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Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

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  • Mohanta, Dusmanta Kumar
  • Sadhu, Pradip Kumar
  • Chakrabarti, R.

Abstract

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.

Suggested Citation

  • Mohanta, Dusmanta Kumar & Sadhu, Pradip Kumar & Chakrabarti, R., 2007. "Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 187-199.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:2:p:187-199
    DOI: 10.1016/j.ress.2005.11.062
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    1. Özgür-Ünlüakın, Demet & Türkali, Busenur & Karacaörenli, Ayşe & Çağlar Aksezer, S., 2019. "A DBN based reactive maintenance model for a complex system in thermal power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    2. Luo, Jianing & Li, Hangxin & Wang, Shengwei, 2022. "A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties," Applied Energy, Elsevier, vol. 328(C).
    3. Cholette, Michael E. & Yu, Hongyang & Borghesani, Pietro & Ma, Lin & Kent, Geoff, 2019. "Degradation modeling and condition-based maintenance of boiler heat exchangers using gamma processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 184-196.
    4. Doostparast, Mohammad & Kolahan, Farhad & Doostparast, Mahdi, 2014. "A reliability-based approach to optimize preventive maintenance scheduling for coherent systems," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 98-106.
    5. Hong Zhang & Hao Sun & Qian Zhang & Guanxun Kong, 2018. "Microgrid Spinning Reserve Optimization with Improved Information Gap Decision Theory," Energies, MDPI, vol. 11(9), pages 1-17, September.
    6. Marhavilas, P.K. & Koulouriotis, D.E., 2012. "A combined usage of stochastic and quantitative risk assessment methods in the worksites: Application on an electric power provider," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 36-46.
    7. Ming Tan, Cher & Raghavan, Nagarajan, 2008. "A framework to practical predictive maintenance modeling for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1138-1150.
    8. Perez-Canto, Salvador & Rubio-Romero, Juan Carlos, 2013. "A model for the preventive maintenance scheduling of power plants including wind farms," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 67-75.
    9. 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.
    10. Mohammad Doostparast & Farhad Kolahan & Mahdi Doostparast, 2015. "Optimisation of PM scheduling for multi-component systems – a simulated annealing approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1199-1207, May.
    11. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    12. Liming Yao & Zhongwen Xu & Ziqiang Zeng, 2020. "A Soft‐Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location‐Allocation Optimization," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1863-1886, September.

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