On Privacy Preservation of Electric Vehicle Charging Control via State Obfuscation

TL;DR

This paper introduces a decentralized, privacy-preserving EV charging control algorithm that ensures optimal load management while protecting user privacy against adversaries, scalable to large EV networks.

Contribution

It proposes a novel state obfuscation method for privacy preservation in decentralized EV charging control, applicable to large-scale cyber-physical systems.

Findings

The algorithm achieves privacy preservation against honest-but-curious adversaries.

It ensures scalable and optimal EV charging solutions.

Simulations validate the approach's efficiency and effectiveness.

Abstract

The electric vehicle (EV) industry is rapidly evolving owing to advancements in smart grid technologies and charging control strategies. While EVs are promising in decarbonizing the transportation system and providing grid services, their widespread adoption has led to notable and erratic load injections that can disrupt the normal operation of power grid. Additionally, the unprotected collection and utilization of personal information during the EV charging process cause prevalent privacy issues. To address the scalability and data confidentiality in large-scale EV charging control, we propose a novel decentralized privacy-preserving EV charging control algorithm via state obfuscation that 1) is scalable w.r.t. the number of EVs and ensures optimal EV charging solutions; 2) achieves privacy preservation in the presence of honest-but-curious adversaries and eavesdroppers; and 3) is applicable to eliminate privacy concerns for general multi-agent optimization problems in large-scale cyber-physical systems. The EV charging control is structured as a constrained optimization problem with coupled objectives and constraints, then solved in a decentralized fashion. Privacy analyses and simulations demonstrate the efficiency and efficacy of the proposed approach.