Two-stage Optimal Scheduling of Virtual Power Plant with Wind-Photovoltaic-Storage-Electric Vehicles in the Day-ahead Market

Over the past decade, Renewable Energy Sources (RES) have been rapidly developed to meet the surging demand for electricity consumption. Besides the benefits, the penetration of RES into the power grid raises multiple issues, including the intermittent nature of power output and technical requirements for grid integration. Therefore, the Virtual Power Plant (VPP) model has been implemented to address the drawbacks of RES through aggregating Distributed Energy Resources (DER). This paper applies a Mixed-Integer Linear Programming (MILP) model to optimize the VPP's operation and maximize VPP's expected profit in the day-ahead market (DAM). The optimization model is solved by GAMS and CPLEX software. This study applies the proposed model to a VPP trial system, including a Wind Power Plant (WP), a Photovoltaic Power Plant (PV), an Energy Storage System (ESS), an Electric Vehicle (EV) charging station, and residential load. WP, PV, and load uncertainties are viewed as stochastic processes and modeled by a set of scenarios. The shifting of the EV charging schedule is considered. The results show that the proposed model has practical significance.