A Jaya-based approach to wind turbine placement problem

ABSTRACT

Renewable energy resources are natural, clean, economical, and never-ending energy resources. Wind energy is an important clean, cheap, and easy applicable energy sources. On account of this, generation of the energy from wind technology has been raised day by day because of the competition with fossil-fuel power production methods. By depending on increases the number of turbines located in the wind farm, the average power obtains from each wind turbine appreciable reduces due to the existence of wake effects within the wind farm. Therefore, the optimal placement of turbines in a wind farm provides to get optimum wind energy from the wind farm. When the place where the wind turbines are located is considered as NxN grid, a wind turbine can be established to each cell of this grid. Whether a wind turbine is replaced to each cell of the grid or not can be modeled as a binary-based optimization problem. In this study, a Jaya-based binary optimization algorithm is proposed to determine which cells are used for wind turbine replacement. In order to justify the efficiency of the proposed approach, two different test cases are considered, and the solutions produced by the proposed approach are compared with the solutions of the swarm intelligence or evolutionary computation methods. According to the experiments and comparisons the Jaya-based binary approach shows a superior performance than compared approaches in terms of cost and power effectiveness. While the efficiency of the Jaya-based approach is 92.2% with 30 turbines replacement on 10 × 10 grid, the efficiency of the Jaya-based binary method is 95.7% with 43 turbines replacement on 20 × 20 grid.

KEYWORDS:

• Clean energy
• Jaya
• renewable energy
• wind energy
• wind turbine replacement

Highlights

• Jaya-based an effective solution proposed for wind farm layout optimization.

• Jaya-based algorithm (for short JayaX) has been analyzed on two different types as $10\times 10$ grid with $100$ possible squares and as $20\times 20$ grid with $400$ possible squares for placement the wind turbines.

• JayaX is compared with state-of-art population-based algorithms.

• The proposed algorithm shows better performance in terms of solution quality and robustness.

Nomenclature

$\mathit{a}$ Axial induction factor$r_r$ Rotor radius$\mathit{z}$ Hub height, $\mathit{m}$$C_T$ Thrust coefficient$z_0$ Surface roughness, $\mathit{m}$$u_0$ Wind velocity, $\mathit{m}/\mathit{s}$$\mathit{\alpha }$ Entrainment constant

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Murat Aslan

Murat Aslan received B.Sc. and M.Sc degrees in computer engineering from Selçuk University in 2011 and 2017 respectively. Recently, he is a research assistant in the Department of the Computer Engineering at Sirnak University and working on his Ph.D. dissertation at Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University. His current interests include applications of Graph Theory, Discrete and Binary Optimization Problems, Swarm Intelligence or Evolutionary Computation Algorithms.

Mesut Gunduz

Mesut Gunduz received M.Sc degree in computer engineering from Selçuk University in 2000, and received Ph.D. degree in Electrical and Electronic Engineering from Selçuk University in 2006. Recently, he is an Associate Professor in the Department of the Computer Engineering at Konya Technical University. His current interests include applications of optimization problems, Artificial Intelligence Algorithms and Machine Learning.

Mustafa Servet Kiran

Mustafa Servet Kiran received B.Sc., M.Sc and Ph.D. degrees in computer engineering from Selçuk University in 2007, 2010 and 2014 respectively. Recently, he is an Associate Professor in the Department of the Computer Engineering at Konya Technical University. His current interests include applications of optimization problems, swarm intelligence Algorithms, Machine Learning and Parallel Computing.