Abstract
Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.
Highlights
In single-objective optimisation, we are looking for one solution only
We claim that for this particular problem, PESA-II did not perform very well. This may be due to the fact that the PESA-II algorithm fitness value is assigned to the hyperboxes, contrary to NSGA-II and strength Pareto evolutionary algorithm 2 (SPEA2) where the fitness value is assigned to the individual solution
This is due to the fact that in case 1, only 10 wind turbines in the top row were upstream wind turbines, and in case 2, around 20 wind turbines were located on the edges, mostly not encountering the wake effect
Summary
To reduce the wake effect, we can optimise the positioning of the wind turbines on the farm This was first accomplished by Mosetti et al [3] in 1994, where they minimised the cost to power ratio under three different wind scenarios using a genetic algorithm. Şişbot et al [15] minimised the cost and maximised the power output of the wind farm on Gökceada Island using a MOGA [16] Such algorithms use a population of candidate solutions and evolve them toward the optimal trade-off between problem objectives using a fitness-based selection mechanism. A comparison of the results will be performed, and in the last section, we will provide the reader with our conclusions
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