Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries

TL;DR

This paper develops a methodology to generate long-term PV generation time series for European countries using reanalysis data, bias correction, and a novel angle inference technique, enabling analysis of high-penetration PV impacts without historical PV data.

Contribution

It introduces a new approach to create PV generation time series from reanalysis data, including bias correction and a novel angle inference method, applicable even without historical PV output data.

Findings

Generated long-term PV time series for European countries.

Analyzed the impact of different PV configurations on electricity demand and generation.

Proposed a bias correction and angle inference methodology for PV data.

Abstract

Long-term hourly time series representing the PV generation in European countries have been obtained and made available under open license. For every country, four different PV configurations, i.e. rooftop, optimum tilt, tracking, and delta have been investigated. These are shown to have a strong influence in the hourly difference between electricity demand and PV generation. To obtain PV time series, irradiance from CFSR reanalysis dataset is converted into electricity generation and aggregated at country level. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology. Moreover, a novel procedure is proposed to infer the orientation and inclination angles representative for PV panels based on the historical PV output throughout the days around summer and winter solstices. A key strength of the methodology is that it doesn't rely on historical PV output data. Consequently, it can be applied in places with no existing knowledge of PV performance.