Journal of Energy Management and Technology

Journal of Energy Management and Technology

Generation Maintenance Scheduling Based on the Remaining Useful Life of Thermal Units Considering Uncertainties via Robust Optimization Approach

Document Type : Original Article

Authors
Mahdi Ramezani 1
Amir Abdollahi 2
Masoud RashidiNejad 1
Ali Riki 1
1 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
10.22109/jemt.2025.537399.1566
Abstract
The efficient and reliable operation of power systems is of critical importance, as unplanned outages resulting from unit failures can cause substantial financial losses, disrupt electricity supply, and compromise grid stability. Maintenance plays a pivotal role in ensuring operational continuity. Traditionally, Generation Maintenance Schedules (GMS) have been either time-based or reactive, which can result in premature servicing or unexpected equipment failures. The prediction of Remaining Useful Life (RUL) addresses these limitations by enabling a more precise estimation of when a component is likely to fail. This paper proposes a comprehensive framework for GMS based on RUL. The degradation states and remaining operational lifespans of thermal units are estimated using a deep learning approach based on Long Short-Term Memory (LSTM) networks. Additionally, a Robust Optimization (RO) method is employed to account for uncertainties in electrical load demand and renewable energy generation. Simulation results demonstrate that the proposed RUL-based GMS framework achieves a 2.41% reduction in total system operation and maintenance costs, highlighting its effectiveness in improving system reliability and cost efficiency.
Keywords
Generation maintenance scheduling
LSTM network
Robust optimization approach
RUL prediction
Subjects
  1. Moyo, N. I. Nwulu, U. E. Ekpenyong, and R. C. Bansal, “A Tri-Objective model for generator maintenance scheduling,” IEEE Access, vol. 9, pp. 136384–136394, Jan. 2021.
  2. Caboz. (Mar. 18, 2019). Business Insider South Africa. Accessed: Jun. 29, 2019. [Online]. Available: https://www.businessinsider.co.za/6-quick-fixes-that-can-save-south-africa-from-more-load-shedding-2019-3.
  3. (May 18, 2019). Interpreting Scedules. Eskom. Accessed: Jun. 29, 2019. [Online]. Available: http://loadshedding.eskom.co.za/LoadShedding/ScheduleInterpretation.
  4. Hassanpour and E. Roghanian, “A two-stage stochastic programming approach for non-cooperative generation maintenance scheduling model design,” International Journal of Electrical Power & Energy Systems, vol. 126, p. 106584, Oct. 2020.
  5. Sharifi, A. Abdollahi, and M. Rashidinejad, “Flexibility-based generation maintenance scheduling in presence of uncertain wind power plants forecasted by deep learning considering demand response programs portfolio,” International Journal of Electrical Power & Energy Systems, vol. 141, p. 108225, Apr. 2022.
  6. Amiri, M. Honarvar, and A. Sadegheih, “Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance,” Energy, vol. 163, pp. 1093–1114, Aug. 2018.
  7. Javidsharifi, T. Niknam, J. Aghaei, and G. Mokryani, “Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices,” Applied Energy, vol. 216, pp. 367–381, Feb. 2018.
  8. Lee, J. Woo, and S. Kim, “A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms,” Applied Energy, vol. 377, Art. 124431, 2025.
  9. Prukpanit, P. Kaewprapha, and N. Leeprechanon, “Optimizing generation maintenance scheduling considering emission factors,” Energies, vol. 16, no. 23, p. 7775, Nov. 2023.
  10. An, G. Si, T. Xia, D. Wang, E. Pan, and L. Xi, “An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs,” Applied Energy, vol. 336, p. 120794, Feb. 2023.
  11. Zhang, X. Wu, B. Cao, X. Wang, and B. Wang, “An efficient generator maintenance scheduling model based on unit clustering and linear relaxation,” Electric Power Systems Research, vol. 230, p. 110300, Mar. 2024.
  12. F. Yu, Q. V. N. Truc, and N. N. Minh, “Integrated maintenance scheduling for generators and transmission lines considering renewable energy sources and energy storages,” Electric Power Systems Research, vol. 246, p. 111696, Apr. 2025.
  13. Liu, Y. Xiao, and J. Yang, “Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power,” Applied Energy, vol. 360, p. 122793, Feb. 2024.
  14. Salarkheili, M. S. Nazar, D. Wozabal, and F. Jabari, “Capacity withholding of GenCos in electricity markets using security-constrained generation maintenance scheduling,” International Journal of Electrical Power & Energy Systems, vol. 146, p. 108771, Nov. 2022.
  15. B. Kivade, N. I. Jamadar, “Prediction of Remaining useful life of Mechanical Components – A Review,” International Journal of Engineering Education, Vol. 2, p. 1-5, Oct. 2014.
  16. Okoh, R. Roy, J. Mehnen, and L. Redding, “Overview of remaining useful life prediction techniques in through-life engineering services,” Procedia CIRP, vol. 16, pp. 158–163, Jan. 2014.
  17. Wang, Y. Zhao, and S. Addepalli, “Remaining Useful Life Prediction using Deep Learning Approaches: A Review,” Procedia Manufacturing, vol. 49, pp. 81–88, Jan. 2020.
  18. She and M. Jia, “A BiGRU method for remaining useful life prediction of machinery,” Measurement, vol. 167, p. 108277, Jul. 2020.
  19. Liang, H. Liu, and N.-C. Xiao, “A hybrid approach based on deep neural network and double exponential model for remaining useful life prediction,” Expert Systems with Applications, vol. 249, Art. 123563, 2024.
  20. Li, D. Yu, V. S. Byg, and S. D. Ioan, “The development of machine learning-based remaining useful life prediction for lithium-ion batteries,” Journal of Energy Chemistry, vol. 82, pp. 103–121, Apr. 2023.
  21. Li, Y. Chen, Z. Hu, and H. Zhang, “Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models,” Reliability Engineering & System Safety, vol. 229, p. 108869, Oct. 2022.
  22. Ma, H. Yin, and K. Wang, “Prediction of the remaining useful life of supercapacitors at different temperatures based on improved Long Short-Term memory,” Energies, vol. 16, no. 14, p. 5240, Jul. 2023.
  23. Lee and M. Mitici, “Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics,” Reliability Engineering & System Safety, vol. 230, p. 108908, Oct. 2022.
  24. Hao, F. Di Maio, and E. Zio, “Monte Carlo Tree Search-based Deep Reinforcement Learning for Flexible Operation & Maintenance Optimization of a Nuclear Power Plant,” Journal of Safety and Sustainability, vol. 1, no. 1, pp. 4–13, Sep. 2023.
  25. Poorvaezi Roukerd, A. Abdollahi, M. Rashidinejad, “Uncertainty-based unit commitment and construction in the presence of fast ramp units and energy storages as flexible resources considering enigmatic demand elasticity,” Journal of Energy Storage, vol. 29, Art. 101290, 2020.
  26. Randall and B. Basciftci, “Risk-averse contextual predictive maintenance and operations scheduling with flexible generation under wind energy uncertainty,” European Journal of Operational Research, vol. 327, no. 1, Art. 06 005, June 2025.
  27. Zhong, N. Fan, and L. Wu, “Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty,” European Journal of Operational Research, vol. 318, no. 1, pp. 179–199, May 2024.
  28. Mirzaei, A. Behbahaninia, A. Abdalisousan, and S. M. M. Lavasani, “A novel approach to repair time prediction and availability assessment of the equipment in power generation systems using fuzzy logic and Monte Carlo simulation,” Energy, vol. 282, p. 128842, Aug. 2023.
  29. A. Moghadam, S. Bagheri, A. H. Salemi, and M. B. Tavakoli, “Long‐term and multi‐objective maintenance scheduling of medium voltage overhead lines based on LP metric method,” IET Generation Transmission & Distribution, vol. 18, no. 7, pp. 1478–1493, Feb. 2024.
  30. Rahmani-Andebili, A. Abdollahi, and M. P. Moghaddam, “An investigation of implementing Emergency Demand Response Program (EDRP) in Unit Commitment problem,” in *Proc. IEEE Power & Energy Society General Meeting*, San Diego, CA, USA, 2011, pp. 1–7.
  31. Salarkheili, M. S. Nazar, D. Wozabal, and F. Jabari, “Capacity withholding of GenCos in electricity markets using security-constrained generation maintenance scheduling,” International Journal of Electrical Power & Energy Systems, vol. 146, p. 108771, Nov. 2022.
  32. Mollahassani-Pour, M. Rashidinejad, A. Abdollahi, and M. A. Forghani, “Demand response Resources’ allocation in Security-Constrained Preventive Maintenance Scheduling via MODM method,” IEEE Systems Journal, vol. 11, no. 2, pp. 1196–1207, Mar. 2016.
  33. W. Chang and H.-J. Lu, “Integrating GRAY Data Preprocessor and Deep Belief Network for Day-Ahead PV Power Output Forecast,” IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 185–194, Dec. 2018.
  34. Yousuf, S. A. Khan, and S. Khursheed, “Remaining useful life (RUL) regression using Long–Short Term Memory (LSTM) networks,” Microelectronics Reliability, vol. 139, p. 114772, Sep. 2022.
  35. Zhu, Z. Yan, W.-J. Lee, X. Yang, Y. Fu, and W. Cao, “Direct load control in microgrids to enhance the performance of integrated resources planning,” IEEE Transactions on Industry Applications, vol. 51, no. 5, pp. 3553–3560, Mar. 2015.
  36. Cho et al., “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” arXiv.org, 2014.
  37. Ben-Tal, A. Goryashko, E. Guslitzer, and A. Nemirovski, “Adjustable robust solutions of uncertain linear programs,” Mathematical Programming, vol. 99, no. 2, pp. 351–376, Mar. 2004.
  38. Hussain, V.-H. Bui, and H.-M. Kim, “Robust Optimization-Based scheduling of Multi-Microgrids considering uncertainties,” Energies, vol. 9, no. 4, p. 278, Apr. 2016.
  39. Mollahassani-Pour, A. Abdollahi, and M. Rashidinejad, “Investigation of Market-Based Demand Response Impacts on Security-Constrained Preventive Maintenance scheduling,” IEEE Systems Journal, vol. 9, no. 4, pp. 1496–1506, Jan. 2014.
  40. Subcommittee, “IEEE Reliability Test System,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047–2054, Nov. 1979.
  41. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” 2008 International Conference on Prognostics and Health Management, Oct. 2008.
Journal of Energy Management and Technology
Volume 9, Issue 2
Spring 2025
Pages 129-138

  • Receive Date 05 August 2025
  • Revise Date 22 August 2025
  • Accept Date 10 September 2025