@article{adef0266b2164557b2141f4373fbd89f,
title = "Evaluation of an offshore wind farm by using data from the weather station, floating LiDAR, MAST, and MERRA",
abstract = "Offshore wind energy is regarded as a key alternative to fossil fuels in many parts of the world. Its exploitation is based on the sound evaluation of wind resources. This study used data from a meteorological mast, a floating light detection and ranging (LiDAR) device, and the Modern-Era Retrospective Analysis for Research and Applications, a reanalysis data set established by the NASA Center for Climate Simulation, to evaluate wind resources of the Changhua-South Offshore Wind Farm. The average wind speeds evaluated at a height of 105 m in the studied wind farm were 7.97 and 8.02 m/s according to the data obtained from the floating LiDAR device and a mast, respectively. The full-load hours were 3320.5 and 3296.5 h per year when data from the LiDAR device and mast were used, respectively. The estimated annual energy production (AEP) with a probability of 50% (P50) reached 314 GWh/y, whereas the AEPs with a probability of 75% (P75) and with a probability of 90% (P90) were 283 GWh/y and 255 GWh/y, respectively. The estimated AEP of P75 was 90% of the AEP of P50, whereas the estimated AEP of P90 was 81% of the AEP of P50. This difference might need to be considered when assessing the risk of financing a wind project.",
author = "Yue, {Cheng Dar} and Chiu, {Yi Shegn} and Tu, {Chien Cheng} and Lin, {Ta Hui}",
note = "Funding Information: This study was conducted under financial support for the project titled “The Technique of Big Data Prediction and Accreditation Standard for Taiwan Offshore Wind Farm” (MOST 108-3116-F-006-009-CC2) financed by the Ministry of Science and Technology of the Republic of China. The authors appreciate the Ministry's support for this study. This manuscript was edited by Wallace Academic Editing. Funding Information: resources of the Changhua‐South Offshore Wind Farm and optimize wind farm design. The power generation potential of the target wind farm was first estimated. The Park Optimizer module of generation potential of the target wind farm was first estimated. The Park Optimizer module of WindSim was then used to identify turbine locations with the highest wind speeds and lowest WindSim was then used to identify turbine locations with the highest wind speeds and lowest turbulence to maximize energy production. Finally, the prediction accuracy of AEP was evaluated on turbulence to maximize energy production. Finally, the prediction accuracy of AEP was evaluated the basis of estimated uncertainty. The crucial findings are as follows. on the basis of estimated uncertainty. The crucial findings are as follows. The average wind speeds evaluated at a height of 105 m in the studied wind farm using WindSim The average wind speeds evaluated at a height of 105 m in the studied wind farm using and MCP data derived from the floating LiDAR device and mast were 7.97 and 8.02 m/s, respectively. WindSim and MCP data derived from the floating LiDAR device and mast were 7.97 and 8.02 m/s, The predictions for energy production of the wind farm indicated that the AEP will become increasingly respectively. The predictions for energy production of the wind farm indicated that the AEP will conservative with an increasing probability value. The estimated AEP of P50 reached 314 GWh/y, become increasingly conservative with an increasing probability value. The estimated AEP of P50 whereas that of P75 and P90 was merely 283 GWh/y and 255 GWh/y, respectively. The estimated AEP reached 314 GWh/y, whereas that of P75 and P90 was merely 283 GWh/y and 255 GWh/y, respectively. of P75 was 90% of that of P50, whereas the estimated AEP of P90 was 81% of that of P50. This difference The estimated AEP of P75 was 90% of that of P50, whereas the estimated AEP of P90 was 81% of that of might need to be considered by investors when assessing the risk of financing a wind project. P50. This difference might need to be considered by investors when assessing the risk of financing a This study offers novel contributions to the literature by examining the applicability of data from wind project. multiple sources, namely a floating LiDAR device, a meteorological mast, and MERRA, for evaluating This study offers novel contributions to the literature by examining the applicability of data from offshore wind resources. The analysis results indicated that the use of MERRA data to conduct MCP multiple sources, namely a floating LiDAR device, a meteorological mast, and MERRA, for enhances the prediction accuracy of energy production of an offshore wind farm. Mast and LiDAR evaluating offshore wind resources. The analysis results indicated that the use of MERRA data to data were used as the target data and MERRA data served as the reference data in MCP for predicting conduct MCP enhances the prediction accuracy of energy production of an offshore wind farm. Mast the offshore wind power generation. and LiDAR data were used as the target data and MERRA data served as the reference data in MCP Future research can be conducted to improve the accuracy of wind power estimation. Although for predicting the offshore wind power generation. hourly data are the most common source for the estimation of wind power generation, methods using Future research can be conducted to improve the accuracy of wind power estimation. Although such data may not be able to track changes in wind direction. The question of whether the accuracy hourly data are the most common source for the estimation of wind power generation, methods using of wind resource estimation can be enhanced by using data with a higher temporal resolution (e.g., such data may not be able to track changes in wind direction. The question of whether the accuracy 10-min data) merits investigation. Moreover, the energy production estimation in this study was based of wind resource estimation can be enhanced by using data with a higher temporal resolution (e.g., 10‐min data) merits investigation. Moreover, the energy production estimation in this study was based on the power curve and wind speed statistics. However, the gust factor and air density also affect the energy production of wind turbines. Further investigation can be undertaken to determine whether more weather data can help improve the accuracy of power generation estimation. analysis, C.-D.Y. and Y.-S.C.; funding acquisition, T.-H.L.; investigation, C.-D.Y. and Y.-S.C.; methodology, C.-D.Y. aAnudthYo.-rS .CCo.;nptrroibjeucttiaodnms: inCiostnrcaetipotnu,aCli.z-aDti.Yo.n;,r eCs.o‐uDr.cYe.s ,aCnd.- CT.T.‐.H; s.oLf.t; wdaartea, Ccu.-rDat.Yio.na,n dC.Y‐D.-S.Y.C.,. ;Ysu.‐pS.eCrv. iasniodn ,CC.‐.-CD.T.Y.;. formal analysis, C.‐D.Y. and Y.‐S.C.; funding acquisition, T.‐H.L.; investigation, C.‐D.Y. and Y.‐S.C.; All authors have read and agreed to the published version of the manuscript. methodology, C.‐D.Y. and Y.‐S.C.; project administration, C.‐D.Y.; resources, C.‐C.T.; software, C.‐D.Y. and Y.‐ Funding: This work was carried out under the financial support of the project entitled “Development and Application of Taipower Offshore Meteorological and Oceanographic Mast Data” (MOST 106-3113-F-006-002) financed by the Ministry of Science and Technology of the Republic of China. Funding Information: Acknowledgments: This study was conducted under financial support for the project titled “The Technique of Big Data Prediction and Accreditation Standard for Taiwan Offshore Wind Farm” (MOST 108-3116-F-006-009-CC2) financed by the Ministry of Science and Technology of the Republic of China. The authors appreciate the Ministry{\textquoteright}s support for this study. This manuscript was edited by Wallace Academic Editing. Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).",
year = "2020",
month = jan,
day = "1",
doi = "10.3390/en13010185",
language = "English",
volume = "13",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "1",
}