New Approaches to Off-Shore Wind Energy Management Exploiting Satellite EO Data
Morelli, Marco1; Masini, Andrea2; Potenza, Marco Alberto Carlo1
1University of Milano, ITALY; 2Flyby S.r.l., ITALY
Wind as an energy resource has been increasingly in focus over the past decades, starting with the global oil crisis in the 1970s. The possibility of expanding wind power production to off-shore locations is attractive, especially in sites where wind levels tend to be higher and more constant.
Off-shore high-potential sites for wind energy plants are currently being looked up by means of wind atlases, which are essentially based on NWP (Numerical Weather Prediction) archive data and that supply information with low spatial resolution and very low accuracy. Moreover, real-time monitoring of active off-shore wind plants is being carried out using in-situ installed anemometers, that are not very reliable (especially on long time periods) and that should be periodically substituted when malfunctions or damages occur.
These activities could be greatly supported exploiting archived and near real-time satellite imagery, that could provide accurate, global coverage and high spatial resolution information about both averaged and near real-time off-shore windiness.
In this work we present new methodologies aimed to support both planning and near-real-time monitoring of off-shore wind energy plants using satellite SAR (Synthetic Aperture Radar) imagery. Such methodologies are currently being developed in the scope of SATENERG, a research project funded by ASI (Italian Space Agency).
SAR wind data are derived from radar backscattering using empirical geophysical model functions, thus achieving greater accuracy and greater resolution with respect to other wind measurement methods. In detail, we calculate wind speed from X-band and C-band satellite SAR data, such as Cosmo-SkyMed (XMOD2) and ERS and ENVISAT (CMOD4) respectively. Then, using also detailed models of each part of the wind plant, we are able to calculate the AC power yield expected behaviour, which can be used to support either the design of potential plants (using historical series of satellite images) or the monitoring and performance analysis of active plants (using near-real-time satellite imagery).
We have applied these methods in several test cases and obtained successful results in comparison with standard methodologies.