Abstract
The low energy yield of solar photovoltaic plants remains a critical challenge, primarily due to elevated operating temperatures and extensive soiling rates. To address these limitations, besides tracking, this paper suggests the integration of solar PVT system for cooling and robotic cleaning system in solar modules. In this work, three performance metrics: energy production, thermal efficiency of PVT system and degradation rate of solar module are optimized simultaneously using MOPSO (Multi-Objective Particle Swarm Optimization) framework coupled with TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) rank based decision algorithm. A comparative analysis is conducted under anisotropic condition for four PV technologies: mono-crystalline, polycrystalline, microcrystalline and thin-film modules. The region chosen is Quaid-e-Azam solar park located in Bahawalpur Pakistan. The MATLAB computational simulation results indicate that mono crystalline modules yield a higher energy production of 16.8 MWH whereas polycrystalline modules demonstrate a superior PVT thermal efficiency of 92.15% (neglecting losses). In contrast, thin-film and polycrystalline modules exhibit only a marginal improvement in life span of approximately 1 year as compared to other modules. This intelligent framework supports global energy enhancement through automated solar PV systems by integrating module type with their life time evaluation.
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