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.
Wan Haiyang,Liu Wenxia,Shi Qingxin等. A Post-Disaster Centralized Matching and Decentralized Dispatch Strategy for Emergency Resources in Urban Power Network[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7463-7480.
[1] 孙建平. 上海城市运行安全发展报告(2019-2020)[M]. 上海: 同济大学出版社, 2021. [2] 何正友, 李波, 廖凯, 等. 新形态城市电网保护与控制关键技术[J]. 中国电机工程学报, 2020, 40(19): 6193-6207. He Zhengyou, Li Bo, Liao Kai, et al.Key technologies for protection and control of novel urban power grids[J]. Proceedings of the CSEE, 2020, 40(19): 6193-6207. [3] 何永秀, 朱茳, 罗涛, 等. 城市电网规划自然灾害风险评价研究[J]. 电工技术学报, 2011, 26(12): 205-210. He Yongxiu, Zhu Jiang, Luo Tao, et al.Risk assessment of natural disaster in urban electric power network planning[J]. Transactions of China Electrotechnical Society, 2011, 26(12): 205-210. [4] 杨海涛, 祝达康, 李晶, 等. 特大型城市电网大停电的机理和预防对策探讨[J]. 电力系统自动化, 2014, 38(6): 128-135. Yang Haitao, Zhu Dakang, Li Jing, et al.A discussion on mechanism and countermeasures of blackouts in super megacity power networks[J]. Automation of Electric Power Systems, 2014, 38(6): 128-135. [5] 周波涛, 钱进. IPCC AR6报告解读:极端天气气候事件变化[J]. 气候变化研究进展, 2021, 17(6): 713-718. Zhou Botao, Qian Jin.Changes of weather and climate extremes in the IPCC AR6[J]. Climate Change Research, 2021, 17(6): 713-718. [6] 孙为民, 孙华东, 何剑, 等. 面向严重自然灾害的电力系统韧性评估技术综述[J]. 电网技术, 2024, 48(1): 129-139. Sun Weimin, Sun Huadong, He Jian, et al.Review of power system resilience assessment techniques for severe natural disasters[J]. Power System Technology, 2024, 48(1): 129-139. [7] Tao Zhengru, Han Lu.Emergency response, influence and lessons in the 2021 compound disaster in Henan Province of China[J]. International Journal of Environmental Research and Public Health, 2022, 19(1): 488. [8] 陶然, 赵冬梅, 徐辰宇, 等. 考虑电-气-热-交通相互依存的城市能源系统韧性评估与提升方法[J]. 电工技术学报, 2023, 38(22): 6133-6149. Tao Ran, Zhao Dongmei, Xu Chenyu, et al.Resilience assessment and enhancement methods for urban energy system considering electricity-gas-heat-transport interdependency[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 6133-6149. [9] Shi Qingxin, Liu Wenxia, Zeng Bo, et al.Enhancing distribution system resilience against extreme weather events: concept review, algorithm summary, and future vision[J]. International Journal of Electrical Power & Energy Systems, 2022, 138: 107860. [10] 许寅, 和敬涵, 王颖, 等. 韧性背景下的配网故障恢复研究综述及展望[J]. 电工技术学报, 2019, 34(16): 3416-3429. Xu Yin, He Jinghan, Wang Ying, et al.A review on distribution system restoration for resilience enhancement[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3416-3429. [11] 王帆. 保定中心城区电网目标网架规划设计[D]. 北京: 华北电力大学, 2014. Wang Fan.Baoding center city power grid planning and design objectives[D]. Beijing: North China Electric Power University, 2014. [12] Gilstrap M, Amin S, DeCorla-Souza K. United States electricity industry primer[R/OL]. Washington D. C.: Office of Electricity Delivery and Energy Reliability, U.S. Department of Energy, 2015. https://www.energy.gov/sites/prod/files/2015/12/f28/united-states-electricity-industry-primer.pdf#page=13&zoom=100,93,172. [13] Electricity Networks Strategy Group. Our electricity transmission network: a vision for2020[R/OL]. London: Electricity Networks Strategy Group, 2012. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/48275/4264-ensg-summary.pdf. [14] 田野, 陈维江, 张薛鸿, 等. 日本配电网中性点接地方式选取原则及接地故障处理方法[J]. 供用电, 2017, 34(5): 14-20. Tian Ye, Chen Weijiang, Zhang Xuehong, et al.Selection principle of neural grounding mode of distribution power network and its fault processing technology in Japan[J]. Distribution & Utilization, 2017, 34(5): 14-20. [15] 彭冬, 吴俊玲, 梁才浩, 等. 城市电网电压序列优化配置[J]. 电网技术, 2010, 34(6): 113-118. Peng Dong, Wu Junling, Liang Caihao, et al.Optimal configuration of voltage grades for urban distribution network[J]. Power System Technology, 2010, 34(6): 113-118. [16] 周念成, 莫复雪, 肖舒严, 等. 计及多电压等级配电网拓扑约束的协调转供优化[J]. 中国电机工程学报, 2021, 41(9): 3106-3120. Zhou Niancheng, Mo Fuxue, Xiao Shuyan, et al.Coordinated power transfer optimization of multi-voltage-level distribution network considering topology constraints[J]. Proceedings of the CSEE, 2021, 41(9): 3106-3120. [17] Shi Qingxin, Li Fangxing, Olama M, et al.Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience[J]. International Journal of Electrical Power & Energy Systems, 2021, 124: 106355. [18] Liu Wenxia, Wang Yuehan, Shi Qingxin, et al. A multi-stage restoration strategy to enhance distribution system resilience with improved conditional generative adversarial nets[J/OL]. CSEE Journal of Power and Energy System, 2022: 1-12[2023-10-08]. doi:10. 17775/CSEEJPES.2021.09080. [19] 陈厚合, 丛前, 姜涛, 等. 多能协同的配电网供电恢复策略[J]. 电工技术学报, 2022, 37(3): 610-622, 685. Chen Houhe, Cong Qian, Jiang Tao, et al.Distribution systems restoration with multi-energy synergy[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 610-622, 685. [20] 卢志刚, 李丹, 吕雪姣, 等. 含分布式电源的冰灾下配电网多故障抢修策略[J]. 电工技术学报, 2018, 33(2): 423-432. Lu Zhigang, Li Dan, Lü Xuejiao, et al.Multiple faults repair strategy under ice storm for distribution network with distributed generators[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 423-432. [21] 朱晓荣, 司羽. 考虑物理—信息—交通网耦合的配电网多时段动态供电恢复策略[J]. 电工技术学报, 2023, 38(12): 3306-3320. Zhu Xiaorong, Si Yu.Multi-period dynamic power supply restoration strategy considering physical-cyber-traffic network coupling[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3306-3320. [22] 王月汉, 刘文霞, 姚齐, 等. 面向配电网韧性提升的移动储能预布局与动态调度策略[J]. 电力系统自动化, 2022, 46(15): 37-45. Wang Yuehan, Liu Wenxia, Yao Qi, et al.Pre-layout and dynamic scheduling strategy of mobile energy storage for resilience enhancement of distribution network[J]. Automation of Electric Power Systems, 2022, 46(15): 37-45. [23] Ye Zhigang, Chen Chen, Chen Bo, et al.Resilient service restoration for unbalanced distribution systems with distributed energy resources by leveraging mobile generators[J]. IEEE Transactions on Industrial Informatics, 2021, 17(2): 1386-1396. [24] 俞拙非, 刘菲, 刘瑞环, 等. 面向配电网弹性提升的源网荷灵活资源优化研究综述及展望[J]. 中国电力, 2022, 55(4): 132-144. Yu Zhuofei, Liu Fei, Liu Ruihuan, et al.Resilience-oriented optimization of source-grid-load flexible resources in distribution systems: review and prospect[J]. Electric Power, 2022, 55(4): 132-144. [25] 李娜. 基于鲁棒规划模型的电力应急物资库网络设计研究[D]. 北京: 华北电力大学, 2019. Li Na.Research on network design of electric power emergency material warehouse based on robust programming model[D]. Beijing: North China Electric Power Unversity, 2019. [26] Shi Qingxin, Li Fangxing, Dong Jin, et al.Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience[J]. Applied Energy, 2022, 318: 119245. [27] Sun Xiaotian, Xie Haipeng, Bie Zhaohong, et al. Restoration of high-renewable-penetrated distribution systems considering uncertain repair workloads[J/OL]. CSEE Journal of Power and Energy System, 2022: 1-12[2023-10-08]. doi: 10.17775/CSEEJPES.2021. 06500. [28] Wan Haiyang, Liu Wenxia, Shi Qingxin, et al. Multi-time-step rolling optimization strategy for post-disaster emergency recovery in distribution system based on model predictive control[J/OL]. CSEE Journal of Power and Energy Systems, 2022: 1-11[2023-10-08]. doi: 10. 17775/CSEEJPES.2021.08050. [29] 万海洋, 刘文霞, 石庆鑫, 等. 计及交通流量动态变化的配电网灾后修复多时间断面优化策略[J]. 电力系统自动化, 2022, 46(12): 119-129. Wan Haiyang, Liu Wenxia, Shi Qingxin, et al.Multiple-time-section optimization strategy for post-disaster recovery of distribution network considering dynamic changes of traffic flow[J]. Automation of Electric Power Systems, 2022, 46(12): 119-129. [30] Yang Lijun, Qin Ying.Research on rolling co-optimization of fault repair and service restoration in distribution network based on combined drive methodology[J]. Electric Power Systems Research, 2023, 220: 109373. [31] Yao Shuhan, Wang Peng, Liu Xiaochuan, et al.Rolling optimization of mobile energy storage fleets for resilient service restoration[J]. IEEE Transactions on Smart Grid, 2020, 11(2): 1030-1043. [32] 李长城, 和敬涵, 王颖, 等. 考虑分布式电源支撑作用的输配电系统协同恢复方法[J]. 电力自动化设备, 2022, 42(2): 112-119. Li Changcheng, He Jinghan, Wang Ying, et al.Coordinated restoration of transmission and distribution systems considering DG support[J]. Electric Power Automation Equipment, 2022, 42(2): 112-119. [33] 王炜歆, 王小君, 许寅, 等. 考虑输配协同的电网并行恢复分区及机组启动次序统一优化决策方法[J]. 中国电机工程学报, 2024, 44(3): 859-872. Wang Weixin, Wang Xiaojun, Xu Yin, et al.A synthetic optimal decision-making method for parallel restoration sectionalizing and generator start-up sequence of power grids considering transmission and distribution system coordination[J]. Proceedings of the CSEE, 2024, 44(3): 859-872. [34] Zhao Jin, Wang Hongtao, Liu Yutian, et al.Coordinated restoration of transmission and distribution system using decentralized scheme[J]. IEEE Transactions on Power Systems, 2019, 34(5): 3428-3442. [35] Wan Haiyang, Liu Wenxia, Zhang Shuai, et al.Pre-disaster and mid-disaster resilience improvement strategy of high proportion renewable energy system considering frequency stability[J]. International Journal of Electrical Power & Energy Systems, 2023, 151: 109135. [36] Jiang Lizhou, Li Xin, Long Tao, et al.Resilient service restoration for distribution systems with mobile resources using Floyd-based network simplification method[J]. IET Generation, Transmission & Distribution, 2022, 16(3): 414-429. [37] 胡振涛, 崔南方, 张艳, 等. 基于动态资源权重的多技能项目调度启发式算法[J]. 控制与决策, 2021, 36(10): 2553-2561. Hu Zhentao, Cui Nanfang, Zhang Yan, et al.Dynamic resource priority-based heuristics for multi-skill resource constrained project scheduling problem[J]. Control and Decision, 2021, 36(10): 2553-2561. [38] 魏恩伟, 李伟华, 张之涵, 等. 基于改进匈牙利算法的非侵入式负荷匹配方法[J]. 电测与仪表, 2019, 56(22): 58-64. Wei Enwei, Li Weihua, Zhang Zhihan, et al.Non-intrusive load matching method based on improved Hungarian algorithm[J]. Electrical Measurement & Instrumentation, 2019, 56(22): 58-64. [39] 韩晓阳, 孟相如, 康巧燕, 等. 基于二分图最优匹配的虚拟网络映射算法[J]. 系统工程与电子技术, 2019, 41(12): 2891-2898. Han Xiaoyang, Meng Xiangru, Kang Qiaoyan, et al.Virtual network embedding algorithm based on bipartite graph optimal matching[J]. Systems Engineering and Electronics, 2019, 41(12): 2891-2898. [40] West D B.Introduction to Graph Theory[M]. 2nd ed. Upper Saddle River: Prentice Hall, 2000. [41] Asratian A S, Denley T M J, Häggkvist R. Bipartite Graphs and Their Applications[M]. Cambridge: Cambridge University Press, 1998. [42] 杨丽君, 赵宇, 赵优, 等. 考虑交通路网应急电源车调度的有源配电网故障均衡恢复[J]. 电力系统自动化, 2021, 45(21): 170-180. Yang Lijun, Zhao Yu, Zhao You, et al.Balanced fault recovery of active distribution network considering emergency power supply vehicle scheduling in traffic network[J]. Automation of Electric Power Systems, 2021, 45(21): 170-180. [43] 赵瑾. 考虑可再生能源参与的时空协调负荷恢复[D]. 济南: 山东大学, 2020. Zhao Jin.Spatio-temporal coordination of load restoration considering renewable energy participation[D]. Jinan: Shandong University, 2020.
2024, Vol. 39 
