Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

Abstract

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.

Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia

“…As far as wind farm layout is concerned, many studies have utilized the aforementioned multi-criteria approaches. Use of weighted aggregation has been reported in various studies [34][35][36][37], whereas Pareto ranking has also been used in a number of research works [38][39][40][41][42]. In addition, a comprehensive literature review covering various aspects of wind farm design has been reported in various research articles [19,21,43,44].…”

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

“…An optimal solution to a multi-objective problem is one of a set of feasible solutions that satisfies the objectives to an acceptable level (i.e., where the trade-off between two or more objectives is the best). Only a few works in the WFDO problem [154,159,163,202,209,250,264] multi-objective approaches. In addition, as pointed out in [153,154], many different multi-objective formulations have not been implemented and/or tested for the solution of the WFDO problem.…”

“…Population-based algorithms rely on the idea in which the best characteristics of the best individuals can be combined to produce a better individual. The Genetic Algorithm (GA) is the most used population-based metaheuristic in the WFDO problem [56,134,135,156,[159][160][161]163,165,199,200,207,235,237,245,250,254,278,301,307,[353][354][355][356][357]. The GA [327,346] refers to a class of adaptive search procedures based on the principles derived from natural evolution and genetics.…”

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

“…There are very few other methods that also account for aspects of turbine selection in tandem with turbine placement; one other example includes the method presented by Chen et al [10], which considers differing hub-heights as an optimization parameter in maximizing wind farm power output. Other powerful wind farm layout optimization methods include those developed in [11][12][13][14][15][16][17][18][19]. The majority of these methods, while providing important and diverse capabilities in the context of turbine micro-siting for a single wind farm, do not simultaneously focus turbine type selection during the layout optimization process, as is required in the explorations presented in this paper.…”

“…The growth of the wake downwind of a turbine, also accounting for wake merging scenarios, is determined using the wake growth model proposed by [27]. The corresponding energy deficit downwind of a turbine is determined using the velocity deficit model developed by [28], which is widely used in wind farm power generation estimation [14,15,[29][30][31]. The UWFLO power generation model also accounts for the possibility of a turbine being 'partially' in the wake of another turbine located upwind.…”