Two-stage Optimal Scheduling of Virtual Power Plant Considering Demand Response and Forecast Errors

Abstract

For the uncertainty of wind power, PV and load in virtual power plant (VPP), a two-stage optimal dispatching model considering the participation of demand-side response resources in VPP is developed from two stages: day-ahead and real-time, taking into account the forecast error and customer-side demand response resources. In the day-ahead stage, a day-ahead VPP optimal economic dispatch model is developed using price-based demand response and incentive-based demand response to optimize customer load curves with VPP operating cost as the target. In the real time stage, taking into account the regulation characteristics of the excitation type demand response, the real time power deviation is smoothed together with the internal generator set of VPP to realize the correction of the power output of the equipment before the day. Both phases of the model are modeled on MATLAB software and solved by the CPLEX optimization solver. The simulation results show that the introduction of price-based demand response and incentive-based demand response together can optimize the load profile of users better and reduce the operating cost of VPP; the two-stage optimal scheduling can smooth out the random fluctuations on both sides of the source load in the real-time phase and realize the economic and stable operation of VPP.