IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v191y2020ics0360544219322091.html
   My bibliography  Save this article

Spatial and temporal correlation analysis of wind power between different provinces in China

Author

Listed:
  • Ren, Guorui
  • Wan, Jie
  • Liu, Jinfu
  • Yu, Daren

Abstract

China has different topographic conditions and multiple weather systems due to its vast geographic areas. As a result, the geographic diversity between different provinces is potential to mitigate wind power variability. This study analyzes the spatial and temporal correlations of wind power on the province level. The results show that a lower correlation is observed for greater distances and smaller time scales. Correlation between different provinces is stronger than that between different wind farms within the same distance. The correlation coefficient of wind power decays more rapidly in China than in Europe and North America within the same distance. The correlation coefficients of wind power variation are generally smaller than the correlation coefficients of instantaneous wind power, and a positive linear relationship is observed between these coefficients. The temporal correlations of wind power between different provinces are analyzed as well. The time lags of wind power between different province pairs are obtained.

Suggested Citation

  • Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322091
    DOI: 10.1016/j.energy.2019.116514
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219322091
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116514?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    2. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Alam, Md. Mahbub, 2017. "Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters," Renewable Energy, Elsevier, vol. 102(PA), pages 35-49.
    3. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    4. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    5. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    6. Bilal, Muhammad & Birkelund, Yngve & Homola, Matthew & Virk, Muhammad Shakeel, 2016. "Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell," Renewable Energy, Elsevier, vol. 99(C), pages 647-653.
    7. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    8. Vandezande, Leen & Meeus, Leonardo & Belmans, Ronnie & Saguan, Marcelo & Glachant, Jean-Michel, 2010. "Well-functioning balancing markets: A prerequisite for wind power integration," Energy Policy, Elsevier, vol. 38(7), pages 3146-3154, July.
    9. Jerez, S. & Thais, F. & Tobin, I. & Wild, M. & Colette, A. & Yiou, P. & Vautard, R., 2015. "The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1-15.
    10. Jahangiri, Mehdi & Ghaderi, Reza & Haghani, Ahmad & Nematollahi, Omid, 2016. "Finding the best locations for establishment of solar-wind power stations in Middle-East using GIS: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 38-52.
    11. Zhang, Hengxu & Cao, Yongji & Zhang, Yi & Terzija, Vladimir, 2018. "Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data," Applied Energy, Elsevier, vol. 216(C), pages 172-182.
    12. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    13. Kamau, J.N. & Kinyua, R. & Gathua, J.K., 2010. "6 years of wind data for Marsabit, Kenya average over 14m/s at 100m hub height; An analysis of the wind energy potential," Renewable Energy, Elsevier, vol. 35(6), pages 1298-1302.
    14. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    15. Katzenstein, Warren & Fertig, Emily & Apt, Jay, 2010. "The variability of interconnected wind plants," Energy Policy, Elsevier, vol. 38(8), pages 4400-4410, August.
    16. Mentis, Dimitrios & Hermann, Sebastian & Howells, Mark & Welsch, Manuel & Siyal, Shahid Hussain, 2015. "Assessing the technical wind energy potential in Africa a GIS-based approach," Renewable Energy, Elsevier, vol. 83(C), pages 110-125.
    17. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    18. Kayalar, Derya Ezgi & Küçüközmen, C. Coşkun & Selcuk-Kestel, A. Sevtap, 2017. "The impact of crude oil prices on financial market indicators: copula approach," Energy Economics, Elsevier, vol. 61(C), pages 162-173.
    19. Ceyhun Yıldız & Mustafa Tekin & Ahmet Gani & Ö. Fatih Keçecioğlu & Hakan Açıkgöz & Mustafa Şekkeli, 2017. "A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    20. He, Gang & Kammen, Daniel M., 2014. "Where, when and how much wind is available? A provincial-scale wind resource assessment for China," Energy Policy, Elsevier, vol. 74(C), pages 116-122.
    21. Yu, Dayang & Liang, Jun & Han, Xueshan & Zhao, Jianguo, 2011. "Profiling the regional wind power fluctuation in China," Energy Policy, Elsevier, vol. 39(1), pages 299-306, January.
    22. Monforti, F. & Huld, T. & Bódis, K. & Vitali, L. & D'Isidoro, M. & Lacal-Arántegui, R., 2014. "Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach," Renewable Energy, Elsevier, vol. 63(C), pages 576-586.
    23. Olauson, Jon & Bergkvist, Mikael, 2016. "Correlation between wind power generation in the European countries," Energy, Elsevier, vol. 114(C), pages 663-670.
    24. Monforti, F. & Gaetani, M. & Vignati, E., 2016. "How synchronous is wind energy production among European countries?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1622-1638.
    25. Moura, Pedro S. & de Almeida, Aníbal T., 2010. "The role of demand-side management in the grid integration of wind power," Applied Energy, Elsevier, vol. 87(8), pages 2581-2588, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    2. Tapia Carpio, Lucio Guido, 2021. "Mitigating the risk of photovoltaic power generation: A complementarity model of solar irradiation in diverse regions applied to Brazil," Utilities Policy, Elsevier, vol. 71(C).
    3. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
    4. Jánosi, Imre M. & Medjdoub, Karim & Vincze, Miklós, 2021. "Combined wind-solar electricity production potential over north-western Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. Hua, Ershi & Sun, Ruyi & Feng, Ping & Song, Lili & Han, Mengyao, 2024. "Optimizing onshore wind power installation within China via geographical multi-objective decision-making," Energy, Elsevier, vol. 307(C).
    6. Li, Ke & Yang, Fan & Wang, Lupan & Yan, Yi & Wang, Haiyang & Zhang, Chenghui, 2022. "A scenario-based two-stage stochastic optimization approach for multi-energy microgrids," Applied Energy, Elsevier, vol. 322(C).
    7. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    8. López Prol, Javier & de Llano Paz, Fernando & Calvo-Silvosa, Anxo & Pfenninger, Stefan & Staffell, Iain, 2024. "Wind-solar technological, spatial and temporal complementarities in Europe: A portfolio approach," Energy, Elsevier, vol. 292(C).
    9. Kena Mi & Rulong Zhuang, 2022. "Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    10. Yaoshan Ma & Qingyu Yao, 2022. "Impact of Producer Service Agglomeration on Carbon Emission Efficiency and Its Mechanism: A Case Study of Urban Agglomeration in the Yangtze River Delta," Sustainability, MDPI, vol. 14(16), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Spatial and temporal assessments of complementarity for renewable energy resources in China," Energy, Elsevier, vol. 177(C), pages 262-275.
    2. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    3. Chiyori T. Urabe & Tetsuo Saitou & Kazuto Kataoka & Takashi Ikegami & Kazuhiko Ogimoto, 2021. "Positive Correlations between Short-Term and Average Long-Term Fluctuations in Wind Power Output," Energies, MDPI, vol. 14(7), pages 1-15, March.
    4. Berger, Mathias & Radu, David & Fonteneau, Raphaël & Henry, Robin & Glavic, Mevludin & Fettweis, Xavier & Le Du, Marc & Panciatici, Patrick & Balea, Lucian & Ernst, Damien, 2020. "Critical time windows for renewable resource complementarity assessment," Energy, Elsevier, vol. 198(C).
    5. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    6. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    7. Hanieh Seyedhashemi & Benoît Hingray & Christophe Lavaysse & Théo Chamarande, 2021. "The Impact of Low-Resource Periods on the Reliability of Wind Power Systems for Rural Electrification in Africa," Energies, MDPI, vol. 14(11), pages 1-18, May.
    8. Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    9. Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
    10. Lindberg, O. & Lingfors, D. & Arnqvist, J., 2022. "Analyzing the mechanisms behind temporal correlation between power sources using frequency separated time scales: A Swedish case study on PV and wind," Energy, Elsevier, vol. 259(C).
    11. Liu, Laibao & Wang, Zheng & Wang, Yang & Wang, Jun & Chang, Rui & He, Gang & Tang, Wenjun & Gao, Ziqi & Li, Jiangtao & Liu, Changyi & Zhao, Lin & Qin, Dahe & Li, Shuangcheng, 2020. "Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    12. Song, Feng & Cui, Jian & Yu, Yihua, 2022. "Dynamic volatility spillover effects between wind and solar power generations: Implications for hedging strategies and a sustainable power sector," Economic Modelling, Elsevier, vol. 116(C).
    13. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    14. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    15. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    16. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    17. Engeland, Kolbjørn & Borga, Marco & Creutin, Jean-Dominique & François, Baptiste & Ramos, Maria-Helena & Vidal, Jean-Philippe, 2017. "Space-time variability of climate variables and intermittent renewable electricity production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 600-617.
    18. Jurasz, Jakub & Beluco, Alexandre & Canales, Fausto A., 2018. "The impact of complementarity on power supply reliability of small scale hybrid energy systems," Energy, Elsevier, vol. 161(C), pages 737-743.
    19. Yang, Yuting, 2022. "Electricity interconnection with intermittent renewables," Journal of Environmental Economics and Management, Elsevier, vol. 113(C).
    20. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.