Posters

Abstract

The performance of offshore wind farms can be accurately predicted with little overall bias using bottom-up wake and blockage models. However, validation against operational data shows that this small average model bias masks a seasonal pattern in which models tend to overestimate the production in the summer months while underestimating it in the winter. Bottom-up approaches for modelling wind farm performance represent wakes and blockages from individual turbines. These single-turbine upstream and downstream effects are combined to calculate the aggregated turbine interaction losses for the wind farm.    As wind farms expand in horizontal scale and turbines grow taller, their interaction with the atmosphere becomes more significant. This interaction is not included in the traditional bottom-up approaches. Experiments and simulations have already shown that the global blockage loss is dependent on the height of the atmospheric boundary layer. In this contribution we present results from simulations using a simple model of a stratified atmosphere coupled with wind turbine wakes and blockage. The combination of buoyancy and turbine drag forces can trigger the formation of atmospheric gravity waves, depending on the stratification at the top of the boundary layer and above. The pressure perturbation from gravity waves feeds back into the wind farm flow, causing additional blockage upstream, but also leads to a downstream acceleration of the flow. The balance of these influences within the wind farm depends on the wind farm layout, the inflow conditions and crucially on the stratification of the atmosphere and the height of the boundary layer.   We analyse the seasonal profile of the atmospheric interaction for offshore wind farms based on radiosonde data and hindcast modelled data. The coupling to the gravity waves leads to increased wind farm losses in the summer compared to a purely bottom-up description of wakes and blockage that neglects the atmospheric interaction. We use this to show how the combination of wakes and an atmospheric perturbation explains seasonal trends in the performance of operational wind farms.