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

Tiejiang YUAN; Yang YANG; Litong DONG

doi:10.12204/j.issn.1000-7229.2022.11.013

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Electric Power Construction ›› 2022, Vol. 43 ›› Issue (11) : 132-141. DOI: 10.12204/j.issn.1000-7229.2022.11.013

New Energy Power Generation

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

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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

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Tiejiang YUAN , Yang YANG , Litong DONG. Construction Method of Wind Power Output Scenario Matching with Typical Daily Load[J]. Electric Power Construction. 2022, 43(11): 132-141 https://doi.org/10.12204/j.issn.1000-7229.2022.11.013

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. https://doi.org/10.1016/j.egypro.2018.09.056 https://linkinghub.elsevier.com/retrieve/pii/S1876610218306003 Cited in this article [3]

[6]

PINTO

E S

, SERRA

L M

, LÁZARO

. Evaluation of methods to select representative days for the optimization of polygeneration systems[J]. Renewable Energy, 2020, 151: 488-502.

https://doi.org/10.1016/j.renene.2019.11.048

https://linkinghub.elsevier.com/retrieve/pii/S0960148119317355

Cited in this article [3]

[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.

Cited in this article [3]

[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. Cited in this article [2]

[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.

Cited in this article [2]

[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.

Cited in this article [2]

[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. Cited in this article [2]

[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(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. Cited in this article [2]

[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.

Cited in this article [2]

[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. Cited in this article [2]

[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. Cited in this article [2]

[15]

YEGANEFAR

, 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.

https://doi.org/10.1016/j.apenergy.2020.115224

https://linkinghub.elsevier.com/retrieve/pii/S0306261920307364

Cited in this article [1]

[16]

ZATTI

, GABBA

, FRESCHINI

, 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.

https://doi.org/10.1016/j.energy.2019.05.044

https://linkinghub.elsevier.com/retrieve/pii/S0360544219309077

Cited in this article [2]

[17]

GUO

, 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.

https://doi.org/10.1016/j.apenergy.2020.114606

https://linkinghub.elsevier.com/retrieve/pii/S0306261920301185

Cited in this article [2]

[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

, 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.

Cited in this article [2]

[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. Cited in this article [2]

[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. Cited in this article [1]

[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.

Cited in this article [1]

[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

, 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.

Cited in this article [1]

[23]

PONCELET

, HÖSCHLE

, DELARUE

, 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.

https://doi.org/10.1109/TPWRS.2016.2596803

http://ieeexplore.ieee.org/document/7527691/

Cited in this article [1]

[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.

Abstract

K-means聚类算法是以确定的类数k为前提对数据集进行聚类的,通常聚类数事先无法确定。从样本几何结构的角度设计了一种新的聚类有效性指标,在此基础上提出了一种新的确定K-means算法最佳聚类数的方法。理论研究和实验结果验证了以上算法方案的有效性和良好性能。

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.

Cited in this article [1] Abstract

K-means clustering algorithm clusters datasets according to the certain clustering number k. However,k cannot be confirmed beforehand. A new clustering validity index was designed from the standpoint of sample geometry. Based on the index, a new method for determining the optimal clustering number in K-means clustering algorithm was proposed. Theoretical research and experimental results demonstrate the validity and good performance of the above-mentioned algorithm.

[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. Cited in this article [1]

[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.