Wind plant blockage is a phenomenon where the presence of a large wind farm creates a disturbance to air flow external to the farm itself that is not accounted for by wake effects. This is typically manifested as a slowdown in wind velocity upstream from a plant, which can be shown to decrease power production. Knowing a priori how a wind farm's layout will generate blockage could help to improve the accuracy of AEP calculations and suggest more blockage-optimal turbine layouts. In this study, we consider the effect of wind plant blockage and perform multiple types of layout optimization to reduce blockage while solving the flow physics using computational fluid dynamics. We present a variety of methods to quantify this blockage effect, ranging from localized measurements taken in the proximity of each turbine to farm-wide integral measurements designed to capture the velocity decrease in a more global way. We then investigate each blockage metric using simple case studies designed to isolate effects due to layout changes. While all metrics show sensitivity during this testing, the integral metrics better avoid spurious effects due to waking and are better suited to evaluating blockage which we find to be a cumulative effect. We then perform multiple farm layout optimizations using these blockage metrics as objectives and find that in the absence of a power production constraint the optimized layouts tend to minimize disturbance to the surrounding flow by creating streamwise rows of turbines. Finally, we present a power-constrained layout optimization which explicitly illustrates the trade-off between designing for blockage and power. This work presents a variety of different blockage definitions and offers a set of recommendations regarding their deployment, expected behavior, and viability for consideration as part of layout planning.
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