面向多场景调节需求的集中式共享储能鲁棒优化配置

doi:10.19595/j.cnki.1000-6753.tces.220726

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (1961 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

With the continuous development of the high proportion new energy power system, the demand for energy storage under different scenarios of source network load is increasing day by day. A robust optimal allocation model of centralized shared energy storage for multi-scenario regulation demand is proposed. Firstly, the operation mode of shared energy storage is designed, which takes into account the coordination of the power supply side output plan and participation in power grid side frequency modulation. The operation strategy of shared energy storage under multi-scenario regulation demand is given. Secondly, considering the joint output constraint and dynamic frequency constraint of source network storage, and describing the uncertainty of power generation and consumption by using the robust theory of accurate probability distribution, a robust optimal allocation model of centralized shared energy storage considering the extreme deviation of random variables is established. Finally, the modified IEEE 39 node is used to simulate and analyze the constructed model, and the key factors affecting the economy and robustness of the capacity allocation model are further discussed to verify the effectiveness of the optimal allocation model proposed in this paper.

Key words： Centralized shared energy storage capacity optimization configuration multi-scenario regulation demand source-load uncertainty

Received: 04 May 2022

PACS:

TM910

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Du Xili
	Li Xiaozhu
	Chen Laijun
	Hao Yibo
	Mei Shengwei

Cite this article:

Du Xili,Li Xiaozhu,Chen Laijun等. 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.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220726 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I23/5911

[1] 伍梦尧. 清洁能源高比例发展看青海[N]. 中国电力报, 2021-07-21(1).
[2] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报: 1-11[2022-06-25]. DOI: 10.19595/j.cnki. 1000-6753.tces.211730.
Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society: 1-11[2022-06-25]. DOI: 10.19595/j.cnki.1000- 6753.tces.211730.
[3] Yang Yuqing, Bremner S, Menictas C, et al.Battery energy storage system size determination in renewable energy systems: a review[J]. Renewable and Sustainable Energy Reviews, 2018, 91: 109-125.
[4] 王霞, 应黎明, 卢少平. 考虑动态频率约束的一次调频和二次调频联合优化模型[J]. 电网技术, 2020, 44(8): 2858-2867.
Wang Xia, Ying Liming, Lu Shaoping.Joint optimization model for primary and secondary frequency regulation considering dynamic frequency constraint[J]. Power System Technology, 2020, 44(8): 2858-2867.
[5] 裴佑, 裴哲义, 邱伟强, 等. 基于区块链的共享储能联合调频分散交易机制设计[J]. 电力自动化设备, 2021, 41(10): 138-145.
Pei You, Pei Zheyi, Qiu Weiqiang, et al.Design of decentralized trading mechanism for shared energy storage joint frequency regulation based on blockchain[J]. Electric Power Automation Equipment, 2021, 41(10): 138-145.
[6] 刘巨, 姚伟, 文劲宇, 等. 一种基于储能技术的风电场虚拟惯量补偿策略[J]. 中国电机工程学报, 2015, 35(7): 1596-1605.
Liu Ju, Yao Wei, Wen Jinyu, et al.A wind farm virtual inertia compensation strategy based on energy storage system[J]. Proceedings of the CSEE, 2015, 35(7): 1596-1605.
[7] 李咸善, 解仕杰, 方子健, 等. 多微电网共享储能的优化配置及其成本分摊[J]. 电力自动化设备, 2021, 41(10): 44-51.
Li Xianshan, Xie Shijie, Fang Zijian, et al.Optimal configuration of shared energy storage for multi-microgrid and its cost allocation[J]. Electric Power Automation Equipment, 2021, 41(10): 44-51.
[8] 李军徽, 侯涛, 穆钢, 等. 电力市场环境下考虑风电调度和调频极限的储能优化控制[J]. 电工技术学报, 2021, 36(9): 1791-1804.
Li Junhui, Hou Tao, Mu Gang, et al.Optimal control strategy for energy storage considering wind farm scheduling plan and modulation frequency limitation under electricity market environment[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1791-1804.
[9] 石涛, 张斌, 晁勤, 等. 兼顾平抑风电波动和补偿预测误差的混合储能容量经济配比与优化控制[J]. 电网技术, 2016, 40(2): 477-483.
Shi Tao, Zhang Bin, Chao Qin, et al.Economic storage ratio and optimal control of hybrid energy capacity combining stabilized wind power fluctuations with compensated predictive errors[J]. Power System Technology, 2016, 40(2): 477-483.
[10] Bludszuweit H, Domínguez J A.Probabilistic energy storage sizing for reducing wind power forecast uncertainty[J]. Renewable Energy and Power Quality Journal, 2010, 1(8): 1153-1157.
[11] 杨帆, 王维庆, 程静, 等. 基于误差分配原则的发电侧共享储能容量规划研究[J]. 电力系统保护与控制, 2022, 50(7): 91-102.
Yang Fan, Wang Weiqing, Cheng Jing, et al.Capacity planning of shared energy storage on the generation side based on the error distribution principle[J]. Power System Protection and Control, 2022, 50(7): 91-102.
[12] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516.
Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516.
[13] 孙偲, 陈来军, 邱欣杰, 等. 基于合作博弈的发电侧共享储能规划模型[J]. 全球能源互联网, 2019, 2(4): 360-366.
Sun Cai, Chen Laijun, Qiu Xinjie, et al.A generation-side shared energy storage planning model based on cooperative game[J]. Journal of Global Energy Interconnection, 2019, 2(4): 360-366.
[14] Hou Kai, Jia Hongjie, Xu Xiandong, et al.A continuous time Markov chain based sequential analytical approach for composite power system reliability assessment[J]. IEEE Transactions on Power Systems, 2016, 31(1): 738-748.
[15] 孙伟卿, 宋赫, 秦艳辉, 等. 考虑灵活性供需不确定性的储能优化配置[J]. 电网技术, 2020, 44(12): 4486-4497.
Sun Weiqing, Song He, Qin Yanhui, et al.Energy storage system optimal allocation considering flexibility supply and demand uncertainty[J]. Power System Technology, 2020, 44(12): 4486-4497.
[16] Akram U, Mithulananthan N, Raza M Q, et al.RoCoF restrictive planning framework and wind speed forecast informed operation strategy of energy storage system[J]. IEEE Transactions on Power Systems, 2021, 36(1): 224-234.
[17] 李笑竹, 王维庆, 王海云, 等. 考虑荷源双侧不确定性的跨区域灵活性鲁棒优化运行策略[J]. 高电压技术, 2020, 46(5): 1538-1549.
Li Xiaozhu, Wang Weiqing, Wang Haiyun, et al.Robust optimized operation strategy for cross-region flexibility with bilateral uncertainty of load source[J]. High Voltage Engineering, 2020, 46(5): 1538-1549.
[18] Zahasky C, Krevor S.Global geologic carbon storage requirements of climate change mitigation scenarios[J]. Energy & Environmental Science, 2020, 13(6): 1561-1567.
[19] 吴杰, 丁明, 张晶晶. 基于云模型和K-means聚类的风电场储能容量优化配置方法[J]. 电力系统自动化, 2018, 42(24): 67-73.
Wu Jie, Ding Ming, Zhang Jingjing.Capacity configuration method of energy storage system for wind farm based on cloud model and K-means clustering[J]. Automation of Electric Power Systems, 2018, 42(24): 67-73.
[20] 李笑竹, 王维庆. 基于贝叶斯理论的分布鲁棒优化在储能配置上的应用[J/OL].电网技术:1-11[2022-06-25].DOI:10.13335/j.1000-3673.pst.2021.1978.
Li Xiaozhu, WANG Weiqing.Distributed robust optimization based on Bayesian theory application in allocation of energy storage[J/OL]. Power System Technology:1-11[2022-06-25].DOI:10.13335/j.1000-3673.pst.2021.1978.
[21] 帅轩越, 王秀丽, 吴雄, 等. 计及电热需求响应的共享储能容量配置与动态租赁模型[J]. 电力系统自动化, 2021, 45(19): 24-32.
Shuai Xuanyue, Wang Xiuli, Wu Xiong, et al.Shared energy storage capacity allocation and dynamic lease model considering electricity-heat demand response[J]. Automation of Electric Power Systems, 2021, 45(19): 24-32.
[22] 李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
Li Junhui, Zhang Jiahui, Li Cuiping, et al.Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
[23] 王少林, 韩鑫磊, 王海磊. 新能源场站储能系统的鲁棒优化配置策略[J]. 现代电力, 2021, 38(6): 636-644.
Wang Shaolin, Han Xinlei, Wang Hailei.Robust optimization configuration strategy for energy storage system of new energy station[J]. Modern Electric Power, 2021, 38(6): 636-644.
[24] 郑乐, 胡伟, 陆秋瑜, 等. 储能系统用于提高风电接入的规划和运行综合优化模型[J]. 中国电机工程学报, 2014, 34(16): 2533-2543.
Zheng Le, Hu Wei, Lu Qiuyu, et al.Research on planning and operation model for energy storage system to optimize wind power integration[J]. Proceedings of the CSEE, 2014, 34(16): 2533-2543.
[25] 梅生伟, 王莹莹, 刘锋. 风-光-储混合电力系统的博弈论规划模型与分析[J]. 电力系统自动化, 2011, 35(20): 13-19.
Mei Shengwei, Wang Yingying, Liu Feng.A game theory based planning model and analysis for hybrid power system with wind generators-photovoltaic panels-storage batteries[J]. Automation of Electric Power Systems, 2011, 35(20): 13-19.
[26] Liu Sai, Zhou Cheng, Guo Haomin, et al.Operational optimization of a building-level integrated energy system considering additional potential benefits of energy storage[J]. Protection and Control of Modern Power Systems, 2021, 6(1): 1-10.