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Deceiving Flexibility: A Stealthy False Data Injection Model in Vehicle-to-Grid Coordination

Kaan T. Gun, Xiaozhe Wang, Danial Jafarigiv

Abstract

Electric vehicles (EVs) in Vehicle-to-Grid (V2G) systems act as distributed energy resources that support grid stability. Centralized coordination such as the extended State Space Model (eSSM) enhances scalability and estimation efficiency but may introduce new cyber-attack surfaces. This paper presents a stealthy False Data Injection Attack (FDIA) targeting eSSM-based V2G coordination. Unlike prior studies that assume attackers can disrupt physical charging or discharging processes, we consider an adversary who compromises only a subset of EVs, and limiting their influence to the manipulation of reported State of Charge (SoC) and power measurements. By doing so, the attacker can deceive the operator's perception of fleet flexibility while remaining consistent with model-based expectations, thus evading anomaly detection. Numerical simulations show that the proposed stealthy FDIA can deteriorate grid frequency stability even without direct access to control infrastructure. These findings highlight the need for enhanced detection and mitigation mechanisms tailored to aggregated V2G frameworks

Deceiving Flexibility: A Stealthy False Data Injection Model in Vehicle-to-Grid Coordination

Abstract

Electric vehicles (EVs) in Vehicle-to-Grid (V2G) systems act as distributed energy resources that support grid stability. Centralized coordination such as the extended State Space Model (eSSM) enhances scalability and estimation efficiency but may introduce new cyber-attack surfaces. This paper presents a stealthy False Data Injection Attack (FDIA) targeting eSSM-based V2G coordination. Unlike prior studies that assume attackers can disrupt physical charging or discharging processes, we consider an adversary who compromises only a subset of EVs, and limiting their influence to the manipulation of reported State of Charge (SoC) and power measurements. By doing so, the attacker can deceive the operator's perception of fleet flexibility while remaining consistent with model-based expectations, thus evading anomaly detection. Numerical simulations show that the proposed stealthy FDIA can deteriorate grid frequency stability even without direct access to control infrastructure. These findings highlight the need for enhanced detection and mitigation mechanisms tailored to aggregated V2G frameworks
Paper Structure (26 sections, 19 equations, 8 figures, 2 tables, 1 algorithm)

This paper contains 26 sections, 19 equations, 8 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Individual Electric Vehicle Model (IEVM)
  4. The Extended State Space Model of Aggregated EVs
  5. The proposed stealthy FDIA model
  6. Stealthy Conditions
  7. Multi-Stage FDI Attack
  8. Collection of compromised EV measurements
  9. IEVM Simulation
  10. Receding Horizon Attack Formulation
  11. Aggregate Stealth Constraint
  12. Individual Physical Feasibility
  13. Conservation Constraint
  14. Maximum Likelihood Assignment
  15. State Transition Across Operating Modes
  16. ...and 11 more sections

Figures (8)

  • Figure 1: Illustration of State Indexes and Transitions.
  • Figure 2: Overview of V2G Communication and eSSM execution timeline
  • Figure 3: FDI Attack Procedure Green and red dashed lines indicate the true and manipulated EV measurements respectively.
  • Figure 4: Illustration of the optimizations cascading timeline. Output of the IEVM $\tilde{\boldsymbol{x}}_{0}(0)$ is manipulated $\tilde{\boldsymbol{x}}_{0}(0)'\!\!=\!E_0\tilde{\boldsymbol{x}}_0(0)$. At the end of period $h$, the manipulated state evolved under SSM becomes the initial state for the next period $h\!+\!1$.
  • Figure 5: Grid Flexibility with No Control. Upper and lower bounds of EVs' flexibility from SSM under attack are represented as $y_{u}$ and $y_{l}$, respectively.
  • ...and 3 more figures