AI 中文总结
针对可再生能源占主导且惯性低的电力系统最优潮流问题，提出基于物理信息神经网络（PINN）的框架，纳入位置感知惯性约束，经6GW测试系统验证，该框架能确保符合物理定律与惯性约束，产生高精度最优潮流解。
AI 中文摘要
最优潮流问题是现代电力系统安全经济运行的核心。然而，可再生能源渗透率的增加和系统惯性的降低给传统基于优化的最优潮流求解器带来了重大挑战。机器学习方法虽提高了计算速度，但纯数据驱动方法存在数据依赖、泛化能力有限和缺乏物理可行性保证等问题。本文提出了一种基于物理信息神经网络（PINN）的框架来解决可再生能源主导的低惯性电力系统中的最优潮流问题。该模型明确纳入了基于系统惯性强度概念的位置感知惯性约束，考虑了发电单元与扰动位置之间的电气距离。在一个6GW测试系统上的仿真结果表明了其高精度，训练和测试数据集的平均绝对误差约为系统总容量的0.045%。研究结果表明，所提出的PINN框架能够在确保符合物理定律和惯性相关约束的同时，产生高精度的最优潮流解。总体而言，这些发现凸显了基于物理信息学习在未来低惯性电力系统中实现安全、高效和可扩展计算的最优潮流的潜力。
英文摘要
The problem of Optimal Power Flow (OPF) is central to the secure and economic operation of modern power systems. However, increasing renewable energy penetration, and decreasing system inertia pose significant challenges to conventional optimization-based OPF solvers. While machine learning approaches have demonstrated substantial computational speed-ups, purely data-driven methods often suffer from data dependency, limited generalization, and lack of guaranteed physical feasibility. This paper suggests a physics-informed neural network (PINN) framework for solving the OPF problem in renewable energy-dominated, low-inertia power systems. In contrast to conventional OPF formulations, the model explicitly incorporates a location-aware inertia constraint based on the concept of system inertia strength, which accounts for the electrical distance between generation units and disturbance locations. Simulation results on a 6 GW test system demonstrate high accuracy. The mean absolute error (MAE) for both the training and testing datasets is approximately 0.045% of the total system capacity. The findings demonstrate that the proposed PINN framework is capable of producing highly accurate OPF solutions while ensuring compliance with both physical laws and inertia-related constraints. Overall, the findings highlight the potential of physics-informed learning to enable secure, efficient, and computationally scalable OPF for future low-inertia power systems.
CommentsPresented in EEEIC 2026 conference
