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Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution

Author

Listed:
  • Muhammad Umar Afzaal

    (O&M Division, KOENERGY Korea for Gulpur Hydro Power Project, Islamabad 44000, Pakistan)

  • Intisar Ali Sajjad

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Ahmed Bilal Awan

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia)

  • Kashif Nisar Paracha

    (Department of Electrical Engineering, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Muhammad Faisal Nadeem Khan

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Abdul Rauf Bhatti

    (Department of Electrical Engineering, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Muhammad Zubair

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia)

  • Waqas ur Rehman

    (Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA)

  • Salman Amin

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Shaikh Saaqib Haroon

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Rehan Liaqat

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
    Department of Electrical Engineering, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Walid Hdidi

    (Department of mathematics, College of Arts and Sciences of Tabrjal, Jouf University, Sakaka 72341, Saudi Arabia)

  • Iskander Tlili

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
    Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam)

Abstract

Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.

Suggested Citation

  • Muhammad Umar Afzaal & Intisar Ali Sajjad & Ahmed Bilal Awan & Kashif Nisar Paracha & Muhammad Faisal Nadeem Khan & Abdul Rauf Bhatti & Muhammad Zubair & Waqas ur Rehman & Salman Amin & Shaikh Saaqib , 2020. "Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2241-:d:332012
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    Cited by:

    1. B. Koti Reddy & Amit Kumar Singh, 2021. "Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods," Energies, MDPI, vol. 14(16), pages 1-28, August.
    2. Sylwester Kaczmarzewski & Piotr Olczak & Maciej Sołtysik, 2021. "The Impact of Electricity Consumption Profile in Underground Mines to Cooperate with RES," Energies, MDPI, vol. 14(18), pages 1-20, September.
    3. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    4. Ali S. Alghamdi, 2021. "Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio," Energies, MDPI, vol. 14(6), pages 1-18, March.
    5. Ahmed Bilal Awan & Mohammed Alghassab & Muhammad Zubair & Abdul Rauf Bhatti & Muhammad Uzair & Ghulam Abbas, 2020. "Comparative Analysis of Ground-Mounted vs. Rooftop Photovoltaic Systems Optimized for Interrow Distance between Parallel Arrays," Energies, MDPI, vol. 13(14), pages 1-21, July.
    6. Akinyemi Ayodeji Stephen & Kabeya Musasa & Innocent Ewean Davidson, 2022. "Modelling of Solar PV under Varying Condition with an Improved Incremental Conductance and Integral Regulator," Energies, MDPI, vol. 15(7), pages 1-22, March.
    7. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    8. Amedeo Buonanno & Martina Caliano & Marialaura Di Somma & Giorgio Graditi & Maria Valenti, 2022. "A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles," Energies, MDPI, vol. 15(23), pages 1-18, November.

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