Offshore Wind Shear Estimations for Wind Power Assessment

2008 ◽  
Author(s):  
Susan W. Stewart
Keyword(s):  
Wind Power ◽  
Wind Shear ◽  
Meteorological Data ◽  
Open Water ◽  
Offshore Wind ◽  
Wind Data ◽  
South Atlantic Bight ◽  
Data Sets ◽  
Time Period ◽  
Bodies Of Water

Appropriate wind shear estimates are extremely important when assessing any regions’ wind power resource. Wind shear is used not only to estimate wind velocity at wind turbine hub heights other than the data collection height, but also as a siting tool to compare the wind resources in different locations when wind data are not available at a consistent height. Models for wind shear over land, as well as simple models for wind shear over open water have been found to correlate poorly with offshore wind data. This is thought to be partially due to the effect of changing wave conditions on wind shear as well as differences in thermal effects over bodies of water. In this study, offshore wind data from the South Atlantic Bight region is used to estimate the offshore wind shear conditions in this area. Data sets include collocated 10 m and 50 m meteorological data as well as wave data, all taken over a three and a half year time period. Offshore wind shear assessments from other studies are analyzed and compared to the current study as well.

Feasibility of Offshore Wind Power in the South Atlantic Bight

2006 ◽  
Author(s):  
W.S. Bulpitt ◽  
S.W. Stewart ◽  
M.H. Hunt ◽  
S.V. Shelton
Keyword(s):  
Wind Power ◽  
South Atlantic ◽  
Offshore Wind ◽  
South Atlantic Bight ◽  
The South ◽  
Offshore Wind Power

scholarly journals Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea

2020 ◽  
Vol 20 (2) ◽  
pp. 143-153
Author(s):  
Nguyen Xuan Tung ◽  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Thi Nhan ◽  
Tran Quang Son
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Geographical Location ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Power Capacity ◽  
Wind Power Density

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.

Effects of different wind data sources in offshore wind power assessment

2015 ◽  
Vol 77 ◽  
pp. 101-114 ◽  
Author(s):  
Takvor H. Soukissian ◽  
Anastasios Papadopoulos
Keyword(s):  
Wind Power ◽  
Data Sources ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Power

scholarly journals Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 10-16
Author(s):  
Muhammad Shoaib ◽  
Saif Ur Rehman ◽  
Imran Siddiqui ◽  
Shafiqur Rehman ◽  
Shamim Khan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Gaussian Mixture ◽  
Descriptive Statistics ◽  
Wind Data ◽  
Weibull Function ◽  
Data Sets ◽  
Data Set ◽  
Wind Speeds ◽  
Power Densities

In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively.

scholarly journals Investigation of wind shear coefficients and their effect on annual energy yields along the coastal sites of Pakistan

2020 ◽  
pp. 014459872093042
Author(s):  
Muhammad Sumair ◽  
Tauseef Aized ◽  
Syed Asad Raza Gardezi ◽  
Syed Muhammad Sohail Rehman ◽  
Syed Ubaid ur Rehman
Keyword(s):  
Wind Speed ◽  
Energy Density ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Shear ◽  
Meteorological Data ◽  
Economic Feasibility ◽  
Wind Power Density ◽  
Cost Of Energy

Ten-year hourly recorded wind meteorological data at six sites along the coastline of Pakistan at two heights (10 and 50 m) were extrapolated to two higher heights (80 and 100 m). Monthly and seasonal analysis of variation in air density (ρ), wind speed, Weibull parameters ( K and C), wind power density, and wind energy density with height was investigated. Analysis shows that wind shear coefficient is highest in winter and lowest in summer. ρ, wind speed, wind power density, and wind energy density all increase with increasing hub height, with the most prominent increment in winter and the lowest in summer. With increasing height, K has been found to decrease slightly while C increases. Techno-economic feasibility analysis of annual energy production using 15 turbines was carried out which shows that capacity factor alone cannot render a turbine feasible but also economic assessment is mandatory to evaluate the feasibility of turbines. G1 and G2 turbines have been found the best options while B5 and V2 as the worst. Comparison among sites shows that Karachi is the most potential site with cost of energy of $0.017/kW h while Jiwani is the worst site with cost of energy of $0.039/kW h both at 100 m height.

scholarly journals Measurements and Modelling of Offshore Wind Profiles in a Semi-Enclosed Sea

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 194 ◽  
Author(s):  
Nina Svensson ◽  
Johan Arnqvist ◽  
Hans Bergström ◽  
Anna Rutgersson ◽  
Erik Sahlée
Keyword(s):  
Marine Boundary Layer ◽  
Continuous Wave ◽  
Wrf Model ◽  
Open Water ◽  
Offshore Wind ◽  
Data Sets ◽  
Average Wind Speed ◽  
Wind Speed Profile ◽  
Stable Conditions ◽  
Wind Profiles

A conically scanning, continuous-wave LIDAR is placed on an island in the central Baltic Sea with large open-water fetch, providing wind and turbulence profiles up to 300 m height. LIDAR and Weather Research and Forecasting (WRF) profiles from one year are used to characterize the marine boundary layer, at the same time performing an evaluation of the WRF model against LIDAR measurements with a focus on low-level jet representation. A good agreement is found between the average wind speed profile in WRF and LIDAR, with the largest bias occurring during stable conditions. The LLJ frequency is highest in May with frequency of occurrence ranging between 18% and 27% depending on the method of detection. Most of the LLJs occur during nighttime, indicating that most of them do not have local origin. For cases with simultaneous LLJs in both data sets the WRF agrees well with the LIDAR. In many cases, however, the LLJ is misplaced in time or space in the WRF simulations compared to the LIDAR. This shows that models still must be improved to capture mesoscale effects in the coastal zone.

The Effect of Wind Data Resolution on the Predicted Energy Generation of an Offshore Wind Farm

2014 ◽  
Author(s):  
Chelsea Drenick ◽  
Ian Prowell ◽  
Dan Dolan
Keyword(s):  
Potential Energy ◽  
Probabilistic Analysis ◽  
Wind Farm ◽  
Time History ◽  
Energy Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Data Sets ◽  
Offshore Wind Farm ◽  
Data Resolution

To estimate the potential energy of an offshore wind farm, wind data and an assessment framework are necessary. Depending on the type of wind data and the software used for the assessment, many techniques are available to calculate the expected energy generation. This paper investigates how the resolution of the wind data affects the estimated final energy output and revenue predicted for an offshore wind farm. Four wind resolution sets are utilized in the analysis: a 5-year 10-minute time history, a 5-year 6-hour time history, a wind rose, and a wind speed and direction average. The first two data sets are analyzed using a time history analysis procedure that determines the energy generated at each time step including wake loss effects. The third and fourth data sets are analyzed using a probabilistic analysis method. The four analysis procedures are utilized at a variety of locations off the East Coast. At each site, the expected energy as well as revenue is presented for each of the data types so that the trends in varying the wind data resolution can be determined. Conclusions are made based on the accuracy and possible bias associated with low resolutions for estimating potential energy for a specific location. Findings illustrate the value of a time history, as compared to a more simplistic probabilistic analysis, to support conclusions about expected energy production and revenue generated.

Effect of Applied Weather Data Sets on the Computational Assessment of Hygrothermal Performance of Historical Masonry

2017 ◽  
Author(s):  
Jan Kocí ◽  
Robert Cerný
Keyword(s):  
Meteorological Data ◽  
Warming Trend ◽  
Weather Data ◽  
Data Sets ◽  
Climatic Data ◽  
Damage Functions ◽  
Historical Building ◽  
Time Period ◽  
Hygrothermal Performance ◽  
Historical Masonry

Several historical wall assemblies together with several weather data sets are investigated in order to study the effect of environmental load on hygrothermal performance of historical buildings. The effect of weather data is assessed using several damage functions with the emphasis placed on frost induced damage. The climatic data are represented by six different weather data sets, namely by the test reference year, positive and critical weather years, together with the meteorological data measured by the autors during the time period of 2013–2015. Special attention is paid to the recent weather data as there is an apparent trend of average temperature increase in the Central Europe in last few years. The results presented in the paper confirm the warming trend which is manifested by virtually no frost induced damage observed for weather years 2014 and 2015 in the analyzed historical building envelopes.

scholarly journals Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 10-16
Author(s):  
Muhammad Shoaib ◽  
Saif Ur Rehman ◽  
Imran Siddiqui ◽  
Shafiqur Rehman ◽  
Shamim Khan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Gaussian Mixture ◽  
Descriptive Statistics ◽  
Wind Data ◽  
Weibull Function ◽  
Data Sets ◽  
Data Set ◽  
Wind Speeds ◽  
Power Densities

In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively.

scholarly journals Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3243
Author(s):  
Zi Lin ◽  
Xiaolei Liu ◽  
Ziming Feng
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Wind Turbines ◽  
Meteorological Data ◽  
Oil Production ◽  
Economic Feasibility ◽  
Offshore Wind ◽  
Levelized Cost Of Electricity ◽  
Small Wind Turbines ◽  
Power Curves

In this paper, the technical and economic feasibility of integrating SWTs (Small Wind Turbines) into remote oil production sites are investigated. Compared to large turbines in onshore and offshore wind farms, SWTs are more suitable for individual power generations. A comprehensive approach based on wind energy assessment, wind power prediction, and economic analysis is then recommended, to evaluate how, where, and when small wind production recovery is achievable in oilfields. Firstly, wind resource in oilfields is critically assessed based on recorded meteorological data. Then, the wind power potential is numerically tested using specified wind turbines with density-corrected power curves. Later, estimations of annual costs and energy-saving are carried out before and after the installation of SWT via the LCOE (Levelized Cost of Electricity) and the EROI (Energy Return on Investment). The proposed methodology was tested against the Daqing oilfield, which is the largest onshore oilfield in China. The results suggested that over 80% of the original annual costs in oil production could be saved through the integrations between wind energy and oil production.

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