Coordinated Power Restoration Strategy of Electric Vehicles and Emergency Power Supply Vehicles Considering Multi-Temporal Domains

XU Yanchun, LI Fang, XI Lei, ZHANG Tao, WANG Lingyun, MI Lu

Electric Power Construction ›› 2026, Vol. 47 ›› Issue (8) : 119-139.

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Electric Power Construction
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Electric Power Construction ›› 2026, Vol. 47 ›› Issue (8) : 119-139. DOI: 10.12204/j.issn.1000-7229.2026.08.009
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Coordinated Power Restoration Strategy of Electric Vehicles and Emergency Power Supply Vehicles Considering Multi-Temporal Domains

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Abstract

[Objective] To address the problem of critical load power restoration after distribution network faults， a coordinated power restoration strategy integrating electric vehicles （EVs） and emergency power supply vehicles （EPSVs） across multiple temporal domains is proposed. [Methods] A multi-temporal EV equivalent power source model is established to characterize the available power capacity of EV fleets under summer， winter， workday， and holiday scenarios. A load outage loss assessment model considering load importance， capacity， and outage duration is developed. On this basis， a two-stage EV-EPSV coordinated scheduling model is constructed and solved using genetic algorithm （GA） and adaptive large neighborhood search （ALNS）. [Results] Simulation results show that， compared with the EV-only scheme， the EV+EPSV heuristic scheme achieves a load outage loss saving rate of approximately 9.51%-16.19%， while the proposed coordinated scheme achieves a load outage loss saving rate of approximately 34.49%-70.36%. Under the same coordinated restoration framework， ALNS outperforms local search （LS） with a saving rate of approximately 15.97%-49.18%. [Conclusions] The proposed strategy can exploit the temporal complementarity between the rapid response of EVs and the mobile compensation capability of EPSVs， effectively reducing load outage losses and improving power restoration efficiency after distribution network faults.

Key words

electric vehicle / emergency power supply vehicle / multi-temporal discharging model / load outage assessment / power supply restoration

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XU Yanchun , LI Fang , XI Lei , et al . Coordinated Power Restoration Strategy of Electric Vehicles and Emergency Power Supply Vehicles Considering Multi-Temporal Domains[J]. Electric Power Construction. 2026, 47(8): 119-139 https://doi.org/10.12204/j.issn.1000-7229.2026.08.009

附录A

表A1 各区域负荷的失电损失值

Table A1 Load-loss cost of various regional load

失电持续时间/min 失电损失/（元·kW⁻¹）
住宅区负荷 工作区负荷 购物区负荷
0 0 0 0
1 0.028 12.073 9.995
20 1.470 21.774 38.119
60 5.292 51.045 104.363
120 11.023 65.118 203.729
240 36.453 84.872 382.565
480 78.002 116.259 770.715
1440 244.199 241.808 979.734
表A2 各类区域负荷重要度参量

Table A2 Importance parameters of various regional load

负荷区域类型 εk，1 εk，2 εk，3
住宅区 1 1 0
工作区 1 4 1
购物区 2 3 2
表A3 各类区域负荷的典型负荷容量

Table A3 Typical load capacity of various regional load

负荷区域 负荷容量/MW
住宅区 10.0
工作区 17.5
购物区 42.0

附录B

图B1 4种场景驻留时长热力图

Fig. B1 Heatmap of dwelling time across four scenarios

附录C

表C1 夏季场景EV分配明细

Table C1 EV allocation under summer scenario

EV所在供电节点 受电负荷 EV所在供电节点 受电负荷 EV所在供电节点 受电负荷
1 L1 12 L2 23 L1
2 L1 13 L1 24 L2
3 L3 14 L2 25 L3
4 L1 15 L1 26 L3
5 L2 16 L2 27 L2
6 L1 17 L1 28 L3
7 L2 18 L2 29 L3
8 L2 19 L1 30 L1
9 L2 20 L1 31 L1
10 L2 21 L1 32 L1
11 L2 22 L1 33 L3
表C2 冬季场景EV分配明细

Table C2 EV allocation under winter scenario

EV所在供电节点 受电负荷 EV所在供电节点 受电负荷 EV所在供电节点 受电负荷
1 L2 12 L2 23 L1
2 L3 13 L2 24 L1
3 L1 14 L2 25 L1
4 L1 15 L1 26 L1
5 L2 16 L2 27 L1
6 L2 17 L2 28 L1
7 L2 18 L1 29 L1
8 L1 19 L1 30 L1
9 L3 20 L1 31 L1
10 L2 21 L1 32 L1
11 L1 22 L2 33 L1
表C3 节假日场景EV分配明细

Table C3 EV allocation under holiday scenario

EV所在供电节点 受电负荷 EV所在供电节点 受电负荷 EV所在供电节点 受电负荷
1 L1 12 L2 23 L3
2 L1 13 L3 24 L1
3 L1 14 L1 25 L2
4 L2 15 L2 26 L3
5 L1 16 L1 27 L1
6 L1 17 L2 28 L3
7 L1 18 L1 29 L2
8 L3 19 L1 30 L3
9 L3 20 L3 31 L1
10 L3 21 L1 32 L3
11 L2 22 L1 33 L1
表C4 工作日场景EV分配明细

Table C4 EV allocation under workday scenario

EV所在供电节点 受电负荷 EV所在供电节点 受电负荷 EV所在供电节点 受电负荷
1 L1 12 L2 23 L2
2 L3 13 L2 24 L2
3 L1 14 L2 25 L2
4 L3 15 L3 26 L2
5 L3 16 L2 27 L2
6 L3 17 L3 28 L2
7 L3 18 L2 29 L2
8 L2 19 L1 30 L2
9 L2 20 L2 31 L2
10 L2 21 L3 32 L2
11 L2 22 L3 33 L2
表C5 不同场景下EPSV分配明细

Table C5 EPSV allocation under different scenarios

场景 受电负荷 供电车数量/辆 供电车编号
夏季 L1 9 1，2，3，4，5，6，7，8，9
L2 16 10，11，12，13，14，15，16，17，18，19，20，21，22，23，24，25
L3 5 26，27，28，29，30
冬季 L1 19 1，2，3，4，5，6，7，8，9，10，11，12，13，14，15，16，17，18，19
L2 8 20，21，22，23，24，25，26，27
L3 3 28，29，30
节假日 L1 5 1，2，3，4，5
L2 6 6，7，8，9，10，11
L3 19 12，13，14，15，16，17，18，19，20，21，22，23，24，25，26，27，28，29，30
工作日 L1 5 1，2，3，4，5
L2 20 6，7，8，9，10，11，12，13，14，15，16，17，18，19，20，21，22，23，24，25
L3 5 26，27，28，29，30
图C1 冬季场景下住宅区失电损失对比

Fig. C1 Comparison of power outage losses in residential districts under winter scenario

图C2 冬季场景下工作区失电损失对比

Fig. C2 Comparison of power outage losses in workplace districts under winter scenario

图C3 冬季场景下购物区失电损失对比

Fig. C3 Comparison of power outage losses in shopping districts under winter scenario

图C4 节假日场景下住宅区失电损失对比

Fig. C4 Comparison of power outage losses in residential districts under holiday scenario

图C5 节假日场景下工作区失电损失对比

Fig. C5 Comparison of power outage losses in workplace districts under holiday scenario

图C6 节假日场景下购物区失电损失对比

Fig. C6 Comparison of power outage losses in shopping districts under holiday scenario

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https://doi.org/10.3969/j.issn.1009-0134.2025.07.008
Cited in this article [1] Abstract

Aiming at the aviation complex rotary parts manufacturing workshop in the material distribution often faced by the material supply is not timely， difficult path planning and other difficult problems， analysis of the workshop material distribution characteristics and constraints， with a time window of the vehicle path optimization problem as the basic model， to minimize the integrated trolley call number， the total distance travelled， the delivery time penalty cost of the distribution cost as the optimization objective， to build the material distribution path planning model， design a hybrid adaptive large neighborhood search genetic algorithm to solve the problem. A hybrid adaptive large neighborhood search genetic algorithm is designed to solve the problem， and the actual shortest feasible path between two workstations is obtained in advance considering the complexity of the logistics channel in the workshop， and then the distribution path planning between workstations is carried out. The optimization effect and performance of the proposed algorithm on different scale distribution problems are evaluated through the validation and comparative analysis of the actual cases in the workshop and the classical standard cases.
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Footnotes

利益冲突声明（Conflict of Interests） 所有作者声明不存在利益冲突。

作者贡献声明（Authors' Contributions） 徐艳春确定研究对象范围、收集数据，李芳进行对比实验、文献调研与整理、起草论文，席磊完成实验并分析数据，张涛参与论文修订和绘制图谱，王凌云参与论文写作和修订，MI Lu修订论文与审核论文。所有作者均阅读并同意了论文终稿内容。

Funding

National Natural Science Foundation of China(52477104)
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