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.

scholarly journals A multi-collocation method for coastal zone observations with applications to Sentinel-3A altimeter wave height data

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 249-268 ◽  
Author(s):  
Johannes Schulz-Stellenfleth ◽  
Joanna Staneva
Keyword(s):  
Collocation Method ◽  
Wave Height ◽  
Numerical Models ◽  
Coastal Areas ◽  
Data Sources ◽  
Data Sets ◽  
Model Data ◽  
Data Set ◽  
Stochastic Errors

Abstract. In many coastal areas there is an increasing number and variety of observation data available, which are often very heterogeneous in their temporal and spatial sampling characteristics. With the advent of new systems, like the radar altimeter on board the Sentinel-3A satellite, a lot of questions arise concerning the accuracy and added value of different instruments and numerical models. Quantification of errors is a key factor for applications, like data assimilation and forecast improvement. In the past, the triple collocation method to estimate systematic and stochastic errors of measurements and numerical models was successfully applied to different data sets. This method relies on the assumption that three independent data sets provide estimates of the same quantity. In coastal areas with strong gradients even small distances between measurements can lead to larger differences and this assumption can become critical. In this study the triple collocation method is extended in different ways with the specific problems of the coast in mind. In addition to nearest-neighbour approximations considered so far, the presented method allows for use of a large variety of interpolation approaches to take spatial variations in the observed area into account. Observation and numerical model errors can therefore be estimated, even if the distance between the different data sources is too large to assume that they measure the same quantity. If the number of observations is sufficient, the method can also be used to estimate error correlations between certain data source components. As a second novelty, an estimator for the uncertainty in the derived observation errors is derived as a function of the covariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlo simulations. The technique is then applied to a data set of Sentinel-3A altimeter measurements, in situ wave observations, and numerical wave model data with a focus on the North Sea. Stochastic observation errors for the significant wave height, as well as bias and calibration errors, are derived for the model and the altimeter. The analysis indicates a slight overestimation of altimeter wave heights, which become more pronounced at higher sea states. The smallest stochastic errors are found for the in situ measurements. Different observation geometries of in situ data and altimeter tracks are furthermore analysed, considering 1-D and 2-D interpolation approaches. For example, the geometry of an altimeter track passing between two in situ wave instruments is considered with model data being available at the in situ locations. It is shown that for a sufficiently large sample, the errors of all data sources, as well as the error correlations of the model, can be estimated with the new method.

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.

scholarly journals Application of ERA5 and MENA Simulations to Predict Offshore Wind Energy Potential

2020 ◽  
Author(s):  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
narjes nabipour ◽  
Kwok-wing Chau
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Power Distribution ◽  
Climate Model ◽  
Surface Wind ◽  
Climate Change Impacts ◽  
Important Variable ◽  
Offshore Wind ◽  
Energy Potential ◽  
Near Surface

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

scholarly journals A multi collocation method for coastal zone observations with applications to SENTINEL-3a altimeter wave height data

2018 ◽  
Author(s):  
Johannes Schulz-Stellenfleth ◽  
Joanna Staneva
Keyword(s):  
Collocation Method ◽  
Wave Height ◽  
Numerical Models ◽  
Coastal Areas ◽  
Data Sources ◽  
Data Sets ◽  
Model Errors ◽  
Model Data ◽  
Data Set ◽  
Stochastic Errors

Abstract. In many coastal areas there is an increasing number and variety of observation data available, which are often very heterogeneous in their temporal and spatial sampling characteristics. With the advent of new systems, like the radar altimeter onboard the SENTINEL-3a satellite, a lot of questions arise concerning the accuracy and added value of different instruments and numerical models. Quantification of errors is a key factor for applications, like data assimilation and forecast improvement. In the past, the triple collocation method to estimate systematic and stochastic errors of measurements and numerical models was successfully applied to different data sets. This method relies on the assumption, that three independent data sets provide estimates of the same quantity. In coastal areas with strong gradients even small distances between measurements can lead to larger differences and this assumption can become critical. In this study the triple collocation method is extended in different ways with the specific problems of the coast in mind. In addition to nearest neighbor approximations considered so far, the presented method allows to use a large variety of interpolation approaches to take spatial variations in the observed area into account. Observation and numerical model errors can therefore be estimated, even if the distance between the different data sources is too big to assume, that they measure the same quantity. If the number of observations is sufficient, the method can also be used to estimate error correlations between certain data source components. As a second novelty, an estimator for the uncertainty of the derived observation errors is derived as a function of the covariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlo simulations. The technique is then applied to a data set of SENTINEL-3a altimeter measurements, insitu wave observation, and numerical wave model data with a focus on the North Sea. Stochastic observation errors for the significant wave height, as well as bias and calibration errors are derived for the model and the altimeter. The analysis indicates a slight overestimation of altimeter wave heights, which becomes more pronounced at higher sea states. The smallest stochastic errors are found for the insitu measurements. Different observation geometries of insitu data and altimeter tracks are furthermore analysed, considering 1D and 2D interpolation approaches. For example, the geometry of an altimeter track passing between two insitu wave instruments is considered with model data being available at the insitu locations. It is shown, that for a sufficiently large sample, the errors of all data sources, as well as the error correlations of the model, can be estimated with the new method.

scholarly journals Clustering wind profile shapes to estimate airborne wind energy production

2020 ◽  
Vol 5 (3) ◽  
pp. 1097-1120 ◽  
Author(s):  
Mark Schelbergen ◽  
Peter C. Kalverla ◽  
Roland Schmehl ◽  
Simon J. Watson
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Wind Profile ◽  
Atmospheric Stability ◽  
Offshore Wind ◽  
Small Scale ◽  
Height Range ◽  
The North ◽  
The Difference

Abstract. Airborne wind energy (AWE) systems harness energy at heights beyond the reach of tower-based wind turbines. To estimate the annual energy production (AEP), measured or modelled wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from modelled datasets that include the variation in the wind speed and direction with height. K-means clustering is used to identify a set of wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the centre of the Netherlands, respectively. The cluster-mean wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with the literature. Finally, it is demonstrated how a set of wind profile shapes is used to estimate the AEP of a small-scale pumping AWE system located at Cabauw, which requires the derivation of a separate power curve for each wind profile shape. Studying the relationship between the estimated AEP and the number of site-specific clusters used for the calculation shows that the difference in AEP relative to the converged value is less than 3 % for four or more clusters.

Offshore Wind Electricity: A Viable Energy Option for the Coastal United States

2007 ◽  
Vol 41 (3) ◽  
pp. 32-43 ◽  
Author(s):  
Walt Musial
Keyword(s):  
United States ◽  
Wind Energy ◽  
Deep Water ◽  
Electric Energy ◽  
The United States ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Wide Range ◽  
Cost Reductions ◽  
Market Variables

U.S. offshore wind energy resources are abundant, indigenous, and broadly dispersed among the most expensive and highly constrained electricity load centers. Economic capacity expansion models developed at the National Renewable Energy Laboratory show that offshore wind energy can compete in future U.S. electric energy markets without major changes in the market variables or revolutionary technological breakthroughs. However, significant research, development, and deployment will be needed to bring the current technology through a course of cost reductions. To maximize the resource potential, these reductions need to be made along parallel technology paths that will expand the available resource by allowing wind turbines to be installed in deep water. Analysis shows that incremental technology improvements leading to moderate cost reductions, and reasonable increases in the cost of conventional energy will help offshore wind achieve cost competitiveness by 2030 and become a major contributor to the energy supply of the United States. This paper describes a wide range of technical research and development that can reduce costs and improve technology for deep water deployment.

Current Profile Data Sources for Engineering Design West of Shetlands

2014 ◽  
Author(s):  
Gus Jeans ◽  
Joe Fox ◽  
Claire Channelliere
Keyword(s):  
Numerical Model ◽  
Engineering Design ◽  
Current Data ◽  
Data Sources ◽  
Model Data ◽  
Current Profile ◽  
Profile Data ◽  
Field Development ◽  
In Situ Data

Current profile data sources considered for derivation of engineering design criteria West of Shetland are described. The region is impacted by a variety of oceanographic processes that combine to produce a complex current regime. Reliable quantification of the resulting current profiles is required for safe and cost effective offshore exploration and field development. A key challenge to all offshore developments is acquisition of appropriate data. Site specific measurement remains the primary current profile data source for engineering applications, with full water column coverage at sufficient resolution required for riser design. Such in-situ data are generally expensive and time consuming to collect, so there is an increasing tendency for numerical model current data to be considered. Model data are often relatively quick and inexpensive to obtain, with the added benefit of a much longer duration, potentially allowing inter-annual variability and extreme events to be captured. However, the accuracy and reliability of numerical model data remains questionable, or unproven, in many deepwater development regions. This paper describes a recent study in which in-situ data remained the primary source for derivation of current profile criteria for engineering design. Short duration proprietary data were supplemented by additional public domain data from nearby sites in a regional synthesis, with critical results. The performance and benefits of readily available model data are also considered.

scholarly journals Doppler Lidar–Based Wind-Profile Measurement System for Offshore Wind-Energy and Other Marine Boundary Layer Applications

2012 ◽  
Vol 51 (2) ◽  
pp. 327-349 ◽  
Author(s):  
Yelena L. Pichugina ◽  
Robert M. Banta ◽  
W. Alan Brewer ◽  
Scott P. Sandberg ◽  
R. Michael Hardesty
Keyword(s):  
Boundary Layer ◽  
Wind Energy ◽  
Measurement System ◽  
Marine Boundary Layer ◽  
Spatial Behavior ◽  
Offshore Wind ◽  
Profile Measurement ◽  
Doppler Lidar ◽  
Offshore Wind Energy ◽  
Near Surface

AbstractAccurate measurement of wind speed profiles aloft in the marine boundary layer is a difficult challenge. The development of offshore wind energy requires accurate information on wind speeds above the surface at least at the levels occupied by turbine blades. Few measured data are available at these heights, and the temporal and spatial behavior of near-surface winds is often unrepresentative of that at the required heights. As a consequence, numerical model data, another potential source of information, are essentially unverified at these levels of the atmosphere. In this paper, a motion-compensated, high-resolution Doppler lidar–based wind measurement system that is capable of providing needed information on offshore winds at several heights is described. The system has been evaluated and verified in several ways. A sampling of data from the 2004 New England Air Quality Study shows the kind of analyses and information available. Examples include time–height cross sections, time series, profiles, and distributions of quantities such as winds and shear. These analyses show that there is strong spatial and temporal variability associated with the wind field in the marine boundary layer. Winds near the coast show diurnal variations, and frequent occurrences of low-level jets are evident, especially during nocturnal periods. Persistent patterns of spatial variability in the flow field that are due to coastal irregularities should be of particular concern for wind-energy planning, because they affect the representativeness of fixed-location measurements and imply that some areas would be favored for wind-energy production whereas others would not.

scholarly journals Clustering wind profile shapes to estimate airborne wind energy production

2020 ◽  
Author(s):  
Mark Schelbergen ◽  
Peter C. Kalverla ◽  
Roland Schmehl ◽  
Simon J. Watson
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Wind Profile ◽  
Atmospheric Stability ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Height Range ◽  
The North ◽  
Wind Resource

Abstract. Airborne wind energy (AWE) systems typically harness energy in an altitude range up to 500 m above the ground. To estimate the annual energy production (AEP), measured wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from reanalysis data or long-term LiDAR measurements that include the vertical variation of the wind speed and direction. K-means clustering is used to identify a set of prevailing wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas and LiDAR observations for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the center of the Netherlands, respectively. The resulting wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with literature. Finally, it is demonstrated how a set of wind profile shapes and their statistics can be used to estimate the AEP of a pumping AWE system. For four or more clusters, the site specific AEP error is within a few percent of the converged value.

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