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

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