Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models

• URL: http://arxiv.org/abs/2504.02855v1
• Date: Wed, 26 Mar 2025 17:58:49 GMT
• Title: Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models
• Authors: Lu Cheng, Qixiu Zhang, Beibei Xu, Zhiwei Huang, Cirun Zhang, Yanan Lyu, Fan Zhang,
• Abstract summary: Generative Large Models (GLMs) provide a data-driven approach to enhancing forecasting, scheduling, and market operations.<n>By leveragingtemporal modeling and reinforcement learning, GLMs enable dynamic energy scheduling, improve grid stability, enhance carbon trading strategies, and strengthen resilience against extreme weather events.
• Score: 4.854786697610143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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