A data-driven architecture for adaptive decentralized charging and energy management
Abstract
Distributed energy resources (DER), renewable energy sources (RES) and electric vehicles (EV) pose considerable challenges with respect to their efficient integration within the power system. Operation and control strategies for energy management systems (EMS) and charging station management systems (CSMS) are required to be highly adaptable and able to rapidly incorporate information received about power system supplies and demands to maintain efficient system operation. Here, information integration and monitoring is particularly crucial in use cases such as smart charging of EV and vehicle-to-grid (V2G) interactions. For this, standardization is a key challenge, as communication standards such as OCPP and EEBus facilitate interoperability, as well as monitoring and controlling aligned devices using shared data models and protocols. In this work, a data-driven architecture for integrated and adaptive charging and energy management is presented, which allows for mapping of logical system states and actions to concrete protocol data models. Based on actual and predicted information received with respect to system state, control strategies can be derived and adapted during run-time to address changed system requirements, behavior or system topologies of DER and charging stations (CS).