2.3 Wave Energy Resource Assessment Methods

Wave energy resource assessment is crucial for harnessing ocean power. It involves measuring waves, predicting their behavior, and analyzing their energy potential. These methods help engineers understand wave patterns and design efficient energy conversion systems.

From buoys to satellites, various techniques capture wave data. Numerical models simulate wave dynamics, while statistical analysis reveals long-term trends and extreme events. This knowledge is vital for developing reliable and cost-effective wave energy technologies.

Wave Measurement Techniques

In-Situ Measurements

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• Wave buoys are floating devices equipped with sensors to measure wave height, period, and direction
  • Accelerometers and inclinometers measure the buoy's motion in response to waves
  • GPS receivers determine the buoy's position and track its movement
  • Data is transmitted to shore stations for analysis (via radio or satellite)
• Pressure transducers can be deployed on the seafloor to measure water pressure fluctuations caused by passing waves
  • Pressure data is converted to wave height using linear wave theory
  • Suitable for shallow water applications where the pressure signal attenuates with depth

Remote Sensing Techniques

• Satellite altimetry uses radar to measure the sea surface elevation from space
  • Altimeters emit microwave pulses and measure the time it takes for the signal to return
  • The distance between the satellite and the sea surface is calculated, revealing wave heights
  • Provides global coverage but has limited spatial and temporal resolution compared to in-situ measurements
• Synthetic Aperture Radar (SAR) satellites can image the sea surface and derive wave parameters
  • SAR measures the backscatter of microwave signals from the sea surface
  • Wave height, wavelength, and direction can be inferred from SAR images
  • Provides high spatial resolution but is affected by environmental factors (wind, rain, etc.)

Numerical Modeling

• Numerical wave models simulate the generation, propagation, and dissipation of ocean waves
  • Models solve the governing equations of fluid dynamics (e.g., Navier-Stokes equations)
  • Wind data, bathymetry, and boundary conditions are used as inputs
  • Models output wave parameters (height, period, direction) over a specified domain and time period
• Spectral wave models describe the sea state as a superposition of many sinusoidal wave components
  • Each component has a specific frequency, amplitude, and direction
  • The wave spectrum evolves in space and time based on physical processes (wind input, nonlinear interactions, dissipation)
  • Examples of spectral models include WAM, SWAN, and WAVEWATCH III

Wave Resource Prediction

Hindcasting

• Hindcasting involves reconstructing past wave conditions using historical meteorological data and numerical models
  • Long-term wind data (from weather stations, reanalysis products) is used to drive the wave model
  • The model simulates wave growth, propagation, and dissipation over the historical period
  • Hindcast results provide a long-term database of wave parameters at the location of interest
• Hindcasting enables the assessment of the long-term wave climate and variability
  • Statistical analysis of hindcast data yields average wave conditions, seasonal patterns, and extreme events
  • Hindcast data is used to estimate the available wave energy resource and design wave conditions for marine structures

Forecasting

• Wave forecasting predicts future wave conditions based on current observations and numerical models
  • Real-time wind forecasts (from weather models) are used as input to the wave model
  • The model simulates the evolution of the wave field over the forecast period (typically a few days ahead)
  • Forecast results provide short-term predictions of wave parameters at specific locations
• Wave forecasts are essential for operational planning and decision-making
  • Offshore industries (oil and gas, renewable energy) rely on forecasts for safe and efficient operations
  • Forecasts inform the scheduling of maintenance activities, vessel routing, and power production
  • Coastal managers use forecasts for beach safety, erosion control, and flood warning

Wave Energy Flux

• Wave energy flux (or wave power) quantifies the rate at which wave energy is transmitted across a vertical plane perpendicular to the wave direction
  • Expressed in units of power per unit width (kW/m) or power per unit area (kW/m²)
  • Calculated as: $P = \frac{1}{2}\rho g^2 H_s^2 T_e$, where $\rho$ is water density, $g$ is gravitational acceleration, $H_s$ is significant wave height, and $T_e$ is energy period
• The available wave energy resource at a location is assessed by analyzing the long-term distribution of wave energy flux
  • Hindcast data or wave measurements are used to compute the wave power time series
  • Statistical analysis yields the average annual wave power, seasonal variability, and power exceedance levels
• Wave energy flux is a key parameter for the design and performance assessment of wave energy converters (WECs)
  • WECs are optimized to capture the maximum available wave power at a site
  • The capacity factor and annual energy production of a WEC depend on the wave power distribution at the deployment location

Statistical Analysis Methods

Probability Distributions

• Exceedance probability describes the likelihood that a given wave parameter (height, period) will be exceeded over a specified time interval
  • Calculated from the cumulative distribution function (CDF) of the wave parameter
  • Expressed as a percentage or a return period (e.g., 1-year return period wave height)
  • Used to determine design wave conditions for marine structures and assess the survivability of WECs
• Joint probability distributions capture the combined occurrence of two or more wave parameters
  • Commonly used to represent the joint distribution of wave height and period (or wave height and direction)
  • Contour plots or tables display the probability of occurrence of different wave height-period combinations
  • Joint distributions are used to assess the performance of WECs under realistic sea states and estimate energy production

Extreme Value Analysis

• Extreme value analysis aims to predict the occurrence of rare, high-magnitude events (e.g., 100-year wave height)
  • Extreme events are important for the design of coastal and offshore structures
  • The Generalized Extreme Value (GEV) distribution is often used to model the distribution of extreme wave heights
  • The GEV distribution combines three types of extreme value distributions (Gumbel, Fréchet, and Weibull) into a single framework
• Extreme value analysis involves fitting the GEV distribution to a sample of extreme wave heights (e.g., annual maxima)
  • The fitted distribution is then extrapolated to estimate the return levels of extreme events
  • Confidence intervals are used to quantify the uncertainty associated with the estimated return levels
• Other methods for extreme value analysis include the Peak-Over-Threshold (POT) approach and the r-largest method
  • POT considers all wave heights above a specified threshold and models their distribution using the Generalized Pareto Distribution (GPD)
  • The r-largest method uses the r-largest wave heights from each year (or season) to fit the GEV distribution