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Sensor network based PV power nowcasting with spatio-temporal preselection for grid-friendly control

Author

Listed:
  • Chen, Xiaoyang
  • Du, Yang
  • Lim, Enggee
  • Wen, Huiqing
  • Jiang, Lin

Abstract

The increasing penetration of photovoltaics (PV) systems introduces more uncertainties to the power system, and has drawn serious concern for maintaining the grid stability. Consequently, the PV power grid-friendly control (GFC) has been imposed by utilities to provide additional flexibilities for power system operations. Conventional GFC strategies show limitations to estimate real-time maximum available power, especially when fast moving clouds occur. In this regards, the spatio-temporal (ST) PV nowcasting using a sensor network provides a remedy to the above issue. However, current ST nowcasting methods suffer from the problems such as predictor mis-selection, inconsistent nowcasting, and poor model adaptability, which still hinder their practical use for GFC. In this paper, a novel ST PV power nowcasting method with predictor preselection is presented, which can be used for GFC. The proposed method enables a fast and precise predictor preselection in different scenarios, and provides consistent PV nowcasts with cloud information interpolated. The effectiveness of the proposed nowcasting method is evaluated in a real sensor network. The experimental results reveal that the proposed method has strong robustness in case of various weather conditions, with fewer training data used. Compared with the conventional methods, the proposed method shows an average nRMSE and nPMAE improvements over 13.5% and 41.3% respectively in the cloudy days. A practice of integrating the proposed nowcasting method to GFC operation is also demonstrated. The results show that the proposed method is promising to improve the performance of GFC.

Suggested Citation

  • Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Jiang, Lin, 2019. "Sensor network based PV power nowcasting with spatio-temporal preselection for grid-friendly control," Applied Energy, Elsevier, vol. 255(C).
  • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919314473
    DOI: 10.1016/j.apenergy.2019.113760
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    Citations

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    Cited by:

    1. Yang, Dazhi & Yagli, Gokhan Mert & Srinivasan, Dipti, 2022. "Sub-minute probabilistic solar forecasting for real-time stochastic simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    3. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    4. Alessandro Niccolai & Alfredo Nespoli, 2020. "Sun Position Identification in Sky Images for Nowcasting Application," Forecasting, MDPI, vol. 2(4), pages 1-17, November.
    5. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    6. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.
    7. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Yan, Ke & Kirtley, James, 2020. "Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling," Applied Energy, Elsevier, vol. 268(C).

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