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Dynamical optimal positioning of a photovoltaic panel in all weather conditions

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  • Gulin, Marko
  • Vašak, Mario
  • Perić, Nedjeljko

Abstract

In this paper we develop and verify a model predictive control algorithm for photovoltaic panel orientation with the aim to maximize the photovoltaic system netto power production. Thereby we take into account local weather forecast with its uncertainty, thermal behavior of the panel, and the positioning system energy consumption with its technical constraints. The model predictive control synthesis procedure comprises two basic steps: (i) identification of solar irradiance model and development of the photovoltaic system model and (ii) development of predictive control algorithm for the photovoltaic panel active surface orientation, based on the obtained models. Performance of the developed algorithm is verified through year-scale simulations based on a large number of solar irradiance and other weather data patterns. It turns out that the proposed algorithm is fully competitive with the mostly used sun tracking or maximum irradiance seeking controls, and that it outperforms them. The other advantages of the proposed algorithm are: (i) the positioning system is controlled smoothly and (ii) prediction of energy yield one day ahead is available together with its uncertainty for easier photovoltaic system integration into the electricity distribution network.

Suggested Citation

  • Gulin, Marko & Vašak, Mario & Perić, Nedjeljko, 2013. "Dynamical optimal positioning of a photovoltaic panel in all weather conditions," Applied Energy, Elsevier, vol. 108(C), pages 429-438.
    • Handle: RePEc:eee:appene:v:108:y:2013:i:c:p:429-438
    DOI: 10.1016/j.apenergy.2013.03.006
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    Cited by:

    1. Nsengiyumva, Walter & Chen, Shi Guo & Hu, Lihua & Chen, Xueyong, 2018. "Recent advancements and challenges in Solar Tracking Systems (STS): A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 250-279.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    3. Gulin, Marko & Pavlović, Tomislav & Vašak, Mario, 2016. "Photovoltaic panel and array static models for power production prediction: Integration of manufacturers’ and on-line data," Renewable Energy, Elsevier, vol. 97(C), pages 399-413.
    4. Li, Qian & Wu, Zhou & Xia, Xiaohua, 2018. "Estimate and characterize PV power at demand-side hybrid system," Applied Energy, Elsevier, vol. 218(C), pages 66-77.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    6. Raptis, P.I. & Kazadzis, S. & Psiloglou, B. & Kouremeti, N. & Kosmopoulos, P. & Kazantzidis, A., 2017. "Measurements and model simulations of solar radiation at tilted planes, towards the maximization of energy capture," Energy, Elsevier, vol. 130(C), pages 570-580.
    7. Sato, Daisuke & Yamagata, Yuki & Hirata, Kenji & Yamada, Noboru, 2020. "Mathematical power-generation model of a four-terminal partial concentrator photovoltaic module for optimal sun-tracking strategy," Energy, Elsevier, vol. 213(C).
    8. Yilmaz, Saban & Riza Ozcalik, Hasan & Dogmus, Osman & Dincer, Furkan & Akgol, Oguzhan & Karaaslan, Muharrem, 2015. "Design of two axes sun tracking controller with analytically solar radiation calculations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 997-1005.
    9. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.

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