Combining economic and fluid dynamic models to determine the optimal spacing in very large wind-farms

Stevens, R.J.A.M.; Hobbs, B.; Ramos, A.; Meneveau, C.

doi:10.1002/we.2016

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind-farms

R.J.A.M. Stevens, B. Hobbs, A. Ramos, C. Meneveau, Wind Energy 20 (3), 465-477 (2017).

Main finding

Under the reference cost-minimization parameterization, conditional optima are approximately 16D offshore and 12D onshore. For profit per fixed area, greater revenue relative to costs shifts the optimum inward: roughly above 16D or 14D at revenue parameter 1.25, near 10D at 1.5, and near 7D at 2.

Optimal wind farm spacing at three revenue parameters with the dependence beneath — **How to read the figure.** Profit per unit area against turbine spacing at three values of the revenue parameter (a-c), with the dependence of the optimum on that parameter beneath (d, e). As revenue rises relative to cost the optimum moves inward, from wider than 14 diameters to about 10 and then about 7. Optimal spacing is a property of the objective and its cost assumptions, not of the flow alone. Figure 7 gives the absolute optima under cost minimisation: about 16 diameters offshore and 12 onshore. Open the full-resolution figure. Figure 4. R.J.A.M. Stevens et al. (2017). No separate licence is stated here; consult the original publication and credited source before reuse.

Why this matters

Supports the qualitative conclusion that “optimal spacing” is objective- and cost-dependent.

Research context

The model balances turbine density against wake loss, infrastructure and land costs, and an assumed turbulence-related maintenance penalty. Its cost inputs are approximate and site dependent, and it omits uncertainty distributions and several project-specific constraints.

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