A two-layer decentralized charging approach for residential electric vehicles based on fuzzy data fusion.

This work presents a two-layer decentralized charging approach (TLDCA) based on fuzzy data fusion concerning the economic and power layers for optimizing the charging cost of residential electric vehicles (EVs). We defined the problem with the fuzzy objective function of minimizing the charging costs and presented a detailed fuzzy integer linear programming formulation for obtaining the optimal solution set. The optimal solution set relies on the decision control variable which is obtained through the fuzzy fusion mechanism that incorporates multiple independent and uncertain day-ahead price patterns and state-of-charge inputs from the utility grid and EV domains. The developed TLDCA reduces the charging cost for EVs while guaranteeing their required energy by determining the optimal charging schedule. We conduct two case studies to investigate the TLDCA behavior, where the first case explores the optimization of charging costs and the changing needs of individual EVs. The second case examines the charging cost, impact on load profile, and peak-to-average ratio against the summer and winter load profiles for the aggregated EVs. The simulation results verify that the developed TLDCA optimizes the charging cost and peak-to-average ratio compared to the uncoordinated charging, standard-rate charging, and time-of-use charging schemes. [ABSTRACT FROM AUTHOR]