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Wind resource assessment and energy potential of selected locations in Fiji

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

Abstract

This study summarizes an assessment of the wind resource at selected locations in Fiji for the potential of future utility-scale wind-power development. We use 2–8 years of near surface wind observations (2011–2018) from thirty automatic weather stations. The standard wind-industry software, WAsP is used to simulate the wind resource in terms of mean wind speed, dominant wind direction, power density and annual energy production (AEP) using a Vergnet 275-kW wind turbine. Our analysis identifies three sites: Rakiraki, Nabouwalu and Udu, which should be considered for a future comprehensive resource assessment for utility-scale wind-power development once further wind resource data is available. High-resolution resource maps for each wind resource parameter at a horizontal resolution of 50 m are produced for 6 km×6 km domains around these sites. Rakiraki, Nabouwalu and Udu have average wind speeds of 7.6 m/s, 7.1 m/s and 7.0 m/s, with an average power density of 401 W/m2, 512 W/m2 and 294 W/m2, and a potential average AEP of 0.91 GWh, 0.80 GWh and 0.72 GWh at 55 m AGL, respectively. The dominant wind direction is southeasterly. Modelling a 10 MW wind farm at each site yields a net AEP of 43 GWh, 42 GWh and 37 GWh for Rakiraki, Nabouwalu and Udu, respectively with capacity factors of 0.42–0.48 and wind farm efficiencies of 97–98%.

Suggested Citation

  • Dayal, Kunal K. & Cater, John E. & Kingan, Michael J. & Bellon, Gilles D. & Sharma, Rajnish N., 2021. "Wind resource assessment and energy potential of selected locations in Fiji," Renewable Energy, Elsevier, vol. 172(C), pages 219-237.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:219-237
    DOI: 10.1016/j.renene.2021.03.034
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    References listed on IDEAS

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    1. Sharma, Kaushik & Ahmed, M. Rafiuddin, 2016. "Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula," Renewable Energy, Elsevier, vol. 89(C), pages 168-180.
    2. de Araujo Lima, Laerte & Bezerra Filho, Celso Rosendo, 2010. "Wind energy assessment and wind farm simulation in Triunfo – Pernambuco, Brazil," Renewable Energy, Elsevier, vol. 35(12), pages 2705-2713.
    3. Kumar, Ajal & Prasad, Shivneel, 2010. "Examining wind quality and wind power prospects on Fiji Islands," Renewable Energy, Elsevier, vol. 35(2), pages 536-540.
    4. Carrasco-Díaz, Magdiel & Rivas, David & Orozco-Contreras, Manuel & Sánchez-Montante, Orzo, 2015. "An assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico," Renewable Energy, Elsevier, vol. 78(C), pages 295-305.
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    Cited by:

    1. Jinjing An & Guoping Chen & Zhuo Zou & Yaojie Sun & Ran Liu & Lirong Zheng, 2021. "An IoT-Based Traceability Platform for Wind Turbines," Energies, MDPI, vol. 14(9), pages 1-17, May.
    2. 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).
    3. Misbah Abdelrahim & Gamal Alkawsi & Ammar Ahmed Alkahtani & Ali M. W. Alhasan & Mohammad Khudari & Mohd Rizuan Abdul Kadir & Janaka Ekanayake & Sieh Kiong Tiong, 2022. "Techno-Economic Analysis of a Wind-Energy-Based Charging Station for Electric Vehicles in High-Rise Buildings in Malaysia," Energies, MDPI, vol. 15(15), pages 1-11, July.
    4. Jung, Christopher & Schindler, Dirk, 2023. "Introducing a new wind speed complementarity model," Energy, Elsevier, vol. 265(C).
    5. Simone Ferrari & Federica Zagarella & Paola Caputo & Marco Beccali, 2023. "Mapping Seasonal Variability of Buildings Electricity Demand profiles in Mediterranean Small Islands," Energies, MDPI, vol. 16(4), pages 1-16, February.
    6. 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).
    7. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).

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