Offshore Solar Farm Assessment and Uncertainty Determination for the United Arab Emirates

Floating offshore solar farms (OSF) are an attractive option for solar energy generation, as they help avoid land competition with other uses. Planning the deployment of OSF requires assessments for site selection, which are primarily based on energy yields, in addition to other considerations. The yields are determined through evaluations using climate and oceanic variables. Uncertainty in these variables can propagate to further uncertainty in yield estimations, which ultimately can lead to significant consequences in the cost of energy. In this work, we propose the development of a novel method to assess the viability of OSF considering insolation uncertainty, with a focus on the United Arab Emirates (UAE) region. Open-source satellite data is utilized to conduct initial site assessments, and produce a set of viable locations based on parameters that include solar irradiance, ambient temperature, sea surface temperature, wind speed and precipitation. Validation of the viable locations will be done through the deployment of meteorological instrumentation, to collect in-situ measurements for a minimum of one year. Machine learning techniques are examined to quantify the uncertainty, followed by determination of impacts on levelized cost of electricity (LCOE) and savings based on uncertainty reduction.