IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2241-d332012.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2241/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/6/2241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2012. "A review of solar energy modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2864-2869.
    2. Byuk-Keun Jo & Gilsoo Jang, 2019. "An Evaluation of the Effect on the Expansion of Photovoltaic Power Generation According to Renewable Energy Certificates on Energy Storage Systems: A Case Study of the Korean Renewable Energy Market," Sustainability, MDPI, vol. 11(16), pages 1-17, August.
    3. Elminir, Hamdy K. & Azzam, Yosry A. & Younes, Farag I., 2007. "Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models," Energy, Elsevier, vol. 32(8), pages 1513-1523.
    4. Deng, Rong & Chang, Nathan L. & Ouyang, Zi & Chong, Chee Mun, 2019. "A techno-economic review of silicon photovoltaic module recycling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 532-550.
    5. Anderson, Dennis & Leach, Matthew, 2004. "Harvesting and redistributing renewable energy: on the role of gas and electricity grids to overcome intermittency through the generation and storage of hydrogen," Energy Policy, Elsevier, vol. 32(14), pages 1603-1614, September.
    6. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    7. Ali, Sajid & Lee, Sang-Moon & Jang, Choon-Man, 2018. "Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea," Renewable Energy, Elsevier, vol. 123(C), pages 652-663.
    8. Paul A. Bekker & Kees E. Bouwman, 2009. "Arbitrage Smoothing In Fitting A Sequence Of Yield Curves," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(05), pages 577-588.
    9. Shoaib, Muhammad & Siddiqui, Imran & Amir, Yousaf Muhammad & Rehman, Saif Ur, 2017. "Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1343-1351.
    10. Pereira da Silva, Patrícia & Dantas, Guilherme & Pereira, Guillermo Ivan & Câmara, Lorrane & De Castro, Nivalde J., 2019. "Photovoltaic distributed generation – An international review on diffusion, support policies, and electricity sector regulatory adaptation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 30-39.
    11. William Horan & Rachel Shawe & Bernadette O’Regan, 2019. "Ireland’s Transition towards a Low Carbon Society: The Leadership Role of Higher Education Institutions in Solar Photovoltaic Niche Development," Sustainability, MDPI, vol. 11(3), pages 1-12, January.
    12. Rahimikhoob, Ali, 2010. "Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment," Renewable Energy, Elsevier, vol. 35(9), pages 2131-2135.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    2. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    3. 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.
    4. Mecibah, Mohamed Salah & Boukelia, Taqiy Eddine & Tahtah, Reda & Gairaa, Kacem, 2014. "Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 194-202.
    5. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    6. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    7. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    8. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    9. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    10. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    11. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
    12. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    13. Krystyna Kurowska & Hubert Kryszk & Stanisław Bielski, 2022. "Location and Technical Requirements for Photovoltaic Power Stations in Poland," Energies, MDPI, vol. 15(7), pages 1-16, April.
    14. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.
    15. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    16. Halawa, Edward & GhaffarianHoseini, AmirHosein & Hin Wa Li, Danny, 2014. "Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview," Renewable Energy, Elsevier, vol. 72(C), pages 149-153.
    17. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    18. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
    19. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    20. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2241-:d:332012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.