Continuous depletion of conventional sources of energy has arisen the need of renewable sources of energy and wind energy is one of the most promising alternative sources in this regard. Wind turbines are utilized to harness wind energy. However, finding the optimal layout (numbers and positions) of wind turbines inside a wind farm is a challenging task as velocity deficits caused by wake effects have to be minimized. In this study, using a linear wake model, a wind farm layout optimization has been carried out with the aim of maximization of energy and the minimization of the investment cost. A novel space decomposition approach is proposed to solve the cost-energy multi-objective optimization problem to determine the optimal layout of turbines. An indexed form of Binary — real coded Non-dominated sorting genetic algorithm, termed as i-NSGA II, has been proposed. The proposed approach is first validated with the rigorously explored literature case study and applied further on a realistic wind farm data. It has been found that the proposed method is able to provide ∼133 % increase in power produced and ∼14% improvement in cost-energy ratio as compared to the benchmark case study. Besides, ∼5% improvement in the cost-energy ratio is also observed as compared to the other reported works. Moreover, in contrast to previous studies, the generated Pareto optimal front (PO) allows a decision maker to choose among various layouts of turbines instead of a single solution.
[1]
Bryony DuPont,et al.
An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm
,
2016
.
[2]
Peng-Yeng Yin,et al.
Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization
,
2017
.
[3]
Krystel K. Castillo-Villar,et al.
A Review of Methodological Approaches for the Design and Optimization of Wind Farms
,
2014
.
[4]
W Huang,et al.
Wind farm micro-siting optimization using novel cell membrane approach
,
2016
.
[5]
Prateek Mittal,et al.
A novel hybrid optimization methodology to optimize the total number and placement of wind turbines
,
2016
.
[6]
Weerakorn Ongsakul,et al.
Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand
,
2016
.
[7]
Carlo Poloni,et al.
Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm
,
1994
.
[8]
R. K. Ursem.
Multi-objective Optimization using Evolutionary Algorithms
,
2009
.
[9]
Victor Parada,et al.
Wind farm layout optimization using a Gaussian-based wake model
,
2017
.