Real-Time Energy Management System Supported on Cloud Services for Cluster of Microgrids

Abstract — An innovative microgrid’s energy management system demands many features under a hierarchical structure perspective: an autonomous and scalable design, massive storage capabilities, real-time information analysis, and fast-paced processing are a few, and others such as cybersecurity issues to maintain trustworthiness and viability, are a must. This research revised most of them before integrating and deploying the proposed cloud-based real-time energy management system architecture in a real-life scenario. The implementation solved an economic dispatch problem, incorporated internet of things materials, and used suitable machine learning functionalities in an interconnected microgrid assemblage. For this, the authors studied and ran microgrid models, deployed the models into hardware-in-the-loop units, linked the consolidated hardware to a production cloud server, and merged the energy management system with machine learning, and the internet of things tools. As established by real-life evidence, this research defines relevant aspects for a fully deployed scalable and autonomous real-time cloud-computing energy management system architecture to optimize energy generation, usage forecasting, and energy trade.

Keywords — Real-Time, Microgrid Cluster, Testbed, Energy Management System, Machine learning, Big Data.