A Review and Comparative Analysis of Solar Tracking Systems

Reza Sadeghi, Mattia Parenti, Samuele Memme, Marco Fossa () and Stefano Morchio
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Reza Sadeghi: Dime Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via Opera Pia 15a, 16145 Genova, Italy
Mattia Parenti: Dime Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via Opera Pia 15a, 16145 Genova, Italy
Samuele Memme: Dime Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via Opera Pia 15a, 16145 Genova, Italy
Marco Fossa: Dime Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via Opera Pia 15a, 16145 Genova, Italy
Stefano Morchio: Dime Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via Opera Pia 15a, 16145 Genova, Italy

Energies, 2025, vol. 18, issue 10, 1-48

Abstract: This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

Keywords: solar tracking system; control strategies; optical cameras; agrivoltaics (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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