Optimal Day-ahead Dispatch of Virtual Power Plant with Aggregated Multi-type Electric Vehicles via a Stackelberg Game Approach

Disorderly charging of large-scale electric vehicles (EVs) would result in the peak electricity consumption, damaging the safe and economic operation of power systems. As an important managing technique of distributed energy resources (DERs), virtual power plant (VPP) can effectively improve the charging strategies of EVs. With this, this paper proposes a bi-level optimal scheduling model of VPP with aggregated plug-in electric vehicles (PEVs) and a battery swapping station, and to optimize the charging/discharging of PEVs and batteries in battery swapping station. Based on the Stackelberg game, the leader is a VPP operator. It guides the charging/discharging behavior of PEVs using reasonable prices. As the followers, PEV owners adjust their charging/discharging strategies based on the leader's price information. The Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level model into a single-level formulation. Numerical results demonstrate that the proposed model can effectively improve overall economic benefits of the VPP, while respecting the interest of each PEV owners. Besides, charging/discharging of EVs are improved sustaining the safe operation of power systems.