Disaster Resistance Enhancement Strategies for Distribution Networks Considering Configuration and Scheduling of Mobile Energy Storage

doi:10.11930/j.issn.1004-9649.202502064

Abstract

Abstract:

To mitigate the impact of high-penetration photovoltaic (PV) integration on power quality and enhance the reliability of power supply under extreme disasters, mobile energy storage (MES) with its greater flexibility and cost-effectiveness has been identified as one of the effective measures to improve the disaster resistance of distribution networks. This paper proposes a multi-objective optimization model for determining the configuration of MES under high-penetration PV integration scenarios, considering three key aspects including economic efficiency, vulnerability and MES capacity. Additionally, a two-stage resilience-enhancing optimization and scheduling model is developed for extreme disaster scenarios, which incorporates the initial deployment of separable MES and their emergency response dispatch. By dynamically scheduling the access locations and charge-discharge power of the separable MES, the proposed method ensures emergency power restoration under extreme disasters while maximizing the reuse value of MES during both normal and disaster conditions. Finally, case studies validate the effectiveness of the proposed method.

Key words: mobile energy storage, configuration, pre-deployment, fault recovery, resilience and disaster resistance enhancement

LIU Yongmei, WU Ming, LI Ying, YUAN Yuqi, XI Yanna, ZHAO Fengzhan. Disaster Resistance Enhancement Strategies for Distribution Networks Considering Configuration and Scheduling of Mobile Energy Storage[J]. Electric Power, 2025, 58(9): 10-22.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202502064

https://www.electricpower.com.cn/EN/Y2025/V58/I9/10

Figures/Tables 15

Fig.1 Process of MES configuration and scheduling strategies

Fig.2 Optimization configuration strategies for MES

Fig.3 Two-stage MES scheduling model before and after extreme disasters

Fig.4 Traffic grid schematic diagram of the distribution network

Fig.5 Topology diagram of the IEEE 33 system with distributed PV integration

Fig.6 Predicted output of PV

Table 1 Optimized configuration results of MES in different scenarios

参数	场景
参数	1	2	3
移动储能峰谷套利C₁/万元		2347.52	2407.77
电网缓建收益补贴C₂/万元		91.28	94.20
移动储能投资成本C₃/万元		1115	1145
移动储能运行成本C₄/万元		10.48	11.77
移动储能投资总效益f₁/万元		1313.32	1345.20
配电网脆弱性f₂	0.3220	0.2640	0.2250
移动储能总容量f₃/(MW·h)		3.725	3.955
移动储能模块投资数量n_MES/块		4	4
移动储能模车投资数量n_CAR/辆		2	2

Fig.7 Fault conditions of the IEEE 33-bus system

Fig.8 Load active power prediction curve

Table 2 Comparison of scheduling schemes for initial positions before a disaster

方案	移动储能车初始位置	开断支路	总成本/元
1	节点1、1	1-4、7-8、9-11、12-13、 14-16、29-30、31-32	3454
2	节点23、24	1-4、7-8、9-11、12-13、 14-16、29-30、31-32	3243

Fig.9 Islanding topology structure of the distribution network

Fig.10 Charge-discharge power and state of charge of the MES

Fig.11 The scheduling process of MES

Fig.12 Load recovery proportion and lost - load power in each time period

Table 3 Analysis of distribution network resilience enhancement strategies for three schemes

抗灾能力指标	方案
抗灾能力指标	1	2	3
负荷削减成本/元	6555	3802	2708
重要负荷恢复速度/(kW·h^–1)	108.81	110.65	111.38
重要负荷恢复率/%	97.29	98.93	99.59
负荷恢复率/%	87.42	91.08	92.62

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