Wind energy has played a major role in the development of our society since 5000 BC when the first sail boats traveled the Nile River. Wind technology continued to advance with the invention of wind-powered pumps and mills in China and the Middle East as early as 200 BC, and by the late 19th century the first electricity-generating wind turbines were built. Today the effects of human driven climate change are pushing the world to transition from a fossil fuel dominated energy diet to a menu composed of a variety of technologies powered by renewable energies, including wind power. Our consumption habits and the historical design of our electricity grids were based on relatively few, centralized, and controllable power plants that use various fossil or nuclear fuels to generate power on demand to match real time use. The weather driven nature and low energy density of renewable sources like solar, wind and hydro lead to a more distributed and fluctuating electricity generation, which results in multiple problems: In Switzerland, under the foreseen future where photovoltaic (PV) panels are predominantly installed in urban areas, the nuclear phase-out will cause large deficits of electricity in winter. This thesis contributes to the solution of these important challenges in two ways: first by presenting a model that combines a variety of renewable energy sources to form a reliable and efficient electrical system, and secondly by improving the assessment of wind power potential in the Alps. The first two parts of this thesis are concerned with the simulation and optimization of the Swiss electrical system, in which the nuclear reactors are replaced by a combination of solar and wind energy sources. A spatially explicit approach is required to consider the spatio-temporal variability in renewable sources. Storage hydropower is a remarkable asset for the energy transition, and thus occupies a central role in the manner the system is modeled and operated (Part I). A novel algorithm explores how the whole system reacts under various combinations of PV and wind energies (Part II). By simultaneously optimizing the generation mix and location of PV panels and wind turbines, while considering the electrical grid and land use, this algorithm showed that a large number of carefully located wind turbines and a small number of PV panels located in the Alps can provide a much more balanced system than the conventional, urban PV scenario. Part III of this thesis addresses the difficulty of wind power potential assessment in mountainous terrain. On such terrain, high horizontal resolution is required in the numerical models that simulate the air flows. This constraint, and the need for long time series, prohibit the simulation of large domains and thus the identification of the best locations for wind turbines in the entire Swiss Alps. Downscaling models for wind are often used to obtain high resolution and long time series, but perform poorly on such terrain. In order to improve these models, a new method based on deep learning is presented. It downscales the outputs of the operational numerical model COSMO-1 used for the Swiss weather forecast, which has a resolution of 1.1 km, to a 50-meter resolution. The validation at 60 measurement stations shows that wind speeds and directions are more accurate than COSMO-1 and present many of the expected orographic effects like deflection, sheltering and ridge acceleration.