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Day-ahead scheduling of air-conditioners based on equivalent energy storage model under temperature-set-point control

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  • Yu, Zhou-Chen
  • Bao, Yu-Qing
  • Yang, Xiao

Abstract

Air-conditioners (ACs) can fully utilize the inherent characteristics of storing heat/cold in demand response (DR), achieving peak load shifting and renewable energy consumption. However, traditional control strategies based on compressor ON/OFF state require retrofitting of the internal components of ACs, making it challenging to implement directly through ACs' infrared control protocol. Given the widespread availability of infrared temperature-set-point (TSP) adjustment functions in existing ACs, this paper proposes a day-ahead optimal scheduling strategy of aggregate ACs under TSP control. Based on the second-order equivalent thermodynamic parameter (ETP) model, the TSP adjustment characteristics are considered to establish the equivalent energy storage (EES) model of ACs. On this basis, taking the overall optimization objectives of power system into account, the optimal scheduling model of aggregate ACs under TSP control is established to solve collaborative optimization problems for power generation and consumption. Case study demonstrates that, compared to optimal scheduling models based on traditional EES model, the proposed model exhibits superior peak load shifting effects and allows for smooth control of ACs through the issuance of control commands.

Suggested Citation

  • Yu, Zhou-Chen & Bao, Yu-Qing & Yang, Xiao, 2024. "Day-ahead scheduling of air-conditioners based on equivalent energy storage model under temperature-set-point control," Applied Energy, Elsevier, vol. 368(C).
    • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s030626192400864x
    DOI: 10.1016/j.apenergy.2024.123481
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    References listed on IDEAS

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    1. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    2. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    3. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
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