Renewable energy resources, such as wind, are available worldwide. Locating areas with high and continual wind sources are crucial in pre-planning of wind farms. Vast offshore areas are characterized with higher and more reliable wind resources in comparison with continental areas. However, offshore wind energy production is in a quite preliminary phase. Elaborating the potential productivity of wind farms over such areas is challenging due to sparse in situ observations. Mediterranean basin is not an exception. The overall aim of this thesis is to perform analysis in model efficiency in estimation of wind energy from regional to local scale.
First, we are proposing numerical simulations of near-surface wind fields from regional climate models (RCMs) in order to obtain and fill the gaps in observations over the Mediterranean basin. Four simulations produced with two regional climate models are examined. Remote sensing observations (QuikSCAT satellite) are used to assess the skill of the simulated fields. A technique in estimation the potential energy from the wind fields over the region is introduced locating the three potentially interesting sub-regions for wind farms.
Then, we use local-scale model (large-eddy simulation) with implemented parameterization of wind turbine in order to simulate real case flow in theoretical wind farm. Information reported with regional climate model would be used to create inflow conditions for the selected sub-region of the Mediterranean Sea for simulating theoretical offshore wind farm.
Finally, we would compare the estimation of wind power potential obtained by regional climate model and power production of theoretical wind farm obtained with large-eddy simulations for chosen sub-region. Within this multi-scale approach, we would present different numerical computational efficiency in application of wind energy and justification in usage of both regional and local scale models. The novelty of this multi-model methodological approach could be considered in offering significant information for wind industry.