Energy Scheduling of Virtual Power Plants: A Data-Driven Enclosing Polyhedron Method

Abstract

Uncertainty sets (USs) based on historical data have been applied for accurately characterizing the uncertainty of renewable energy resource (RES) unit outputs in robust energy scheduling involving virtual power plants (VPPs). However, it remains highly challenging to develop scheduling solutions that optimally balance between security and economic efficiency and the lowest computational burden. This involves constructing the smallest possible linear-form US that encompasses RES uncertainty data with a minimum number of vertices. The present work addresses these challenges by developing a data-driven minimum-volume ellipsoid US (EUS) with flexible confidence levels. The number of vertices in the obtained EUS is reduced to improve the computational efficiency of the solution process by approximating the EUS using a hybrid polyhedron US (HPUS) composed of rectangular and diamond USs. Finally, a vertex-based column-and-constraint generation algorithm, which can avoid falling into locally optimal solutions, is designed to solve the robust VPP energy scheduling model with the HPUS. The effectiveness and superiority of the proposed US approach and algorithm are verified based on a practical VPP system in South China.