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Predicting the behavior of a grid-connected photovoltaic system from measurements of solar radiation and ambient temperature

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  • Hernandez, J.
  • Gordillo, G.
  • Vallejo, W.

Abstract

This paper presents a methodology to predict in a statistically reliable way the behavior of a grid-connected photovoltaic system. The methodology developed can be implemented either in common programming software or through an off-the-shelf simulation of electrical systems. Initially, the atmospheric parameters that influence the behavior of PV generators (radiation and temperature) are characterized in a probabilistic manner. In parallel, a model compound by various PV generator components is defined: the modules (and their electrical and physical characteristics), their connection to form the generator, and the inverter type. This model was verified for comparing their behavior with output measured on a real installed system of 3.6kWp. The solar resource characterized and the photovoltaic system model are integrated in a non-deterministic approach using the stochastic Monte Carlo method, developed in the programming language DPL of the electrical-systems simulation software DIGSILENT®. It is done to estimate the steady-state electrical parameters describing the influence of the grid-connected photovoltaic system. Specifically, we estimated the nominal peak power of the PV generator to minimize network losses, subject to constraints on nodes voltages and conductor currents.

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  • Hernandez, J. & Gordillo, G. & Vallejo, W., 2013. "Predicting the behavior of a grid-connected photovoltaic system from measurements of solar radiation and ambient temperature," Applied Energy, Elsevier, vol. 104(C), pages 527-537.
    • Handle: RePEc:eee:appene:v:104:y:2013:i:c:p:527-537
    DOI: 10.1016/j.apenergy.2012.10.022
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    References listed on IDEAS

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    1. Alonso García, M.C. & Balenzategui, J.L., 2004. "Estimation of photovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations," Renewable Energy, Elsevier, vol. 29(12), pages 1997-2010.
    2. Ayompe, L.M. & Duffy, A. & McCormack, S.J. & Conlon, M., 2010. "Validated real-time energy models for small-scale grid-connected PV-systems," Energy, Elsevier, vol. 35(10), pages 4086-4091.
    3. Paatero, Jukka V. & Lund, Peter D., 2007. "Effects of large-scale photovoltaic power integration on electricity distribution networks," Renewable Energy, Elsevier, vol. 32(2), pages 216-234.
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    Cited by:

    1. Roy, Sanjoy, 2015. "Statistical estimates of short duration power generated by a photovoltaic unit in environment of scattered cloud cover," Energy, Elsevier, vol. 89(C), pages 14-23.
    2. Tovilović, Duško M. & LJ. Rajaković, Nikola, 2015. "The simultaneous impact of photovoltaic systems and plug-in electric vehicles on the daily load and voltage profiles and the harmonic voltage distortions in urban distribution systems," Renewable Energy, Elsevier, vol. 76(C), pages 454-464.
    3. Hammad, Bashar & Al–Abed, Mohammad & Al–Ghandoor, Ahmed & Al–Sardeah, Ali & Al–Bashir, Adnan, 2018. "Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2218-2234.
    4. Speidel, Stuart & Bräunl, Thomas, 2016. "Leaving the grid—The effect of combining home energy storage with renewable energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1213-1224.
    5. Renzi, M. & Egidi, L. & Comodi, G., 2015. "Performance analysis of two 3.5kWp CPV systems under real operating conditions," Applied Energy, Elsevier, vol. 160(C), pages 687-696.
    6. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    7. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.

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