IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2742-d789745.html
   My bibliography  Save this article

Generalized Stochastic Petri Nets for Planning and Optimizing Maintenance Logistics of Small Hydroelectric Power Plants

Author

Listed:
  • Arthur Henrique de Andrade Melani

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Miguel Angelo de Carvalho Michalski

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Carlos Alberto Murad

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Adherbal Caminada Netto

    (Department of Mechanical Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-010, Brazil)

  • Gilberto Francisco Martha de Souza

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

Abstract

Maintenance plays a crucial role in the availability of an asset. In particular, when a company’s assets are decentralized, logistical aspects directly impact maintenance management and, consequently, productivity. In the energy generation sector, this scenario is common in enterprises and projects in which distributed energy resources (DERs), such as small hydroelectric power plants (SHPPs), are considered. Hence, the objective of this work is to propose an application of generalized stochastic Petri nets (GSPN) for the planning and optimization of the maintenance logistics of a DER enterprise with two SHPPs. In the presented case study, different scenarios are modeled considering logistical aspects related to the availability of spare parts and the sharing of maintenance teams between plants. From the financial return resulting from the estimated energy generation and the operating cost of each simulated scenario, the most profitable one can be estimated. The results demonstrate the ability of GSPNs to estimate the influence of the number of spare parts and maintenance teams on the availability of DERs, allowing the optimization of costs related to maintenance logistics.

Suggested Citation

  • Arthur Henrique de Andrade Melani & Miguel Angelo de Carvalho Michalski & Carlos Alberto Murad & Adherbal Caminada Netto & Gilberto Francisco Martha de Souza, 2022. "Generalized Stochastic Petri Nets for Planning and Optimizing Maintenance Logistics of Small Hydroelectric Power Plants," Energies, MDPI, vol. 15(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2742-:d:789745
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2742/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2742/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    2. Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.
    3. Sungeun Jung & Younghye Bae & Jongsung Kim & Hongjun Joo & Hung Soo Kim & Jaewon Jung, 2021. "Analysis of Small Hydropower Generation Potential: (1) Estimation of the Potential in Ungaged Basins," Energies, MDPI, vol. 14(11), pages 1-20, May.
    4. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    5. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    6. Tobi Elusakin & Mahmood Shafiee & Tosin Adedipe & Fateme Dinmohammadi, 2021. "A Stochastic Petri Net Model for O&M Planning of Floating Offshore Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-18, February.
    7. Jaewon Jung & Sungeun Jung & Junhyeong Lee & Myungjin Lee & Hung Soo Kim, 2021. "Analysis of Small Hydropower Generation Potential: (2) Future Prospect of the Potential under Climate Change," Energies, MDPI, vol. 14(11), pages 1-26, May.
    8. Teegala Srinivasa Kishore & Epari Ritesh Patro & V. S. K. V. Harish & Ali Torabi Haghighi, 2021. "A Comprehensive Study on the Recent Progress and Trends in Development of Small Hydropower Projects," Energies, MDPI, vol. 14(10), pages 1-31, May.
    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. Ewa Chomać-Pierzecka & Andrzej Kokiel & Joanna Rogozińska-Mitrut & Anna Sobczak & Dariusz Soboń & Jacek Stasiak, 2022. "Hydropower in the Energy Market in Poland and the Baltic States in the Light of the Challenges of Sustainable Development-An Overview of the Current State and Development Potential," Energies, MDPI, vol. 15(19), pages 1-19, October.
    2. Huang, Xiaoxun & Hayashi, Kiichiro & Fujii, Minoru & Villa, Ferdinando & Yamazaki, Yuri & Okazawa, Hiromu, 2023. "Identification of potential locations for small hydropower plant based on resources time footprint: A case study in Dan River Basin, China," Renewable Energy, Elsevier, vol. 205(C), pages 293-304.
    3. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    4. Efthymios Moutsiakis & Athena Yiannakou, 2023. "Small Hydroelectric Energy and Spatial Planning: A Methodology Introducing the Concept of Territorial Carrying Capacity," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    5. Tattini, Jacopo & Ramea, Kalai & Gargiulo, Maurizio & Yang, Christopher & Mulholland, Eamonn & Yeh, Sonia & Karlsson, Kenneth, 2018. "Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model," Applied Energy, Elsevier, vol. 212(C), pages 265-282.
    6. Larisa Vazhenina & Elena Magaril & Igor Mayburov, 2022. "Resource Conservation as the Main Factor in Increasing the Resource Efficiency of Russian Gas Companies," Resources, MDPI, vol. 11(12), pages 1-14, December.
    7. Tae-Woo Kim & Yenjae Chang & Dae-Wook Kim & Man-Keun Kim, 2020. "Preventive Maintenance and Forced Outages in Power Plants in Korea," Energies, MDPI, vol. 13(14), pages 1-12, July.
    8. Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.
    9. Wassim Salameh & Jalal Faraj & Elias Harika & Rabih Murr & Mahmoud Khaled, 2021. "On the Optimization of Electrical Water Heaters: Modelling Simulations and Experimentation," Energies, MDPI, vol. 14(13), pages 1-12, June.
    10. Fortes, Patrícia & Simoes, Sofia G. & Gouveia, João Pedro & Seixas, Júlia, 2019. "Electricity, the silver bullet for the deep decarbonisation of the energy system? Cost-effectiveness analysis for Portugal," Applied Energy, Elsevier, vol. 237(C), pages 292-303.
    11. Xexakis, Georgios & Hansmann, Ralph & Volken, Sandra P. & Trutnevyte, Evelina, 2020. "Models on the wrong track: Model-based electricity supply scenarios in Switzerland are not aligned with the perspectives of energy experts and the public," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    12. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    13. Yue, Xiufeng & Deane, J.P. & O'Gallachoir, Brian & Rogan, Fionn, 2020. "Identifying decarbonisation opportunities using marginal abatement cost curves and energy system scenario ensembles," Applied Energy, Elsevier, vol. 276(C).
    14. Nolting, Lars & Praktiknjo, Aaron, 2022. "The complexity dilemma – Insights from security of electricity supply assessments," Energy, Elsevier, vol. 241(C).
    15. Gyanwali, Khem & Komiyama, Ryoichi & Fujii, Yasumasa, 2020. "Representing hydropower in the dynamic power sector model and assessing clean energy deployment in the power generation mix of Nepal," Energy, Elsevier, vol. 202(C).
    16. Wen, Xin & Heinisch, Verena & Müller, Jonas & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2023. "Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale," Energy, Elsevier, vol. 285(C).
    17. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    18. Guangyuan Xing & Youheng Zhang & Ju’e Guo, 2023. "Environmental Regulation in Evolution and Governance Strategies," IJERPH, MDPI, vol. 20(6), pages 1-25, March.
    19. Lopion, Peter & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "A review of current challenges and trends in energy systems modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 156-166.
    20. Ramea, Kalai & Bunch, David S. & Yang, Christopher & Yeh, Sonia & Ogden, Joan M., 2018. "Integration of behavioral effects from vehicle choice models into long-term energy systems optimization models," Energy Economics, Elsevier, vol. 74(C), pages 663-676.

    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:15:y:2022:i:8:p:2742-:d:789745. 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.