The increasing global energy demand and the urgent need to transition to sustainable practices have made the integration of renewable energy sources (RESs) into distribution power networks imperative. This paper main objective is to implement rooftop photovoltaic (PV) systems as an essential part of low-voltage (LV) DC networks' building-integrated centralized generation. By ensuring maximum power extraction from photovoltaic (PV) panels, a Maximum Power Point Tracking (MPPT) approach based on Artificial Neural Networks (ANNs) is utilized to enhance power generation efficiency, particularly in partially shaded scenarios. After that, the PV system's generated power is sent to a Luo converter, which effectively tracks and optimizes the power production. The Luo converter's output is then fed into a high-frequency converter to enable wireless power transfer. This wireless power transmission improves the system's flexibility and scalability by facilitating energy transfer without the requirement for physical connections. An isolation transformer is attached to the high-frequency converter's output to guarantee the system's dependability and safety. The high-frequency converter is isolated from the downstream components by the isolation transformer, which also offers protection against electrical risks. Lastly, the transformer's isolated output is connected to DC loads as well as a battery intended for use in electric vehicle (EV) applications. Lastly, we will use the MATLAB 2021a / Simulink program to do a number of numerical simulations in order to validate the proposed controls. The efficiency achieved with the Luo converter is 93.5% and tracing efficiency of the ANN based MPPT is 92%. © 2024 Elsevier B.V., All rights reserved.