储能电站在多站融合场景下的运行策略优化

doi:10.11930/j.issn.1004-9649.202005076

中国电力 ›› 2021, Vol. 54 ›› Issue (6): 136-144.DOI: 10.11930/j.issn.1004-9649.202005076

储能电站在多站融合场景下的运行策略优化

白中华¹, 李强², 陈晶³, 袁福生¹, 徐文波¹, 孙奉昌¹, 余宗泽¹

1. 国网信息通信产业集团有限公司北京分公司，北京 100031;
2. 国网信息通信产业集团有限公司，北京 100031;
3. 国网北京市电力公司北京电力经济技术研究院，北京 100008

收稿日期:2020-05-11 修回日期:2020-08-04 发布日期:2021-06-05
作者简介:白中华(1981-),男,硕士,高级工程师,从事节能环保发电、储能技术研究,E-mail:baizhonghua@sgitg.sgcc.com.cn;袁福生(1974-),男,硕士,高级工程师,从事多站融合、边缘计算、电力基础资源运营研究,E-mail:yuanfusheng@sgitg.sgcc.com.cn;徐文波(1989-),男,通信作者,硕士,工程师,从事节能、储能技术研究,E-mail:xuwenbo@sgitg.sgcc.com.cn;余宗泽(1996-),女,助理工程师,从事电力物联网、智能电网研究,E-mail:yuzongze@sgitg.sgcc.com.cn
基金资助:
国家电网有限公司科技项目(多站融合关键技术及商业模式研究，5200-201941477A-0-0-00)

Operation Strategy Optimization of Energy Storage Power Station in Multi-Station Integration Scenario

BAI Zhonghua¹, LI Qiang², CHEN Jing³, YUAN Fusheng¹, XU Wenbo¹, SUN Fengchang¹, YU Zongze¹

1. Beijing Branch of State Grid Information &Telecommunication Group, Beijing 100031, China;
2. State Grid Information &Telecommunication Group, Beijing 100031, China;
3. Beijing Electric Power Economic Research Institute, State Grid Beijing Electric Power Company, Beijing 100008, China

Received:2020-05-11 Revised:2020-08-04 Published:2021-06-05
Supported by:
This work is supported by Science and Technology Project of SGCC(Research on Key Technology and Business Model of Multi-Station Integration, No.5200-201941477A-0-0-00)

摘要/Abstract

摘要： 为降低多站融合的运行费用，提升多站融合的经济性，充分发挥储能电站的价值，保证储能电站经济运行，构建储能电站的寿命模型、边缘数据中心和充电站的负荷模型、电储能交易模型，采用双层优化方法，通过粒子群优化算法，提出储能电站在多站融合中发挥多重功能的复杂场景下优化运行策略。结果表明：在电价相同的连续时段组内，储能电站以相同的倍率充放电为最佳运行策略；在不同电价的时段组间，储能电站的最佳运行策略由分时电价、电池寿命特性和多站负荷特性共同决定。通过算例验证了目前常见的运行策略并非最佳。

关键词: 多站融合, 储能电站, 寿命特性, 优化运行, 双层优化

Abstract: In order to reduce the operation cost of multi-station integration, improve the economy of multi-station integration, give full play to the value of energy storage power station, and ensure the economic operation of energy storage power station. In this paper, the life model of energy storage station, the load model of edge data center and charging station, and the energy storage transaction model are constructed. By using the method of two-level optimization and particle swarm optimization algorithm, the optimal operation strategy of energy storage station in the complex scene of multi-function in multi station fusion is proposed. The simulation results show that the optimal operation strategy of energy storage power station is the same rate of charge and discharge in the same continuous period group with the same electricity price; among the groups with different electricity prices, the optimal operation strategy of energy storage power station is determined by the time-sharing electricity price, battery life characteristics and load characteristics of multiple stations. The case study shows that the current simple operation strategy is not the best.

Key words: multi-station integration, energy storage power station, life characteristics, optimal operation, double level optimization

白中华, 李强, 陈晶, 袁福生, 徐文波, 孙奉昌, 余宗泽. 储能电站在多站融合场景下的运行策略优化[J]. 中国电力, 2021, 54(6): 136-144.

BAI Zhonghua, LI Qiang, CHEN Jing, YUAN Fusheng, XU Wenbo, SUN Fengchang, YU Zongze. Operation Strategy Optimization of Energy Storage Power Station in Multi-Station Integration Scenario[J]. Electric Power, 2021, 54(6): 136-144.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202005076

https://www.electricpower.com.cn/CN/Y2021/V54/I6/136

参考文献

[1] 黄际元, 李欣然, 常敏, 等. 考虑储能电池参与一次调频技术经济模型的容量配置方法[J]. 电工技术学报, 2017, 32(21):112-121
HUANG Jiyuan, LI Xinran, CHANG Min, et al. Capacity allocation of BESS in primary frequency regulation considering its technical-economic model[J]. Transactions of China Electrotechnical Society, 2017, 32(21):112-121
[2] 崔红芬, 杨波, 蒋叶, 等. 于模糊控制和SOC自恢复储能参与二次调频控制策略[J]. 电力系统保护与控制, 2019, 47(22):89-97
CUI Hongfen, YANG Bo, JIANG Ye,et al. Strategy based on fuzzy control and self adaptive modification of SOC involved in secondary frequency regulation with battery energy storage[J]. Power System Protection and Control, 2019, 47(22):89-97
[3] MA YR, HAN XS, YANG M, et al. Multi-timescale robust dispatching for coordinated automatic generation control and energy storage[J]. Global Energy Interconnection, 2020, 3(4):355-364.
[4] 赵健, 王奕凡, 谢桦, 等. 高渗透率可再生能源接入系统中储能应用综述[J]. 中国电力, 2019, 52(4):167-177
ZHAO Jian, WANG Yifan, XIE Hua,et al. An overview of energy storage applications in power systems with high penetration renewable energy resources[J]. Electric Power, 2019, 52(4):167-177
[5] 姚方, 王佳伟, 文福拴, 等. 计及碳税的含风电和储能的电力系统经济调度[J]. 电力科学与技术学报, 2019, 34(1):37-46
YAO Fang, WANG Jiawei, WEN Fushuan, et al. Economic dispatch for a power system containing wind power and energy storage with carbon tax considered[J]. Journal of Electric Power Science and Technology, 2019, 34(1):37-46
[6] 曹敏, 徐杰彦, 巨健, 等. 用户侧储能设备参与电网辅助服务的技术经济性分析[J]. 电力需求侧管理, 2019, 21(1):52-55
CAO Min, XU Jieyan, JU Jian, et al. Technical and economic analysis of user side energy storage equipment participating in power grid ancillary services[J]. Power Demand Side Management, 2019, 21(1):52-55
[7] 王德顺, 薛金花, 叶季蕾, 等. 基于粒子群算法的储能电站经济优化调度策略[J]. 可再生能源, 2019, 37(5):714-719
WANG Deshun, XUE Jinhua, YE Jilei, et al. Economic optimization scheduling strategy for battery energy storage system based on particle swarm optimization[J]. Renewable Energy Resources, 2019, 37(5):714-719
[8] 张文亮, 丘明, 来小康. 储能技术在电力系统中的应用[J]. 电网技术, 2008, 32(7):1-9
ZHANG Wenliang, QIU Ming, LAI Xiaokang. Application of energy storage technologies in power grids[J]. Power System Technology, 2008, 32(7):1-9
[9] 王永灿. 电力系统发储共体电站运行优化及储能容量配置研究[D]. 武汉:华中科技大学, 2019.
WANG Yongcan. Research on operation optimization and energy storage capacity allocation for storage integrated power plants in power system[D]. Wuhan:Huazhong University of Science and Technology, 2019.
[10] 祁明, 张逸婕, 于晓蒙. 基于储能电站调度的光储发电系统运行优化措施[J]. 中国科技信息, 2019(24):80-81
[11] 艾瑶瑶. 储能在电网中的应用价值及其商业模式研究[D]. 北京:华北电力大学, 2019.
AI Yaoyao. Research on application value of energy storage systems in the power grid and its commercial modes[D]. Beijing:North China Electric Power University, 2019.
[12] 张新松, 袁越, 曹阳. 考虑损耗成本的电池储能电站建模及优化调度[J]. 电网技术, 2017, 41(5):1541-1548
ZHANG Xinsong, YUAN Yue, CAO Yang. Modeling and scheduling for battery energy storage station with consideration of wearing costs[J]. Power System Technology, 2017, 41(5):1541-1548
[13] 李嘉琛, 韩肖清, 刘毅敏. 可调度光伏电站中混合储能容量的优化配置[J]. 电源技术, 2016, 40(2):392-396
LI Jiachen, HAN Xiaoqing, LIU Yimin. Capacity optimization of hybrid storage in schedulable PV power station[J]. Chinese Journal of Power Sources, 2016, 40(2):392-396
[14] 李春华, 朱新坚. 基于混合储能的光伏微网动态建模与仿真[J]. 电网技术, 2013, 37(1):39-46
LI Chunhua, ZHU Xinjian. Dynamic modeling and simulation of hybrid energy storage-based photovoltaic microgrid[J]. Power System Technology, 2013, 37(1):39-46
[15] 娄宇成, 张宇, 贾玉健, 等. 充放储一体化站对电网负荷削峰填谷作用的研究[J]. 电气自动化, 2013, 35(5):41-43
LOU Yucheng, ZHANG Yu, JIA Yujian, et al. Research on shifting the grid peak load by EV charging discharging and storage integrative station[J]. Electrical Automation, 2013, 35(5):41-43
[16] 薛琰, 殷文倩, 杨志豪, 等. 电力市场环境下独立储能电站的运行策略研究[J]. 电力需求侧管理, 2018, 20(6):12-15
XUE Yan, YIN Wenqian, YANG Zhihao, et al. Study on operation strategy of independent energy storage power station in electricity market[J]. Power Demand Side Management, 2018, 20(6):12-15
[17] 徐文波, 程华福, 白中华, 等. 多站融合模式下储能电站的优化设计和运行[J]. 供用电, 2019, 36(11):84-91
XU Wenbo, CHENG Huafu, BAI Zhonghua, et al. Optimal design and operation of energy storage power station under multi-station fusion mode[J]. Distribution & Utilization, 2019, 36(11):84-91
[18] 孙玮泽. 基于大规模储能电站的控制研究[D]. 吉林:东北电力大学, 2019.
SUN Weize. Research on control based on large scale energy storage power station[D]. Jilin, China:Northeast Dianli University, 2019.
[19] 张宇丰. 基于多目标粒子群算法的多约束组合优化问题研究[D]. 西安:西安理工大学, 2019.
ZHANG Yufeng. Research on multi-constraint combinatorial optimization problem based on muliti-objective particle swarm optimization algorithm[D]. Xi'an:Xi'an University of Technology, 2019.
[20] 范宏, 周献远. 基于智能微网的混合储能配置方法[J]. 电网与清洁能源, 2017, 33(9):99-103
FAN Hong, ZHOU Xianyuan. Hybrid energy storage allocation method based on smart microgrid[J]. Power System and Clean Energy, 2017, 33(9):99-103
[21] 李建林, 房凯, 黄际元. 电池储能系统调频技术[M]. 北京:机械工业出版社, 2018.
[22] 姚磊, 余斌, 张涛. 加速放电倍率下锂离子电池健康预测[J]. 电池, 2019, 49(4):283-286
YAO Lei, YU Bin, ZHANG Tao. Health prediction for Li-ion battery under accelerated discharge rates[J]. Battery Bimonthly, 2019, 49(4):283-286
[23] 张谦, 王众, 谭维玉, 等. 基于MDP随机路径模拟的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2018, 42(20):59-66
ZHANG Qian, WANG Zhong, TAN Weiyu, et al. Spatial-temporal distribution prediction of charging load for electric vehicle based on MDP random path simulation[J]. Automation of Electric Power Systems, 2018, 42(20):59-66
[24] 杨波, 陈卫, 文明浩, 等. 电动汽车充电站的概率负荷建模[J]. 电力系统自动化, 2014, 38(16):67-73
YANG Bo, CHEN Wei, WEN Minghao, et al. Probabilistic load modeling of electric vehicle charging stations[J]. Automation of Electric Power Systems, 2014, 38(16):67-73
[25] 张洪财, 胡泽春, 宋永华, 等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014, 38(1):13-20
ZHANG Hongcai, HU Zechun, SONG Yonghua, et al. A prediction method for electric vehicle charging load considering spatial and temporal distribution[J]. Automation of Electric Power Systems, 2014, 38(1):13-20

储能电站在多站融合场景下的运行策略优化

Operation Strategy Optimization of Energy Storage Power Station in Multi-Station Integration Scenario

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	鲁玲, 苑涛, 杨波, 李欣, 鲁洋, 蒲秋平, 张鑫. 计及㶲效率和多重不确定性的区域综合能源系统双层优化[J]. 中国电力, 2025, 58(1): 128-140.
[2]	李江, 范袁铮, 刘博. 计及水泥厂直接碳排放碳责任的源-荷低碳优化运行方法[J]. 中国电力, 2025, 58(1): 141-152.
[3]	张旭, 王淳, 胡奕涛, 陈锐凯, 刘昆, 郭志东, 钟俊勋. 面向短时过载及长期轻载的配变侧储能配置与调度双层优化[J]. 中国电力, 2025, 58(1): 174-184.
[4]	田刚领, 武鸿鑫, 李娟, 张柳丽, 谢佳, 李爱魁. 风电场多功能储能电站功率分配策略[J]. 中国电力, 2024, 57(9): 247-256.
[5]	周健, 冯楠, 季怡萍, 冯煜尧, 王帅, 李少岩. 考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法[J]. 中国电力, 2024, 57(10): 150-157.
[6]	王世杰, 冯天波, 孙宁, 何可, 李嘉文, 杨程, 崔昊杨. 考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略[J]. 中国电力, 2024, 57(1): 101-114.
[7]	薛贵元, 吴垠, 诸晓骏, 谈健, 明昊. 计及用能权配额约束的发电商竞价策略双层优化方法[J]. 中国电力, 2023, 56(5): 51-61.
[8]	薛溟枫, 毛晓波, 肖浩, 周毅斌, 浦骁威, 裴玮. 基于联邦学习的综合能源微网群协同优化运行方法[J]. 中国电力, 2023, 56(12): 164-173.
[9]	孙志媛, 孙艳, 刘默斯, 宋益. 考虑碳流需求响应的电力系统低碳运行策略[J]. 中国电力, 2023, 56(11): 95-103.
[10]	李茜, 苟璐旸, 李樊, 张安安, 郑雅迪, 周熙朋. 面向提供系统灵活性的风电场内机组优化运行[J]. 中国电力, 2022, 55(8): 23-30.
[11]	曹新慧, 车勇, 司政, 开赛江, 周专, 袁铁江. 广域储能电站定容-选址一体规划[J]. 中国电力, 2022, 55(7): 110-120.
[12]	卢炳文, 魏震波, 魏平桉, 郭毅, 胡蓉. 考虑多重区间不确定性的用户侧综合能源系统双层优化配置[J]. 中国电力, 2022, 55(3): 193-202.
[13]	陈娟, 惠东, 范茂松, 胡娟, 褚永金. 基于粗糙集的电池储能电站海量数据处理方法[J]. 中国电力, 2022, 55(2): 44-50,208.
[14]	张嘉诚, 夏向阳, 邓子豪, 陈贵全. 储能电站安全参与电网一次调频的优化控制策略[J]. 中国电力, 2022, 55(2): 19-27.
[15]	谭鸣骢, 王玲玲, 蒋传文, 刘航航, 巫里尔沙, 唐炯. 考虑负荷聚合商调节潜力的需求响应双层优化模型[J]. 中国电力, 2022, 55(10): 32-44.

模态框（Modal）标题

储能电站在多站融合场景下的运行策略优化

Operation Strategy Optimization of Energy Storage Power Station in Multi-Station Integration Scenario

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics