Offshore Wind Potential of West Central Taiwan: A Case Study

Abstract

In this study, we present the wind distributions from a long-term offshore met mast and a novel approach based on the measure–correlate–predict (MCP) method from short-term onshore-wind-turbine data. The annual energy production (AEP) and capacity factors (CFs) of one onshore and four offshore wind-turbine generators (WTG) available on the market are evaluated on the basis of wind-distribution analysis from both the real met mast and the MCP method. Here, we also consider the power loss from a 4-month light detection and ranging (LiDAR) power-curve test on an onshore turbine to enhance the accuracy of further AEP and CF evaluations. The achieved Weibull distributions could efficiently represent the probability distribution of wind-speed variation, mean wind speed (MWS), and both the scale and shape parameters of Weibull distribution in Taiwan sites. The power-loss effect is also considered when calculating the AEPs and CFs of different WTGs. Successful offshore wind development requires (1) quick, accurate, and economical harnessing of a wind resource and (2) selection of the most suitable and efficient turbine for a specific offshore site.

Keywords

• measure–correlate–predict (MCP)
• wind-turbine generator (WTG)
• annual energy production (AEP)
• capacity factors (CFs)
• light detection and ranging (LiDAR)
• mean wind speed (MWS)