SREM: Smart renewable energy management scheme with distributed learning and EV network

In this article, aiming to develop the Green Internet of Vehicles (G-IoV), we propose a smart energy management system that leverages the intelligence edge clients and the distributed electric vehicles (EVs). The system proposed in this article incorporates the benefits of both software, specifically in terms of the user interface, and hardware, specifically in terms of edge clients. In particular, this system integrates intelligence edge clients with an EV CAN bus network as an electronic control unit. By leveraging the intelligent edge clients recommendation system, EVs can make informed decisions on battery charging or discharging actions. As a result, a virtual-power-plant (VPP) can treat the EVs network as a vast intelligent energy storage facility, efficiently managing the battery energy of all distributed EVs connected to the platform and fully utilizing the electricity generated from renewable energy sources. We experimentally verify that using federal learning to train models in EV networks versus training models directly in EVs, using federal learning in EV networks yields better experimental results. AI-empowered virtual power plant (VPP) for smart renewable energy management system.image

Automated summary

This article proposes a smart energy management system that utilizes intelligent edge clients and distributed electric vehicles to improve energy utilization and maximize the use of renewable energy. The system treats a virtual power plant as an energy storage facility and efficiently manages battery energy.