Congestion-aware multi-objective scheduling and control for a user-centered EV charging hub

This paper presents a congestion-aware Energy Management System (EMS) for Electric Vehicle (EV) charging hubs with on-site PV generation and energy storage in the built environment. The concept of the system is based on a time-discretized scheduling approach that incorporates all relevant assets at the hub to charge the EVs without creating grid congestion problems. Since EV charging schedules in general have to be determined based on incomplete and often inaccurate forecasts and information, the scheduler is combined with an online control policy that operates to compensate for forecast errors or that can be used to react on external market price signals or congestion information. A simulation study of the scheduling concept and its underlying model shows that incorporating the current peak-tariff structure used in the Netherlands into the EMS can contribute to reducing the peak demand on the grid throughout the year for the proposed charging hub by 8.4%. The simulations furthermore show that the application of a peak shaving approach can lead to a peak load reduction of up to 36%, at only a 2.6% increase in total operational costs. The research objective of this study is to investigate how the theoretical model of the EMS can be applied in a real-world implementation. For this, the EMS is implemented in a real-life operational demonstration of the concept with real devices, data and users. The demonstration of the EMS in a real-world implementation advances the state of knowledge on these topics by demonstrating conflicting interests the lack of information-exchange between different stakeholders and the effect of this on the EV charging ecosystem. Further research is necessary, specifically on the interaction between EVs, Charge Point Operators, Mobility Service Providers and other relevant market parties such as energy traders.