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.
@article{arxiv.2412.01080,
title = {Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters},
author = {Linna Xu and Jian Huang and Shan Yang and Yongli Zhu},
journal= {arXiv preprint arXiv:2412.01080},
year = {2024}
}
Comments
This paper has been accepted by and presented in ICSGSC 2024, Shanghai, China