A Distributionally Robust Defender-Attacker-Defender Model for Resilience Enhancement of Power Systems Against Malicious Cyberattacks

弹性(材料科学) 电力系统 计算机科学 可靠性工程 资源配置 过程(计算) 计算机安全 资源(消歧) 智能电网 关键基础设施 网格 可靠性(半导体) 风险分析(工程) 功率(物理) 工程类 计算机网络 业务 电气工程 物理 操作系统 几何学 热力学 量子力学 数学
作者
Zhaoxi Liu,Lingfeng Wang
出处
期刊:IEEE Transactions on Power Systems [Institute of Electrical and Electronics Engineers]
卷期号:38 (6): 4986-4997 被引量:17
标识
DOI:10.1109/tpwrs.2022.3222309
摘要

The cybersecurity of electric power grids is emerging as a critical challenge for the power industry in the transformation of modern power systems towards the future smart grid. It is of great importance to enhance the resilience of power systems against potential cyber threats. In this paper, a distributionally robust recovery resource allocation method based on the tri-level defender-attacker-defender (D-A-D) model is proposed to enhance the resilience of power systems in the face of malicious cyberattacks. The proposed recovery resource allocation method is able to optimally distribute the recovery resources among the substations in the grid to mitigate the impacts of successful cyberattacks during the recovery process, and improve the resilience performance of power systems against the attacks. In the proposed model, the recovery resource represents the available resource that can be expended to support the recovery efforts of the cyber-physical power system. It is expected to accelerate the recovery process of the system after successful attacks. The recovery resource may include necessary software, hardware, and labor force. Meanwhile, a distributionally robust optimization (DRO) model is proposed to address the uncertainty of the power system operation conditions, e.g., renewable energy resources (RESs). In order to verify the proposed system defense method, case studies were conducted on the IEEE Reliability Test System RTS-79. The results of the case studies show that the proposed method can provide a robust solution to recovery resource allocation for mitigating the risk of potential cyberattacks.
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