Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan

Abstract

This study analyzed the wind speed data of the met mast in the first commercial-scale offshore wind farm of Taiwan from May 2017 to April 2018. The mean wind speed and standard deviation, wind rose, histogram, wind speed profile, and diurnal variation of wind speed with associated changes in wind direction revealed some noteworthy findings. First, the standard deviation of the corresponding mean wind speed is somewhat high. Second, the Hellmann exponent is as low as 0.05. Third, afternoons in winter and nights and early mornings in summer have the highest and lowest wind speed in a year, respectively. Regarding the histogram, the distribution probability of wind is bimodal, which can be depicted as a mixture of two gamma distributions. In addition, the corresponding change between the hourly mean wind speed and wind direction revealed that the land–sea breeze plays a significant role in wind speed distribution, wind profile, and wind energy production. The low Hellmann exponent is discussed in detail. To further clarify the effect of the land–sea breeze for facilitating future wind energy development in Taiwan, we propose some recommendations.

Managing the Volatility Risk of Renewable Energy: Index Insurance for Offshore Wind Farms in Taiwan

“…A recent project was conducted by Cheng et al [52] for the first offshore wind farm in Taiwan; the Formosa 1 offshore wind farm was completed in October 2019, which has a total capacity of 128 MW and is located about 6 km off the coast of Miaoli in northwestern Taiwan. This study used the met mast wind speed data for one year of the farm, from May 2017 to April 2018, to investigate the wind speed characteristics of the offshore wind farm, modeling the probability distribution and characterizing the seasonal variation of the at-site wind speed.…”

Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico

“…In a generalized way, the Weibull probability density function has been used for this purpose, but different wind regimes could be better represented by other functions. According to Chang [10] and Cheng [11], the wind speed distribution for a particular location determines the available wind energy and the performance of an energy conversion system. Therefore, determining the function that best represents the wind regime at a location will contribute to a better estimation.…”

“…Therefore, determining the function that best represents the wind regime at a location will contribute to a better estimation. In this regard, several studies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] have used different probability density functions, such as Weibull, Gamma, Raleigh, Beta, log-normal, and some combinations with them. On the other hand, Wais [12,13] mentions that the two-parameter Weibull distribution is recognized as an appropriate model and the most widely used in the wind industry, but also concludes that the two-parameter Weibull distribution is not always sufficient to specify the wind speed distribution and evaluate the available wind energy.…”

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects

“…[25] found horizontal and vertical turbulence intensities measurements from two different cities to be in general agreement, suggesting that near-surface turbulent characteristics may be similar across urban areas. Other case studies have examined wind profiles specific to an area of interest for wind energy assessment [27, 28, 29]. Though these efforts have all contributed to our collective knowledge of near-surface wind patterns, we still do not have a comprehensive understanding of the fine-scale wind direction and wind speed variability which occurs across environments of varying surface complexity in daytime convective processes.…”