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

doi:10.19595/j.cnki.1000-6753.tces.231828

Abstract
Figure/Table
References (0)
Related Citation (7)

Download: PDF (2041 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

The urban distribution system (DS) serves as a crucial link between end-users and the transmission grid, having a direct impact on the quality of power supply. However, DS is relatively more vulnerable to extreme events in comparison to the transmission network. Hence, it has been an urgent priority to enhance the resilience of DS. In recent years, the approach to enhancing DS resilience moving away from traditional load tracking of power sources to a source-grid-load-storage perspective as the structure evolution of DS caused by the high proportion of renewable generation. However, the study of load behavior on the demand side still has limitations, particularly in terms of precise modeling. Representatively, most literature consider the thermal control loads (TCL) as rigid loads or characterizes their cold load pick-up (CLPU) phenomenon as a fixed curve neglecting the dependence on the outage duration. Similarly, the time-varying, diverse, and complex nature of urban building loads under extreme conditions is seldom addressed in relevant papers. This significant deviation from real-world scenarios greatly impacts the applicability of relevant theoretical models and directly restrict the analysis of the load restoration process.
Therefore, this paper conducts the following works to address the actual scenario of load restorations in DS. Firstly, the study delves into the dynamic coupling evolution law between urban time-varying loads and DS decision sequences under extreme conditions, considering both objective and subjective reasons. It accurately quantifies the complex behavior of urban building loads during the process of DS restoration in a universal and low-dimensional manner. The paper also explains the dependence of loads on DS restoration sequences, analyzing the decision-dependency characteristics of load from both subjective and objective perspectives. Secondly, the interaction between the DS and end-user decision-making constitutes a bi-level model. This paper employs the Karush-Kuhn-Tucker (KKT) conditions to solve and analyze a bi-level model that integrates the load side of the distribution network. This simplifies the model calculation while accurately identifying the optimal solution for the upper distribution network and the lower load side. Additionally, the paper proposes a data-driven modeling method and introduces a differential calculation adaptive model based on "time tag" technology and load restoration decision sequences. This adaptive model enables dynamic selection of diversified load behavior in distribution networks. Finally, the paper summarizes various indicators for evaluating the resilience of distribution networks and utilizes existing research results to compare and analyze the traditional double-layer model with the proposed adaptive model from six perspectives. The evaluation takes the system function curve of DS during the load recovery process as a starting point, allowing for an assessment of the advantages and disadvantages of the two models.
Case studies shows that the lack of consideration for loads’ decision-dependency characteristics leads to the violations of node voltage and line flow, which positively correlated with penetration of TCLs and subjective driven load. Additionally, case studies also shows that the adaptive model can achieve fault restoration without affecting the resilience of the distribution network. While better protecting user privacy, it only takes 33% of the time of traditional bi-level models.
Overall, this research provides valuable insights into load restoration under extreme conditions for urban DS. It provides a comprehensive analysis and optimization method ensures more efficient and effective decision-making processes. Meanwhile, the proposed method facilitates prompt decision-making without affecting the resilience of DS. Its solution time cost only accounts for 33% compared to the traditional bi-level model.

Key words： Distribution system resilience cold load pick-up load restoration decision-dependent behaviors

Received: 03 November 2023

PACS:

TM712

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Deng Rongnan
	Song Meng
	Gao Ciwei
	Yan Xingyu
	Bai Wenchao

Cite this article:

Deng Rongnan,Song Meng,Gao Ciwei等. Comparative Analysis of Distribution System Load Restoration considering Decision-Dependent Behaviors of Customers[J]. Transactions of China Electrotechnical Society, 0, (): 2024031403-2024031403.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231828 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/2024031403

[1] 高海翔, 陈颖, 黄少伟, 等. 配电网韧性及其相关研究进展[J]. 电力系统自动化, 2015, 39(23): 1-8.
Gao Haixiang, Chen Ying, Huang Shaowei, et al.Distribution systems resilience: an overview of research progress[J]. Automation of Electric Power Systems, 2015, 39(23): 1-8.
[2] Chen Kening, Wu Wenchuan, Zhang Boming, et al.Robust restoration decision-making model for distribution networks based on information gap decision theory[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 587-597.
[3] 国家发改委. “十四五”全国城市基础设施建设规划[R/OL].
[2022-07-31]. https://www.gov.cn/zhengce/zhengceku/2022-07/31/5703690/files/d4ebd608827e41138701d06fe6133cdb.pdf
[4] Liu Rongpeng, Hou Yunhe, Li Yujia, et al.Sample robust scheduling of electricity-gas systems under wind power uncertainty[J]. IEEE Transactions on Power Systems, 2021, 36(6): 5889-5900.
[5] Wang Chong, Wang Zhaoyu, Hou Yunhe, et al.Dynamic game-based maintenance scheduling of integrated electric and natural gas grids with a bilevel approach[J]. IEEE Transactions on Power Systems, 2018, 33(5): 4958-4971.
[6] Chen Chen, Wang Jianhui, Qiu Feng, et al.Resilient distribution system by microgrids formation after natural disasters[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 958-966.
[7] 赵冬梅, 王浩翔, 陶然. 计及风电-负荷不确定性的风-火-核-碳捕集多源协调优化调度[J]. 电工技术学报, 2022, 37(3): 707-718.
Zhao Dongmei, Wang Haoxiang, Tao Ran.A multi-source coordinated optimal scheduling model considering wind-load uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 707-718.
[8] 许寅, 王颖, 和敬涵, 等. 多源协同的配电网多时段负荷恢复优化决策方法[J]. 电力系统自动化, 2020, 44(2): 123-131.
Xu Yin, Wang Ying, He Jinghan, et al.Optimal decision-making method for multi-period load restoration in distribution network with coordination of multiple sources[J]. Automation of Electric Power Systems, 2020, 44(2): 123-131.
[9] 罗伟, 李长城, 琚上纯, 等. 考虑孤岛融合的配电系统多源协同恢复策略[J]. 电网技术, 2022, 46(4): 1485-1495.
Luo Wei, Li Changcheng, Ju Shangchun, et al.Multi-source cooperative restoration strategy for distribution system considering islanding integration[J]. Power System Technology, 2022, 46(4): 1485-1495.
[10] Ma Shanshan, Chen Bokan, Wang Zhaoyu.Resilience enhancement strategy for distribution systems under extreme weather events[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 1442-1451.
[11] 李超, 苗世洪, 盛万兴, 等. 考虑动态网络重构的主动配电网优化运行策略[J]. 电工技术学报, 2019, 34(18): 3909-3919.
Li Chao, Miao Shihong, Sheng Wanxing, et al.Optimization operation strategy of active distribution network considering dynamic network reconfiguration[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3909-3919.
[12] 李晓宇. 基于机器学习的配电网络拓扑生成及重构优化研究[D]. 北京: 北京邮电大学, 2019.
[13] 王维. 考虑间歇性分布式电源出力不确定性的配电网故障恢复策略[D]. 北京: 北京交通大学, 2022.
[14] 王颖, 祝士焱, 许寅, 等. 考虑多类型储能协同的重要负荷恢复方法[J]. 电力自动化设备, 2022, 42(1): 72-78.
Wang Ying, Zhu Shiyan, Xu Yin, et al.Critical load restoration method considering coordination of multiple types of energy storage[J]. Electric Power Automation Equipment, 2022, 42(1): 72-78.
[15] 刘峰伟, 陈佳佳, 赵艳雷, 等. 端对端交易模式下基于移动储能共享的配电系统韧性提升[J]. 电力系统自动化, 2022, 46(16): 151-159.
Liu Fengwei, Chen Jiajia, Zhao Yanlei, et al.Resilience enhancement for distribution system based on mobile energy storage sharing in peer-to-peer transaction mode[J]. Automation of Electric Power Systems, 2022, 46(16): 151-159.
[16] 陈碧云, 金洋, 徐旗, 等. 计及灾后恢复全过程储能应急响应能力的配电网韧性规划[J/OL]. 电网技术: 1-8[2023-08-18].
Chen Biyun, Jin Yang, Xu Qi, et al.Resilience planning of distribution network considering emergency response capability of energy storage throughout the entire process of post-disaster recovery[J]. Power System Technology, 1-8[2023-08-18].
[17] Poudel S, Dubey A, Schneider K P.A generalized framework for service restoration in a resilient power distribution system[J]. IEEE Systems Journal, 2022, 16(1): 252-263.
[18] 陈厚合, 丛前, 姜涛, 等. 多能协同的配电网供电恢复策略[J]. 电工技术学报, 2022, 37(3): 610-622, 685.
Chen Houhe, Cong Qian, Jiang Tao, et al.Distribution systems restoration with multi-energy synergy[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 610-622, 685.
[19] Poudel S, Dubey A, Schneider K P.A generalized framework for service restoration in a resilient power distribution system[J]. IEEE Systems Journal, 2022, 16(1): 252-263.
[20] Mirza O H.Usage of CLPU curve to deal with the cold load pickup problem[J]. IEEE Transactions on Power Delivery, 1997, 12(2): 660-667.
[21] Schneider K P, Sortomme E, Venkata S S, et al.Evaluating the magnitude and duration of cold load pick-up on residential distribution using multi-state load models[J]. IEEE Transactions on Power Systems, 2016, 31(5): 3765-3774.
[22] Uçak C, Pahwa A.Optimal step-by-step restoration of distribution systems during excessive loads due to cold load pickup[J]. Electric Power Systems Research, 1995, 32(2): 121-128.
[23] Arif A, Ma Shanshan, Wang Zhaoyu, et al.Optimizing service restoration in distribution systems with uncertain repair time and demand[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6828-6838.
[24] Li Yiyan, Yan Zheng, Chen Sijie, et al.Operation strategy of smart thermostats that self-learn user preferences[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 5770-5780.
[25] 宋梦, 周佳妮, 高赐威, 等. CPSS视角下城市建筑与配电网高韧性协调运行:研究述评与展望[J]. 电力系统自动化, 2023, 47(23): 105-121.
Song Meng, Zhou Jiani, Gao Ciwei, et al.High-resilience coordinated operation of urban buildings and distribution networks from cyber-physical-social system perspective: research review and prospect[J]. Automation of Electric Power Systems, 2023, 47(23): 105-121.
[26] 范睿, 孙润稼, 刘玉田. 考虑空调负荷需求响应的负荷恢复量削减方法[J]. 电工技术学报, 2022, 37(11): 2869-2877.
Fan Rui, Sun Runjia, Liu Yutian.A load restoration amount reduction method considering demand response of air conditioning loads[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2869-2877.
[27] Song Meng, Nejad R R, Sun Wei.Robust distribution system load restoration with time-dependent cold load pickup[J]. IEEE Transactions on Power Systems, 2021, 36(4): 3204-3215.
[28] 聂世豪, 李桐, 陈磊, 等. 投切型温控负荷一次调频策略及电网侧聚合建模[J]. 中国电机工程学报, 2022, 42(增刊1): 1-11.
Nie Shihao, Li Tong, Chen Lei, et al.Primary frequency modulation strategy of switching temperature control load and aggregation modeling of power grid side[J]. Proceedings of the CSEE, 2022, 42(S1): 1-11.
[29] 王栋. 参与电网快速调频的变频空调负荷虚拟同步机调控技术研究[D]. 南京: 东南大学, 2020.
[30] 吴润基, 王冬骁, 谢昌鸿, 等. 空调负荷参与配电网电压管理的分布式控制方法[J]. 电力系统自动化, 2021, 45(6): 215-222.
Wu Runji, Wang Dongxiao, Xie Changhong, et al.Distributed control method for air-conditioning load participating in voltage management of distribution network[J]. Automation of Electric Power Systems, 2021, 45(6): 215-222.
[31] Song Meng, Gao Ciwei, Shahidehpour M, et al.State space modeling and control of aggregated TCLs for regulation services in power grids[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 4095-4106.
[32] 陈磊, 邓欣怡, 陈红坤, 等. 电力系统韧性评估与提升研究综述[J]. 电力系统保护与控制, 2022, 50(13): 11-22.
Chen Lei, Deng Xinyi, Chen Hongkun, et al.Review of the assessment and improvement of power system resilience[J]. Power System Protection and Control, 2022, 50(13): 11-22.
[33] 谢小杰, 梁英, 王梓森, 等. 社交网络用户隐私泄露量化评估方法[J]. 计算机工程与科学, 2021, 43(8): 1376-1386.
Xie Xiaojie, Liang Ying, Wang Zisen, et al.A quantitative evaluation method of social network users' privacy leakage[J]. Computer Engineering & Science, 2021, 43(8): 1376-1386.
[34] 陆秋瑜, 江里舟, 别朝红, 等. 考虑用户能量优化调度的配电网灾后故障恢复策略[J]. 电力系统自动化, 2023, 47(1): 44-54.
Lu Qiuyu, Jiang Lizhou, Bie Chaohong, et al.Post-disaster fault restoration strategy for distribution network considering optimal scheduling of user-side energy[J]. Automation of Electric Power Systems, 2023, 47(1): 44-54.