PHANTOM: Physics-Aware Adversarial Attacks against Federated Learning-Coordinated EV Charging Management System

TL;DR

This paper introduces PHANTOM, a physics-aware adversarial framework using federated learning and reinforcement learning to generate false data injection attacks on EV charging systems, revealing vulnerabilities in grid stability.

Contribution

It presents a novel physics-informed neural network digital twin combined with multi-agent RL to craft adversarial attacks on EVCS, highlighting cybersecurity risks in smart grid management.

Findings

Learned attack policies can disrupt load balancing.

Attacks induce voltage instabilities propagating across grid boundaries.

Physics-aware modeling enhances attack realism and effectiveness.

Abstract

The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks necessitates intelligent and adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network that is trained and optimized through a multi-agent reinforcement learning model. PHANTOM integrates a physics-informed neural network (PINN) enabled by federated learning (FL) that functions as a digital twin of EVCS-integrated systems, ensuring physically consistent modeling of operational dynamics and constraints. Building on this digital twin, we construct a multi-agent RL environment that utilizes deep Q-networks (DQN) and soft actor-critic (SAC) methods to derive adversarial false data injection (FDI) strategies capable of bypassing conventional detection mechanisms. To examine the broader grid-level consequences, a transmission and distribution (T and D) dual simulation platform is developed, allowing us to capture cascading interactions between EVCS disturbances at the distribution level and the operations of the bulk transmission system. Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T and D boundaries. These findings highlight the critical need for physics-aware cybersecurity to ensure the resilience of large-scale vehicle-grid integration.