Objectives: To propose a novel hybrid control plan to enhance the power quality (PQ) of distributed energy sources (DERs) such as solar energy (SE), wind energy (WE), and battery energy storage system (BEES) technologies. Method: A BEES, a PV farm, and a wind farm are integrated into the grid to supply home loads. The ABMO-ANN control system is a novel method that combines the Hybrid Barnacles Mating Optimization Algorithm (HBMO) and the Artificial Neural Network (ANN) (ANN). The power is balanced by balancing the power consumed by BEES and the power provided by the VSI inverter in this new proposed algorithm. Findings: In this HBMO-ANN control model, the proportional integral (PI) gain parameter controller generates the signals based on the load variation to manage the hybrid RES energy sources optimally. The assumed variables as system parameters in this suggested prediction approach are direct current (DC) voltage and active with reactive power. Novelty: The new HBMO-ANN method creates optimal control, aiming to improve power system damping and sustain line voltage with the reactive power compensation provided. However, when compared to existing control methods, the performance of the HBMO-ANN approach with a PI controller is also justified. The suggested methodology is compared to existing methods in MATLAB/Simulink.