Distributed Control of Charging for Electric Vehicle Fleets under Dynamic Transformer Ratings

TL;DR

This paper develops and compares distributed control algorithms for EV charging to prevent transformer overheating, balancing data communication, computational efficiency, and optimality, validated through simulations on residential and commercial fleets.

Contribution

It introduces a novel ALADIN-based control method and a low-information PEM approach for EV charging under nonlinear transformer constraints, with comprehensive analysis and comparison.

Findings

ALADIN outperforms classical methods in optimality and efficiency.

Low-information PEM reduces communication needs significantly.

Simulation results confirm the effectiveness of the proposed methods.

Abstract

Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This manuscript presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the trade-offs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods such as those based on dual decomposition and alternating direction method of multipliers (ADMM) are compared against the new Augmented Lagrangian-based Alternating Direction Inexact Newton (ALADIN) method and a novel low-information, look-ahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets. Simulation results validate the new methods and provide insights into key trade-offs.