Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images

Solar photovoltaic self-consumption is an attractive approach to increase autarky and reduce emissions in the building sector. However, a successful deployment in urban rooftops requires both accurate and low-computational-cost methods to estimate the self-consumption potential and economic feasibility, which is especially scarce in the literature on net billing schemes. In the first part of this study, a bottom-up GIS-based techno-economic model has helped compare the self-consumption potential with net metering and net billing in a Mediterranean municipality of Spain, with 3734 buildings in total. The capacity was optimized according to load profiles obtained from aggregated real measurements. Multiple load profile scenarios were assessed, revealing that the potential self-sufficiency of the municipality ranges between 21.9% and 42.5%. In the second part of the study, simplified regression-based models were developed to estimate the self-sufficiency, self-consumption, economic payback and internal rate of return at a building scale, providing nRMSE values of 3.9%, 3.1%, 10.0% and 1.5%, respectively. One of the predictors with a high correlation in the regressions is a novel coefficient that measures the alignment between the load and the hours with higher irradiance. The developed correlations can be employed for any other economic or demand scenario.

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Techno-Economic Potential of Urban Photovoltaics: Comparison of Net Billing and Net Metering in a Mediterranean Municipality

Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method

Won Joon Yoo

Papers

Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method