| dc.description.abstract | This work presents an approach for optimizing Vertical Axis Wind Turbine (VAWT) clusters and Wind Farm Layout Optimization (WFLO), employing a combination of Computational Fluid Dynamics (CFD) simulations and a Genetic Algorithm (GA) to maximize the Annual Energy Production (AEP) from a wind farm comprising Norhybrid 3.7 kW VAWTs.
An optimized arrangement for the VAWT cluster was determined using Three-Dimensional (3D) CFD simulations. The cluster configuration was defined by an array angle of 60 degrees and a spacing of two turbine diameters (D) between the downwind (DW) and respective upwind (UW) turbines. This arrangement was in turn incorporated into a GA to improve the layout of a wind farm.
The GA was employed to evaluate potential cluster positions, utilizing selection, crossover, and mutation processes to gradually evolve towards the optimal solution. Despite variations in population sizes, generations, and wind conditions across multiple iterations, the algorithm consistently demonstrated its robustness by converging on similar solutions.
In the GA WFLO case study involving 15 Norhybrid 3.7 kW VAWTs, the optimized wind farm layout was estimated to result in an AEP of 65643.13 kWh. The capacity factor was calculated to be around 13.5\%. This relatively low value was recognized as a constraint to the wind park feasibility, and could be attributed to numerous factors inherent in the multifaceted optimization problem. Conservative wind speed values for the studies were highlighted.
The work underscores the viability and flexibility of using GA in combination with CFD simulations for the optimization of VAWT clusters and WFLO. It also emphasizes the importance of accurate input data, validation, modeling and wind resource assessment for proper performance estimations. | |