用基础模型加速电网仿真，让小机构也能低成本用AI。
Accelerating Quasi-Static Time Series Simulations with Foundation Models
- 用电网基础模型降低神经功率流求解器的训练成本
- 仿真速度提升3到4个数量级，且收敛性更好
- 适合电网规划与运行团队，尤其资源有限的机构
准静态时间序列（QSTS）仿真在评估大规模分布式能源接入电网能力方面潜力巨大。然而，随着电网扩展和运行接近极限，迭代式潮流求解器变得计算代价高昂且收敛困难。神经网络潮流求解器可将计算速度提升3至4个数量级，但训练成本高。本文提出利用近期出现的电网基础模型，通过一次预训练支持多种电网运行与规划任务，仅需少量微调即可应用，从而摊薄训练成本。我们呼吁人工智能与电网领域合作，共同开发并开源此类模型，使所有运营商，包括资源有限者，都能无需从零构建即可享受AI带来的效率提升。
原文摘要 · Abstract (English)
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch.
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