New applications of remote sensing technology for offshore wind power
University of Delaware
Efficient development of offshore wind power will require accurate information about the wind field on a wide range of spatial and temporal scales. Lidar and Satellite Microwave Radar/Radiometry have been evaluated and used for wind speed measurement. Numerous studies have been published that examine the error, bias, and performance characteristics of variants of both technologies under a range of conditions. This paper reviews recent research and technological advances and outlines strategies for applying the technologies to reduce costs, increase energy production and improve energy forecasting through advanced rotor controls and more accurate resource estimation and mapping. A literature search was conducted to identify the most recent and relevant correlation and validation studies of Lidar, synthetic aperture radar, scatterometers, and radiometers used for estimating wind speed. Database queries were conducted to estimate inventory for satellite wind data. Estimates of the accuracy (bias and uncertainty) and availability (sample density) of these technologies were developed based on the literature search and database queries. Both “snapshot” wind speed and energy density estimates were compared for satellite microwave systems and Lidar technologies. Offshore, where turbulence is lower, Lidar is found to have very high accuracy and availability, comparable to cup anemometers at a range of up to 200m on fixed platforms. Floating Lidar is rapidly approaching the same level of accuracy and availability, and is easily repositioned. However, the short time series of Lidar is less useful for long term indexing, and it is limited to a single site per sensor. Satellite microwave wind retrievals are available over a 20 year period and are found to have good time-averaged accuracy at 10 meters above sea level for wind speeds between 3 and 15 m/s, but are subject to minor bias (below +/- 0.2 m/s) from the use of inaccurate shear profiles, from diurnal effects, and from local metocean conditions. Three strategies for use of these technologies are outlined and evaluated. ● Siting and Resource Assessment - By processing all available satellite microwave data sets, calibrated with data from a one year field campaign using floating Lidar systems, cross-correlated through a parametric geophysical model function, bias and error of wind speeds generated from the satellite data can be reduced, and wind mapping can be significantly improved in resolution and accuracy. ● Energy Production Estimates - By using wind profile data from floating Lidars, deployed on site, and indexed to a 20 year time series from calibrated satellite wind data, Annual Energy Production estimates can be greatly improved by reducing uncertainty (and thus, the risk premium on financing). In the near future, this methodology can obviate the need for a met tower for resource assessment. ● Rotor Control - By using nacelle or hub mounted Lidar to look upstream, new Lidar-assisted control systems can adjust blade pitch and nacelle yaw pro-actively to match rapid changes in wind speed or direction. This can reduce fatigue and extreme gust loading on components, allowing longer blades and greater swept area. It can also improve efficiency be reducing yaw mis-alignment. In addition to power production benefits, rough, first-order costs were developed to check economic justification, and the expected change in Breakeven Price was calculated for two different build-out scenarios of the study area. The analysis indicates that the recommended strategies for improving Rotor Control and reducing uncertainty of AEP estimates can reduce the Breakeven Price of power for the base case wind farm by at least 4%. The benefits of improved mapping are more difficult to monetize due to high levels of uncertainty in all the primary factors, so two different scenarios are considered. If the benefits of improved mapping and better siting (2% to 3% lower BP) are available to the first ten or twelve projects, and the mapping effort is federally funded, or the costs are somehow distributed industry-wide over full build-out of the study area, the Breakeven Price for the first phase of wind farms could be reduced by a total of around 6% to 7%. If mapping benefits are assumed to diminish over time as the study area builds out, the long term, annualized reduction in Breakeven Price over the entire study area will be lower, at around 4% to 5%. In either case the mapping effort is justified, and the cost can be reduced by about $120 million using Lidar equipped met buoys.