Ideal site selection of fast electrical vehicle charging stations within urban environments: A GIS-AHP approach



Last Updated: 3-2016


Electric mobility and electric vehicles (EVs) have been recent areas of focus for much of the developed world. One country in particular, the Netherlands, has experienced a striking acceptance of EVs in the last years highlighting their commitment to alternative forms of mobility. This trend will continue as the Dutch government aims for 1 million EVs on the road by 2025. An impressive supporting network of public slow-charging points has been placed throughout the Netherlands to support EV acceptance and curb range anxiety. Long charging times and capacity limitations with slow-chargers have created an inefficient charging network that has a low charge-to-vehicle turnover rate. A new charging technology, fast-charging, significantly reduces charging time to 20 minutes or less, considerably quicker than slow-charging. So far, fast-charging only makes up about two percent of the public charging infrastructure in the Netherlands and the majority of these stations are located in rural areas along highways. Urban environments are prime areas for fast-charging stations but have largely been unrealized. This study uses a geographical information system (GIS) based analytic hierarchy process (AHP) approach for selecting ideal locations for fast electrical vehicle charging stations in Dutch urban environments. The synergies of a GIS-AHP tool allow for effective decision making while considering multiple, and often conflicting urban factors. The study has been teamed with Fastned, an innovative Dutch company who is leading placement of public fast-charging stations in the Netherlands. Fastned’s expert knowledge is used to judge the importance of selection criteria, resulting in criteria weights for created GIS layers. GIS layers are combined following the AHP structure to reach the final objective layer. The synergistic method develops an efficient, realistic and geographically substantial solution to the complex decision-making problem of fast-charging site selection within Dutch urban environments, and will examine the case study city of Amsterdam. An analysis of top site selections and their urban characteristics are presented. The sensitivity analysis proves the tool is effective at incorporating expert knowledge into the tool. The working GIS-AHP tool has proven to be flexible and powerful for decision makers and will act as evidence that a selected fast-charging station location is truly ideal based off of urban characteristics. This study has been the first of its kind to use a GIS-AHP approach towards location-allocation for electric vehicle fast-charging stations.