Optimization of Power System Flexibility Through AI-Driven Dynamic Load Management and Renewable Integration

Abstract

This paper introduces an advanced framework to enhance power system flexibility through AI-driven dynamic load management and renewable energy integration. Leveraging a transformer-based predictive model and MATPOWER simulations on the IEEE 14-bus system, the study achieves significant improvements in system efficiency and stability. Key contributions include a 44% reduction in total power losses, enhanced voltage stability validated through the Fast Voltage Stability Index (FVSI), and optimized renewable energy utilization. Comparative analyses demonstrate the superiority of AI-based approaches over traditional models such as ARIMA, with the transformer model achieving significantly lower forecasting errors. The proposed methodology highlights the transformative potential of AI in addressing the challenges of modern power grids, paving the way for more resilient, efficient, and sustainable energy systems.

Keywords

AI-driven strategies / dynamic load management / power system flexibility / predictive modeling / renewable energy integration / voltage stability

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2025 The Author(s). Battery Energy published by Xijing University and John Wiley & Sons Australia, Ltd.