Abstract：The urban power network (UPN) is a critical infrastructure that supports the normal operation of cities. The timely development of optimal post-disaster emergency resource (ER) allocation strategies for the UPN is of great significance for maintaining the functionality of various lifeline systems in cities. Unlike a single transmission and distribution network, the UPN has the characteristics of multiple voltage levels and complex topologies. This paper proposes a centralized matching and decentralized dispatch strategy for post-disaster ERs in the UPN, aiming at the problem of limited emergency resources, numerous fault components (FC), and information barriers for collaborative repair of FCs among multi-voltage power networks in the post-disaster restoration of the UPN. First, the matching relationship between ER and FC is established using the bipartite graph method based on real-time information and characteristic parameters. Second, with the objective of maximizing the net benefit of matching and using the modified Kuhn-Munkres algorithm, the optimal matching between the ER and the FC is formulated centrally. Additionally, considering the coupling relationship between multi-voltage-level power systems within the UPN, a bi-level optimization model is established. The upper-level high-voltage power system maximizes the load restoration of the substation buses, while the lower-level medium-voltage power system minimizes the load loss cost, and a decentralized recovery strategy for loads of each voltage level is developed. Furthermore, by timely updating the information of newly discovered FC and ER in UPN, the optimal matching method and load restoration strategy are re-determined to ensure that the proposed method is optimal at each time step. Finally, the case study employs real-world UPN data to perform a simulation analysis of the method proposed in this paper. In comparison to commonly used centralized and rolling optimization approaches in existing research, the method proposed in this paper can swiftly formulate solutions that approach global optimality within a relatively short timeframe. For instance, in Scenario 1, although the load restoration achieved by the proposed method is only 98.42% and 99.76% of the centralized and rolling optimization strategies, respectively, its solving efficiency is 38 times and 10 times faster than the two, respectively. Given the significance of scheduling plan formulation time during post-disaster load restoration, the paper argues that sacrificing some precision for higher solving efficiency is acceptable. The following conclusions can be drawn from the simulation analysis: (1) The method proposed in this paper independently resolves the ER and FC matching process, significantly reducing the computational complexity of the post-disaster fault repair and load restoration model. It can swiftly and efficiently formulate precise emergency plans in a relatively short time. Also, the algorithm demonstrates good convergence, thereby validating the effectiveness of the method proposed in this paper. (2) The quantities of pre-allocation ERs of different types in UPN has a substantial impact on post-disaster load loss and economic costs. In future research, it is imperative to further explore, based on predictive disaster information, the mechanisms through which different extreme disasters affect UPN components, analyze the location of vulnerable UPN components, and formulate more economical and rational resource pre-allocation strategies.
万海洋, 刘文霞, 石庆鑫, 刘佳怡, 张帅. 城市电网灾后应急资源的集中匹配-分布调度策略[J]. 电工技术学报, 2024, 39(23): 7463-7480.
Wan Haiyang, Liu Wenxia, Shi Qingxin, Liu Jiayi, Zhang Shuai. A Post-Disaster Centralized Matching and Decentralized Dispatch Strategy for Emergency Resources in Urban Power Network. Transactions of China Electrotechnical Society, 2024, 39(23): 7463-7480.
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2024, Vol. 39 
