Posters

Abstract

Coastal regions offer an ideal environment for wind farms due to the consistent and strong winds that blow in from the sea. Coastal wind farms can achieve high energy yield because the sea breeze tends to be more consistent and stronger than inland winds, resulting in increased efficiency and a more reliable power supply. The expansive open spaces and proximity to populated areas make these locations attractive for the development of wind energy projects, leading to a growing trend in the construction of wind farms along coastal areas. To study wind flow in coastal regions, impacts from both microscale topography and regional climate conditions should be considered. These effects are coupled between them and can be correctly reproduced only with an appropriately designed model chain covering both microscale and mesoscale phenomena. In Meteodyn WT, the mesoscale-microscale coupling method combines mesoscale models which capture the climate conditions and introduce them in the micro-scale model, with microscale model that focus on the terrain complexity to improve the wind resource modelling at the coastal area. The statistic wind profiles from meso-scale model are used not only as the initial and boundary conditions but also as forces in the computational domain to maintain the statistic meso-scale wind profiles in meso-scale zone. The Monin-Obukhov length obtained from the mesoscale data is used to choose thermal stability classes. Here, we investigate a coastal site equipped with seven meteorological masts using various methodologies: the Computational Fluid Dynamics (CFD) modelling approach, the mesoscale-microscale coupling approach, and the mesoscale modelling approach (WRF simulation). The results indicate that the mesoscale simulation tends to underestimate the wind in this coastal region. The mesoscale-microscale coupling method yields an average Root Mean Square Error (RSME) of 2.4% using the met mast measurement as the reference, outperforming the microscale simulation with an average RSME of 10%. Notably, in the absence of met mast measurements, the wind speeds obtained through the microscale approach are more sensitive to the reference provided by the mesoscale simulation compared to the mesoscale-microscale coupling approach. Therefore, in the absence of met mast measurements, utilizing multiple reference points obtained through mesoscale simulation can effectively mitigate Root Mean Square Error (RMSE) errors, as opposed to relying on a single reference point. While various approaches, such as mesoscale modeling, microscale modeling, and meso-micro coupling modeling, each demonstrate their advantages in wind modeling, the choice depends on factors such as climate conditions, the topography conditions and measurement availability. At coastal sites, the mesoscale-microscale coupling approach is currently experiencing significant development and is increasingly being validated at wind farms.