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Virtual Battery Modeling of Air Conditioning Loads in the Presence of Unknown Heat Disturbances

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  • Seung-Jun Hahm

    (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Ye-Eun Jang

    (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Young-Jin Kim

    (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

Abstract

Air conditioning loads (ACLs) are potential flexible resources that can provide various grid services to the power system. Recent studies have attempted to represent their flexibility using a virtual battery (VB) model for quantification, but the modeling process requires information on thermal parameters and heat disturbances (e.g., solar irradiation and internal heat load) that are difficult to measure. In this paper, we present a new method that models a VB without prior knowledge of such information. First, we construct a thermal dynamic model of an individual ACL using historical input-output data. The linear regression model parameters are identified without using the measurements of disturbances. Second, we derive a VB model from the linear regression parameters using a change of variable technique. We show that the VB can be directly modeled from the regression model of thermal dynamics without estimating the exact thermal parameters and heat disturbances. Third, aggregation of the VB models is implemented. The energy limits of aggregate VB models are designed considering the baseline load prediction error caused by disturbance uncertainty. Finally, simulation results verify the accuracy and effectiveness of the proposed VB modeling strategy.

Suggested Citation

  • Seung-Jun Hahm & Ye-Eun Jang & Young-Jin Kim, 2022. "Virtual Battery Modeling of Air Conditioning Loads in the Presence of Unknown Heat Disturbances," Energies, MDPI, vol. 15(24), pages 1-15, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9354-:d:999381
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    References listed on IDEAS

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    1. Papaefthymiou, Georgios & Haesen, Edwin & Sach, Thobias, 2018. "Power System Flexibility Tracker: Indicators to track flexibility progress towards high-RES systems," Renewable Energy, Elsevier, vol. 127(C), pages 1026-1035.
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    4. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
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