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An assessment of power flexibility from commercial building cooling systems in the United States

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  • Huang, Sen
  • Ye, Yunyang
  • Wu, Di
  • Zuo, Wangda

Abstract

Understanding the varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning loads in commercial buildings in the U.S. using a simulation-based method. Commercial buildings are first grouped by building type and climate region. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that cooling loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while medium office buildings have the most flexibility as a percentage of the rated power consumption.

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  • Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544220326785
    DOI: 10.1016/j.energy.2020.119571
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    Cited by:

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    6. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    7. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    8. Gallardo, Andres & Berardi, Umberto, 2022. "Evaluation of the energy flexibility potential of radiant ceiling panels with thermal energy storage," Energy, Elsevier, vol. 254(PC).
    9. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    10. Huang, Bowen & Huang, Sen & Ma, Xu & Katipamula, Srinivas & Wu, Di & Lutes, Robert, 2023. "Stochastic scheduling for commercial building cooling systems: considering uncertainty in zone temperature prediction," Applied Energy, Elsevier, vol. 346(C).

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