Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

TL;DR

This paper investigates how 5G networks can support near real-time distributed state estimation in smart grids by integrating AI/ML techniques, comparing graphical models and graph neural networks for improved energy management.

Contribution

It provides an overview of integrating distributed state estimation with 5G network architecture and compares two advanced ML-based methods for real-time performance.

Findings

Graph neural networks outperform belief propagation in accuracy.

Communication delays impact real-time state estimation performance.

The integration enhances smart grid data-driven services.

Abstract

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.