Distribution Network Flexibility Evaluation Method Considering Collaborative Interaction of Flexible Resources

doi:10.11930/j.issn.1004-9649.202403021

Abstract

Abstract:

A distribution network flexibility evaluation method considering collaborative interaction of flexible resources is proposed. Firstly, a flexibility evaluation indicator system is constructed, which includes three aspects: collaborative regulation capacity, collaborative efficiency quality, and power grid reliability. Secondly, a Gaussian mixture model is used to construct typical operating scenarios, and the subjective and objective weights of evaluation indicators are determined using Analytic Hierarchy Process and Random Forest Model, respectively. Through a double-layer evaluation process, the evaluation indicator values for each typical scenario are calculated. Then, based on the probability of typical scenarios appearing, the comprehensive flexibility evaluation index values of each flexible resource access scheme are weighted. Finally, the feasibility and effectiveness of the proposed method are verified through the case study on a 54-bus distribution network.

Key words: distribution network, flexible resources, flexibility evaluation method, random forest

Zhiwen LIU, Yan LI, Chong SHAO, Xinming FAN, Qinghui ZENG. Distribution Network Flexibility Evaluation Method Considering Collaborative Interaction of Flexible Resources[J]. Electric Power, 2024, 57(10): 158-165.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202403021

https://www.electricpower.com.cn/EN/Y2024/V57/I10/158

Figures/Tables 10

References 21

1	郑国光. 支撑“双碳” 目标实现的问题辨识与关键举措研究[J]. 中国电力, 2023, 56 (11): 1- 8.
	ZHENG Guoguang. Problem identification and key measures to support the achievement of carbon peak and carbon neutrality[J]. Electric Power, 2023, 56 (11): 1- 8.
2	叶宇剑, 袁泉, 汤奕. 面向双碳目标的交通网-电网耦合网络中电动汽车负荷低碳优化方法[J]. 中国电力, 2023, 56 (5): 72- 79.
	YE Yujian, YUAN Quan, TANG Yi. Electric vehicle charging demand low carbon optimization in traffic-grid coupling networks towards "dual carbon" goal[J]. Electric Power, 2023, 56 (5): 72- 79.
3	王彩霞, 时智勇, 梁志峰, 等. 新能源为主体电力系统的需求侧资源利用关键技术及展望[J]. 电力系统自动化, 2021, 45 (16): 37- 48. DOI
	WANG Caixia, SHI Zhiyong, LIANG Zhifeng, et al. Key technologies and prospects of demand-side resource utilization for power systems dominated by renewable energy[J]. Automation of Electric Power Systems, 2021, 45 (16): 37- 48. DOI
4	卓映君, 周保荣, 姚文峰, 等. “双碳” 目标下南方区域能源电力消费及负荷特性预测[J]. 南方电网技术, 2023, 17 (9): 132- 140.
	ZHUO Yingjun, ZHOU Baorong, YAO Wenfeng, et al. Prediction of energy and electricity consumption and load characteristics in the southern region under the carbon peak and carbon neutrality target[J]. Southern Power System Technology, 2023, 17 (9): 132- 140.
5	彭道刚, 税纪钧, 王丹豪, 等. “双碳” 背景下虚拟电厂研究综述[J]. 发电技术, 2023, 44 (5): 602- 615. DOI
	PENG Daogang, SHUI Jijun, WANG Danhao, et al. Review of virtual power plant under the background of "dual carbon"[J]. Power Generation Technology, 2023, 44 (5): 602- 615. DOI
6	任海波, 余波, 王奎, 等. “双碳” 背景下抽水蓄能电站的发展与展望[J]. 内蒙古电力技术, 2022, 40 (3): 25- 30.
	REN Haibo, YU Bo, WANG Kui, et al. Development and prospect of pumped storage power station under double-carbon background[J]. Inner Mongolia Electric Power, 2022, 40 (3): 25- 30.
7	刘鹏, 崔雪. 双碳背景下考虑市场份额偏好的发电侧市场均衡分析[J]. 电力科学与技术学报, 2023, 38 (2): 9- 17, 39.
	LIU Peng, CUI Xue. Equilibrium analysis of power generation market considering market share preference un-der carbon-neutral goal[J]. Journal of Electric Power Science and Technology, 2023, 38 (2): 9- 17, 39.
8	GE S Y, XU Z Y, LIU H, et al. Flexibility evaluation of active distribution networks considering probabilistic characteristics of uncertain variables[J]. IET Generation, Transmission & Distribution, 2019, 13 (14): 3148- 3157.
9	米伟铭, 叶鹏, 张明理, 等. 基于云模型的新型配电系统灵活性评估方法[J]. 电网技术, 2024, 48 (6): 2532- 2540.
	MI Weiming, YE Peng, ZHANG Mingli, et al. Novel flexibility evaluation for distribution systems based on cloud models[J]. Power System Technology, 2024, 48 (6): 2532- 2540.
10	LIU S K, LIU L H, FAN Y P, et al. An integrated scheme for online dynamic security assessment based on partial mutual information and iterated random forest[J]. IEEE Transactions on Smart Grid, 2020, 11 (4): 3606- 3619. DOI
11	高亚静, 朱静, 程华新, 等. 计及不确定性因素基于多场景的主动配电系统短期供电能力评估[J]. 中国电机工程学报, 2016, 36 (22): 6076- 6085.
	GAO Yajing, ZHU Jing, CHENG Huaxin, et al. Evaluation on the short-term power supply capacity of active distribution system based on multiple scenarios considering uncertainties[J]. Proceedings of the CSEE, 2016, 36 (22): 6076- 6085.
12	马杰, 李秋燕, 丁岩, 等. 含高渗透率可再生能源的配电网灵活性评价指标体系及计算方法[J]. 电力系统及其自动化学报, 2020, 32 (9): 99- 104, 112.
	MA Jie, LI Qiuyan, DING Yan, et al. Flexibility evaluation index system and calculation method for distribution network with High-permeability renewable energy[J]. Proceedings of the CSU-EPSA, 2020, 32 (9): 99- 104, 112.
13	陈垚煜, 江全元, 周自强, 等. 考虑典型场景的配电网调控方案灵活性评估方法[J]. 电力建设, 2019, 40 (7): 34- 40. DOI
	CHEN Yaoyu, JIANG Quanyuan, ZHOU Ziqiang, et al. An evaluation method on the flexibility of regulation methods in distribution network considering typical scene sets[J]. Electric Power Construction, 2019, 40 (7): 34- 40. DOI
14	李亚平, 周竞, 鞠平, 等. 柔性负荷互动影响量化评估方法[J]. 电力系统自动化, 2015, 39 (17): 26- 32, 67. DOI
	LI Yaping, ZHOU Jing, JU Ping, et al. Quantitative assessment method for interactive impact of flexible load[J]. Automation of Electric Power Systems, 2015, 39 (17): 26- 32, 67. DOI
15	陈海鹏, 周越豪, 赵畅, 等. 考虑高载能负荷参与的多时间尺度风电消纳调度[J]. 东北电力大学学报, 2022, 42 (6): 39- 51.
	CHEN Haipeng, ZHOU Yuehao, ZHAO Chang, et al. Multi-time scale wind power consumption scheduling with high energy load participation is considered[J]. Journal of Northeast Electric Power University, 2022, 42 (6): 39- 51.
16	张蓬, 吕恭祥, 刘志杰, 等. 含电能路由器的配电网转供灵活性量化分析[J]. 上海交通大学学报, 2023, 57 (5): 513- 520.
	ZHANG Peng, LÜ Gongxiang, LIU Zhijie, et al. Load transfer flexibility analysis of distribution network with electric energy routers[J]. Journal of Shanghai Jiao Tong University, 2023, 57 (5): 513- 520.
17	潘忠志, 孔宁, 王燕涛. 改进新能源消纳的配电网资源优化配置研究[J]. 东北电力大学学报, 2023, 43 (6): 71- 78.
	PAN Zhongzhi, KONG Ning, WANG Yantao. Joint optimal allocation method of energy-load-storage in power SupplyArea to improve new energy consumption capacity[J]. Journal of Northeast Electric Power University, 2023, 43 (6): 71- 78.
18	凡鹏飞, 李宝琴, 侯江伟, 等. 配电网分布式电源经济可承载力评估[J]. 中国电力, 2024, 57 (7): 196- 202.
	FAN Pengfei, LI Baoqin, HOU Jiangwei, et al. Economic capacity assessment of renewables in distribution networks[J]. Electric Power, 2024, 57 (7): 196- 202.
19	杨彪, 颜伟, 莫静山. 考虑源荷功率随机性和相关性的主导节点选择与无功分区方法[J]. 电力系统自动化, 2021, 45 (11): 61- 67.
	YANG Biao, YAN Wei, MO Jingshan. Pilot-bus selection and network partitioning method considering randomness and correlation of source-load power[J]. Automation of Electric Power Systems, 2021, 45 (11): 61- 67.
20	李彬, 白雪峰, 王京菊, 等. 新型电力负荷管理系统发展的关键支撑技术研究[J]. 内蒙古电力技术, 2023, 41 (2): 1- 6.
	LI Bin, BAI Xuefeng, WANG Jingju, et al. Research on key supporting technology for development of new power load management system[J]. Inner Mongolia Electric Power, 2023, 41 (2): 1- 6.
21	MIRANDA V, RANITO J V, PROENCA L M. Genetic algorithms in optimal multistage distribution network planning[J]. IEEE Transactions on Power Systems, 2002, 9 (4): 1927- 1933.

编号	接入节点	柔性资源类型	各节点接入容量
方案1	—	—	—
方案2	4、9、10、35	分布式储能	500 kW/1 MW·h
方案3	4、9、10、35	电动汽车充电站	1 MW
方案4	6、12、23、36	分布式储能	500 kW/1 MW·h
方案5	6、12、23、36	电动汽车充电站	1 MW

编号	接入节点	柔性资源类型	各节点接入容量
方案1	—	—	—
方案2	4、9、10、35	分布式储能	500 kW/1 MW·h
方案3	4、9、10、35	电动汽车充电站	1 MW
方案4	6、12、23、36	分布式储能	500 kW/1 MW·h
方案5	6、12、23、36	电动汽车充电站	1 MW

典型日场景编号	数量	概率
1	63	0.1726
2	134	0.3671
3	71	0.1945
4	97	0.2657

典型日场景编号	数量	概率
1	63	0.1726
2	134	0.3671
3	71	0.1945
4	97	0.2657

指标	主观权重	客观权重	组合权重
$ {\lambda _{{\text{RC}}}} $	0.1218	0.0909	0.10635
$ {\lambda _{{\text{RE}}}} $	0.1157	0.0964	0.10605
$ {\lambda _{{\rm{p {\text -} v}}}} $	0.1113	0.1818	0.14655
$ {\lambda _{{\text{loss}}}} $	0.0268	0.0455	0.03615
$ {\lambda _{{{\Delta \rm V}}}} $	0.2253	0.1055	0.16540
$ {\lambda _{{\text{FR,max}}}} $	0.0919	0.1364	0.11415
$ {\lambda _{{\text{LF}}}} $	0.1235	0.0271	0.07530
${\lambda _{{\text{IE}}}}$	0.1837	0.3164	0.25005