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Optimization Chapter | 9 243
FIG. 9.3 Layout of the Middelgrunden offshore wind farm in various scenarios: (B) actual, (C)
optimized with constraints on the arrangement, and (D) optimized without constraints on locations
of the wind turbines [4,5]. (A) is the yearly wind energy rose.
farm within these recommended areas. Macro-siting can also be treated as an
optimization problem, which is not discussed here.
After selection of a location/plot for an offshore wind farm, the best layout of
turbines within a farm needs to be determined. The layout, in general, consists
of the number of wind turbines, and the size and geographical location of each
turbine. In many cases, the maximum investment, or the capacity of a farm,
is decided in the first steps of a study as a constraint; therefore, if the size of
the individual turbines is also established, the optimization problem reduces
to finding the best/optimum geographical location for each turbine. This is
referred to as micro-siting of offshore wind farms. Fig. 9.3 shows an example
of wind farm optimization that can lead to around 5% increase in annual energy
production (AEP; [4]).
Wake Effect
The layout of wind turbines in a wind farm affects AEP of the array. As we
discussed in Chapter 4, wind has spatial variability; therefore, if the farm is large
enough, in some places wind energy is higher than other places. If variability of
the available resource was the only reason, we could just locate wind turbines in
places that have the highest energy. A more complicated issue is the interaction
of a wind turbine with neighbouring turbines. Fig. 9.4 shows how turbines can
be located in the wake of each other under particular wind conditions. The wind
speed in the wake of a turbine is significantly lower than the undisturbed wind
speed (in the absence of the upwind turbine). This reduction of speed adversely
affects total AEP. It is possible to place turbines very far from each other to avoid
wake effects, but this can lead increased cost of cabling, electrical connections