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Article

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

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O&M Division, KOENERGY Korea for Gulpur Hydro Power Project, Islamabad 44000, Pakistan
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Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
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Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia
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Department of Electrical Engineering, Government College University Faisalabad, Faisalabad 38000, Pakistan
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Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
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Department of mathematics, College of Arts and Sciences of Tabrjal, Jouf University, Sakaka 72341, Saudi Arabia
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Author to whom correspondence should be addressed.
Sustainability 2020, 12(6), 2241; https://doi.org/10.3390/su12062241
Received: 31 December 2019 / Revised: 6 March 2020 / Accepted: 11 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Large-Scale Solar Electricity Networks and the Energy Transition)
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. View Full-Text
Keywords: solar power generation; Weibull distribution; irradiance patterns solar power generation; Weibull distribution; irradiance patterns
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MDPI and ACS Style

Afzaal, M.U.; Sajjad, I.A.; Awan, A.B.; Paracha, K.N.; Khan, M.F.N.; Bhatti, A.R.; Zubair, M.; Rehman, W.u.; Amin, S.; Haroon, S.S.; Liaqat, R.; Hdidi, W.; Tlili, I. Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution. Sustainability 2020, 12, 2241. https://doi.org/10.3390/su12062241

AMA Style

Afzaal MU, Sajjad IA, Awan AB, Paracha KN, Khan MFN, Bhatti AR, Zubair M, Rehman Wu, Amin S, Haroon SS, Liaqat R, Hdidi W, Tlili I. Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution. Sustainability. 2020; 12(6):2241. https://doi.org/10.3390/su12062241

Chicago/Turabian Style

Afzaal, Muhammad U., Intisar A. Sajjad, Ahmed B. Awan, Kashif N. Paracha, Muhammad F.N. Khan, Abdul R. Bhatti, Muhammad Zubair, Waqas u. Rehman, Salman Amin, Shaikh S. Haroon, Rehan Liaqat, Walid Hdidi, and Iskander Tlili. 2020. "Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution" Sustainability 12, no. 6: 2241. https://doi.org/10.3390/su12062241

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