计及调节能力薄弱环节识别的储-输联合规划方法

doi:10.19595/j.cnki.1000-6753.tces.251194

Abstract
Figure/Table
References
Related Citation (3)

Download: PDF (1912 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the advancement of the “dual carbon” strategy and the rapid construction of a new energy system, the proportion of renewable energy in the power system is continuously increasing. The grid connection of large-scale renewable energy has brought the challenge of insufficient regulation capacity, manifested as the dual risks of power curtailment and shortage. Relying solely on the regulation capacity resources on the generation side to follow net load change can no longer meet system operation requirements. Existing research on regulation capacity assessment and resource allocation has made progress, but most remain at the level of global indicators, lacking spatiotemporal identification of links with insufficient regulation capacity. At the planning level, the dynamic coupling between energy storage configuration and transmission expansion is generally ignored, which can lead to wasted storage investment and redundant line expansion, making it difficult to meet the flexibility and economic needs of a high-proportion renewable energy system. Therefore, this paper proposes a storage-transmission joint planning method considering the identification of weak links in regulation capability. This method conducts spatiotemporal assessment of regulation capability, tracing the source of regulation requirements to when and where they originate, thereby achieving precise localization of weak links and providing a scientific basis for accurate storage configuration and efficient transmission expansion.
Firstly, considering that insufficient regulation capability mainly stems from inadequate peak shaving and transmission congestion, a source tracing method for power system supply-demand imbalance is proposed. This method clarifies the causes of insufficient regulation capability by characterizing the imbalance between supply and demand.
Secondly, a regulation capability deficiency index and a line full-load duration ratio index are constructed to identify when and where deficiencies occur, precisely locating weak peak shaving nodes and bottleneck lines in the power system, providing scientific guidance for subsequent planning and reducing the solution domain.
Finally, the weak link identification results are incorporated into the storage-transmission planning model. Considering the dynamic coupling mechanism between storage configuration and transmission expansion, the model achieves an integrated planning chain of “imbalance tracing-weak link identification-precise configuration” based on both economy and flexibility. To verify the effectiveness of this closed-loop model, five comparative schemes are set up, and the effectiveness and rationality of the proposed method are verified by using the Garver-6 system and the power grid of a northeastern province.
The following conclusions can be drawn: (1) The imbalance tracing method provided can effectively clarify the main causes of supply-demand imbalance, and the proposed insufficient regulation index and line full-load duration ratio can accurately identify weak nodes and bottleneck transmission lines. (2) The storage-transmission planning method considering weak link identification has clear economic advantages. Considering the dynamic coupling between storage and transmission avoids wasted storage investment and redundant expansion, significantly improving economy of grid operation. (3) The method has the best effect on weak links. Compared with the scheme of “planning lines first and then configuring storage”, the regulation capability shortage index decreases by 78.1%, and the line full-load duration ratio decreases by 63.5%, further proving the superiority of the proposed method.

Key words： High-penetration renewable energy supply-demand imbalance traceability weak link assessment joint expansion planning

Received: 08 July 2025

PACS:

TM715

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yang Xiuyu
	Fan Zekun
	Yan Gangui
	Yang Cheng

Cite this article:

Yang Xiuyu,Fan Zekun,Yan Gangui等. Joint Storage-Transmission Planning Method Considering Identification of Weak Links in Regulation Capacity[J]. Transactions of China Electrotechnical Society, 2026, 41(15): 5132-5145.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251194 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I15/5132

[1] 黄大为, 郭念康, 于娜, 等. 面向系统灵活性提升的大用户激励型需求响应优化策略[J]. 电工技术学报, 2024, 39(21): 6778-6792.
Huang Dawei, Guo Niankang, Yu Na, et al.Optimization strategy for enhancing system flexibility through incentive demand response of large-scale users[J]. Transactions of China Electrotechnical Society, 2024, 39(21): 6778-6792.
[2] 陈胜, 陈明健, 卫志农, 等. 电-气-氢混联综合能源配网灵活运行域模型[J]. 电工技术学报, 2025, 40(19): 6101-6114.
Chen Sheng, Chen Mingjian, Wei Zhinong, et al.Flexible operation region for integrated electric-gas-hydrogen distribution network[J]. Transactions of China Electrotechnical Society, 2025, 40(19): 6101-6114.
[3] 徐峰亮, 王克谦, 王文豪, 等. 计及运行灵活性的中压配电系统源-网-储协同扩展规划[J]. 中国电力, 2024, 57(7): 98-108.
Xu Fengliang, Wang Keqian, Wang Wenhao, et al.Collaborative expansion planning of source-grid-storage in medium voltage distribution system considering operational flexibility[J]. Electric Power, 2024, 57(7): 98-108.
[4] 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.
[5] 章枫, 樊恒建, 邓晖, 等. 基于数学形态学的电力系统灵活调节能力充裕度分析[J]. 中国电力, 2025, 58(3): 43-54.
Zhang Feng, Fan Hengjian, Deng Hui, et al.Analysis of power system flexibility regulation adequacy based on mathematical morphology[J]. Electric Power, 2025, 58(3): 43-54.
[6] 鲁宗相, 林弋莎, 乔颖, 等. 极高比例可再生能源电力系统的灵活性供需平衡[J]. 电力系统自动化, 2022, 46(16): 3-16.
Lu Zongxiang, Lin Yisha, Qiao Ying, et al.Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(16): 3-16.
[7] 林芝羽, 李华强, 苏韵掣, 等. 计及灵活性承载度的电网评估与扩展规划方法[J]. 电力系统保护与控制, 2021, 49(5): 46-57.
Lin Zhiyu, Li Huaqiang, Su Yunche, et al.Evaluation and expansion planning method of a power system considering flexible carrying capacity[J]. Power System Protection and Control, 2021, 49(5): 46-57.
[8] 臧延雪, 边晓燕, 梁思琪, 等. 计及线路传输能力的新能源电力系统灵活性评估及优化调度方法[J]. 电力系统保护与控制, 2023, 51(11): 15-26.
Zang Yanxue, Bian Xiaoyan, Liang Siqi, et al.Flexibility evaluation and optimal dispatching method of a renewable energy power system considering line transmission capacity[J]. Power System Protection and Control, 2023, 51(11): 15-26.
[9] Dai Xinyin.Flexibility evaluation of a new distribution system based on co-operative game-Gaussian cloud model[J]. Electrical Engineering, 2025, 107(4): 4187-4202.
[10] 张海波, 胡玉康, 李正荣, 等. 负荷高密度地区中计及灵活性不足风险的储能优化配置[J]. 电网技术, 2023, 47(12): 4926-4940.
Zhang Haibo, Hu Yukang, Li Zhengrong, et al.Optimal configuration of energy storage considering the risk of insufficient flexibility in high load density areas[J]. Power System Technology, 2023, 47(12): 4926-4940.
[11] 邹世豪, 曹永吉, 张恒旭, 等. 考虑碳捕集电厂运行灵活性的网-储联合规划[J]. 高电压技术, 2024, 50(11): 5164-5173.
Zou Shihao, Cao Yongji, Zhang Hengxu, et al.Joint grid-storage planning considering operational flexibility of carbon capture power plant[J]. High Voltage Engineering, 2024, 50(11): 5164-5173.
[12] 黄玲玲, 卞雅婕, 付张杰, 等. 考虑网络传输灵活性动态变化的输电网多元灵活性资源协调规划方法[J]. 中国电机工程学报, 2026, 46(4): 1458-1470.
Huang Lingling, Bian Yajie, Fu Zhangjie, et al.Multi flexible resource coordination planning considering the matching of transmission requirements and transmission capabilities in the process of flexible resource transmission[J]. Proceedings of the CSEE, 2026, 46(4): 1458-1470.
[13] 路帅超, 孙英云, 赵鹏飞, 等. 新型电力系统多阶段输-储协同分布鲁棒规划[J]. 电力系统自动化, 2024, 48(15): 15-24.
Lu Shuaichao, Sun Yingyun, Zhao Pengfei, et al.Multi-stage transmission-storage cooperative distr-ibutionally robust planning for new power system[J]. Automation of Electric Power Systems, 2024, 48(15): 15-24.
[14] 李军徽, 陈国航, 马腾, 等. 高风电渗透率下液流电池储能系统调峰优化控制策略[J]. 发电技术, 2024, 45(3): 434-447.
Li Junhui, Chen Guohang, Ma Teng, et al.Optimal control strategy of peak shaving of flow battery energy storage system under high wind power permeability[J]. Power Generation Technology, 2024, 45(3): 434-447.
[15] 赵冬梅, 宋晨铭, 冯向阳, 等. 100%新能源场景下考虑频率稳定约束的源网荷储一体化系统储能优化配置[J]. 电工技术学报, 2025, 40(7): 2146-2161.
Zhao Dongmei, Song Chenming, Feng Xiangyang, et al.The optimal configuration of energy storage in the source-grid-load-storage integrated system considering frequency stability constraints in 100% new energy scenarios[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2146-2161.
[16] 肖娟霞, 李勇, 韩宇, 等. 计及台风时空特性和灵活性资源协同优化的配电网弹性提升策略[J]. 电工技术学报, 2024, 39(23): 7430-7446.
Xiao Juanxia, Li Yong, Han Yu, et al.Resilience enhancement strategy for distribution networks considering the spatiotemporal characteristics of typhoon and the collaborative optimization of flexible resources[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7430-7446.
[17] 王杰, 徐立军, 李笑竹, 等. 计及灵活性不足风险的配电网智能软开关与多类型共享储能协调优化[J]. 发电技术, 2025, 46(4): 758-767, 634.
Wang Jie, Xu Lijun, Li Xiaozhu, et al.Coordinated optimization of intelligent soft open points and multiple types of shared energy storage in distribution networks considering flexibility deficiency risk[J]. Power Generation Technology, 2025, 46(4): 758-767, 634.
[18] 郇政林, 刘杰, 徐沈智, 等. 面向高比例新能源接入的源-荷-储灵活性资源协调规划[J]. 电网与清洁能源, 2022, 38(7): 107-117.
Huan Zhenglin, Liu Jie, Xu Shenzhi, et al.Source-load-storage flexibility resource coordinated planning for high proportion of renewable energy[J]. Power System and Clean Energy, 2022, 38(7): 107-117.
[19] 季克勤, 卢昊威. 考虑多元灵活性资源的配电网协同优化调度[J]. 电气技术, 2024, 25(7): 23-31.
Ji Keqin, Lu Haowei.Coordinative optimization and dispatching of distribution network considering multi-flexible resources[J]. Electrical Engineering, 2024, 25(7): 23-31.
[20] Fu Yu, Bai Hao, Cai Yongxiang, et al.Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration[J]. Scientific Reports, 2024, 14: 7658.
[21] 吕婉煜, 赵红生, 韩应生, 等. 供需不确定性下抽水蓄能提升系统灵活性的分层优化调度[J]. 电工技术学报, 2025, 40(21): 6984-6999.
Lü Wanyu, Zhao Hongsheng, Han Yingsheng, et al.Hierarchical optimal scheduling for pumped storage to enhance system flexibility under supply-demand uncertainty[J]. Transactions of China Electrote-chnical Society, 2025, 40(21): 6984-6999.
[22] 杨心刚, 臧延雪, 张雅君, 等. 计及线路-源-储灵活性的电力系统分层优化调度方法[J]. 电力自动化设备, 2024, 44(8): 185-192, 209.
Yang Xingang, Zang Yanxue, Zhang Yajun, et al.Hierarchical optimal scheduling method for power system considering line-source-storage flexibility[J]. Electric Power Automation Equipment, 2024, 44(8): 185-192, 209.
[23] 郑爽, 杨晓楠, 句荣滨, 等. 两种启发式电网规划算法的比较与改进[J]. 电力系统保护与控制, 2019, 47(17): 109-116.
Zheng Shuang, Yang Xiaonan, Ju Rongbin, et al.Comparison and improvement of two heuristic algorithms for power network planning[J]. Power System Protection and Control, 2019, 47(17): 109-116.