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Dynamic thermal modelling for the prediction of the operating temperature of a PV panel with an integrated cooling system

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  • Osma-Pinto, German
  • Ordóñez-Plata, Gabriel

Abstract

Water-based cooling methods increase the output power of photovoltaic (PV) panels. One of the most promising methods is an irrigation of the front surface of a PV panel. Although some studies have experimentally investigated this method, there remains a lack of information for predicting the operating temperature of a PV panel (TPV) during the irrigation cycle. Therefore, in this study, a dynamic thermal model (RC model) is proposed to predict the TPV and water temperature (Twtq) during an intermittent irrigation cycle relying on the energy balance of the PV panel and irrigation water, which in turn is based on a dynamic thermal model of a non-irrigated PV panel that shows a good performance for quick variations in the solar irradiance and air velocity. The use of both models allows studying the three thermal transients that a PV panel can experiment during intermittent irrigation, direct cooling, indirect cooling, and heating, as well as the processes of heating and convective cooling of water in the tank. Two experiments were conducted to establish the thermal capacitance per unit area of a PV panel (csPV) and superficial conductance (h) for three air velocities (0.0, 1.15, and 2.10 m/s). The models were validated using an experimental setup with PV panels (255 W); three validation cases (for a typical day, with forced ventilation, and constraining the heat transfer) were conducted for a non-irrigated operation using NRMSETPV of 4.0%–5.2%; in addition, the model was validated for an intermittent irrigation operation during the day with NRMSETPV and NRMSETwtq of 5.5% and 5.6%, respectively. Results indicate that both RC models can predict successfully the behaviour TPV, even when face intense thermal transients caused by abrupt changes of solar irradiance and air velocity and the intermittent irrigation on front surface of the PV panel.

Suggested Citation

  • Osma-Pinto, German & Ordóñez-Plata, Gabriel, 2020. "Dynamic thermal modelling for the prediction of the operating temperature of a PV panel with an integrated cooling system," Renewable Energy, Elsevier, vol. 152(C), pages 1041-1054.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:1041-1054
    DOI: 10.1016/j.renene.2020.01.132
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    2. Mariyam Sattar & Abdul Rehman & Naseem Ahmad & AlSharef Mohammad & Ahmad Aziz Al Ahmadi & Nasim Ullah, 2022. "Performance Analysis and Optimization of a Cooling System for Hybrid Solar Panels Based on Climatic Conditions of Islamabad, Pakistan," Energies, MDPI, vol. 15(17), pages 1-22, August.
    3. Bevilacqua, Piero & Bruno, Roberto & Rollo, Antonino & Ferraro, Vittorio, 2022. "A novel thermal model for PV panels with back surface spray cooling," Energy, Elsevier, vol. 255(C).
    4. Li, Qingxiang & Zhu, Li & Sun, Yong & Lu, Lin & Yang, Yang, 2020. "Performance prediction of Building Integrated Photovoltaics under no-shading, shading and masking conditions using a multi-physics model," Energy, Elsevier, vol. 213(C).
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    6. Yamuna Munusamy & John Wong Lin Onn & Mohammed Alquraish & Mohamed Kchaou & Sumathi Sethupathi, 2021. "Thermal Performance of Finned Heat Sinks Embedded with Form-Stable Myristic Acid Phase Change Material in Photovoltaic Cooling for Green Energy Storage," Energies, MDPI, vol. 14(21), pages 1-14, October.
    7. Yildirim, Mehmet Ali & Cebula, Artur & Sułowicz, Maciej, 2022. "A cooling design for photovoltaic panels – Water-based PV/T system," Energy, Elsevier, vol. 256(C).
    8. Keddouda, Abdelhak & Ihaddadene, Razika & Boukhari, Ali & Atia, Abdelmalek & Arıcı, Müslüm & Lebbihiat, Nacer & Ihaddadene, Nabila, 2024. "Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation," Applied Energy, Elsevier, vol. 363(C).

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