Machine Learning-Assisted Distribution System Network Reconfiguration Problem

TL;DR

This work has devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations featuring multiple substations that can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder.

Abstract

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

Figures (4)

• Figure 1: The modified IEEE 37-Bus system with flexible network topology configuration.
• Figure 2: Overall architecture of the machine learning framework
• Figure 3: The deep neural network framework for learning the optimal network reconfiguration
• Figure 4: Different network configuration outputs of the Neural Networks.