Multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs

Abstract

Nowadays, the importance of the coordinated operation of power system assets has caused severe challenges that can be addressed through some recently emerged concepts, such as microgrids (MGs) and virtual power plants (VPPs). In this paper, a hierarchical model is proposed for simultaneous modeling of an MG scheduling and VPP energy management problems. Given the stochastic nature of the scheduling inputs, power production and also, load demand uncertainties are modeled using a scenario-based method. Therefore, the final model is presented as a stochastic mixed-integer linear programming (MILP) model. It is possible to cover the fluctuations at the minimum cost using demand response (DR) programs and electric vehicles (EVs). In addition, the possibility of power transaction between the adjacent MGs is analyzed in scheduling model, preventing unreal power exchanges. The simulation results indicate that the coordinated operation of MGs can improve load restoration, and reduce the load supply costs in each MG. It is also observed that the profit of the VPP would be higher in the case with the time-of-use (TOU) tariff compared to the cases with the real-time pricing (RTP) and critical peak pricing (CPP) mechanisms.