Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization

Partha P. Biswas, P.N. Suganthan and Gehan A.J. Amaratunga

Renewable Energy, 2018, vol. 115, issue C, 326-337

Abstract: An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study.

Keywords: Wind turbine data; Windfarm turbine placement; Power output; Efficiency; Multi-objective evolutionary algorithm; Hub heights (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:115:y:2018:i:c:p:326-337

DOI: 10.1016/j.renene.2017.08.041

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