Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models
It addresses the problem of managing intelligent, low-carbon power systems for energy providers and grid operators, but it appears incremental as it applies existing GLM methods to a specific domain without claiming major breakthroughs.
This paper tackles the optimization of modern power systems for renewable energy integration and carbon reduction by exploring Generative Large Models (GLMs), resulting in improved grid stability, dynamic energy scheduling, and enhanced carbon trading strategies.
The transition to intelligent, low-carbon power systems necessitates advanced optimization strategies for managing renewable energy integration, energy storage, and carbon emissions. Generative Large Models (GLMs) provide a data-driven approach to enhancing forecasting, scheduling, and market operations by processing multi-source data and capturing complex system dynamics. This paper explores the role of GLMs in optimizing load-side management, energy storage utilization, and electricity carbon, with a focus on Smart Wide-area Hybrid Energy Systems with Storage and Carbon (SGLSC). By leveraging spatiotemporal modeling and reinforcement learning, GLMs enable dynamic energy scheduling, improve grid stability, enhance carbon trading strategies, and strengthen resilience against extreme weather events. The proposed framework highlights the transformative potential of GLMs in achieving efficient, adaptive, and low-carbon power system operations.