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Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm

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  • Yi-Zeng Hsieh

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Shih-Syun Lin

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • En-Yu Chang

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Kwong-Kau Tiong

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Shih-Wei Tan

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Chiou-Yi Hor

    (Green Energy and System Integration R&D Department, China Steel Corporation, Kaohsiung 81233, Taiwan)

  • Shyi-Chy Cheng

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Yu-Shiuan Tsai

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Chao-Rong Chen

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

The focus of this study is under the auspices of China Steel Corporation, Taiwan, in carrying out the national energy policy of 2025 Non-Nuclear Home. Under this policy, an estimated 600 offshore wind turbines will be installed by 2025. In order to carry out the wind energy project effectively, a preliminary study must be conducted. In this article, we investigated the influence of the wake effect on the efficiency of the turbines’ layout in a windfarm. A distributed genetic algorithm is deployed to study the wind turbines’ layout in order to alleviate the detrimental wake effect. In the current stage of this research, the historical weather data of weather stations near the site of the 29th windfarm, Taiwan, were collected by Academia Sinica. Our wake effect resilient optimized windfarm showed superior performance over that of the conventional windfarm. Additionally, an operation cost minimization process is also demonstrated and implemented using an ant colony optimization algorithm to optimize the total length of the power-carrying interconnecting cables for the turbines inside the optimized windfarm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4125-:d:590767
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    References listed on IDEAS

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    1. Xiaojuan Han & Fang Chen & Xiwang Cui & Yong Li & Xiangjun Li, 2012. "A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology," Energies, MDPI, vol. 5(5), pages 1-20, May.
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    Cited by:

    1. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    2. 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.

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