面向全场景安全的储能投资高效规划方法

doi:10.19595/j.cnki.1000-6753.tces.231501

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Abstract With the continuous increase in the penetration rate of renewable energy sources such as wind and photovoltaics, the randomness and volatility of the power system are constantly growing. Investing energy storage is considered one of the most effective ways to alleviate the uncertainty of renewable energy and enhance the security and flexibility of the power system. However, existing planning methods, to ensure the computational efficiency, usually select only a few key scenarios to formulate energy storage planning, which cannot guarantee its security under all scenarios. If security verification is performed for the planning across all scenarios, it will be difficult to accept due to the large model size leading to unacceptable solution time. Therefore, this paper proposes an efficient method for energy storage planning considering full-scenario security.
Firstly, addressing the security risks brought by open-loop scenario clustering and planning solutions in existing planning methods, a closed-loop energy storage planning framework oriented towards security under all scenarios is proposed. Secondly, by constructing a relaxed energy storage planning model based on fixed 0-1 variables to obtain a ranking result of the full scenario set reflecting the criticality of different scenarios, guiding the update of key scenarios based on this ranking result can ensure full-scenario security while balancing computational efficiency. Thirdly, this method requires a given set of initial key scenario sets. To address the issues of traditional clustering methods such as K-means, which require a predefined number of clusters, are prone to submerge extreme scenarios' information, and can not accurately reflect the critical information of all scenarios, a method for generating an initial scenario set based on self-organizing maps (SOM) neural network and scenario critical indicator ranking is further proposed. This method does not require a predefined number of clusters and can accurately reflect critical information under all scenarios, further improving computational efficiency.
To validate the effectiveness of the proposed energy storage planning method, case studies are conducted on the IEEE 30 bus system and an actual 341-bus system in a domestic province. Scenarios are considered with full scenario set sizes of 30 and 365, and different levels of renewable energy fluctuations and line transmission capacity limits are taken into account. The results indicate that, considering the detailed system operational characteristics, existing closed-loop energy storage planning methods, due to the lack of guidance from key scenarios' information, take an excessively long time to solve when dealing with a large number of scenarios in practical systems, exceeding two weeks in some cases. The proposed method can ensure the security and optimality of planning under all scenarios while reducing the number of scenarios to be considered and improving solution efficiency. In practical systems, the proposed method can obtain energy storage planning solutions within 4 hours.
From the numerical analysis, the following conclusions can be drawn: (1) Considering the fine temporal granularity of the power system operational characteristics in the planning year, existing energy storage planning methods are computationally expensive, even at the planning level. (2) The proposed energy storage planning method accurately considers the fine temporal granularity of the power system operational characteristics of the planning year. While ensuring the security of planning under all scenarios, it improves solution efficiency by reducing the number of scenarios to be considered and selecting more reasonable initial key scenarios. (3) In actual power system operations, the proposed energy storage planning method can consider the benefits obtained through peak-to-valley electricity price differentials and has certain economic value.

Key words： Energy storage expansion planning multi-scenario planning scenario clustering security check self-organizing maps (SOM) neural network

Received: 12 September 2023

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	Cheng Caoyang
	Yang Zhifang
	Yu Juan
	Wang Xingang
	Zhou Zhuan

Cite this article:

Cheng Caoyang,Yang Zhifang,Yu Juan等. An Efficient Method for Energy Storage Planning Considering Full-Scenario Security[J]. Transactions of China Electrotechnical Society, 2025, 40(1): 64-79.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231501 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I1/64

[1] 中华人民共和国国务院. 国务院关于印发2030年前碳达峰行动方案的通知[J]. 中华人民共和国国务院公报, 2021(31): 48-58.
State Council of the People's Republic of China. Circular of the state council on printing and issuing the action plan for carbon dioxide peaking before2030[J]. Gazette of the State Council of the People's Republic of China, 2021(31): 48-58.
[2] 鲁宗相, 李海波, 乔颖. 含高比例可再生能源电力系统灵活性规划及挑战[J]. 电力系统自动化, 2016, 40(13): 147-158.
Lu Zongxiang, Li Haibo, Qiao Ying.Power system flexibility planning and challenges considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2016, 40(13): 147-158.
[3] 陈红坤, 王雪纯, 陈磊. 考虑灵活性供给约束的区域综合能源系统节点边际能价分解分析[J]. 电工技术学报, 2023, 38(13): 3576-3589.
Chen Hongkun, Wang Xuechun, Chen Lei.Decomposition analysis on locational marginal energy prices in regional integrated energy system considering flexibility provision constraints[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3576-3589.
[4] 孙玉树, 杨敏, 师长立, 等. 储能的应用现状和发展趋势分析[J]. 高电压技术, 2020, 46(1): 80-89.
Sun Yushu, Yang Min, Shi Changli, et al.Analysis of application status and development trend of energy storage[J]. High Voltage Engineering, 2020, 46(1): 80-89.
[5] 毕皓淳, 祁琪, 刘向军, 等. 计及5G基站运行协同的分布式储能优化配置方法[J]. 电工技术学报, 2024, 39(21): 6819-6833.
Bi Haochun, Qi Qi, Liu Xiangjun, et al.An optimal configuration method for DES considering coordinated operation of 5G base station[J]. Transactions of China Electrotechnical Society, 2024, 39(21): 6819-6833.
[6] Gabrielli P, Gazzani M, Martelli E, et al.Optimal design of multi-energy systems with seasonal storage[J]. Applied Energy, 2018, 219: 408-424.
[7] 姜海洋, 杜尔顺, 金晨, 等. 高比例清洁能源并网的跨国互联电力系统多时间尺度储能容量优化规划[J]. 中国电机工程学报, 2021, 41(6): 2101-2115.
Jiang Haiyang, Du Ershun, Jin Chen, et al.Optimal planning of multi-time scale energy storage capacity of cross-national interconnected power system with high proportion of clean energy[J]. Proceedings of the CSEE, 2021, 41(6): 2101-2115.
[8] 林雨眠, 熊厚博, 张笑演, 等. 计及新能源机会约束与虚拟储能的电-热系统分布式多目标优化调度[J]. 电工技术学报, 2024, 39(16): 5042-5059.
Lin Yumian, Xiong Houbo, Zhang Xiaoyan, et al.Distributed multi-objective optimal scheduling of integrated electric-heat system considering chance constraint of new energy and virtual storage[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5042-5059.
[9] Zhang Ning, Jiang Haiyang, Du Ershun, et al.An efficient power system planning model considering year-round hourly operation simulation[J]. IEEE Transactions on Power Systems, 2022, 37(6): 4925-4935.
[10] 胡俊杰, 童宇轩, 刘雪涛, 等. 计及精细化氢能利用的综合能源系统多时间尺度鲁棒优化策略[J]. 电工技术学报, 2024, 39(5): 1419-1435.
Hu Junjie, Tong Yuxuan, Liu Xuetao, et al.Multi-time-scale robust optimization strategy for integrated energy system considering the refinement of hydrogen energy use[J]. Transactions of China Electrote-chnical Society, 2024, 39(5): 1419-1435.
[11] PJM. PJM region transmission planning process[EB/OL]. (2021-12). http://www.pjm.com/-/media/documents/ manuals/m14b.ashx.
[12] 高海淑, 张玉敏, 吉兴全, 等. 基于场景聚类的主动配电网分布鲁棒综合优化[J]. 电力系统自动化, 2020, 44(21): 32-41.
Gao Haishu, Zhang Yumin, Ji Xingquan, et al.Scenario clustering based distributionally robust comprehensive optimization of active distribution network[J]. Automation of Electric Power Systems, 2020, 44(21): 32-41.
[13] Zheng Fangfang, Meng Xiaofang, Wang Lidi, et al.Operation optimization method of distribution network with wind turbine and photovoltaic considering clustering and energy storage[J]. Sustainability, 2023, 15(3): 2184.
[14] 吴俊, 薛禹胜, 舒印彪, 等. 大规模可再生能源接入下的电力系统充裕性优化 (三)多场景的备用优化[J]. 电力系统自动化, 2019, 43(11): 1-7, 76.
Wu Jun, Xue Yusheng, Shu Yinbiao, et al.Adequacy optimization for a large-scale renewable energy integrated power system part three reserve optimization in multiple scenarios[J]. Automation of Electric Power Systems, 2019, 43(11): 1-7, 76.
[15] Tso W W, Demirhan C D, Heuberger C F, et al.A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage[J]. Applied Energy, 2020, 270: 115190.
[16] 李军徽, 赵寒杰, 朱星旭, 等. 基于区域间场景等值的配电网随机潮流加速算法[J]. 电工技术学报, 2024, 39(7): 1957-1970.
Li Junhui, Zhao Hanjie, Zhu Xingxu, et al.Stochastic power flow acceleration algorithm of distribution network based on scene equivalence between regions[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 1957-1970.
[17] 张斌, 庄池杰, 胡军, 等. 结合降维技术的电力负荷曲线集成聚类算法[J]. 中国电机工程学报, 2015, 35(15): 3741-3749.
Zhang Bin, Zhuang Chijie, Hu Jun, et al.Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J]. Proceedings of the CSEE, 2015, 35(15): 3741-3749.
[18] Tejada-Arango D A, Domeshek M, Wogrin S, et al. Enhanced representative days and system states modeling for energy storage investment analysis[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6534-6544.
[19] 郭力, 杨书强, 刘一欣, 等. 风光储微电网容量规划中的典型日选取方法[J]. 中国电机工程学报, 2020, 40(8): 2468-2479.
Guo Li, Yang Shuqiang, Liu Yixin, et al.Typical day selection method for capacity planning of microgrid with wind turbine-photovoltaic and energy storage[J]. Proceedings of the CSEE, 2020, 40(8): 2468-2479.
[20] 潘超, 范宫博, 王锦鹏, 等. 灵活性资源参与的电热综合能源系统低碳优化[J]. 电工技术学报, 2023, 38(6): 1633-1647.
Pan Chao, Fan Gongbo, Wang Jinpeng, et al.Low-carbon optimization of electric and heating integrated energy system with flexible resource participation[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1633-1647.
[21] García-Cerezo Á, García-Bertrand R, Baringo L.Enhanced representative time periods for transmission expansion planning problems[J]. IEEE Transactions on Power Systems, 2021, 36(4): 3802-3805.
[22] 黄森. 面向高比例可再生能源接入的分布式储能优化规划[D]. 北京: 华北电力大学, 2022.
Huang Sen.Optimal planning of distributed energy storage for high proportion of renewable energy[D]. Beijing : North China Electric Power University, 2022.
[23] 杜锡力, 李笑竹, 陈来军, 等. 面向多场景调节需求的集中式共享储能鲁棒优化配置[J]. 电工技术学报, 2022, 37(23): 5911-5921.
Du Xili, Li Xiaozhu, Chen Laijun, et al.Robust and optimized configuration of centralized shared energy storage for multi-scenario regulation demand[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5911-5921.
[24] Pineda S, Morales J M.Chronological time-period clustering for optimal capacity expansion planning with storage[J]. IEEE Transactions on Power Systems, 2018, 33(6): 7162-7170.
[25] Novo R, Marocco P, Giorgi G, et al.Planning the decarbonisation of energy systems: the importance of applying time series clustering to long-term models[J]. Energy Conversion and Management: X, 2022, 15: 100274.
[26] Tejada-Arango D A, Wogrin S, Centeno E. Representation of storage operations in network-constrained optimization models for medium- and long-term operation[J]. IEEE Transactions on Power Systems, 2018, 33(1): 386-396.
[27] Zhang Yufan, Ai Qian, Xiao Fei, et al.Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network[J]. International Journal of Electrical Power & Energy Systems, 2020, 114: 105388.
[28] Chen Yize, Wang Yishen, Kirschen D, et al.Model-free renewable scenario generation using generative adversarial networks[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3265-3275.
[29] 朱玲, 李威, 王骞, 等. 基于校正条件生成对抗网络的风电场群绿氢储能系统容量配置[J]. 电工技术学报, 2024, 39(3): 714-730.
Zhu Ling, Li Wei, Wang Qian, et al.Wind farms-green hydrogen energy storage system capacity sizing method based on corrected-conditional generative adversarial network[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 714-730.
[30] Bahl B, Kümpel A, Seele H, et al.Time-series aggregation for synthesis problems by bounding error in the objective function[J]. Energy, 2017, 135: 900-912.
[31] Liu Yixian, Sioshansi R, Conejo A J.Hierarchical clustering to find representative operating periods for capacity-expansion modeling[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3029-3039.
[32] García-Cerezo Á, García-Bertrand R, Baringo L.Priority chronological time-period clustering for generation and transmission expansion planning problems with long-term dynamics[J]. IEEE Transactions on Power Systems, 2022, 37(6): 4325-4339.
[33] 解蛟龙. 风/光/负荷典型场景缩减方法及在电网规划中的应用[D]. 合肥: 合肥工业大学, 2017.
Xie Jiaolong.A typical reduced-scenario analysis method for wind photoelectric, load scene and its application in power system planning[D]. Hefei: Hefei University of Technology, 2017.
[34] 赵书强, 要金铭, 李志伟. 基于改进K-means聚类和SBR算法的风电场景缩减方法研究[J]. 电网技术, 2021, 45(10): 3947-3954.
Zhao Shuqiang, Yao Jinming, Li Zhiwei.Wind power scenario reduction based on improved K-means clustering and SBR algorithm[J]. Power System Technology, 2021, 45(10): 3947-3954.
[35] 张沛, 田佳鑫, 谢桦. 计及多个风场预测误差的电力系统风险快速计算方法[J]. 电工技术学报, 2021, 36(9): 1876-1887.
Zhang Pei, Tian Jiaxin, Xie Hua.A fast risk assessment method with consideration of forecasting errors of multiple wind farms[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1876-1887.
[36] 王群, 董文略, 杨莉. 基于Wasserstein距离和改进K-medoids聚类的风电/光伏经典场景集生成算法[J]. 中国电机工程学报, 2015, 35(11): 2654-2661.
Wang Qun, Dong Wenlue, Yang Li.A wind power/photovoltaic typical scenario set generation algorithm based on Wasserstein distance metric and revised K-medoids cluster[J]. Proceedings of the CSEE, 2015, 35(11): 2654-2661.
[37] 陈宝林. 最优化理论与算法[M]. 2版. 北京: 清华大学出版社, 2005.
[38] 高倩, 杨知方, 李文沅, 等. 分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法[J]. 电工技术学报, 2023: 2024, 39(13): 3990-4002.
Gao Qian, Yang Zhifang, Li Wenyuan, et al.Dispatch acceleration of integrated electricity and gas system using branch-and-bound search information[J]. Tran-sactions of China Electrotechnical Society, 2023: 2024, 39(13): 3990-4002.
[39] 汪洋, 夏清, 康重庆. 机组组合算法中起作用整数变量的辨识方法[J]. 中国电机工程学报, 2010, 30(13): 46-52.
Wang Yang, Xia Qing, Kang Chongqing.Identi-fication of the active integer variables in security constrained unit commitment[J]. Proceedings of the CSEE, 2010, 30(13): 46-52.
[40] Kohonen T.The self-organizing map[J]. Proceedings of the IEEE, 1990, 78(9): 1464-1480.
[41] 周楠, 徐潇源, 严正, 等. 基于宽度学习系统的光伏发电功率超短期预测[J]. 电力系统自动化, 2021, 45(1): 55-64.
Zhou Nan, Xu Xiaoyuan, Yan Zheng, et al.Ultra-short-term forecasting of photovoltaic power genera-tion based on broad learning system[J]. Automation of Electric Power Systems, 2021, 45(1): 55-64.
[42] 陈宇航, 王渝红, 南璐, 等.基于自组织映射-前馈神经网络和先知混合模型的短期负荷预测[J/OL]. 现代电力, 2023: 1-9. https://doi.org/10.19725/j.cnki. 1007- 2322.2023.0036.
Chen Yuhang, Wang Yuhong, Nan Lu.Short-Term load forecasting based on SOM-BP and prophet hybrid model[J/OL]. Modern Electric Power, 2023: 1-9. https://doi.org/10.19725/j.cnki.1007-2322.2023.0036.
[43] 康重庆, 姚良忠. 高比例可再生能源电力系统的关键科学问题与理论研究框架[J]. 电力系统自动化, 2017, 41(9): 2-11.
Kang Chongqing, Yao Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 2-11.
[44] 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.
[45] Cheng Caoyang. Energy storage expansion planning [EB/OL].2023. https://github.com/cyangcheng/code-for-papers-230616.
[46] 浙江省人民政府. 浙江省发展改革委关于调整我省目录销售电价有关事项的通知[EB/OL].2021-10-25. https://www.zj.gov.cn/art/2021/10/25/art_1229203592_ 2370712.html.
[47] 李中浩, 余娟, 杨知方, 等. 精准计及大规模储能电池寿命的电力系统经济调度[J]. 中国电机工程学报, 2023, 43(19): 7371-7383.
Li Zhonghao, Yu Juan, Yang Zhifang, et al.Economic dispatch of power system accurately considering the life of large-scale energy storage battery[J]. Proceedings of the CSEE, 2023, 43(19): 7371-7383.