Approximating Energy-Regulation Feasible Regions of Virtual Power Plants: A Data-Driven Inverse Optimization Approach

System operators can allow virtual power plants (VPPs) to submit their feasible region (FR) for market clearing and dispatch. A VPP as a whole must determine its FR to submit based on the individual operation model of its internal distributed energy resources (DERs), which is an FR aggregation problem. Existing FR aggregation approaches rely on analytical methods, which have issues with generality and adaptability. In this paper, we propose a data-driven approach to approximate the energy-regulation FR of VPPs. We adopt the virtual battery model to approximate the aggregate FR of a VPP, and we determine the model parameters through inverse optimization based on generated multiscenario operation data by using the original operation model. Numerical tests verify the accuracy of the proposed method. We believe that our work helps to better leverage the flexibility of DERs.