Energy Scheduling of VPPs with Renewable Generation: A Hybrid Data-Driven Convex Hull Approach

This paper addresses the challenges of energy scheduling under uncertainty for virtual power plants (VPPs) with renewable energy sources. While existing robust scheduling methods using uncertainty sets offer solutions to handle renewable generation variability, they often struggle to balance between conservatism and computational efficiency. We propose a novel data-driven approach that combines ellipsoidal uncertainty sets (EUS) with a hybrid convex hull uncertainty set (HCHUS) to overcome these limitations. First, we develop a minimum-volume ellipsoid method to construct an EUS that comprehensively covers potential renewable energy outputs while minimizing over-conservatism. To enhance computational efficiency, we then approximate the non-linear EUS using an HCHUS composed of rectangular and diamond convex hulls. Results demonstrate that our proposed approach achieves an effective balance between solution accuracy and computational tractability, offering a practical tool for VPP energy scheduling in both day-ahead and real-time markets.