Active Support Strategies for Power Supply in Extreme Scenarios with Synergies Between Idle and Emergency Resources in the City
Abstract
:1. Introduction
2. Modeling of Urban Idle and Mobile Emergency Resources
2.1. Dispatch Model for Participation of Urban Idle Resources in Emergency Power Supply
2.1.1. Simulation of Idle EV Data in Public Parking Lots
2.1.2. Emergency Dispatch Model for Idle EVs in Public Parking Lots
2.2. Emergency Power Vehicle Scheduling Model Considering Road Congestion
2.2.1. Roadway Resistance Modeling Considering Real-Time Traffic
2.2.2. Emergency Power Vehicle Dispatch Model
2.3. Emergency Response Team Model
2.3.1. Repair Team Maintenance Status Constraints
2.3.2. Spatio-Temporal Scheduling Constraints for Emergency Teams
3. Multi-Source Synergistic Power Supply Active Support Strategy
3.1. Objective Function
3.2. Supporting Power Supply Constraints
3.2.1. Emergency Power Vehicle Output Constraints
3.2.2. Idle Electric Vehicles’ Output
3.2.3. Distributed Power Output
3.2.4. Load Reduction Constraints
3.2.5. Radial Constraints on the Distribution Network
3.2.6. Distribution Network Current Constraints
3.3. Second-Order Cone Relaxation
4. Case Analysis
4.1. Parameterization
4.2. Analysis of Results
4.2.1. Economic Analysis
4.2.2. Mobile Resource Scheduling Analysis
4.2.3. Analysis of Emergency Resource Capacity
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
EPSVs | Emergency Power Supply Vehicles |
EV | Electric Vehicle |
SOC | State of Charge |
μ | Mean of the distribution function |
σ | Standard deviation of the distribution function |
λ | Shape parameters of the distribution function |
k | Proportionality parameter of a distribution function |
tst | Start time of the distribution network fault |
ted | End time of the distribution network fault |
Charging identifiers of the EV at time t | |
Discharging identifiers of the EV at time t | |
EV’s charging active at time t | |
EV’s discharging active at time t | |
Maximum EVs power | |
Minimum EVs power | |
Ωp | Set of idle EVs that can be involved in the support |
S(t) | Road congestion degree at time t |
Wij | Roadway weight |
Lij | Road section impedance |
Ni | Intersection impedance |
α | Coefficients for adjusting the road section resistance |
β | Coefficients for adjusting the road section resistance |
b | Length of the complete cycle of the traffic signal |
c | Proportion of the green effective time to the signal cycle |
d | Frequency of vehicle arrivals on the roadway |
Scheduling variables of the kth EPSVs at the initial moment t0 | |
n | Set of EPSVs |
Initial position of the kth EPSVs | |
Ωd | Set of urban road network nodes |
T | Power supply restoration time |
Nr | Set of faulty lines |
D | Upper limit of the number of lines to be repaired |
uij | Line Connection Status Variables |
fPV | Cost of distributed PV output |
fEPSV | Cost of supplying power to EPSV |
fEV | Cost of subsidizing unused EV output in public parking lots |
Critical load reduction cost factor | |
Ordinary load reduction cost factor | |
λ1 | Distributed PV Output Subsidy Factor |
λ2 | Distributed EPSVs Output Subsidy Factor |
λ3 | EV Output Subsidy Factor for Public Parking Lots |
c(t) | Price of electricity |
Active charging power of EPSVs at time t | |
Active discharging power of EPSVs at time t | |
EPSVs Charge Marking at time t | |
EPSVs Discharge Marking at time t | |
Maximum EPSVs Discharge | |
Maximum EPSVs Charge | |
Electricity of the ith EPSVs at moment t | |
Minimum EPSVs Electricity at time t | |
Maximum EPSVs Electricity at time t | |
ηch | Charging identifiers of the EPSVs |
ηdis | Discharging identifiers of the EPSVs at time t |
EPSVs scheduling variable at time t | |
Active power after aggregation of the kth public parking lot at time t | |
Distributed PV Output at time t | |
Maximum Distributed PV Output | |
Minimum Distributed PV Output | |
Np | Public parking lot Collection |
Npv | Distributed PV Access Point Collection |
Nl | Load node |
Load reduction at node i at time t | |
Actual Load demand at node i at time t | |
Hjs | Virtual power flow |
Hij | Virtual power flow |
Nb | Number of distribution nodes |
NDG | Number of distributed power sources |
Square of node i voltage at time t | |
Square of node j voltage at time t | |
Square of line current ij at time t | |
Pij,t | Active power flowing through branches ij at time t |
Pki,t | Active power flowing through branches ki at time t |
Qij,t | Reactive power flowing through branches ij at time t |
Qki,t | Reactive power flowing through branches ki at time t |
Iij,t | Current flowing through the branch ij at time t |
Vi,t | Square of node voltage at time t |
rij | Resistance of branch ij |
xij | Reactance of branch ij |
References
- Xu, L.; Zeng, H.; Lin, N.; Yang, Y.; Guo, Q.; Poor, H.V. Entropic Value-at-Risk Constrained Optimal Power Flow Considering Distribution Network Outages During Extreme Events. IEEE Trans. Power Syst. 2025, 40, 1184–1187. [Google Scholar] [CrossRef]
- Lee, J.; Paal, S.G. Knowledge Transfer Predictive Models for Power Outage Caused by Various Types of Extreme Weather Events. In Proceedings of the IEEE International Conference on Big Data (BigData), Washington, DC, USA, 15–18 December 2024; pp. 8227–8229. [Google Scholar] [CrossRef]
- Deng, Y.; Jiang, W.; Hu, F.; Sun, K.; Yu, J. Resilience-Oriented Dynamic Distribution Network With Considering Recovery Ability of Distributed Resources. EEE J. Emerg. Sel. Top. Circuits Syst. 2022, 12, 149–160. [Google Scholar] [CrossRef]
- Chang, W.; Shou, G.; Liu, Y.; Guo, Z.; Hu, Y.; Liu, J. An efficient topology reconfiguration algorithm under targeted attacks and failures. In Proceedings of the NOMS 2018—2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, 23–27 April 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Kim, J.-E.; Hong, C.-H. A Study on Methodology of Digital-SOP System for Effective Disaster Response. In Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2), Casablanca, Morocco, 14–17 October 2019; pp. 741–746. [Google Scholar] [CrossRef]
- Hu, Y.; Dong, A. Research on Vehicle Resource Allocation Strategy Assisted by Idle Resources in Complex Urban Channel Environments. In Proceedings of the 2024 7th International Conference on Computer Information Science and Application Technology (CISAT), Hangzhou, China, 12–14 July 2024; pp. 1398–1402. [Google Scholar] [CrossRef]
- Tetik, A.F.; Yigit, H.; Erenoglu, A.K.; Erdinc, O.; Boynuegri, A.R. Optimizing Parking Lot Management with Mobile Energy Suppliers for Electric Vehicles. In Proceedings of the 2024 Global Energy Conference (GEC), Batman, Turkiye, 4–6 December 2024; pp. 6–11. [Google Scholar] [CrossRef]
- Gao, Z.; Wu, X.; Zhou, S.; Du, Q.; Peng, L.; Ji, Q. Dynamic Reconfiguration of Microgrids Considering Mobile Energy Storage System. In Proceedings of the 2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2), Hangzhou, China, 15–18 December 2023; pp. 5087–5092. [Google Scholar] [CrossRef]
- Huang, Z.; Sun, L.; Yi, K.; Jin, X. Cooperative Repair Scheduling Strategy of Distribution Systems and Traffic Roads. In Proceedings of the 2023 IEEE 6th International Electrical and Energy Conference (CIEEC), Hefei, China, 12–14 May 2023; pp. 960–965. [Google Scholar] [CrossRef]
- Keerthisinghe, C.; Ahumada-Paras, M.; Pozzo, L.D.; Kirschen, D.S.; Pontes, H.; Tatum, W.K.; Matos, M.A. PV-Battery Systems for Critical Loads During Emergencies: A Case Study from Puerto Rico After Hurricane Maria. IEEE Power Energy Mag. 2019, 17, 82–92. [Google Scholar] [CrossRef]
- Qi, Z.; Zhang, Y.; Li, S.; Gu, X. Research on Power System Restoration Strategy with Collaborative Participation of Multi-Pumped Storage and Wind Power. In Proceedings of the 2024 9th Asia Conference on Power and Electrical Engineering (ACPEE), Shanghai, China, 11–13 April 2024; pp. 1722–1726. [Google Scholar] [CrossRef]
- Gholami, A.; Aminifar, F.; Shahidehpour, M. Front Lines Against the Darkness: Enhancing the Resilience of the Electricity Grid Through Microgrid Facilities. IEEE Electrif. Mag. 2016, 4, 18–24. [Google Scholar] [CrossRef]
- Shi, J.; Chen, Y.; Xie, X.; Gao, Y. Two-stage Power Supply Restoration Strategy of Urban Distribution Network Based on Local Flexible Resources. In Proceedings of the 2024 9th Asia Conference on Power and Electrical Engineering (ACPEE), Shanghai, China, 11–13 April 2024; pp. 796–800. [Google Scholar] [CrossRef]
- Yang, Z.; Martí, A.; Chen, Y.; Martí, J.R. Optimal Resource Allocation to Enhance Power Grid Resilience Against Hurricanes. IEEE Trans. Power Syst. 2022, 38, 2621–2629. [Google Scholar] [CrossRef]
- Xu, Y.; Wang, Y.; He, J.; Su, M.; Ni, P. Resilience-Oriented Distribution System Restoration Considering Mobile Emergency Resource Dispatch in Transportation System. IEEE Access 2019, 7, 73899–73912. [Google Scholar] [CrossRef]
- Kim, J.; Dvorkin, Y. Enhancing Distribution System Resilience With Mobile Energy Storage and Microgrids. IEEE Trans. Smart Grid 2018, 10, 4996–5006. [Google Scholar] [CrossRef]
- Lei, S.; Chen, C.; Zhou, H.; Hou, Y. Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement. IEEE Trans. Smart Grid 2019, 10, 5650–5662. [Google Scholar] [CrossRef]
- Cain, S.R. Distinguishing between lognormal and Weibull distributions [time-to-failure data]. IEEE Trans. Reliab. 2002, 51, 32–38. [Google Scholar] [CrossRef]
- Anderson, C.L.; Davison, M. An aggregate Weibull approach for modeling short-term system generating capacity. IEEE Trans. Power Syst. 2005, 20, 1783–1789. [Google Scholar] [CrossRef]
- Yu, Q.; Hu, F.; Ye, Z.; Chen, C.; Sun, L.; Luo, Y. High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology. IEEE Trans. Intell. Transp. Syst. 2022, 23, 17530–17545. [Google Scholar] [CrossRef]
- Soofi, A.; Manshadi, F.S.D.; Liu, G.; Dai, R. A SOCP Relaxation for Cycle Constraints in the Optimal Power Flow Problem. IEEE Trans. Smart Grid 2021, 12, 1663–1673. [Google Scholar] [CrossRef]
- Wu, C.; Han, H.; Gao, S.; Liu, Y. Coordinated scheduling for multimicrogrid systems considering mobile energy storage characteristics of electric vehicles. IEEE Trans. Transp. Electrif. 2023, 9, 1775–1783. [Google Scholar] [CrossRef]
- Yao, S.; Wang, P.; Liu, X.; Zhang, H.; Zhao, T. Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration. IEEE Trans. Smart Grid 2020, 11, 1030–1043. [Google Scholar] [CrossRef]
- Bhat, P.K.; Wu, Z.; Chen, B. Long Trip Charging Planning of Battery Electric Vehicle Considering Vehicle Waiting Time Forecast at Fast Charging Stations: A Mixed-Integer Dynamic Programming Approach. IEEE Access 2025, 13, 52100–52113. [Google Scholar] [CrossRef]
- More Than 17,000 Vehicle Trips to Participate in Shenzhen to Start a Large-Scale Car Network Interaction Test. Xinhua News. Available online: https://www.news.cn/local/20250329/80dc0392658f4f09b144a327c8b3d50f/c.html (accessed on 29 March 2025).
Flow Situation | Fast-Track | Pass Through Normally | Crawl Through | Hard Pass |
---|---|---|---|---|
S(t) | [0,0.6] | (0.6,0.8] | (0.8,1.0] | (1.0,2.0] |
Category | Parameter | Value |
---|---|---|
EPSVs | Power supply capacity (kWh) | 800 [22] |
Discharge power (kW) | 300 [23] | |
initial position | 4, 17 | |
Subsidy coefficient | 1.5 | |
Number of units configured | 2 | |
Idle EV in public parking lots | Battery Pack Capacity(kWh) | 80 [24] |
Charge/discharge power(kW) | 60 [25] | |
Number of EVs in Parking Lot 1 | 12 | |
Number of EVs in Parking Lot 2 | 20 | |
Subsidy factor | 0.9 | |
Shape parameters of arrival and departure times k1, k2 | 0.9831, 4.665 | |
Scale parameter of arrival and departure time λ1, λ2 departure time | 16.8, 50 | |
Distributed Power | access location | {3, 11, 15, 28, 32} |
Subsidy factor | 0.6 |
Scenario | Load Loss Costs | Photovoltaic Dispatch Costs | Emergency Power Vehicle Output Costs | Public Parking Lot EV Output Subsidy | Total Cost |
---|---|---|---|---|---|
Case 1 | 62,475 | 1107.9 | - | - | 63,582.9 |
Case 2 | 44,361 | 1244.4 | 1930.2 | - | 47,535.6 |
Case 3 | 34,490 | 1461.6 | - | 1640.1 | 37,591.7 |
Case 4 | 15,668 | 1485.6 | 2171.9 | 2631.1 | 21,956.6 |
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Zhao, R.; Lu, J.; Chen, Y.; Gao, Y.; Li, M.; Wei, C.; Li, J. Active Support Strategies for Power Supply in Extreme Scenarios with Synergies Between Idle and Emergency Resources in the City. Energies 2025, 18, 1940. https://doi.org/10.3390/en18081940
Zhao R, Lu J, Chen Y, Gao Y, Li M, Wei C, Li J. Active Support Strategies for Power Supply in Extreme Scenarios with Synergies Between Idle and Emergency Resources in the City. Energies. 2025; 18(8):1940. https://doi.org/10.3390/en18081940
Chicago/Turabian StyleZhao, Ruifeng, Jiangang Lu, Yizhe Chen, Yifan Gao, Ming Li, Chengzhi Wei, and Junhao Li. 2025. "Active Support Strategies for Power Supply in Extreme Scenarios with Synergies Between Idle and Emergency Resources in the City" Energies 18, no. 8: 1940. https://doi.org/10.3390/en18081940
APA StyleZhao, R., Lu, J., Chen, Y., Gao, Y., Li, M., Wei, C., & Li, J. (2025). Active Support Strategies for Power Supply in Extreme Scenarios with Synergies Between Idle and Emergency Resources in the City. Energies, 18(8), 1940. https://doi.org/10.3390/en18081940