Towards Balanced Three-phase Charging: Phase Optimization in Adaptive Charging Networks

TL;DR

This paper introduces the PXA algorithm for phase optimization in EV charging networks, effectively balancing phases to minimize losses and improve energy delivery, with proven convergence to the global optimum under certain conditions.

Contribution

The paper proposes a novel PXA algorithm for phase optimization in EV charging, including an online MPC-based version, demonstrating significant performance improvements over existing strategies.

Findings

PXA algorithm converges to the global optimum under certain conditions.

PXA significantly improves charging performance compared to other strategies.

Real-world data validates the effectiveness of the proposed methods.

Abstract

We study the problem of phase optimization for electric-vehicle (EV) charging. We formulate our problem as a non-convex mixed-integer programming problem whose objective is to minimize the charging loss. Despite the hardness of directly solving this non-convex problem, we solve a relaxation of the original problem by proposing the PXA algorithm where "P", "X", and "A" stand for three variable matrices in the formed phase optimization problems. We show that under certain conditions, the solution is given by the PXA precisely converges to the global optimum. In addition, using the idea of model predictive control (MPC), we design the {PXA-MPC}, which is an online implementation of the PXA. Compared to other empirical phase balancing strategies, the PXA algorithm significantly improves the charging performance by maximizing energy delivery, minimizing charging price, and assisting future energy planning. The efficacy of our algorithm is demonstrated using data collected from a real-world adaptive EV charging network (ACN).