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Modelling generator maintenance scheduling costs in deregulated power markets

Author

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  • Dahal, Keshav
  • Al-Arfaj, Khalid
  • Paudyal, Krishna

Abstract

Generating companies use the maintenance cost function as the sole or main objective for creating the maintenance schedule of power generators. Usually only maintenance activities related costs are considered to derive the cost function. However, in deregulated markets, maintenance related costs alone do not represent the full costs of generators. This paper models various cost components that affect the maintenance activities in deregulated power markets. The costs that we model include direct and indirect maintenance, failures, interruptions, contractual compensation, rescheduling, and market opportunity. The loss of firm’s reputation and selection of loyalty model are also considered using the Analytic Hierarchy Process (AHP) within an opportunity cost model. A case study is used to illustrate the modelling activities. The enhanced model is utilised in generator maintenance scheduling cases. The experimental results demonstrate the importance and impact of market related costs in maintenance schedules.

Suggested Citation

  • Dahal, Keshav & Al-Arfaj, Khalid & Paudyal, Krishna, 2015. "Modelling generator maintenance scheduling costs in deregulated power markets," European Journal of Operational Research, Elsevier, vol. 240(2), pages 551-561.
    • Handle: RePEc:eee:ejores:v:240:y:2015:i:2:p:551-561
    DOI: 10.1016/j.ejor.2014.07.008
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    References listed on IDEAS

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    Cited by:

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    2. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Liang Shen & Fei Lin & T. C. E. Cheng, 2022. "Low-Carbon Transition Models of High Carbon Supply Chains under the Mixed Carbon Cap-and-Trade and Carbon Tax Policy in the Carbon Neutrality Era," IJERPH, MDPI, vol. 19(18), pages 1-21, September.
    4. Ivlev, Ilya & Vacek, Jakub & Kneppo, Peter, 2015. "Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty," European Journal of Operational Research, Elsevier, vol. 247(1), pages 216-228.
    5. Wenchang Luo & Rylan Chin & Alexander Cai & Guohui Lin & Bing Su & An Zhang, 2022. "A tardiness-augmented approximation scheme for rejection-allowed multiprocessor rescheduling," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 690-722, August.
    6. Wang, Dujuan & Yin, Yunqiang & Cheng, T.C.E., 2018. "Parallel-machine rescheduling with job unavailability and rejection," Omega, Elsevier, vol. 81(C), pages 246-260.
    7. Pablo David Necoechea-Porras & Asunción López & Juan Carlos Salazar-Elena, 2021. "Deregulation in the Energy Sector and Its Economic Effects on the Power Sector: A Literature Review," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    8. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    9. Omid Sadeghian & Arash Moradzadeh & Behnam Mohammadi-Ivatloo & Mehdi Abapour & Fausto Pedro Garcia Marquez, 2020. "Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach," Energies, MDPI, vol. 13(11), pages 1-25, June.
    10. Yin, Yunqiang & Cheng, T.C.E. & Wang, Du-Juan, 2016. "Rescheduling on identical parallel machines with machine disruptions to minimize total completion time," European Journal of Operational Research, Elsevier, vol. 252(3), pages 737-749.

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