A statistical approach for hourly photovoltaic power generation modeling with generation locations without measured data

• A Monte Carlo based methodology to analyze photovoltaic generation is proposed.
• Able to assess photovoltaic generation locations without measurement data.
• Combines a statistical irradiance model with a photovoltaic power generation model.
• The methodology is verified against solar irradiance measurements.
• Case studies are presented to demonstrate the applicability of the methodology.

The use of solar energy is becoming increasingly widespread in many countries at the time of writing. Due to its stochastic nature, the increasing amount of solar generation in the generation mix has to be taken into account when planning electric power systems at both distribution and transmission system levels. The presented Monte Carlo simulation based statistical methodology is able to analyze photovoltaic generation scenarios comprising new generation locations without measured data from those locations. The introduced model is able to assess the spatial and temporal correlations between the generation locations in geographical areas of varying size and amount of installed photovoltaic generation. The model is verified against measured solar irradiance data from Finland. In addition, the paper couples a polycrystalline silicon photovoltaic panel power generation model with the statistical model and presents a case study to illustrate the applicability of the methodology for analyzing large scale solar generation.