Resilience assessment framework for cyber-physical distribution power system based on coordinated cyber-physical attacks under dynamic game
Owing to the advanced communication networks and intelligent electronic devices, the cyber-physical distribution systems (CPDSs) possess the capability to perform flexible economic dispatch and achieve rapid self-healing from extreme events. Meanwhile, the deep integration of cyber and physical systems makes CPDS vulnerable to coordinated cyber-physical attacks. In this paper, a resilience assessment framework for the CPDS under coordinated cyber-physical attacks is proposed to investigate the impact of the coordinated attacks on load loss and service restoration in CPDS. First, a three-stage defender-attacker-defender dynamic game model considering fake base station (FBS) and physical attacks for CPDS is established, aiming at seeking the optimal defense resource deployment strategy to enhance the resilience of the CPDS. The physical attack is launched to cause faults on the power lines, and the FBS attack is employed to interrupt the service of wireless cellular network to hinder the self-healing process of the CPDS. The lognormal shadowing model and search theory are applied to quantitatively describe the process of the coordinated cyber-physical attacks. Further, the constructed three-stage dynamic game model is equivalently recast as a tri-level max-min-max optimization model, which is solved using column-and-constraint generation combined with enumeration method. Finally, the effectiveness of the proposed resilience assessment framework and solution strategy is demonstrated by conducting simulation analysis on the modified IEEE 33-node CPDS and a real-world 47-node CPDS in China.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Formal Resilience Framework for Cyber-Physical Embodied Systems under Device-Level Cyberattacks
In cyber-physical systems (CPSs), fault tolerance is traditionally achieved by analysing sensor and actuator outputs, detecting progressive drift or sudden failures, and initiating suitable tolerance mechanisms. Reasonab…
AI-Enhanced Resilience in Power Systems: Adversarial Deep Learning for Robust Short-Term Voltage Stability Assessment under Cyber-Attacks
In the era of Industry 4.0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical. This study proposes a pioneering AI-based control framework that enhances short-…
Generative Adversarial NetworkGraph AttentionGame-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning
The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematic…
Multi-agent Reinforcement LearningCyber-Physical Vulnerability Assessment of P2P Energy Exchanges in Active Distribution Networks
Owing to the decreasing costs of distributed energy resources (DERs) as well as decarbonization policies, power systems are undergoing a modernization process. The large deployment of DERs together with internet of thing…
energy tradingARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning
Resilience of safety-critical systems is gaining importance, particularly with the increasing number of cyber and physical threats. Cyber-physical threats are becoming increasingly prevalent, as digital systems are ubiqu…
Metric Learningreinforcement-learningReinforcement Learning