Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters

Abstract

In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a cloud server at the far-end control center, reducing the communication time the inverters need to wait. Experimental results on an ARM-based smart meter board demonstrate the feasibility and correctness of the proposed approach by comparing against the results on the desktop PC.

This paper has been accepted by and presented in ICSGSC 2024, Shanghai, China