OPFormer: Real-Time Optimal Power Flow with CNN-Based Transformer

Abstract

In the global energy crisis, optimal power flow (OPF) has attracted increasing attention as a tool for power system analysis and energy conservation. However, due to the non-convex and nonlinear nature of the OPF problem, it is often challenging to solve. This paper proposes a data-driven approach based on Convolutional Neural Networks (CNN) and transformers for predicting OPF solutions. Traditional studies have typically focused on either complete topological structures or modifications within small bus system test sets. In contrast, the proposed method applies topology labels as inputs and employs one-hot encoding to represent the topology change, allowing for effective computation across networks with varying topological structures. Experiments were conducted using the IEEE 30-bus system and 118-bus system, incorporating N-1 topology changes within the bus system. Compared to recent studies, the errors for P<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> and V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> are reduced by 57.73% and 45.12% on the IEEE 30-bus system, and by 36.26% and 38.41% on the IEEE 118-bus system, respectively.