CURRENT stands for “charging infrastructure for electric vehicles analysis tool.” It is a microscopic charging demand tool for electric vehicles (EVs) providing time and location-specific information about charging. CURRENT gives information on the hourly electricity demand of an electric fleet over the course of a week and shows flexibility potential for controlled charging.
Two assumptions form the basis for CURRENT’s calculations. First, EV users do not significantly change their travel pattern. Second, users predominantly charge where they already park. These assumptions are consistent with other research and the anticipated user behavior for a mass-market of EVs. We use the German household travel survey (MiD) for the input data. Next, we outline the workings of the model.
First, CURRENT takes the household travel dataset and creates 24-hour vehicle diaries with information for all trips and parking events for one day. Specific information for each vehicle is added to model it as an electric vehicle (e.g., electric range, charging power capacity). We then define the availability of charging infrastructure at different locations (e.g., home, work, shopping), available charging power, and a minimum parking time for a charging event. Using these assumptions and the vehicle diaries, every vehicle must run through the charging algorithm as an EV. In the charging algorithm, every vehicle must complete every activity of the day (i.e., trip and parking event). Based on charging preferences and depending on charging opportunities during a day, an EV is charged while parking or during a trip interruption at a fast charging station. This results in an aggregated charging demand per hour of the week and per location for an entire fleet of EVs.
A charging event occurs stochastically based on a multinomial logit approach. The model enables users to account for charging preferences in their decision algorithm. A utility-based approach gives the probability of charging at each activity of the day and also for interrupting the trip for a fast charging event. The utility function of each activity considers the preference of charging per location and price per kWh. The charging decision is made by a Monte Carlo simulation according to the charging probability at the charging point. In addition, the probability of finding available charging infrastructure differs for each location (i.e., home, work, shopping, leisure, other). For each household with a vehicle and a private parking spot at home, we assign a private charging infrastructure with a maximum charging power. When interrupting a trip for a fast charging event, the probability to find a fast charging station is 100 percent. The model has both normal charging and fast charging infrastructure. Due to a lack of empirical information, we make assumptions on the probability of finding available charging infrastructure and assign the information for each parking event by a Monte-Carlo simulation.
Further detailed information on CURRENT, all assumptions, and the detailed algorithm are summarized in the following articles.