Presentations

Abstract

Current wind farm energy yield assessment methodology is based on the assumption that historical energy resource is representative of future expectations. However, this assumption is challenged by the realities of a changing climate. Accurately capturing the impact of climate change on future energy yields is a complex task due to several factors: (a) significant variation in climate projections under different emissions scenarios (b) large variation in the sign and magnitude of change of meteorological variables from various climate models (c) geographical variation in projected changes, and (d) interactions between meteorological variables that influence energy yield. Scientific research suggests that while model uncertainties and spread in wind resource projections can be reduced by advancing our understanding of model performance, the significant remaining spread poses challenges for industry decision-making, highlighting the need for robust risk assessment frameworks and adaptive planning strategies as the best way forward. This study presents several case studies from locations across Europe, utilising climate projections from several CMIP6 models and various SSP emissions scenarios. The research focuses on key meteorological variables that affect energy yields and investigates: (a) projected rate of change in key meteorological variables, (b) the range of projected changes across different emissions scenarios and climate models, (c) methods for translating changes in meteorological variables into changes in energy yield, and (d) an evaluation of other climate related risks that could impact the example wind farm projects in those locations.  The findings underscore the variations in projected future outcomes, driven by factors such as climate model selection, emissions scenario, and project location. This highlights the importance of thorough screening and thoughtful consideration of both climate models and emissions scenarios, which may result in useful strategies for narrowing the range of projected outcomes by evaluating model suitability and the likelihood of various future emissions scenarios.