用快速直流解做基底，神经网络补足非线性误差，实现近实时交流潮流优化。
Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
- 以直流优化解为起点，用图神经网络学习非线性修正项。
- 在57、118、2000节点系统上，误差降低25%，求解快13倍。
- 适用于电网实时调度，尤其适合大电网和故障场景下的快速决策。
求解非线性的交流最优潮流（AC OPF）问题仍是实时电网运行的主要计算瓶颈。本文提出一种残差学习范式，以快速的直流最优潮流（DC OPF）解作为基线，仅学习达到完整交流潮流可行解所需的非线性修正量。方法采用拓扑感知的图神经网络，结合局部注意力机制与两级直流特征融合，并通过物理信息损失函数训练，确保交流潮流可行性和运行约束满足。在OPFData数据集的57、118和2000节点系统上的评估显示，相比传统AC OPF求解器，均方误差降低约25%，可行性误差减少最高达3倍，求解时间提速最高达13倍。模型在N-1故障条件下仍保持高精度，且可高效扩展至大规模网络。结果表明，残差学习是连接线性近似与交流可行解之间的实用且可扩展的桥梁，支持近实时运行决策。
原文摘要 · Abstract (English)
Solving the nonlinear AC optimal power flow (AC OPF) problem remains a major computational bottleneck for real-time grid operations. In this paper, we propose a residual learning paradigm that uses fast DC optimal power flow (DC OPF) solutions as a baseline, and learns only the nonlinear corrections required to provide the full AC-OPF solution. The method utilizes a topology-aware Graph Neural Network with local attention and two-level DC feature integration, trained using a physics-informed loss that enforces AC power-flow feasibility and operational limits. Evaluations on OPFData for 57-, 118-, and 2000-bus systems show around 25% lower MSE, up to 3X reduction in feasibility error, and up to 13X runtime speedup compared to conventional AC OPF solvers. The model maintains accuracy under N-1 contingencies and scales efficiently to large networks. These results demonstrate that residual learning is a practical and scalable bridge between linear approximations and AC-feasible OPF, enabling near real-time operational decision making.
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。