Comparative studies of EV fleet smart charging approaches for demand response in solar-powered building communities

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios.