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

Abstract

This study carries out an analysis of the 10 MW Butoni wind farm in the tropical southwest Pacific island of Fiji using 6 years of uninterrupted near-surface wind observations (2013–2018). The standard wind-industry software, WAsP is used to analyse and evaluate the wind characteristics of the wind farm and the surrounding areas. The modelled and operational AEP are discussed with the related economic analysis together with the main causes for the under-performance of the wind farm. The results revealed that the mean wind speed, power density and the AEP at the Butoni wind farm are below the utility-scale standard of 6.4 m/s, 300 W/m2 and 500 MWh/year/turbine respectively, at 55 m above ground level (AGL). The main reason for the under-performance of the wind farm is that it was commissioned for a low mean wind speed regime of Wind Power Class 1. The wind farm has a lower-than-expected capacity factor of 5.4% and a higher wind shear coefficient of 0.35. An economic analysis revealed that the payback time is 24.5 years, and the cost of energy generation is FJD $ 0.55/kWh.

Evaluation of the mesoscale–microscale (WRF–WAsP) coupling methodology for wind resource parameters in Fiji

“…The deviations are between the observed (measured) wind resource parameters and the simulated coupled model wind resource parameters. For statistical significance, RMSE, Bias, STDE, and correlation have already been computed and evaluated in (Dayal, 2021) for measurements against WRF 1 km × 1 km results. The results of which revealed that they are within acceptable limits and the mesoscale model physics is correct (Dayal, 2021).…”

“…For statistical significance, RMSE, Bias, STDE, and correlation have already been computed and evaluated in (Dayal, 2021) for measurements against WRF 1 km × 1 km results. The results of which revealed that they are within acceptable limits and the mesoscale model physics is correct (Dayal, 2021). The WRF-WAsP coupling provides opportunities for further improvement should wind measurements be available at higher elevations, where the influence of the local topography and vegetation characteristics are lower.…”

“…The dominant wind direction is southeasterly as Fiji falls in the Southeast Trade Wind Zone. The Southeast Trade Winds are mostly dominant during austral winter, while during austral summer the winds are mostly dominated by other climate systems such as low pressures systems and tropical cyclones (Dayal, 2021).…”

“…A number of wind resource assessment studies have been conducted in different areas of Fiji using measured wind data at heights of 10-34 m. These study areas include Kadavu and Suva Peninsula (Sharma and Ahmed, 2016), Bligh Waters (Dayal, 2015), Rakiraki, Nabouwalu and Udu (Dayal et al, 2021b), Butoni Wind Farm (Dayal et al, 2022), Suva (Kutty et al, 2019), and Nasawana (Nair and Kumar, 2021). Most of these studies aimed to assist rural electrification in the interiors of larger islands and in the smaller outer islands, while relatively few studies have examined utility-scale wind farms for onshore and offshore applications (Dayal, 2015(Dayal, , 2021Dayal et al, 2021b). The measured wind speed and the power density at studied sites within Fiji varies from 1.94 to 6.31 m/s and 11 to 241 W/m 2 at heights of 10-34 m, respectively.…”

“…Apart from the microscale studies, a few mesoscale studies including a Weather Research and Forecasting (WRF) model grid sensitivity study (Dayal et al, 2020a), a WRF model evaluation study (Dayal et al, 2020b) and a decade long mesoscale wind resource assessment study have been conducted for Fiji at a high-resolution of 1 km × 1 km at applicable wind turbine hub-heights to identify potential wind resource areas (Dayal et al, 2021a). A recent study also analyzed the wind resources at the existing 10 MW Butoni wind farm and reported that it is located on a weak wind speed regime of Wind Power Class 1 and has a capacity factor of 5.4% (Dayal et al, 2022). Wind resource data is scare, and the wind energy potential is unknown in most locations around Fiji.…”