Generate EV schedules

[rajeee]

Pull Request Description

We leverage the existing American Time Use Survey (ATUS) guided stochastic occupancy generator to generate the EV battery charging and discharging schedule. The schedule generator takes total hours driven per week as an input to the EV schedule generator and produces the charging and discharging schedule. The battery model takes care of the actual power draw during the charging and discharging period. An example of what these schedules look like is presented in Figure 1. This schedule shows that during an “away period” for the occupant, the vehicle is discharged after the occupant leaves the home. Then later in the day the vehicle is discharged again while the occupant travels home.

Figure 1 Illustration of EV battery discharge schedule generation.

Discharging Schedule Generation

The guiding theme for the discharge schedule is the assumption that the EV is owned/associated with just one of occupant in the house. We look at the away duration for each occupant and eliminate any occupant whose away hours would not be sufficient to fit the given number of driving hours per year. Then we randomly pick one of the occupant out of the eligible occupant. If no occupant happens to have sufficient away hours then we pick the occupant with the highest away hours and truncate the driving hours to whatever hours the occupant away schedule supports.

Once the occupant is chosen, we assume that the EV battery discharge will start immediately after they leave the home, and immediately before they arrive – simulating the scenario that they left the home driving the EV, stayed somewhere for a while and then returned home. Following that assumption, we sum the total away duration for the occupant, and proportionally assign the driving hours to each away period. The driving hours assigned to each away period is distributed symmetrically at the start and end – always making sure to leave the center 20% as idle to allow for some idle duration at the destination away from home. This algorithm is illustrated in the Figure 1.

Charging Schedule Generation

Currently, we assume that the occupant plugs in their vehicle as soon as they are home and keeps it plugged in until they leave. So, the charging schedule is identical to the occupancy schedule of the EV owning occupant. This makes the battery eligible to be charged anytime the EV is at home if the state of charge is below 100%. Whether or not the charging happens and at what power level is managed by the battery model. The TEMPO model makes similar assumptions in its “immediate” or “ASAP” charging strategy, as described in Yip et al. (2023).

Handling of fractional charging at home

Not all EV owners charge exclusively at home. RECS 2020 survey has a question about what fraction of their charging is done at home with options being 100%, 80 to 99%, 60 to 79% etc. This diversity of charging behavior is handled by proportionally reducing the driving hours and calculating effective driving hours. The underlying assumption in using this data is that respondents answered the fraction of their charging in terms of energy, as opposed to time. The "hours driven per week" fed into the schedule generator is assumed to have already applied this adjustment.

Checklist

Not all may apply:

• Schematron validator (EPvalidator.xml) has been updated
• Sample files have been added/updated (openstudio tasks.rb update_hpxmls)
• Tests have been added/updated (e.g., HPXMLtoOpenStudio/tests/test*.rb and/or workflow/tests/test*.rb)
• Documentation has been updated
• Changelog has been updated
• openstudio tasks.rb update_measures has been run
• No unexpected changes to simulation results of sample files