Python-Based Implementation of Metaheuristic MPPT Techniques: A Cost-Effective Framework for Solar Photovoltaic Systems in Developing Nations

Syed Majed Ashraf, M. Saad Bin Arif (), Mohammed Khouj, Shahrin Md. Ayob () and Muhammad I. Masud ()
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Syed Majed Ashraf: Bharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi, New Delhi 110016, India
M. Saad Bin Arif: Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh 202002, India
Mohammed Khouj: Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
Shahrin Md. Ayob: Faculty of Electrical Engineering, Universiti Teknologi Malaysia UTM, Johor Bharu 81310, Malaysia
Muhammad I. Masud: Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia

Energies, 2025, vol. 18, issue 12, 1-18

Abstract: Despite the convenience of solar potential and the magnitude of energy received by the Earth from the sun, solar photovoltaic systems have failed to meet the growing energy demand. This can be attributed to various factors such as low cell efficiency, environmental conditions, and improper tracking of operating points, which further worsen the system’s performance. Various advanced metaheuristic-based Maximum Power Point Tracking (MPPT) techniques were reported in the literature. Most available techniques were designed and tested in subscription-based/paid software such as MATLAB/Simulink, PSIM simulator, etc. Due to this, the simulation and analysis of these MPPT algorithms for developing and underdeveloped countries added an extra economic burden. Many open-source PV libraries are computationally intensive, lack active support, and prove impractical for MPPT testing on resource-constrained hardware. Their complexity and absence of optimization for edge devices limit their viability for the edge device. This issue is addressed in this research by designing a robust framework using an open-source programming language i.e., Python. For demonstration purposes, we simulated and analyzed a solar PV system and benchmarked its performance against the JAP6 solar panel. We implemented multiple metaheuristic MPPT algorithms including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), evaluating their efficacy under both Standard Test Conditions (STC) and complex partial shading scenarios. The results obtained validate the feasibility of the implementation in Python. Therefore, this research provides a comprehensive framework that can be utilized to implement sophisticated designs in a cost-effective manner for developing and underdeveloped nations.

Keywords: photovoltaic; P-V curve; metaheuristic MPPT; Python (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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