Validation of offshore wind data in the Canary Islands and development of typical wind resource years

Abstract

In a global context of rising demand for renewable energies, wind turbines are playing an increasingly important role in the energy landscape. This is particularly true in the Canary Islands, where the installation of several offshore wind farms are planned for 2030. In order to assess the energy potential of these wind farms, and to ensure the harmonious integration of their energy production into the islands’ electricity grid, it is essential to have wind speed data on the areas for the different wind farms. In this context, the aim of this Master Thesis carried out at the University of Gran Canaria, one of the islands concerned by these projects, is to rapidly and efficiently establish a complete and coherent database presenting the hour-by-hour evolution of wind speeds at a height of 10 metres for a typical year on the different studied sites. To achieve this, wind data from different sources were analyzed and compared in order to gradually converge to a reliable database for each of the sites considered for future wind farm installations. Three database sources were selected and used: onshore experimental measurement data, offshore experimental measurement data and modelling data from SIMAR, a software developed by the Spanish government. Firstly, onshore and offshore datasets were compared to demonstrate correlation between trends observed on three different time scales: daily, monthly and annual. SIMAR data were then compared with onshore experimental data with the same purpose. Then, a typical wind year was identified for each of the zones studied, thanks to a statistical analysis based on the Conover-Iman test. Finally, based on all the above analyses, the SIMAR databases were completed to ensure continuity of wind data, and adjusted using a daily weighting coefficient to reproduce the daily behaviour observed in the experimental databases. This enabled hourly wind speeds for a typical year to be generated for each of the studied sites. In conclusion, this Master Thesis work has enabled the rapid set up of consistent databases in areas where future offshore wind farms will be sited, paving the way for further work to estimate the energy production potential of these farms.