A comparison between Horizontal and Vertical axis wind turbines in a wind farm perspective

One of the most pressing challenges of the 21st century is the search for sustainable energy sources able of meeting the growing global demand. This increase is driven primarily by the exponential rise in population, the continuous expansion of technological infrastructure (such as cloud computing) and the economic growth of developing nations. At the same time, the need to address climate change is becoming ever more urgent, as evidenced by rising global temperatures and more frequent extreme weather events caused by greenhouse gas emissions. The resulting dual pressure, the rising energy demand and the environmental imperatives, has accelerated the energy transition, fostering a shift away from fossil fuels and toward renewable energy technologies. Among the most promising renewable energy sources is wind power, particularly offshore wind energy, which offers high production potential with a low environmental impact. Within this field, vertical-axis wind turbines (VAWTs) have emerged as an innovative alternative to traditional horizontal-axis wind turbines (HAWTs), due to their advantages in terms of structural simplicity, scalability, and potential for deployment in dense or urban environments. However, one of the major technical challenge in optimizing wind farm performance lies in accurately modeling wake effects, evaluating the turbulent airflow and the influence on downstream turbines. In this context, there is a growing need for fast and reliable modeling tools to enable efficient preliminary assessments in the early stages of wind farm design. This thesis compares three widely used analytical wake models, Jensen, Top-Hat, and Gaussian, to assess their predictive accuracy and suitability for different turbine configurations (HAWTs and VAWTs) in wind farms. A simulation environment is developed in MATLAB to model the geometric and aerodynamic interaction between upstream and downstream turbines, with particular attention to wake expansion, velocity recovery, and three-dimensional diffusion. Multiple wake geometries (circular and elliptical) are considered, and a variable-coefficient version of the Gaussian model is introduced to improve accuracy and overallperformances. To verify the models under realistic conditions, simulations are performed using wind data from real-world sites: the Swedish coast and the French coast. Performance indicators such as wind speed recovery, Annual Energy Production (AEP), and Capacity Factor (CF) are evaluated. Results show that the optimized Gaussian model for VAWTs offers superior performance, especially in compact symmetric layouts, by reducing wake losses and improving energy yield. These findings support the strategic potential of VAWTs in future wind farm development, particularly in scenarios requiring high efficiency in limited space.