IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v231y2021ics0360544221011713.html
   My bibliography  Save this article

A global wind farm potential index to increase energy yields and accessibility

Author

Listed:
  • Jung, Christopher
  • Schindler, Dirk

Abstract

The preconditions for wind farm installation and operation are high energy yields and accessibility. However, so far, no attempts were made to develop a global scale index integrating energy yields and accessibility of wind farms. Thus, the goal of this study was to create a universally applicable wind farm potential index that enables finding productive and accessible wind farm sites around the world. The wind farm potential index was developed at a very high horizontal resolution (2000 m × 2000 m) using the Global Wind Speed Model and comprehensive land use data. The wind farm capacity factor's global pattern was estimated based on Kappa and Wakeby distributions, and a generic 3.3 MW wind turbine power curve yielding the resource potential index. The geographical potential index integrates 16 geographical restrictions, including the accessibility to the power grid. The correlation coefficients between the resource potential index and geographical potential index were below 0.10 in many countries (61%). The areas with high resource potential and geographical potential were often divergent, e.g., in areas with poorly developed infrastructure. Applying the new wind farm potential index allows a global, consistent assessment of areas suitable for installing and operating wind farms.

Suggested Citation

  • Jung, Christopher & Schindler, Dirk, 2021. "A global wind farm potential index to increase energy yields and accessibility," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011713
    DOI: 10.1016/j.energy.2021.120923
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221011713
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120923?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. Sofia Spyridonidou & Dimitra G. Vagiona, 2020. "Systematic Review of Site-Selection Processes in Onshore and Offshore Wind Energy Research," Energies, MDPI, vol. 13(22), pages 1-26, November.
    2. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    3. Jain, Anjali & Das, Partha & Yamujala, Sumanth & Bhakar, Rohit & Mathur, Jyotirmay, 2020. "Resource potential and variability assessment of solar and wind energy in India," Energy, Elsevier, vol. 211(C).
    4. Miao, Haozeyu & Dong, Danhong & Huang, Gang & Hu, Kaiming & Tian, Qun & Gong, Yuanfa, 2020. "Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets," Energy, Elsevier, vol. 200(C).
    5. Lopez, Anthony & Mai, Trieu & Lantz, Eric & Harrison-Atlas, Dylan & Williams, Travis & Maclaurin, Galen, 2021. "Land use and turbine technology influences on wind potential in the United States," Energy, Elsevier, vol. 223(C).
    6. Nayyar, Zeeshan Alam & Ali, Ahmed, 2020. "Roughness classification utilizing remote sensing techniques for wind resource assessment," Renewable Energy, Elsevier, vol. 149(C), pages 66-79.
    7. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    8. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    9. Leonie Grau & Christopher Jung & Dirk Schindler, 2017. "On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany," Sustainability, MDPI, vol. 9(7), pages 1-11, July.
    10. Eichhorn, Marcus & Masurowski, Frank & Becker, Raik & Thrän, Daniela, 2019. "Wind energy expansion scenarios – A spatial sustainability assessment," Energy, Elsevier, vol. 180(C), pages 367-375.
    11. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    12. Xu, Ye & Li, Ye & Zheng, Lijun & Cui, Liang & Li, Sha & Li, Wei & Cai, Yanpeng, 2020. "Site selection of wind farms using GIS and multi-criteria decision making method in Wafangdian, China," Energy, Elsevier, vol. 207(C).
    13. Obane, Hideaki & Nagai, Yu & Asano, Kenji, 2020. "Assessing land use and potential conflict in solar and onshore wind energy in Japan," Renewable Energy, Elsevier, vol. 160(C), pages 842-851.
    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. Dong, Jizhe & Han, Shunjie & Shao, Xiangxin & Tang, Like & Chen, Renhui & Wu, Longfei & Zheng, Cunlong & Li, Zonghao & Li, Haolin, 2021. "Day-ahead wind-thermal unit commitment considering historical virtual wind power data," Energy, Elsevier, vol. 235(C).
    2. Kınas, Zeynep & Karabiber, Abdulkerim & Yar, Adem & Ozen, Abdurrahman & Ozel, Faruk & Ersöz, Mustafa & Okbaz, Abdulkerim, 2022. "High-performance triboelectric nanogenerator based on carbon nanomaterials functionalized polyacrylonitrile nanofibers," Energy, Elsevier, vol. 239(PD).

    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. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(C).
    3. Jung, Christopher & Schindler, Dirk, 2023. "Introducing a new wind speed complementarity model," Energy, Elsevier, vol. 265(C).
    4. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    5. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    6. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    7. Lv, Furong & Tang, Haiping, 2024. "Sustainable photovoltaic power generation spatial planning through ecosystem service valuation: A case study of the Qinghai-Tibet plateau," Renewable Energy, Elsevier, vol. 222(C).
    8. Christopher Jung & Dirk Schindler, 2023. "Reasons for the Recent Onshore Wind Capacity Factor Increase," Energies, MDPI, vol. 16(14), pages 1-17, July.
    9. Louise Christine Dammeier & Joyce H. C. Bosmans & Mark A. J. Huijbregts, 2023. "Variability in greenhouse gas footprints of the global wind farm fleet," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 272-282, February.
    10. Elkadeem, Mohamed R. & Younes, Ali & Mazzeo, Domenico & Jurasz, Jakub & Elia Campana, Pietro & Sharshir, Swellam W. & Alaam, Mohamed A., 2022. "Geospatial-assisted multi-criterion analysis of solar and wind power geographical-technical-economic potential assessment," Applied Energy, Elsevier, vol. 322(C).
    11. Kumbuso Joshua Nyoni & Anesu Maronga & Paul Gerard Tuohy & Agabu Shane, 2021. "Hydro–Connected Floating PV Renewable Energy System and Onshore Wind Potential in Zambia," Energies, MDPI, vol. 14(17), pages 1-42, August.
    12. Hwangbo, Soonho & Heo, SungKu & Yoo, ChangKyoo, 2022. "Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: Towards decarbonization petrochemical industry," Energy, Elsevier, vol. 238(PC).
    13. Wei Fang & Cheng Yang & Dengfeng Liu & Qiang Huang & Bo Ming & Long Cheng & Lu Wang & Gang Feng & Jianan Shang, 2023. "Assessment of Wind and Solar Power Potential and Their Temporal Complementarity in China’s Northwestern Provinces: Insights from ERA5 Reanalysis," Energies, MDPI, vol. 16(20), pages 1-23, October.
    14. Hosseini Dehshiri, Seyyed Shahabaddin & Firoozabadi, Bahar, 2022. "A new application of measurement of alternatives and ranking according to compromise solution (MARCOS) in solar site location for electricity and hydrogen production: A case study in the southern clim," Energy, Elsevier, vol. 261(PB).
    15. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    16. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).
    18. 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).
    19. Zhang, Zeyu & Liang, Yushi & Xue, Xinyue & Li, Yan & Zhang, Mulan & Li, Yiran & Ji, Xiaodong, 2024. "China's future wind energy considering air density during climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    20. Efthymios Moutsiakis & Athena Yiannakou, 2023. "Small Hydroelectric Energy and Spatial Planning: A Methodology Introducing the Concept of Territorial Carrying Capacity," Sustainability, MDPI, vol. 15(6), pages 1-15, March.

    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:231:y:2021:i:c:s0360544221011713. 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.