Save Money, Get Charged: Facility-Tied Energy Management with Unknown and Unscheduled EV Charging

Energy Management Systems (EMS) for extensive facilities often experience significant unplanned loads from electric vehicle (EV) operators connected to Electric Vehicle Supply Equipment (EVSE). Facility EMS often statically curtails the installed EVSE units to mitigate costly peak infringement. This results in many vehicles being insufficiently charged or routinely exceeding peak limits. The developed EVSE EMS module is designed to reduce peak infringements while ensuring a fast and complete charge for EV operators. This tool leverages recent advances in machine learning (transformers, RegNet, and Deep Q Reinforcement Learning), coupled with classical algorithms such as Naive Bayes to learn, adapt, and anticipate impacts of unknown charging events. This module is implemented alongside the operating EMS at the USU Electric Vehicle Roadway (EVR) facility. It significantly reduces costs while ensuring efficient charging for unknown and unscheduled user EVs.