IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6960-d1254100.html
   My bibliography  Save this article

Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System

Author

Listed:
  • Ivan Postnikov

    (School of Power Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Ekaterina Samarkina

    (School of Power Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Andrey Penkovskii

    (School of Power Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Vladimir Kornev

    (Corporate Training & Research Center “Eurosibenergo”, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Denis Sidorov

    (Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

Abstract

This research focuses on exploring various techniques and models for simulating the random behavior of energy facilities or systems. These simulations are essential in identifying the likelihood of component failures within the studied facilities. By assessing the potential consequences of emergency scenarios, this analysis serves as a fundamental aspect of synthesizing and analyzing reliability in the cyber-physical system. Ultimately, the study aims to enhance the management and control of reliability and safety for these facilities. In this study, a unified heating source is considered as an energy facility (as part of district heating systems), for example, a combined heat and power plant. However, the developed methods and models have sufficient universality for their adaptation to other energy facilities without significant changes. The research methodology is based on the use of Markov random processes and laws of the probability theory. The basic model of the energy facilities is formulated for the conditions of the simplest events flow with appropriate assumptions and constraints, in particular, ordinary events and independence of events (failures and restorations). To take into account the non-ordinary events (failures) and dependences between some failures, corresponding modifications of the basic model are proposed. A computational experiment was carried out using the developed models, and graphical interpretations of the results are presented. The obtained results allow us to formulate some preliminary conclusions about the range of influence of the simulated factors on the reliability analysis of studied facilities and to outline conditions and areas of their admissible application.

Suggested Citation

  • Ivan Postnikov & Ekaterina Samarkina & Andrey Penkovskii & Vladimir Kornev & Denis Sidorov, 2023. "Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System," Energies, MDPI, vol. 16(19), pages 1-11, October.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6960-:d:1254100
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6960/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/19/6960/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George C. Konstantopoulos & Antonio T. Alexandridis & Panos C. Papageorgiou, 2020. "Towards the Integration of Modern Power Systems into a Cyber–Physical Framework," Energies, MDPI, vol. 13(9), pages 1-20, May.
    2. Lisnianski, Anatoly & Elmakias, David & Laredo, David & Ben Haim, Hanoch, 2012. "A multi-state Markov model for a short-term reliability analysis of a power generating unit," Reliability Engineering and System Safety, Elsevier, vol. 98(1), pages 1-6.
    3. Lund, Henrik & Østergaard, Poul Alberg & Chang, Miguel & Werner, Sven & Svendsen, Svend & Sorknæs, Peter & Thorsen, Jan Eric & Hvelplund, Frede & Mortensen, Bent Ole Gram & Mathiesen, Brian Vad & Boje, 2018. "The status of 4th generation district heating: Research and results," Energy, Elsevier, vol. 164(C), pages 147-159.
    4. Brange, Lisa & Englund, Jessica & Lauenburg, Patrick, 2016. "Prosumers in district heating networks – A Swedish case study," Applied Energy, Elsevier, vol. 164(C), pages 492-500.
    5. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    6. Shu, Liwei & Chen, Lingen & Jin, Jiashan & Yu, Jun & Sun, Fengrui & Wu, Chih, 2005. "Functional reliability simulation for a power-station's steam-turbine," Applied Energy, Elsevier, vol. 80(1), pages 61-66, January.
    7. David Huber & Viktoria Illyés & Veronika Turewicz & Gregor Götzl & Andreas Hammer & Karl Ponweiser, 2021. "Novel District Heating Systems: Methods and Simulation Results," Energies, MDPI, vol. 14(15), pages 1-23, July.
    8. Revesz, Akos & Jones, Phil & Dunham, Chris & Davies, Gareth & Marques, Catarina & Matabuena, Rodrigo & Scott, Jim & Maidment, Graeme, 2020. "Developing novel 5th generation district energy networks," Energy, Elsevier, vol. 201(C).
    9. Danhong Wang & Jan Carmeliet & Kristina Orehounig, 2021. "Design and Assessment of District Heating Systems with Solar Thermal Prosumers and Thermal Storage," Energies, MDPI, vol. 14(4), pages 1-27, February.
    10. Li, Haoran & Hou, Juan & Tian, Zhiyong & Hong, Tianzhen & Nord, Natasa & Rohde, Daniel, 2022. "Optimize heat prosumers' economic performance under current heating price models by using water tank thermal energy storage," Energy, Elsevier, vol. 239(PB).
    11. Gross, Michel & Karbasi, Babak & Reiners, Tobias & Altieri, Lisa & Wagner, Hermann-Josef & Bertsch, Valentin, 2021. "Implementing prosumers into heating networks," Energy, Elsevier, vol. 230(C).
    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. Wirtz, Marco, 2023. "nPro: A web-based planning tool for designing district energy systems and thermal networks," Energy, Elsevier, vol. 268(C).
    2. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    3. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Gjoka, Kristian & Rismanchi, Behzad & Crawford, Robert H., 2023. "Fifth-generation district heating and cooling systems: A review of recent advancements and implementation barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    5. Dino, Giuseppe Edoardo & Catrini, Pietro & Buscemi, Alessandro & Piacentino, Antonio & Palomba, Valeria & Frazzica, Andrea, 2023. "Modeling of a bidirectional substation in a district heating network: Validation, dynamic analysis, and application to a solar prosumer," Energy, Elsevier, vol. 284(C).
    6. Bogdanovics, Raimonds & Zemitis, Jurgis & Zajacs, Aleksandrs & Borodinecs, Anatolijs, 2024. "Small-scale district heating system as heat storage for decentralized solar thermal collectors during non-heating period," Energy, Elsevier, vol. 298(C).
    7. Li, Haoran & Hou, Juan & Hong, Tianzhen & Nord, Natasa, 2022. "Distinguish between the economic optimal and lowest distribution temperatures for heat-prosumer-based district heating systems with short-term thermal energy storage," Energy, Elsevier, vol. 248(C).
    8. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2023. "Potential for supply temperature reduction of existing district heating substations," Energy, Elsevier, vol. 285(C).
    9. Chicherin, Stanislav, 2020. "Methodology for analyzing operation data for optimum district heating (DH) system design: Ten-year data of Omsk, Russia," Energy, Elsevier, vol. 211(C).
    10. Hrvoje Dorotić & Kristijan Čuljak & Josip Miškić & Tomislav Pukšec & Neven Duić, 2022. "Technical and Economic Assessment of Supermarket and Power Substation Waste Heat Integration into Existing District Heating Systems," Energies, MDPI, vol. 15(5), pages 1-29, February.
    11. Wirtz, Marco & Kivilip, Lukas & Remmen, Peter & Müller, Dirk, 2020. "5th Generation District Heating: A novel design approach based on mathematical optimization," Applied Energy, Elsevier, vol. 260(C).
    12. Lund, Henrik & Østergaard, Poul Alberg & Nielsen, Tore Bach & Werner, Sven & Thorsen, Jan Eric & Gudmundsson, Oddgeir & Arabkoohsar, Ahmad & Mathiesen, Brian Vad, 2021. "Perspectives on fourth and fifth generation district heating," Energy, Elsevier, vol. 227(C).
    13. Stanislav Chicherin & Vladislav Mašatin & Andres Siirde & Anna Volkova, 2020. "Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy," Energies, MDPI, vol. 13(17), pages 1-15, September.
    14. Millar, Michael-Allan & Yu, Zhibin & Burnside, Neil & Jones, Greg & Elrick, Bruce, 2021. "Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks," Applied Energy, Elsevier, vol. 291(C).
    15. Wheatcroft, Edward & Wynn, Henry P. & Lygnerud, Kristina & Bonvicini, Giorgio & Bonvicini, Giorgio & Lenote, Daniela, 2020. "The role of low temperature waste heat recovery in achieving 2050 goals: a policy positioning paper," LSE Research Online Documents on Economics 104136, London School of Economics and Political Science, LSE Library.
    16. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    17. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.
    18. Alessandro Guzzini & Marco Pellegrini & Edoardo Pelliconi & Cesare Saccani, 2020. "Low Temperature District Heating: An Expert Opinion Survey," Energies, MDPI, vol. 13(4), pages 1-34, February.
    19. Reiners, Tobias & Gross, Michel & Altieri, Lisa & Wagner, Hermann-Josef & Bertsch, Valentin, 2021. "Heat pump efficiency in fifth generation ultra-low temperature district heating networks using a wastewater heat source," Energy, Elsevier, vol. 236(C).
    20. Gross, Michel & Karbasi, Babak & Reiners, Tobias & Altieri, Lisa & Wagner, Hermann-Josef & Bertsch, Valentin, 2021. "Implementing prosumers into heating networks," Energy, Elsevier, vol. 230(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:16:y:2023:i:19:p:6960-:d:1254100. 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.