Posters

Abstract

Wake effects have a significant impact on wind farm production and asset lifetime. Regions of slow, highly turbulent wind lead to a power reduction and an increased fatigue for the turbines within or at the edge of the wake. Wind farm control (WFC) is an efficient way to mitigate the production losses, for example using wake steering which consists in voluntarily misaligning upstream turbines in order to steer their wakes away from downstream turbines. Optimising the misalignment of each turbine throughout the farm can increase the global wind farm production. Before implementing a WFC system, the achievable production gains must be predicted with sufficient accuracy, along with the control implementation impact, beyond an idealised mathematical optimisation. Our estimation method considers the determination of freestream conditions, including measurement error and wind condition variability, the operational state of the turbines (unavailability, curtailment, etc.) and the representativity of the wake model accuracy. These factors can be parameterised and combined into scenarios with different assumptions on the control implementation method (e.g. open loop control system, online optimization, sensor accuracy, closed-loop control system, integrated uncertainty management, etc.). This allows to quantify the relative efficiency of each method. This work complements [1] by focusing on the yearly production rather than a single set of conditions. This evaluation method can also help improve the understanding of factors that make a wind farm more or less suitable for WFC with wake steering. Parametric studies of the farm layout and wind resources are carried out to measure their impact on WFC. Knowing these critical factors empowers farm operators to evaluate the relevance of WFC for their asset. We develop a robust WFC systems that intrinsically reduces the losses due to these errors and uncertainties (for example with added sensors or ad-hoc online optimisation) and will be field-tested in 2025.