A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknow...

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Bibliographic Details
Published in:Energies (Basel) 2020-12, Vol.13 (24), p.6712
Main Authors: Guzman Razo, Dorian Esteban, Müller, Björn, Madsen, Henrik, Wittwer, Christof
Format: Article
Language:English
Subjects:
Albedo
Algorithms
Ambient temperature
Artificial intelligence
auto-calibrated algorithms
Azimuth
digital simulation
Efficiency
Genetic algorithms
Irradiance
Machine learning
Methods
Neural networks
Optimization
Optimization algorithms
Optimization techniques
parameter estimation
Photovoltaic cells
photovoltaic systems
Photovoltaics
Simulation
Subsystems
Temperature dependence
Twinning
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Summary:A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13246712