Wind resource assessment and energy potential of selected locations in Fiji

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 windindustry 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 windpower 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/m 2 , 512 W/m 2 and 294 W/m 2 , 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 %.

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

“…Comparing the results of this study with a recent WAsP microscale wind resource study undertaken by the authors [23], the WRF model is able to successfully identify the three reported potential utility-scale wind farm sites of Rakiraki, Nabouwalu and Udu. Looking at the seasonal cycle of wind speed, the measured and the modelled wind speed seasonal variability is small (< 1 m/s) over a period of 3 -10 years at all sites where measurements are The WRF model has a tendency to overestimate lower wind speeds and underestimate higher wind speeds [34].…”

“…Moreover, a recent study in Fiji using 5 -6 years of measured automatic weather station wind data in WAsP identified three potential wind farm sites that can be further investigated for utility-scale wind power applications. The WAsP modelling revealed that each of these three sites could incorporate a minimum of 10 MW using Vergnet 275-kW wind turbines to support the electricity grid network [23]. To the knowledge of the authors, to date, no validated mesoscale wind-resource assessment has been carried out for the SIDS of Fiji.…”

An analysis of the 10 MW Butoni wind farm in the Tropical Southwest Pacific Island of Fiji

“…The wind resource at the Sigatoka AWS can be classified as Wind Power Class 1 using the NREL standard classification at 10 m AGL (NREL, 2020). The 10 m wind speed, power density and the Weibull A and k values at the Sigatoka AWS are less than those reported in the literature for wind energy studies in Fiji (Dayal, 2015;Dayal et al, 2021b;Gosai, 2015;Kumar and Nair, 2012Kumar and Prasad, 2010;Pratap, 2016;Sharma and Ahmed, 2016;Singh, 2015). On average, the 10 m wind speed is lower by 2192%, power density is lower by 21654%, the Weibull A parameter is lower by 2190% and the Weibull k parameter is lower by 236% at the Sigatoka AWS in comparison with the wind energy studies reported in the literature (Dayal, 2015;Dayal et al, 2021b;Gosai, 2015;Kumar and Nair, 2012Kumar and Prasad, 2010;Pratap, 2016;Sharma and Ahmed, 2016;Singh, 2015).…”

“…The 10 m wind speed, power density and the Weibull A and k values at the Sigatoka AWS are less than those reported in the literature for wind energy studies in Fiji (Dayal, 2015;Dayal et al, 2021b;Gosai, 2015;Kumar and Nair, 2012Kumar and Prasad, 2010;Pratap, 2016;Sharma and Ahmed, 2016;Singh, 2015). On average, the 10 m wind speed is lower by 2192%, power density is lower by 21654%, the Weibull A parameter is lower by 2190% and the Weibull k parameter is lower by 236% at the Sigatoka AWS in comparison with the wind energy studies reported in the literature (Dayal, 2015;Dayal et al, 2021b;Gosai, 2015;Kumar and Nair, 2012Kumar and Prasad, 2010;Pratap, 2016;Sharma and Ahmed, 2016;Singh, 2015). The major reason for the larger differences observed between the Sigatoka AWS and other stations is the geographical location, as the wind resource parameters vary in space depending on the location, surrounding terrain and topography.…”

“…Several wind-resource assessment studies have been conducted in Fiji using the Wind Atlas Analysis and Application Programme (WAsP) (Gosai, 2015;Kumar and Nair, 2012Kumar and Prasad, 2010;Pratap, 2016;Sharma and Ahmed, 2016;Singh, 2015) and WindPRO (Dayal, 2015), with mean wind speed, power density and Weibull A and k parameters ranging from 4.6 to 6.5 m/s, 139 to 241 W/m 2 , 5.2 to 7.35 m/s and 1.68 to 2.63, respectively at 10 m elevation, but none have led to developing new wind farms to support the electricity grid. However, a recent study identified three potential wind farm sites that can be further investigated for utility-scale wind power applications with each site capable of incorporating a minimum installed capacity of 10 MW using Vergnet 275-kW wind turbines (Dayal et al, 2021b).…”