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Solar photovoltaic (PV) systems play a crucial role in addressing the growing demand for clean energy and mitigating climate change impacts. However, PV system performance is heavily influenced by the incident solar radiation on panel surfaces, with suboptimal tilt angles leading to significant power losses. Despite the critical importance of tilt angle optimization, many existing PV installations worldwide operate suboptimally due to simplified estimation methods or lack of site-specific optimization. This study presents a novel hybrid approach combining empirical and computational methods to determine optimal annual and monthly PV panel tilt angles using long-term hourly ERA5 reanalysis radiation data. Our results validate the effectiveness of ERA5 data for global tilt angle optimization, demonstrating a strong correlation with established cubic relations. Analysis of spatial and temporal patterns of optimized tilt angles reveals the influence of latitude, local atmospheric conditions, and seasonal variations on optimal PV panel inclination. A comprehensive assessment of the global PV inventory in 2018 shows that 44.6% of installed capacity is located in regions with solar power losses exceeding 1%, resulting in a total loss of 6154 GWh yr ^−1 —equivalent to Luxembourg’s annual electricity consumption. Comparison between optimized tilt angles and those estimated using empirical cubic schemes reveals significant discrepancies in some regions, with annual power losses surpassing 3% when using empirical methods. These findings underscore the importance of accurate, location-specific tilt angle optimization to minimize solar power losses and maximize global PV inventory performance. Our research highlights the potential for substantial energy yield improvements through widespread adoption of optimized tilt angles in PV system design and retrofitting, contributing to enhanced renewable energy production and accelerated progress towards global sustainability goals.
Solar photovoltaic (PV) systems play a crucial role in addressing the growing demand for clean energy and mitigating climate change impacts. However, PV system performance is heavily influenced by the incident solar radiation on panel surfaces, with suboptimal tilt angles leading to significant power losses. Despite the critical importance of tilt angle optimization, many existing PV installations worldwide operate suboptimally due to simplified estimation methods or lack of site-specific optimization. This study presents a novel hybrid approach combining empirical and computational methods to determine optimal annual and monthly PV panel tilt angles using long-term hourly ERA5 reanalysis radiation data. Our results validate the effectiveness of ERA5 data for global tilt angle optimization, demonstrating a strong correlation with established cubic relations. Analysis of spatial and temporal patterns of optimized tilt angles reveals the influence of latitude, local atmospheric conditions, and seasonal variations on optimal PV panel inclination. A comprehensive assessment of the global PV inventory in 2018 shows that 44.6% of installed capacity is located in regions with solar power losses exceeding 1%, resulting in a total loss of 6154 GWh yr ^−1 —equivalent to Luxembourg’s annual electricity consumption. Comparison between optimized tilt angles and those estimated using empirical cubic schemes reveals significant discrepancies in some regions, with annual power losses surpassing 3% when using empirical methods. These findings underscore the importance of accurate, location-specific tilt angle optimization to minimize solar power losses and maximize global PV inventory performance. Our research highlights the potential for substantial energy yield improvements through widespread adoption of optimized tilt angles in PV system design and retrofitting, contributing to enhanced renewable energy production and accelerated progress towards global sustainability goals.
Global optimization of photovoltaic tilt angles: reducing solar power losses using reanalysis data
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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