Evaluating the Role of Blockage Deficit Models in Robust Wind Farm Design

Abstract

Uncertainty in wind farm layout optimization regarding model choice and the impact of global blockage always exists. This paper evaluates these interactions by extending a multi-objective approach that maximizes mean Annual Energy Production (AEP) from a model ensemble while minimizing their variance. By incorporating the Self Similar blockage model into an ensemble of five wake models, we assess the impact of blockage physics on layout robustness. Results from a linear mixed-effects model indicate that including blockage leads to a non-significant average AEP reduction of 0.0624 GWh ($p$-value = 0.323). Conversely, it caused a highly statistically significant increase in uncertainty, with model disagreement rising by 0.177 GWh ($p$-value < 0.001). Additionally, computational runtime increased nearly nine times. These findings highlight an accuracy vs. certainty paradox, where theoretically necessary physics can compromise model consensus if implemented without re-calibration. Ultimately, this work suggests that simple blockage couplings act as a diagnostic for model incompatibility, emphasizing the necessity of careful model tuning for wind farm design.