Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060

The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration.