Electric vehicles charging infrastructure location: a genetic algorithm approach

Abstract

Introduction As part of the overall goal of carbon emissions reduction, European cities are expected to encourage the electrification of urban transport. In order to prepare themselves to welcome the increased number of electric vehicles circulating in the city networks in the near future, they are expected to deploy networks of public electric vehicle chargers. The Electric Vehicle Charging Infrastructure Location Problem is an optimization problem that can be approached by linear programming, multi-objective optimization and genetic algorithms. Methods In the present paper, a genetic algorithm approach is presented. Since data from electric vehicles usage are still scarce, origin -destination data of conventional vehicles are used and the necessary assumptions to predict electric vehicles' penetration in the years to come are made. The algorithm and a user-friendly tool have been developed in R and tested for the city of Thessaloniki. ResultsThe results indicate that 15 stations would be required to cover 80% of the estimated electric vehicles charging demand in 2020 in the city of Thessaloniki and their optimal locations to install them are identified. Conclusions The tool that has been developed based on the genetic algorithm, is open source and freely available to interested users. The approach can be used to allocate charging stations at high-level, i.e. to zones, and the authors encourage its use by local authorities of other cities too, in Greece and worldwide, in order to deploy a plan for installing adequate charging infrastructure to cover future electric vehicles charging demand and reduce the electric vehicle Bdriver anxiety( i.e. the driver's concern of running out of battery) encouraging the widespread adoption of electromobility.

Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction

“…The genetic algorithm is a heuristic optimization algorithm widely used in the field of artificial intelligence. On the location problem for charging stations, Dong J et al proposed an activity-based assessment method to analyze the impact of public charging infrastructure construction on increasing driving mileage [11]; Chen S et al set up an optimization model with minimum investment cost and transportation cost as objective function, considering the constraints of capacity, coverage and charging convenience [12]; He F et al modeled the initial charging state of batteries and the car owner's preference for power consumption uncertainty [13], all of which are solved by the genetic algorithm. Efthymiou D et al proposed that the charging station location problem can be solved by linear programming, multi-objective optimization and genetic algorithm [14].…”

Development of an assessment model for predicting public electric vehicle charging stations

“…For this purpose, they developed a calculation model which is applied to cumulated PEV sales and the inventory of publicly accessible charging points, in Germany with the spatial resolution of administrative districts. Comprehensive PEV and public charging [24] developed a genetic algorithm to calculate the required number of EV charging stations. Unfortunately, this model is too complex for it to be adapted in the U.S.…”

Using Electric Vehicles and Demand Side Response to Unlock Distribution Network Flexibility

“…According to the Electric Vehicle Charging Information System, EVs reduce around 2.3 tons of carbon dioxide annually [5]. EVs also have a higher efficiency and a quieter operation than ICE vehicles [6], [7]. Moreover, EVs can take part in demand response (DR) programs by operating as a storage device or controllable load to facilitate the integration of renewable energy systems (RESs) into the grid [8].…”

“…On the contrary, the main concerns with the EV technology include the limited capacity of existing power networks, lack of charging infrastructures, limited charging options, long charging durations, limited driving ranges of vehicles, and high investment costs due to high battery prices [6], [9]. Among the EV drivers, 'range anxiety' is one of the main issues due to long charging durations and lack of charging infrastructures.…”