Abstract
Advances in the field of edge computing and the emergence of edge-cloud has enabled the monitoring and management of one of the most complex existing systems, the electric grid, which is undergoing a decentralized and ‘smart transition’ towards ‘smart grid’. Advanced Monitoring Infrastructure (AMI) and sensing devices (PMUs, RTUs etc.) combined with advanced communications networking allow the monitoring of the grid parameters, while edge computing capabilities (including hardware acceleration engines, reconfigurable hardware, etc.) enable real-time Transient State Estimation (TSE) of the grid nodes and therefore enabling other smart grid applications, such as preventive maintenance, control and data analytics. In this work, we focus on the processing requirements of TSE and we present an edge-cloud architecture for efficient TSE application. The architecture leverages low-cost hardware acceleration engines at the edge (devices enhanced with FPGA resources) in order to solve the TSE equations effectively and in tight time thresholds, while reducing the transmission of traffic towards the cloud and thus saving bandwidth for more sophisticated smart grid applications.