Guidelines for Wind Resource Assessment

Best Practices for Countries Initiating Wind Development

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• Publisher: Asian Development Bank
• Year: 2014
• Print ISBN: 9789292545628
• eBook ISBN: 9789292545635
• Topic: Commerce
• Subtopic: Industrie de l'énergie

Wind Resource Assessment (WRA) is a pivotal step in the development phase because it determines the bankability of wind projects. The Asian Development Bank's Quantum Leap in Wind Power Development in Asia and the Pacific project has developed WRA guidelines that encapsulate best practices for new and emerging wind energy markets with the goal of accelerating wind energy development. The guidelines address challenges to policy support for WRA, wind measurement, wind data processing, wind flow modeling, and estimation of losses and uncertainty. These are challenges faced in these markets by policy makers, implementation agencies, utilities, developers, and financiers.

Appendix 1: Nuts and Bolts of Wind Resource Assessments

This section will give readers a quick preview of detailed parameters of interest in a wind resource assessment.

A. Parameters Associated with Wind

A wind measurement campaign will capture the parameters listed in Table A1.1.

Table A1.1 List of Measured Parameters in a Wind Resource Assessment

	Parameter	Detailed Description
1	Wind speed at height h1, h2, h3 For example h1= 80 m, h2= 60 m, h3= 40 m	Wind speed statistics are recorded every 10 minutes for at least 1 year
2	Wind direction at height h4 and h5. For example, h4= 75 m, h5 = 50 m	Wind direction statistics are recorded every 10 minutes for at least 1 year
3	Wind statistics (mentioned in items 1 and 2 above) for wind speed and wind direction	Within a 10-minute interval, the following statistics are recorded: • mean wind speed, standard deviation of wind speed, maximum wind speed, and minimum wind speed • mean wind direction, standard deviation of wind direction, maximum wind direction, and minimum wind direction

m = meter.

Source: Jain (2010).

The parameters in Table A1.2 are computed from the measurement data.

Table A1.2 List of Computed Parameters in a Wind Resource Assessment

	Parameter	Detailed Description
1	Average annual wind speed (AWS) at hub height	Computed at each turbine location by extrapolating AWS from measurement locations. WAsP, a leading software for wind resource modeling, is one methodology for extrapolation.
2	Turbulence intensity (TI)	Ratio of standard deviation of wind speed to the average wind speed. TI is used to select the class of turbine.
3	Shear	Measure of how wind speed changes with elevation from the ground. This is used to project wind speed from measurement heights to turbine hub height.
4	Weibull parameters	The Weibull function describes the probability density function of wind speed. It has two parameters: shape factor and scale factor. These are used in annual energy production (AEP) calculation.
5	Air density	Computed based on elevation from sea level, temperature, humidity, and barometric pressure. It is used in the AEP computation.
6	Average annual energy production	Computed for each turbine in the planned wind farm based on wind speed profile and the turbine’s power production curve.
7	Long-term correction to AEP	Computed using high-quality long-term wind data and a technique called measure–correlate–predict.
8	Net long-term corrected AEP	Estimated losses are subtracted from Gross AEP in items 6 and 7 to obtain net AEP. This is the amount of energy delivered to the grid.
9	AEP for different exceedance probabilities	Net long-term corrected AEP is reduced by multiples of standard deviation of AEP to compute P75, P90, P95, and other AEP.

Source: Jain (2010).

Appendix 2: Wind Measurement

At least 1 year of on-site wind measurement is a prerequisite for a bankable wind resource assessment. Broadly speaking, there are two types of wind measurements: in situ and remote sensing. In situ measurements are done with meteorological towers (also called met-masts or met-towers). There are two options for remote sensing: Sonic Detection and Ranging (SODAR), which is based on sound waves and Light Detection and Ranging (LIDAR), which is based on light waves. Both types of measurement provide wind speed, wind direction, temperature, barometric pressure, relative humidity, solar radiation, and a few other atmospheric conditions.

A. Wind Measurement Based on Meteorological Towers

A met-tower is the most popular method of measuring wind speed. The most commonly used configuration of a met-tower is shown in Table A2.1.

Table A2.1 Description of Met-Towers, Sensors, and Configuration

Item	Description
Types of met-towers	Temporary: Met-towers with a life of 3–5 years, used for project-specific assessment of wind resources Permanent: Met-towers with a life of 20 years, used for long-term measurements or used in wind farm as a reference mast
Size and type	Temporary: 80 m monopole (or tubular) towers are recommended for new projects. A 60-m monopole tower is the most popular. Permanent: Lattice towers
Number of anemometers	Two Class I calibrated anemometers at 80 m Two calibrated anemometers at 60 m Two calibrated anemometers at 40 m
Number of wind vanes	Two wind vanes: One at 75 m and a second at 55 m
Orientation of anemometers	If wind direction is predominantly from one direction, then place the two anemometers at +45/−45-degree angles to the primary wind direction, such that the two anemometers make an angle of 90 degrees.
Orientation of wind vane	The wind vane must be parallel to one of the anemometers. Both wind vanes must be parallel.
Boom length	The boom on which the anemometers are mounted must be at least six times the diameter of the pole.
Other sensors	Temperature, barometric pressure, relative humidity, and solar radia...

Table of contents

• Front Cover
• Title Page
• Copyright Page
• Contents
• List of Tables and Figures
• Foreword
• Executive Summary
• Abbreviations
• I. Introduction to Wind Resource Assessments
• II. Wind Energy Project Life Cycle
• III. Importance of Accurate Wind Resource Assessments
• IV. Wind Resource Assessment Process
• V. Best Practices for Wind Resource Assessments
• VI. Conclusions
• VII. References
• Appendixes
• Back Cover