Wind farm layout optimization for levelized cost of energy minimization with combined analytical wake model and hybrid optimization strategy

Wind farm layout optimization is a crucial stage for wind farm projects. Most of previous studies aim to maximize the power generation by optimizing turbine locations using analytical wake models and heuristic optimization techniques. However, the performance of analytical wake models was not properly evaluated before being applied on the layout optimization, which may lead a large discrepancy. The efficiency of strategies to solve this optimization problem also needs to be improved with the increasing scale of wind farm. Moreover, the minimization of levelized cost of energy of wind farm is considered to be a more appropriate optimization objective than the power maximization. In this study, the combined analytical wake model considering wake loss, added turbulence and wake superposition model is presented and evaluated by comparing with numerical simulation data. A hybrid optimization strategy assembling genetic algorithm and particle swarm optimization is proposed and used to minimize the levelized cost of energy under different scenarios. The results show that the power generation is underestimated by up to 31% if neglecting the added turbulence model and the combined analytical wake model has an averaged error of 3%. The levelized cost of energy is decreased by 1.9% and the annual energy production, capacity factor and efficiency are improved by 2.3%, 2.3% and 1.3% respectively after the layout optimization for the full various wind scenario with 49 wind turbines. The proposed hybrid optimization strategy increases the optimization effect by up to 0.9% compared with the individual genetic algorithm strategy and improves the efficiency by up to 26% than the individual particle swarm optimization strategy.