scholarly journals Lidar uncertainty and beam averaging correction

2015 ◽  
Vol 12 (1) ◽  
pp. 85-89 ◽  
Author(s):  
A. Giyanani ◽  
W. Bierbooms ◽  
G. van Bussel
Keyword(s):  
Remote Sensing ◽  
Wind Energy ◽  
Complex Terrain ◽  
New Technology ◽  
Resource Assessment ◽  
International Guideline ◽  
Wind Resource Assessment ◽  
Draft Version ◽  
Wind Resource ◽  
Atmospheric Variables

Abstract. Remote sensing of the atmospheric variables with the use of Lidar is a relatively new technology field for wind resource assessment in wind energy. A review of the draft version of an international guideline (CD IEC 61400-12-1 Ed.2) used for wind energy purposes is performed and some extra atmospheric variables are taken into account for proper representation of the site. A measurement campaign with two Leosphere vertical scanning WindCube Lidars and metmast measurements is used for comparison of the uncertainty in wind speed measurements using the CD IEC 61400-12-1 Ed.2. The comparison revealed higher but realistic uncertainties. A simple model for Lidar beam averaging correction is demonstrated for understanding deviation in the measurements. It can be further applied for beam averaging uncertainty calculations in flat and complex terrain.

Wind Resource Assessment Using a Mesoscale Model: The Effect of Horizontal Resolution

2012 ◽  
Author(s):  
S. Jafari ◽  
T. Sommer ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Energy ◽  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Energy Resource ◽  
Resource Assessment ◽  
Land Area ◽  
Wind Resource Assessment ◽  
Wind Resource ◽  
Horizontal Grid

Prospecting for wind farm sites and pre-development studies of wind energy projects require knowledge of the wind energy resource over large areas (that is, areas of the order of 10’000 km2 and greater). One approach to detail this wind resource is the use of mesoscale numerical weather prediction models. In this paper, the mesoscale Weather Research and Forecasting (WRF) model is used to examine the effect of horizontal grid resolution on the fidelity of the predictions of the wind resource. The simulations are made for three test cases, Switzerland (land area 39’770 km2), Iowa (land area 145,743 km2) and Oregon (land area 248’647 km2), representing a range of terrain types, from complex terrain to flat terrain, over the period from 2006–2010. On the basis of comparisons to the data from meteorological masts and tall communication towers, guidelines are given for the horizontal grid required in the use of mesoscale models of large area wind resource assessment, especially over complex terrain.

scholarly journals Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Matthew A. Lackner ◽  
Anthony L. Rogers ◽  
James F. Manwell
Keyword(s):  
Wind Energy ◽  
Energy Production ◽  
Closed Form Solution ◽  
Resource Assessment ◽  
Form Solution ◽  
Mathematical Framework ◽  
Site Assessment ◽  
Wind Resource Assessment ◽  
Uncertainty Sources ◽  
Wind Resource

This paper presents a mathematical framework to properly account for uncertainty in wind resource assessment and wind energy production estimation. A meteorological tower based wind measurement campaign is considered exclusively, in which measure-correlate-predict is used to estimate the long-term wind resource. The evaluation of a wind resource and the subsequent estimation of the annual energy production (AEP) is a highly uncertain process. Uncertainty arises at all points in the process, from measuring the wind speed to the uncertainty in a power curve. A proper assessment of uncertainty is critical for judging the feasibility and risk of a potential wind energy development. The approach in this paper provides a framework for an accurate and objective accounting of uncertainty and, therefore, better decision making when assessing a potential wind energy site. It does not investigate the values of individual uncertainty sources. Three major aspects of site assessment uncertainty are presented here. First, a method is presented for combining uncertainty that arises in assessing the wind resource. Second, methods for handling uncertainty sources in wind turbine power output and energy losses are presented. Third, a new method for estimating the overall AEP uncertainty when using a Weibull distribution is presented. While it is commonly assumed that the uncertainty in the wind resource should be scaled by a factor between 2 and 3 to yield the uncertainty in the AEP, this work demonstrates that this assumption is an oversimplification and also presents a closed form solution for the sensitivity factors of the Weibull parameters.

Urban wind resource assessment in changing climate: Case study

2016 ◽  
Vol 41 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Djordje Romanic ◽  
Ashkan Rasouli ◽  
Horia Hangan
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Resource Assessment ◽  
Urban Environments ◽  
Wind Resource Assessment ◽  
Changing Climate ◽  
Energy Maps ◽  
Wind Resource ◽  
The City

Urban wind resource assessment in changing climate has not been studied so far. This study presents a methodology for microscale numerical modelling of urban wind resource assessment in changing climate. The methodology is applied for a specific urban development in the city of Toronto, ON, Canada. It is shown that the speed of the southwest winds, that is, the most frequent winds increased for .8 m s−1 in the period from 1948 to 2015. The generated wind energy maps are used to estimate the influences of climate change on the available wind energy. It is shown that the geometry of irregularly spaced and located obstacles in urban environments has to be taken into consideration when performing studies on urban wind resource assessment in changing climate. In the analysed urban environment, peak speeds are more affected by climate change than the mean speeds.

scholarly journals Performance analysis of wind resource assessment programs in complex terrain

2006 ◽  
Vol 1 (04) ◽  
pp. 301-306 ◽  
Author(s):  
A. Llombart ◽  
◽  
A. Talayero ◽  
A. Mallet ◽  
A. Pera ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Complex Terrain ◽  
Resource Assessment ◽  
Wind Resource Assessment ◽  
Wind Resource
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