“双碳”目标下共享储能多场景递进规划优化

张婷, 马裕泽, 乌云娜

电力建设 ›› 2025, Vol. 46 ›› Issue (1) : 134-146.

PDF(3489 KB)
PDF(3489 KB)
电力建设 ›› 2025, Vol. 46 ›› Issue (1) : 134-146. DOI: 10.12204/j.issn.1000-7229.2025.01.012
新能源与储能

“双碳”目标下共享储能多场景递进规划优化

作者信息 +

Multi-Scenario Progressive Planning Optimization of Shared Energy Storage Under Dual-Carbon Goals

Author information +
文章历史 +

摘要

碳中和目标引导下,共享储能规划优化已成为解决以新能源为主体的新型电力系统供电波动性与供需不匹配性的关键问题。在深入分析新型电力系统典型灵活性调节资源的物理特性、状态表征函数及成本收益模型的基础上,以系统灵活性调节需求特征分析为指引,以社会灵活性调节福利最大化为目标,提出了锂电池-超级电容器共享储能系统在多灵活性调节资源协同下平抑波动、削峰填谷、缓解输配电线路阻塞与延缓电网升级改造、减少弃风弃光、实现供需平衡、降低切负荷六种场景的运行策略与多时域递进规划优化模型及其求解方法,从而更好地服务于新型电力系统的动态建设。算例结果表明,所提模型可提高风光消纳水平,并有效降低系统灵活性调节成本。

Abstract

Guided by the goal of carbon neutrality,the optimization of shared energy-storage planning has become key for solving (i) the volatility and uncertainty of power supply for new power systems with new energy as the main body and (ii) the mismatch with user demands. Therefore,based on a comprehensive analysis of the physical characteristics,operation characterization function,and cost-benefit model of typical flexibility-regulation resources of new power systems,this study performs a characteristic analysis of system flexibility-regulation demand as guidance and aims to maximize the social welfare of flexibility regulation. The operation strategy and progressive planning-optimization model for six scenarios of lithium battery-supercapacitor shared energy-storage system under the coordination of multiple flexible-regulation resources,such as smoothing fluctuations,peaking and valley filling,alleviating transmission and distribution circuit congestion and delaying grid upgrading,reducing wind and power rejection,achieving supply-and-demand balance,and reducing load cutting,are proposed. Additionally,a scientific and effective solution method is proposed to facilitate the dynamic construction of new power systems. Numerical results show that the proposed model can improve the absorption level of renewable energy and effectively reduce the system flexibility-adjustment cost.

关键词

共享储能 / 递进规划优化 / 多灵活性资源协同 / 多场景分析 / 新型电力系统

Key words

shared energy storage / progressive planning optimization / multi-flexible adjustment resource coupling / multi-scenario analysis / new power system

引用本文

导出引用
张婷, 马裕泽, 乌云娜. “双碳”目标下共享储能多场景递进规划优化[J]. 电力建设. 2025, 46(1): 134-146 https://doi.org/10.12204/j.issn.1000-7229.2025.01.012
ZHANG Ting, MA Yuze, WU Yunna. Multi-Scenario Progressive Planning Optimization of Shared Energy Storage Under Dual-Carbon Goals[J]. Electric Power Construction. 2025, 46(1): 134-146 https://doi.org/10.12204/j.issn.1000-7229.2025.01.012
中图分类号: TM715   

参考文献

[1]
汤易. 面向配电网的分布式储能优化规划及经济性量化评估研究[D]. 杭州: 浙江大学, 2020.
TANG Yi. Research on optimal planning and quantitative evaluation of distributed energy storage for distribution network[D]. Hangzhou: Zhejiang University, 2020.
[2]
王颖, 祝士焱, 许寅, 等. 考虑多类型储能协同的重要负荷恢复方法[J]. 电力自动化设备, 2022, 42(1): 72-78.
WANG Ying, ZHU Shiyan, XU Yin, et al. Critical load restoration method considering coordination of multiple types of energy storage[J]. Electric Power Automation Equipment, 2022, 42(1): 72-78.
[3]
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.
[4]
WALKER A, KWON S. Analysis on impact of shared energy storage in residential community: individual versus shared energy storage[J]. Applied Energy, 2021, 282, 116172.
[5]
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.
[6]
SONG M, MENG J, LIN G J, et al. Applications of shared economy in smart grids: shared energy storage and transactive energy[J]. The Electricity Journal, 2022, 35(5): 107128.
[7]
WALKER A, KWON S. Design of structured control policy for shared energy storage in residential community: a stochastic optimization approach[J]. Applied Energy, 2021, 298: 117182.
[8]
李建林, 崔宜琳, 马速良, 等. 需求侧共享储能的运营模式优化及其经济效益分析研究[J]. 电网技术, 2022, 46 (12): 4954-4969.
LI Jianlin, CUI Yilin, MA Suliang, et al. Operation mode optimization and economic benefit analysis of demand-side shared energy storage[J]. Power System Technology, 2022, 46 (12): 4954-4969.
[9]
CUI S C, WANG Y W, LIU X K, et al. Economic storage sharing framework: asymmetric bargaining-based energy cooperation[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7489-7500.
[10]
王俐英, 林嘉琳, 宋美琴, 等. 考虑需求响应激励机制的园区综合能源系统博弈优化调度[J]. 控制与决策, 2023, 38(11): 3192-3200.
WANG Liying, LIN Jialin, SONG Meiqin, et al. Optimal dispatch of park integrated energy system considering demand response incentive mechanism[J]. Control and Decision, 2023, 38(11): 3192-3200.
[11]
XIAO J W, YANG Y B, CUI S C, et al. A new energy storage sharing framework with regard to both storage capacity and power capacity[J]. Applied Energy, 2022, 307: 118171.
[12]
LAI S Y, QIU J, TAO Y C. Credit-based pricing and planning strategies for hydrogen and electricity energy storage sharing[J]. IEEE Transactions on Sustainable Energy, 2022, 13(1): 67-80.
[13]
MA M T, HUANG H J, SONG X L, et al. Optimal sizing and operations of shared energy storage systems in distribution networks: a bi-level programming approach[J]. Applied Energy, 2022, 307: 118170.
[14]
肖峻, 张泽群, 梁海深. 配电网络公共储能位置与容量的优化方法[J]. 电力系统自动化, 2015, 39(19): 54-60, 67.
XIAO Jun, ZHANG Zequn, LIANG Haishen. Optimal method for placement and capacity of energy storage in distribution system[J]. Automation of Electric Power Systems, 2015, 39(19): 54-60, 67.
[15]
王梓旭, 林伟, 杨知方, 等. 考虑负荷弹性空间的配电网可靠性扩展规划方法[J]. 中国电机工程学报, 2022, 42(18): 6655-6667.
WANG Zixu, LIN Wei, YANG Zhifang, et al. A reliability-constrained distribution network expansion planning method considering flexibility space of power demand[J]. Proceedings of the CSEE, 2022, 42(18): 6655-6667.
[16]
黄弦超. 计及可控负荷的独立微网分布式电源容量优化[J]. 中国电机工程学报, 2018, 38(7): 1962-1970, 2211.
HUANG Xianchao. Capacity optimization of distributed generation for stand-alone microgrid considering controllable load[J]. Proceedings of the CSEE, 2018, 38(7): 1962-1970, 2211.
[17]
刘辉, 刘强, 张立, 等. 考虑需求侧协同响应的热电联供微网多目标规划[J]. 电力系统保护与控制, 2019, 47(5): 43-51.
LIU Hui, LIU Qiang, ZHANG Li, et al. Multi-objective planning for combined heat and power microgrid considering demand side cooperative response[J]. Power System Protection and Control, 2019, 47(5): 43-51.
[18]
邵志芳, 赵强, 张玉琼. 独立型微电网源荷协调配置优化[J]. 电网技术, 2021, 45(10): 3935-3946.
SHAO Zhifang, ZHAO Qiang, ZHANG Yuqiong. Source side and load side coordinated configuration optimization for stand-alone micro-grid[J]. Power System Technology, 2021, 45(10): 3935-3946.
[19]
袁霜晨, 蔡声霞, 王守相, 等. 用户侧热/电综合储能系统经济性建模与分析[J]. 储能科学与技术, 2017, 6(5): 1099-1104.
摘要
随着智能电网的发展,用户的用能需求呈现多元化,对用户侧储能系统的经济性分析也需要考虑用户的多元用能需求。为此,考虑用户的热能需求,在用户侧引入热储能,与电储能一起构成热/电综合储能系统,并构建了其数学模型;然后,建立了该综合储能系统的经济模型,给出了其初始投资成本、年均成本和年收益的计算方法。算例部分通过对不含热储能的场景和考虑热储能的场景分析,比较了两者的经济性。结果表明,引入热储能系统虽然会提高初始投资成本和年运行成本,但也会提高整个储能系统的经济性,使储能系统的投资回报年限缩短,说明了在传统电储能系统引入热储能形成与热/电综合储能系统的必要性和有效性。
YUAN Shuangchen, CAI Shengxia, WANG Shouxiang, et al. Economic modeling and analysis of user-side electrical/thermal comprehensive energy storage system[J]. Energy Storage Science and Technology, 2017, 6(5): 1099-1104.
The demand for energy gradually diversifies on user side along with the development of smart grid. Therefore, demand for diversified energy on user side needs to be taken into account based on economic analysis of electrical energy storage system (ESS). This paper firstly introduces thermal ESS to form an electrical/thermal comprehensive ESS together with electrical ESS considering the users’ demand for thermal energy. Then the economic model of the comprehensive ESS is built and the initial investment cost, average annual cost and annual earnings of new ESS are analyzed. A test case including two scenarios in which the first does not have thermal ESS while the second takes it into consideration is given to compare the economic performance. The results show that introducing thermal ESS will improve the initial investment cost and annual operating cost, but the whole economy of ESS will rise, thus shortening the life of return of investment, which illustrates the necessity and effectiveness of introducing thermal ESS.
[20]
鲁宗相, 李海波, 乔颖. 高比例可再生能源并网的电力系统灵活性评价与平衡机理[J]. 中国电机工程学报, 2017, 37(1):9-20.
LU Zhongxiang, LI Haibo, QIAO Ying. Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity[J]. Proceedings of the CSEE, 2017, 37(1):9-20.
[21]
JO J, PARK J. Demand-side management with shared energy storage system in smart grid[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 4466-4476.
[22]
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): 4403-4412.
XIE Yulong, LUO Yiyang, LI Zhiwei, et al. Optimal allocation of shared energy storage considering the economic consumption of microgrid new energy[J]. High Voltage Engineering, 2022, 48(11): 4403-4412.
[23]
吴玮坪, 胡泽春, 宋永华. 结合随机规划和序贯蒙特卡洛模拟的风电场储能优化配置方法[J]. 电网技术, 2018, 42(4): 1055-1062.
WU Weiping, HU Zechun, SONG Yonghua. Optimal sizing of energy storage system for wind farms combining stochastic programming and sequential Monte Carlo simulation[J]. Power System Technology, 2018, 42(4): 1055-1062.
[24]
李成, 杨秀, 张美霞, 等. 基于成本分析的超级电容器和蓄电池混合储能优化配置方案[J]. 电力系统自动化, 2013, 37(18): 20-24.
LI Cheng, YANG Xiu, ZHANG Meixia, et al. Optimal configuration scheme for hybrid energy storage system of super-capacitors and batteries based on cost analysis[J]. Automation of Electric Power Systems, 2013, 37(18): 20-24.
[25]
李则衡, 陈磊, 路晓敏, 等. 基于系统灵活性的可再生能源接纳评估[J]. 电网技术, 2017, 41(7): 2187-2194.
LI Zeheng, CHEN Lei, LU Xiaomin, et al. Assessment of renewable energy accommodation based on system flexibility analysis[J]. Power System Technology, 2017, 41(7): 2187-2194.
[26]
朱少闻. 考虑需求侧管理的综合能源系统均衡交互策略研究[D]. 北京: 华北电力大学, 2020.
ZHU Shaowen. Research on balanced interaction strategy of integrated energy system considering demand side management[D]. Beijing: North China Electric Power University, 2020.
[27]
HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[28]
叶泽, 李湘旗, 姜飞, 等. 考虑最优弃能率的风光火储联合系统分层优化经济调度[J]. 电网技术, 2021, 45(6): 2270-2280.
YE Ze, LI Xiangqi, JIANG Fei, et al. Hierarchical optimization economic dispatching of combined wind-PV-thermal-energy storage system considering the optimal energy abandonment rate[J]. Power System Technology, 2021, 45(6): 2270-2280.
[29]
郭玲娟, 魏斌, 韩肖清, 等. 基于集合经验模态分解的交直流混合微电网混合储能容量优化配置[J]. 高电压技术, 2020, 46(2): 527-537.
GUO Lingjuan, WEI Bin, HAN Xiaoqing, et al. Capacity optimal configuration of hybrid energy storage in hybrid AC/DC micro-grid based on ensemble empirical mode decomposition[J]. High Voltage Engineering, 2020, 46(2): 527-537.
[30]
孙可. 几种类型发电公司环境成本核算的分析研究[J]. 能源工程, 2004, 24(3): 23-26.
SUN Ke. Environmental cost analysis and research of different power plants[J]. Energy Engineering, 2004, 24(3): 23-26.

基金

国家自然科学基金青年项目(72303057)
河北省教育厅青年拔尖人才项目(BJK2023115)
河北省自然科学基金青年项目(G202302009)
河北省社会科学基金青年项目(HB23GL029)

编辑: 张小飞
PDF(3489 KB)

Accesses

Citation

Detail

段落导航
相关文章
AI小编
你好!我是《电力建设》AI小编,有什么可以帮您的吗?

/