基于SPEA2-SA算法的锂电池云边协同储能系统优化配置

吕云鹏; 兰叶深

doi:10.19912/j.0254-0096.tynxb.2024-1899

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (3) : 514-524. DOI: 10.19912/j.0254-0096.tynxb.2024-1899

基于SPEA2-SA算法的锂电池云边协同储能系统优化配置

吕云鹏, 兰叶深

作者信息 +

OPTIMAL ALLOCATION OF LITHIUM BATTERY CLOUD-EDGE COLLABORATIVE ENERGY STORAGE SYSTEM BASED ON SPEA2-SA ALGORITHM

Lyu Yunpeng, Lan Yeshen

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摘要

综合考虑锂电池储能系统优化配置方案及运行策略,提出一种兼顾经济性及可靠性的锂电池云边协同储能系统优化配置模型。首先,以锂电池云边协同储能成本、储能系统释放电能的收益及电网可靠性为目标函数建立数学模型。在此基础上,考虑配电网及储能系统两方面约束条件,对锂电池云边协同储能系统进行优化设计,提高光伏等可再生能源消纳能力,提升电力系统稳定性;其次,采用SPEA2-SA算法对多目标模型进行求解,找寻锂电池云边协同储能系统多目标情形下的最优解,克服电网功率波动问题,获得储能系统最优配置运行方案;最后,采用上述研究模型及算法,以IEEE-33节点模型针对锂电池云边协同储能系统的有效性展开仿真实验。算例仿真结果表明,SPEA2-SA算法优化下的锂电池云边协同储能系统优化配置方案能在显著降低运行成本的同时有效提高光伏电网的运行稳定性,从而验证SPEA2-SA算法的优良性及有效性。

Abstract

Comprehensively considering the optimal allocation scheme and operation strategy of lithium battery energy storage system, an optimal allocation model of lithium battery cloud-edge collaborative energy storage system is proposed, which takes into account economic performance and reliability. Firstly, a mathematical model is established with the objective functions of the cost of the lithium battery cloud-edge collaborative energy storage system, the benefit of energy release by the energy storage system and the reliability of the power grid. On this basis, considering the constraints of distribution network and energy storage system, lithium battery cloud-edge collaborative energy storage system is optimized to improve the absorption capacity of renewable energy such as photovoltaic power and improve the stability of the power system. Secondly, SPEA2-SA algorithm was used to solve the multi-objective model to find the optimal solution of lithium battery cloud-edge collaborative energy storage system under the multi-objective situation, overcome the power grid fluctuation problem, and obtain the optimal allocation and operation scheme of the energy storage system. Finally, using the above research model and algorithm, the IEEE-33 node model is used to conduct simulation experiments on the effectiveness of the lithium battery cloud-edge collaborative energy storage system. The simulation results of the example show that the optimal allocation scheme of the lithium battery cloud-edge collaborative energy storage system optimized by the SPEA2-SA algorithm can significantly reduce the operating cost and effectively improve the operating stability of the photovoltaic power grid, thus validating the superiority and effectiveness of the SPEA2-SA algorithm.

导出引用

吕云鹏, 兰叶深. 基于SPEA2-SA算法的锂电池云边协同储能系统优化配置[J]. 太阳能学报. 2026, 47(3): 514-524 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1899

Lyu Yunpeng, Lan Yeshen. OPTIMAL ALLOCATION OF LITHIUM BATTERY CLOUD-EDGE COLLABORATIVE ENERGY STORAGE SYSTEM BASED ON SPEA2-SA ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 514-524 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1899

中图分类号： TM912

参考文献

[1] 张宝锋, 宋子琛, 王剑彬, 等. 风储一体化系统无功响应技术研究[J]. 热力发电, 2024, 53(8): 135-142.
ZHANG B F, SONG Z C, WANG J B, et al.Reactive power response technology of wind-storage integrated system[J]. Thermal power generation, 2024, 53(8): 135-142.
[2] 韩晓, 王涛, 韦晓广, 等. 考虑阵列间时空相关性的超短期光伏出力预测[J]. 电力系统保护与控制, 2024, 52(14): 82-94.
HAN X, WANG T, WEI X G, et al.Ultrashort-term photovoltaic output forecasting considering spatiotemporal correlation between arrays[J]. Power system protection and control, 2024, 52(14): 82-94.
[3] 李富春, 田旭, 党楠, 等. 光伏发电与塔式太阳能热发电联合电站配置特性研究[J]. 太阳能学报, 2025, 46(2): 457-463.
LI F C,TIAN X,DANG N, et al.Research on configuration characteristics of combined photovoltaic and tower solar thermal power plant[J]. Acta energiae solaris sinica, 2025, 46(2): 457-463.
[4] 沈颖, 孙继欣, 陈炜琦. 面向有源配电网的业务驱动的数字孪生数据质量模型研究[J]. 东华大学学报(自然科学版), 2024, 50(2) : 144-152.
SHEN Y, SUN J X, CHEN W Q, et al.Business-driven digital twin data quality model for active distribution networks[J]. Journal of Donghua University(natural science edition), 2024, 50(2): 144-152.
[5] 滕伟, 许庆祥, 王亚军, 等. 平滑风电波动的飞轮协同电池混合储能系统经济性配置[J]. 动力工程学报, 2024, 44(7): 1101-1108.
TENG W, XU Q X, WANG Y J, et al.Economic configuration of flywheel collaborative battery hybrid energy storage system for smoothing wind power fluctuations[J]. Journal of Chinese Society of Power Engineering, 2024, 44(7): 1101-1108.
[6] ATWA Y M, EL-SAADANY E F. Optimal allocation of ESS in distribution systems with a high penetration of wind energy[J]. IEEE transactions on power systems, 2010, 25(4): 1815-1822.
[7] 朱娟娟, 段奕琳, 闫群民, 等. 基于BAS-IMOPSO算法的风电系统储能优化配置[J]. 电力工程技术, 2023, 42(2) : 180-187.
ZGU J J, DUAN Y L, YAN Q M, et al.Optimal allocation of energy storage in wind power system based on BAS-IMOPSO algorithm[J]. Electric power engineering technology, 2023, 42(2): 180-187.
[8] 闫涛, 唐巍, 王越, 等. 基于储能多状态模型的含微网配电系统可靠性评估[J]. 电网技术, 2017, 41(7): 2222-2228.
YAN T, TANG W, WANG Y, et al.Reliability evaluation of distribution network with microgrid based on multi-state energy storage model[J]. Power system technology, 2017, 41(7): 2222-2228.
[9] 栗然, 吕慧敏, 彭湘泽, 等. 阶梯成本下考虑混合租建模式的云储能优化配置[J]. 太阳能学报, 2024, 45(2): 263-273.
LI R, LYU H M, PENG X Z, et al.Optimal configuration of cloud energy storage consideing hybrid self-built and lease mode under tiered cost[J]. Acta energiae solaris sinica, 2024, 45(2): 263-273.
[10] 张世旭, 李姚旺, 刘伟生, 等. 面向微电网群的云储能经济-低碳-可靠多目标优化配置方法[J]. 电力系统自动化, 2024, 48(1): 21-30.
ZHANG S X, LI Y W, LIU W S, et al.Economic, low-carbon and reliable multi-objective optimal configuration method of cloud energy storage for microgrid clusters[J]. Automation of electric power systems, 2024, 48(1): 21-30.
[11] 刘沛津, 史洁琳, 孙昱, 等. 边缘计算模式下的电力用户能效评估方法[J]. 哈尔滨工业大学学报, 2023, 55(12): 93-103.
LIU P J, SHI J L, SUN Y, et al.Energy efficiency evaluation method for power users in edge computing mode[J]. Journal of Harbin Institute of Technology, 2023, 55(12): 93-103.
[12] 徐策, 王俊江, 钟浩, 等. 基于物联网边缘计算的配电网故障定位方法[J]. 计算机应用与软件, 2024, 41(10): 95-103, 109.
XU C, WANG J J, ZHONG H, et al.Design and application of distribution network fault location method based on iot edge computing[J]. Computer applications and software, 2024, 41(10): 95-103, 109.
[13] 朱斌, 刘东, 刘天元, 等. 边缘计算在电力系统供需互动应用的研究进展与展望[J]. 电网技术, 2024, 48(10): 4327-4341.
ZHU B, LIU D, LIU T Y, et al.Research progresses and prospects on application of edge computing in power system supply-demand interaction[J]. Power system technology, 2024, 48(10): 4327-4341.
[14] 贾东卫, 任永峰, 李莉美, 等. 基于集合经验模态分解的微电网混合储能优化配置[J]. 太阳能学报, 2023, 44(2): 239-246.
JIA D W, REN Y F, LI L M, et al.Research on optimization of hybrid energy storage capacity using ensemble empirical mode decomposition and fuzzy control[J]. Acta energiae solaris sinica, 2023, 44(2): 239-246.
[15] 姜淇, 常玉红, 衣传宝, 等. 基于NSGA-Ⅱ串行模式搜索的新能源发电与抽水蓄能电站联合系统多时间尺度优化调度方法[J]. 太阳能学报, 2024, 45(4): 434-441.
JIANG Q,CHANG Y H,YI C B, et al.Multi-time scale optimization scheduling of combined system of new energy power generation and pumped storage power station based on NSGA-Ⅱ serial pattern search[J]. Acta energiae solaris sinica, 2024, 45(4): 434-441.
[16] 史林军, 端木陈睿, 杨冬梅, 等. 基于分组控制策略的风电场储能容量优化配置[J]. 太阳能学报, 2024, 45(7): 340-349.
SHI L J, DUANMU C R, YANG D M, et al.Optimal allocation of wind farm energy storage capacity based on group control strategy[J]. Acta energiae solaris sinica, 2024, 45(7): 340-349.
[17] 李鑫, 李俊伟, 陈薇, 等. 基于SPEA2的风光柴储独立微电网多目标容量优化配置[J]. 热力发电, 2024, 53(8): 9-19.
LI X, LI J W, CHEN W, et al.Multi-objective capacity optimization allocation of wind-PV-diesel-battery stand-alone microgrid based on SPEA2[J]. Thermal power generation, 2024, 53(8): 9-19.
[18] 刘伟, 夏敏学, 梁晨鹏. 高压直挂大容量电池储能系统实时仿真及控制研究[J]. 电力系统保护与控制, 2024, 52(14): 154-166.
LIU W, XIA M X, LIANG C P.Real-time simulation and control of a large capacity bettery energy storage system directly connecting to a high-voltage grid without a transformer[J]. Power system protection and control, 2024, 52(14): 154-166.
[19] 郑凯元, 张维戈, 邓岳, 等. 基于差额功率转换的锂电池储能系统建模与可靠性分析[J]. 电网技术, 2023, 47(7) : 2630-2638.
ZHENG K Y, ZHANG W G, DENG Y, et al.Modeling and reliability analysis of lithium battery energy storage system based on differential power conversion[J]. Power system technology, 2023, 47(7): 2630-2638.
[20] 王康, 张树生, 何卫平, 等. 基于SPEA2的复杂机械产品选择装配方法[J]. 上海交通大学学报, 2016, 50(7): 1047-1053.
WANG K, ZHANG S S, HE W P, et al.Selective assembly of complicated mechanical product based on SPEA2[J]. Journal of Shanghai Jiao Tong University, 2016, 50(7): 1047-1053.
[21] 解良彬, 向月, 王世谦, 等. 计及经济性与可靠性的台区储能优化配置[J]. 电力工程技术, 2024, 43(4) : 56-66, 87.
XIE L B, XIANG Y, WANG S Q, et al.Optimal configuration of distributed generation based on improved grey optimization algorithm[J]. Electric power engineering technology, 2024, 43(4): 56-66, 87.
[22] 孙培锋, 陆王琳, 白鹏, 等. 锂电池和超级电容混合储能辅助火电调频技术发展现状和趋势[J]. 动力工程学报, 2024, 44(3): 418-429.
SUN P F, LU W L, BAI P, et al.Development status and trends of lithium battery and supercapacitor hybrid energy storage assisted thermal power frequency regulation technology[J]. Journal of Chinese Society of Power Engineering, 2024, 44(3): 418-429.
[23] 王书勤, 黄茜. 基于GA-SA的多点封控行动中的兵力分配研究[J]. 运筹与管理, 2024, 33(4): 1-6.
WANG S Q, HUANG Q.Research on force assignment in multipoint sealing and controlling action based on GA-SA[J]. Operations research and management science, 2024, 33(4): 1-6.