. In this context, the PV-storage-load Virtual Power Plant (VPP), which integrates PV, energy storage system (ESS), electric vehicles (EVs) and flexible loads, has become an important method to enhance flexibility of the power systems. Therefore, a day-ahead optimization scheduling strategy for a PV-storage-load VPP considering EVs is proposed. First, the important role of the PV-storage-load VPP in enhancing the flexibility of the power system under a high proportion of renewable energy is analyzed. Then, with the objective of minimizing the total operational cost of the VPP’s day-ahead scheduling, a day-ahead optimization scheduling model for the PV-storage-load VPP considering EV constraints is constructed. Finally, a case study is conducted on a PV-storage-load VPP in a certain region of Zhejiang province of China. The simulation results show that the proposed model is applicable to day-ahead scheduling scenarios of the VPP in different seasons and can effectively promote the consumption of renewable energy and reduce the operational costs of the VPP.