Optimizing coordinated spatio-temporal control of electric vehicles for enhanced energy sharing and performance across building communities

Most of the existing electric vehicles (EVs) charging controls consider power balance regulation in only one community while neglecting their mobility capability. EVs can be considered as mobile batteries and thus deliver and share electricity between multiple building communities, i.e., Community-to-EV-to-Community (C2V2C). Since different communities can have different power regulation needs, it is challenging to consider the various spatio-temporal needs and balance them. This study aims to bridge the research gap by developing a genetic algorithm based advanced C2V2C control to optimize EV charging and electricity delivery between multiple communities. The developed control considers the spatio-temporal power regulating needs of multiple communities and balances these needs based on the users' requirements via a weighting factor. Using two building communities as case studies, the developed control is compared with a conventional independent control under two control objectives. The results demonstrated the effectiveness of the C2V2C service in reducing the aggregated peak power exchanges with the grid (e.g., by 1 %∼6%) and reducing the total electricity costs (e.g., by up to 282 %) of multiple communities. The impacts of the users' preferences are also discussed. This study provides a novel and resource-efficient solution for electricity sharing between different buildings.