Machine Learning-Assisted Distribution System Network Reconfiguration Problem

Conference · 21 February 2025

DOI:https://doi.org/10.1109/GridEdge61154.2025.10887483· OSTI ID:2558977

Asiamah, Richard; Zhou, Yuqi; Zamzam, Ahmed S.

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2558977
• Report Number(s): NREL/CP-5D00-94350; MainId:96132; UUID:c792ed07-405d-4152-857d-d9cb69b48e55; MainAdminId:76655
• Country of Publication: United States
• Language: English

Related Subjects

MATHEMATICS AND COMPUTING
POWER TRANSMISSION AND DISTRIBUTION
deep neural networks
microgrids
optimal power flow