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

Author

Listed:
  • Dorian Esteban Guzman Razo

    (Fraunhofer ISE, Fraunhofer Institute for Solar Energy Systems, Heidenhofstrasse 2, 79110 Freiburg, Germany)

  • Björn Müller

    (Fraunhofer ISE, Fraunhofer Institute for Solar Energy Systems, Heidenhofstrasse 2, 79110 Freiburg, Germany)

  • Henrik Madsen

    (Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Lyngby, Denmark)

  • Christof Wittwer

    (Fraunhofer ISE, Fraunhofer Institute for Solar Energy Systems, Heidenhofstrasse 2, 79110 Freiburg, Germany)

Abstract

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.

Suggested Citation

  • Dorian Esteban Guzman Razo & Björn Müller & Henrik Madsen & Christof Wittwer, 2020. "A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning," Energies, MDPI, vol. 13(24), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6712-:d:465067
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    References listed on IDEAS

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    2. Jesús Polo & Nuria Martín-Chivelet & Carlos Sanz-Saiz, 2022. "BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin," Energies, MDPI, vol. 15(11), pages 1-11, June.

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