Sequential Control of Individual Switches for Real-Time Distribution Network Reconfiguration Using Deep Reinforcement Learning

This paper presents a study on Dynamic Sequential Distribution Network Reconfiguration (DSDNR), which considers the sequential control of individual switches in real-time distribution network operation. Traditional Distribution Network Reconfiguration (DNR) is formulated as an optimization problem that determines optimal switch states for network conditions.With the increasing variability introduced by distributed energy resources (DERs), real-time DNR research has evolved toward continuously identifying optimal switch states over time. However, in practical operations, the simultaneous change of multiple switches can compromise system stability, making it essential to optimize the sequential order of individual switch operations. This study introduces a new DNR formulation that integrates sequential switching optimization into real-time applications, enabling fast and stable operation u…