IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1719-d341409.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1719/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/7/1719/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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. 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.
    3. 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.

    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. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    2. Aviad Navon & Gefen Ben Yosef & Ram Machlev & Shmuel Shapira & Nilanjan Roy Chowdhury & Juri Belikov & Ariel Orda & Yoash Levron, 2020. "Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review," Energies, MDPI, vol. 13(15), pages 1-35, August.
    3. Dharmesh Dabhi & Kartik Pandya & Joao Soares & Fernando Lezama & Zita Vale, 2022. "Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets," Energies, MDPI, vol. 15(13), pages 1-20, July.
    4. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    5. Yinuo Huang & Licheng Wang & Kai Wang, 2019. "Investigation of Var Compensation Schemes in Unbalanced Distribution Systems," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    6. 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.
    7. Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2021. "Adaptive Control for Energy Exchange with Probabilistic Interval Predictors in Isolated Microgrids," Energies, MDPI, vol. 14(2), pages 1-23, January.
    8. Koos van der Linden & Natalia Romero & Mathijs M. de Weerdt, 2021. "Benchmarking Flexible Electric Loads Scheduling Algorithms," Energies, MDPI, vol. 14(5), pages 1-16, February.

    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:13:y:2020:i:7:p:1719-:d:341409. 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.