基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究

doi:10.11930/j.issn.1004-9649.202212032

摘要/Abstract

摘要： 新能源出力的波动性、随机性使得新型电力系统在规划以及运行分析时，如何获得典型出力场景是亟须解决的关键问题之一。提出了基于皮尔逊相关系数融合密度峰值和熵权法的典型场景聚类中心确定方法。首先，采用密度峰值方法选取初始聚类中心；然后，基于熵权法选取后续聚类中心；接着，采用后向场景缩减方法获得最终典型场景，以该方法为核心，构建了确定新能源典型出力场景的整体方案；最后，以2021年华北某地风电与光伏的日出力场景进行算例分析及对比验证，验证所提方法的准确性和有效性。研究成果将为电力系统规划与运行提供更为准确的数据支撑。

关键词: 典型出力场景, 密度峰值, 熵权法, 场景聚类, 场景缩减

Abstract: The volatility and randomness of new energy output make it one of the key problems that need to be solved urgently to obtain typical output scenarios in the planning and operation analysis of new power systems. In this paper, a typical scenario clustering center determination method based on fusion density peak value and entropy weight method of Pearson’s correlation coefficient is proposed. The method first selects the initial clustering center by the density peak value method and then selects the subsequent clustering center based on the entropy weight method. Then, it obtains the final typical scenario by using the backward scenario reduction method. Therefore, this paper takes this method as the core to construct an overall scheme for determining the typical output scenarios of new energy. Finally, this paper uses the daily output scenario of wind power and photovoltaic power in North China in 2021 to carry out an analysis of examples and verification through comparison, which proves the accuracy and effectiveness of the proposed method. The research results will provide more accurate data support for power system planning and operation.

Key words: typical output scenario, density peak value, entropy weight method, scenario clustering, scenario reduction

赵源上, 林伟芳. 基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究[J]. 中国电力, 2023, 56(5): 193-202.

ZHAO Yuanshang, LIN Weifang. Research on Typical Scenarios Based on Fusion Density Peak Value and Entropy Weight Method of Pearson’s Correlation Coefficient[J]. Electric Power, 2023, 56(5): 193-202.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202212032

https://www.electricpower.com.cn/CN/Y2023/V56/I5/193

参考文献

[1] 许金华. 加快建设新型电力系统科学有序推进实现“双碳”目标[N]. 科技日报, 2021-10-28(5).
[2] 黎静华, 骆怡辰, 杨舒惠, 等. 可再生能源电力不确定性预测方法综述[J]. 高电压技术, 2021, 47(4): 1144–1157
LI Jinghua, LUO Yichen, YANG Shuhui, et al. Review of uncertainty forecasting methods for renewable energy power[J]. High Voltage Engineering, 2021, 47(4): 1144–1157
[3] 肖先勇, 郑子萱. “双碳”目标下新能源为主体的新型电力系统: 贡献、关键技术与挑战[J]. 工程科学与技术, 2022, 54(1): 47–59
XIAO Xianyong, ZHENG Zixuan. New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality: contribution, key techniques, and challenges[J]. Advanced Engineering Sciences, 2022, 54(1): 47–59
[4] ACERO J F C, VIAMONTE W R L, VELASQUEZ O C, et al. Modeling of the load duration curve considering the uncertainty of renewable generation and load. case study for the Peruvian power system[C]//2021 IEEE PES/IAS Power Africa. Nairobi, Kenya. IEEE, 2021: 1–5.
[5] 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
[6] 张宁, 杜尔顺, 李晖. 强不确定环境下的电力系统优化规划[M]. 北京: 中国电力出版社, 2021: 16–39.
[7] 董骁翀, 张姝, 李烨, 等. 电力系统中时序场景生成和约简方法研究综述[J]. 电网技术, 2023, 47(2): 709–721
DONG Xiaochong, ZHANG Shu, LI Ye, et al. Review of power system temporal scenario generation and reduction methods[J]. Power System Technology, 2023, 47(2): 709–721
[8] SURAJIT S, ROY G S, PARIMAL A. Multi-scenario based Bi-level coordinated planning of active distribution system under uncertain environment[J]. IEEE Transactions on Industry Applications, 2020, 56(1): 850–863.
[9] 卢炳文, 魏震波, 魏平桉, 等. 考虑消纳风电的区域综合能源系统电转气与储能设备优化配置[J]. 智慧电力, 2021, 49(5): 7–14, 68
LU Bingwen, WEI Zhenbo, WEI Ping'an, et al. Optimal configuration of PtG and energy storage equipment in regional integrated energy system considering wind power consumption[J]. Smart Power, 2021, 49(5): 7–14, 68
[10] 艾力米努尔·库尔班, 谢娟英, 姚若侠. 融合最近邻矩阵与局部密度的自适应K-means聚类算法[J]. 计算机科学与探索, 2023, 17(2): 355–366
KUERBAN Ailiminuer, XIE Juanying, YAO Ruoxia, et al. Adaptive K-means algorithm combining nearest-neighbor matrix and local density[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(2): 355–366
[11] 王帅, 杜欣慧, 姚宏民, 等. 面向含多种用户类型的负荷曲线聚类研究[J]. 电网技术, 2018, 42(10): 3401–3412
WANG Shuai, DU Xinhui, YAO Hongmin, et al. Research on load curve clustering with multiple user types[J]. Power System Technology, 2018, 42(10): 3401–3412
[12] 宋学伟, 刘玉瑶. 基于改进K-means聚类的风光发电场景划分[J]. 发电技术, 2020, 41(6): 625–630
SONG Xuewei, LIU Yuyao. Wind and photovoltaic generation scene division based on improved K-means clustering[J]. Power Generation Technology, 2020, 41(6): 625–630
[13] 郑焕坤, 曾凡斐, 傅钰, 等. 基于E-C-K-均值聚类和SOP优化的分布式电源双层规划[J]. 太阳能学报, 2022, 43(2): 127–135
ZHENG Huankun, ZENG Fanfei, FU Yu, et al. Bi-level distributed power planning based on e-c-k-means clustering and sop optimization[J]. Acta Energiae Solaris Sinica, 2022, 43(2): 127–135
[14] 要金铭, 赵书强, 韦子瑜, 等. 基于场景分析的电力系统日前调度及其快速求解方法[J]. 电力自动化设备, 2022, 42(9): 102–110
YAO Jinming, ZHAO Shuqiang, WEI Ziyu, et al. Day-ahead dispatch and its fast solution method of power system based on scenario analysis[J]. Electric Power Automation Equipment, 2022, 42(9): 102–110
[15] 白斌, 韩明亮, 林江, 等. 含风电和光伏的可再生能源场景削减方法[J]. 电力系统保护与控制, 2021, 49(15): 141–149
BAI Bin, HAN Mingliang, LIN Jiang, et al. Scenario reduction method of renewable energy including wind power and photovoltaic[J]. Power System Protection and Control, 2021, 49(15): 141–149
[16] 高鑫, 唐飞, 王晨旭, 等. 含分布式电源的配电网韧性与评估方法研究[J/OL]. 武汉大学学报(工学版), 2022: 1–8[2022-05-05].https://kns.cnki.net/kcms/detail/42.1675.T.20220505.1410.002.html.
GAO Xin, TANG Fei, WANG Chenxu, et al. Research on resilience and assessment method of distribution network integrated with distributed generation[J/OL]. Engineering Journal of Wuhan University, 2022: 1–8[2022-05-05].https://kns.cnki.net/kcms/detail/42.1675.T.20220505.1410.002.html.
[17] 党倩, 崔阿军, 尚闻博, 等. 采用欧式形态距离的负荷曲线近邻传播聚类方法[J]. 西安交通大学学报, 2022, 56(1): 165–176
DANG Qian, CUI Ajun, SHANG Wenbo, et al. Affinity propagation clustering method of typical load curve with euclidean morphological distance[J]. Journal of Xi’an Jiaotong University, 2022, 56(1): 165–176
[18] 余姚果, 梅亚东, 王现勋, 等. 基于改进SBR的风电出力典型场景提取与分析[J]. 武汉大学学报(工学版), 2021, 54(4): 346–353
YU Yaoguo, MEI Yadong, WANG Xianxun, et al. Extraction and analysis of wind power typical scenarios based on the improved SBR algorithm[J]. Engineering Journal of Wuhan University, 2021, 54(4): 346–353
[19] 卜凡鹏, 陈俊艺, 张琪祁, 等. 一种基于双层迭代聚类分析的负荷模式可控精细化识别方法[J]. 电网技术, 2018, 42(3): 903–913
BU Fanpeng, CHEN Junyi, ZHANG Qiqi, et al. A controllable refined recognition method of electrical load pattern based on bilayer iterative clustering analysis[J]. Power System Technology, 2018, 42(3): 903–913
[20] 王瑞峰, 王庆荣. 基于改进双层聚类多目标优化的配电网动态重构[J]. 电力系统保护与控制, 2019, 47(21): 92–99
WANG Ruifeng, WANG Qingrong. Multi-objective optimization of dynamic reconfiguration of distribution network based on improved Bilayer clustering[J]. Power System Protection and Control, 2019, 47(21): 92–99
[21] 夏飞, 张洁, 张浩, 等. 基于BIC准则和加权皮尔逊距离的居民负荷模式精细识别及预测[J]. 电子测量与仪器学报, 2020, 34(11): 33–42
XIA Fei, ZHANG Jie, ZHANG Hao, et al. Fine recognition and prediction of resident load pattern based on BIC criterion and weighted Pearson distance[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(11): 33–42
[22] 南钰, 宋瑞卿, 陈鹏, 等. 基于改进熵权-灰色关联法的配电网可靠性影响因素分析[J]. 电力系统保护与控制, 2019, 47(24): 101–107
NAN Yu, SONG Ruiqing, CHEN Peng, et al. Study on the factors influencing the reliability analysis in distribution network based on improved entropy weight gray correlation analysis algorithm[J]. Power System Protection and Control, 2019, 47(24): 101–107
[23] 杨晶显, 刘俊勇, 韩晓言, 等. 基于深度嵌入聚类的水光荷不确定性源场景生成方法[J]. 中国电机工程学报, 2020, 40(22): 7296–7306
YANG Jingxian, LIU Junyong, HAN Xiaoyan, et al. An uncertain hydro/PV/load typical scenarios generation method based on deep embedding for clustering[J]. Proceedings of the CSEE, 2020, 40(22): 7296–7306
[24] 王星华, 陈卓优, 彭显刚. 一种基于双层聚类分析的负荷形态组合识别方法[J]. 电网技术, 2016, 40(5): 1495–1501
WANG Xinghua, CHEN Zhuoyou, PENG Xiangang. A new combinational electrical load analysis method based on bilayer clustering analysis[J]. Power System Technology, 2016, 40(5): 1495–1501
[25] 唐锦, 张书怡, 吴秋伟, 等. 基于Copula函数与等概率逆变换的风电出力场景生成方法[J]. 电力工程技术, 2021, 40(6): 86–94
TANG Jin, ZHANG Shuyi, WU Qiuwei, et al. Wind power output scenario generation method based on Copula function and equal probability inverse transformation[J]. Electric Power Engineering Technology, 2021, 40(6): 86–94
[26] 赵书强, 要金铭, 李志伟. 基于改进K-means聚类和SBR算法的风电场景缩减方法研究[J]. 电网技术, 2021, 45(10): 3947–3954
ZHAO Shuqiang, YAO Jinming, LI Zhiwei. Wind power scenario reduction based on improved K-means clustering and SBR algorithm[J]. Power System Technology, 2021, 45(10): 3947–3954
[27] 丁明, 宋晓皖, 孙磊, 等. 考虑时空相关性的多风电场出力场景生成与评价方法[J]. 电力自动化设备, 2019, 39(10): 39–47
DING Ming, SONG Xiaowan, SUN Lei, et al. Scenario generation and evaluation method of multiple wind farms output considering spatial-temporal correlation[J]. Electric Power Automation Equipment, 2019, 39(10): 39–47
[28] 张辰毓, 许刚. 分布式多元随机动态场景生成及快速精准场景降维算法[J]. 电网技术, 2022, 46(2): 671–681
ZHANG Chenyu, XU Gang. Distributed multivariate random dynamic scenario generation and fast & accurate scenario simplified algorithm[J]. Power System Technology, 2022, 46(2): 671–681
[29] 唐海国, 张志丹, 康童, 等. 考虑场景聚类的配电网-天然气联合系统双层随机运行优化[J]. 现代电力, 2021, 38(6): 681–694
TANG Haiguo, ZHANG Zhidan, KANG Tong, et al. Bi-level stochastic operation optimization of distribution-natural gas combined system considering scenario clustering[J]. Modern Electric Power, 2021, 38(6): 681–694