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Optimizing Residential Electricity Demand with Bipartite Models for Enhanced Demand Response

Author

Listed:
  • Jonathan Campoverde

    (Electrical Engineering Department, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    These authors contributed equally to this work.)

  • Marcelo Garcia Torres

    (Electrical Engineering Department, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    These authors contributed equally to this work.)

  • Luis Tipan

    (Electrical Engineering Department, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    These authors contributed equally to this work.)

Abstract

This study presents an advanced energy demand management approach within residential microgrids using bipartite models for optimal demand response. The methodology relies on linear programming, specifically the Simplex algorithm, to optimize power distribution while minimizing costs. The model aims to reduce residential energy consumption by flattening the demand curve through demand response programs. Additionally, the Internet of Things (IoT) is integrated as a communication channel to ensure efficient energy management without compromising user comfort. The research evaluates energy resource allocation using bipartite graphs, modeling the generation of energy from renewable and conventional high-efficiency sources. Various case studies analyze scenarios with and without market constraints, assessing the impact of demand response at different levels (5%, 10%, 15%, and 20%). Results demonstrate a significant reduction in reliance on external grids, with optimized energy distribution leading to potential cost savings for consumers. The findings suggest that intelligent demand response strategies can enhance microgrid efficiency, supporting sustainability and reducing carbon footprints.

Suggested Citation

  • Jonathan Campoverde & Marcelo Garcia Torres & Luis Tipan, 2025. "Optimizing Residential Electricity Demand with Bipartite Models for Enhanced Demand Response," Energies, MDPI, vol. 18(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3819-:d:1704213
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    References listed on IDEAS

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    5. S. Sofana Reka & Prakash Venugopal & V. Ravi & Tomislav Dragicevic, 2023. "Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
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