Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation

The utility-scale photovoltaic systems have been widely integrated for sustainable urban development. However, the varying photovoltaic conversion efficiency (PVCE) affected by dynamic urban thermal environment causes uncontrollable uncertainty in electricity generation, which challenges installed-capacity planning and load-balancing operations. To tackle this problem, this study develops a PVCE estimation model containing four hierarchical modules. First, photovoltaic surface temperatures (PVSTs) are retrieved from satellite imagery, and meteorological features are collected to represent the dynamic thermal environment. Second, the contribution of each feature to the PVST estimation is evaluated, referring to the Mean Decrease in Impurity, Permutation Importance, and SHapley Addictive exPlanations. Third, machine learning models using Support Vector Machine, Random Forest, and XGBoost are developed to establish robust regressions between PVSTs and the selected features, enabling an accurate estimation of PVST spatio-temporal heterogeneity. Finally, the spatio-temporally corresponding PVCEs are calculated, resulting in a refined estimation of annual electricity generation. The investigation of four floating PV systems in Singapore found that their PVCEs vary insignificantly throughout the year, which is probably because of its stable climate and the cooling effect of water. The proposed model is simple and effective, demonstrating its impact on PV potential estimation when the urban thermal effect becomes significant.