IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v47y2016icp121-128.html
   My bibliography  Save this article

Evaluation of wind energy resource and wind turbine characteristics at two locations in China

Author

Listed:
  • Li, Chong
  • Liu, Youying
  • Li, Gang
  • Li, Jianyan
  • Zhu, Dasheng
  • Jia, Wenhua
  • Li, Guo
  • Zhi, Youran
  • Zhai, Xinyu

Abstract

The ten-year monthly mean wind speed data at 10, 50,100, 150 and 300 m heights over a typical year were statistically analyzed in this study to determine the potential for wind power generation at two locations (Urumqi and Xining) in China, using the two-parameters Weibull distribution function. The shape factor k and scale factor c were estimated by the maximum likelihood method. Fourteen small to medium sized commercially available wind turbines were selected for the two regions, and their mean energy outputs and capacity factors were all analyzed. Results showed that the maximum monthly mean wind speeds at different heights in Urumqi and Xining occur in November and December, respectively; while the minimum values of the two locations occur in June and July, respectively. The wind power class and potential of Urumqi and Xining are “class 1” and “poor” and “class 2” and “Marginal”, respectively. Urumqi is not suitable for large-scale electric wind-power application; however, small-scale wind turbines or wind-hybrid power systems might be a reasonable option for this site for supplying power. Xining has a better wind resource than Urumqi, and medium-scale wind turbines might be a reasonable option in Xining for supplying more electric energy. The mean energy outputs for the selected wind turbines range from 491 kWh/yr to 87,333 kWh/yr in Urumqi, while the values range from 1,071 kWh/yr to 167,237 kWh/yr in Xining. The capacity factors for these wind turbines in Urumqi and Xining range from 4.3% to 19.5% and 10.4%–27.9%, respectively. The aim of this paper is to promote the development of wind power in China by contributing to scholarly understanding of its impact on the geographical regions studied.

Suggested Citation

  • Li, Chong & Liu, Youying & Li, Gang & Li, Jianyan & Zhu, Dasheng & Jia, Wenhua & Li, Guo & Zhi, Youran & Zhai, Xinyu, 2016. "Evaluation of wind energy resource and wind turbine characteristics at two locations in China," Technology in Society, Elsevier, vol. 47(C), pages 121-128.
    • Handle: RePEc:eee:teinso:v:47:y:2016:i:c:p:121-128
    DOI: 10.1016/j.techsoc.2016.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X16301531
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2016.09.003?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. Katinas, Vladislovas & Sankauskas, Donatas & Markevičius, Antanas & Perednis, Eugenijus, 2014. "Investigation of the wind energy characteristics and power generation in Lithuania," Renewable Energy, Elsevier, vol. 66(C), pages 299-304.
    2. Abbes, Mohamed & Belhadj, Jamel, 2012. "Wind resource estimation and wind park design in El-Kef region, Tunisia," Energy, Elsevier, vol. 40(1), pages 348-357.
    3. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    4. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    5. Ohunakin, O.S. & Adaramola, M.S. & Oyewola, O.M., 2011. "Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria," Applied Energy, Elsevier, vol. 88(9), pages 3197-3206.
    6. Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
    7. Kaplan, Yusuf Alper, 2015. "Overview of wind energy in the world and assessment of current wind energy policies in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 562-568.
    8. Mostafaeipour, A. & Sedaghat, A. & Dehghan-Niri, A.A. & Kalantar, V., 2011. "Wind energy feasibility study for city of Shahrbabak in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2545-2556, August.
    9. Daphne Ngar‐yin Mah & Peter Hills, 2012. "Collaborative governance for sustainable development: wind resource assessment in Xinjiang and Guangdong Provinces, China," Sustainable Development, John Wiley & Sons, Ltd., vol. 20(2), pages 85-97, March.
    10. Adaramola, M.S. & Oyewola, O.M., 2011. "Evaluating the performance of wind turbines in selected locations in Oyo state, Nigeria," Renewable Energy, Elsevier, vol. 36(12), pages 3297-3304.
    11. Baseer, M.A. & Meyer, J.P. & Alam, Md. Mahbub & Rehman, S., 2015. "Wind speed and power characteristics for Jubail industrial city, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1193-1204.
    12. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    13. Ohunakin, Olayinka S., 2011. "Assessment of wind energy resources for electricity generation using WECS in North-Central region, Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1968-1976, May.
    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. Al-Shetwi, Ali Q. & Atawi, Ibrahem E. & Abuelrub, Ahmad & Hannan, M.A., 2023. "Techno-economic assessment and optimal design of hybrid power generation-based renewable energy systems," Technology in Society, Elsevier, vol. 75(C).
    2. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    3. He, Zhengxia & Cao, Changshuai & Kuai, Leyi & Zhou, Yanqing & Wang, Jianming, 2022. "Impact of policies on wind power innovation at different income levels: Regional differences in China based on dynamic panel estimation," Technology in Society, Elsevier, vol. 71(C).
    4. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    5. Li, Chong & Zhou, Dequn & Wang, Hui & Lu, Yuzheng & Li, Dongdong, 2020. "Techno-economic performance study of stand-alone wind/diesel/battery hybrid system with different battery technologies in the cold region of China," Energy, Elsevier, vol. 192(C).
    6. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    7. Soheil Mohseni & Alan C. Brent & Daniel Burmester, 2021. "Off-Grid Multi-Carrier Microgrid Design Optimisation: The Case of Rakiura–Stewart Island, Aotearoa–New Zealand," Energies, MDPI, vol. 14(20), pages 1-28, October.

    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. Mohammadi, Kasra & Mostafaeipour, Ali & Sabzpooshani, Majid, 2014. "Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran," Energy, Elsevier, vol. 67(C), pages 117-128.
    2. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    3. 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.
    4. Aliyu, Abubakar Sadiq & Dada, Joseph O. & Adam, Ibrahim Khalil, 2015. "Current status and future prospects of renewable energy in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 336-346.
    5. Brimmo, Ayoola T. & Sodiq, Ahmed & Sofela, Samuel & Kolo, Isa, 2017. "Sustainable energy development in Nigeria: Wind, hydropower, geothermal and nuclear (Vol. 1)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 474-490.
    6. Amer Al-Hinai & Yassine Charabi & Seyed H. Aghay Kaboli, 2021. "Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    7. Khahro, Shahnawaz Farhan & Tabbassum, Kavita & Mahmood Soomro, Amir & Liao, Xiaozhong & Alvi, Muhammad Bux & Dong, Lei & Manzoor, M. Farhan, 2014. "Techno-economical evaluation of wind energy potential and analysis of power generation from wind at Gharo, Sindh Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 460-474.
    8. González-Longatt, Francisco & Medina, Humberto & Serrano González, Javier, 2015. "Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 1-16.
    9. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    10. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    11. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    12. Alrashidi, Musaed & Rahman, Saifur & Pipattanasomporn, Manisa, 2020. "Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds," Renewable Energy, Elsevier, vol. 149(C), pages 664-681.
    13. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    14. Diaf, S. & Notton, G., 2013. "Technical and economic analysis of large-scale wind energy conversion systems in Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 37-51.
    15. Chadee, Xsitaaz T. & Clarke, Ricardo M., 2018. "Wind resources and the levelized cost of wind generated electricity in the Caribbean islands of Trinidad and Tobago," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2526-2540.
    16. Wasiu Olalekan Idris & Mohd Zamri Ibrahim & Aliashim Albani, 2020. "The Status of the Development of Wind Energy in Nigeria," Energies, MDPI, vol. 13(23), pages 1-16, November.
    17. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
    18. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    19. Olatomiwa, Lanre & Mekhilef, Saad & Huda, A.S.N. & Ohunakin, Olayinka S., 2015. "Economic evaluation of hybrid energy systems for rural electrification in six geo-political zones of Nigeria," Renewable Energy, Elsevier, vol. 83(C), pages 435-446.
    20. Jiang, He & Wang, Jianzhou & Dong, Yao & Lu, Haiyan, 2015. "Comprehensive assessment of wind resources and the low-carbon economy: An empirical study in the Alxa and Xilin Gol Leagues of inner Mongolia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1304-1319.

    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:teinso:v:47:y:2016:i:c:p:121-128. 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: https://www.journals.elsevier.com/technology-in-society .

    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.