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An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III

Author

Listed:
  • Senthilkumar Subramanian

    (Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India)

  • Chandramohan Sankaralingam

    (Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India)

  • Rajvikram Madurai Elavarasan

    (Clean and Resilient Energy Systems Laboratory, Texas A&M University, Galveston, TX 77553, USA)

  • Raghavendra Rajan Vijayaraghavan

    (Research and Development Laboratory, Innovate Educational Institute, Chennai 600069, India)

  • Kannadasan Raju

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India)

  • Lucian Mihet-Popa

    (Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway)

Abstract

Wind energy is an abundant renewable energy resource that has been extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for a wind energy system. The proposed algorithm consists of non-dominated sorting which focuses to maximize the power extraction of the wind turbine, minimize the cost of generating energy, and the lifetime of the battery. Additionally, the performance characteristics of the wind turbine and battery energy storage system (BESS) are analyzed specifically torque, current, voltage, state of charge (SOC), and internal resistance. The complete analysis is carried out in the MATLAB/Simulink platform. The simulated results are compared with existing optimization techniques such as single-objective, multi-objective, and non-dominating sorting GA II (Genetic Algorithm-II). From the observed results, the non-dominated sorting genetic algorithm (NSGA III) optimization algorithm offers superior performance notably higher turbine power output with higher torque rate, lower speed variation, reduced energy cost, and lesser degradation rate of the battery. This result attested to the fact that the proposed optimization tool can extract a higher rate of power from a self-excited induction generator (SEIG) when compared with a conventional optimization tool.

Suggested Citation

  • Senthilkumar Subramanian & Chandramohan Sankaralingam & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan & Kannadasan Raju & Lucian Mihet-Popa, 2021. "An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III," Sustainability, MDPI, vol. 13(1), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:1:p:410-:d:474813
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    References listed on IDEAS

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    Cited by:

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    2. Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    3. Balasubbareddy Mallala & Venkata Prasad Papana & Ravindra Sangu & Kowstubha Palle & Venkata Krishna Reddy Chinthalacheruvu, 2022. "Multi-Objective Optimal Power Flow Solution Using a Non-Dominated Sorting Hybrid Fruit Fly-Based Artificial Bee Colony," Energies, MDPI, vol. 15(11), pages 1-16, June.
    4. Varadharajan Sankaralingam Sriraja Balaguru & Nesamony Jothi Swaroopan & Kannadasan Raju & Mohammed H. Alsharif & Mun-Kyeom Kim, 2021. "Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors," Sustainability, MDPI, vol. 13(4), pages 1-31, February.
    5. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
    6. Chen, Jie & Huang, Shoujun & Shahabi, Laleh, 2021. "Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm," Applied Energy, Elsevier, vol. 298(C).

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