IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p5215-d1188577.html
   My bibliography  Save this article

Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management

Author

Listed:
  • Daniel Cardoso

    (Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal)

  • Daniel Nunes

    (C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal)

  • João Faria

    (Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Paulo Fael

    (Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal)

  • Pedro D. Gaspar

    (Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal)

Abstract

This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.

Suggested Citation

  • Daniel Cardoso & Daniel Nunes & João Faria & Paulo Fael & Pedro D. Gaspar, 2023. "Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management," Energies, MDPI, vol. 16(13), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5215-:d:1188577
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/5215/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/5215/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Onovwiona, H.I. & Ugursal, V.I., 2006. "Residential cogeneration systems: review of the current technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(5), pages 389-431, October.
    2. Chicco, Gianfranco & Mancarella, Pierluigi, 2008. "Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part I: Models and indicators," Energy, Elsevier, vol. 33(3), pages 410-417.
    3. Darcovich, K. & Henquin, E.R. & Kenney, B. & Davidson, I.J. & Saldanha, N. & Beausoleil-Morrison, I., 2013. "Higher-capacity lithium ion battery chemistries for improved residential energy storage with micro-cogeneration," Applied Energy, Elsevier, vol. 111(C), pages 853-861.
    4. Eun-Chul Kang & Euy-Joon Lee & Mohamed Ghorab & Libing Yang & Evgueniy Entchev & Kwang-Seob Lee & Nam-Jin Lyu, 2016. "Investigation of Energy and Environmental Potentials of a Renewable Trigeneration System in a Residential Application," Energies, MDPI, vol. 9(9), pages 1-17, September.
    5. Mancarella, Pierluigi & Chicco, Gianfranco, 2008. "Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part II: Analysis techniques and application cases," Energy, Elsevier, vol. 33(3), pages 418-430.
    6. Mandelli, Stefano & Brivio, Claudio & Colombo, Emanuela & Merlo, Marco, 2016. "A sizing methodology based on Levelized Cost of Supplied and Lost Energy for off-grid rural electrification systems," Renewable Energy, Elsevier, vol. 89(C), pages 475-488.
    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. Dawid Czajor & Łukasz Amanowicz, 2024. "Methodology for Modernizing Local Gas-Fired District Heating Systems into a Central District Heating System Using Gas-Fired Cogeneration Engines—A Case Study," Sustainability, MDPI, vol. 16(4), pages 1-30, February.

    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. Antonio Rosato & Antonio Ciervo & Giovanni Ciampi & Michelangelo Scorpio & Sergio Sibilio, 2020. "Integration of Micro-Cogeneration Units and Electric Storages into a Micro-Scale Residential Solar District Heating System Operating with a Seasonal Thermal Storage," Energies, MDPI, vol. 13(20), pages 1-40, October.
    2. Sibilio, Sergio & Rosato, Antonio & Ciampi, Giovanni & Scorpio, Michelangelo & Akisawa, Atsushi, 2017. "Building-integrated trigeneration system: Energy, environmental and economic dynamic performance assessment for Italian residential applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 920-933.
    3. Mancarella, Pierluigi & Chicco, Gianfranco, 2009. "Global and local emission impact assessment of distributed cogeneration systems with partial-load models," Applied Energy, Elsevier, vol. 86(10), pages 2096-2106, October.
    4. Monica Costea & Michel Feidt, 2022. "A Review Regarding Combined Heat and Power Production and Extensions: Thermodynamic Modelling and Environmental Impact," Energies, MDPI, vol. 15(23), pages 1-25, November.
    5. Rosato, Antonio & Ciervo, Antonio & Ciampi, Giovanni & Scorpio, Michelangelo & Guarino, Francesco & Sibilio, Sergio, 2020. "Impact of solar field design and back-up technology on dynamic performance of a solar hybrid heating network integrated with a seasonal borehole thermal energy storage serving a small-scale residentia," Renewable Energy, Elsevier, vol. 154(C), pages 684-703.
    6. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Energy trends in Palestinian territories of West Bank and Gaza Strip: Possibilities for reducing the reliance on external energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 117-129.
    7. Jiang-Jiang, Wang & Chun-Fa, Zhang & You-Yin, Jing, 2010. "Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China," Applied Energy, Elsevier, vol. 87(4), pages 1247-1259, April.
    8. Ciampi, Giovanni & Rosato, Antonio & Sibilio, Sergio, 2018. "Thermo-economic sensitivity analysis by dynamic simulations of a small Italian solar district heating system with a seasonal borehole thermal energy storage," Energy, Elsevier, vol. 143(C), pages 757-771.
    9. Farah Mneimneh & Hasan Ghazzawi & Seeram Ramakrishna, 2023. "Review Study of Energy Efficiency Measures in Favor of Reducing Carbon Footprint of Electricity and Power, Buildings, and Transportation," Circular Economy and Sustainability,, Springer.
    10. Jing, Rui & Wang, Meng & Brandon, Nigel & Zhao, Yingru, 2017. "Multi-criteria evaluation of solid oxide fuel cell based combined cooling heating and power (SOFC-CCHP) applications for public buildings in China," Energy, Elsevier, vol. 141(C), pages 273-289.
    11. Caresana, Flavio & Brandoni, Caterina & Feliciotti, Petro & Bartolini, Carlo Maria, 2011. "Energy and economic analysis of an ICE-based variable speed-operated micro-cogenerator," Applied Energy, Elsevier, vol. 88(3), pages 659-671, March.
    12. Sheykhi, Mohammad & Chahartaghi, Mahmood & Safaei Pirooz, Amir Ali & Flay, Richard G.J., 2020. "Investigation of the effects of operating parameters of an internal combustion engine on the performance and fuel consumption of a CCHP system," Energy, Elsevier, vol. 211(C).
    13. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Particle swarm optimization for redundant building cooling heating and power system," Applied Energy, Elsevier, vol. 87(12), pages 3668-3679, December.
    14. Dib, Ghady & Haberschill, Philippe & Rullière, Romuald & Perroit, Quentin & Davies, Simon & Revellin, Rémi, 2020. "Thermodynamic simulation of a micro advanced adiabatic compressed air energy storage for building application," Applied Energy, Elsevier, vol. 260(C).
    15. Franki, Vladimir & Višković, Alfredo, 2021. "Multi-criteria decision support: A case study of Southeast Europe power systems," Utilities Policy, Elsevier, vol. 73(C).
    16. Miao Li & Hailin Mu & Huanan Li, 2013. "Analysis and Assessments of Combined Cooling, Heating and Power Systems in Various Operation Modes for a Building in China, Dalian," Energies, MDPI, vol. 6(5), pages 1-22, May.
    17. Carvalho, Monica & Serra, Luis Maria & Lozano, Miguel Angel, 2011. "Optimal synthesis of trigeneration systems subject to environmental constraints," Energy, Elsevier, vol. 36(6), pages 3779-3790.
    18. Yang, Kun & Ding, Yan & Zhu, Neng & Yang, Fan & Wang, Qiaochu, 2018. "Multi-criteria integrated evaluation of distributed energy system for community energy planning based on improved grey incidence approach: A case study in Tianjin," Applied Energy, Elsevier, vol. 229(C), pages 352-363.
    19. Mallikarjun, Sreekanth & Lewis, Herbert F., 2014. "Energy technology allocation for distributed energy resources: A strategic technology-policy framework," Energy, Elsevier, vol. 72(C), pages 783-799.
    20. Xiaoming Zhou & Maosheng Sang & Minglei Bao & Yi Ding, 2022. "Tracing and Evaluating Life-Cycle Carbon Emissions of Urban Multi-Energy Systems," Energies, MDPI, vol. 15(8), pages 1-19, April.

    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:13:p:5215-:d:1188577. 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.