S+M.ai
Dec 1, 2021
Summarized Description: Due to higher energy efficiency and lower emissions, electric vehicles (EVs) have become attractive transportation means in developing cleaner mobility systems. Moreover, many future automated vehicles (AV) can be electrified. Hence, the existing market will experience a drastic growth in automated electric vehicles (AEVs). For infrastructure enabled automation (IEA), charging facility planning is required to accommodate the increasing AEV charging demand. The planning process must also account for their impact on the power grid. This study presents an integrated demand coverage optimization model over a coupled power-transportation (CPT) network. This model aims to pinpoint candidate locations of AEV charging stations that would serve the most charging demand in the transportation network, considering the upcoming technologies in AEV also will affect the charging behavior that can influence the charging system. Besides, power grid limitations at each charging station are considered for the minimal power cost of the network. The developed model is applied to Utah state road network to determine the optimal charging station locations.