Abstract
This study presents a methodology for wind farm set point optimization, which allows including both loads and power output as optimization criteria. The primary control strategy investigated involves de-rating of individual turbines in the wind farm. The optimal de-rating level of each individual turbine is determined by using accurate and computationally efficient surrogate models, mapping the dependency between the choice of de-rating strategy for a given turbine and the load and power outputs of downwind turbines. A case study based on the Lillgrund offshore wind farm shows that for specific wind directions with strong wake interactions, it may be possible to achieve a net gain in power output in the order of 5% at specific mean wind speeds. Alternatively, maintaining nominal power output but optimizing the load distribution could lead to substantial fatigue load reductions.
Highlights
Introduction and study objectivesIt is estimated that optimization of wind farm operations through improvement of the farm control strategies can potentially lead to 1-5% increase of the wind farm power outputs [1]
A case study based on the Lillgrund offshore wind farm shows that for specific wind directions with strong wake interactions, it may be possible to achieve a net gain in power output in the order of 5% at specific mean wind speeds
Maintaining nominal power output but optimizing the load distribution could lead to substantial fatigue load reductions
Summary
Introduction and study objectivesIt is estimated that optimization of wind farm operations through improvement of the farm control strategies can potentially lead to 1-5% increase of the wind farm power outputs [1]. In its most general form, such an optimization task would consist of modifying the control set points of individual turbines in the wind farm, and use the total profits (i.e., considering the balance of costs and income) as an objective function. Since estimating profits can be challenging, very often the total power output of the wind farm is used as an objective function instead of the profits Such an approach still provides useful results since the power output is very closely related to the income from energy. While the relationship between loads and costs is less explicit than the relationship between power output and income, loads can still be a useful criterion as the fatigue damage accumulation typically governs the actual turbine lifetime In this respect, any fatigue damage increase could be considered as an increase in the turbine lifetime consumption and a reduction of the turbine lifetime. If increased loading leads to the need of increased turbine maintenance, load levels will affect operation & maintenance costs
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