面向含拓扑重构的配电网多相交流最优潮流的可扩展自监督学习
Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration
浏览论文内容
中文总结 AI 辅助
该研究提出Penalty+SLFS自监督学习算法，用于含拓扑重构的配电网多相AC-OPF，在IEEE馈线上比IPOPT快三个数量级，实现可忽略最优间隙与近零约束违反，为大规模配电网实时AC-OPF提供可行方案。
中文摘要 AI 辅助
配电网中分布式能源资源（DER）的普及使得这些资产能够主动协调，以降低成本并实现更清洁的运行。要实现这一潜力，需要在不同负荷、DER可用性和拓扑重构情况下，快速求解多相交流最优潮流（AC-OPF），且速度和规模远超传统非线性求解器。基于学习的替代模型可提供毫秒级推理，但现有方法主要针对平衡输电系统，无法扩展到 utility 级配电网馈线的多相、不平衡和可重构特性。本文提出了惩罚+序列线性化可行性搜索（SLFS）算法，这是一种适用于开关诱导拓扑变化下多相配电网AC-OPF的自监督学习框架。Penalty+SLFS无需标记最优解，通过可微定点潮流求解器直接从AC-OPF目标和约束进行训练，避免了昂贵的标签生成并实现了鲁棒的训练流程；使用导纳矩阵逆的Sherman-Morrison-Woodbury更新高效处理拓扑变化，同时采用M步雅可比近似加速潮流求解器的求导。推理时，SLFS可修复任何不可行的预测，以低计算开销提供可行性保证。在IEEE 13节点至8500节点馈线上，Penalty+SLFS实现了可忽略的最优性间隙和近零约束违反，比IPOPT快三个数量级，且在大分布偏移下仍保持鲁棒性，为大规模配电网的实时、拓扑感知AC-OPF提供了可行路径。
英文摘要
The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and enable cleaner operations. Realizing this potential requires solving multiphase AC optimal power flow (AC-OPF) quickly across varying loads, DER availabilities, and topology reconfigurations, at much greater speed and scale than conventional nonlinear solvers. Learning-based surrogates can offer millisecond inference, yet existing methods target largely balanced transmission systems and do not scale to the multiphase, unbalanced, and reconfigurable nature of distribution feeders at utility scale. We present the Penalty + Sequential Linearized Feasibility Seeking (SLFS) algorithm, a self-supervised learning framework for multiphase distribution AC-OPF under switch-induced topology changes. Penalty+SLFS requires no labeled optimal solutions and trains directly from the AC-OPF objective and constraints through a differentiable fixed-point power flow solver, avoiding expensive label generation and admitting robust training procedures. Topology changes are handled efficiently using Sherman-Morrison-Woodbury updates of the admittance-matrix inverse, while an M-step Jacobian approximation accelerates differentiation through the power flow solver. At inference, SLFS repairs any infeasible predictions, providing feasibility guarantees with low computational overhead. On IEEE feeders ranging from 13 to 8,500 nodes, Penalty+SLFS achieves negligible optimality gaps and near-zero constraint violations, delivers up to three orders of magnitude speedups over IPOPT, and remains robust under large distributional shifts, demonstrating a viable path toward real-time, topology-aware AC-OPF for large-scale distribution grids.
发表机构
- Massachusetts Institute of Technology（麻省理工学院）
- GE Vernova Advanced Research Center（GE Vernova先进研究中心）
机构由 AI 辅助整理，请以论文原文为准。
