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Demand response via optimal pre-cooling combined with temperature reset strategy for air conditioning system: A case study of office building

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  • Wang, Jiewei
  • Wei, Ziqing
  • Zhu, Yikang
  • Zheng, Chunyuan
  • Li, Bin
  • Zhai, Xiaoqiang

Abstract

Demand response (DR) through air conditioning (AC) systems in buildings is a promising way to balance the power grid. However, the majority of existing DR strategies are rule-based. The other ones are model-based but generally have adopted oversimplified AC system models. Consequently, the effect of model-based optimal control cannot be evaluated properly. To address these limitations, a co-simulation framework is developed to minimize power consumption and electricity costs by adopting both pre-cooling and temperature reset strategies. The co-simulation framework combines an EnergyPlus model that is calibrated by measured data and the genetic algorithm through Functional Mock-up Unit socket. Both time-of-use electricity prices and the constraints of thermal comfort are considered in the optimization problem. A real office building with a variable refrigerant flow (VRF) system is used to test the co-simulation framework. Compared with rule-based strategies, the power consumption of the VRF system following the optimal control strategy during the DR period is reduced by 80.12%, 74.70% and 55.60%, and the daily electricity cost is reduced by 14.98%, 12.11% and 8.35%, respectively. The quantitative analysis also reveals that 37.96% of the cold energy stored by the envelope in the pre-cooling period is released into the air during the DR period.

Suggested Citation

  • Wang, Jiewei & Wei, Ziqing & Zhu, Yikang & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2023. "Demand response via optimal pre-cooling combined with temperature reset strategy for air conditioning system: A case study of office building," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s036054422302145x
    DOI: 10.1016/j.energy.2023.128751
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    References listed on IDEAS

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    1. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    2. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
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    1. 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).

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