提出物理感知对抗攻击，破坏智能充电系统稳定性。
PHANTOM: Physics-Aware Adversarial Attacks against Federated Learning-Coordinated EV Charging Management System
- 用联邦学习构建电力系统数字孪生，融合物理规律建模
- 通过多智能体强化学习生成可绕过检测的虚假数据攻击
- 验证攻击引发电压不稳并跨配电网与主网级联失效
配电网络中电动汽车充电站（EVCS）的快速部署需要智能自适应控制以维持电网弹性与可靠性。本文提出PHANTOM，一种基于多智能体强化学习训练的物理感知对抗网络。该方法利用联邦学习（FL）增强的物理信息神经网络（PINN）构建EVCS集成系统的数字孪生，确保运行动态与约束的物理一致性。在此数字孪生基础上，构建多智能体强化学习环境，采用深度Q网络（DQN）与软演员-评论家（SAC）方法，生成可规避传统检测机制的虚假数据注入（FDI）攻击策略。为评估更广泛的电网层级影响，开发了输配电网（T and D）双层仿真平台，捕捉分布层面充电扰动与主网运行之间的级联交互。结果表明，所学攻击策略会破坏负荷平衡并引发电压失稳，且影响跨输配边界传播。研究凸显了面向大规模车网融合场景的物理感知网络安全防护的紧迫性。
原文摘要 · Abstract (English)
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.
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