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HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response

Author

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  • Nikolaos Kampelis

    (Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece)

  • Nikolaos Sifakis

    (Renewable and Sustainable Energy Systems Laboratory, School of Environmental Engineering, Technical University of Crete, Kounoupidiana, GR 73100 Chania, Greece)

  • Dionysia Kolokotsa

    (Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece)

  • Konstantinos Gobakis

    (Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece)

  • Konstantinos Kalaitzakis

    (Electric Circuits and Renewable Energy Sources Laboratory, Technical University of Crete; Kounoupidiana, GR 73100 Chania, Greece)

  • Daniela Isidori

    (Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy)

  • Cristina Cristalli

    (Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy)

Abstract

Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.

Suggested Citation

  • Nikolaos Kampelis & Nikolaos Sifakis & Dionysia Kolokotsa & Konstantinos Gobakis & Konstantinos Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2019. "HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response," Energies, MDPI, vol. 12(11), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2177-:d:238017
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    References listed on IDEAS

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    Cited by:

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    5. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, vol. 12(19), pages 1-22, September.
    6. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Krzysztof Wąs & Jan Radoń & Agnieszka Sadłowska-Sałęga, 2020. "Maintenance of Passive House Standard in the Light of Long-Term Study on Energy Use in a Prefabricated Lightweight Passive House in Central Europe," Energies, MDPI, vol. 13(11), pages 1-22, June.
    8. Ahmad, Ashfaq & Khan, Jamil Yusuf, 2020. "Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings," Applied Energy, Elsevier, vol. 259(C).
    9. Krzysztof Wąs & Jan Radoń & Agnieszka Sadłowska-Sałęga, 2022. "Thermal Comfort—Case Study in a Lightweight Passive House," Energies, MDPI, vol. 15(13), pages 1-21, June.
    10. Bartosz Radomski & Tomasz Mróz, 2021. "The Methodology for Designing Residential Buildings with a Positive Energy Balance—General Approach," Energies, MDPI, vol. 14(15), pages 1-16, August.
    11. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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