Browse Publications Technical Papers 13-02-01-0001

Optimal Planning of Charging Stations Based on Charging Behavior of Electric Vehicles and Charging Selection 13-02-01-0001

This also appears in SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy-V130-13EJ

Today, due to the shortage of fuel resources, the penetration rate of electric vehicles is increasing. An important part of the electric vehicle industry is the charging part. The interactive objects in this part are the electric vehicles and charging stations controlled by the operators. The demand for electric vehicles is generally just to be able to fill their own power. Under the premise of meeting this requirement, how to optimize the planning of charging stations and improve revenue is worth discussing. In this article, three aspects (charging behavior, charging equipment selection, and energy selection) are researched in depth. The Gaussian model is used to fit the data of charging time, charging start time, and charging amount. The fitting results are good and the RSME values of the six sets of data are all less than 1. Based on the fitting curve as a probability density function, using the Monte Carlo model can predict the electricity demand (260,000 MWh) of the commonly used electric vehicles in Wuhan. For the charging load and cost issues that the operators care about, this article uses genetic algorithm to solve and obtain a reasonable number of three charging power levels. Through calculation, when the ratio of the number of AC Level1, AC Level2, and DC charging equipment is approximately 75:47:35, it can better meet the charging load and cost requirements. In addition, through calculating the revenue of photovoltaic power generation equipment, the results show that the use of photovoltaic power generation brings 30.11% more profit than the use of photovoltaic power.


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