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A Mixed Binary Linear Programming Model for Optimal Energy Management of Smart Buildings

Author

Listed:
  • Zahra Foroozandeh

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Sérgio Ramos

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • João Soares

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Fernando Lezama

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Zita Vale

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • António Gomes

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Rodrigo L. Joench

    (GECAD, Institute of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal
    Instituto Federal de Santa Catarina (IFSC), Florianopólis 88020-300, Brazil)

Abstract

Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs.

Suggested Citation

  • Zahra Foroozandeh & Sérgio Ramos & João Soares & Fernando Lezama & Zita Vale & António Gomes & Rodrigo L. Joench, 2020. "A Mixed Binary Linear Programming Model for Optimal Energy Management of Smart Buildings," Energies, MDPI, vol. 13(7), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1719-:d:341409
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    References listed on IDEAS

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    1. João Soares & Tiago Pinto & Fernando Lezama & Hugo Morais, 2018. "Survey on Complex Optimization and Simulation for the New Power Systems Paradigm," Complexity, Hindawi, vol. 2018, pages 1-32, August.
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    Cited by:

    1. Cindy Paola Guzman & Nataly Bañol Arias & John Fredy Franco & Marcos J. Rider & Rubén Romero, 2020. "Enhanced Coordination Strategy for an Aggregator of Distributed Energy Resources Participating in the Day-Ahead Reserve Market," Energies, MDPI, vol. 13(8), pages 1-22, April.
    2. Patrick Wilk & Ning Wang & Jie Li, 2024. "Multi-Agent Reinforcement Learning for Smart Community Energy Management," Energies, MDPI, vol. 17(20), pages 1-21, October.
    3. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    4. Joao Soares & Bruno Canizes & Zita Vale, 2021. "Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation," Energies, MDPI, vol. 14(23), pages 1-4, November.

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