A Hybrid Power Heronian Function-Based Multicriteria Decision-Making Model for Workplace Charging Scheduling Algorithms

This study proposes a new multicriteria decision-making (MCDM) model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at workplaces. An optimal station is incorporated into for quantitative evaluation. The proposed based on hybrid power Heronian functions in which linear normalization method improved by applying inverse sorting algorithm rational and objective decision-making. enables EV users specify evaluate considering their aspects Five different algorithms with ac dual-port L2 dc fast (DCFC) supply equipment (EVSE) are investigated. Based from field, required time, EVSE occupancy, number of units, flexibility found have highest importance degree, while cost has lowest degree. experimental results show terms meeting users’ workplaces, EVs energy needs performs better when compared them arrival departure times. While alternatives display similar ranking behavior both types, alternative may differ type. To validate model, comparison against three traditional models performed. It demonstrated yields same order as approaches. Sensitivity analysis validates worst alternatives.