Data-Driven Wind Generation Admissibility Assessment of Integrated Electric-Heat Systems: A Dynamic Convex Hull-Based Approach

Abstract

This paper proposes a data-driven approach to assess the wind generation accommodation capability of the integrated electric-heat system. The overall assessment model is constructed based on the two-stage robust decision-making framework, where the uncertainty of wind generation is described by a convex hull formed by historical data. To take a balance between modeling accuracy and computation tractability, the convex hull is firstly approximated by the intersection of a set of low-dimensional convex hulls, named the approximated convex hull, and then the approximated convex hull based uncertainty set is further simplified by the vertex coefficient discretization treatment. A uniform-compression-expansion scheme for the identified worst-case data samples is derived to realize the evolution of the dynamic convex hull as well as to bridge the gap between the first- and the second-stage problems. A modified column-and-constraint generation algorithm is devised to solve the overall model. Simulation results on two test systems verify the effectiveness and the scalability of the proposed method.