Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Abstract

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

“…Other engineering wake models have also been developed for various wind energy applications [25]. For example, Bastankhah and Porté-Agel [26] proposed a Gaussian shape wake model, which has been applied for offshore wind farm layout optimization in [27]; a similar model has also been developed by Gao et al [10]; Sun and Yang [28] developed an analytical threedimensional wind turbine wake model which accounts for the wind variation in the vertical direction; and more recently Cheng et al [29] developed a new analytical wake model based on the Monin-Obukhov similarity theory. Nevertheless, most engineering wake models, such as the aforementioned ones, only work for wind farms on flat terrain or at offshore sites.…”

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

“…A heuristic is a method of searching for an optimal solution based on probabilistic theory, and mathematical programming is a method of formulating and optimizing the variables and boundary conditions of a problem. Algorithms that use heuristic methods for the WFLO problem include the GA [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], evolutionary strategy [20][21][22], particle swarm optimization [23][24][25], and greedy heuristic [26,27]. Moreover, other works on the development of various algorithms have been conducted [28][29][30][31][32][33].…”

Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

“…Obviously, the predominate wind direction is between 75 • and 105 • . Notice that, although the indicated wind condition is employed for wind farm layout optimization, a finer 3 • wind direction division (angular resolution) will be applied for power output evaluation analysis due to the reported remark of potentially favorable wind power performance with a resolution coarser than 3 • in reference [33,34].…”