| 47 | 0 | 1398 |
| 下载次数 | 被引频次 | 阅读次数 |
为了在电力系统灵活性分析过程中，提取对系统灵活性影响较大的典型光伏日出力率曲线，提出一种考虑灵活性分析的典型光伏日出力率曲线提取方法， 确定对灵活性影响较大的光伏日发电曲线范围， 利用斯皮尔曼相关系数和随机贪心算法改进 K-means 聚类算法， 对光伏日出力率曲线进行聚类， 利用灰狼算法提取典型光伏日出力率曲线。 算例分析以蒙西电网2019—2022年光伏日出力数据为研究对象， 结果表明， 利用所提方法提取的典型光伏日出力率曲线， 能够有效反映光伏发电对系统灵活性的影响。
Abstract：In the process of analyzing the flexibility of the power system, it is necessary to extract typical photovoltaic(PV) daily output curves that have a significant impact on the system flexibility. This paper proposes a method for extracting typical PV daily output curves considering flexibility analysis. The method determines the range of PV daily generation curves that have a significant impact on flexibility, improves the K-means clustering algorithm using Spearman correlation coefficient and random greedy algorithm, clusters the PV daily output rate curves, and extracts typical PV daily output rate curves using the grey wolf algorithm. A case study is conducted using the PV output data from the Inner Mongolia west power grid from 2019 to 2022. The results show that the typical PV daily output rate curves extracted using the proposed method can effectively reflect the impact of PV power generation on system flexibility.
[1] 鲁宗相,李海波,乔颖.含高比例可再生能源电力系统灵活性规划及挑战[J].电力系统自动化,2016,40(13):147-158. LU Zongxiang, LI Haibo, QIAO Ying. Planning and Challenges of Power Systems with a High Proportion of Renewable Energy Resources[J]. Automation of Electric Power Systems, 2016, 40(13): 147-158.
[2] 鲁宗相,林弋莎,乔颖,等.极高比例可再生能源电力系统的灵活性供需平衡[J].电力系统自动化,2022,46(16):3-16. LU Zongxiang, LIN Yisha, QIAO Ying, et al. Flexibility Supply and Demand Balance in Power Systems with an Extremely High Proportion of Renewable Energy[J]. Automation of Electric Power Systems, 2022, 46(16): 3-16.
[3] 陈雯,刘娟,赵爽,等.适应于高比例可再生能源系统灵活性资源配置[J].云南电力技术,2023,51(5):2-5,9. CHEN Wen, LIU Juan, ZHAO Shuang, et al. Rese on Peak Shaving and Energy Storage Requirements Suitable for High Proportion Renewable Energy Systems[J]. Yunnan Electric Power, 2023, 51(5): 2-5, 9.
[4] 童宇轩,金超.新型电力系统典型负荷侧资源灵活性价值分析[J].东北电力技术,2023,44(12):49-54,67. TONG Yuxuan, JIN Chao. Analysis of Resource Flexibility Value on Typical Load Side of New Power System[J]. Northeast Electric Power Technology, 2023, 44(12): 49-54, 67.
[5] Publishing O, Agency I E.Harnessing variable renewables[M]. Paris: OECD/IEA, 2011.
[6] 黄冯.面向电力系统分析的可再生能源典型场景生成方法[D].合肥:合肥工业大学,2019.
[7] 郝浩伟,周博文,张馨文.计及碳交易与需求响应的综合能源系统优化研究[J].东北电力技术,2022,43(10):32-37,43. HAO Haowei, ZHOU Bowen, ZHANG Xinwen. Research of Integrated Energy System Optimization Considering Carbon Trading and Demand Response[J]. Northeast Electric Power Technology, 2022, 43(10): 32-37, 43.
[8] 王建学,张耀,万筱钟.光伏出力特性指标体系和分类典型曲线研究[J].电力需求侧管理,2017,19(5):8-12. WANG Jianxue, ZHANG Yao, WAN Xiaozhong. Research on Photovoltaic Output Characteristics Indicator System and Classification of Typical Curves[J]. Power Demand Side Management, 2017, 19(5): 8-12.
[9] 王建学,张耀,万筱钟,等.面向电网运行的新能源出力特性指标体系研究--风电出力特性指标体系[J].电网与清洁能源,2016, 32(2):42-51,57. WANG Jianxue, ZHANG Yao, WAN Xiaozhong, et al. Research on New Energy Output Characteristics Indicator System for Power Grid Operation- Wind Power Output Characteristics Indicator System[J]. Power System and Clean Energy, 2016, 32(2): 42-51, 57.
[10] 王群,董文略,杨莉.基于Wasserstein距离和改进K-medoids聚类的风电/光伏经典场景集生成算法[J].中国电机工程学报,2015, 35(11):2654-2661. WANG Qun, DONG Wenlue, YANG Li. Algorithm for Generating Classical Scenario Sets for Wind and Photovoltaic Based on Wasserstein Distance and Improved K- medoids Clustering[J]. Proceedings of the CSEE, 2015, 35(11): 2654- 2661.
[11] 邬浩泽,朱晨烜,张贻山,等.一种基于改进模糊聚类算法的自适应典型日选取方法[J].智慧电力,2022,50(1):60-67. WU Haoze, ZHU Chenxuan, ZHANG Yishan, et al. An Adaptive Typical Day Selection Method Based on Improved Fuzzy Clustering Algorithm[J]. Smart Power, 2022, 50(1): 60- 67.
[12] 林俐,费宏运,刘汝琛,等.基于分层聚类算法的地区风电出力典型场景选取方法[J].电力系统保护与控制,2018,46(7):1-6. LIN Li, FEI Hongyun, LIU Ruchen, et al. Regional Wind Power Output Typical Scenario Selection Method Based on Hierarchical Clustering Algorithm[J]. Power System Protection and Control, 2018, 46(7): 1-6.
[13] 胡啸,王玲燕,张浩宇,等.基于狮群优化的改进K-Means聚类算法研究[J].控制工程,2022,29(11):1996-2002. HU Xiao, WANG Lingyan, ZHANG Haoyu, et al. Study on Improved K-Means Clustering Algorithm Based on Lion Group Optimization[J]. Control Engineering of China, 2022, 29(11): 1996-2002.
[14] 吴亚雄,高崇,曹华珍,等.基于灰狼优化聚类算法的日负荷曲线聚类分析[J].电力系统保护与控制,2020,48(6):68-76. WU Yaxiong, GAO Chong, CAO Huazhen, et al. Day Load Curve Clustering Analysis Based on Grey Wolf Optimization Clustering Algorithm[J]. Power System Protection and Control, 2020, 48(6): 68-76.
[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. Renewable Energy Scenario Curtailment Methods Including Wind and Photovoltaic[J]. Power System Protection and Control, 2021, 49(15): 141-149.
[16] 郑超,杨明亮,吴琛,等.计算步长对新能源高占比电网调度运行优化的影响[J].云南电力技术,2022,50(3):36-41. ZHENG Chao, YANG Mingliang, WU Chen, et al. The Effect of Sampling in the Optimal Operation of the Renewable-Source- Dominated Power Gird[J]. Yunnan Electric Power, 2022, 50(3): 36-41.
[17] 张勋奎.以新能源为主体的新型电力系统发展路线图[J].分布式能源,2021,6(6):1-8. ZHANG Xunkui. A Roadmap for Developing a New Power System With New Energy as the Main Body[J]. Distributed Energy, 2021, 6(6): 1-8.
[18] LiKas A, Vlassis N, VerbeeK J J. The Global K-Means Clustering Algorithm[J]. Pattern Recognition, 2002, 36(2): 451-461.
[19] Zhou H B, Gao J T. Automatic Method for Determining Cluster Number Based on Silhouette Coefficient[J]. Advanced Materials Research, 2014(951): 227-230.
[20] Lovie, A. D. Who discovered Spearman's rank correlation[J]. British Journal of Mathematical& Statistical Psychology, 2011, 48(2): 255-269.
[21] Bouamama S, Blum C. A randomized population-based iterated greedy algorithm for the minimum weight dominating set problem[C]//IEEE. 20156th International Conference on Information and Communication Systems. Amman: Jordan, 2015: 7-12.
[22] Mirjalili S, Mirjalili S M, Lewis A. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69(3): 46-61.
[23] Saremi S, Mirjalili S Z, Mirjalili S M. Evolutionary population dynamics and grey wolf optimizer[J]. Neural Computing& Applications, 2015(26): 1257-1263.
基本信息:
DOI：10.19929/j.cnki.nmgdljs.2024.0036
中图分类号:
引用信息:
[1]呼斯乐1,2,于源1,王渊3等.考虑灵活性分析的典型光伏日出力率曲线提取方法[J].内蒙古电力技术,2024,42(03):20-27.DOI:10.19929/j.cnki.nmgdljs.2024.0036.
基金信息:
内蒙古电力（集团）有限责任公司科技项目“电力系统灵活调节资源优化配置方法及智能决策系统研究”（2022-07）