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 article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs 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 with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs.
Highlights
A S RENEWABLE generation is increasingly deployed, powering our transportation system from the electricManuscript received June 24, 2021; accepted September 20, 2021
We present a novel modeling framework for an EV charging (EVC) hub that enables the synchronous distributed EVC algorithms to apply to fleets of electric vehicles (EVs) with known, but time-varying arrivals and departures
A piecewise linear (PWL) approach is used to formulate the nonlinear problem as a quadratic program (QP), which improves the numerics of the problem significantly
Summary
A S RENEWABLE generation is increasingly deployed, powering our transportation system from the electric. We compare the privacy offered by the classical algorithms of dual ascent and ADMM and new EVC algorithms, i.e., the augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method [29] and the packetized energy management (PEM) [30]–[33] This comparison is based on protecting valuable customer information, such as personal travel schedules. This article leverages a new distributed optimization method with quadratic convergence, i.e., ALADIN [29], and it proposes the new iteration-free, packet-based coordination scheme [30]–[33] These different methods have hitherto not been developed or analyzed for the EVC problem under dynamic coupling constraints.
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