Weather window and efficiency assessment of offshore wind power construction in China adjacent seas using the calibrated SWAN model

Yang, Zihao; Lin, Yifan; Dong, Sheng

doi:10.1016/j.oceaneng.2022.111933

Cited by 11 publications

(4 citation statements)

References 84 publications

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“…This closely aligns with the strategic objectives outlined in China's 14th Five-Year Plan, which advocates for the establishment of offshore wind en ergy infrastructure within these geographically specified regions. Owing to the limited publicly accessible long-term wind data, this study necessitates the utilization of reanalysis products, as corroborated by the extant literature [53][54][55] Among the various reanalysis datasets, the European Centre for Medium-Range Weathe Forecasts (ECMWF) ERA5 was selected, given its robust alignment with in situ measure ments along China's coastlines [56,57]. An extensive analysis was conducted using a 30 year span of ERA5 data, from 1993 to 2022, to investigate the features of wind energy.…”

Section: Application Of the Clustering Algorithm For Wind Energy Asse...mentioning

confidence: 99%

Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation

Huang,

Zhu,

Xia

et al. 2023

JMSE

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In wind resource assessment research, mixture models are gaining importance due to the complex characteristics of wind data. The precision of parameter estimations for these models is paramount, as it directly affects the reliability of wind energy forecasts. Traditionally, the expectation–maximization (EM) algorithm has served as a primary tool for such estimations. However, challenges are often encountered with this method when handling complex probability distributions. Given these limitations, the objective of this study is to propose a new clustering algorithm, designed to transform mixture distribution models into simpler probability clusters. To validate its efficacy, a numerical experiment was conducted, and its outcomes were compared with those derived from the established EM algorithm. The results demonstrated a significant alignment between the new method and the traditional EM approach, indicating that comparable accuracy can be achieved without the need for solving complex nonlinear equations. Moreover, the new algorithm was utilized to examine the joint probabilistic structure of wind speed and air density in China’s coastal regions. Notably, the clustering algorithm demonstrated its robustness, with the root mean square error value being notably minimal and the coefficient of determination exceeding 0.9. The proposed approach is suggested as a compelling alternative for parameter estimation in mixture models, particularly when dealing with complex probability models.

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Section: Application Of the Clustering Algorithm For Wind Energy Asse...mentioning

confidence: 99%

Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation

Huang,

Zhu,

Xia

et al. 2023

JMSE

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show abstract

“…Forcing wind fields significantly affect the accuracy of wave models (Kutupoglu et al, 2018;Yang et al, 2022). In this study, we utilized 10-m wind speeds (U 10 ) from three high-quality wind products to drive the wave model, namely the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5 3 ), the Cross-Calibrated Multi-Platform Version 2.0 (CCMP 4 ), and the National Centers for Environmental Prediction (NCEP) Final Reanalysis Data (FNL 5 ).…”

Section: Atmospheric Forcing Data and Observationsmentioning

confidence: 99%

“…The dataset used in this study has a horizontal resolution of 0.25°× 0.25°and a temporal resolution of 1 hour. Previous studies have demonstrated the exceptional performance of ERA5 in our study area (Zhang et al, 2020;Feng et al, 2022;Yang et al, 2022;Zhai et al, 2023). Therefore, ERA5 was selected as the primary forcing wind field to drive the model.…”

Section: Atmospheric Forcing Data and Observationsmentioning

confidence: 99%

A general method to determine the optimal whitecapping dissipation coefficient in the SWAN model

Lei,

Wu,

et al. 2023

Front. Mar. Sci.

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Whitecapping dissipation is a critical term in affecting the accuracy of wave height modeling. However, the whitecapping dissipation coefficient (Cds), as a primary factor influencing whitecapping, is commonly determined through trial and error in various studies. In this study, we present a general method for calibrating the Simulating Waves Nearshore (SWAN) wave model using the whitecapping dissipation term, demonstrated through a detailed study in the South China Sea (SCS). Theoretical analysis reveals that the optimal Cds value shows a one-to-one correspondence with the applied wind field. Expectedly, under high-quality wind field conditions, the optimal Cds values tend to fall within a narrow range, regardless of the model domain or time span. Numerical experiments executed in the SCS further consolidated this inference, encompassing two common wind input schemes (ST6 and YAN) and three distinct whitecapping dissipation schemes (KOMEN, JANSSEN, and WST). Based on the experimental results, we have identified an optimal Cds range for each whitecapping dissipation scheme. Cds values within the optimal range consistently outperformed the default Cds in the SWAN model. Subsequent experiments verified the method’s applicability to the Gulf of Mexico and the Mediterranean Sea. The findings suggest that this research holds substantial promise for practical applications on a global scale.

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“…Recent examples of published weather window analyses include sites in the North Atlantic Ocean near Portugal , the North Sea and Southern Baltic region (Gintautas & Sørensen, 2017), Japan (Japan Sea and Paci c Ocean) (Taniguchi et al, 2016), China (Yang et al, 2022), and the Black Sea (Onea & Rusu, 2019). Signi cant wave height (Hs) is the primary parameter used in these studies (O'Connor et al, 2013b(O'Connor et al, , 2013c(O'Connor et al, , 2013a.…”

Section: Introductionmentioning

confidence: 99%

Weather Window Analysis for the Deployment, Operation, and Maintenance of Marine Renewable Energy Devices in Irish Coastal Waters

Moore,

Eftekhari,

Nash

2024

Preprint

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This study presents a detailed analysis of weather window accessibility for marine renewable energy (MRE) sites along Ireland's coast, utilizing a robust 12-year met-ocean dataset. The research focuses on key test sites - the Atlantic Marine Energy Test Site (AMETS), the Galway Bay Test Site (GBTS), and the Westwave Demonstration Site - and expands to a broader spatial analysis of Irish coastal waters. By integrating significant wave height and wind data, the study evaluates site accessibility, emphasizing the paramount role of wave height in determining access. Findings reveal substantial spatial variability in accessibility, with high-resource areas like AMETS facing greater access challenges due to harsher conditions, as opposed to the more accessible GBTS. The study underscores the need for a nuanced, region-specific approach to MRE development in Ireland, highlighting how strategic planning and technological advancements are crucial in exploiting the country's significant MRE potential. The results also stress the importance of long-term data for accurate environmental variability assessment, offering vital insights for future MRE site viability and strategy development.

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Weather window and efficiency assessment of offshore wind power construction in China adjacent seas using the calibrated SWAN model

Cited by 11 publications

References 84 publications

Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation

Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation

A general method to determine the optimal whitecapping dissipation coefficient in the SWAN model

Weather Window Analysis for the Deployment, Operation, and Maintenance of Marine Renewable Energy Devices in Irish Coastal Waters

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