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Wind farm layout optimization (WFLO) seeks to alleviate the wake loss andmaximize wind farm power output efficiency, and is a crucial process in thedesign of wind energy projects.Since the optimization algorithms typicallyrequire thousands of numerical evaluations of the wake effects, conventionalWFLO studies are usually carried out with the low-fidelity analytical wakemodels.In this paper, we develop an optimization framework for wind farm layoutdesign using CFD-based Kriging model to maximize the annual energy production(AEP) of wind farms. This surrogate-based optimization (SBO) framework useslatin hypercube sampling to generate a group of wind farm layout samples, basedon which CFD simulations are carried out to obtain the corresponding AEPs.Thiswind farm layout dataset is used to train the Kriging model, which is thenintegrated with an optimizer based on genetic algorithm (GA). As theoptimization progresses, the intermediate optimal layout designs are again fedinto the dataset.Such adaptive update of wind farm layout dataset continuesuntil the algorithm converges.To evaluate the performance of the proposed SBOframework, we apply it to three representative wind farm cases.Compared to theconventional staggered layout, the optimized wind farm produces significantlyhigher total AEP.In particular, the SBO framework requires significantlysmaller number of CFD calls to yield the optimal layouts that generates almostthe same AEP with the direct CFD-GA method.Further analysis on the velocityfields show that the optimization framework attempts to locate the downstreamturbines away from the the wakes of upstream ones.The proposed CFD-basedsurrogate model provides a more accurate and flexible alternative to theconventional analytical-wake-model-based methods in WFLO tasks, and has thepotential to be used for designing efficient wind farm projects.

An optimization framework for wind farm layout design using CFD-based Kriging model