Exploration and research on elastic enhancement strategies for distribution networks using electric vehicle exchange stations during disconnection events

doi:10.19799/j.cnki.2095-4239.2024.0265

Abstract

Abstract:

Frequent natural disasters in recent years have posed significant threats to the safe operation of distribution power systems. The energy storage batteries in electric vehicle exchange stations, known for their easy transportability, show great promise for emergency power supply scenarios. In order to enhance the resilience of distribution networks, this paper proposes an operational scheduling method that utilizes electric vehicles to transport energy storage batteries from exchange stations. The study begins by developing an optimization model for the distribution network that integrates the EV transportation traffic network, aiming to minimize system operating costs during emergencies. This model considers factors such as load loss cost, generation cost, and transportation cost. Then, the nonlinear parts of the optimization problem are analyzed, and the problem is then reformulated into a mixed-integer optimization problem, making it easier to solve. The proposed strategy is verified through case studies demonstrating its effectiveness under certain boundary conditions. This method enables the intelligent dispatch of swappable electric vehicles, effectively transferring energy storage batteries from areas with sufficient energy to those experiencing shortages. This approach not only reduces the operating costs of the power system during emergencies but also significantly enhances the overall resilience of the power system's operations.

Key words: contingency scenarios, power exchange stations, mobile energy storage, power system resilience, linear mixed integer optimization

CLC Number:

TM 73

Tianao ZHANG, Yongchong CHEN, Yixian YANG, Hong QI, Gang WU. Exploration and research on elastic enhancement strategies for distribution networks using electric vehicle exchange stations during disconnection events[J]. Energy Storage Science and Technology, 2024, 13(11): 3961-3970.

Figures/Tables 14

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Table 1

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Table 1

Constant parameter table of control strategy"

参数	数值	参数	数值
优化间隔 $Δ T$	1 h	负荷损失成本 $W 1 / W 2 / W 3$	50/50/50(元/kW·h)
储能电池放电效率 $η$	0.98	储能电池总数量N_bat	21
储能电池最大容量 $E m d i s c h$	150 kW·h	储能电池功率渗透率k	0.4
储能电池最大输出功率 $P m m a x, ∀ m ∈ M b a t$	150 kW	电压型电源最大输出功率 $P q, v o l m a x, ∀ q ∈ M p$	300 kW
储能电池控制上下限SOC_min/SOC_max	0.10/0.98	—	—

Table 1

Table 2

Constant parameter table of SOC"

参数	数值	参数	数值
$S O C 10 / S O C 20 / S O C 30 / S O C 40$	0.95/0.85/0.92/0.95	$S O C 50 / S O C 60 / S O C 70 / S O C 80$	0.85/0.92/0.92/0.85
$S O C 90 / S O C 100 / S O C 110 / S O C 120$	0.75/0.95/0.85/0.75	$S O C 130 / S O C 140 / S O C 150 / S O C 160$	0.95/0.95/0.96/0.85
$S O C 170 / S O C 180 / S O C 190 / S O C 200$	0.97/0.96/0.85/0.97	$S O C 210$	0.97

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Table 3

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参数	数值
C₂₃/ C₃₁/ C₃₂	45/31/50(元/次)
C₁₂/ C₁₃/ C₂₁	30/28/35(元/次)
t₂₃/ t₃₁/ t₃₂	0.40/0.28/0.45(小时)
t₁₂/ t₁₃/ t₂₁	0.10/0.30/0.12(小时)
V₂₃/ V₃₁/ V₃₂ V₁₂/ V₁₃/ V₂₁	0.95/0.65/1(%/次) 0.8/0.6/0.85(%/次)

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