Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
This work addresses the need for efficient energy management in smart grids by leveraging 5G networks, though it appears incremental as it compares existing methods in a new context.
The paper explores integrating distributed state estimation for smart grids with AI/ML-enabled 5G networks, comparing graphical models and graph neural networks to support near real-time performance while accounting for communication delays.
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.