IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v231y2025icp120-127.html
   My bibliography  Save this article

Multi-objective model for residential energy management in context of individual self-consumption

Author

Listed:
  • Ramos, Sérgio
  • Roque, Luís A.C.
  • Gomes, António
  • Soares, João
  • Rolle, José Calvo
  • Vale, Zita

Abstract

The European Union (EU) strongly urged its member states to take action to establish energy communities. Domestic consumers now have the opportunity to organize themselves to play an active and decisive role in the electricity supply chain and make a solid contribution to an ecological footprint. This work proposes residential energy management, considering renewable resources production (photovoltaic panels) and energy storage systems for individual self-consumption approach and boosting future participation in energy community interactions. A multi-objective optimization model using Biased Random Key Genetic Algorithm (BRKGA) was adapted and implemented to minimize electricity consumption costs and maximize self-consumption usage. An energy storage system with 10 kW was considered to optimize electricity management. Two scenarios were proposed, one considering a residential prosumer with a photovoltaic (PV) power installed value of 3.56kWp and the other with 4.20kWp. The results showed a reduction in electricity consumption between 7 % and 8 % and an increase in self-consumption supported by a battery energy storage system of nearly 50 %. Data from an actual system were used to obtain realistic scenarios.

Suggested Citation

  • Ramos, Sérgio & Roque, Luís A.C. & Gomes, António & Soares, João & Rolle, José Calvo & Vale, Zita, 2025. "Multi-objective model for residential energy management in context of individual self-consumption," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 231(C), pages 120-127.
    • Handle: RePEc:eee:matcom:v:231:y:2025:i:c:p:120-127
    DOI: 10.1016/j.matcom.2024.11.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847542400466X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2024.11.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Fouad El Gohary & Sofie Nyström & Lizette Reitsma & Cajsa Bartusch, 2021. "Identifying Challenges in Engaging Users to Increase Self-Consumption of Electricity in Microgrids," Energies, MDPI, vol. 14(5), pages 1-27, February.
    2. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    3. Bertsch, Valentin & Geldermann, Jutta & Lühn, Tobias, 2017. "What drives the profitability of household PV investments, self-consumption and self-sufficiency?," Applied Energy, Elsevier, vol. 204(C), pages 1-15.
    4. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    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. Ramallo-González, Alfonso P. & Loonen, Roel & Tomat, Valentina & Zamora, Miguel Ángel & Surugin, Dmitry & Hensen, Jan, 2020. "Nomograms for de-complexing the dimensioning of off-grid PV systems," Renewable Energy, Elsevier, vol. 161(C), pages 162-172.
    2. Liu, Yiwei & Tang, Yinggan & Hua, Changchun, 2025. "Multi-objective nutcracker optimization algorithm based on fast non-dominated sorting and elite strategy for grid-connected hybrid microgrid system scheduling," Renewable Energy, Elsevier, vol. 242(C).
    3. Sadat, Seyyed Ali & Roy, Riya & Pearce, Joshua M., 2025. "Technical, economic and environmental potential of recycled polycarbonate solar photovoltaic frames," Renewable Energy, Elsevier, vol. 242(C).
    4. Angenendt, Georg & Zurmühlen, Sebastian & Axelsen, Hendrik & Sauer, Dirk Uwe, 2018. "Comparison of different operation strategies for PV battery home storage systems including forecast-based operation strategies," Applied Energy, Elsevier, vol. 229(C), pages 884-899.
    5. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    6. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    7. Thopil, George Alex & Sachse, Christiaan Eddie & Lalk, Jörg & Thopil, Miriam Sara, 2020. "Techno-economic performance comparison of crystalline and thin film PV panels under varying meteorological conditions: A high solar resource southern hemisphere case," Applied Energy, Elsevier, vol. 275(C).
    8. Oscar Villegas Mier & Anna Dittmann & Wiebke Herzberg & Holger Ruf & Elke Lorenz & Michael Schmidt & Rainer Gasper, 2023. "Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast," Energies, MDPI, vol. 16(19), pages 1-19, October.
    9. Liu, Jia & Chen, Xi & Yang, Hongxing & Li, Yutong, 2020. "Energy storage and management system design optimization for a photovoltaic integrated low-energy building," Energy, Elsevier, vol. 190(C).
    10. Jonatas B. C. Chagas & Julian Blank & Markus Wagner & Marcone J. F. Souza & Kalyanmoy Deb, 2021. "A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem," Journal of Heuristics, Springer, vol. 27(3), pages 267-301, June.
    11. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    12. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    13. Neetzow, Paul & Mendelevitch, Roman & Siddiqui, Sauleh, 2019. "Modeling coordination between renewables and grid: Policies to mitigate distribution grid constraints using residential PV-battery systems," Energy Policy, Elsevier, vol. 132(C), pages 1017-1033.
    14. Wisam Kareem Meteab & Salwan Ali Habeeb Alsultani & Francisco Jurado, 2023. "Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer," Energies, MDPI, vol. 16(16), pages 1-18, August.
    15. Vladimir Z. Gjorgievski & Nikolas G. Chatzigeorgiou & Venizelos Venizelou & Georgios C. Christoforidis & George E. Georghiou & Grigoris K. Papagiannis, 2020. "Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus," Energies, MDPI, vol. 13(8), pages 1-18, April.
    16. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    17. Xiaoyu Yu & Jingyi Qian & Yajing Zhang & Min Kong, 2023. "Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
    18. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    19. Gallego-Castillo, Cristobal & Heleno, Miguel & Victoria, Marta, 2021. "Self-consumption for energy communities in Spain: A regional analysis under the new legal framework," Energy Policy, Elsevier, vol. 150(C).
    20. Schick, Christoph & Hufendiek, Kai, 2023. "Assessment of the regulatory framework in view of effectiveness and distributional effects in the context of small-scale PV—The German experience," Energy Policy, Elsevier, vol. 172(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:matcom:v:231:y:2025:i:c:p:120-127. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.