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Wind power characteristics of Oahu, Hawaii

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  • Argüeso, D.
  • Businger, S.

Abstract

Renewable energy is a main avenue to reduce greenhouse gas emissions and mitigate climate change, as well as health impacts, associated with mining, refining and burning fossil fuels. Isolated locations with consistent natural energy resources patterns, such as Hawaii, have great potential to reduce their dependence on fossil fuels and generate energy locally. Using a regional atmospheric model, we explored the wind-power potential of Oahu at high resolution (1 km) and over a period (2005–2014) that allowed the assessment of variability from hourly to interannual. A validation of the model using both weather stations and wind farms showed the need for observational data at the turbine hub height to correctly estimate model errors for wind power applications because the model response can be quite different at standard near-surface wind measurement heights. The model performance at larger timescales evidences the potential for long-term assessment of wind characteristics. On the other hand, the model errors at sub-daily timescales indicated limitations of short-term planning, except for sudden changes in wind speed, which were accurately simulated. Our results identify optimal locations for wind power plants from capacity factor estimates, which include analysis of mean, variability at different timescales, ramps, and sustained periods of low generation.

Suggested Citation

  • Argüeso, D. & Businger, S., 2018. "Wind power characteristics of Oahu, Hawaii," Renewable Energy, Elsevier, vol. 128(PA), pages 324-336.
  • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:324-336
    DOI: 10.1016/j.renene.2018.05.080
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    3. Liu, Zhenqing & Diao, Zheng & Ishihara, Takeshi, 2019. "Study of the flow fields over simplified topographies with different roughness conditions using large eddy simulations," Renewable Energy, Elsevier, vol. 136(C), pages 968-992.
    4. Tian, Qun & Huang, Gang & Hu, Kaiming & Niyogi, Dev, 2019. "Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016," Energy, Elsevier, vol. 167(C), pages 1224-1235.
    5. D’Isidoro, Massimo & Briganti, Gino & Vitali, Lina & Righini, Gaia & Adani, Mario & Guarnieri, Guido & Moretti, Lorenzo & Raliselo, Muso & Mahahabisa, Mabafokeng & Ciancarella, Luisella & Zanini, Gabr, 2020. "Estimation of solar and wind energy resources over Lesotho and their complementarity by means of WRF yearly simulation at high resolution," Renewable Energy, Elsevier, vol. 158(C), pages 114-129.
    6. Dayal, Kunal K. & Bellon, Gilles & Cater, John E. & Kingan, Michael J. & Sharma, Rajnish N., 2021. "High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model," Energy, Elsevier, vol. 232(C).
    7. Perini de Souza, Noele Bissoli & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Wind mapping using the mesoscale WRF model in a tropical region of Brazil," Energy, Elsevier, vol. 240(C).
    8. Aifeng Lv & Taohui Li & Wenxiang Zhang & Yonghao Liu, 2022. "Spatiotemporal Distribution and Complementarity of Wind and Solar Energy in China," Energies, MDPI, vol. 15(19), pages 1-16, October.
    9. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).

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