用生成大模型优化电网调度与碳管理，提升低碳系统韧性
Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models
- 结合时空建模与强化学习，实现动态能源调度
- 提升电网稳定性，碳交易策略更高效，抗极端天气能力增强
- 适合电力系统智能化、低碳化研究者参考
向智能低碳电力系统转型亟需先进优化策略以应对可再生能源接入、储能利用和碳排放管理。生成式大模型（GLMs）通过处理多源数据并捕捉复杂系统动态，为预测、调度和市场运营提供数据驱动方法。本文探讨了GLMs在负荷侧管理、储能利用及电力碳排放优化中的作用，聚焦于含储电与碳控的智能广域混合能源系统（SGLSC）。借助时空建模与强化学习，GLMs实现动态能源调度，提升电网稳定性，优化碳交易策略，并增强对极端天气事件的韧性。该框架展示了生成式大模型在实现高效、自适应、低碳电力系统运行中的变革潜力。
原文摘要 · Abstract (English)
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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