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Method for Resilience-oriented Optimization and Evaluation of Distribution Networks Considering Load Restoration Prioritization and Dynamic Repair Under Typhoon Disasters
YAN Renwu, GUO Yumin, LI Peiqiang
Electric Power Construction ›› 2026, Vol. 47 ›› Issue (3) : 93-105.
PDF(2075 KB)
PDF(2075 KB)
Method for Resilience-oriented Optimization and Evaluation of Distribution Networks Considering Load Restoration Prioritization and Dynamic Repair Under Typhoon Disasters
[Objective] To enhance the resilience of distribution networks under typhoon disasters and mitigate the risks of load interruptions and power supply losses caused by natural disasters， this paper proposes a resilience-oriented optimization strategy and evaluation method that accounts for load restoration priority and dynamic repair. [Methods] Typhoon-induced fault scenarios are constructed by integrating the Batts typhoon model with a line fault model， thereby capturing the impacts of typhoon intensity and trajectory on distribution networks. On this basis， a multi-source collaborative optimization model is developed with the core objective of prioritizing the restoration of critical loads. The model couples dynamic reconfiguration， fault repair， and the dynamic output characteristics of distributed energy resources （DERs） to enable rapid response and efficient resource dispatch during disasters. A set of resilience evaluation metrics of load average recovery level considering load weights is proposed to quantitatively evaluate system resilience under different scenarios. [Results] Case studies on a modified IEEE 33-bus distribution system demonstrate that the proposed strategy effectively reduces overall system load losses and significantly improves the restoration level of critical buses and essential users under typhoon scenarios. The simulation results also validate the applicability and effectiveness of the proposed evaluation metrics in distinguishing the merits and demerits of different recovery strategies. [Conclusions] The proposed strategy achieves dynamic optimization of distribution networks throughout the disaster impact and recovery process. Compared with conventional approaches， it exhibits distinct advantages in terms of load restoration speed， supply reliability， and resource utilization efficiency. In addition， the proposed resilience evaluation metrics provide a more scientific characterization of system resilience under disaster conditions， compensating for the limitations of conventional metrics. Overall， this paper offers valuable insights and references for fault recovery and resilience evaluation of future power systems under typhoon disasters.
typhoon disaster / distribution network resilience / multi-source collaborative optimization / resilience evaluation metrics
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Urban power grids have high load densities and are subjected to many critical loads. Improving the extreme survival capacity of an urban power grid under extreme conditions and events help to ensure uninterrupted power supply for important users, improve the anti-vulnerability capacity of the power grid, and reduce the impact and losses caused by extreme events. In this study, an anti-fragile planning method for a large-scale urban distribution network is developed to improve the extreme survival capacity. First, a two-step decision-making framework for urban distribution network resilience planning based on the stochastic programming theory is proposed. The first step of the framework is to determine the set of candidate line reinforcement/upgrading schemes, and the second step is to determine the optimal deployment scheme of distributed power sources in the line-planning scheme based on the stochastic programming theory. The final resilience planning scheme is determined by considering factors, such as investment economy and extreme survival capacity improvement effect. Among different candidate line reinforcement/upgrading schemes, an extreme scene generation and representative scene-screening method based on Monte Carlo simulation and K-means clustering is proposed, considering the possible typhoon extreme disasters. Next, the planning problem of distributed generation is constructed as a two-stage random mixed-integer programming to optimize investment economy and maximize extreme survival capacity, and the random programming problem is transformed into a deterministic mixed-integer linear-programming problem based on the above extreme scenarios. The IEEE 33-node and 123-node distribution systems are used to verify the effectiveness of the proposed method. |
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