V2G-Sim calculates the driving and charging energy exchanges for individual vehicles, up to any total number of vehicles. When information is provided to V2G-Sim on the location of individual vehicles, grid impacts and opportunities can be resolved down to any spatial resolution. For example, grid loading from plug-in vehicles can be resolved at the wholesale market level, or down to the distribution system level where the EV loading can be visualized on individual transformers. This case study illustrates the spatial resolution capabilities of V2G-Sim with two examples, 1) where EV charging loads are resolved by location type, and 2) where EV charging loads are spatially resolved by state at the national level for the United States.
Vehicle usage data is provided to V2G-Sim using the National Household Travel Survey (NHTS), for only the San Francisco Bay Area in case 1, and for the entire United States in case 2. Spatial information is included in the NHTS in terms of location type, metropolitan area, or state. Assumptions are provided in the inputs for each case on the types of chargers that vehicles plug in to. For case 1 vehicles are assumed to use L2 chargers at work and L1 chargers at home. For case 2, all vehicles are assumed to charge on L1 chargers, and assumptions are made for the levels of EV adoption in each state (80% for this illustrative example).
Case 1 Results - EV charging resolved by location type
The vehicle activity profiles for the San Francisco Bay Area are used as an input to V2G-Sim to compute the overall grid load from charging 659 EVs. The charging load, resolved second-by-second for 24 hours is shown in Figure 1.
Figure 1: Total EV charging load for 659 vehicles in the San Francisco Bay Area
assuming L2 charging at work locations and L1 charging at home locations
Within the NHTS database, the location type for each vehicle is also provided. The cumulative number of vehicles in each location type is shown in Figure 2.
Figure 2: Cumulative number of vehicles in each location type by time of day
Using this type of locational data for each vehicle, the charging load at each location can be predicted by V2G-Sim, as shown in Figure 3.
Figure 3: EV charging load resolved by location type for 659 vehicles in the San Francisco Bay Area
The results in Figure 3 show a sharp peak in workplace EV charging load around 9 am, with the peak having a similar magnitude as the evening peak at home locations. This high and sharp day time peak occurs in this case because L2 chargers are used at workplaces while L1 chargers are used at home. As most vehicles are fully charged over night at home locations, the magnitude of energy required to fill EV batteries during their morning workplace charging session is minimal. As each car is plugged into a relatively fast charger at work, the batteries draw a lot of power for only a short time, causing the sharp peak.
Case 2 Results - EV charging resolved by State
For the second example, NHTS vehicle activity data is used for the entire United States to predict the EV charging demand by state across the United States. Vehicles are assumed to charge with L1 chargers, and an 80% EV adoption is assumed for each state. Time zone differences between each region are factored into the results, and the colormap depicting total charging demand is in units of Watts. The spatially resolved results by state are shown in the animation in Figure 4.
Figure 4: EV Charging load for the United States resolved
by State, assuming L1 charging and 80% adoption of EVs
ConclusionsThis case study demonstrated the spatial resolution capabilities of V2G-Sim. Spatial resolution by location type and by state in the United States were demonstrated, however V2G-Sim can be used to demonstrate spatial resolution to any scale. One particular application of this spatial resolution capability is in resolving EV charging loads at the distribution systems scale, for instance by predicting the EV loading on individual transformers within a utilities' service territory.