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

Author

Listed:
  • Muhammad Nabeel Hussain

    (Department of Mechanical Engineering, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan)

  • Nadeem Shaukat

    (Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan
    Department of Physics and Applied Mathematics, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan)

  • Ammar Ahmad

    (Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan)

  • Muhammad Abid

    (Department of Mechanical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
    Interdisciplinary Research Center, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan)

  • Abrar Hashmi

    (Department of Electrical Engineering, Capital University and Technology, Islamabad 45750, Pakistan)

  • Zohreh Rajabi

    (Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia)

  • Muhammad Atiq Ur Rehman Tariq

    (Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia
    College of Engineering, IT & Environment, Charles Darwin University, Darwin, NT 0810, Australia)

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.

Suggested Citation

  • Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8458-:d:859893
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    References listed on IDEAS

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    1. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    2. Pookpunt, Sittichoke & Ongsakul, Weerakorn, 2013. "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, Elsevier, vol. 55(C), pages 266-276.
    3. Yi-Zeng Hsieh & Shih-Syun Lin & En-Yu Chang & Kwong-Kau Tiong & Shih-Wei Tan & Chiou-Yi Hor & Shyi-Chy Cheng & Yu-Shiuan Tsai & Chao-Rong Chen, 2021. "Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm," Energies, MDPI, vol. 14(14), pages 1-17, July.
    4. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm," Energy, Elsevier, vol. 73(C), pages 430-442.
    5. Zergane, Saïd & Smaili, Arezki & Masson, Christian, 2018. "Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method," Renewable Energy, Elsevier, vol. 125(C), pages 166-171.
    6. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    7. Song, Dongran & Liu, Junbo & Yang, Jian & Su, Mei & Wang, Yun & Yang, Xuebing & Huang, Lingxiang & Joo, Young Hoon, 2020. "Optimal design of wind turbines on high-altitude sites based on improved Yin-Yang pair optimization," Energy, Elsevier, vol. 193(C).
    8. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    9. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    10. Hegazy Rezk & Ahmed Fathy & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2019. "The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms," Energies, MDPI, vol. 12(22), pages 1-19, November.
    11. Paxis Marques João Roque & Shyama Pada Chowdhury & Zhongjie Huan, 2021. "Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study," Energies, MDPI, vol. 14(14), pages 1-22, July.
    12. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    13. Kuo, Jim Y.J. & Romero, David A. & Amon, Cristina H., 2015. "A mechanistic semi-empirical wake interaction model for wind farm layout optimization," Energy, Elsevier, vol. 93(P2), pages 2157-2165.
    14. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    15. El-Shorbagy, M.A. & Mousa, A.A. & Nasr, S.M., 2016. "A chaos-based evolutionary algorithm for general nonlinear programming problems," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 8-21.
    16. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    17. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    18. Wagner, Markus & Day, Jareth & Neumann, Frank, 2013. "A fast and effective local search algorithm for optimizing the placement of wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 64-70.
    19. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2018. "Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming," Renewable Energy, Elsevier, vol. 126(C), pages 341-353.
    20. Mamdouh Abdulrahman & David Wood, 2019. "Wind Farm Layout Upgrade Optimization," Energies, MDPI, vol. 12(13), pages 1-25, June.
    21. Biswas, Partha P. & Suganthan, P.N. & Amaratunga, Gehan A.J., 2018. "Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization," Renewable Energy, Elsevier, vol. 115(C), pages 326-337.
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