Distributed coordination of electric vehicles charging station and home energy management systems in residential neighborhood

The uncoordinated management of Electric Vehicle (EV) Charging Station Management Systems (CSMS) and Home Energy Management Systems (HEMSs) has been shown to have detrimental effects on the distribution system, leading to the creation of new demand peaks (rebound) and increased power loss in the grid. This paper develops a distributed coordination approach for managing CSMS and HEMSs agents, aimed at mitigating the negative impacts of uncoordinated consumers within a neighborhood. A comprehensive model of consumer flexibility is developed by integrating residential demands with detailed CSMS features, including EV charging schedules and energy requirements, as well as the impact of temperature on charging duration. The proposed coordination technique not only fulfills individual objectives of agents but also addresses shared objectives of the neighborhood, which are distributed among all agents by a coordinator. The technique aims to harmonize HEMSs and CSMS consumption profiles to smooth out the aggregated profile and reduce the neighborhood’s total energy costs. Afterward, an incentive allocation mechanism has been devised to assess the marginal contributions of agents and distribute rewards accordingly. The proposed CSMS and HEMSs coordination is evaluated through case studies encompassing diverse preferences, coordination levels, as well as parameters uncertainties. Additionally, the proposed approach is compared against both the uncoordinated and indirect coordination cases, implemented using proximal dynamic prices. The evaluation demonstrates that, compared to the baseline scenario, the load factor improves significantly by up to 35%, and the total neighborhood discounted bill is reduced by up to 27%. • Distributed coordination harmonizes CSMS and HEMS profiles to flatten aggregated demand. • Incentive allocation mechanism evaluates agent contributions and distributes rewards fairly. • Coordination reduces total neighborhood bill by up to 27% and improves load factor by 35%. • Sensitivity analysis confirms framework’s robustness against parameter uncertainties.