AUTHOR=Guzman Razo Dorian Esteban , Madsen Henrik , Wittwer Christof 

TITLE=Genetic algorithm optimization for parametrization, digital twinning, and now-casting of unknown small- and medium-scale PV systems based only on on-site measured data

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1060215

DOI=10.3389/fenrg.2023.1060215

ISSN=2296-598X

ABSTRACT=Accurately predicting and balancing energy generation and consumption is crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, those predictions ideally should be performed based only on on-site measured data and data available within the monitoring platforms, data which is scarce for small and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a Genetic Algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of -1.14 W/kWp and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.