Inputs for Vehicle Usage by Drivers

V2G-Sim enables predictions of vehicle energy usage and vehicle-grid interactions for any number of PEVs (e.g. 100, 103, 106, etc.) enabling the understanding of grid impacts and opportunities at various levels (e.g. distribution system, transmission system, wholesale market, etc.). Vehicle usage profiles for each individual vehicle are required, which describe characteristics such as trip start time, trip end time, trip distance/duration, and times and locations when vehicles are parked or plugged in. The current version of V2G-Sim enables specification of this input data in the following ways:


  1. Stochastic approach – Input statistics describing how/when/where a collection of N number of vehicles travel are provided to V2G-Sim. These statistics include average and standard deviation values describing characteristics such as the time when vehicles depart in the morning, how long vehicles are parked at work, whether there are morning trips, lunch trips, evening trips, etc. Vehicles can be separated into any number of statistical bins for the purposes of entering input statistics . V2G-Sim uses a Monte Carlo sampling approach from the statistical inputs to create vehicle usage profiles describing when each of the N vehicles drives, charges, or is parked.

  2. Derivation of stochastic inputs from a deterministic data source – Algorithms are included in V2G-Sim to automatically derive input statistics from deterministic data sources, such as travel surveys. Following execution of the statistical generation algorithm, the stochastic approach described above is followed.

  3. Deterministic approach – When V2G-Sim users have actual vehicle usage data, this can be directly used as the input data source to simulate the vehicle energy usage and grid interactions for a collection of PEVs. Examples of this deterministic input data includes:
    • Travel survey data such as the National Household Travel Survey (NHTS), fleet scheduling data such as from a fleet dispatch algorithm (i.e. such as the type that deploys delivery vehicles in a courier’s fleet, or maintenance vehicles in a utility or phone network operator’s fleet).

    • Real-time vehicle mobility data, for example using mobility patterns from phone GPS or cell phone tower data.