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A hybrid risk analysis model for wind farms using Coloured Petri Nets and interpretive structural modelling

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  • Zeinalnezhad, Masoomeh
  • Chofreh, Abdoulmohammad Gholamzadeh
  • Goni, Feybi Ariani
  • Hashemi, Leila Sadat
  • Klemeš, Jiří Jaromír

Abstract

This research presents an integrated approach combining Coloured Petri Nets and Interpretive Structural Modelling, called hybrid ISM-CPN model, to risk assessment of wind farms development. The dynamic nature of the component elements of wind farms and considering the risks interdependencies motivate this combination. A questionnaire survey targeting experts is conducted for calculating the modified Risk Priority Numbers (RPNs). Thirty-four factors are ranked, and the nine critical risks identified as “Political instability”, “Sanctions”, “Economic insecurity”, “Interest rate fluctuations”, “Exchange rate fluctuations”, “Inflation rate fluctuations”, “Feasibility risk”, “Shortage of capital risk”, and “Supplier risk”. The values of RPNT = 260.26, RPNC = 251.31, and RPNQ = 238.77 indicate that “Exchange rate fluctuations” is the most important critical risk. The second one, is “Political instability” with RPNT = 255.35, RPNC = 247.28, and RPNQ = 230.56. The simulation results of a 50 MW wind farm reveal, with a 90% confidence level, “Sanctions” would cause 43.9% increase in project execution time and 28% decrease in project quality, and “Shortage of capital risk” has the greatest impact on project cost, with a 25.79% increase. This work further proposes several strategies to respond to the CRs and concludes that investments in REs can support post-COVID-19 economic recovery.

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  • Zeinalnezhad, Masoomeh & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Hashemi, Leila Sadat & Klemeš, Jiří Jaromír, 2021. "A hybrid risk analysis model for wind farms using Coloured Petri Nets and interpretive structural modelling," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009440
    DOI: 10.1016/j.energy.2021.120696
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    Cited by:

    1. Chong, Cheng Tung & Fan, Yee Van & Lee, Chew Tin & Klemeš, Jiří Jaromír, 2022. "Post COVID-19 ENERGY sustainability and carbon emissions neutrality," Energy, Elsevier, vol. 241(C).
    2. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    3. Hasan Mahmud & Joyashree Roy, 2021. "Barriers to Overcome in Accelerating Renewable Energy Penetration in Bangladesh," Sustainability, MDPI, vol. 13(14), pages 1-28, July.
    4. Nadirah Hazwani Najib & Syuhaida Ismail & Rohayah Che Amat & Serdar Durdyev & Zdeňka Konečná & Abdoulmohammad Gholamzadeh Chofreh & Feybi Ariani Goni & Chitdrakantan Subramaniam & Jiří Jaromír Klemeš, 2022. "Stakeholders’ Impact Factors of the COVID-19 Pandemic on Sustainable Mixed Development Projects: A Systematic Review and Meta-Analysis," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    5. Muhammad Zeeshan Shaukat & Madiha Saleem & Muhammad Usman Ajmal Mirza & Abdul Basit & Abdul Aziz Khan Niazi, 2023. "Using Interpretive Structural Modelling (ISM) to Impose Hierarchy on Critical Issues of Contractual Bargaining: A Study of Construction Industry of Pakistan," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(3), pages 69-84.
    6. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).

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