Artificial Intelligence-Driven Energy Management in Grid Energy Storage Systems: Design and Performance Evaluation

Abstract

Grid energy storage systems are a trending technology, attracting researchers due to their significant market and need. An update is essential for this technology, as it has several drawbacks, such as improper charging, high cost, resource limitations, and weak grid stability. To overcome these drawbacks, this study presents a Solar PV system with grid stability (SPGSEV) model. This model integrates core modules, such as mobility and charging systems, charging stations, and a solar-powered photovoltaic system with battery storage and PV modules. The grid stability management system is developed to achieve effective resource utilization and, with the Multi-Objective Remora Optimization Algorithm (MOROA), to support proper decision-making and charge scheduling. The overall framework is implemented in MATLAB, and the parameters measured for result analysis include Electricity Price, Voltage, PVs Capacity, PVs Output Power, Power Loss, Voltage Magnitude, and Stay Time. The results are also compared with earlier models such as EMEVC, OEPSM, and EVESS. The outcome proves the stability of the proposed model by attaining lower loss and high PV output power, which leads to maximum energy utilization and grid stability for the energy storage systems.