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Demand-Side Management Optimization Using Genetic Algorithms: A Case Study

Author

Listed:
  • Lauro Correa dos Santos Junior

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Jonathan Muñoz Tabora

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
    Electrical Engineering Department, National Autonomous University of Honduras (UNAH), Tegucigalpa 04001, Honduras)

  • Josivan Reis

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Vinicius Andrade

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Carminda Carvalho

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Allan Manito

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Maria Tostes

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Edson Matos

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

  • Ubiratan Bezerra

    (Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil)

Abstract

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption.

Suggested Citation

  • Lauro Correa dos Santos Junior & Jonathan Muñoz Tabora & Josivan Reis & Vinicius Andrade & Carminda Carvalho & Allan Manito & Maria Tostes & Edson Matos & Ubiratan Bezerra, 2024. "Demand-Side Management Optimization Using Genetic Algorithms: A Case Study," Energies, MDPI, vol. 17(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1463-:d:1359304
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    References listed on IDEAS

    as
    1. Mahmoud Reza Ramezanpour & Mostafa Farajpour, 2022. "Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-12, February.
    2. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    3. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
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