Research on user-side energy storage coordinated and optimized scheduling mechanism under cloud energy storage mode

doi:10.3969/j.issn.2097-0706.2023.09.003

Abstract

Abstract:

With the continuous advancement of the "dual carbon" target， energy storage application scenarios are emerging endlessly. Small energy storage units on the user side have advantages of small size， convenient deployment and flexible application. However， the overly random scheduling mode also brings hidden dangers to the operation of the power grid. Based on the concept of cloud energy storage， the interconnection and interoperability of small energy storage devices on the user side can be realized， and the architecture and operation mode of cloud energy storage system are proposed. Then， a cluster scheduling strategy for small energy storage devices under cloud energy storage mode is designed， whose feasibility is verified by simulation examples. The simulation results show that the proposed operation mode and optimized scheduling scheme are feasible， easy to implement， and effective， which can facilitate the application of energy storage units in new scenarios.

Key words: user side energy storage, cloud energy storage, optimize scheduling, "dual carbon" target, operation of power grid, cluster scheduling strategy

CLC Number:

TK02

CUI Jindong, WANG Yuqing. Research on user-side energy storage coordinated and optimized scheduling mechanism under cloud energy storage mode[J]. Integrated Intelligent Energy, 2023, 45(9): 18-25.

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References 22

[1]	积极稳妥推进碳达峰碳中和[EB/OL]. (2023-04-26)[2023-05-01]. https://www.gov.cn/yaowen/2023-04/06/content_5750183.htm.
[2]	“两个替代”深度推进面临多重难题[EB/OL]. (2018-10-10)[2023-05-01]. http://ccnews.people.com.cn/n1/2018/1010/c141677-30331917.htm.
[3]	李姚旺, 张宁, 张世旭, 等. 面向电力系统的多能源云储能模式:基本概念与研究展望[J]. 中国电机工程学报, 2023, 43(6):2179-2190.
	LI Yaowang, ZHANG Ning, ZHANG Shixu, et al. Multi-energy cloud energy storage for power systems: Basic concepts and research prospects[J]. Proceeding of the CSEE, 2023, 43(6): 2179-2190.
[4]	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]. Power System Automation, 2019, 43(10): 10-18.
[5]	麻秀范, 孟祥玉, 朱秋萍, 等. 计及通信负载5G基站储能调控策略[J]. 电工技术学报, 2022, 37(11):2878-2887.
	MA Fanxiu, MENG Xiangyu, ZHU qiuping, et al. Control strategy of 5G base station energy storage considering communication load[J]. Transaction of China Electrotechnical Society, 2022, 37(11): 2878-2887.
[6]	LOMBARDI P, SCHWABE F. Sharing economy as a new business model for energy storage systems[J]. Applied Energy, 2017, 188: 485-496. doi: 10.1016/j.apenergy.2016.12.016
[7]	袁晓冬, 费骏韬, 胡波, 等. 资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型[J]. 电力系统保护与控制, 2019, 47(22):17-26.
	YUAN Xiaodong, FEI Juntao, HU Bo, et al. Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode[J]. Power System Protection and Control, 2019, 47(22): 17-26.
[8]	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 Si, 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.
[9]	NIKOOBAKHT A, AGHAEI J, SHAFIE-KHAH M, et al. Assessing increased flexibility of energy storage and demand response to accommodate a high penetration of renewable energy sources[J]. IEEE Transactions on Sustainable Energy, 2019, 10(2): 659-669. doi: 10.1109/TSTE.5165391
[10]	马云聪, 武传涛, 林湘宁, 等. 一种基于半分布式结构化拓扑的云储能点对点交易策略研究[J]. 中国电机工程学报, 2022, 42(21):7731-7745.
	MA Yuncong, WU Chuantao, LIN Xiangning, et al. Research on peer-to-peer transaction strategy of cloud energy storage based on semi-distributed structured topology[J]. Proceedings of the CSEE, 2022, 42(21): 7731-7745.
[11]	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):3822-3829.
	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): 3822-3829.
[12]	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.
	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.
[13]	朱国海. 基于云储能的微电网多源协调优化[J]. 电源技术, 2021, 45(1):85-88.
	ZHU Guohai. Multi-source coordination and optimization of microgrid based on cloud energy storage[J]. Chinese Journal of Power Sources, 2021, 45(1): 85-88.
[14]	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.
[15]	杨艳红, 裴玮, 邓卫, 等. 计及蓄电池储能寿命影响的微电网日前调度优化[J]. 电工技术学报, 2015, 30(22):172-180.
	YANG Yanhong, PEI Wei, DENG Wei, et al. Day-ahead scheduling optimization for microgrid with battery life model[J]. Transactions of China Electrotechnical Society, 2015, 30(22): 172-180.
[16]	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.
	KANG Chongqing, LIU Jingkun, ZHANG Ning. Medium and long-term electric power development considering operating characteristics of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(21): 2-8.
[17]	张巍, 缪辉. 基于云储能租赁服务的风储参与能量-调频市场竞价策略研究[J]. 电网技术, 2021, 45(10):3840-3852.
	ZHANG Wei, LIAO Hui. Bidding strategies of wind power and energy storage participating in energy and frequency regulation market based on cloud energy storage leasing services[J]. Power System Technology, 2021, 45(10): 3840-3852.
[18]	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. doi: 10.12204/j.issn.1000-7229.2020.05.012
	LIN 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. doi: 10.12204/j.issn.1000-7229.2020.05.012
[19]	赵显秋, 秦立军, 段惠. 基于聚合效应的配电网分布式储能优化调度[J]. 电力电容器与无功补偿, 2020, 41(4):228-234.
	ZHAO Xianqiu, QIN Lijun, DUAN Hui. Distributed energy storage optimal scheduling of distribution networks based on aggregation effect[J]. Power Capacitors & Reactive Power Compensation, 2020, 41(4): 228-234.
[20]	ERDIWANSYAH, MAHIDIN, HUSIN H, et al. A critical review of the integration of renewable energy sources with various technologies[J]. Protection and Control of Modern Power Systems, 2021, 6(1).DOI:10.1186/s41601-021-00181-3.
[21]	徐天韵, 陈涛, 高赐威. 电力市场背景下的近用户侧储能站容量优化配置研究[J]. 综合智慧能源, 2023, 45(2):77-84. doi: 10.3969/j.issn.2097-0706.2023.02.010
	XU Tianyun, CHEN Tao, GAO Ciwei. Research on capacity optimization for the user-side energy storage station participating in electric power market[J]. Integrated Intelligent Energy, 2023, 45(2): 77-84. doi: 10.3969/j.issn.2097-0706.2023.02.010
[22]	刘静, 史梦鸽, 胡永锋. 含电池储能系统的智能楼宇多阶段能量管理策略[J]. 综合智慧能源, 2022, 44(3): 29-37. doi: 10.3969/j.issn.2097-0706.2022.03.005
	LIU Jing, SHI Mengge, HU Yongfeng. Multi-stage energy management strategy for smart buildings with BESS[J]. Integrated Intelligent Energy, 2022, 44(3): 29-37. doi: 10.3969/j.issn.2097-0706.2022.03.005

储能装置	配置容量/（kW·h）	荷电状态上限	荷电状态下限
1	300	0.9	0.1
2	100	0.9	0.1
3	350	0.9	0.1
4	150	0.9	0.1
5	200	0.9	0.1

储能装置	第1轮	第2轮	第3轮	第4轮	第5轮
1	0.487 3	—	—	—	—
2	0.493 9	0.476 3	0.456 3	0.442 1	—
3	0.499 8	0.463 5	0.461 1	0.452 3	0.450 7
4	0.502 1	0.454 6	—	—	—
5	0.503 4	0.462 8	0.453 9	—	—

竞价轮次	供需双方匹配结果	交易电量/（kW·h）	成交价格/[元·（kW·h）^-1]
1	储能装置1- 配电网	265	0.487 3
2	储能装置4- 配电网	125	0.454 6
3	储能装置5- 配电网	175	0.453 9
4	储能装置2- 配电网	80	0.442 1
5	储能装置3- 配电网	30	0.450 7