Two-Stage Optimization for Efficient V2G Coordination in Distribution Power System

TL;DR

This paper introduces a two-stage optimization approach for efficient vehicle-to-grid coordination in power systems, combining difference of convex relaxation with trust region methods to improve computational speed and solution feasibility.

Contribution

The paper presents a novel two-stage optimization framework that effectively relaxes and solves the complex mixed-integer nonlinear problem in V2G coordination.

Findings

Significantly faster computation compared to SCIP.

Achieves near-optimal solutions.

Improves feasibility of solutions.

Abstract

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV scheduling control strategies have been developed to manage vehicle-to-grid (V2G) in coordination with the optimal power flow. In existing studies, such coordination optimization is formulated as a mixed-integer nonlinear programming (MINP), which is computationally challenging due to the binary EV charging and discharging variables. To address this challenge, we develop an efficient two-stage optimization method for this mixed-integer nonlinear coordination problem. This method first employs an efficient technique called the difference of convex (DC) to relax the integrality and reformulate MINP into a series of path-following continuous programming. Although the DC approach shows promising efficiency for solving MINP, it cannot guarantee the feasibility of the solutions. Consequently, we propose a trust region optimization method in stage two that constructs a trust region around DC's solution and then searches for the best feasible solution within this region. Our simulation results demonstrate that, compared to the open-source optimization solver SCIP, our proposed method significantly enhances computational efficiency while achieving near optimality.