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A robust resilient-oriented design for the cyber-physical distribution system against sequential typhoons.

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This study enhances cyber-physical distribution system resilience against typhoons using a robust optimization model. It integrates pre-typhoon prevention and during-typhoon emergency response for cost-effective performance.

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Area of Science:

  • Electrical Engineering
  • Power Systems
  • Disaster Management

Background:

  • Cyber-physical distribution systems face significant operational risks from high-impact, low-probability disasters like typhoons.
  • The deep coupling of cyber and physical components exacerbates system vulnerabilities during extreme weather events.

Purpose of the Study:

  • To propose a resilience-oriented robust optimization model for cyber-physical distribution systems facing typhoon threats.
  • To incorporate planning-operational restoration measures within a comprehensive prevention and emergency response framework.

Main Methods:

  • Developed a resilience-oriented robust optimization model integrating pre-typhoon prevention (line hardening, battery storage, soft open points, wireless communication) and during-typhoon emergency response.
  • Constructed a spatially and temporally extended N-k uncertainty set to model time-varying typhoon paths and overhead line status.
  • Recast the robust optimization model into a mixed-integer linear programming problem solvable via a nested column-and-constraint generation algorithm.

Main Results:

  • The proposed model effectively enhances the resilience of cyber-physical distribution systems against typhoon-induced disruptions.
  • Numerical results demonstrate rapid response capabilities to worst-case typhoon scenarios.
  • The implemented strategies offer cost-effective performance in mitigating power outages.

Conclusions:

  • The integrated prevention and emergency response framework significantly improves system resilience against typhoons.
  • Robust optimization provides a viable approach for managing uncertainties in cyber-physical distribution systems during extreme events.
  • The study offers practical, cost-effective solutions for enhancing grid reliability in disaster-prone areas.