Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant

Highlights: • Multi-objective scheduling of a VPP to maximize profit and minimize emissions. • Considering various DERs including wind turbine, PV, CHP, EES and heat-only unit. • Using heuristic algorithms to solve non-linear and non-convex problem of scheduling. • Modeling the uncertainties of wind speed, solar radiation, market price, and load. • Using two-stage stochastic programming for modeling the optimization problem. -- Abstract: The reduction of global greenhouse gas emissions is one of the key steps towards sustainable development. The integration of Distributed Energy Resources (DERs) in power systems will help with emissions reduction. Virtual Power Plants (VPPs) can overcome barriers to participation of DERs in system operation. In this paper, a model is proposed for the energy management of a VPP including PhotoVoltaic (PV) modules, wind turbines, Electrical Energy Storage (EES) systems, Combined Heat and Power (CHP) units, and heat-only units. The multi-objective operational scheduling of DERs in the VPP focuses on maximizing the expected day-ahead profit of the VPP and minimizing the expected day-ahead emissions. The uncertainty of wind speed, solar radiation, market price, and electrical load is modeled using scenario based approach. Also, two-stage stochastic programming is implemented for modeling the VPP energy management. Three cases have been investigated for evaluating the proposed method: single-objective scheduling of VPP to maximize profit, single-objective scheduling of VPP to minimize emission and multi-objective economic/emission scheduling of VPP. The results indicate the appropriate economic and environmental performance of the proposed method, which provides the possibility of selecting a compromise solution for the VPP operator in accordance with environmental restrictions and economic constraints.