  Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and
maximize wind farm power output efficiency, and is a crucial process in the
design of wind energy projects.Since the optimization algorithms typically
require thousands of numerical evaluations of the wake effects, conventional
WFLO studies are usually carried out with the low-fidelity analytical wake
models.In this paper, we develop an optimization framework for wind farm layout
design using CFD-based Kriging model to maximize the annual energy production
(AEP) of wind farms. This surrogate-based optimization (SBO) framework uses
latin hypercube sampling to generate a group of wind farm layout samples, based
on which CFD simulations are carried out to obtain the corresponding AEPs.This
wind farm layout dataset is used to train the Kriging model, which is then
integrated with an optimizer based on genetic algorithm (GA). As the
optimization progresses, the intermediate optimal layout designs are again fed
into the dataset.Such adaptive update of wind farm layout dataset continues
until the algorithm converges.To evaluate the performance of the proposed SBO
framework, we apply it to three representative wind farm cases.Compared to the
conventional staggered layout, the optimized wind farm produces significantly
higher total AEP.In particular, the SBO framework requires significantly
smaller number of CFD calls to yield the optimal layouts that generates almost
the same AEP with the direct CFD-GA method.Further analysis on the velocity
fields show that the optimization framework attempts to locate the downstream
turbines away from the the wakes of upstream ones.The proposed CFD-based
surrogate model provides a more accurate and flexible alternative to the
conventional analytical-wake-model-based methods in WFLO tasks, and has the
potential to be used for designing efficient wind farm projects.
