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

TL;DR

This work tackles the computational challenge of coordinating vehicle-to-grid (V2G) scheduling with optimal power flow (OPF) in distribution networks, formulated as a mixed-integer nonlinear program (MINP) due to binary EV charging/discharging decisions. It introduces a two-stage approach: Stage 1 applies a difference-of-convex (DC) relaxation to convert the MINP into a sequence of path-following SOCPs, producing a fast but potentially infeasible solution, and Stage 2 uses a trust-region optimization that builds around the DC solution to find a feasible, near-optimal solution within a controlled region. The method demonstrates dramatic speedups and near-optimal performance compared with an open-source solver (SCIP) on radial networks Case 18 and Case 69, with sensitivity analyses showing how the trust-region radius $\Delta$ affects feasibility and optimality. The approach offers a practical path toward real-time or large-scale V2G-OPF coordination in distribution grids, with potential extensions to distributed computation and uncertainty handling for EVs.

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.

Figures (2)

• Figure 1: Electricity price in a day.
• Figure 2: The comparison of solving time between the SCIP and our two-stage method among test data samples on Case 18.