Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management

Abstract

Towards zero CO2 emissions society, large shares of renewable energy sources and storage systems are integrated into microgrids as part of the electrical grids for energy exchange aiming to effectively reduce the stress from the transmission grid. However, energy management within and across microgrids is complicated due to many uncertainties such as imprecise knowledge on energy production and demand, which makes energy optimization challenging. In this paper, we present an open architecture that uses machine learning algorithms at the edge to predict energy consumption and production for energy management in smart microgrids. Such predictions are aggregated across different prosumers at a centralized marketplace in the Cloud using Kafka Streams and OpenSource IoT platforms. Using pluggable optimization algorithms, different microgrids can implement different strategies for real-time optimal energy schedules. The proposed architecture is evaluated in terms of scalability and accuracy of predictions. Our heuristics can effectively optimize medium-sized microgrids.