Complementary characteristics of wind-photovoltaic-hydropower output in basin

doi:10.3785/j.issn.1008-973X.2024.07.020

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (7): 1505-1515 DOI: 10.3785/j.issn.1008-973X.2024.07.020

Complementary characteristics of wind-photovoltaic-hydropower output in basin

Xinyang LI1(

),Weifeng LIU2,*(

),Xuning GUO2,Yunling LI2,Feilin ZHU3,Ping’an ZHONG3

1. School of Civil Engineering, Tianjin University, Tianjin 300072, China
2. Water Strategy Research Unit One, MWR General Institute of Water Resources and Hydropower Planning and Design, Beijing 100120, China
3. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

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Abstract

The Quantitative assessment of the complementary characteristics of wind-photovoltaic-hydropower power output is important for the layout and optimized scheduling of wind-photovoltaic-hydropower systems. Existing indicators for evaluating complementary characteristics are only applicable to assessing the complementary characteristics between two energy sources. Indicators suitable for evaluating the complementary characteristics of three energy sources were established. Combining historical meteorological data, the long-term complementary characteristics of wind-photovoltaic-hydropower output in the Yalong River basin were revealed. In response to the significant impact of meteorological conditions on wind-photovoltaic-hydropower output and the complexity of short-term complementary characteristics, an intelligent screening model for short-term typical days of wind-photovoltaic-hydropower output was proposed, integrating with machine learning theory. The differences in evaluation results of wind-photovoltaic-hydropower output complementary characteristics under different time calculation steps were analyzed. The results show that at a long-term scale, wind and solar power output compensates for hydropower output, enhancing the hydroelectric energy generation efficiency by 3%. At a short-term scale, hydropower output demonstrated favorable electricity compensation benefits for wind and solar power output, with the wind and solar curtailment rate reduced by 18 percentage points and the output shortage rate reduced by 28 percentage points after hydropower compensation.

Key words： wind-photovoltaic-hydropower output complementarity analysis tri-energy complementarity index machine learning Yalong River basin

Received: 11 June 2023 Published: 01 July 2024

CLC:

TP 393

Fund: 国家重点研发计划重点专项（2022YF3202300）；水利部水利青年拔尖人才项目；水利水电规划设计总院“揭榜挂帅”项目；中国博士后科学基金资助项目（2021M702313）；中央高校基本科研业务费专项资金资助项目（B240201123）.

Corresponding Authors: Weifeng LIU E-mail: 842212007@qq.com;weifliu@qq.com

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Cite this article:

Xinyang LI,Weifeng LIU,Xuning GUO,Yunling LI,Feilin ZHU,Ping’an ZHONG. Complementary characteristics of wind-photovoltaic-hydropower output in basin. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1505-1515.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.07.020 OR https://www.zjujournals.com/eng/Y2024/V58/I7/1505

流域风光水电出力互补特性

定量评估风光水能源出力互补特性对风光水电站规模布局以及互补优化调度具有重要意义，现有互补特性评价指标仅适用于评价两能源互补特性. 为此建立适合三能源互补特性评价的指标体系，结合历史气象资料，探明雅砻江流域风光水电出力长期互补特性. 针对风光水电出力受气象条件的显著影响和短期互补特性的复杂性，结合机器学习理论，提出风光水电出力短期典型日智能筛选模型，剖析不同时间计算步长对风光水电出力互补特性评价结果的差异. 计算结果表明，长期尺度风光能源出力对水电能源呈现电量补偿特性，使水电能源的发电效益提高3%；短期尺度水电能源出力对风光能源出力具有较好的电力补偿效益，水电补偿后风光弃电率减小了18个百分点，出力短缺率减小了28个百分点.

关键词： 风光水电出力, 互补性分析, 三能源互补性指标, 机器学习, 雅砻江流域

Fig.1 Schematic of two energy sources output complementarity

Fig.2 Schematic of comprehensive vector for wind-photovoltaic-hydropower output

Fig.3 Schematic of Euclidean distance calculation

Fig.4 Schematic of alignment and curved path of dynamic time warping algorithm

Fig.5 Flowchart of K-Means-DTW algorithm for screening of typical days

Fig.6 Average monthly output of wind-photovltatic-hydropower after standardization

Fig.7 Wind and solar power provide energy compensation for hydropower at long-term scale

Fig.8 Determine optimal number of clusters

Tab.1 With-cluster sum of squared errors for different algorithms

Fig.9 Screening of typical days based on K-Means-ED algorithm

Fig.10 Screening of typical days based on K-Means-DTW algorithm

Fig.11 Power output process of wind-photovoltaic-hydro for different typical days

Tab.2 Comprehensive coefficients of wind-photovoltaic-hydropower output for different typical days

Fig.12 Contribution of energy paired combination outputs to comprehensive complementary coefficient at hour scale

Fig.13 Hydropower output provides power compensation for wind-photovoltaic output at short-term scale

Tab.3 Impact of with and without hydropower compensation on wind-photovoltaic output at short-term scale %

Tab.4 Complementary of wind-photovoltaic-hydropower output in different calculation periods

Fig.14 Contribution of energy paired combination outputs to comprehensive complementary coefficient in different calculation periods


[1]	WANG X, MEI Y, KONG Y, et al Improved multi-objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic system[J]. Energy, 2017, 134: 813- 839 doi: 10.1016/j.energy.2017.06.047

[2]	JURASZ J, BELUCO A, CANALES F A The impact of complementarity on power supply reliability of small scale hybrid energy systems[J]. Energy, 2018, 161: 737- 743 doi: 10.1016/j.energy.2018.07.182

[3]	MONFORTI F, HULD T, BÓDIS K, et al Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach[J]. Renewable Energy, 2014, 63: 576- 586 doi: 10.1016/j.renene.2013.10.028

[4]	DENAULT M, DUPUIS D, COUTURE-CARDINAL S Complementarity of hydro and wind power: improving the risk profile of energy inflows[J]. Energy Policy, 2009, 37 (12): 5376- 5384 doi: 10.1016/j.enpol.2009.07.064

[5]	徐维超. 相关系数研究综述[J]. 广东工业大学学报. 2012, 29(3): 12−17. XU Weichao. A review on correlation coefficients [J]. Journal of Guangdong University of Technology , 2012, 29(3): 12−17.

[6]	MYERS J L, WELL A D, LORCH R F. Research design and statistical analysis [M]. New York: Routledge, 2010: 271−290.

[7]	LIU Y, XIAO L, WANG H, et al Analysis on the hourly spatiotemporal complementarities between China’s solar and wind energy resources spreading in a wide area[J]. Science China Technological Sciences, 2013, 56: 683- 692 doi: 10.1007/s11431-012-5105-1

[8]	ZHOU H, WU H, YE C, et al Integration capability evaluation of wind and photovoltaic generation in power systems based on temporal and spatial correlations[J]. Energies, 2019, 12 (1): 171 doi: 10.3390/en12010171

[9]	SANTOS-ALAMILLOS F J, POZO-VÁZQUEZ D, RUIZ-ARIAS J A, et al Combining wind farms with concentrating solar plants to provide stable renewable power[J]. Renewable Energy, 2015, 76: 539- 550 doi: 10.1016/j.renene.2014.11.055

[10]	LI Y, AGELIDIS V G, SHRIVASTAVA Y. Wind-solar resource complementarity and its combined correlation with electricity load demand [C]// 2009 4th IEEE Conference on Industrial Electronics and Applications . Xi’an: IEEE, 2009: 3623−3628.

[11]	DOS ANJOS P S, DA SILVA A S A, STO ŠIĆ B, et al Long-term correlations and cross-correlations in wind speed and solar radiation temporal series from Fernando de Noronha Island, Brazil[J]. Physica A: Statistical Mechanics and its Applications, 2015, 424: 90- 96 doi: 10.1016/j.physa.2015.01.003

[12]	王浩, 王旭, 雷晓辉, 等梯级水库群联合调度关键技术发展历程与展望[J]. 水利学报, 2019, 50 (1): 25- 37 WANG Hao, WANG Xu, LEI Xiaohui, et al The development and prospect of key techniques in the cascade reservoir operation[J]. Journal of Hydraulic Engineering, 2019, 50 (1): 25- 37

[13]	SAILOR D J, SMITH M, HART M Climate change implications for wind power resources in the Northwest United States[J]. Renewable Energy, 2008, 33 (11): 2393- 2406

[14]	CROOK J A, JONES L A, FORSTER P M, et al Climate change impacts on future photovoltaic and concentrated solar power energy output[J]. Energy and Environmental Science, 2011, 4 (9): 3101- 3109

[15]	LIU W, ZHU F, CHEN J, et al Multi-objective optimization scheduling of wind–photovoltaic–hydropower systems considering riverine ecosystem[J]. Energy Conversion and Management, 2019, 196: 32- 43 doi: 10.1016/j.enconman.2019.05.104

[16]	CANTÃO M P, BESSA M R, BETTEGA R, et al Evaluation of hydro-wind complementarity in the Brazilian territory by means of correlation maps[J]. Renewable Energy, 2017, 101: 1215- 1225 doi: 10.1016/j.renene.2016.10.012

[17]	KOTSIANTIS S B. Supervised machine learning: a review of classification techniques [C]// Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering . [S. l.]: IOS Press, 2007: 3−24.

[18]	PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al Scikit-learn: machine learning in Python[J]. Journal of Machine Learning Research, 2011, 12: 2825- 2830

[19]	LOYD S Least squares quantization in PCM[J]. IEEE Transactions on Information Theory, 1982, 28 (2): 129- 137 doi: 10.1109/TIT.1982.1056489

[20]	李映辉. 基于数据挖掘的相似洪水动态识别方法研究及应用 [D]. 南京: 河海大学, 2019. LI Yinghui. Similar flood dynamic recognition method based on data mining [D]. Nanjing: Hohai University, 2019.

[21]	王小锋, 丁义. 雅砻江流域水调自动化系统建设及应用研究 [J]. 水电与新能源, 2014(6): 46−48. WANG Xiaofeng, DING Yi. Development and application of automatic water diversion system of Yalong River valley [J]. Hydropower and New Energy , 2014(6): 46−48.

[22]	李长春, 高仕春. 保证出力在水库调度中的应用探讨[J]. 水力发电. 2008, 34(8): 97−99. LI Changchun, GAO Shichun. Discussion on application of firm power in reservoir operation [J]. Water Power , 2008, 34(8): 97−99.

[23]	李科. 基于多属性的车辆重识别方法研究[D]. 厦门: 厦门大学, 2019. LI Ke. Vehicle re-identification based on multi-attribute [D]. Xiamen: Xiamen University, 2019.

[24]	朱连江, 马炳先, 赵学泉基于轮廓系数的聚类有效性分析[J]. 计算机应用, 2010, 30 (Suppl.2): 139- 141 ZHU Lianjiang, MA Bingxian, ZHAO Xuequan Clustering validity analysis based on silhouette coefficient[J]. Journal of Computer Applications, 2010, 30 (Suppl.2): 139- 141

[25]	TIBSHIRANI R, WALTHER G, HASTIE T Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society: Statistical Methodologys, 2001, 63 (2): 411- 423

[26]	国家能源局. 2019年风电并网运行情况[EB/OL]. (2020-02-28)[2023-05-13]. https://www.nea.gov.cn/2020-02/28/c_138827910.htm.

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