考虑多重不确定性的广义共享储能优化配置方法

doi:10.19595/j.cnki.1000-6753.tces.240248

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2548 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The volatility and intermittency of renewable energy sources will threaten the safe and stable operation of the power system. The "energy timing transfer" feature of energy storage (ES) broadens the path for the power system to realize the balance between supply and demand, and can alleviate the plight of renewable energy consumption. However, up to now, the large-scale application of ES still exists development bottlenecks such as high investment costs, long construction cycle and the lack of commercial profit model. Shared energy storage(SES) can effectively reduce redundant investment in ES resources and improve the utilization rate of it by decoupling the ownership and usage of ES resources and utilizing the complementary nature of users' needs.
The optimal configuration of SES resources is the basis for its efficient operation. Currently, research on the allocation of SES resources focuses on conventional electrochemical ES devices, and lacks unified modeling of multi-energy generalized ES devices, thus failing to realize the full exploitation of the large number of existing idle multi-energy generalized ES resources. In addition, most of the studies have not yet fully considered the uncertainty of user energy storage demand and the uncertainty impact brought by the introduction of multi-energy generalized ES resources, resulting in an uneconomical SES configuration scheme. For this reason, this paper proposes a framework for optimized configuration of generalized SES resources, i.e., the SES operator will optimize the construction scale of centralized electrochemical ES equipment on the basis of leasing the generalized distributed generalized ES resources, so as to reduce the upfront investment cost under the premise of meeting the users' demand for SES services.
Firstly, this paper models and aggregates the generalized energy storage(GES) resources, comprehensively considers the operational characteristics of GES resources, respectively models 5G base station backup energy storage, electric vehicle charging station, and air conditioner, utilizes Minkowski sum method to aggregate and encapsulate the large-scale GES resources, so as to achieve a concise representation of the GES model and to provide a model basis for the following text. Then, the generalized SES optimal configuration model is designed considering multiple uncertainties. SES operators and user groups form a cooperative alliance by ceding the right to use ES resources, and the generalized SES optimal configuration model is decomposed into two sub-problems of alliance energy cost minimization and internal payment bargaining based on the Nash bargaining theory; based on the deterministic model, the fuzzy opportunity constrained planning theory is used to quantify the risk caused by the uncertainty of the user group's source-load output and GES parameters.
Case study first verifies the effectiveness of the generalized SES optimal configuration model based on Nash bargaining. By leasing idle GES resources and taking advantage of the complementary feature of user demand, the SES is able to effectively reduce the configuration of centralized ES scale, and ultimately achieve a win-win situation between the SES operator and the user group. In addition, the results of optimal configuration taking into account multiple uncertainties show that the complementary role of GES resources is beneficial to the SES operator in reducing configuration costs and increasing net returns. Finally, compared to the uncertainty of GES parameters, the uncertainty of the source-load-side output brings more riskiness to the configuration decision of the SES operator, and the configuration scale of centralized electrochemical ES equipment increases with the increase of the uncertainty ambiguity; the configuration under high confidence can be realized by sacrificing some of the gains, thus achieving the balance between economy and reliability.

Key words： Shared energy storage generalized energy storage optimize configuration uncertainty fuzzy chance-constrained programming Nash bargaining

Received: 06 February 2024

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Song Meng
	Lin Gujing
	Gao Ciwei
	Zhang Zitao
	Chen Tao

Cite this article:

Song Meng,Lin Gujing,Gao Ciwei等. A Generalized Shared Energy Storage Optimization Configuration Method Considering Multiple Uncertainties[J]. Transactions of China Electrotechnical Society, 2025, 40(5): 1521-1539.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240248 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I5/1521

[1] 武昭原, 周明, 王剑晓, 等. 双碳目标下提升电力系统灵活性的市场机制综述[J]. 中国电机工程学报, 2022, 42(21): 7746-7763.
Wu Zhaoyuan, Zhou Ming, Wang Jianxiao, et al.Review on market mechanism to enhance the flexibility of power system under the dual-carbon target[J]. Proceedings of the CSEE, 2022, 42(21): 7746-7763.
[2] 陈启鑫, 房曦晨, 郭鸿业, 等. 储能参与电力市场机制: 现状与展望[J]. 电力系统自动化, 2021, 45(16): 14-28.
Chen Qixin, Fang Xichen, Guo Hongye, et al.Participation mechanism of energy storage in electricity market: status quo and prospect[J]. Automation of Electric Power Systems, 2021, 45(16): 14-28.
[3] Dai Rui, Esmaeilbeigi R, Charkhgard H.The utilization of shared energy storage in energy systems: a comprehensive review[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3163-3174.
[4] 宋梦, 林固静, 蒙璟, 等. 共享储能关键技术与应用[J]. 上海交通大学学报, 2024, 58(5): 585-599.
Song Meng, Lin Gujing, Meng Jing, et al.Key technologies and applications of shared energy storage[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 585-599.
[5] 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.
[6] 简川黔, 刘继春, 蒲天骄, 等. 共建共享模式下考虑需求响应的多售电主体风光储容量优化方法[J]. 电力自动化设备, 2021, 41(9): 206-214.
Jian Chuanqian, Liu Jichun, Pu Tianjiao, et al.Wind-photovoltaic-storage capacity optimization method for multiple electricity sell entities considering demand response under co-construction and sharing mode[J]. Electric Power Automation Equipment, 2021, 41(9): 206-214.
[7] 叶晨, 王蓓蓓, 薛必克, 等. 考虑超售的共享分布式光储混合运营模式协同策略研究[J]. 电工技术学报, 2022, 37(7): 1836-1846.
Ye Chen, Wang Beibei, Xue Bike, et al.Study on the coordination strategy of sharing distributed photovoltaic energy storage hybrid operation mode considering overselling[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1836-1846.
[8] 张思远, 钟浩. 聚合用户储能的云储能容量配置与定价[J]. 南方电网技术, 2022, 16(11): 9-19.
Zhang Siyuan, Zhong Hao.Cloud energy storage capacity configuration for aggregated user energy storage and its transaction pricing[J]. Southern Power System Technology, 2022, 16(11): 9-19.
[9] Song Meng, Gao Ciwei.Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy: A Case Study of TCLs[M]. Singapore: Springer, 2022.
[10] Song Meng, Gao Ciwei, Yan Huaguang, et al.Thermal battery modeling of inverter air conditioning for demand response[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 5522-5534.
[11] Song Meng, Gao Ciwei, Shahidehpour M, et al.Multi-time-scale modeling and parameter estimation of TCLs for smoothing out wind power generation variability[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 105-118.
[12] 电力需求侧响应潜力评估-北极星售电网[EB/OL]. [2024-02-13].https://news.bjx.com.cn/html/20201013/1109397.shtml.
[13] 雍培, 张宁, 慈松, 等. 5G通信基站参与需求响应: 关键技术与前景展望[J]. 中国电机工程学报, 2021, 41(16): 5540-5551.
Yong Pei, Zhang Ning, Ci Song, et al.5G communication base stations participating in demand response: key technologies and prospects[J]. Proceedings of the CSEE, 2021, 41(16): 5540-5551.
[14] 曹茜琳. 基于纳什议价博弈的用户侧共享储能优化配置研究[D]. 武汉: 中国地质大学, 2022.
Cao Xilin.Research on optimal allocation of shared energy storage on user side based on Nash bargaining game[D]. Wuhan: China University of Geosciences, 2022.
[15] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8214.
Zuo Fengyuan, Zhang Yuqiong, Zhao Qiang, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8214.
[16] Bayram I S, Abdallah M, Tajer A, et al.A stochastic sizing approach for sharing-based energy storage applications[J]. IEEE Transactions on Smart Grid, 2017, 8(3): 1075-1084.
[17] 孙伟卿, 向威, 裴亮, 等. 电力辅助服务市场下的用户侧广义储能控制策略[J]. 电力系统自动化, 2020, 44(2): 68-76.
Sun Weiqing, Xiang Wei, Pei Liang, et al.Generalized energy storage control strategies on user side in power ancillary service market[J]. Automation of Electric Power Systems, 2020, 44(2): 68-76.
[18] 刘轶涵, 徐青山, 杨永标, 等. 计及配电网潮流约束下基于广义纳什议价理论的工业用户共享储能配置[J]. 电网技术, 2023, 47(2): 571-585.
Liu Yihan, Xu Qingshan, Yang Yongbiao, et al.Distribution network power flow constrained shared energy storage configuration for industrial consumers based on generalized Nash bargaining theory[J]. Power System Technology, 2023, 47(2): 571-585.
[19] 董雷, 李扬, 陈盛, 等. 考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J]. 电工技术学报, 2024, 39(9): 2635-2651.
Dong Lei, Li Yang, Chen Sheng, et al.Multi-microgrid cooperative game optimization scheduling considering multiple uncertainties and coupled electricity-carbon transactions[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2635-2651.
[20] 段翩, 朱建全, 刘明波. 基于双层模糊机会约束规划的虚拟电厂优化调度[J]. 电工技术学报, 2016, 31(9): 58-67.
Duan Pian, Zhu Jianquan, Liu Mingbo.Optimal dispatch of virtual power plant based on bi-level fuzzy chance constrained programming[J]. Transactions of China Electrotechnical Society, 2016, 31(9): 58-67.
[21] 吴雄, 王秀丽, 王建学, 等. 微网经济调度问题的混合整数规划方法[J]. 中国电机工程学报, 2013, 33(28): 1-9.
Wu Xiong, Wang Xiuli, Wang Jianxue, et al.Economic generation scheduling of a microgrid using mixed integer programming[J]. Proceedings of the CSEE, 2013, 33(28): 1-9.
[22] 周宸宇, 冯成, 王毅. 基于移动用户接入控制的5G通信基站需求响应[J]. 中国电机工程学报, 2021, 41(16): 5452-5461.
Zhou Chenyu, Feng Cheng, Wang Yi.Demand response of 5G communication base stations based on admission control of mobile users[J]. Proceedings of the CSEE, 2021, 41(16): 5452-5461.
[23] Song Meng, Sun Wei, Wang Yifei, et al.Hierarchical scheduling of aggregated TCL flexibility for transactive energy in power systems[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2452-2463.
[24] 詹祥澎, 杨军, 韩思宁, 等. 考虑电动汽车可调度潜力的充电站两阶段市场投标策略[J]. 电力系统自动化, 2021, 45(10): 86-96.
Zhan Xiangpeng, Yang Jun, Han Sining, et al.Two-stage market bidding strategy of charging station considering schedulable potential capacity of electric vehicle[J]. Automation of Electric Power Systems, 2021, 45(10): 86-96.
[25] 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.
[26] 吴盛军, 李群, 刘建坤, 等. 基于储能电站服务的冷热电多微网系统双层优化配置[J]. 电网技术, 2021, 45(10): 3822-3832.
Wu Shengjun, Li Qun, Liu Jiankun, et al.Bi-level optimal configuration for combined cooling heating and power multi-microgrids based on energy storage station service[J]. Power System Technology, 2021, 45(10): 3822-3832.
[27] 刘亚锦, 代航, 刘志坚, 等. 面向多类型工业用户的分散式共享储能配置及投资效益分析[J]. 电力自动化设备, 2021, 41(10): 256-264.
Liu Yajin, Dai Hang, Liu Zhijian, et al.Configuration and investment benefit analysis of decentralized shared energy storage for multiple types of industrial users[J]. Electric Power Automation Equipment, 2021, 41(10): 256-264.
[28] 程静, 谭智钢, 岳雷. 考虑负荷综合需求响应的CCHP-SESS双层优化配置[J]. 电网技术, 2023, 47(3): 918-931.
Cheng Jing, Tan Zhigang, Yue Lei.CCHP-SESS bi-layer optimal configuration considering comprehensive load demand response[J]. Power System Technology, 2023, 47(3): 918-931.