@article {1415970, title = {Optimal planning of intra-city public charging stations}, journal = {Energy}, volume = {238}, year = {2022}, pages = {121948}, abstract = {Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and\ urban land uses\ on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on\ Geographic Information System\ is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. V{\"a}ster{\r a}s, a Swedish city, is used as a case study to demonstrate the model{\textquoteright}s effectiveness. It is found that the charging demand served by a PCS is critical to its\ profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.}, url = {https://www.sciencedirect.com/science/article/pii/S0360544221021964?via\%3Dihub$\#$!}, author = {Lin, Haiyang and Caiyun Bian and Wang, Yu and Hailong Li and Sun, Qie and Fredrik Wallen} }