Article, 2023

An Adaptive V2G Capacity-Based Frequency Regulation Scheme With Integral Reinforcement Learning Against DoS Attacks

IEEE Transactions on Smart Grid, ISSN 1949-3061, 1949-3053, Volume 15, 1, Pages 834-847, 10.1109/tsg.2023.3270564

Contributors

Sun, Jian 0000-0003-0683-9096 [1] [2] Qi, Guanqiu 0000-0001-9562-3865 [3] Chai, Yanxia 0000-0002-2717-5981 [4] Zhu, Zhiqin 0000-0002-3883-2529 (Corresponding author) [5] Guerrero, Josep M 0000-0001-5236-4592 [2]

Affiliations

  1. [1] Southwest University
    2. [NORA names: China; Asia, East];
  2. [2] Aalborg University
    3. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  3. [3] State University of New York
    4. [NORA names: United States; America, North; OECD];
  4. [4] Chongqing University
    5. [NORA names: China; Asia, East];
  5. [5] Chongqing University of Posts and Telecommunications
    6. [NORA names: China; Asia, East]

Abstract

Aggregated electrical vehicles (EVs) can flexibly serve grid frequency regulation (FR) with adjustable FR capacity (FRC) via vehicle-to-grid (V2G) technology. Therefore, current research applies integral reinforcement learning (IRL) to FR as it can easily solve difficult modeling and optimization problems. However, communication between EVs is vulnerable to denial of service (DoS) attacks, which can significantly degrade FR performance and even destabilize V2G FR systems. This paper proposes an adaptive V2G FRC-based FR scheme with IRL to improve FR resilience and mitigate FR performance degradation against DoS attacks. The proposed scheme optimizes V2G control by IRL without analytical models, integrating an event-triggered mechanism to reduce communication burden. It adapts V2G FRC to attack intensity to minimize frequency deviation and mitigate the impact of DoS attacks. The analytical estimation of convergence rate establishes the quantitative relationships among control performance, V2G FRC, and attack intensity, thereby deriving an adaptive mechanism. The theoretical analysis of the proposed scheme yields stability conditions that guarantee the FR performance. The simulations were performed on the IEEE 39-bus test system to verify the effectiveness and advantages of the proposed scheme. The results indicate that V2G FRC should be scaled down to mitigate performance degradation when intensive attacks occur.

Keywords

DoS, DoS attacks, FR capacity, FR performance, FR schemes, G control, IEEE, IEEE 39-bus test system, V2G control, adaptive mechanisms, aggregated electric vehicles, analysis, analytical estimates, analytical model, attack intensity, attacks, burden, capacity, communication, communication burden, conditions, control performance, convergence rate, degradation, denial, denial of service, deviation, effect, electric vehicles, estimates of convergence rates, event-triggered mechanism, frequency, frequency deviation, frequency regulation, frequency regulation scheme, grid, grid frequency regulation, impact, impact of DoS attacks, integral reinforcement learning, integration, intense attack, intensity, learning, mechanism, mitigate performance degradation, model, optimization, optimization problem, performance, performance degradation, problem, quantitative relationship, rate, reduce communication burden, regulation, regulation scheme, reinforcement learning, relationship, research, resilience, results, scheme, services, simulation, stability, stability conditions, system, technology, test system, theoretical analysis, vehicle, vehicle-to-grid (V2G) technology

Funders

  • National Natural Science Foundation of China
  • Chongqing Municipality Education Commission

Data Provider: Digital Science

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