Two-Layer Optimization Operation Model of Cloud Energy Storage Based on Improved Myerson Value Method

doi:10.11930/j.issn.1004-9649.202212071

Abstract

Abstract: To deeply explore the role of centralized shared energy storage and distributed energy storage in the future new energy structure, this paper proposes a two-layer optimization operation model of cloud energy storage considering multi-agent value coordination. Firstly, a two-layer cloud energy storage model is built based on the scale effect of shared energy storage and the complementarity of distributed energy storage users. The upper objective function is the optimal daily economic benefit considering the full lifecycle cost of shared energy storage. The lower layer aims at the lowest daily operation cost of the user alliance and is solved by two-layer parallel optimization of analytic target cascading (ATC). To take into account the interests of each participant and the realization paths, the paper puts forward an improved Myerson value method to address the curse of dimensionality caused by cost allocation and user groups of chain alliance. Finally, the simulation results show that the model can further improve the consumption capacity of clean energy in the whole society, and the proposed cost allocation method can yield win-win results, providing references for future user-side large-scale energy storage applications.

Key words: cloud energy storage, new energy consumption, analytic target cascading, improved Myerson value method, cost allocation

WEI Zhenbo, LI Yinjiang, ZHANG Wenwen, YANG Chao. Two-Layer Optimization Operation Model of Cloud Energy Storage Based on Improved Myerson Value Method[J]. Electric Power, 2023, 56(7): 198-206.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202212071

https://www.electricpower.com.cn/EN/Y2023/V56/I7/198

References

[1] 赵一男, 宋斌, 钱振宇, 等. 未来配电网的分布式形态及规划方法[J]. 中国电力, 2022, 55(4): 70–77
ZHAO Yinan, SONG Bin, QIAN Zhenyu, et al. Dispatching architecture and planning method of future distribution network[J]. Electric Power, 2022, 55(4): 70–77
[2] 张旭, 李阳, 刘晓, 等. 含高渗透率新能源的新型交直流储能系统的配电网规划[J]. 南方电网技术, 2022, 16(4): 60–67
ZHANG Xu, LI Yang, LI Xiao, et al. Distribution network planning of new AC/DC energy storage system with high penetration new energy[J]. Southern Power System Technology, 2022, 16(4): 60–67
[3] 周姝灿, 卢洵, 刘新苗, 等. 大容量锂电池储能电站的等值仿真方法[J]. 南方电网技术, 2022, 16(4): 30–38
ZHOU Zhucan, LU Xun, LIU Xinmiao, et al. Equivalent simulation method for large capacity lithium battery energy storage power station[J]. Southern Power System Technology, 2022, 16(4): 30–38
[4] 李建林, 武亦文, 王楠, 等. 吉瓦级电化学储能电站研究综述及展望[J]. 电力系统自动化, 2021, 45(19): 2–14
LI Jianlin, WU Yiwen, WANG Nan, et al. Review and prospect of gigawatt-level electrochemical energy storage power station[J]. Automation of Electric Power Systems, 2021, 45(19): 2–14
[5] 任大伟, 侯金鸣, 肖晋宇, 等. 能源电力清洁化转型中的储能关键技术探讨[J]. 高电压技术, 2021, 47(8): 2751–2759
REN Dawei, HOU Jinming, XIAO Jinyu, et al. Exploration of key technologies for energy storage in the cleansing transformation of energy and power[J]. High Voltage Engineering, 2021, 47(8): 2751–2759
[6] 王枭, 何怡刚, 马恒瑞, 等. 面向电网辅助服务的虚拟储能电厂分布式优化控制方法[J]. 电力系统自动化, 2022, 46(10): 181–188
WANG Xiao, HE Yigang, MA Hengrui, et al. Distributed optimization control method of virtual energy storage plants for power grid ancillary services[J]. Automation of Electric Power Systems, 2022, 46(10): 181–188
[7] 林振锋, 郑常宝, 芮涛, 等. 用户侧分布式储能鲁棒博弈优化调度方法[J]. 中国电力, 2022, 55(2): 35–43, 114
LIN Zhenfeng, ZHENG Changbao, RUI Tao, et al. Robust game optimization scheduling method for user-side distributed energy storage[J]. Electric Power, 2022, 55(2): 35–43, 114
[8] 王仕俊, 平常, 薛国斌. 考虑共享储能的社区综合能源系统协同优化研究[J]. 中国电力, 2018, 51(8): 77–84
WANG Shijun, PING Chang, XUE Guobin. Synergic optimization of community energy Internet considering the shared energy storage[J]. Electric Power, 2018, 51(8): 77–84
[9] 李淋, 徐青山, 王晓晴, 等. 基于共享储能电站的工业用户日前优化经济调度[J]. 电力建设, 2020, 41(5): 100–107
LI Lin, XU Qingshan, WANG Xiaoqing, et al. Optimal economic scheduling of industrial customers on the basis of sharing energy-storage station[J]. Electric Power Construction, 2020, 41(5): 100–107
[10] 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
[11] 李咸善, 陈奥博, 程杉, 等. 基于生态博弈的含云储能微电网多智能体协调优化调度[J]. 中国电力, 2021, 54(7): 166–177
LI Xianshan, CHEN Aobo, CHENG Shan, et al. Multi-agent coordination and optimal dispatch of microgrid with CES based on ecological game[J]. Electric Power, 2021, 54(7): 166–177
[12] 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
[13] 康重庆, 刘静琨, 张宁. 未来电力系统储能的新形态: 云储能[J]. 电力系统自动化, 2017, 41(21): 2–8, 16
KANG Chongqing, LIU Jingkun, ZHANG Ning. A new form of energy storage in future power system: cloud energy storage[J]. Automation of Electric Power Systems, 2017, 41(21): 2–8, 16
[14] 刘静琨, 张宁, 康重庆. 电力系统云储能研究框架与基础模型[J]. 中国电机工程学报, 2017, 37(12): 3361–3371, 3663
LIU Jingkun, ZHANG Ning, KANG Chongqing. Research framework and basic models for cloud energy storage in power system[J]. Proceedings of the CSEE, 2017, 37(12): 3361–3371, 3663
[15] 李咸善, 方子健, 李飞, 等. 含多微电网租赁共享储能的配电网博弈优化调度[J]. 中国电机工程学报, 2022, 42(18): 6611–6625
LI Xianshan, FANG Zijian, LI Fei, et al. Game optimal scheduling of distribution network with multi-microgrid leasing and shared energy storage[J]. Proceedings of the CSEE, 2022, 42(18): 6611–6625
[16] 陈岑, 武传涛, 康慨, 等. 基于改进Owen值法的分布式储能双层合作博弈优化策略[J]. 中国电机工程学报, 2022, 42(11): 3924–3936
CHEN Cen, WU Chuantao, KANG Kai, et al. Optimal strategy of distributed energy storage two-layer cooperative game based on improved owen-value method[J]. Proceedings of the CSEE, 2022, 42(11): 3924–3936
[17] 陈思源. 合作博弈Shapley值的扩展与应用[D]. 西安: 西安电子科技大学, 2011.
CHEN Siyuan. The extension and application of Shapley value in the cooperative game[D]. Xi'an: Xidian University, 2011.
[18] CUI S C, WANG Y W, SHI Y, et al. An efficient peer-to-peer energy-sharing framework for numerous community prosumers[J]. IEEE Transactions on Industrial Informatics, 2020, 16(12): 7402–7412.
[19] ZENG A D, XU Q S, DING M S, et al. A classification control strategy for energy storage system in microgrid[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2015, 10(4): 396–403.
[20] 谢雨龙, 罗逸飏, 李智威, 等. 考虑微网新能源经济消纳的共享储能优化配置[J]. 高电压技术, 2022, 48(11): 4403–4413
XIE Yulong, LUO Yiyang, LI Zhiwei, et al. Optimal allocation of shared energy storage considering the economic consumption of new energy in microgrid[J]. High Voltage Engineering, 2022, 48(11): 4403–4413
[21] 吴盛军, 刘建坤, 周前, 等. 考虑储能电站服务的冷热电多微网系统优化经济调度[J]. 电力系统自动化, 2019, 43(10): 10–18
WU Shengjun, LIU Jiankun, ZHOU Qian, et al. Optimal economic scheduling for multi-microgrid system with combined cooling, heating and power considering service of energy storage station[J]. Automation of Electric Power Systems, 2019, 43(10): 10–18
[22] 江润洲, 邱晓燕, 李丹, 等. 含储能系统的多微网智能配电系统经济运行[J]. 电网技术, 2013, 37(12): 3596–3602
JIANG Runzhou, QIU Xiaoyan, LI Dan, et al. Economic operation of smart distribution network containing multi microgrids and energy storage system[J]. Power System Technology, 2013, 37(12): 3596–3602
[23] 温可瑞, 李卫东, 韩松, 等. 考虑日历寿命的电池储能系统参与一次调频服务配置容量与控制参数协同优化[J]. 高电压技术, 2019, 45(7): 2185–2193
WEN Kerui, LI Weidong, HAN Song, et al. Optimization of deployment with control for battery energy storage system participating in primary frequency regulation service considering its calendar life[J]. High Voltage Engineering, 2019, 45(7): 2185–2193
[24] 徐若晨, 张江涛, 刘明义, 等. 电化学储能及抽水蓄能全生命周期度电成本分析[J]. 电工电能新技术, 2021, 40(12): 10–18
XU Ruochen, ZHANG Jiangtao, LIU Mingyi, et al. Analysis of life cycle cost of electrochemical energy storage and pumped storage[J]. Advanced Technology of Electrical Engineering and Energy, 2021, 40(12): 10–18