With all the major car manufacturers entering the market, electric vehicles are gaining not just in popularity but also in market share. It is not difficult to imagine a future where a majority of the city-dwellers in a tropical mega-city like Singapore will be driving in electric vehicles. This transformation could have a significant impact on the power infrastructure of the city due to the power demand of charging stations. A relevant question that needs to be answered is how should the charging infrastructure of a city like Singapore be developed to most efficiently meet the energy demands and to encourage this transformation to electro-mobility. We propose a simulation-assisted method for determining charging station locations.
We use SEMSim Traffic to simulate the movement of vehicles and their energy consumption during a typical day in Singapore. As can be seen in the first half of the video below, an analysis of the state-of-charge (SoC) of vehicles revealed that during the course of the day these locations of low SoC are rather distributed over the island. Also, the fact that even super-fast charging can take more than 15 minutes has to be taken into account when developing charging infrastructure. These factors suggest a need for an alternative approach to analyzing the simulation data. Since we use an agent-based simulation, it is possible to track the complete journeys of vehicles over the course of a day. Thus, instead of identifying the locations with low SoC, a heatmap of origins of the vehicles with low SoC is obtained. This reveals (in red) some clear candidate locations for initial charging station placement like some areas in the Central Business District of Singapore.
This process can be iterated several times with new charging structure locations to determine the temporal order of charging infrastructure development. This could also be combined with SEMSim Power to ensure that charging infrastructure is developed in a way that does not adversely affect the power grid. The quality of the results produced by this method depends on the quality of the input data. For the Singapore case study we used available data about the road network and origin/destination distributions derived from the Household Interview and Travel Survey 2012 which covers roughly 1% of the population. This amount of data may not be enough to come to any definite conclusions regarding charging infrastructure development.