基于改进场景聚类算法的海上风电储能优化配置研究

doi:10.11930/j.issn.1004-9649.202201065

摘要/Abstract

摘要： 在平滑海上风电出力波动的应用需求下，提出一种储能优化配置方法。利用小波包分解算法对海上风电出力曲线进行分解，得到储能系统全年功率响应曲线。采用基于云模型和模糊C均值聚类算法相结合的改进场景聚类算法，对储能全年功率响应曲线进行聚合，生成储能功率响应典型场景。以储能年综合成本最低为目标，构建储能优化配置模型。采用粒子群算法对海上风电储能优化配置模型进行求解，最后通过算例仿真对所提方法和模型进行分析验证。结果表明：所提模型和方法能综合考虑海上风电场侧储能的实际运行特性，可有效指导海上风电场的储能配置和建设规划。

关键词: 海上风电, 小波包分解, 场景聚类, 粒子群算法, 储能配置

Abstract: As demands on smoothing the output fluctuation of offshore wind power increase, this paper proposes an optimal configuration method for offshore wind power storage. The wavelet packet decomposition algorithm is used to process the output curve of the wind power, and an annual power response curve of the power storage system is obtained. In addition, the paper adopts an improved scene clustering algorithm combining a cloud model with a fuzzy c-means clustering algorithm to aggregate the annual power response curve and generate typical scenes of the power response. Furthermore, to minimize the annual comprehensive cost of the power storage, the paper constructs an optimal configuration model for offshore wind power storage and uses the particle swarm optimization algorithm to solve the optimal configuration model. Finally, the proposed method and model are analyzed and verified by typical examples. The results show that the proposed model and method can comprehensively consider the actual operating characteristics of the power storage system on the side of offshore wind farms and effectively guide the power storage configuration and construction planning of offshore wind farms.

Key words: offshore wind power, wavelet packet decomposition, scene clustering, particle swarm optimization algorithm, energy storage configuration

易锦桂, 朱自伟, 谢青. 基于改进场景聚类算法的海上风电储能优化配置研究[J]. 中国电力, 2022, 55(12): 2-10.

YI Jingui, ZHU Ziwei, XIE Qing. Research on Optimal Configuration of Offshore Wind Power Energy Storage Based on Improved Scene Clustering Algorithm[J]. Electric Power, 2022, 55(12): 2-10.

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https://www.electricpower.com.cn/CN/Y2022/V55/I12/2

参考文献

[1] 刘卫东, 李奇南, 王轩, 等. 大规模海上风电柔性直流输电技术应用现状和展望[J]. 中国电力, 2020, 53(7): 55–71
LIU Weidong, LI Qinan, WANG Xuan, et al. Application status and prospect of VSC-HVDC technology for large-scale offshore wind farms[J]. Electric Power, 2020, 53(7): 55–71
[2] 凡航, 张雪敏, 梅生伟, 等. 基于时空神经网络的风电场超短期风速预测模型[J]. 电力系统自动化, 2021, 45(1): 28–35
FAN Hang, ZHANG Xuemin, MEI Shengwei, et al. Ultra-short-term wind speed prediction model for wind farms based on spatiotemporal neural network[J]. Automation of Electric Power Systems, 2021, 45(1): 28–35
[3] 王红星, 郭敬梅, 谢志文, 等. 海上风电次/超同步振荡的网侧附加阻尼抑制方法[J]. 南方电网技术, 2021, 15(11): 49–55
WANG Hongxing, GUO Jingmei, XIE Zhiwen, et al. Grid-side supplementary damping suppression method of sub-& super-synchronous oscillation in offshore wind farms[J]. Southern Power System Technology, 2021, 15(11): 49–55
[4] 时智勇, 王彩霞, 李琼慧. “十四五”中国海上风电发展关键问题[J]. 中国电力, 2020, 53(7): 8–17
SHI Zhiyong, WANG Caixia, LI Qionghui. Key issues of China’s offshore wind power development in the “14th Five-Year plan”[J]. Electric Power, 2020, 53(7): 8–17
[5] 刘扬波, 陈俊生, 李全皎, 等. 海上风电水下压缩空气储能系统运行及变工况分析[J]. 南方电网技术, 2022, 16(4): 50–59
LIU Yangbo, CHEN Junshegn, LI Quanjiao, et al. Operation and varying load analysis of offshore wind-underwater compressed air energy storage system[J]. Southern Power System Technology, 2022, 16(4): 50–59
[6] NIKOOBAKHT A, AGHAEI J, SHAFIE-KHAH M, et al. Allocation of fast-acting energy storage systems in transmission grids with high renewable generation[J]. IEEE Transactions on Sustainable Energy, 2020, 11(3): 1728–1738.
[7] BRAZ PONTES L R, PERCY MOLINA RODRIGUEZ Y, LUYO KUONG J, et al. Optimal allocation of energy storage system in distribution systems with intermittent renewable energy[J]. IEEE Latin America Transactions, 2021, 19(02): 288–296.
[8] 张江丰, 苏烨, 孙坚栋, 等. 电网侧电化学储能电站AGC控制策略优化及试验分析[J]. 电力科学与技术学报, 2022, 37(2): 173–180
ZHANG Jiangfeng, SU Ye, SUN Jiandong, et al. Optimization and test analysis of AGC control strategy for the grid-side elctrochemical energy storage power station[J]. Journal of Electric Power Science and Technology, 2022, 37(2): 173–180
[9] 刘志成, 彭道刚, 赵慧荣, 等. 双碳目标下储能参与电力系统辅助服务发展前景[J]. 储能科学与技术, 2022, 11(2): 704–716
LIU Zhicheng, PENG Daogang, ZHAO Huirong, et al. Development prospects of energy storage participating in auxiliary services of power systems under the targets of the dual-carbon goal[J]. Energy Storage Science and Technology, 2022, 11(2): 704–716
[10] ZHOU T P, SUN W. Optimization of battery–supercapacitor hybrid energy storage station in wind/solar generation system[J]. IEEE Transactions on Sustainable Energy, 2014, 5(2): 408–415.
[11] 修晓青, 唐巍, 马健. 用于储能容量配置的典型负荷曲线提取方法[J]. 太阳能学报, 2018, 39(8): 2234–2242
XIU Xiaoqing, TANG Wei, MA Jian. Typical load curve extraction method for energy storage capacity configuration[J]. Acta Energiae Solaris Sinica, 2018, 39(8): 2234–2242
[12] ABDULLAH M A, MUTTAQI K M, SUTANTO D, et al. An effective power dispatch control strategy to improve generation schedulability and supply reliability of a wind farm using a battery energy storage system[J]. IEEE Transactions on Sustainable Energy, 2015, 6(3): 1093–1102.
[13] 王苏蓬, 张新慧, 吴文浩, 等. 用于风电平抑的混合储能选型和容量优化配置方法[J]. 智慧电力, 2021, 49(9): 16–23
WANG Supeng, ZHANG Xinhui, WU Wenhao, et al. Hybrid energy storage selection and capacity optimization configuration method for wind power smoothing[J]. Smart Power, 2021, 49(9): 16–23
[14] 丁明, 解蛟龙, 刘新宇, 等. 面向风电接纳能力评价的风资源/负荷典型场景集生成方法与应用[J]. 中国电机工程学报, 2016, 36(15): 4064–4072
DING Ming, XIE Jiaolong, LIU Xinyu, et al. The generation method and application of wind resources/load typical scenario set for evaluation of wind power grid integration[J]. Proceedings of the CSEE, 2016, 36(15): 4064–4072
[15] 李笑彤, 宋宝同, 吕风波, 等. 基于负荷数据聚类的充电站储能容量规划方法[J]. 电网与清洁能源, 2021, 37(1): 90–96
LI Xiaotong, SONG Baotong, Lü Fengbo, et al. An energy storage capacity planning method of charging station based on load data clustering[J]. Power System and Clean Energy, 2021, 37(1): 90–96
[16] DU R, ZOU P H, MA C. Multi-objective optimal sizing of hybrid energy storage systems for grid-connected wind farms using fuzzy control[J]. Journal of Renewable and Sustainable Energy, 2021, 13(1): 014103.
[17] SHEN Y P, LI Y F, TANG Y H, et al. An energy management strategy based on fuzzy logic for hybrid energy storage system in electric vehicles[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2022, 17(1): 53–60.
[18] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 风电场接入电力系统技术规定: GB/T 19963—2011[S]. 北京: 中国标准出版社, 2012.
[19] 郄朝辉, 李兆伟, 王维洲, 等. 直流闭锁故障下风电并网功率和直流输送功率的耦合关系分析[J]. 中国电力, 2021, 54(6): 175–182
QIE Zhaohui, LI Zhaowei, WANG Weizhou, et al. Coupling relationship between wind power grid-connected power and DC transmission power with DC block fault[J]. Electric Power, 2021, 54(6): 175–182
[20] 付斌, 李道国, 王慕快. 云模型研究的回顾与展望[J]. 计算机应用研究, 2011, 28(2): 420–426
FU Bin, LI Daoguo, WANG Mukuai. Review and prospect on research of cloud model[J]. Application Research of Computers, 2011, 28(2): 420–426