Comparison of wind profile estimation methods for calculating offshore wind potential for the Northeast region of Brazil

2021 ◽  
Author(s):  
Luiz Felipe Rodrigues do Carmo ◽  
Ana Cristina Pinto de Almeida Palmeira ◽  
Carlos Felipe de Jesus Lauriano Antonio ◽  
Ronaldo Maia de Jesus Palmeira
Keyword(s):  
Wind Profile ◽  
Offshore Wind ◽  
Estimation Methods ◽  
Northeast Region ◽  
Wind Potential ◽  
Profile Estimation

scholarly journals Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli
Keyword(s):  
Data Analysis ◽  
Wind Energy ◽  
Wind Turbines ◽  
Energy Resource ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Energy ◽  
Offshore Wind Turbines ◽  
Wind Potential

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

Assessment of Offshore Wind potential in Morocco

2021 ◽  
Vol 17 (5) ◽  
pp. 1
Author(s):  
Mourad TRIHI ◽  
Mostapha TARFAOUI ◽  
Mourad NACHTANE ◽  
Houda LAAOUIDI
Keyword(s):  
Offshore Wind ◽  
Wind Potential

A New Assessment of Offshore Wind Profile Relationships

2018 ◽  
Author(s):  
Gus Jeans ◽  
Dave Quantrell ◽  
Andrew Watson ◽  
Laure Grignon ◽  
Gil Lizcano
Keyword(s):  
Wind Energy ◽  
Engineering Design ◽  
Wind Profile ◽  
Numerical Models ◽  
Data Sources ◽  
Offshore Wind ◽  
Wind Gust ◽  
Model Data ◽  
Near Surface ◽  
Wide Range

Engineering design codes specify a variety of different relationships to quantify vertical variations in wind speed, gust factor and turbulence intensity. These are required to support applications including assessment of wind resource, operability and engineering design. Differences between the available relationships lead to undesirable uncertainty in all stages of an offshore wind project. Reducing these uncertainties will become increasingly important as wind energy is harnessed in deeper waters and at lower costs. Installation of a traditional met mast is not an option in deep water. Reliable measurement of the local wind, gust and turbulence profiles from floating LiDAR can be challenging. Fortunately, alternative data sources can provide improved characterisation of winds at offshore locations. Numerical modelling of wind in the lower few hundred metres of the atmosphere is generally much simpler at remote deepwater locations than over complex onshore terrain. The sophistication, resolution and reliability of such models is advancing rapidly. Mesoscale models can now allow nesting of large scale conditions to horizontal scales less than one kilometre. Models can also provide many decades of wind data, a major advantage over the site specific measurements gathered to support a wind energy development. Model data are also immediately available at the start of a project at relatively low cost. At offshore locations these models can be validated and calibrated, just above the sea surface, using well established satellite wind products. Reliable long term statistics of near surface wind can be used to quantify winds at the higher elevations applicable to wind turbines using the wide range of existing standard profile relationships. Reduced uncertainty in these profile relationships will be of considerable benefit to the wider use of satellite and model data sources in the wind energy industry. This paper describes a new assessment of various industry standard wind profile relationships, using a range of available met mast datasets and numerical models.

Application of nasal profile estimation methods for Korean population

2021 ◽  
Vol 48 ◽  
pp. 101823
Author(s):  
Dong-Ho Kim ◽  
Hankyu Kim ◽  
U-Young Lee ◽  
Yi-Suk Kim
Keyword(s):  
Korean Population ◽  
Estimation Methods ◽  
Profile Estimation

Investigation on offshore wind energy potential in Benin Republic

2019 ◽  
pp. 0309524X1987276 ◽  
Author(s):  
Maurel R Aza-Gnandji ◽  
François-Xavier Fifatin ◽  
Frédéric Dubas ◽  
Télesphore C Nounangnonhou ◽  
Christophe Espanet ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Wind Energy ◽  
Exclusive Economic Zone ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Energy Potential ◽  
Wind Potential ◽  
Wind Energy Potential ◽  
Economic Zone ◽  
Benin Republic

This article presents a study on offshore wind energy viability in Benin Republic. Weibull law has been used to model the spatial distribution of daily wind speed data in Benin Republic’s Exclusive Economic Zone. The spatial distribution of wind energy potential in Benin’s exclusive economic zone has been obtained at several heights by extrapolating Weibull parameters. Wind resource has then been categorized using National Renewable Energy Laboratory standards. Bathymetric data in the exclusive economic zone are used to determine areas showing good compromise between exploitable wind potential and turbine’s foundation. We have shown that Benin’s offshore resources can reach Class 7 at 100 m height, Class 6, respectively, at 100 and 80 m heights and finally Class 5 at 50 m height. We have also shown that locations close to the shore are the most suitable to offshore wind power generation in Benin’s exclusive economic zone.

Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way

2018 ◽  
Vol 99 (6) ◽  
pp. 1155-1176 ◽  
Author(s):  
Robert M. Banta ◽  
Yelena L. Pichugina ◽  
W. Alan Brewer ◽  
Eric P. James ◽  
Joseph B. Olson ◽  
...  
Keyword(s):  
Wind Energy ◽  
Sea Level ◽  
Wind Profile ◽  
Gulf Of Maine ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Model Errors ◽  
High Quality ◽  
Forecast Lead Time ◽  
Nwp Model

AbstractTo advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

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