Scalable Electric Vehicle Charging Protocols

TL;DR

This paper introduces scalable, decentralized electric vehicle charging protocols that optimize power grid usage, improve security, and protect user privacy, addressing the challenges of large fleet management and grid constraints.

Contribution

It presents a novel decentralized charging protocol using the Frank-Wolfe method and a network-constrained optimization approach with ADMM, enhancing scalability and security.

Findings

Proposed protocols outperform existing methods in speed and security.

Numerical tests confirm the optimality and efficiency of the schemes.

The methods effectively manage grid constraints and user privacy.

Abstract

Although electric vehicles are considered a viable solution to reduce greenhouse gas emissions, their uncoordinated charging could have adverse effects on power system operation. Nevertheless, the task of optimal electric vehicle charging scales unfavorably with the fleet size and the number of control periods, especially when distribution grid limitations are enforced. To this end, vehicle charging is first tackled using the recently revived Frank-Wolfe method. The novel decentralized charging protocol has minimal computational requirements from vehicle controllers, enjoys provable acceleration over existing alternatives, enhances the security of the pricing mechanism against data attacks, and protects user privacy. To comply with voltage limits, a network-constrained EV charging problem is subsequently formulated. Leveraging a linearized model for unbalanced distribution grids, the goal is to minimize the power supply cost while respecting critical voltage regulation and substation capacity limitations. Optimizing variables across grid nodes is accomplished by exchanging information only between neighboring buses via the alternating direction method of multipliers. Numerical tests corroborate the optimality and efficiency of the novel schemes.