On the impact of probabilistic weather data on the economically optimal design of renewable energy systems – a case study on La Gomera island

Abstract

 Renewable energy and storage systems are widely discussed to minimise the impact of global warming. In addition to the temporal resolution of simulation tools, also the chosen input data might have a strong impact on the performance of renewable energy systems, and energy storage systems in particular. This study analyses the impact of probabilistic weather data on the design of renewable energy systems. The main objective is hereby the determination of the robustness of a recently state-of-the-art design process of a 100% renewable energy and storage system with varying probabilistic input data. The island of La Gomera, Canary Islands, is taken as a case study. Although all analysed systems show some variance in their results, the combination of vehicle-to-grid and power-to-hydrogen shows the best economic performance. Hereby, small island energy systems depending heavily on wind energy show higher variations than those with high shares of solar energy. This analysis illustrates clearly that the choice of one historical reference year is not suitable to determine the expected performance of an energy system. To learn about their sensitivity, synthetic probabilistic inputs as applied in this study are a good way to determine both the expected mean values and their variance.