The Optimal Power Flow (OPF) problem is pivotal for power system operations,
guiding generator output and power distribution to meet demand at minimized
costs, while adhering to physical and engineering constraints. The integration
of renewable energy sources, like wind and solar, however, poses challenges due
to their inherent variability. This variability, driven largely by changing
weather conditions, demands frequent recalibrations of power settings, thus
necessitating recurrent OPF resolutions. This task is daunting using
traditional numerical methods, particularly for extensive power systems. In
this work, we present a cutting-edge, physics-informed machine learning
methodology, trained using imitation learning and historical European weather
datasets. Our approach directly correlates electricity demand and weather
patterns with power dispatch and generation, circumventing the iterative
requirements of traditional OPF solvers. This offers a more expedient solution
apt for real-time applications. Rigorous evaluations on aggregated European
power systems validate our method's superiority over existing data-driven
techniques in OPF solving. By presenting a quick, robust, and efficient
solution, this research sets a new standard in real-time OPF resolution, paving
the way for more resilient power systems in the era of renewable energy.

借助物理启示的机器学习方法，使用模仿学习和历史欧洲天气数据集，直接将电力需求和天气模式与电力调度和发电相互关联，从而实现实时最优功率流解决方案，并验证效果优于现有数据驱动技术，有助于可再生能源时代构建更具韧性的电力系统。