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Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

Author

Listed:
  • Mohammed Olama

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Jin Dong

    (Energy and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Isha Sharma

    (Energy and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Yaosuo Xue

    (Electrical and Electronics Systems Research Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Teja Kuruganti

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

Abstract

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

Suggested Citation

  • Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4593-:d:408719
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    References listed on IDEAS

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    1. Drgoňa, Ján & Picard, Damien & Kvasnica, Michal & Helsen, Lieve, 2018. "Approximate model predictive building control via machine learning," Applied Energy, Elsevier, vol. 218(C), pages 199-216.
    2. Mohammed M. Olama & Teja Kuruganti & James Nutaro & Jin Dong, 2018. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid," Energies, MDPI, vol. 11(7), pages 1-15, July.
    3. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
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