WAsP model performance verification using lidar data

Indasi, Victor S.; Lynch, M. J.; McGann, B.T.; Yu, Frank; Jeanneret, François; Sutton, John F.

doi:10.1007/s40095-015-0189-6

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Both data should correspond to the same coordinates and period of time to make sure there is no bias in the comparison. A similar study using the mean difference to identify the accuracy of WAsP model is reported by [48]. Only three sites were qualified for accuracy analysis as only those sites have nearby meteorological data.…”

Section: Dependence Of Power Law Index (Pli) Upon Temperaturementioning

confidence: 98%

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Albani

Ibrahim

2017

Energies

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This paper investigated the wind energy potential by analysing a certain amount of gathered 10-min measured data at four stations located at coastal sites in Malaysia, i.e., Kudat, Mersing, Kijal, and Langkawi. The wind data are collected from a total of four new wind measurement masts with sensors mounted at various heights on the tower. The measured data have enabled the establishment of wind resource maps and the power law indexes (PLIs) analysis. In addition, the dependence of PLI upon surface temperature and terrain types is studied, as they are associated to the form of exponential fits. Moreover, the accuracy of exponential fits is assessed by comparing the results with the 1/7 law via the capacity factor (CF) discrepancies. In order to do so, the wind turbine with a hub-height similar to the maximum height of the measured data at each site is selected to simulate energy production. Accordingly, the discrepancy of CF based on the extrapolated data by employing 1/7 laws and exponential fits, in spite of being computed using measured data, is determined as well. Furthermore, the large discrepancy of the wind data and the CF, which has been determined with the application of 1/7, is compared to the exponential fits. This is because; discrepancy in estimation of vertical wind speed could lead to inaccurate CF computation. Meanwhile, from the energy potential analysis based on the computed CF, only Kudat and Mersing display a promising potential to develop a medium capacity of wind turbine power, while the other sites may be suitable for wind turbines at a small scale.

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Section: Dependence Of Power Law Index (Pli) Upon Temperaturementioning

confidence: 98%

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Albani

Ibrahim

2017

Energies

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“…(2016) also conducted a similar study using the mean difference to identify the accuracy of the WAsP model. 47 This study has obtained good predictions for the wind resource map in Kudat with only a 10% discrepancy. The WAsP numerical model would create a higher uncertainty over the flow of wind speed over rugged terrain.…”

Section: Resultsmentioning

confidence: 66%

The wind energy potential in Kudat Malaysia by considering the levelized cost of energy for combined wind turbine capacities

Albani

Ibrahim

Yong

et al. 2020

Energy & Environment

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This paper presents the wind energy potential at Kudat Malaysia by considering the Levelized cost of energy (LCOE) model for combined wind turbine capacities. The combination of small- and utility-scale wind turbines is the key to the success of the operation of a wind park in the lower wind speed region. In a combination approach, the small-scale wind turbines provide the power required by the utility-scale wind turbines to start the blade rotation. For this reason, the particular closed-form equation was modified to determine the LCOE of a wind park with combined turbine capacities. The modified LCOE model can be used as a basis for setting tariff rates or define the economic feasibility of wind energy projects with combined wind turbine capacities.

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“…The landcover to roughness length approach is used extensively in wind engineering (Indasi et al 2016). For the offshore sites, we assume homogeneous terrain in all directions and z 0 = 0.0002 m as recommended by Wieringa (1993), although z 0 depends on wind speed (Charnock 1955).…”

Section: Methodsmentioning

confidence: 99%

Using Observed and Modelled Heat Fluxes for Improved Extrapolation of Wind Distributions

Floors

Troen

Peña

2023

Boundary-Layer Meteorol

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Modelling the horizontal and vertical variation of wind speed is crucial for wind energy applications. A model frequently used for this purpose is part of the Wind Atlas Analysis and Application program (WAsP). Here, we modify the model in WAsP to account for local atmospheric stability parameters. Atmospheric stability effects are treated by using the impact of a temperature scale on the geostrophic drag law and the diabatic logarithmic wind profile. Using this approach, wind-direction dependent mean and standard deviation of a surface-layer temperature scale and a mean boundary-layer height scale can be obtained from either numerical weather prediction model output or observations to improve vertical extrapolations of Weibull wind speed distribution parameters. The modified atmospheric stability model is validated at six flat sites in northwestern Europe. The surface-layer temperature scale is available from sonic anemometer measurements at three of the sites. At all sites the temperature scale is also estimated from reanalysis data and from mesoscale model output. The modified model improves the aggregated estimations of power density distributions when extrapolating to nearby locations from 5.2 to 3%, when using the temperature scale derived from either observations or mesoscale/reanalysis output compared to the current model.

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WAsP model performance verification using lidar data

Cited by 10 publications

References 14 publications

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

The wind energy potential in Kudat Malaysia by considering the levelized cost of energy for combined wind turbine capacities

Using Observed and Modelled Heat Fluxes for Improved Extrapolation of Wind Distributions

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