Work

Advancing performance guarantees for DNN-based OPF in topology-flexible distribution networks

Year: 2025

Type: article

Abstract: We tackled the lack of solution-quality guarantees for deep neural network (DNN)-based optimal power flow (OPF) in topology-flexible distribution networks (TFDNs), despited the black-box nature of DNN predictors. We formulated a bi-level min-max (BLMM) problem, whose inner level certifies the performance of a fixed DNN-based OPF solution and whose outer level updates the DNN to improve the certified bound. To solve the BLMM problem, we developed an iterative scheme grounded in Danskin’s theorem. In particular, we attained the exact solution by explicitly reformulating the BLMM problem through binary variables. To alleviate the computational burden from binary variables, we combined convex relaxation with pattern recognition, thereby reducing the required number of binary variables. The effectiveness of the proposed method is demonstrated on a 4-bus system and the IEEE 136-bus test system.

Source: Cyber-Physical Energy Systems

DOI: 10.1016/j.cpes.2025.07.001

Authors: Wei Lin, Qingyue Ran, Zhifang Yang, Juan Yu, Chongyu Wang, Suhan Zhang

Institutions: Chongqing University, Illinois Institute of Technology, University of Sheffield

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: