Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization. (January 2018)

• Record Type: Journal Article
• Authors: Biswas, Partha P.; Suganthan, P.N.; Amaratunga, Gehan A.J.
• Is Part Of: Renewable energy. Volume 115(2018)
• Page Start: 326
• Page End: 337
• Publication Date: 2018-01
• Subjects: Wind turbine data -- Windfarm turbine placement -- Power output -- Efficiency -- Multi-objective evolutionary algorithm -- Hub heights; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.08.041
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: Windfarm layout optimization is formulated as a multi-objective problem. Maximizing output power and windfarm efficiency are set as the two objectives. Optimizations with same and different hub heights of turbines are performed. Each Pareto solution represents an efficient layout with certain no. of turbines. Any layout from the pool of optimal solutions can be applied based on requirement.