1. State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing 400044 China 2. Department of Electrical Engineering City University of Hong Kong Hong Kong 610200 China
Abstract：Hydrogen energy system, with its inter temporal and spatial transfer characteristics, shows great potential for enhancing the resilience of distribution grids. However, few literatures have considered the inter temporal and spatial flexibility of hydrogen energy system and the inter-regional support capability of mobile emergency resources, and the post-disaster collaborative recovery mechanism of multi-region electric-hydrogen integrated energy system (MR-EH-IES) is still unclear, which makes it difficult to exploit the inter-regional support potential of mobile resilience resources. Aiming at the above problems, this paper proposes a post-disaster recovery strategy for MR-EH-IES with cross-regional resource sharing. This paper firstly proposes a two-layer MR-EH-IES disaster recovery framework based on the idea of “intra-regional autonomy, resource integration, inter-regional sharing”. In the lower layer, the electric-hydrogen integrated energy system (EH-IES) carries out intra-zone autonomy. The potential of synergistic cooperation between mobile electric energy storage, hydrogen fuel power generation vehicles, maintenance personnel and hydrogen energy system in disaster recovery is fully considered, and the EH-IES disaster recovery model considering the synergistic scheduling of distributed power sources and maintenance personnel is established. At the upper level, the joint disaster resilience center carries out the coordinated allocation of mobile resilience resource (MRR). Starting from the disaster recovery mechanism of different types of MRR, the key factors affecting its allocation are analyzed, the MRR disaster recovery mechanism considering cross-region support is proposed, and the MRR disaster allocation model considering cross-region resource sharing is established. Then, based on the above framework and strategy, the MR-EH-IES two-layer disaster recovery model considering cross-region resource sharing is proposed. The simulation analysis shows that the total cut-load loss of MR-EH-IES decreases by 22.4% after considering cross-region resource sharing, in which the cut-load loss of region 1 and region 3 increases slightly by ¥1.3×103 and ¥7.3×103, respectively, while the cut-load loss of region 2 and region 4 decreases by ¥157.8×103 and ¥62.5×103, respectively. Specifically, in the early stage of disaster recovery, when the mobile power supply left from region 1 and region 3 to support region 2 and region 4, the weighted load recovery rate of region 1 and region 3 showed a short drop, with the maximum drop of 0.5% and 1.1%, respectively, but both of them were higher than the weighted proportion of important loads. Meanwhile, the load-weighted recovery rates of region 2 and region 4 increased significantly, with maximum enhancements of 11.0% and 4.2%, respectively. In addition, when region 1 and region 3 were restored, idle mobile power supplies and maintenance personnel were the first to support other regions. The following conclusions can be drawn from the simulation analysis: (1) The post-disaster recovery strategy proposed in this paper is able to rapidly restore the supply of important loads and reduce the system damage in the early stage of disaster recovery through the reasonable allocation of mobile emergency resources, and improve the utilization rate of mobile emergency resources in the later stage of disaster recovery. (2) The inter temporal and spatial flexibility of the hydrogen system and the long tube trailer can increase the energy transfer channels of MR-EH-IES in time and space scales, giving full play to the ability of hydrogen energy system to support the power grid in disaster recovery.
[1] Panteli M, Mancarella P.Modeling and evaluating the resilience of critical electrical power infrastructure to extreme weather events[J]. IEEE Systems Journal, 2017, 11(3): 1733-1742. [2] 李博达, 熊宇峰, 任正伟, 等. 台风期间考虑配电终端功能可用性的配电网重构方法[J]. 电力系统自动化, 2021, 45(4): 38-44. Li Boda, Xiong Yufeng, Ren Zhengwei, et al.Distribution network reconfiguration method considering availability of distribution terminal functions during typhoon disaster[J]. Automation of Electric Power Systems, 2021, 45(4): 38-44. [3] 肖娟霞, 李勇, 韩宇, 等. 计及台风时空特性和灵活性资源协同优化的配电网弹性提升策略[J]. 电工技术学报, 2024, 39(23): 7430-7446. Xiao Juanxia, Li Yong, Han Yu, et al.Resilience enhancement strategy for distribution networks considering the spatiotemporal characteristics of typhoon and the collaborative optimization of flexible resources[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7430-7446. [4] 阮前途, 叶荣. 保障极端天气下供需安全的新型电力系统电源规划[J/OL]. 电力系统自动化, 2024: 1-15. http://kns.cnki.net/kcms/detail/32.1180.TP.20240927.1026.006.html. Ruan Qiantu, Ye Rong. Power source planning of new power system for guaranteeing supply-demand security under extreme weather[J/OL]. Automation of Electric Power Systems, 2024: 1-15. http://kns.cnki.net/kcms/detail/32.1180.TP.20240927.1026.006.html. [5] 蔡胜, 谢云云, 张玉坪, 等. 考虑孤岛微电网建立过程功率冲击的弹性配电网主动预防调度[J]. 电工技术学报, 2023, 38(23): 6419-6432. Cai Sheng, Xie Yunyun, Zhang Yuping, et al.Proactive scheduling of resilient distribution systems considering power impact during islanded microgrid formation process[J]. Transactions of China Electro-technical Society, 2023, 38(23): 6419-6432. [6] 程欢, 任洲洋, 孙志媛, 等. 电能-甲醇跨区协同输运下的电-氢耦合系统调度[J]. 电工技术学报, 2024, 39(3): 731-744. Cheng Huan, Ren Zhouyang, Sun Zhiyuan, et al.A dispatching for the electricity-hydrogen coupling systems considering the coordinated inter-region transportation of electricity and methanol[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 731-744. [7] 夏威夷, 任洲洋, 潘珍. 考虑子母站灵活互联的分布式供氢网和配电网多主体协调规划方法[J]. 中国电机工程学报, 2024, 44(23): 9187-9200. Xia Weiyi, Ren Zhouyang, Pan Zhen.A multi-agent cooperative planning method for the distributed hydrogen supply network and the power distribution network considering the flexible interconnections between on-site and off-site hydrogen refueling stations[J]. Proceedings of the CSEE, 2024, 44(23): 9187-9200. [8] 谭洪, 王宇炜, 王秋杰, 等. 基于氢能固态运输的电-氢综合能源系统双层调度模型[J]. 电工技术学报, 2025, 40(3): 744-758. Tan Hong, Wang Yuwei, Wang Qiujie, et al.A bi-level dispatching model for electricity-hydrogen integrated energy system based on hydrogen solidity transport[J]. Transactions of China Electrotechnical Society, 2025, 40(3): 744-758. [9] Zhao Yuxuan, Lin Jin, Song Yonghua, et al.A hierarchical strategy for restorative self-healing of hydrogen-penetrated distribution systems considering energy sharing via mobile resources[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1388-1404. [10] 张小彤. 计及电源容量配置的极端灾害下主动配电网应急弹性提升研究[D]. 徐州: 中国矿业大学, 2023. Zhang Xiaotong.Research on resilience improvement of active distribution network under extreme disasters considering capacity configuration[D]. Xuzhou: China University of Mining and Technology, 2023. [11] Lei Shunbo, Wang Jianhui, Chen Chen, et al.Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters[J]. IEEE Transactions on Smart Grid, 2018, 9(3): 2030-2041. [12] Saboori H, Mehrjerdi H, Jadid S.Mobile battery storage modeling and normal-emergency operation in coupled distribution-transportation networks[J]. IEEE Transactions on Sustainable Energy, 2022, 13(4): 2226-2238. [13] Alizadeh M, Jafari-Nokandi M.A bi-level resilience-oriented islanding framework for an active distribution network incorporating electric vehicles parking lots[J]. Electric Power Systems Research, 2023, 218: 109233. [14] 孙科, 陈文钢, 陈佳佳, 等. 基于电动汽车的极端场景多微电网韧性提升策略研究[J]. 电力系统保护与控制, 2023, 51(24): 53-65. Sun Ke, Chen Wengang, Chen Jiajia, et al.A resilience enhancement strategy for multi-microgrid in extreme scenarios based on electric vehicles[J]. Power System Protection and Control, 2023, 51(24): 53-65. [15] 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. [16] Lei Shunbo, Chen Chen, Li Yupeng, et al.Resilient disaster recovery logistics of distribution systems: co-optimize service restoration with repair crew and mobile power source dispatch[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6187-6202. [17] Arif A, Ma Shanshan, Wang Zhaoyu, et al.Optimizing service restoration in distribution systems with uncertain repair time and demand[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6828-6838. [18] 徐岩, 张荟, 马天祥, 等. 含分布式电源的配电网故障紧急恢复与抢修协调优化策略[J]. 电力系统自动化, 2021, 45(22): 38-46. Xu Yan, Zhang Hui, Ma Tianxiang, et al.Coordinated optimization strategy of fault emergency recovery and repair for distribution network with distributed generators[J]. Automation of Electric Power Systems, 2021, 45(22): 38-46. [19] 刘玉玲, 张峰, 张刚, 等. 考虑分布式电源的配电网灾中-灾后两阶段协同韧性恢复决策[J]. 电力系统自动化, 2024, 48(2): 48-59. Liu Yuling, Zhang Feng, Zhang Gang, et al.In-and post-disaster two-stage coordinated resilience recovery decision of distribution network considering distributed generators[J]. Automation of Electric Power Systems, 2024, 48(2): 48-59. [20] 王伟, 何雨霏, 谢开贵, 等. 面向电网灾后恢复过程的移动应急资源分体式调度方法[J]. 中国电机工程学报, 2024, 44(8): 3046-3059. Wang Wei, He Yufei, Xie Kaigui, et al.Component-separating scheduling method of mobile emergency resources oriented to post-disaster restoration of power system[J]. Proceedings of the CSEE, 2024, 44(8): 3046-3059. [21] 张亚超, 谢仕炜. 面向配电网弹性提升的多时间尺度恢复策略协调优化框架[J]. 电力系统自动化, 2021, 45(18): 28-34. Zhang Yachao, Xie Shiwei.Coordinated optimization framework of multi-timescale restoration strategies for resilience enhancement of distribution networks[J]. Automation of Electric Power Systems, 2021, 45(18): 28-34. [22] Lei Shunbo, Chen Chen, Song Yue, et al.Radiality constraints for resilient reconfiguration of distribution systems: formulation and application to microgrid formation[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 3944-3956. [23] 彭佳盛, 文云峰, 梁晓锐, 等. 基于弹性系数的配电网重构与故障抢修协同优化[J]. 电力系统自动化, 2025, 49(6): 88-95. Peng Jiasheng, Wen Yunfeng, Liang Xiaorui, et al.Resilience coefficient based collaborative optimization of distribution network reconfiguration and fault repair[J]. Automation of Electric Power Systems, 2025, 49(6): 88-95. [24] Pang Kaiyuan, Wang Chongyu, Hatziargyriou N D, et al.Dynamic restoration of active distribution networks by coordinated repair crew dispatch and cold load pickup[J]. IEEE Transactions on Power Systems, 2024, 39(2): 4699-4713. [25] Su Jinshun, Mehrani S, Dehghanian P, et al.Quasi second-order stochastic dominance model for balancing wildfire risks and power outages due to proactive public safety de-energizations[J]. IEEE Transactions on Power Systems, 2024, 39(2): 2528-2542. [26] Qiu Dawei, Wang Yi, Wang Jianhong, et al.Resilience-oriented coordination of networked microgrids: a shapley Q-value learning approach[J]. IEEE Transactions on Power Systems, 2024, 39(2): 3401-3416. [27] 姜云鹏, 任洲洋, 李秋燕, 等. 考虑多灵活性资源协调调度的配电网新能源消纳策略[J]. 电工技术学报, 2022, 37(7): 1820-1835. Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835. [28] Cao Xiaoyu, Cao Tianxiang, Xu Zhanbo, et al.Resilience constrained scheduling of mobile emergency resources in electricity-hydrogen distribution network[J]. IEEE Transactions on Sustainable Energy, 2023, 14(2): 1269-1284. [29] 陈韵含, 许寅, 王颖, 等. 考虑潜在恢复需求的城市配电网移动应急资源灾前布点[J]. 电力系统自动化, 2023, 47(14): 105-113. Chen Yunhan, Xu Yin, Wang Ying, et al.Pre-disaster positioning of mobile emergency resources for urban distribution network considering potential restoration demand[J]. Automation of Electric Power Systems, 2023, 47(14): 105-113. [30] 陈朝旭, 张亚超, 朱蜀, 等. 考虑多电解槽多工况组合运行的电-氢-热综合能源系统优化调度[J]. 电网技术, 2025, 49(2): 542-551. Chen Zhaoxu, Zhang Yachao, Zhu Shu, et al.Optimal scheduling of electricity-hydrogen-heat integrated energy system considering combined operation of multi-electrolyzers under multiple conditions[J]. Power System Technology, 2025, 49(2): 542-551.
2025, Vol. 40 
