Data-Driven Framework for Mitigating EV-Based Load-Altering Attacks on LFC Model of Microgrid

Abstract

The deployment of electric vehicles (EVs) in different grid domains, such as microgrids (MGs), has increased considerably. To fully realize the advantages of EV ecosystems and integrate them with the MG control schemes, the use of information and communication technologies is required, making the EV ecosystem prone to data manipulation and malware injection. On this basis, the potential vulnerabilities of MGs, such as the load frequency control (LFC) model, that plays an important role in keeping a balance between generation and demand, will be discussed. Then, a switching attack vector originating from EV ecosystems is leveraged to launch coordinated EV-based load-altering attacks (EV-LAAs) based on the frequency of lightly damped modes in MGs. A multi-agent cooperative reinforcement learning (RL) control framework based on the actor-critic proximal policy optimization (PPO) model is designed to mitigate the switching attack vectors. A Lyapunov function is developed using the PPO to provide monotonic policies and guarantee MG’s stability. The performance and robustness of the proposed method are compared with a model-based controller and a centralized RL framework for several attack scenarios during disturbances in wind speed, solar irradiation, and parametric uncertainties under a testbed that integrates a virtual sphere (vSphere) of an EV ecosystem with an islanded MG simulated in OPAL-RT 5650.