Maximum Power Point Tracking Control for Photovoltaic Battery Systems using Deep Q Network Algorithm - Dong, Tran (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Currently, many countries around the world have taken specific steps to gradually replace traditional fossil energy sources with renewable energy sources, of which solar energy is an appropriate choice. Power generation using photovoltaic (PV) batteries is becoming increasingly important because this is a renewable energy source with many advantages such as no fuel costs, no environmental pollution, requiring little maintenance, and does not emit noise compared to other energy sources. However, PV modules when working with inappropriate load impedance still have low conversion efficiency, so maximum power point tracking (MPPT) for PV is essential in a PV system. The amount of electricity generated depends on the operating voltage of the PV. On the and characteristics of PV, there exists only one maximum power point (MPP), this MPP point changes depending on radiation and environmental temperature. The MPPT's mission is to find and maintain the most efficient working mode. Therefore, many MPPT methods have been studied to determine the optimal working point. In this article, we propose to use the Deep Q Network (DQN) algorithm to maximize the energy from solar panels when there are changes in radiation intensity and environmental temperature. The results have been simulated and verified on MATLAB/SIMULINK, showing the feasibility and quality of the response when applying the new algorithm.