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Real Time Maximum Power Point Tracking Algorithm Optimization and Levelized Cost Reduction in Solar Arrays
Subject area: Science,Engineering and Technology · Area of research: Solar Arrays
Abstract
The economic viability of solar photovoltaic systems hinges on two interlinked factors: how efficiently energy is extracted in real time, and how that efficiency translates into lower lifecycle costs. Maximum Power Point Tracking algorithms are the primary mechanism for reducing immediate power extraction, but typical approaches are often delayed under dynamic environmental conditions, leading to energy losses that accumulate over a system’s 20-25-year lifespan. This paper develops a conceptual framework for optimizing real-time Maximum Power Point Tracking algorithms to fully reduce the Levelized Cost of Energy in both residential and commercial solar arrays. We argue that optimizing should be treated as a multi-objective problem in which tracking speed, stability, computational load, and adaptability are balanced against hardware constraints and maintenance costs. The review examines conventional, intelligent, and unique MPPT strategies, emphasizing their implications for energy yield, inverter stress, and system degradation. The analysis suggests that even modest gains in tracking efficiency, when sustained over decades, can meaningfully lower the Levelized Cost Of Energy, by improving energy yield without proportional increases in capital expenditure. The paper concludes by outlining design considerations for implementing such optimization in real-world systems and identifying conceptual gaps that future research should address.
Keywords
Real-time optimization, Solar Photovoltaic, Levelized Cost of Energy, Adaptive control, Energy yield, Lifecycle cost, and Maximum Power Point Tracking.
References
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[3] Esram, Trishan & Chapman, P.L.. (2007). Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques. Energy Conversion, IEEE Transactions on. 22. 439 – 449. 10.1109/TEC.2006.874230.
[4] Harrison, A. & Nfah Mbaka, Eustace & Nguimfack Ndongmo, Jean De Dieu & Alombah, Njimboh. (2022). An Enhanced P&O MPPT Algorithm for PV Systems with Fast Dynamic and Steady-State Response under Real Irradiance and Temperature Conditions. International Journal of Photoenergy. 2022. 10.1155/2022/6009632.
[5] Mohammed, Faiz & Hasan, Fattah & Kareem, Parween & Hasan, Raed & Saleh, Khalid & Ahmed, Ahmed. (2025). Optimization and Simulation of the Perturb and Observe Algorithm for Maximum Power Point Tracking in Photovoltaic Systems EISSN: 2788-9920 NTU Journal for Renewable Energy. NTU Journal of Renewable Energy. 9. 73-81. 10.56286/02qc5x54.
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How to cite this paper
@article{1719718,
author = {Aniekan Oliseh Eno-Ibanga, Dr. Samir Abood},
title = {Real Time Maximum Power Point Tracking Algorithm Optimization and Levelized Cost Reduction in Solar Arrays},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {2065-2069},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719718.pdf},
abstract = {The economic viability of solar photovoltaic systems hinges on two interlinked factors: how efficiently energy is extracted in real time, and how that efficiency translates into lower lifecycle costs. Maximum Power Point Tracking algorithms are the primary mechanism for reducing immediate power extraction, but typical approaches are often delayed under dynamic environmental conditions, leading to energy losses that accumulate over a system’s 20-25-year lifespan. This paper develops a conceptual framework for optimizing real-time Maximum Power Point Tracking algorithms to fully reduce the Levelized Cost of Energy in both residential and commercial solar arrays. We argue that optimizing should be treated as a multi-objective problem in which tracking speed, stability, computational load, and adaptability are balanced against hardware constraints and maintenance costs. The review examines conventional, intelligent, and unique MPPT strategies, emphasizing their implications for energy yield, inverter stress, and system degradation. The analysis suggests that even modest gains in tracking efficiency, when sustained over decades, can meaningfully lower the Levelized Cost Of Energy, by improving energy yield without proportional increases in capital expenditure. The paper concludes by outlining design considerations for implementing such optimization in real-world systems and identifying conceptual gaps that future research should address.},
keywords = {Real-time optimization, Solar Photovoltaic, Levelized Cost of Energy, Adaptive control, Energy yield, Lifecycle cost, and Maximum Power Point Tracking.},
month = {March},
doi = {https://doi.org/10.64388/IREV8I9-1719718}
}