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| A Service Restoration Method for Resilient Distribution Systems Considering Time-Varying Characteristic of Cold Load Pickup |
| Cai Sheng1, Xu Zhenqin1, Xie Yunyun1, Ding Bo2, Zhang Menglin3 |
1. School of Automation Nanjing University of Science and Technology Nanjing 210094 China;
2. State Grid Huai' an Power Supply Company Huai' an 223002 China;
3. School of Automation China University of Geosciences Wuhan 430074 China |
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Abstract When an outage occurs in the distribution system due to extreme disaster events, the distributed generators are used to form microgrids and restore critical loads, which is essential for reducing outage duration and enhancing the resilience of distribution systems. The existing work utilizes the constant load model when designing the service restoration (SR) schemes. However, the ignorance of current unbalance caused by load peak when picking up cold loads may threaten the security of microgrids. Recently, SR approaches considering the cold load pickup (CLPU) were presented to ensure the supply-demand balance when restoring the distribution systems, but most of them simply assumed the system is three-phase balanced. This was insufficient to ensure a successful restoration because the violation of current unbalance factor may lead to the cut-off of distributed generators in microgrids. To address this issue, this paper proposed a SR method for three-phase unbalanced distribution systems considering the time-varying characteristic of CLPU. By restricting the current unbalance factor during the SR process, the security and reliability of microgrids can be improved.
First, a time-dependent CLPU model was formulated when considering the relationship between CLPU characteristic and outage duration, and the sampling method was used to generate load demand profiles. Then, considering the constraints such as power balance constraint and current unbalance factor constraint, a SR model was formulated to maximize the amount of load restoration. Next, nonlinear constraints in the original model were linearized to facilitate the solution. The main differences between the proposed SR model and those in existing literature lie in the current unbalance factor constraints, by which the load pickup sequence at each phase is optimized to reduce the current unbalance of distributed generators. In addition, zone sectionalization, restoration paths, and load pickup sequences were jointly optimized to enhance the effectiveness of the microgrid-based SR scheme.
Simulation results on the modified IEEE 37 node distribution system show that, after the event strikes, two microgrids are sequentially formed to restore the critical loads. Specifically, the black-start units are used to crank the non-black start units, and the line switches are closed orderly to extend the microgrids. The total generation of power sources in one MG is equal to the total amount of load consumption for each time step. The comparison with the SR method using constant load model shows that, the consideration of CLPU can improve the security of microgrid by restricting the current unbalance factor. The comparison with the SR method using traditional delayed exponential-based CLPU model shows that, the proposed time-dependent CLPU model can reduce the conservation of SR schemes. Comparison of different operation methods shows that, the utilization rate of the distributed generator for method with/without considering optimal microgrid topology is 100% and 85.5%, respectively.
The following conclusions can be drawn from the simulation analysis: (1) Compared with traditional distribution system SR method, considering the CLPU characteristics can reduce the current unbalance factor, thereby enhancing the security and reliability of microgrids during the SR process. (2) The proposed model considers the relationship between CLPU characteristic and outage duration. Compared with the traditional CLPU model, a less conservative scheduling strategy can be obtained. (3) The proposed model jointly optimizes the network topology and restoration sequences. Therefore, more flexible MG topology can be obtained to satisfy the supply-and-demand balance and more critical loads can be supplied.
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Received: 04 December 2023
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[1] Perera A T D, Nik V M, Chen Deliang, et al. Quantifying the impacts of climate change and extreme climate events on energy systems[J]. Nature Energy, 2020, 5(2): 150-159.
[2] 别朝红, 林雁翎, 邱爱慈. 弹性电网及其恢复力的基本概念与研究展望[J]. 电力系统自动化, 2015, 39(22): 1-9.
Bie Zhaohong, Lin Yanling, Qiu Aici.Concept and research prospects of power system resilience[J]. Automation of Electric Power Systems, 2015, 39(22): 1-9.
[3] 陶然, 赵冬梅, 徐辰宇, 等. 考虑电-气-热-交通相互依存的城市能源系统韧性评估与提升方法[J]. 电工技术学报, 2023, 38(22): 6133-6149.
Tao Ran, Zhao Dongmei, Xu Chenyu.Resilience assessment and enhancement methods for urban energy system considering electricity-gas-heat-transport interdependy[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 6133-6149.
[4] 蔡胜, 谢云云, 张玉坪, 等. 考虑孤岛微电网建立过程功率冲击的弹性配电网主动预防调度[J]. 电工技术学报, 2023, 38(23): 6419-6432.
Cai Sheng, Xie Yunyun, Zhang Yuping, et al.Proactive scheduling of resilient distribution systems considering power impact during islanded microgrid formation process[J]. Transactions of China Electro-technical Society, 2023, 38(23): 6419-6432.
[5] 王晓卫, 康干坤, 梁振锋, 等. 考虑5G基站储能参与配电网供电恢复研究[J]. 电工技术学报, 2024, 39(11): 3539-3555.
Wang Xiaowei, Kang Qiankun, Liang Zhenfeng, et al.Distribution network restoration supply method considers 5G base station energy storage participa-tion[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3539-3555.
[6] 詹红霞, 肖竣文, 邓小勇, 等. 计及柔性负荷的高比例风光渗透下配电网孤岛划分策略[J]. 电力工程技术, 2022, 41(4): 108-116.
Zhan Hongxia, Xiao Junwen, Deng Xiaoyong, et al.Islanding strategy for distribution network with high proportion of wind/photovoltaic penetration considering flexible load[J]. Electric Power Engineering Technology, 2022, 41(4): 108-116.
[7] 齐郑, 张首魁, 李志, 等. 考虑时间尺度的含DG配电网故障动态恢复策略[J]. 电力系统保护与控制, 2017, 45(16): 31-38.
Qi Zheng, Zhang Shoukui, Li Zhi, et al.Dynamic service restoration strategy considering time scale for distribution network with DGs[J]. Power System Protection and Control, 2017, 45(16): 31-38.
[8] Che Liang, Shahidehpour M.Adaptive formation of microgrids with mobile emergency resources for critical service restoration in extreme conditions[J]. IEEE Transactions on Power Systems, 2019, 34(1): 742-753.
[9] Cai Sheng, Xie Yunyun, Wu Qiuwei, et al.Robust coordination of multiple power sources for sequential service restoration of distribution systems[J]. International Journal of Electrical Power & Energy Systems, 2021, 131: 107068.
[10] 梁远升, 徐真理, 李海锋, 等. 基于随机响应面法的配电网故障恢复全时段不确定性优化方法[J]. 中国电机工程学报, 2024, 44(23): 9200-9213.
Liang Yuansheng, Xu Zhenli, Li Haifeng, et al.A stochastic optimization method for fault recovery in distribution networks based on stochastic response surface method[J]. Proceeding of the CESS, 2024, 44(23): 9200-9213.
[11] 刘菲, 林超凡, 陈晨, 等. 考虑分布式新能源动态不确定性的配电网灾后时序负荷恢复方法[J]. 电力自动化设备, 2022, 42(7): 159-167.
Liu Fei, Lin Chaofan, Chen Chen, et al.Post-disaster time-series load restoration method for distribution network considering dynamic uncertainty of distributed renewable energy[J]. Electric Power Automation Equipment, 2022, 42(7): 159-167.
[12] Cai Sheng, Xie Yunyun, Wu Qiuwei, et al.Two-stage mobile emergency generator dispatch for sequential service restoration of microgrids in extreme conditions[J]. International Journal of Electrical Power & Energy Systems, 2023, 153: 109312.
[13] Zhang Qianzhi, Ma Zixiao, Zhu Yongli, et al.A two-level simulation-assisted sequential distribution system restoration model with frequency dynamics constraints[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 3835-3846.
[14] Cai Sheng, Xie Yunyun, Zhang Menglin, et al.A stochastic sequential service restoration model for distribution systems considering microgrid inter-connection[J]. IEEE Transactions on Smart Grid, 2024, 15(3): 2396-2409.
[15] Chen Bo, Ye Zhigang, Chen Chen, et al.Toward a MILP modeling framework for distribution system restoration[J]. IEEE Transactions on Power Systems, 2019, 34(3): 1749-1760.
[16] Arif A, Cui Bai, Wang Zhaoyu.Switching device-cognizant sequential distribution system restoration[J]. IEEE Transactions on Power Systems, 2022, 37(1): 317-329.
[17] 杨明, 翟鹤峰, 马嘉翼, 等. 计及分布式电源发电不平衡度约束的三相不对称配电网动态重构[J]. 中国电机工程学报, 2019, 39(12): 3486-3499.
Yang Ming, Zhai Hefeng, Ma Jiayi, et al.Dynamic reconfiguration of three-phase unbalanced distribution networks considering unbalanced operation constraint of distributed generation[J]. Proceedings of the CSEE, 2019, 39(12): 3486-3499.
[18] Wang Zeyu, Wang Jianhui, Chen Chen.A three-phase microgrid restoration model considering unbalanced operation of distributed generation[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3594-3604.
[19] Bassey O, Butler-Purry K L, Chen Bo. Dynamic modeling of sequential service restoration in islanded single master microgrids[J]. IEEE Transactions on Power Systems, 2020, 35(1): 202-214.
[20] Mohy-ud-din G, Muttaqi K M, Sutanto D. A hierarchical service restoration framework for unbalanced active distribution networks based on DSO and VPP coordination[J]. IEEE Transactions on Industry Applications, 2022, 58(2): 1756-1770.
[21] 刘志伟, 苗世洪, 杨炜晨, 等. 计及温度不确定性的配电网广义储能分层调控策略[J]. 电工技术学报, 2023, 38(21): 5794-5807.
Liu Zhiwei, Miao Shihong, Yang Weichen, et al.Generalized energy storage hierarchical regulation strategy for distribution network considering temperature uncertainty[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5794-5807.
[22] 张潼, 于鹤洋, 田江, 等. 基于非侵入式负荷辨识的聚合负荷需求响应能力在线评估[J]. 电力工程技术, 2020, 39(6): 19-25, 65.
Zhang Tong, Yu Heyang, Tian Jiang, et al.Online aggregation monitoring of low-voltage power load demand response capability based on non-intrusive load identification[J]. Electric Power Engineering Technology, 2020, 39(6): 19-25, 65.
[23] 邓荣楠, 宋梦, 高赐威, 等. 考虑用户负荷决策依赖特性的配电网灾后恢复方法对比分析[J]. 电工技术学报, 2024, 39(23): 7447-7462.
Deng Rongnan, Song Meng, Gao Ciwei, et al.Comparative analysis of distribution system load restoration considering decision-dependent behaviors of customers[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7447-7462.
[24] 丁江, 孙磊, 丁明. 计及冷负荷效应时变特性的含风储联合系统负荷恢复策略[J]. 电力自动化设备, 2023, 43(6): 108-115.
Ding Jiang, Sun Lei, Ding Ming.Load restoration strategy with wind farm-battery energy storage system considering time-variation characteristic of cold load pickup[J]. Electric Power Automation Equipment, 2023, 43(6): 108-115.
[25] Chen Bo, Chen Chen, Wang Jianhui, et al.Sequential service restoration for unbalanced distribution systems and microgrids[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1507-1520.
[26] Li Y L, Sun Wei, Yin Wenqian, et al.Restoration strategy for active distribution systems considering endogenous uncertainty in cold load pickup[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 2690-2702.
[27] Xie Dunjian, Xu Yunyun, Nadarajan Sivakumar, et al.Dynamic frequency-constrained load restoration considering multi-phase cold load pickup behaviors[J]. IEEE Transactions on Power Systems, 2024, 39(1): 107-118.
[28] Wang Yifei, Su Xiaoyun, Song Meng, et al.Sequential load restoration with soft open points and time-dependent cold load pickup for resilient distribution systems[J]. IEEE Transactions on Smart Grid, 2023, 14(5): 3427-3438.
[29] Ahmadi H, Martı´ J R, von Meier A. A linear power flow formulation for three-phase distribution systems[J]. IEEE Transactions on Power Systems, 2016, 31(6): 5012-5021.
[30] Ferreira R S, Borges C L T, Pereira M V F. A flexible mixed-integer linear programming approach to the AC optimal power flow in distribution systems[J]. IEEE Transactions on Power Systems, 2014, 29(5): 2447-2459. |
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2025, Vol. 40 
