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Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization

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  • Yin, Peng-Yeng
  • Wu, Tsai-Hung
  • Hsu, Ping-Yi

Abstract

Wind farm micro-siting is the decision problem for determining the optimal placement of wind turbines in consideration of the wake effect. Existing micro-siting models seek to minimize the cost of energy (COE). However, little literature addresses the production risk under wind uncertainty. To this end, we develop several versions of the simulation optimization based risk management (SORM) model which embeds the Monte Carlo simulation component for obtaining a large number of samples from the wind probability density function. Our SORM model is flexible and allowing the decision makers to conduct various forms of what-if analysis trading profit, cost and risk according to their business value. Then we propose an enhanced genetic algorithm (EGA) which is customized to the properties of wind farm dimensions. The experimental results show that the EGA can obtain the SORM decision both effectively and efficiently as compared to other metaheuristic approaches. We demonstrate how the risk under wind uncertainty can be effectively handled with our SORM models. The simulations with what-if analyses are conducted to disclose important characteristics of the risky micro-siting problem.

Suggested Citation

  • Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization," Renewable Energy, Elsevier, vol. 107(C), pages 508-521.
    • Handle: RePEc:eee:renene:v:107:y:2017:i:c:p:508-521
    DOI: 10.1016/j.renene.2017.02.036
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    4. Wang, Longyan & Cholette, Michael E. & Tan, Andy C.C. & Gu, Yuantong, 2017. "A computationally-efficient layout optimization method for real wind farms considering altitude variations," Energy, Elsevier, vol. 132(C), pages 147-159.
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    6. Dhunny, A.Z. & Doorga, J.R.S. & Allam, Z. & Lollchund, M.R. & Boojhawon, R., 2019. "Identification of optimal wind, solar and hybrid wind-solar farming sites using fuzzy logic modelling," Energy, Elsevier, vol. 188(C).
    7. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    8. Nagababu, Garlapati & Puppala, Harish & Pritam, Kocherlakota & Kantipudi, MVV Prasad, 2022. "Two-stage GIS-MCDM based algorithm to identify plausible regions at micro level to install wind farms: A case study of India," Energy, Elsevier, vol. 248(C).
    9. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    10. Saraswat, S.K. & Digalwar, Abhijeet K. & Yadav, S.S. & Kumar, Gaurav, 2021. "MCDM and GIS based modelling technique for assessment of solar and wind farm locations in India," Renewable Energy, Elsevier, vol. 169(C), pages 865-884.
    11. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. Vujičić, Slađana & Nikitović, Zorana & Golubović-Stojanović, Aleksandra & Ravić, Nenad & Djuričić, Milan, 2018. "Information system for wind energy trade and gross domestic product (GDP) estimating from small wind farm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 702-706.

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