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High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model

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  • Dayal, Kunal K.
  • Bellon, Gilles
  • Cater, John E.
  • Kingan, Michael J.
  • Sharma, Rajnish N.

Abstract

This study presents a high-resolution mesoscale wind-resource assessment of the small island developing state (SIDS) of Fiji using a 10-year simulation of the Weather Research and Forecasting (WRF) model with convection-permitting resolution. Our analysis evaluates the wind speed and Weibull distributions, diurnal and seasonal wind speed patterns, resource maps of annual and seasonal wind speed, power density, model statistical analysis and interannual wind speed variability. The results reveal that the WRF-model simulated wind resource parameters are in good agreement with observations at 24 existing weather stations. At 55 m above ground, the annual mean wind speed and wind power density varies from 1.5 m/s to 8 m/s and 50 W/m2 to 300 W/m2, respectively, for onshore land areas. Higher wind speeds are observed during austral winter than in austral summer. Forty high wind-resource areas are identified in this study, which were previously unknown. This indicates that there is potential for utility-scale wind power generation at selected locations with wind speed and power density greater than 6.4 m/s and 300 W/m2 (NREL, Wind Power Class 3). An estimated 1000 MW theoretical potential installed capacity is available for utility-scale wind power applications on Viti Levu and Vanua Levu.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012950
    DOI: 10.1016/j.energy.2021.121047
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    Cited by:

    1. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    2. Yu, Chuanjin & Li, Yongle & Zhao, Liyang & Chen, Qian & Xun, Yuxing, 2023. "A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions," Energy, Elsevier, vol. 262(PB).
    3. Gil Ruiz, Samuel Andrés & Cañón Barriga, Julio Eduardo & Martínez, J. Alejandro, 2022. "Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia," Energy, Elsevier, vol. 244(PB).

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