Input Convex Neural Network-Assisted Optimal Power Flow in Distribution Networks: Modeling, Algorithm Design, and Applications

TL;DR

This paper introduces an innovative approach combining input convex neural networks with optimal power flow in distribution networks, enabling efficient, convex optimization-based solutions with proven convergence and broad application potential.

Contribution

It develops a novel ICNN-assisted OPF framework that integrates machine learning with convex optimization, providing fast algorithms and convergence guarantees.

Findings

The proposed method achieves faster solution times compared to traditional approaches.

Convergence and optimality of the algorithm are theoretically established.

Applications demonstrate the effectiveness of ICNN-assisted OPF in real distribution networks.

Abstract

This paper proposes an input convex neural network (ICNN)-Assisted optimal power flow (OPF) in distribution networks. Instead of relying purely on optimization or machine learning, the ICNN-Assisted OPF is a combination of optimization and machine learning. It utilizes ICNN to learn the nonlinear but convex mapping from control variables to system state variables, followed by embedding into constrained optimization problems as convex constraints. Utilizing a designed ICNN structure, a fast primal-dual gradient method is developed to solve the ICNN-Assisted OPF, with the chain rule of deep learning applied to accelerate the algorithmic implementation. Convergence and optimally properties of the algorithm design are further established. Finally, different distribution network applications are discussed and proposed by means of the ICNN-Assisted OPF.