Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas

Households present a significant contribution in the national energy consumption, and photovoltaics (PV) has become an economically feasible technology that can play an important role to lower this consumption and the associated emissions. Nevertheless, there is still a gap between too in-depth technical models for detailed studies and what urban energy planners need, which are simpler, yet reliable techno-economical tools to select which roofs of city buildings are the best candidates for PV production. In order to face this gap, a multiple linear regression (MLR) model has been developed to determine the economic payback using dimensionless parameters. The methodology has been adopted in the city of Valencia (Spain) for a large sample of multi-storey buildings, which are the most common typology. The approach has a high replicability since it can be applied for different countries. The MLR model provides a payback root mean squared error (RMSE) of 0.48 years in comparison with a complex techno-economic model which was previously developed and validated with the software System Advisor Model (SAM). The variables which have a bigger weight in the payback are the shadow losses and the power unit cost due to the economy of scale. With the current Spanish regulation, PV installations on multi-storey buildings can reach paybacks of around 7-15 years and the best option is to have large economies of scales together with a low energy surplus.

Automated summary

Researchers have developed an MLR model to determine the economic payback of photovoltaic installations on multi-storey buildings in Valencia, Spain. The model shows that shadow losses and power unit cost are key factors affecting payback, with economies of scale playing a significant role. This approach can lead to paybacks of 7-15 years under current Spanish regulations, highlighting the importance of large economies of scale and low energy surplus.