Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty

The integration of renewable energy and electric vehicles into the smart grid is transforming the energy landscape, and Virtual Power Plant (VPP) is at the forefront of this change, aggregating distributed energy resources to optimize supply and demand balance. In this study, we propose a two-stage distributionally robust optimization framework for day-ahead energy scheduling and real-time power scheduling in VPP energy management system. Considering the uncertainty of power deviation in renewable energy generation, we design a coordinated charging and discharging strategy which integrates electric vehicles and energy storage systems to maintain a balance between supply and demand. To efficiently solve the tri-level min-max-min optimization problem with mixed-integer recourse variables in the second stage, we develop an improved nested C&CG algorithm to make real-time decisions on energy storage, which exhibits superior computational performance. The numerical results further demonstrate the effectiveness of the optimal energy scheduling strategy and provide some valuable insights. Moreover, our strategy not only proves cost-effective but also outperforms other comparable approaches in achieving superior peak shaving and valley filling effects. By guiding VPP operators to develop a reasonable energy scheduling solution, we can effectively balance economic and environmental sustainability.