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Performance improvement of air cooled photo-voltaic thermal panel using economic model predictive control and vortex generators

Author

Listed:
  • Karkaba, H.
  • Etienne, L.
  • Pelay, U.
  • Russeil, S.
  • Simo tala, J.
  • Boonaert, J.
  • Lecoeuche, S.
  • Bougeard, D.

Abstract

Photo-Voltaic/Thermal (PVT) system performance is defined by two main factors, the electric power generated from the Photo-Voltaic (PV) module, and the thermal power that is extracted from the PVT module. To increase the energy output, several control techniques can be applied. In the present work, a Economic model predictive Control (EMPC) strategy is used to enhance the performance of the PVT system. A dynamic model for the PVT system is developed using the Modelica language in the Dynamic Modeling Laboratory software (DYMOLA). Then, EMPC controller is defined in Matlab/Simulink. Two geometrical cases for the duct side of the PVT system are studied as different heat intensification techniques. First, an empty channel is considered and then vortex generators (VGs) are inserted into the channel. Simulations are carried out with summer and winter days in the north of France with two energy use scenarios referred to as no heat recovery (NHR) and heat recovery (HR) scenarios. The results showed that when using an EMPC controller with a heat recovery scenario the energy gain increases by 174% and 234% for empty channel and for vortex generator geometrical cases respectively. In order to better analyze the obtained results, cell temperature and mass flow rate are plotted for all the studied scenarios as a function of time. Finally, power generation as a function of irradiance is plotted in order to distinguish when the benefits of cooling out-weight its cost.

Suggested Citation

  • Karkaba, H. & Etienne, L. & Pelay, U. & Russeil, S. & Simo tala, J. & Boonaert, J. & Lecoeuche, S. & Bougeard, D., 2023. "Performance improvement of air cooled photo-voltaic thermal panel using economic model predictive control and vortex generators," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012478
    DOI: 10.1016/j.renene.2023.119332
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