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Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

Author

Listed:
  • Keda Pan

    (Department of Control Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Changhong Xie

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Chun Sing Lai

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
    Brunel Institute of Power Systems, Department of Electronic & Electrical Engineering, Brunel University London, London UB8 3PH, UK)

  • Dongxiao Wang

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
    System Design and Engineering Department, Australia Energy Market Operator, Melbourne 3000, Australia)

  • Loi Lei Lai

    (Department of Control Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
    Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

Suggested Citation

  • Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:4:p:25-487:d:438948
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    References listed on IDEAS

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    Cited by:

    1. Sonia Leva, 2021. "Editorial for Special Issue: “Feature Papers of Forecasting”," Forecasting, MDPI, vol. 3(1), pages 1-3, February.
    2. Alessandro Niccolai & Emanuele Ogliari & Alfredo Nespoli & Riccardo Zich & Valentina Vanetti, 2022. "Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection," Energies, MDPI, vol. 15(24), pages 1-16, December.
    3. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.

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