A bi-level optimization framework for virtual power plants integrating electric vehicles and demand response

Abstract

The increasing penetration of wind and photovoltaic (PV) generation introduces significant uncertainty and volatility to power systems. To address these challenges, this study proposes a bi-level optimization framework for virtual power plants (VPPs) that integrates electric vehicles (EVs) and demand response (DR) to enhance renewable energy utilization, reduce carbon emissions, and coordinate the economic interests between the VPP operator (OPE) and the aggregator (AGG). The upper level maximizes the OPE’s revenue through dynamic electricity pricing, while the lower level minimizes the AGG’s cost via adaptive load scheduling. Simulation results show that, compared to a baseline case without EV and DR coordination, the proposed framework reduces peak demand by 3.02%, lowers total carbon emissions by 10.13%, and decreases renewable energy curtailment by 37.5% for wind and 42.85% for PV. To further validate robustness, the model was tested under diverse weather conditions over a one-week period, achieving even greater reductions in curtailment: 51.07% for wind and 51.38% for PV. This framework provides a scalable solution for high renewable integration, enabling both economic and environmental benefits.