Optimization of Mobile Power Resource Allocation and Scheduling for Multi-point Emergency Response

doi:10.16182/j.issn1004731x.joss.26-0275

Abstract

Abstract:

In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rationalallocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based on the allocation results to generate the specific sequence of the scheduling scheme. Simulation results show that, under resource-constrained scenarios, the proposed method can satisfy minimum guarantee requirements and achieve prioritized supply for high-priority demand points. Compared with the distance-based greedy assignment (DGA), it reduces the weighted shortage by 16.3%, average scheduling distance by 5.04%, and total scheduling distance by 5.1%. The model can adjust the scale of resource utilization, spatial scheduling range, and demand guarantee level according to objective preferences and the degree of supply-demand conditions.

Key words: extreme event, emergency power supply, mobile power resource, resource allocation, scheduling

CLC Number:

TP391.9

Ma Ding, Zhang Tao, Jiang Bo, Liu Yajie, Li Shuijia. Optimization of Mobile Power Resource Allocation and Scheduling for Multi-point Emergency Response[J]. Journal of System Simulation, 2026, 38(10): 2964-2979.

Figures/Tables 16

Table 1

Fig. 1

Table 2

Table 3

Table 4

Table 5

Table 6

Results of relevant evaluation indicators

$Q 1$	$Q 2$	$Q 3$	$Q 4$	$Q 5$	$Q a v g$	$θ$	U
0.999	0.999	0.998	0.424	0.995	0.883	0.712	1.000

Table 6

Fig. 2

Fig. 3

Fig. 4

Table 7

Comparison of result indicators for different methods

指标	本文方法	DGA
$Q a v g$	0.883	0.864
$θ$	0.712	0.707
$U$	1	1
平均调度距离/km	6.97	7.34
总调度距离/km	1 393.16	1 468.05
加权短缺量/(kWˑh)	579.674	692.588

Table 7

Table 8

Table 9

Comparison of result indicators under different weighting coefficients

方案	$Q a v g$	$θ$	$U$	平均调度距离/km	总调度距离/km
I	0.885	0.712	1	6.98	1 396.08
II	0.883	0.712	1	6.97	1 393.16
III	0.719	0.488	0.365	6.18	451.48

Table 9

Fig. 5

Table 10

Comparison of result indicators under different supply-demand levels

场景	$Q a v g$	$θ$	$U$	平均调度距离/km	总调度距离/km
I	0.883	0.712	1.000	6.97	1 393.16
II	0.978	0.947	1.000	7.27	1 453.95
III	0.999	1.012	0.400	6.85	547.75

Table 10

Fig. 6

References 27

[1]	刘文霞, 刘佳怡, 万海洋, 等. 面向多类型极端天气的新型电力系统规划方案全场景风险评估[J]. 电力系统自动化, 2025, 49(4): 65-78.
	Liu Wenxia, Liu Jiayi, Wan Haiyang, et al. Full-scenario Risk Assessment of New Power System Planning Scheme for Multiple Types of Extreme Weather[J]. Automation of Electric Power Systems, 2025, 49(4): 65-78.
[2]	Sayarshad H R. Securing Power Grids and Charging Infrastructure: Cyberattack Resilience and Vehicle-to-grid Integration[J]. Journal of Transport Geography, 2025, 126: 104231.
[3]	鞠冠章, 王靖然, 崔琛, 等. 极端天气事件对新能源发电和电网运行影响研究[J]. 智慧电力, 2022, 50(11): 77-83.
	Ju Guanzhang, Wang Jingran, Cui Chen, et al. Impact of Extreme Weather Events on New Energy Power Generation and Power Grid Operation[J]. Smart Power, 2022, 50(11): 77-83.
[4]	Dani Antonini Bromberger, Vinicius Jacques Garcia, Stéfane Dias Rodrigues, et al. A Taxonomy for Extreme Weather Events and Their Impacts on Power Systems: A Comprehensive Review of Classification, Effects, and Mitigation Strategies[J]. Renewable and Sustainable Energy Reviews, 2026, 227: 116498.
[5]	Liu Hanchen, Wang Chong, Ju Ping, et al. A Sequentially Preventive Model Enhancing Power System Resilience against Extreme-weather-triggered Failures[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111945.
[6]	Hamdare S, Kaiwartya O, Aljaidi Mohammad, et al. Cybersecurity Risk Analysis of Electric Vehicles Charging Stations[J]. Sensors, 2023, 23(15): 6716.
[7]	陈朔, 胡昊, 方慧敏, 等. 基于虚拟储能的电动汽车充放电调度策略及激励评价[J]. 系统仿真学报, 2025, 37(3): 732-741.
	Chen Shuo, Hu Hao, Fang Huimin, et al. Electric Vehicle Dispatching Strategy and Incentive Evaluation Based on Virtual Energy Storage[J]. Journal of System Simulation, 2025, 37(3): 732-741.
[8]	Kempton W, Tomić Jasna. Vehicle-to-grid Power Implementation: From Stabilizing the Grid to Supporting Large-scale Renewable Energy[J]. Journal of Power Sources, 2005, 144(1): 280-294.
[9]	Liu Ke, Liu Yanli, Si Gang, et al. Punctual V2G Scheduling Circle: Evaluate and Enhance Transfer V2G Capability Through Adaptive Redistribution[J]. Applied Energy, 2025, 383: 125290.
[10]	Heshmati Yazdan, Saboori Hedayat. Exploiting Implicit Storage Capacity of Residential EVs for Smart Distribution Networks Restoration[J]. Sustainable Cities and Society, 2025, 118: 106070.
[11]	Wang Tingwang, Zhao Qi, Hu Shuai, et al. Post-event Electric Taxis Dispatch for Enhancing Resilience of Distribution Systems[J]. Energy Reports, 2022, 8(S5): 8-19.
[12]	于仲安, 肖宏亮, 夏强威, 等. 基于V2G模式下电动汽车参与的微电网优化调度仿真研究[J]. 系统仿真学报, 2025, 37(6): 1412-1426.
	Yu Zhongan, Xiao Hongliang, Xia Qiangwei, et al. Simulation Study on Optimizing Microgrid Scheduling with Electric Vehicle Participation Under V2G Mode[J]. Journal of System Simulation, 2025, 37(6): 1412-1426.
[13]	Yin Wanjun, Jia Leilei, Ji Jianbo. Energy Optimal Scheduling Strategy Considering V2G Characteristics of Electric Vehicle[J]. Energy, 2024, 294: 130967.
[14]	Zhang Qianzhi, Yan Jinyue, Gao H O, et al. A Systematic Review on Power Systems Planning and Operations Management with Grid Integration of Transportation Electrification at Scale[J]. Advances in Applied Energy, 2023, 11: 100147.
[15]	Hakam Youness, Gaga Ahmed, Elhadadi Benachir. Exploring the State of Electric Vehicles: An Evidence-based Examination of Current and Future Electric Vehicle Technologies and Smart Charging Stations[J]. Energy Reports, 2024, 11: 4102-4114.
[16]	于振, 张泽浩, 叶宇剑, 等. 基于用户停电损失值的移动应急电源调度决策方法[J]. 供用电, 2025, 42(5): 3-9, 22.
	Yu Zhen, Zhang Zehao, Ye Yujian, et al. Mobile Emergency Power Dispatching Decision-making Method based on Customer Outage Loss Value[J]. Distribution & Utilization, 2025, 42(5): 3-9, 22.
[17]	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.
[18]	Zhao Shihao, Li Kang, Yin Mingjia, et al. Transportable Energy Storage Assisted Post-disaster Restoration of Distribution Networks with Renewable Generations[J]. Energy, 2024, 295: 131105.
[19]	Xu Ruipeng, Zhang Cuo, Zhang Daming, et al. Adaptive Robust Load Restoration via Coordinating Distribution Network Reconfiguration and Mobile Energy Storage[J]. IEEE Transactions on Smart Grid, 2024, 15(6): 5485-5499.
[20]	Zhang Xi, Dong Zihang, Huangfu Fenyu, et al. Strategic Dispatch of Electric Buses for Resilience Enhancement of Urban Energy Systems[J]. Applied Energy, 2024, 361: 122897.
[21]	冯飞波, 闫兴德, 郑宝强, 等. 蓄电-氢储混合储能系统的配电网双层优化[J]. 系统仿真学报, 2022, 34(7): 1405-1416.
	Feng Feibo, Yan Xingde, Zheng Baoqiang, et al. Bi-level Optimization of Distribution Network for Hybrid Energy Storage System of Storage Battery and Hydrogen Storage[J]. Journal of System Simulation, 2022, 34(7): 1405-1416.
[22]	Kavousi-Fard A, Wang Mengqi, Su Wencong. Stochastic Resilient Post-hurricane Power System Recovery Based on Mobile Emergency Resources and Reconfigurable Networked Microgrids[J]. IEEE Access, 2018, 6: 72311-72326.
[23]	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.
[24]	刘子裕, 刘达夫, 杨祺铭, 等. 风涝复合灾害下配电网恢复资源灾前部署及灾后调度方法[J]. 电力系统自动化, 2025, 49(17): 154-164.
	Liu Ziyu, Liu Dafu, Yang Qiming, et al. Pre-disaster Deployment and Post-disaster Dispatching Method for Distribution Network Restoration Resources Under Compound Typhoon and Waterlog Disaster[J]. Automation of Electric Power Systems, 2025, 49(17): 154-164.
[25]	Xu Jie, Huang Yuping. The Short-term Optimal Resource Allocation Approach for Electric Vehicles and V2G Service Stations[J]. Applied Energy, 2022, 319: 119200.
[26]	Algafri Mohammed, Alghazi Anas, Almoghathawi Yasser, et al. Smart City Charging Station Allocation for Electric Vehicles Using Analytic Hierarchy Process and Multiobjective Goal-programming[J]. Applied Energy, 2024, 372: 123775.
[27]	He Zhiye, Tian Yuning, Miao Haoyu, et al. Electric Vehicle Routing Problem with Capacitated Charging Stations and Dynamic Electricity Prices[J]. IEEE Internet of Things Journal, 2025, 12(13): 23763-23772.

类型	主要优点	运行与供电特性	建模考虑
电动汽车	数量多、机动性强、空间分布广	单车电池容量较小，放电效率较高，行驶速度快，但续航里程有限	对行驶电量消耗和可调度容量建模
电动公交车	电池容量大，单车供电能力强	车身尺寸大、行驶速度较慢、对道路条件敏感	建模时考虑行驶能耗
移动储能车	专用于储能与应急供电，可提供稳定支撑	储能电池系统与驱动系统独立	建模时不考虑行驶能耗，考虑其与接口适配关系
应急电源车	具备稳定的发电能力和大功率输出	车载发电机供电，发电能力与车辆行驶能耗独立	不考虑电池约束，重点刻画可供电时间和接口类型

需求点	电力需求/(kWˑh)	重要程度	最低需求满足率/%
1	5 000	3	30
2	8 000	2	40
3	500	4	50
4	15 000	1	30
5	1 500	5	60

类型	电池容量或等效电量/(kWˑh)	百公里耗电量/(kWˑh)	平均速度/(km/h)
纯电动汽车	48, 53.4, 62.5, 78.4, 94.3	11,11.2,12.3	40
增程式电动汽车	37, 53.4, 62.8	13.2,17.9	40
电动公交车	200.296, 255, 310.665	91,94,97	20
移动储能车	313, 626, 516	/	30
应急电源车	800	/	30

接口类型	纯电动汽车	增程式电动汽车	电动公交车	移动储能车	应急电源车
国标直流接口(m=1)	√	√	√	√	×
国标交流接口(m=2)	√	√	×	×	×
工业标准接口(m=3)	×	×	×	×	√

需求点	电动汽车	电动公交车	移动储能车	应急电源车	短缺量/（kW·h）
D1	[1,3,5-6,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,67,85,87,88,89,90,91,92,151,152,153,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,172,173,174,175,176,177,178,179,180]	[132,133,134,135,137,138,139,140,141,142,144,147,150]	/	/	3
D2	[2,4,7-27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,62,64,65,71,72,73,74,75,76,77,169,171]	[109,112,113,114,116,117,120,124,125,130,131,143,145,146,148]	[182,184,185,186,187]	[192,193]	3.5
D3	[63,68,70,97,99,198,199]	[129]	/	/	0.9
D4	[69,79,80,81,82,83,84,86,93,94,95,100,154,196,197,200]	[101,102,103,104,105,107,119,121,122,123,126,128,136,149]	[181,183,188,189,190]	[191,194,195]	8 636.2
D5	[66,78,96,98]	[106,108,110,111,115,118,127]	/	/	7