Field-dataset-based and PVGIS simulation-based comparative assessment of fixed-tilt, single-axis, and dual-axis photovoltaic systems: Dhaka–Sivas case study

Öz

Photovoltaic (PV) output is governed by solar irradiance, cell temperature, electrical operating point, and the incidence angle of incoming solar radiation, each of which is affected differently by fixed-tilt, single-axis, and dual-axis mounting configurations. This study presents a comparative, three-stage performance assessment of these three configurations, combining field measurements, annual PVGIS simulations, MPPT sensitivity analysis, and a techno-economic screening. Solar-tracking mechanisms increase the plane-of-array irradiance captured by the module surface, whereas maximum power point tracking (MPPT) improves electrical energy extraction by forcing the PV generator to operate near its maximum power point. The first stage analyzes a six-month field dataset measured in Dhaka, Bangladesh, between January and June 2025; the second and third stages use PVGIS-based annual simulations to compare the same configurations for Dhaka and Sivas, Türkiye. The Dhaka field dataset contains 2880 records at 30-minute intervals and includes irradiance, temperature, voltage, current, and power measurements for fixed and tracking PV systems. The field results show that the dual-axis tracking system generated 2066.20 Wh compared with 1184.30 Wh for the fixed system, corresponding to a 74.48% energy gain. Annual simulations indicate single-axis and dual-axis tracking gains of 21.13% and 31.97% in Dhaka and 18.44% and 32.90% in Sivas, respectively. A sensitivity-based MPPT analysis is included to distinguish mechanical tracking gain from converter-control gain, and a levelized-cost screening indicates that single-axis tracking can be economically favorable under benchmark cost assumptions, while dual-axis tracking requires careful cost and maintenance evaluation. The field-based findings are reported together with two candid instrumentation limitations, namely the use of a shared ambient-temperature sensor for both PV systems and the influence of the battery charge controller on the panel operating voltage, which are discussed in Section 6.

Anahtar Kelimeler

• Photovoltaic systems
• Solar tracking
• MPPT
• PVGIS
• Techno-economic analysis

Teşekkür

The author acknowledges the European Commission Joint Research Centre for open access to the PVGIS platform. The author has reviewed and edited all output and takes full responsibility for the content of this publication.

Kaynakça

1. [1] Mendeley Data. Real-time solar field dataset. [Online] Available at: https://data.mendeley.com/datasets/x8wg7shknb (Accessed: 10/07/2026).
2. [2] Esram T, Chapman PL. Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion 2007; 22 (2): 439-449.
3. [3] European Commission Joint Research Centre. PVGIS API non-interactive service. [Online] Available from: https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis/using-pvgis-5/api-non-interactive-service_en (Accessed: 10/07/2026).
4. [4] U.S. Department of Energy. Solar photovoltaic system cost benchmarks. [Online] Available from: https://www.energy.gov/cmei/systems/solar-photovoltaic-system-cost-benchmarks (Accessed: 10/07/2026).
5. [5] Ramasamy V, Feldman D, Desai J, Margolis R. U.S. solar photovoltaic system and energy storage cost benchmark: Q1 2021. National Renewable Energy Laboratory, NREL/TP-7A40-80694, 2021.
6. [6] Pelaez SA, Deline C, Greenberg P, Stein JS, Kostuk RK. Model and validation of single-axis tracking with bifacial PV. IEEE Journal of Photovoltaics 2019; 9 (3): 715-721.
7. [7] Sadeghi R, Parenti M, Memme S, Fossa M, Morchio S. A review and comparative analysis of solar tracking systems. Energies 2025; 18 (10): 2553.
8. [8] Ishaque K, Salam Z, Lauss G. The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions. Applied Energy 2014; 119: 228-236.

9. [9] Gasparin FP, Kipper FD, Oliveira FS, Krenzinger A. Assessment on the variation of temperature coefficients of photovoltaic modules with solar irradiance. Solar Energy 2022; 244: 126-133.
10. [10] Gómez-Gil FJ, Wang X, Barnett A. Energy production of photovoltaic systems: Fixed, tracking, and concentrating. Renewable and Sustainable Energy Reviews 2012; 16 (1): 306-313.
11. [11] Eke R, Senturk A. Performance comparison of a double-axis sun tracking versus fixed PV system. Solar Energy 2012; 86 (9): 2665-2672.
12. [12] Hammad B, Al-Sardeah A, Al-Abed M, Nijmeh S, Al-Ghandoor A. Performance and economic comparison of fixed and tracking photovoltaic systems in Jordan. Renewable and Sustainable Energy Reviews 2017; 80: 827-839.
13. [13] Howlader MN, Chowdhury S, Al Imon KA, Khan GJ, Mehedi MKH, Rabbi MF, Islam A, Alif SAZA. Design and implementation of a low-cost IoT-enabled dual-axis photovoltaic tracking system with experimental and simulation-based validation. Journal of Electrical Systems and Information Technology 2026; 13: 44.
14. [14] Mousazadeh H, Keyhani A, Javadi A, Mobli H, Abrinia K, Sharifi A. A review of principle and sun-tracking methods for maximizing solar systems output. Renewable and Sustainable Energy Reviews 2009; 13 (8): 1800-1818.
15. [15] Podder AK, Habibullah M, Roy NK, Pota HR. A chronological review of prospects of solar photovoltaic systems in Bangladesh: Feasibility study analysis, policies, barriers, and recommendations. IET Renewable Power Generation 2021; 15 (10): 2109-2132.
16. [16] Huld T, Müller R, Gambardella A. A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy 2012; 86 (6): 1803-1815.
17. [17] Podder AK, Roy NK, Pota HR. MPPT methods for solar PV systems: A critical review based on tracking nature. IET Renewable Power Generation 2019; 13 (10): 1615-1632.
18. [18] Ispir M, Aksoy MH, Kalyoncu M. Estimation of solar radiation and photovoltaic power potential of Türkiye using ANFIS. Journal of King Saud University - Engineering Sciences 2025; 37 (1): 2.
19. [19] Özüpak Y, Alpsalaz F, Aslan E, Uzel H. An interpretable multi-level stacking framework for photovoltaic power prediction and fault analysis. Energy 2026; 360: 141715.
20. [20] Alpsalaz F, Özüpak Y, Aslan E, Uzel H. Hybrid machine learning approach for enhanced fault detection and power estimation in photovoltaic systems. IET Renewable Power Generation 2026; 20: e70153.
21. [21] Altun BE, Alpsalaz F, Uzel H, Türkay Y. Explainable DL-based classification for power quality disturbances in renewable-energy-integrated distribution networks. IET Renewable Power Generation 2026; 20 (1): e70269.
22. [22] Keçecioğlu ÖF, Tekin M, Gani A, Açıkgöz H, Gemci A, Şekkeli M. Bir güneş enerji santralinin elektrik şebekesindeki güç kalitesi parametrelerine etkisinin incelenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 2015; 18 (2): 17-24.
23. [23] Kececioglu OF, Acikgoz H, Gani A. Fuzzy-PI based MPPT control for photovoltaic systems. 2018 Innovations in Intelligent Systems and Applications Conference (ASYU), Adana, Türkiye, 2018: 1-4.
24. [24] Gani A, Acikgoz H, Sekkeli M. Fotovoltaik sistemlerde değişken yük ve güneş ışınımı altında sinirsel-bulanık denetleyici ile maksimum güç noktası takibi. Avrupa Bilim ve Teknoloji Dergisi 2020; (19): 734-745.