GTS Press Journal of Sustainable Energy Environment and Green Technologies 1 1 2026 08 04 Balancing Storage Load Variability through Real-Time Energy Optimization for Stable System Performance 120 130 36 EN JSEEGT Author 5 Independent Researcher Journal Article 2026 06 25 Modern microgrids have started to integrate renewable energy resources and Battery Energy Storage Systems (BESS) in a rapid manner, which has caused the need of intelligent energy management to keep the stability of the energy storage and the support of real-time decision making. Despite these successes, issues of intermittence of renewables, load variability, synchronization, multi-agent coordination and uncertainty are still difficult for existing forecasting and optimization techniques. To resolve these problems, this research proposed an optimized energy storage load balancing system based on Twin-Driven Multi-Agent Storage Optimization Network (TMASON) for energy real-time optimization and load balancing of energy storage. TMASON combines the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) algorithms to predict load demand, renewable generation and battery State of Charge, while Digital Twin based on Extended Kalman Filter (EKF) provides state estimation in real time. Multi-Agent Deep Reinforcement Learning (MADRL) and Deep Q-Network (DQN) are used to jointly decide the charging and discharging process, while Model Predictive Control (MPC) and Genetic Algorithm (GA) are used to optimize the process of energy dispatch within the operating constraints. Neuro-Fuzzy inference further manages renewable and load uncertainties. Experimental evaluation shows that TMASON achieves an accuracy of 99.31%, demonstrating improved forecasting, storage utilization, decision-making and energy management performance. Overall, the proposed method provides an adaptive and integrated solution for stable and efficient operation of renewable-integrated microgrids. #