•  
  •  
 

Keywords

new distribution network, optimal power flow solving, deep learning, Sinkhorn algorithm, feasibility restoration

Abstract

As the core of optimal dispatching decision-making for distribution networks, optimal power flow (OPF) urgently requires fast calculation methods under large-scale grid frameworks. A deep learning-based OPF solving method oriented toward feasibility restoration was proposed. First, an OPF solving architecture based on state-control variable decomposition was constructed, and an OPF state variable solving model was built based on a deep neural network. Second, to address the problem that OPF results based on deep neural networks fail to satisfy control variable constraints, samples with inequality constraint violations were screened to construct a correction sample set; considering actual physical constraints and supply-demand balance relationships, joint correction constraint conditions based on overall control variables were proposed to establish correction intervals. Finally, the Sinkhorn algorithm based on multi-marginal distributions was used to adjust control variable solutions, and constraint-violating variables were iteratively projected into correction intervals to meet actual physical constraint conditions. The proposed method is verified based on an improved IEEE 123-node distribution network case. Experimental results show that the proposed method can effectively achieve feasibility restoration of control variables, balance the mean absolute error of each control variable, and improve solution accuracy.

DOI

10.19781/j.issn.1673-9140.2026.03.008

First Page

78

Last Page

88

Download

Share

COinS