AI based Microgrids Performance Optimization using Integrated Solar techniques | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Volume 34, Issue 1, January 2025, Page 13-25 PDF (1.4 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2025.330808.1097 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Phlip Youssef Najeeb  1; Ashraf M. Aboshosha2; Ayman S. Haggag1
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| 1Electronics Technology Department, Faculty of Technology and Education, Helwan University, Cairo, Egypt | ||||
| 2Egyptian Atomic Energy Authority | ||||
| Abstract | ||||
| Many countries are working to install small solar power plants on residential and local buildings' roofs. In Egypt, numerous public buildings, such as schools, have empty and unutilized roofs that are mostly free from shadows or obstacles. The research suggests the installation of small-scale solar stations on the building’s rooftops. These stations would make use of large portions of the roofs to produce clean electricity, powering the institutions and connecting them to the public electrical grid. The research explores ways to enhance the efficiency of these small-scale independent solar stations by examining various factors such as building dimensions, site coordinates, panel size, roof area available for installation, Sun angles each day, and roof heights. Key factors that affect photovoltaic panels include regional climatic conditions, panel inclination angles, distances between panel rows, solar radiation, and panel temperature. To analyze these variables, the research utilized numerous programs involving artificial intelligence, including PVSYST, Skelion, and Google Earth. These tools helped determine optimal distances between rows and optimal inclination angles for solar panel installation. They also enabled the assessment of climatic factors' impact on panel performance, calculations of the produced electrical energy, and estimations of annual energy production losses. The findings indicated that the optimal inclination angle for solar module ranges from 18 to 22º to minimize shadows’ effects on energy production. Furthermore, the results emphasized the significance of solar radiation, demonstrating a direct relationship between the average monthly sun radiation and the power produced by the panels. | ||||
| Keywords | ||||
| PV simulation; PV radiation effect; PVSYST software; Skelion software; Tilt angle | ||||
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