Construction Method of Wind Power Output Scenario Matching with Typical Daily Load

doi:10.12204/j.issn.1000-7229.2022.11.013

Abstract

Abstract:

The randomness of wind power output makes it difficult to balance the robustness and computational efficiency of microgrid grid connection planning. A wind power output scenario construction method matching with typical daily load scenarios is proposed. The daily load trend and the location of peak and valley periods should be considered in the microgrid planning. The daily load curve trend and peak and valley period information are extracted by using the membership function, and combined with the improved ordered clustering, a typical daily load selection method is proposed; In the effective time of typical daily load, using the maximum increase and decrease of wind power output, combined with interpolation method, a wind power scene construction method is proposed. Then an evaluation index system is established to evaluate the selection of typical daily load and the construction effect of corresponding wind power scenarios. Finally, the effectiveness of the proposed model is verified by power grid data.

Key words: microgrid planning and operation, scene construction, ordered clustering, evaluating indicator

CLC Number:

TM714

YUAN Tiejiang, YANG Yang, DONG Litong. Construction Method of Wind Power Output Scenario Matching with Typical Daily Load[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(11): 132-141.

Figures/Tables 14

Fig.1 Daily load curve

Fig.2 Flow chart of load scenario extraction and wind power scenario construction

Fig.3 Flow chart of load scene extraction

Fig.4 Annual load curve of a region

Fig.5 Annual wind power output curve of a region

Fig.6 Number of clusters and small probability scenarios

Fig.7 Curve of improved BWP index changing with cluster number

Table 1 Scene extraction results

Fig.8 Typical daily and original load curves of load scenarios

Fig.9 Typical daily output and original output fluctuation curve of wind power scenarios

Table 2 Comparison of different selection methods of typical days

Fig.10 Relationship between typical daily load evaluation index and original load

Fig.11 Probability index of load change in the same direction and original load

Fig.12 Wind power index and original wind power output

References 27

[1]	林俐, 周鹏, 王世谦, 等. 考虑相关性的地区风电出力对调峰容量的影响分析[J]. 现代电力, 2016, 33(6): 21-26.
	LIN Li, ZHOU Peng, WANG Shiqian, et al. Analysis impact on regional wind power to peak regulation capacity by considering the correlation[J]. Modern Electric Power, 2016, 33(6): 21-26.
[2]	李震. 基于时序生产模拟的区域电网新能源消纳能力研究[J]. 现代工业经济和信息化, 2020, 10(12): 68-69.
	LI Zhen. Study on new energy consumption capacity of regional power grid based on time series production simulation[J]. Modern Industrial Economy and Informationization, 2020, 10(12): 68-69.
[3]	辛锐, 张瑜, 郝保中, 等. 考虑响应不确定性的高比例可再生能源微电网优化策略[J]. 供用电, 2021, 38(9): 35-41.
	XIN Rui, ZHANG Yu, HAO Baozhong, et al. Optimization strategy of high proportion renewable energy microgrid considering response uncertaintye[J]. Distribution & Utilization, 2021, 38(9): 35-41.
[4]	谢姿, 张惠娟, 刘琪, 等. 考虑蓄电池寿命的分布式电源容量优化配置[J]. 太阳能学报, 2021, 42(10): 424-430.
	XIE Zi, ZHANG Huijuan, LIU Qi, et al. Optimal configeration of distributed power supply capacity considering battery life[J]. Acta Energiae Solaris Sinica, 2021, 42(10): 424-430.
[5]	GAO Y, LIU Q Y, WANG S X, et al. Impact of typical demand day selection on CCHP operational optimization[J]. Energy Procedia, 2018, 152: 39-44. doi: 10.1016/j.egypro.2018.09.056 URL
[6]	PINTO E S, SERRA L M, LÁZARO A. Evaluation of methods to select representative days for the optimization of polygeneration systems[J]. Renewable Energy, 2020, 151: 488-502. doi: 10.1016/j.renene.2019.11.048 URL
[7]	袁铁江, 车勇, 孙谊媊, 等. 基于时序仿真和GA的风储系统储能容量优化配比[J]. 高电压技术, 2017, 43(7): 2122-2130.
	YUAN Tiejiang, CHE Yong, SUN Yiqian, et al. Optimized proportion of energy storage capacity in wind-storage system based on timing simulation and GA algorithm[J]. High Voltage Engineering, 2017, 43(7): 2122-2130.
[8]	韩宏志, 杨洋, 郜宁, 等. 基于正态分布的典型负荷日拟合方法[J]. 分布式能源, 2020, 5(4): 69-73.
	HAN Hongzhi, YANG Yang, GAO Ning, et al. Fitting method of typical daily load based on normal distribution[J]. Distributed Energy, 2020, 5(4): 69-73.
[9]	蔡国伟, 王大亮, 王燕涛, 等. 一种基于最短距离聚类与关联度分析的典型日选取新方法[J]. 中国电力, 2008, 41(4): 15-18.
	CAI Guowei, WANG Daliang, WANG Yantao, et al. A new selection of the typical day based on the analysis of minimum distance cluster and interconnect degree theory[J]. Electric Power, 2008, 41(4): 15-18.
[10]	孟令奎, 段红伟, 黄长青, 等. 一种基于语义聚类的典型日负荷曲线选取方法[J]. 华北电力大学学报(自然科学版), 2013, 40(1): 43-48.
	MENG Lingkui, DUAN Hongwei, HUANG Changqing, et al. A semantic clustering method for selecting the typical day load curve[J]. Journal of North China Electric Power University (Natural Science Edition), 2013, 40(1): 43-48.
[11]	徐邦恩, 蔺红. 基于改进模糊聚类的典型日负荷曲线选取方法[J]. 电测与仪表, 2019, 56(4): 21-26.
	XU Bangen, LIN Hong. Selection method of typical daily load curve based on improved fuzzy clustering[J]. Electrical Measurement & Instrumentation, 2019, 56(4): 21-26.
	徐邦恩, 蔺红. 基于改进模糊聚类的典型日负荷曲线选取方法[J]. 电测与仪表, 2019(4):21-26.
	XU Bangen, LIN Hong. Selection method of typical daily load curve based on improved fuzzy clustering[J]. Electrical Measurement & Instrumentation, 2019(4):21-26.
[12]	杨恒岳, 刘青荣, 阮应君. 基于k-means聚类算法的分布式能源系统典型日冷热负荷选取[J]. 热力发电, 2021, 50(3): 84-90.
	YANG Hengyue, LIU Qingrong, RUAN Yingjun. Selection of typical daily cooling and heating load of CCHP system based on k-means clustering algorithm[J]. Thermal Power Generation, 2021, 50(3): 84-90.
[13]	袁铁江, 曹继雷. 计及风电-负荷不确定性的风氢低碳能源系统容量优化配置[J]. 高电压技术, 2022, 48(6): 2037-2044.
	YUAN Tiejiang, CAO Jilei. Capacity optimization allocation of wind hydrogen low-carbon energy system considering wind power-load uncertainty[J]. High Voltage Engineering, 2022, 48(6): 2037-2044.
[14]	张国庆, 张海静, 杨东亮, 等. 一种基于反一致自适应聚类的典型日选取方法[J]. 电力科学与工程, 2017, 33(7): 26-31.
	ZHANG Guoqing, ZHANG Haijing, YANG Dongliang, et al. A typical day selection method based on anti-uniform adaptive clustering algorithm[J]. Electric Power Science and Engineering, 2017, 33(7): 26-31.
[15]	YEGANEFAR A, AMIN-NASERI M R, SHEIKH-EL-ESLAMI M K. Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources[J]. Applied Energy, 2020, 272: 115224. doi: 10.1016/j.apenergy.2020.115224 URL
[16]	ZATTI M, GABBA M, FRESCHINI M, et al. K-MILP: a novel clustering approach to select typical and extreme days for multi-energy systems design optimization[J]. Energy, 2019, 181: 1051-1063. doi: 10.1016/j.energy.2019.05.044 URL
[17]	GUO L, HOU R S, LIU Y X, et al. A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid[J]. Applied Energy, 2020, 263: 114606. doi: 10.1016/j.apenergy.2020.114606 URL
[18]	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. A regional wind power typical scenarios’ selection method based on hierarchical clustering algorithm[J]. Power System Protection and Control, 2018, 46(7): 1-6.
[19]	刘汝琛. 基于实测数据的地区风电出力的典型场景选取[D]. 北京: 华北电力大学, 2016.
	LIU Ruchen. Selection of typical scenarios of regional wind power output based on measured data[D]. Beijing: North China Electric Power University, 2016.
[20]	赵岳恒, 刘民伟, 王文飞, 等. 采用谱聚类的风电典型出力场景选取方法[J]. 云南电力技术, 2020, 48(1): 17-20.
	ZHAO Yueheng, LIU Minwei, WANG Wenfei, et al. Generation of typical wind power scenario based on spectral clustering method[J]. Yunnan Electric Power, 2020, 48(1): 17-20.
[21]	谢永胜, 杨洋, 荆世博, 等. 基于时序生产模拟和弃风率约束的电解槽额定功率边界计算方法[J]. 电器与能效管理技术, 2021(7): 23-28.
	XIE Yongsheng, YANG Yang, JING Shibo, et al. Calculation method of rated power boundary of electrolytic cell based on sequential production simulation and air rejection rate constraint[J]. Electrical & Energy Management Technology, 2021(7): 23-28.
[22]	郭力, 杨书强, 刘一欣, 等. 风光储微电网容量规划中的典型日选取方法[J]. 中国电机工程学报, 2020, 40(8): 2468-2479.
	GUO Li, YANG Shuqiang, LIU Yixin, et al. Typical day selection method for capacity planning of microgrid with wind turbine-photovoltaic and energy storage[J]. Proceedings of the CSEE, 2020, 40(8): 2468-2479.
[23]	PONCELET K, HÖSCHLE H, DELARUE E, et al. Selecting representative days for capturing the implications of integrating intermittent renewables in generation expansion planning problems[J]. IEEE Transactions on Power Systems, 2017, 32(3): 1936-1948. doi: 10.1109/TPWRS.2016.2596803 URL
[24]	何韩吉, 邓光明. 基于共同趋势提取的多维有序聚类方法[J]. 统计与信息论坛, 2020, 35(12): 15-20.
	HE Hanji, DENG Guangming. Multi-dimensional ordered clustering method based on common trend extraction[J]. Statistics & Information Forum, 2020, 35(12): 15-20.
[25]	周世兵, 徐振源, 唐旭清. K-means算法最佳聚类数确定方法[J]. 计算机应用, 2010, 30(8): 1995-1998.
	ZHOU Shibing, XU Zhenyuan, TANG Xuqing. Method for determining optimal number of clusters in K-means clustering algorithm[J]. Journal of Computer Applications, 2010, 30(8): 1995-1998.
[26]	韩彦林. 关于小概率事件的认识以及应用[J]. 科技视界, 2017(19): 14-15.
	HAN Yanlin. Understanding and application of small probability events[J]. Science & Technology Vision, 2017(19): 14-15.
[27]	杨富炜. 基于弃风消纳的冷热电联供微网优化配置[D]. 沈阳: 沈阳工业大学, 2020.
	YANG Fuwei. Optimizing allocation of CCHP micro-grid based on wind power curtailment consumption[D]. Shenyang: Shenyang University of Technology, 2020.