Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines

Muhammad Nabeel Hussain, Nadeem Shaukat, Ammar Ahmad, Muhammad Abid, Abrar Hashmi, Zohreh Rajabi, Muhammad Atiq Ur Rehman Tariq

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the meta-heuristic multi-objective elitist teaching–learning based optimization technique is implemented for wind farm layout discrete optimization problem. The optimization of wind farm layout addresses the optimum siting among the wind turbines within the wind farm to accomplish economical, profitable, and technical features. The presented methodology is implemented with multi-objective optimization problem through different targets such as minimizing cost, power output maximization, and the saving of the number of turbines. These targets are investigated with some case studies of multi-objective optimization problems in three scenarios of wind (Scenario-I: fixed wind direction and constant speed, Scenario-II: variable wind direction and constant speed, and Scenario-III: variable wind direction and variable speed) for the optimal micro-siting of wind turbines in a given land area that maximizes the power production while minimizing the total cost. To check the effectiveness of the algorithm, firstly, the results obtained for the three different scenarios have been compared with past studies available in the literature. Secondly, the numbers of turbines have also been optimized by using teaching–learning based optimization. It has been observed that the proposed algorithm shows the optimal layouts along with the optimal number of turbines with minimum fitness evaluation. Finally, the concept of elitism has been introduced in the teaching–learning based optimization algorithm. It is proposed that if elitist-teaching–learning based optimization with elite size of 15% is used, computational expense can be significantly reduced. It can be concluded that that the results obtained by the proposed algorithm are more accurate and advantageous than others.

Original languageEnglish
Article number8458
Pages (from-to)1-24
Number of pages24
JournalSustainability
Volume14
Issue number14
DOIs
Publication statusPublished - Jul 2022

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