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Management and Supervision of Solar Battery Charge and Discharge Using Artificial Neural Networks

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Growing concerns about the depletion of the world's natural resources and future energy supply have spurred the development, the adoption, and the integration of renewable energy sources (solar, wind and biomass ...). The latter is characterized by the non-polluting, green, environmentally friendly. One of the most widely known advances in the development of solar technology has been the photovoltaic (PV), a clean, green and renewable energy system that uses photovoltaic cells to convert solar radiation into electricity. In the presence of the many advantages of using photovoltaic energy sources, their random operation that depends on the presence of solar irradiation and temperature presents a challenge. In this paper, the focus is on a PV system that stores energy in a battery using a bidirectional DC-DC converter controlled by a neural network. The overall system has been designed and simulated on MATLAB Simulink. The simulation results are used to verify the performance of the management system under different test conditions.
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Artificial Neural Networks; Bidirectional DC/DC; Energy Management; Photovoltaic Panel; Renewable Energy; Solar Battery

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