用自监督模型学习电网功率流，提升运维效率。
Optimal Power Grid Operations with Foundation Models
- 构建自监督模型捕捉电网功率流动态
- 利用少量数据完成多任务下游应用
- 适合电力系统智能化研究者参考
能源转型对应对气候危机至关重要，需将大量分布式可再生能源接入现有电网。伴随气候变化和用户行为改变，发电与负荷模式日益波动，给电网规划与运行带来显著复杂性和不确定性。尽管行业已开始运用人工智能应对传统电网仿真工具的计算挑战，本文提出利用人工智能基础模型（FMs）与图神经网络进展，高效利用稀缺的电网数据，服务于多种下游任务，从而提升电网运行水平。我们认为，建立一个学习功率流动态的自监督模型，是构建电网基础模型的关键第一步。该方法有望弥合产业需求与当前电网分析能力之间的差距，推动电力行业向最优运行与规划迈进。
原文摘要 · Abstract (English)
The energy transition, crucial for tackling the climate crisis, demands integrating numerous distributed, renewable energy sources into existing grids. Along with climate change and consumer behavioral changes, this leads to changes and variability in generation and load patterns, introducing significant complexity and uncertainty into grid planning and operations. While the industry has already started to exploit AI to overcome computational challenges of established grid simulation tools, we propose the use of AI Foundation Models (FMs) and advances in Graph Neural Networks to efficiently exploit poorly available grid data for different downstream tasks, enhancing grid operations. For capturing the grid's underlying physics, we believe that building a self-supervised model learning the power flow dynamics is a critical first step towards developing an FM for the power grid. We show how this approach may close the gap between the industry needs and current grid analysis capabilities, to bring the industry closer to optimal grid operation and planning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。