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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. 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).
    5. 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.
    6. Ju, Xinglong & Liu, Feng, 2019. "Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation," Applied Energy, Elsevier, vol. 248(C), pages 429-445.
    7. Atul Patil & Chaitanya Pathak & Bejoy Alduse, 2023. "Review of Natural Hazard Risks for Wind Farms," Energies, MDPI, vol. 16(3), pages 1-29, January.
    8. 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).
    9. 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).
    10. Cansu Kandemir & Holly A. H. Handley, 2019. "Work process improvement through simulation optimization of task assignment and mental workload," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 389-427, December.
    11. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
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