Hussain, Muhammad Nabeel ORCID: 0000-0002-2652-9741, Shaukat, Nadeem ORCID: 0000-0002-4655-0476, Ahmad, Ammar, Abid, Muhammad, Hashmi, Abrar ORCID: 0000-0002-8683-7965, Rajabi, Zohreh ORCID: 0000-0002-7479-7652 and Tariq, Muhammad Atiq Ur Rehman M ORCID: 0000-0002-0226-7310 (2022) Micro-siting of wind turbines in an optimal wind farm area using teaching–learning-based optimization technique. Sustainability (Switzerland), 14 (14). ISSN 2071-1050

Abstract

Nowadays, wind energy is receiving considerable attention due to its availability, low cost, and environment-friendly operation. Wind turbines are rarely placed individually but rather in the form of a wind farm with a group of several wind turbines. The purpose of this research is to perform studies on wind turbine farms in order to find the best distribution for wind turbines that maximizes the produced power, hence minimizing the wind farm area. Wind Farm Area Optimization (WFAO) is performed for optimal placement of wind turbines using elitist teaching–learning-based optimization (ETLBO) techniques. Three different scenarios of wind (first is fixed wind direction and constant speed, second is variable wind direction and constant speed, and third is variable wind direction and variable speed) are considered to find the optimal number of turbines and turbine positioning in a minimized squared land area that maximizes the power production while minimizing the total cost. Other research carried out in the past was to find the optimal placement of the wind turbines in a fixed squared land area of (Formula presented.). In the present study, WFAO–ETLBO algorithm has been implemented to get the optimal land area for the placement of the same number of turbines used in the past research. For Case 1, there is a significant reduction in land area by approximately 30.75%, 45.25%, and 51.75% for each wind scenario, respectively. For Case 2, the reductions in land area for three different wind scenarios are respectively 30.75%, 7.2%, and 7.2%. For Case 3, there is a reduction of 7.2% in land area for each wind scenario. It has been observed that the results obtained by the WFAO–ETLBO algorithm with a significant reduction in the land area along with optimal placement of wind turbines are better than the results obtained from the wind turbines placement in the fixed land area of (Formula presented.).

Search Google Scholar

Repository staff login