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Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms

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  • Dhiman, Harsh S.
  • Deb, Dipankar

Abstract

Wind energy installation numbers have witnessed a sharp increase in the recent past. Additionally, wind farms are seen as an effective and potent part of the interconnected power system. Significant variations in the wind speed pose a challenge for wind farm operators to provide accurate forecasts. In this manuscript, three hybrid wind farms, each comprising of wind turbines and battery energy storage systems, are located in the vicinity of each other and are assumed to deliver power to a utility grid. Fuzzy-based Multi-criteria decision-making techniques are applied to this cluster of three hybrid wind farms to determine the best strategy. Machine learning-based LSSVR method is utilized for wind speed forecasting and penalty cost estimation. Fuzzy TOPSIS and Fuzzy COPRAS evaluated and potential reversal of rankings is also explored. With a cumulative priority score of 0.4573 and 99.3 for dataset X1, both, fuzzy TOPSIS and fuzzy COPRAS respectively indicate that A3, that is, paying penalty for power borrowed from a neighboring wind farm is the best alternative for a hybrid wind farm. This study gives new insights into decision-making, specifically for hybrid wind farms.

Suggested Citation

  • Dhiman, Harsh S. & Deb, Dipankar, 2020. "Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308628
    DOI: 10.1016/j.energy.2020.117755
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    References listed on IDEAS

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    1. Henriot, Arthur, 2015. "Economic curtailment of intermittent renewable energy sources," Energy Economics, Elsevier, vol. 49(C), pages 370-379.
    2. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    3. Beşkirli, Mehmet & Koç, İsmail & Haklı, Hüseyin & Kodaz, Halife, 2018. "A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 301-308.
    4. Jafarian, M. & Ranjbar, A.M., 2010. "Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine," Renewable Energy, Elsevier, vol. 35(9), pages 2008-2014.
    5. Onar, Sezi Cevik & Oztaysi, Basar & Otay, İrem & Kahraman, Cengiz, 2015. "Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets," Energy, Elsevier, vol. 90(P1), pages 274-285.
    6. Shafiqur Rehman & Salman A. Khan, 2016. "Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia," Energies, MDPI, vol. 9(11), pages 1-26, October.
    7. Cavallaro, Fausto & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Mardani, Abbas, 2019. "Assessment of concentrated solar power (CSP) technologies based on a modified intuitionistic fuzzy topsis and trigonometric entropy weights," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 258-270.
    8. Sánchez-Lozano, J.M. & García-Cascales, M.S. & Lamata, M.T., 2016. "GIS-based onshore wind farm site selection using Fuzzy Multi-Criteria Decision Making methods. Evaluating the case of Southeastern Spain," Applied Energy, Elsevier, vol. 171(C), pages 86-102.
    9. Wu, Yunna & Xu, Chuanbo & Zhang, Buyuan & Tao, Yao & Li, Xinying & Chu, Han & Liu, Fangtong, 2019. "Sustainability performance assessment of wind power coupling hydrogen storage projects using a hybrid evaluation technique based on interval type-2 fuzzy set," Energy, Elsevier, vol. 179(C), pages 1176-1190.
    10. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences," Energy, Elsevier, vol. 168(C), pages 450-461.
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