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Improved Air-Conditioning Demand Response of Connected Communities over Individually Optimized Buildings

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  • Nicolas A. Campbell

    (School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Patrick E. Phelan

    (School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Miguel Peinado-Guerrero

    (School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Jesus R. Villalobos

    (School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA)

Abstract

Connected communities potentially offer much greater demand response capabilities over singular building energy management systems (BEMS) through an increase of connectivity. The potential increase in benefits from this next step in connectivity is still under investigation, especially when applied to existing buildings. This work utilizes EnergyPlus simulation results on eight different commercial prototype buildings to estimate the potential savings on peak demand and energy costs using a mixed-integer linear programming model. This model is used in two cases: a fully connected community and eight separate buildings with BEMS. The connected community is optimized using all zones as variables, while the individual buildings are optimized separately and then aggregated. These optimization problems are run for a range of individual zone flexibility values. The results indicate that a connected community offered 60.0 % and 24.8 % more peak demand savings for low and high flexibility scenarios, relative to individually optimized buildings. Energy cost optimization results show only marginally better savings of 2.9 % and 6.1 % for low and high flexibility, respectively.

Suggested Citation

  • Nicolas A. Campbell & Patrick E. Phelan & Miguel Peinado-Guerrero & Jesus R. Villalobos, 2021. "Improved Air-Conditioning Demand Response of Connected Communities over Individually Optimized Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5926-:d:638334
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    References listed on IDEAS

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    Cited by:

    1. Triolo, Ryan C. & Rajagopal, Ram & Wolak, Frank A. & de Chalendar, Jacques A., 2023. "Estimating cooling demand flexibility in a district energy system using temperature set point changes from selected buildings," Applied Energy, Elsevier, vol. 336(C).

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