Experimental and simulation analysis of grid-connected rooftop photovoltaic system for a large-scale facility. (October 2022)

• Record Type: Journal Article
• Authors: Khan, M. Arsalan; Islam, Nazrul; Abdul Mateen Khan, Meer; Irshad, Kashif; Hanzala, Mohammad; Ali Pasha, Amjad; Mursaleen, Mohammad
• Is Part Of: Sustainable energy technologies and assessments. Volume 53:Part D(2022)
• Publication Date: 2022-10
• Subjects: Solar Photovoltaics -- Rooftop PV -- Grid-connected system -- Performance evaluation -- PVsyst analysis -- Energy yield; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102773
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: The growth of photovoltaic (PV) in developing countries remains a major challenge due to a lack of clarity on the performance of the grid-connected PV system. This paper illustrates some of the key features of the operating performance of the 81.9 kWp PV system installed on the roof of academic buildings. Real-time data was monitored over 12 months to evaluate the performance ratio, energy yield, efficiency, and losses associated with the system. The performance evaluation was done by PVsyst and was validated using the experimental data generated from the PV grid-connected system. The experimental result shows that the maximum final yield of 4.5 kWh/kW/day was obtained during April and the minimum of 2.4 kWh/kW/day was obtained during November. The annual average array and system efficiency was 13.1 % and 12.8 %, with July reported maximum efficiency of 13.4 % and 13.1 % respectively. The effect of high temperature and solar irradiance was observed in May, resulting in the lowest array efficiency of 12.6 %. System performance is seen to have seasonal implications, this was verified through simulation studies, and it was found that to overcome energy shortages, especially during winter seasons, PVsyst analysis predicts such shortcomings more accurately.