Optimization for allocating BEV recharging stations in urban areas by using hierarchical clustering

The Battery Electric Vehicle (BEV) prototypes are evolving into a reality in a foreseeable future. Before BEV can embark on a mass adoption by road commuters, a new infrastructure for battery charging will have to be ready in place. By 2015, access to BEV charging will be available at nearly one million charge points in the United States, as claimed by CleanMPG. Early BEV adopters will primarily charge their vehicles at home, in their private garages. However, for many Asia-Pacific regions where people live in concrete ghettos, public charging will play a more central role due to reduced access to convenient home charging. This research focuses on planning BEV charging locations to be installed in urbanized areas where they are usually characterized by dense traffic concentrations, restricted street spaces and other complex factors such as the distribution of power grids. We proposed a two-steps model that first quantified the road information into data points, and subsequently they are converged into 'demand clusters' over an urbanized area by hierarchical clustering analysis. Optimization techniques then applied on the demand clusters with the aim of meeting the supplies and demands, while at the same time certain constraints and cost factors are considered. The model is believed to be an important decision-support tool for city planning and BEV charging stations allocation.