IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2302-d354451.html
   My bibliography  Save this article

Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes

Author

Listed:
  • Alberto Reyes

    (Instituto Nacional de Electricidad y Energías Limpias (INEEL), Cuernavaca 62490, Mexico)

  • L. Enrique Sucar

    (Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), San Andrés Cholula 72840, Mexico)

  • Pablo H. Ibargüengoytia

    (Instituto Nacional de Electricidad y Energías Limpias (INEEL), Cuernavaca 62490, Mexico)

  • Eduardo F. Morales

    (Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), San Andrés Cholula 72840, Mexico
    Centro de Investigación en Matemáticas (CIMAT), Guanajuato 36023, Mexico)

Abstract

Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its importance and usage in power plants and power systems. In this paper, three different applications using this approach are described: (i) optimal dam management in hydroelectric power plants, (ii) inspection and surveillance in electric substations, and (iii) optimization of steam generation in a combined cycle power plant. For each case, the technique has demonstrated to find optimal action policies in uncertain settings, present good response and compilation times, deal with stochastic variables and be a good alternative to traditional control systems. The main contributions of this work are as follows, a methodology to approximate a decision model using machine learning techniques, and examples of how to specify and solve problems in the electric power domain in terms of a FMDP.

Suggested Citation

  • Alberto Reyes & L. Enrique Sucar & Pablo H. Ibargüengoytia & Eduardo F. Morales, 2020. "Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes," Energies, MDPI, vol. 13(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2302-:d:354451
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2302/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/9/2302/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Dongyan & Trivedi, Kishor S., 2005. "Optimization for condition-based maintenance with semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 25-29.
    2. Petras Punys & Antanas Dumbrauskas & Algis Kvaraciejus & Gitana Vyciene, 2011. "Tools for Small Hydropower Plant Resource Planning and Development: A Review of Technology and Applications," Energies, MDPI, vol. 4(9), pages 1-20, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chia-Sheng Tu & Chung-Yuen Yang & Ming-Tang Tsai, 2020. "An Optimal Phase Arrangement of Distribution Transformers under Risk Assessment," Energies, MDPI, vol. 13(21), pages 1-16, November.

    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. Marco van Dijk & Stefanus Johannes van Vuuren & Giovanna Cavazzini & Chantel Monica Niebuhr & Alberto Santolin, 2022. "Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    2. Huixia Huo, 2024. "Optimal Corrective Maintenance Policies via an Availability-Cost Hybrid Factor for Software Aging Systems," Mathematics, MDPI, vol. 12(5), pages 1-14, February.
    3. Zhao, Yunfei & Huang, Linan & Smidts, Carol & Zhu, Quanyan, 2020. "Finite-horizon semi-Markov game for time-sensitive attack response and probabilistic risk assessment in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.
    5. Punys, Petras & Kasiulis, Egidijus & Kvaraciejus, Algis & Dumbrauskas, Antanas & Vyčienė, Gitana & Šilinis, Linas, 2017. "Impacts of the EU and national environmental legislation on tapping hydropower resources in Lithuania – A lowland country," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 495-504.
    6. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    7. Olde Keizer, Minou & Teunter, Ruud, 2014. "Opportunistic condition-based maintenance and aperiodic inspections for a two-unit series system," Research Report 14033-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    8. Girish Kumar & Vipul Jain & Umang Soni, 2019. "Modelling and simulation of repairable mechanical systems reliability and availability," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1221-1233, October.
    9. Prasenjit Mondal, 2016. "On undiscounted semi-Markov decision processes with absorbing states," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(2), pages 161-177, April.
    10. Maxim Zhelonkin & Andrey Kurkin & Alexey Loskutov & Alexander Plekhov & Dmitry Malyarov & Evgeny Kryukov, 2023. "Developing a Simulation Model to Numerically Estimate Energy Parameters and Wave Energy Converter Efficiency of a Floating Wave Power Plant," Energies, MDPI, vol. 16(10), pages 1-18, May.
    11. Zhang, Xueqing & Gao, Hui, 2012. "Road maintenance optimization through a discrete-time semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 110-119.
    12. Insua, David Rios & Ruggeri, Fabrizio & Soyer, Refik & Wilson, Simon, 2020. "Advances in Bayesian decision making in reliability," European Journal of Operational Research, Elsevier, vol. 282(1), pages 1-18.
    13. Müller, Marc F. & Thompson, Sally E. & Kelly, Maggi N., 2016. "Bridging the information gap: A webGIS tool for rural electrification in data-scarce regions," Applied Energy, Elsevier, vol. 171(C), pages 277-286.
    14. Sayyideh Mehri Mousavi & Hesam Shams & Shahrzad Ahmadi, 2017. "Simultaneous optimization of repair and control-limit policy in condition-based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 245-254, January.
    15. Ossai, Chinedu I., 2019. "Remaining useful life estimation for repairable multi-state components subjected to multiple maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 142-151.
    16. Prasenjit Mondal, 2020. "Computing semi-stationary optimal policies for multichain semi-Markov decision processes," Annals of Operations Research, Springer, vol. 287(2), pages 843-865, April.
    17. Wang, W. & Zhang, W., 2008. "An asset residual life prediction model based on expert judgments," European Journal of Operational Research, Elsevier, vol. 188(2), pages 496-505, July.
    18. Petras Punys & Linas Jurevičius, 2022. "Assessment of Hydropower Potential in Wastewater Systems and Application in a Lowland Country, Lithuania," Energies, MDPI, vol. 15(14), pages 1-23, July.
    19. Apichonnabutr, W. & Tiwary, A., 2018. "Trade-offs between economic and environmental performance of an autonomous hybrid energy system using micro hydro," Applied Energy, Elsevier, vol. 226(C), pages 891-904.
    20. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

    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:9:p:2302-:d:354451. 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.