IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i20p5381-d428576.html
   My bibliography  Save this article

Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling

Author

Listed:
  • Pavel Y. Gubin

    (Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Vladislav P. Oboskalov

    (Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Anatolijs Mahnitko

    (Institute of Power Engineering, Riga Technical University, LV-1048 Riga, Latvia)

  • Roman Petrichenko

    (Institute of Power Engineering, Riga Technical University, LV-1048 Riga, Latvia)

Abstract

Generator maintenance scheduling presents many engineering issues that provide power system personnel with a variety of challenges, and one can hardly afford to neglect these engineering issues in the future. Additionally, there is vital need for further development of the repair planning task complexity in order to take into account the vast majority of power flow constraints. At present, the question still remains as to which approach is the simplest and most effective, as well as appropriate for further application in the power flow-oriented statement of the repair planning problem. This research compared directed search, differential evolution, and very fast simulated annealing methods based on a number of numerical calculations and made conclusions about their prospective utilization in terms of a more complicated mathematical formulation of the repair planning task. A comparison of results shows that the effectiveness of directed search methods should not be underestimated, and that the pure differential evolution and very fast simulated annealing approaches are not essentially reliable for repair planning. The experimental results demonstrate the perspectivity of unifying single-procedure methods in order to net out risk associated with specific features of these approaches.

Suggested Citation

  • Pavel Y. Gubin & Vladislav P. Oboskalov & Anatolijs Mahnitko & Roman Petrichenko, 2020. "Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling," Energies, MDPI, vol. 13(20), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5381-:d:428576
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/20/5381/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/20/5381/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jie Cai & Shuyu Guo & Shuang Liao & Xing Chen & Shihong Miao & Yaowang Li, 2020. "Optimization Model of Key Equipment Maintenance Scheduling for an AC/DC Hybrid Transmission Network Based on Mixed Integer Linear Programming," Energies, MDPI, vol. 13(4), pages 1-26, February.
    3. 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.
    4. L. Ingber, 1993. "Simulated annealing: Practice versus theory," Lester Ingber Papers 93sa, Lester Ingber.
    5. Wai Foong & Angus Simpson & Holger Maier & Stephen Stolp, 2008. "Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system," Annals of Operations Research, Springer, vol. 159(1), pages 433-450, March.
    6. Volkanovski, Andrija & Mavko, Borut & Boševski, Tome & Čauševski, Anton & Čepin, Marko, 2008. "Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 779-789.
    7. Gokturk Poyrazoglu & HyungSeon Oh, 2019. "Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization," Energies, MDPI, vol. 12(15), pages 1-18, July.
    8. Hyeongon Park & Joonhyung Park & Jong-Young Park & Jae-Haeng Heo, 2017. "Considering Maintenance Cost in Unit Commitment Problems," Energies, MDPI, vol. 10(11), pages 1-12, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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.
    3. Lin, Boliang & Wu, Jianping & Lin, Ruixi & Wang, Jiaxi & Wang, Hui & Zhang, Xuhui, 2019. "Optimization of high-level preventive maintenance scheduling for high-speed trains," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 261-275.
    4. Bergey, Paul K. & Ragsdale, Cliff, 2005. "Modified differential evolution: a greedy random strategy for genetic recombination," Omega, Elsevier, vol. 33(3), pages 255-265, June.
    5. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2020. "Establishing optimal forest harvesting regulation with continuous approximation," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. David Easterling & Layne Watson & Michael Madigan & Brent Castle & Michael Trosset, 2014. "Parallel deterministic and stochastic global minimization of functions with very many minima," Computational Optimization and Applications, Springer, vol. 57(2), pages 469-492, March.
    7. Mayer, D. G. & Belward, J. A. & Burrage, K., 1996. "Use of advanced techniques to optimize a multi-dimensional dairy model," Agricultural Systems, Elsevier, vol. 50(3), pages 239-253.
    8. Rokhforoz, Pegah & Montazeri, Mina & Fink, Olga, 2023. "Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Eryilmaz, Serkan & Navarro, Jorge, 2022. "A decision theoretic framework for reliability-based optimal wind turbine selection," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    10. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    11. Mayer, D. G. & Belward, J. A. & Burrage, K., 2001. "Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models," Agricultural Systems, Elsevier, vol. 69(3), pages 199-213, September.
    12. Van den Broeke, Maud & Boute, Robert & Cardoen, Brecht & Samii, Behzad, 2017. "An efficient solution method to design the cost-minimizing platform portfolio," European Journal of Operational Research, Elsevier, vol. 259(1), pages 236-250.
    13. Vu, Hai Canh & Do, Phuc & Barros, Anne & Bérenguer, Christophe, 2014. "Maintenance grouping strategy for multi-component systems with dynamic contexts," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 233-249.
    14. Do, Phuc & Vu, Hai Canh & Barros, Anne & Bérenguer, Christophe, 2015. "Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 56-67.
    15. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    16. Chuck Holland & Jack Levis & Ranganath Nuggehalli & Bob Santilli & Jeff Winters, 2017. "UPS Optimizes Delivery Routes," Interfaces, INFORMS, vol. 47(1), pages 8-23, February.
    17. L. Ingber, 2018. "Model of Models (MOM)," Lester Ingber Papers 18mo, Lester Ingber.
    18. Podesta, Guillermo & Letson, David & Messina, Carlos & Royce, Fred & Ferreyra, R. Andres & Jones, James & Hansen, James & Llovet, Ignacio & Grondona, Martin & O'Brien, James J., 2002. "Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience," Agricultural Systems, Elsevier, vol. 74(3), pages 371-392, December.
    19. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    20. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5381-:d:428576. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.