Solar power at new heights: comparing photovoltaic performance across altitudes
Year: 2025
Type: article
Abstract: The transition to renewable energy is essential for mitigating climate change, especially in mountainous regions where energy access and climate vulnerability pose challenges. This study explores photovoltaic (PV) system performance across high- and low-altitude sites in Lebanon, Italy, France, and Switzerland using simulations from the Photovoltaic Geographical Information System (PVGIS). Results show that while low-altitude areas typically yield higher annual energy outputs, high-altitude locations demonstrated seasonal advantages, particularly in spring and summer. Solar irradiation was up to 17.2% higher at lower altitudes overall. However, Performance Ratios, ranging from 73.73% to 88.64%, were generally higher year-round in mountainous areas, indicating greater efficiency at cooler temperatures. A strong inverse correlation between Performance Ratio and module temperature highlights the effect of temperature on PV performance. The Capacity Utilization Factor also varied seasonally, with higher values observed at high-altitude sites during certain months of spring, summer, and autumn. This factor strongly correlated with in-plane radiation, emphasizing irradiation’s role in energy yield. Conducted in accordance with IEC 61724 standards, this research supports energy planning in remote regions. The findings underline the importance of both altitude and geography in solar energy performance, offering valuable insights for expanding renewable energy in diverse and challenging terrains.
Source: Solar Energy
Institutions: Centre National de la Recherche Scientifique, Laboratoire Optimisation de la Conception et Ingénierie de l'Environnement, National Interuniversity Consortium of Materials Science and Technology, University of Turin, Université Savoie Mont Blanc, Centre d'Étude et de Recherche de Djibouti, University of Rome Tor Vergata, Institute of Science, Technology and Sustainability for Ceramics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,900
OpenAlex ID: W4417476318
