TURBO-AI: a transformative utility resource balancing optimization algorithm for real-time energy management in distribution networks with high penetration of non-linear industrial loads

Abstract Modern distribution networks are increasingly challenged by the integration of non-linear industrial loads (NILs), which induce significant harmonic distortions, reaching up to 38.6% total harmonic distortion (THD), and voltage fluctuations of up to ± 15%, threatening grid stability and operational efficiency. This paper presents TURBO-AI, a hybrid machine learning and optimization-based algorithm designed to address these challenges in real-time. TURBO-AI leverages advanced forecasting, state estimation, and a reinforcement learning-enhanced optimization framework to manage distribution systems with high NIL penetration. Simulation results demonstrate that TURBO-AI achieves a 29.6% reduction in mean absolute percentage error (MAPE) for arc furnace load forecasting, a 12.3% reduction in operating costs in industrial park scenarios, and a 48.9% improvement in voltage stability compared to conventional energy management approaches. The algorithm enhances renewable energy utilization and maintains power quality within regulatory limits across diverse network configurations. These results establish TURBO-AI as a scalable and adaptive solution for intelligent energy management in modern distribution networks with complex industrial load profiles.