2.1 Wind resource assessment and characterization
5 min read•july 30, 2024
Wind resource assessment is crucial for evaluating potential wind energy sites. It involves collecting and analyzing wind data to estimate energy production. Techniques include using meteorological towers, remote sensing, and long-term measurement campaigns to capture wind patterns and variability.
Key metrics like distribution, wind rose diagrams, and capacity factors help quantify a site's potential. Advanced analysis techniques assess , extreme events, and spatial correlations, providing a comprehensive understanding of the wind resource for project planning and turbine selection.
Wind Energy Potential Evaluation
Site Assessment Techniques
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Top images from around the web for Site Assessment Techniques
AMT - Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA ... View original
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AMT - Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer View original
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WES - Improving mesoscale wind speed forecasts using lidar-based observation nudging for ... View original
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AMT - Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA ... View original
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- Wind resource assessment collects and analyzes wind data to estimate energy production potential of specific locations
- Meteorological towers with anemometers and wind vanes measure wind speed and direction at multiple heights
- Remote sensing technologies (, ) provide vertical wind profile measurements without tall towers
- Wind measurement campaigns typically last 1-3 years to capture seasonal variations and long-term trends
- (W/m²) quantifies wind energy potential at a site
- represents the ratio of actual energy production to theoretical maximum output
- Used to evaluate site suitability for wind energy projects
- Example: A capacity factor of 0.35 means the turbine produces 35% of its theoretical maximum output
- accounts for measurement errors, data extrapolation, and long-term variability
- Example: Monte Carlo simulations to quantify overall uncertainty in energy yield predictions
Key Metrics and Analysis
- Wind speed frequency distribution typically represented by
- Characterized by shape and scale parameters
- Example: A shape parameter of 2 indicates a Rayleigh distribution, common in many wind regimes
- Wind rose diagram visually represents frequency of wind directions and associated wind speeds
- Example: A wind rose showing prevailing winds from the southwest with highest speeds
- quantifies wind variability
- Ratio of wind speed standard deviation to mean wind speed
- Example: Turbulence intensity of 0.1 indicates relatively smooth wind conditions
- quantifies change in wind speed with height
- Estimated using power law or log law profiles
- Example: A power law exponent of 0.2 indicates moderate wind shear in open terrain
Wind Resource Variability Analysis
Temporal Patterns and Long-term Correlation
- Diurnal and seasonal wind patterns analyzed to understand temporal variations in resource availability
- Example: Stronger winds during afternoon hours due to thermal effects
- Example: Monsoon seasons causing distinct wind patterns in certain regions
- Long-term correlation techniques extend short-term on-site measurements to long-term estimates
- (MCP) method commonly used
- Example: Correlating 1-year on-site data with 20-year reference station data to estimate long-term wind resource
- characterize wind resource variability and uncertainty
- Probability distributions and time series analysis employed
- Example: Using autocorrelation functions to identify cyclic patterns in wind speed data
Advanced Analysis Techniques
- Wind speed ramp events analyzed for grid integration studies
- Rapid changes in wind speed affecting power output
- Example: Identifying frequency and magnitude of 1-hour wind speed changes exceeding 25%
- assesses maximum wind speeds for turbine design and safety
- Methods like Annual Maximum Series or Peak Over Threshold used
- Example: Estimating 50-year return period gust speed for turbine survival wind speed specification
- of wind speeds across a wind farm site evaluated
- Important for assessing overall farm output variability
- Example: Using kriging techniques to interpolate wind speeds between measurement points
Wind Resource Mapping for Site Selection
Map Types and Data Sources
- provide spatial representations of wind parameters across geographical regions
- Wind speed, wind power density, and other relevant parameters visualized
- Example: Color-coded map showing annual average wind speeds at 100m height
- Global wind atlases offer preliminary wind resource data for initial site screening
- provides worldwide coverage
- Example: Using Global Wind Atlas to identify high-potential regions for further investigation
- Mesoscale and techniques create high-resolution wind resource maps
- Nested modeling approaches refine global or regional data to local scales
- Example: WRF (Weather Research and Forecasting) model downscaling to 1km resolution
Interpretation and Application
- Wind resource classification systems categorize sites based on wind characteristics
- define site conditions for turbine selection
- Example: IEC Class I sites with annual average wind speeds above 10 m/s at hub height
- (GIS) overlay wind resource data with other spatial information
- Comprehensive site evaluation considering multiple factors
- Example: Combining wind resource maps with land use, grid infrastructure, and environmental constraint layers
- Wind resource maps often include information on wind speed at multiple heights
- Allows assessment of wind conditions at various turbine hub heights
- Example: Comparing wind speeds at 80m, 100m, and 120m to optimize turbine selection
- Limitations and uncertainties of wind resource maps considered in project planning
- Resolution, model assumptions, and validation status assessed
- Example: Understanding the impact of complex terrain on the accuracy of mesoscale model predictions
Topography and Surface Roughness Impact
Terrain Effects on Wind Flow
- Topographic features significantly influence local wind patterns
- Hills, ridges, and valleys create speed-up and channeling effects
- Example: Wind speed acceleration of 20% observed at the crest of a smooth, isolated hill
- (RIX) quantifies terrain complexity and its potential impact
- Higher RIX values indicate more complex terrain and potential flow separation
- Example: RIX > 30% suggesting high uncertainty in flow model predictions
- (CFD) modeling simulates wind flow over complex terrain
- Assesses local wind resource variations and turbulence patterns
- Example: CFD simulation revealing recirculation zones in the lee of steep ridges
Surface Characteristics and Micro-siting
- Surface roughness affects vertical wind speed profile and turbulence intensity
- Characterized by parameter
- Example: Roughness length of 0.03m for agricultural land vs. 1m for forests
- Wind flow separation and turbulence generation occur in lee of obstacles
- Reduces wind resource quality and increases turbine fatigue loads
- Example: Turbulence intensity doubling downwind of a forest edge
- adjusts wind speed profiles in areas with tall vegetation or buildings
- Effectively raises the ground level for wind profile calculations
- Example: Displacement height of 10m used for wind speed extrapolation over a dense forest
- optimize turbine placement within given topography
- Energy yield optimization and wake effect minimization considered
- Example: Shifting turbine locations to maximize exposure to prevailing winds while minimizing wake losses
Key Terms to Review (28)
Anemometry: Anemometry is the technique used to measure wind speed and direction. This measurement is crucial for understanding wind behavior, which directly impacts various applications, including energy generation and meteorology. By accurately assessing wind resources, anemometry helps in characterizing the potential for harnessing wind energy effectively.
Capacity Factor: Capacity factor is a measure of how efficiently a power generation system operates, defined as the ratio of actual output over a specified period to the maximum possible output if it operated at full capacity for the same period. Understanding capacity factor helps compare different energy systems and their performance in real-world conditions.
Computational Fluid Dynamics: Computational Fluid Dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and algorithms to solve and analyze problems involving fluid flows. It connects mathematical models with computer simulations to predict the behavior of fluids in various environments, making it essential for assessing wind resources, understanding forces on airborne devices, and optimizing layouts for energy generation systems.
Displacement Height: Displacement height refers to the height above ground level at which wind speed measurements become representative of the free atmosphere, effectively accounting for obstacles such as buildings and trees. This concept is crucial in understanding how these obstacles can reduce wind speeds at lower elevations, making it essential for accurately assessing wind resources for energy generation.
Extreme Value Analysis: Extreme value analysis is a statistical method used to assess and characterize the behavior of extreme events, such as high wind speeds or low temperatures, over a specific period. This approach helps in understanding the tail end of the distribution of wind data, which is crucial for predicting the potential for extreme weather conditions that can impact energy generation and infrastructure. By focusing on these extreme values, it provides valuable insights for optimizing design and safety measures in airborne wind energy systems.
Geographical Information Systems: Geographical Information Systems (GIS) are powerful tools used for capturing, storing, analyzing, and managing spatial and geographic data. These systems allow users to visualize and interpret data related to locations on the Earth's surface, making them invaluable in assessing and characterizing wind resources by integrating various datasets, such as topography, land use, and meteorological information.
Global Wind Atlas: The Global Wind Atlas is a comprehensive online resource that provides high-resolution wind data for various locations around the world. This tool is essential for assessing wind energy resources, allowing stakeholders to evaluate potential sites for wind energy projects based on factors like wind speed, direction, and turbulence. By offering standardized data, it helps in the effective characterization and assessment of wind resources globally.
IEC Wind Classes: IEC Wind Classes are a classification system defined by the International Electrotechnical Commission (IEC) that categorizes wind conditions for the design and operation of wind turbines. This system helps in assessing the suitability of specific wind turbine models for various wind environments, enabling more effective resource assessment and characterization. By understanding these classes, developers and engineers can better plan wind energy projects, ensuring that turbines operate efficiently and safely in their designated locations.
Lidar: Lidar, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser light to measure distances and create detailed maps of the environment. This technology is essential in various applications, especially for assessing wind resources, enabling accurate wind profile measurements, and optimizing airborne wind energy systems. By providing precise topographical data and wind velocity profiles, lidar is a key player in improving the efficiency and effectiveness of energy generation from wind sources.
Measure-correlate-predict: Measure-correlate-predict (MCP) is a statistical method used in wind resource assessment that combines on-site measurements of wind speed with data from remote sensing or nearby meteorological stations to estimate wind energy potential at a specific location. This approach helps in creating reliable predictions about wind behavior over time, enabling better decision-making for wind energy projects. By integrating different data sources, MCP enhances the accuracy of wind resource characterization, which is essential for optimizing energy capture and ensuring economic feasibility.
Mesoscale modeling: Mesoscale modeling refers to a type of numerical modeling that focuses on atmospheric phenomena occurring on scales ranging from a few kilometers to hundreds of kilometers. This approach is crucial for accurately assessing wind resources, as it captures the interactions between large-scale weather patterns and local terrain features that influence wind behavior. By simulating these interactions, mesoscale models provide valuable insights into wind speed, direction, and turbulence at various altitudes, essential for effective wind resource assessment and characterization.
Micro-siting Techniques: Micro-siting techniques refer to the methods used to optimize the placement of wind energy systems by assessing specific site conditions to enhance energy capture and reduce environmental impacts. These techniques take into account factors such as terrain, wind patterns, and obstacles to ensure that each unit is positioned for maximum efficiency. By using detailed assessments and simulations, developers can strategically place turbines or other components to make the most out of the available wind resource.
Microscale modeling: Microscale modeling refers to the detailed simulation and analysis of wind flow and turbulence on a small scale, often capturing the effects of local terrain, buildings, and vegetation. This approach is crucial for understanding how these factors influence wind patterns and energy potential at specific locations, making it essential for accurately assessing wind resources for energy generation.
Roughness Length: Roughness length is a crucial parameter in wind resource assessment that quantifies the effect of surface roughness on wind speed and flow characteristics. It represents the height above the ground at which the wind speed theoretically becomes zero due to surface friction, providing a critical link between ground features and atmospheric behavior. Understanding roughness length helps in estimating wind profiles and optimizing energy production in airborne wind energy systems.
Ruggedness Index: The ruggedness index is a quantitative measure used to characterize the variability and complexity of terrain, indicating how rugged or smooth a landscape is. This index is crucial in assessing wind resource potential, as terrain features can significantly affect wind patterns and energy generation. By understanding the ruggedness of an area, stakeholders can make informed decisions about the suitability of locations for wind energy installations.
Sodar: Sodar, short for sonic detection and ranging, is an acoustic remote sensing technology used to measure wind profiles in the atmosphere. By emitting sound waves and analyzing the returned echoes, sodar systems can provide critical data on wind speed, direction, and turbulence at various heights, making it an essential tool for wind resource assessment and characterization. This technology helps in understanding the vertical wind profile, which is vital for optimizing the placement and efficiency of airborne wind energy systems.
Spatial Correlation: Spatial correlation refers to the degree to which a particular phenomenon or variable is related to its location in space. This concept is crucial for understanding how wind resources are distributed and how they can be assessed and characterized in a specific area, leading to more effective planning and implementation of wind energy systems. Analyzing spatial correlation helps identify patterns and trends in wind data, which is essential for optimizing energy capture and enhancing efficiency in airborne wind energy technologies.
Statistical Methods: Statistical methods refer to the techniques used to collect, analyze, interpret, and present data in order to gain insights and make informed decisions. These methods are crucial for evaluating wind resource potential by allowing researchers and engineers to characterize wind patterns, assess variability, and estimate energy production from wind energy systems.
Statistical Modeling: Statistical modeling is the process of creating mathematical representations of complex real-world phenomena using statistical methods and techniques. This approach is particularly useful for analyzing data, making predictions, and understanding the relationships between different variables, especially in contexts like wind resource assessment and characterization, where accurate data interpretation is essential for efficient energy production.
Temporal Patterns: Temporal patterns refer to the variations and trends in data over time, highlighting how specific phenomena, such as wind speeds and directions, change at different times of the day, week, or year. Understanding these patterns is crucial for evaluating wind resources because they inform the predictability of energy generation and help optimize the placement and operation of airborne wind energy systems.
Turbulence Intensity: Turbulence intensity is a measure of the degree of turbulence in a fluid flow, typically expressed as the ratio of the root mean square of turbulent velocity fluctuations to the mean wind speed. It is crucial in understanding the characteristics of wind resources and their impact on various systems, including airborne wind energy. High turbulence intensity can significantly influence the performance, stability, and energy extraction efficiency of airborne systems, making it an important factor in site assessments and layout designs.
Uncertainty Analysis: Uncertainty analysis is the process of quantifying and understanding the uncertainty associated with a particular model, measurement, or prediction. This concept is crucial in assessing how variations in input data can impact the results of a model, particularly in fields that rely on statistical methods and predictive analytics. In the context of evaluating wind resources, it helps in determining the reliability of predictions made about wind speeds and energy production.
Weibull Probability Density Function: The Weibull probability density function is a statistical tool used to model the distribution of wind speeds in wind resource assessment. This function helps to characterize the variability of wind speeds over time, making it crucial for predicting energy production from wind energy systems. By analyzing historical wind data, the Weibull distribution allows for better decision-making regarding site selection and turbine technology.
Wind Direction: Wind direction refers to the direction from which the wind is coming, typically expressed in degrees from true north. Understanding wind direction is crucial for effective resource assessment and characterization, as it impacts turbine placement, efficiency, and energy production. Accurate measurements and analysis of wind direction help in forecasting wind patterns and assessing site suitability for airborne wind energy systems.
Wind Power Density: Wind power density is a measure of the amount of wind power available per unit area at a specific location, typically expressed in watts per square meter (W/m²). This metric is crucial for evaluating the potential energy that can be harnessed from wind, allowing for effective site selection and optimization of wind energy systems. Understanding wind power density helps in assessing the efficiency and feasibility of wind energy projects, as it directly relates to the expected energy output and economic viability of installations.
Wind resource maps: Wind resource maps are graphical representations that display the distribution and intensity of wind resources across a specific area. These maps are essential tools in assessing and characterizing wind energy potential, enabling developers to identify suitable sites for wind energy projects based on wind speed, direction, and other atmospheric conditions.
Wind Shear: Wind shear is the change in wind speed or direction with height in the atmosphere. This phenomenon is crucial for understanding how winds behave, especially in the context of energy generation, as it affects the performance and efficiency of airborne wind energy systems, the design and layout of wind farms, and the overall assessment of wind resources.
Wind speed: Wind speed refers to the rate at which air moves in a specific direction, typically measured in meters per second (m/s) or kilometers per hour (km/h). Understanding wind speed is crucial for assessing the potential of wind energy generation, as it directly influences the amount of energy that can be harvested from the wind. Various factors, such as terrain and atmospheric conditions, can affect wind speed and are essential for characterizing wind resources accurately.