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A simple stochastic method for modelling the uncertainty of photovoltaic power production based on measured data

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  • Barukčić, M.
  • Hederić, Ž.
  • Hadžiselimović, M.
  • Seme, S.

Abstract

This paper describes statistical quantification tools for predicting photovoltaic (PV) production considering uncertainty in PV production at same irradiation levels and PV panel temperatures. When analysing measured data, it is observed that there are different PV power production levels for the same irradiation levels and panel temperatures. These PV power spread out can be caused by different causes, such as dust deposition over the panel, non-ideal working of maximal power point tracking devices, device efficiencies' dependence on power, different temperatures over the PV panel, and others. Due to the stochastic character of these occurrences, they can be challenging when considered in the deterministic mathematical models usually used for PV power prediction. The probabilistic method for PV power production is proposed based on the probability density function with respect to the solar irradiation and the panel temperature. The simulation results are compared among the different models based on the probability density function with respect to the solar irradiation and panel temperature. The best overlapping between measured and calculated PV power production gives the proposed stochastic models dependent only on irradiance. The proposed stochastic model gives a PV energy prediction on a yearly basis with an error of less than 1%.

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  • Barukčić, M. & Hederić, Ž. & Hadžiselimović, M. & Seme, S., 2018. "A simple stochastic method for modelling the uncertainty of photovoltaic power production based on measured data," Energy, Elsevier, vol. 165(PB), pages 246-256.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:246-256
    DOI: 10.1016/j.energy.2018.09.134
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    1. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    2. Zhu, Y. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2015. "A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty," Energy, Elsevier, vol. 88(C), pages 636-649.
    3. Laib, I. & Hamidat, A. & Haddadi, M. & Ramzan, N. & Olabi, A.G., 2018. "Study and simulation of the energy performances of a grid-connected PV system supplying a residential house in north of Algeria," Energy, Elsevier, vol. 152(C), pages 445-454.
    4. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
    5. Moslehi, Salim & Reddy, T. Agami & Katipamula, Srinivas, 2018. "Evaluation of data-driven models for predicting solar photovoltaics power output," Energy, Elsevier, vol. 142(C), pages 1057-1065.
    6. Savvakis, Nikolaos & Tsoutsos, Theocharis, 2015. "Performance assessment of a thin film photovoltaic system under actual Mediterranean climate conditions in the island of Crete," Energy, Elsevier, vol. 90(P2), pages 1435-1455.
    7. Su, Yan & Chan, Lai-Cheong & Shu, Lianjie & Tsui, Kwok-Leung, 2012. "Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems," Applied Energy, Elsevier, vol. 93(C), pages 319-326.
    8. Kusakana, Kanzumba, 2015. "Operation cost minimization of photovoltaic–diesel–battery hybrid systems," Energy, Elsevier, vol. 85(C), pages 645-653.
    9. Antonio Bracale & Pierluigi Caramia & Guido Carpinelli & Anna Rita Di Fazio & Gabriella Ferruzzi, 2013. "A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control," Energies, MDPI, vol. 6(2), pages 1-15, February.
    10. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    11. Silvestre, Santiago & Tahri, Ali & Tahri, Fatima & Benlebna, Soumiya & Chouder, Aissa, 2018. "Evaluation of the performance and degradation of crystalline silicon-based photovoltaic modules in the Saharan environment," Energy, Elsevier, vol. 152(C), pages 57-63.
    12. Heydarabadi, Hoda & Abdolzadeh, Morteza & Lari, Khosro, 2017. "Simulation of airflow and particle deposition settled over a tilted Photovoltaic module," Energy, Elsevier, vol. 139(C), pages 1016-1029.
    13. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
    14. Najibi, Fatemeh & Niknam, Taher & Kavousi-Fard, Abdollah, 2016. "Optimal stochastic management of renewable MG (micro-grids) considering electro-thermal model of PV (photovoltaic)," Energy, Elsevier, vol. 97(C), pages 444-459.
    15. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin & Parastegari, Moein, 2016. "Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties," Energy, Elsevier, vol. 117(P1), pages 176-189.
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