Stability Optimization of High-Renewable Penetration Grids with Energy Storage Synergy

Abstract

This paper presents a new AI-based real-time scheduling algorithm to schedule wind power, solar power, and hydrogen storage units to alleviate grid fluctuation issues in high-renewable penetration scenarios. Integrated intermittent renewable resources destabilize the grid with power imbalance challenges, which require intelligent solutions that can predict and mitigate power imbalances. We advance a deep reinforcement learning (DRL) framework that optimizes energy dispatch across various time horizons and manages synergistic utilization of diverse storage technologies. The algorithm outperforms baseline methods in experimental evaluation by reducing grid frequency fluctuations by 42% and voltage deviations by 36% compared to conventional methods. Economic optimization achieves an operating cost savings of 27% with the AI-coordinated storage system. This research contributes to the growth of smart grid technology by demonstrating a new approach for maintaining grid stability in more than 80% renewable integration systems, paving the way for more reliable and sustainable power infrastructure.