Open AccessCuckoo search for wind farm optimization with auxiliary infrastructure
Author(s) -
Afanasyeva Svetlana,
Saari Jussi,
Pyrhönen Olli,
Partanen Jarmo
Publication year - 2018
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2199
Subject(s) - cuckoo search , wind power , dijkstra's algorithm , genetic algorithm , turbine , mathematical optimization , benchmark (surveying) , metaheuristic , population , computer science , shortest path problem , engineering , particle swarm optimization , mathematics , geography , electrical engineering , mechanical engineering , graph , demography , geodesy , theoretical computer science , sociology
This paper focuses on the optimization problem of a wind farm layout. This area of research is currently receiving widespread attention, as optimal positioning of the turbines promotes the financial viability of the wind farm and enhances the competitiveness of wind projects in the energy market. In this work, cuckoo search (CS), a modern population‐based metaheuristic optimization algorithm, is used. The objective is to find the turbine layout and types that maximize the net present value of the wind farm, while constraints on the turbine positions have to be met. The following constraints are considered: Firstly, the minimum distance between turbines for safe operation; secondly, a realistic wind farm shape including forbidden zones for installation and the existing infrastructure. Furthermore, the optimization of the wind farm includes an algorithm to find the least expensive layout of the wind farm roads and the electrical collector system. The algorithm is based on Dijkstra's shortest path and Prim's minimum spanning tree algorithms. The test results indicate that the infrastructure cost has a significant effect on the optimum wind farm solution. A genetic algorithm, commonly applied to wind farm micro‐siting problems, is used to benchmark the performance of the CS. The results show that the CS is capable of consistently finding better solutions than the genetic algorithm.