Coastal wind speed modelling for wind energy applications

Barthelmie, R.J.; Palutikof, Jean

doi:10.1016/s0167-6105(96)00079-7

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…An alternative model developed by Sempreviva et al [64] is based on the concept of the Internal Boundary Layer (IBL) [65,66] which develops from the point of change. When there is a roughness change from z 01 to z 02 the IBL height h can be defined at a distance x downstream from the change as:…”

Section: Roughness Modeling In Linearized Modelsmentioning

confidence: 99%

State of the Art and Trends in Wind Resource Assessment

Probst

Cárdenas

2010

Energies

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Given the significant rise of the utilization of wind energy the accurate assessment of the wind potential is becoming increasingly important. Direct applications of wind assessment techniques include the creation of wind maps on a local scale (typically 5-20 km) and the micrositing of wind turbines, the estimation of vertical wind speed variations, prospecting on a regional scale (>100 km), estimation of the long-term wind resource at a given site, and forecasting. The measurement of wind speed and direction still widely relies on cup anemometers, though sonic anemometers are becoming increasingly popular. Moreover, remote sensing by Doppler techniques using the backscattering of either sonic beams (SODAR) or light (LIDAR) allowing for vertical profiling well beyond hub height are quickly moving into the mainstream. Local wind maps are based on the predicted modification of the regional wind flow pattern by the local atmospheric boundary layer which in turn depends on both topographic and roughness features and the measured wind rose obtained from one or several measurement towers within the boundaries of the planned development site. Initial models were based on linearized versions of the Navier-Stokes equations, whereas more recently full CFD models have been applied to wind farm micrositing. Linear models tend to perform well for terrain slopes lower than about 25% and have the advantage of short execution times. Long-term performance is frequently estimated from correlations with nearby reference stations with concurrent information and continuous time series over a period of at least 10 years. Simple methods consider only point-to-point linear correlations; more advanced methods like multiple regression techniques and methods based on the theory of distributions will be discussed. Both for early prospecting in regions where only scarce or unreliable reference OPEN ACCESSEnergies 2010, 3 1088 information is available, wind flow modeling on a larger scale (mesoscale) is becoming increasingly popular.

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Section: Roughness Modeling In Linearized Modelsmentioning

confidence: 99%

State of the Art and Trends in Wind Resource Assessment

Probst

Cárdenas

2010

Energies

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“…Due to the complexity of coastal wind flows, these locations may be particularly subject to highly localised climates [42]. For example, the wind speed and direction at a nearby Rf site are likely to depend on the coastal orientation, proximity of the sea and local stability, attributes that may vary significantly over short distances.…”

Section: Effects Of Terrainmentioning

confidence: 99%

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict

et al. 2015

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Highlights Wind resource predicted at 37 sites using operational forecast data and MCP  Forecast reference data highly competitive with nearby meteorological observations  Systematic improvement when using forecast data at coastal sites AbstractOutput from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects.2

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“…It includes studies concerning investigations of wind energy prospects in different countries and regions . According to the literature, there is a number of wind studies on modeling related to the evaluation of models designed for siting wind turbines in areas of complex terrain [32], coastal wind speed modeling [33], evaluation of different wind field modeling techniques for wind energy applications over complex topography [34], application of the wind resource estimation program WAsP for offshore applications [35], wind energy utilization in several regions and modeling studies [36], wind resource assessment of an area using short term data correlated to a long term data [37], critical evaluation of methods for wind-power appraisal [38], maximum wind energy penetration in autonomous electrical generation systems [39], skill forecasting from ensemble predictions of wind power [40] and autonomous wind-diesel hybrid system for various representative wind potential cases [41].…”

Section: Literature Reviewmentioning

confidence: 99%