Identifying charging zones to allocate public charging stations for electric vehicles - Faustino, Fausta J.; Lopes, José Calixto; Melo, Joel D.; Sousa, Thales; Padilha-Feltrin, Antonio [UNESP]; Brito, José A.S.; Garcia, Claudio O. (Elsevier, 2023)
In recent years, several techniques have been presented in the specialized literature to identify the best location for installing public charging stations, considering the demand for charging the batteries of electric vehicles that travel along the main roads in urban areas. However, in cities with different travel patterns for electric vehicle drivers, such consideration may result in charging equipment serving few electric vehicles during high recharging demand. Thus, planning by zone can determine more chargers per station with greater utilization during the operation of charging stations. For this, this work presents a methodology that uses the concept of a charging zone, defined as a circular geographic space with charging equipment that will satisfy the charging demand of electric vehicles in their surroundings. Identifying the centroids of these zones is formulated as a p-median problem, solved by the Teitz-Bart algorithm to provide more coverage to the demands for electric charging in large urban areas. The proposed methodology was applied in a Brazilian city with approximately 3 million inhabitants to find the spatial distribution of public charging zones, considering six scenarios of the global penetration of electric vehicles. Furthermore, we compared the allocation of the centroids in these zones with the solution determined by commercial geoprocessing software. This comparison shows that the proposal determines a spatial distribution of 10% more of these zones with a load factor closer to 0.5 than the results of the commercial geoprocessing software. More zones with this load factor value contribute to better use of the power distribution network installations. In addition, the proposed methodology finds an average reduction of 319 kW of peak demand in regions with a low flow of electric vehicles to meet their charging needs in each analyzed scenario. This peak-demand reduction may allow less investment in connecting future charging stations to the power distribution network. Therefore, the proposed methodology can help public and private agents to disseminate electric mobility, finding public charging areas with greater utilization of chargers per station during the operation of charging stations.
Keywords: charging infrastructure planning; Location-allocation; Optimization; Power distribution system planning; Public charging station