Data-Driven Robust Planning Method for Resilient Distribution Networks Considering High-Permeability Distributed Generation

doi:10.12204/j.issn.1000-7229.2023.06.009

Abstract

Abstract:

By considering the impact of high-permeability distributed generation access on distribution networks, a data-driven robust planning method that accounts for the resilience of a distribution network is proposed. First, a comprehensive resilience evaluation system with high-permeability distributed generation is proposed. The three-stage planning model is then transformed into a two-stage data-driven robust planning model to complete the decision-making of the line-reinforcement scheme and the optimal allocation of distributed generation location capacity. An improved column and constraint generation algorithm based on the limit scenario method is used to solve the robust model. Finally, an example is provided to analyze the effects of the proposed resilience improvement measures. The results show that the proposed method can effectively quantify the resilience of a distribution network and provide a reference for resilient distribution network planning with high-permeability distributed generation.

Key words: high permeability, distributed generation (DG), resilience, data driven, two stage robust programming

CLC Number:

TM73

HUANG Mengqi, LI Yonghui, ZENG Haiyan, CHEN Yan, WANG Xuan, WANG Dongxu, YANG Jun. Data-Driven Robust Planning Method for Resilient Distribution Networks Considering High-Permeability Distributed Generation[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 79-90.

Figures/Tables 18

Fig.1 Resilience index evaluation system

Fig.2 Distribution network system function curve

Table 1 Uncertain problem processing model

Fig.3 Three stage programming model

Fig.4 Interval uncertain sets and ellipsoidal uncertain sets

Fig.5 Solution process of data-driven robust planning model

Fig.6 Adapted IEEE-33 node distribution system

Fig.7 Reference load of each node

Fig.8 Set of alternative reinforcement lines

Table 2 Failure rate of each line

Fig.9 DG alternative connection points

Fig.10 Scenario 1 planning results

Fig.11 Scenario 2 planning results

Fig.12 Scenario 3 planning results

Table 3 Resilience index value

Table 4 Normalized value

Table 5 Final score of each scheme

Table 6 DG capacity under different permeability

References 31

[1]	刘天浩, 朱元振, 孙润稼, 等. 极端自然灾害下电力信息物理系统韧性增强策略[J]. 电力系统自动化, 2021, 45(3): 40-48.
	LIU Tianhao, ZHU Yuanzhen, SUN Runjia, et al. Resilience-enhanced strategy for cyber-physical power system under extreme natural disasters[J]. Automation of Electric Power Systems, 2021, 45(3): 40-48.
[2]	CHEN T, YANG X Z, CHEN C, et al. Evaluation for the resilience of distribution network[C]// 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE). IEEE, 2020: 2071-2076.
[3]	NOURIAN S, KAZEMI A. Resilience enhancement of active distribution networks in the presence of wind turbines and energy storage systems by considering flexible loads[J]. Journal of Energy Storage, 2022, 48: 104042. doi: 10.1016/j.est.2022.104042 URL
[4]	黄亦昕. 考虑严重运行受限场景的含高渗透率可再生能源电网规划研究[D]. 杭州: 浙江大学, 2021.
	HUANG Yixin. Research on power system planning with high penetration of renewable energy considering severely restricted scenarios[D]. Hangzhou: Zhejiang University, 2021.
[5]	马丽叶, 王海锋, 卢志刚, 等. 计及相关性影响的增强台风灾害下配电网韧性灵活性资源规划[J]. 电力系统自动化, 2022, 46(7): 60-68.
	MA Liye, WANG Haifeng, LU Zhigang, et al. Flexible resource planning for improving distribution network resilience under typhoon disasters considering relevance impact[J]. Automation of Electric Power Systems, 2022, 46(7): 60-68.
[6]	PANTELI M, MANCARELLA P, WILKINSON S, et al. Assessment of the resilience of transmission networks to extreme wind events[C]// 2015 IEEE Eindhoven PowerTech. IEEE, 2015: 1-6.
[7]	MIN O Y, DUEÑAS-OSORIO L, XING M. A three-stage resilience analysis framework for urban infrastructure systems[J]. Structural Safety, 2012, 36/37: 23-31. doi: 10.1016/j.strusafe.2011.12.004 URL
[8]	何维国, 王赛一, 许唐云, 等. 城市韧性配电网建设与发展路径[J]. 电网技术, 2022, 46(2): 680-690.
	HE Weiguo, WANG Saiyi, XU Tangyun, et al. Construction and development path of the urban resilient distribution network[J]. Power System Technology, 2022, 46(2): 680-690.
[9]	徐悦, 李博, 孙建军, 等. 基于运行韧性评价的配电网电压暂降治理评估[J]. 电力系统自动化, 2021, 45(5): 104-110.
	XU Yue, LI Bo, SUN Jianjun, et al. Evaluation of voltage sag management in distribution network based on operation resilience assessment[J]. Automation of Electric Power Systems, 2021, 45(5): 104-110.
[10]	于艾清, 金彪, 王育飞. 台风天气下提升配电网韧性的储能规划策略[J]. 电力系统及其自动化学报, 2022, 34(8): 60-67.
	YU Aiqing, JIN Biao, WANG Yufei. Energy storage planning strategy for improving resilience of distribution network under typhoon weather[J]. Proceedings of the CSU-EPSA, 2022, 34(8): 60-67.
[11]	周晓敏, 葛少云, 李腾, 等. 极端天气条件下的配电网韧性分析方法及提升措施研究[J]. 中国电机工程学报, 2018, 38(2): 505-513, 681.
	ZHOU Xiaomin, GE Shaoyun, LI Teng, et al. Assessing and boosting resilience of distribution system under extreme weather[J]. Proceedings of the CSEE, 2018, 38(2): 505-513, 681.
[12]	勇蔚柯, 李扬, 曹阳. 基于需求响应的配电网韧性提升技术研究[J]. 电力需求侧管理, 2022, 24(2): 20-26.
	YONG Weike, LI Yang, CAO Yang. Research on improvement technology of distribution network resilience based on demand response[J]. Power Demand Side Management, 2022, 24(2): 20-26.
[13]	周士超, 刘晓林, 熊展, 等. 考虑韧性提升的交直流配电网线路加固和储能配置策略[J]. 上海交通大学学报, 2021, 55(12): 1619-1630.
	ZHOU Shichao, LIU Xiaolin, XIONG Zhan, et al. Line hardening and energy storage system configuration strategies for resilience enhancement of a hybrid AC-DC distribution system[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1619-1630.
[14]	陶然, 赵冬梅, 王浩翔. 基于信息间隙决策理论的配电网韧性提升规划方法[J]. 电力系统自动化, 2022, 46(9): 32-41.
	TAO Ran, ZHAO Dongmei, WANG Haoxiang. Planning method for resilience enhancement of distribution network based on information gap decision theory[J]. Automation of Electric Power Systems, 2022, 46(9): 32-41.
[15]	马伸铜. 极端自然灾害下计及储能配置的韧性配电网规划方法[D]. 北京: 华北电力大学, 2020.
	MA Shentong. Planning method of resilient distribution networks considering ESS deployment under extreme natural disasters[D]. Beijing: North China Electric Power University, 2020.
[16]	张成昊. 考虑微能源网支撑作用的配电网韧性评估方法和提升策略研究[D]. 天津: 天津大学, 2019.
	ZHANG Chenghao. Research on resilience evaluation method and enhancing strategy for distribution network considering micro-energy grid supporting function[D]. Tianjin:Tianjin University, 2019.
[17]	李京. 冰雪灾害环境下电网韧性提升研究[D]. 北京: 中国科学院大学, 2020.
	LI Jing. Research on the enhancement of the resilience of power grids under ice storms disaster[D]. Beijing: University of Chinese Academy of Sciences, 2020.
[18]	BATTS M E, SIMIU E, RUSSELL L R. Hurricane wind speeds in the United States[J]. Journal of the Structural Division, 1980, 106(10): 2001-2016. doi: 10.1061/JSDEAG.0005541 URL
[19]	张秀钊, 陈姝敏, 钱纹, 等. 含分布式电源配电网抗灾害特性评估方法[J]. 广东电力, 2018, 31(1): 51-56.
	ZHANG Xiuzhao, CHEN Shumin, QIAN Wen, et al. Evaluation method for anti-disaster characteristic of power distribution network with distributed generation[J]. Guangdong Electric Power, 2018, 31(1): 51-56.
[20]	朱兰, 杨淑红, 唐陇军, 等. 微网规划综合评价精细化指标体系研究[J]. 现代电力, 2018, 35(3): 54-61.
	ZHU Lan, YANG Shuhong, TANG Longjun, et al. Study on fine index system for comprehensive evaluation of microgrid planning[J]. Modern Electric Power, 2018, 35(3): 54-61.
[21]	张涛, 朱彤, 高乃平, 等. 分布式冷热电能源系统优化设计及多指标综合评价方法的研究[J]. 中国电机工程学报, 2015, 35(14): 3706-3713.
	ZHANG Tao, ZHU Tong, GAO Naiping, et al. Optimization design and multi-criteria comprehensive evaluation method of combined cooling heating and power system[J]. Proceedings of the CSEE, 2015, 35(14): 3706-3713.
[22]	张晓明, 吴焯军, 甘艳, 等. 一种基于改进层次分析法的输电线路雷害风险评估模型[J]. 电力建设, 2012, 33(8): 35-39.
	ZHANG Xiaoming, WU Zhuojun, GAN Yan, et al. Risk evaluation model of lightning disturbance for transmission lines based on improved AHP[J]. Electric Power Construction, 2012, 33(8): 35-39.
[23]	陈非凡, 高亚静, 梁海峰, 等. 基于地理分区的综合能源园区用能特性评价模型及供能分区方法[J]. 电力建设, 2019, 40(6): 23-32. doi: 10.3969/j.issn.1000-7229.2019.06.003
	CHEN Feifan, GAO Yajing, LIANG Haifeng, et al. Energy consumption characteristic evaluation model and energy supply partition method of integrated energy park based on geographical partition[J]. Electric Power Construction, 2019, 40(6): 23-32. doi: 10.3969/j.issn.1000-7229.2019.06.003
[24]	张宇威, 肖金星, 杨军, 等. 基于风光数据驱动不确定集合的配电网与多微网鲁棒经济调度[J]. 电力建设, 2021, 42(10): 40-50. doi: 10.12204/j.issn.1000-7229.2021.10.005
	ZHANG Yuwei, XIAO Jinxing, YANG Jun, et al. Data-driven robust economic dispatch for distribution network and multiple micro-grids considering correlativity of wind and solar output[J]. Electric Power Construction, 2021, 42(10): 40-50. doi: 10.12204/j.issn.1000-7229.2021.10.005
[25]	陈泽雄, 张新民, 王雪锋, 等. 分布式光伏电站接入配电网的分布鲁棒优化配置方法[J]. 电力系统保护与控制, 2021, 49(13): 30-42.
	CHEN Zexiong, ZHANG Xinmin, WANG Xuefeng, et al. A distributionally robust optimal allocation method for distributed photovoltaic generation stations integrated into a distribution network[J]. Power System Protection and Control, 2021, 49(13): 30-42.
[26]	臧海祥, 马铭欣, 周亦洲, 等. 电力市场环境下风电-光热-生物质混合电站鲁棒优化调度模型[J]. 电力系统保护与控制, 2022, 50(5): 1-11.
	ZANG Haixiang, MA Mingxin, ZHOU Yizhou, et al. Robust optimal scheduling model for a ‘wind power-concentrating solar power-biomass’ hybrid power plant in the electricity market[J]. Power System Protection and Control, 2022, 50(5): 1-11.
[27]	张玮, 荀超, 黄夏楠, 等. 计及价格弹性负荷及风电的随机安全约束经济调度[J]. 智慧电力, 2021, 49(6): 116-123.
	ZHANG Wei, XUN Chao, HUANG Xianan, et al. Stochastic safety constrained economic dispatch considering price elastic load and wind power[J]. Smart Power, 2021, 49(6): 116-123.
[28]	杨贤东, 袁旭峰, 熊炜, 等. 考虑源荷不确定性的风光火储系统低碳经济调度[J]. 智慧电力, 2022, 50(8): 22-29, 53.
	YANG Xiandong, YUAN Xufeng, XIONG Wei, et al. Low-carbon economic dispatch of wind-solar-fired-storage system considering source-load uncertainty[J]. Smart Power, 2022, 50(8): 22-29, 53.
[29]	王贵龙, 赵庆生, 梁定康, 等. 计及机会约束规划的园区能量枢纽经济调度[J]. 电力系统保护与控制, 2021, 49(13): 21-29.
	WANG Guilong, ZHAO Qingsheng, LIANG Dingkang, et al. Economic dispatch of an energy hub in a business park considering chance constrained programming[J]. Power System Protection and Control, 2021, 49(13): 21-29.
[30]	周晓敏. 极端天气条件下的配电网韧性分析及提升措施研究[D]. 天津: 天津大学, 2018.
	ZHOU Xiaomin. Assessing and boosting resilience of distribution system under extreme weather[D]. Tianjin:Tianjin University, 2018.
[31]	DING T, LV J J, BO R, et al. Lift-and-project MVEE based convex hull for robust SCED with wind power integration using historical data-driven modeling approach[J]. Renewable Energy, 2016, 92: 415-427. doi: 10.1016/j.renene.2016.01.001 URL