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A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas

Author

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  • Vaclav Kaczmarczyk

    (CEITEC—Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic)

  • Zdenek Bradac

    (CEITEC—Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
    These authors contributed equally to this work.)

  • Petr Fiedler

    (CEITEC—Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
    These authors contributed equally to this work.)

Abstract

We present the conceptual design of a collective control scheme for appliances within a smart home. Based on the relevant energy acquisition procedures, three appliance groups are defined, modeled, and completed with an energy storage as well as a generator using renewable sources. At the following stage, a mixed quadratic optimization problem is presented, with the solution consisting in a time plan to regulate the operation of the individual devices. Importantly, the paper also proposes a heuristic algorithm securing consistent functionality of the computational process even despite the varying input and user conditions given in the receding horizon.

Suggested Citation

  • Vaclav Kaczmarczyk & Zdenek Bradac & Petr Fiedler, 2017. "A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas," Energies, MDPI, vol. 10(7), pages 1-28, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1049-:d:105308
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    References listed on IDEAS

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    1. João Soares & Bruno Canizes & Cristina Lobo & Zita Vale & Hugo Morais, 2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators," Energies, MDPI, vol. 5(6), pages 1-19, June.
    2. Chang-Soon Kang & Jong-Il Park & Mignon Park & Jaeho Baek, 2014. "Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control," Energies, MDPI, vol. 7(6), pages 1-19, June.
    3. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
    4. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    5. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
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