Experimental Evaluation of Energy Efficiency of Four Sun-Tracking Photovoltaic Configurations

Abdellatif Hraich (), Ali Haddi, Abdellah El Fadar and Oussama Achkari Begdouri
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Abdellatif Hraich: Laboratory of Industrial and Civil Sciences and Technologies (Lab STIC), National School of Applied Sciences, Abdelmalek Essaadi University, Tetouan 93000, Morocco
Ali Haddi: Laboratory of Industrial and Civil Sciences and Technologies (Lab STIC), National School of Applied Sciences, Abdelmalek Essaadi University, Tetouan 93000, Morocco
Abdellah El Fadar: Laboratory of Innovative Technologies, National School of Applied Sciences, Tangier, Abdelmalek Essaadi University, Tetouan 90000, Morocco
Oussama Achkari Begdouri: National School of Applied Sciences, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco

Energies, 2025, vol. 18, issue 22, 1-22

Abstract: The sun tracker plays a major role in improving the energy efficiency of a solar power system. To address this role, this study experimentally explores the energy efficiency of three sun-tracking systems with three types of degrees of freedom (DOFs)—namely, single-axis for both elevation (1DOF_Elev) and azimuth (1DOF_Azim), and dual-axis (2DOF)—integrated in photovoltaic (PV) panels. The three sun-tracking configurations are assessed and compared with the fixed system (0DOF), considering both the net electricity output of the studied photovoltaic system and the energy consumption of each configuration during operation. To accomplish this objective, hardware and software tools were deployed to create a prototype. The sun-tracking techniques are based on the sun position algorithm (astronomical calculations). The different data (time, voltage, current, power, azimuth, and elevation) are stored in real time within a locally developed database which represents crucial data within SCADA systems embedded in smart grids. The results revealed that the 2DOF system exhibits the highest energy efficiency (37.23%), followed by 1DOF_Azim (12.86%), and then by 1DOF_Elev (10.05%), when compared to 0DOF. Overall, this study provides solutions for optimizing photovoltaic energy production and could be integrated into battery-powered devices to accelerate battery recharging, achieving time savings of over 30%.

Keywords: battery; data digitization; energy efficiency; photovoltaic; optimizing energy; smart-grid; sun-tracking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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