基于电网脆弱性评估的储能规划选址研究

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Abstract Against the backdrop of the continuous development of new power system, various regions across the country are actively carrying out new energy storage construction to enhance the regulation capacity of the power system and meet the peak shaving and demand of the power grid. With the characteristics of bidirectional transmission and fast response speed, it is a key issue that should be considered in the site selection stage of energy storage planning, which involves how to effectively improve the security of the power grid and enhance the ability to resist the impact of faults after the new energy storage is access to the power system. A multi-objective decision-making model for energy storage site selection is constructed to address the impact of new energy storage access on the vulnerability of partitioned power grids. Faced with the shortcomings of traditional power grid vulnerability analysis methods, a new vulnerability assessment method based on k-core decomposition is proposed. The system vulnerability indicators under different typical operation scenarios after energy storage access to the power grid are taken as decision-making sub-targets, and the technique for order preference by similarity to an ideal solution (TOPSIS) decision method is used to comprehensively evaluate the optimal solution of energy storage target access point. The rationality and effectiveness of the proposed vulnerability assessment method and the energy storage site selection method are verified through the analysis of the IEEE 39-node system.

Key words： energy storage site selection power grid vulnerability k-core decomposition typical operation scenario technique for order preference by similarity to an ideal solution (TOPSIS) multi-objective decision-making

Received: 14 November 2024

About author:: 江苏省基础研究计划（自然科学基金）项目（BK20210048）

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	SUN Shunxiang
	LI Jinke
	ZHEN Hongning
	YANG Yun
	HAN Zhikun

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SUN Shunxiang,LI Jinke,ZHEN Hongning等. Research on energy storage site selection based on power grid vulnerability assessment[J]. Electrical Engineering, 2025, 26(3): 7-14.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I3/7

[1] 王国春, 董昱, 许涛, 等. 巴西“8.15”大停电事故分析及启示[J].中国电机工程学报, 2023, 43(24): 9461-9470.
[2] 顾雪平, 白岩松, 李少岩, 等. 电力系统黑启动恢复问题的研究评述[J].电工技术学报, 2022, 37(13): 3183-3200.
[3] 顾雪平, 魏佳俊, 白岩松, 等. 基于分层模型预测控制的含风电电力系统恢复在线决策方法[J/OL].电工技术学报, 1-16 [2024-11-14].https://doi.org/10. 19595/j.cnki.1000-6753.tces.240331.
[4] 刘士亚, 郭静, 岑钊华. 城市电网监控应急系统的设计[J].电气技术, 2021, 22(2): 11-16.
[5] 周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J].中国电机工程学报, 2018, 38(7): 1893-1904.
[6] 商立群, 张建涛. 计及储能电池寿命衰减的居民小区光储优化配置[J].电气技术, 2024, 25(2): 1-11.
[7] 杨天鑫, 黄云辉, 何珍玉, 等. 基于多时间尺度调节的构网型储能电站定容选址优化配置[J/OL].电力系统自动化, 1-18 [2024-11-02].http://kns.cnki.net/ kcms/detail/32.1180.TP.20240718.1759.006.html.
[8] 张光儒, 任浩栋, 马振祺, 等. 提升配电网承载力和调节能力的整县分布式光伏储能配置方法[J].电气技术, 2022, 23(11): 49-55, 61.
[9] 程浩, 秦文萍, 韩肖清, 等. 基于功角稳定性的区域电网储能选址定容方法[J].电力自动化设备, 2024, 44(7): 21-29.
[10] 李滨, 谢旭槟, 梁振成, 等. “双高”电力系统集中式储能选址定容规划策略[J].电力系统及其自动化学报, 2024, 36(9): 31-43.
[11] 徐吉智, 张新燕, 常喜强, 等. 基于PV曲线和改进遗传算法储能选址定容研究[J].太阳能学报, 2022, 43(1): 263-268.
[12] 马一然, 彭乔, 刘天琪, 等.基于质心理论的光伏配电网电池储能选址定容方法[J/OL].现代电力, 1-11 [2024-11-02].https://doi.org/10.19725/j.cnki.1007- 2322.2023.0271.
[13] 吴琛, 刘晨曦, 黄伟, 等. 提升新能源电力系统稳定性的构网型变流器选址定容方法[J].电力系统自动化, 2023, 47(12): 130-136.
[14] 王瑜, 王浩. 电池储能削峰填谷选址定容方法研究[J].电气应用, 2024, 43(7): 99-106.
[15] 严奕陆, 刘文霞, 石庆鑫, 等. 基于电-水跨层耦合模型的城市电网脆弱性评估[J].电工技术学报, 2024, 39(16): 5075-5090.
[16] WATTS D J, STRONGATZ S H.Collective dynamics of ‘small-world’ networks[J].Nature, 1998, 393: 440-442.
[17] KITSAK M, GALLOS L K, HAVLIN S, et al.Identification of influential spreaders in complex networks[J].Nature Physics, 2010, 6: 888-893.
[18] 丁明, 韩平平. 基于小世界拓扑模型的大型电网脆弱性评估算法[J].电力系统自动化, 2006, 30(8): 7-10.
[19] 汪小帆, 李翔, 陈关荣. 复杂网络理论及其应用[M].北京: 清华大学出版社, 2006.
[20] 徐玖平, 吴巍. 多属性决策的理论与方法[M].北京: 清华大学出版社, 2006.
[21] 鞠文云, 李银红. 基于最大流传输贡献度的电力网关键线路和节点辨识[J].电力系统自动化, 2012, 36(9): 6-12.
[22] 刘利民, 刘俊勇, 魏震波, 等. 基于协同效应分析的输电线路脆弱评估方法[J].电力自动化设备, 2016, 36(5): 30-37.
[23] 蔡晔, 曹一家, 李勇, 等. 考虑电压等级和运行状态的电网脆弱线路辨识[J].中国电机工程学报, 2014, 34(13): 2124-2131.
[24] 徐林, 王秀丽, 王锡凡. 电气介数及其在电力系统关键线路识别中的应用[J].中国电机工程学报, 2010, 30(1): 33-39.