Scenario file description

The models runs through a series of scenarios. For each scenario, you need to create a scenario file in the scenarios/your_case folder (or use a variant). This page describes the elements of the scenario file (your_scenario.toml).

run

Run parameters give the elements to produce the time tags of the run range.

use_day_types_in_charge_computing

If set to true, the charging computations will be done per day type rather than for all days of the run, which fastens the computations.

start

This part gives the year, month, day, hour, and minute of the start of the computations. It is best to set this to a day hour start for consistency and then cut off the start for display (see below). For example, start on 31/12/2019 at 05:00 if your day start hour is 05:00 and if you want to show results starting on 1/1/2020 at 00:00.

end

This part gives the year, month, day, hour, and minute of the end of the computations. Note that the end time tag is not included in the run, so put 1/1/2021 at 00:00 if you want to compute for 2020.

display_start

This is the first time tag you want to show in your end results.

display_end

This is the first time tag that does not appear in your displayed results.

frequency

The time frequency of the run (size and type). For the type, use 'H' for hours, 'min' for minutes, 'S' for seconds, 'D' for days. For more info and other types, see here. The current model uses hourly (so 1 and 'h'). Changing that might require some adjustments in the code.

display frequency

The frequency of the displayed values can be different than the frequency of the compuatations. In the current version, this needs to be the same or less frequent than the computation frequencey above.

extra_downloads

This is to decide if you want to dowload some files (note that this concerns the currently inactive weather module).

locations

vehicle

connectivity

charging_power

charger_efficiency

latitude

longitude

charging_price

charging_desirability

An indicator (0-1) of how much people like to charge at this location

percentage_in_location_at_run_start

If filling it directly instead of computing it (controlled by compute_start_location_split in mobility_module below)

vehicle_discharge_power

proportion_of_discharge_to_network

time_modulation_factors

Starts at midinight

legs

vehicle

distance

duration

hour_in_day_factors

This factor adds a correction to the vehicle consumption that depends on the hour in day (the first is for 00:00 to 00:59, the last for 23:00 to 23:59). Note that these are actual hours, not hours in the user day (which can start and end at other moments than midnight). This is used to take into account things like traffic jams. The default value is 1.

locations

road_type_mix

Give the road type mix We need a value for each of the road_types (see above in [transport_factors]) If the road type does not occur in the leg, simply put 0 Note that the total should be 1 (there are not checks to ensure this, so you need to make sure you do this correctly) road_types = ['highway', 'city']

mix

vehicle

These parameters define the scenario's vehicle. If you want profiles for several vehicles, run separate scenarios.

name

This is the name of the vehicle, which will be checked to see if trips and locations are actually declared.

base_location

not relevant for cars

yearly_kilometrage

Not relevant for cars, as it is built bottom-up

kilometers_column_for_consumption

use_weighted

battery_capacity

solar_panel_size_kWp

base_consumption_per_km

electricity_kWh = 0.76 gasoline_litres = 0.3399 diesel_litres = 0.2917 hydrogen_kg = 0.0398 CNG_kg = 0.2415 LNG_kg = 0.2415

trips

vehicle

legs

time_between_legs

This is the time spent at each interim location (same units as used general)

percentage_station_users

start_probabilities

repeated_sequence

repetition_amounts

time_between_repetitions

mobility_module

day_start_hour

This parameter defines the start of the day for the scenario. It is chosen so that there are no movements during that hour number. It represents the shift from midnight, so a day start hour of 5 means that the day starts at hour 5 (i.e. 05.00). The idea is to choose an hour that is logical for the users/matches their patterns. This is connected to the day types, as a given day will correspond to a day type. For example, the hours between 00:00 and 05:00 on a Saturday belong to the Thursday before and are as such on a week day (as opposed to a weekend day).

compute_start_location_split

day_types

mobility_quantities

battery_space_shift_quantities

location_connections_headers

mobility_index_names

kilometers_driven_headers

holiday_weeks

number_of_holiday_weeks

We need to provide the number of holliday weeks seprately, as counting the elements of the list above might not work, such as when the above list contains weeks 1 and 53 (which just represent the fact that years don't necessarily start and/or end at the start/end of a week).

holiday_departures_in_weekend_week_numbers

holiday_returns_in_weekend_week_numbers

work_hours_in_a_work_day

hours_worked_per_work_week

hours_in_a_standard_work_week

percentage_working_on_a_work_week

worked_hours_per_year

leisure_trips_per_weekend

leisure_trips_per_week_outside_weekends

maximal_fill_percentage_leisure_trips_on_non_work_weekdays

weekday_leisure_trips

percentage_of_weekday_leisure_trips_on_a_work_day

weekend_trips_per_year

holiday_trips_taken

time_spent_at_holiday_destination

trips_per_day_type

For vehicles other than cars, we assume that there is one trip per day type. This is where we match the trips to their day type

transport_factors

road_types

weights

weather

weather_factors_table_root_name

source_data

start_year

end_year

quantities

raw_data_folder

The following define the area over which the raw weather data will be downloaded. The corresponding in the processed weather database is below.

latitude_min

latitude_max

longitude_min

longitude_max

processed_data

raw_data_folder

processed_folder

weather_database_file_name

chunk_size

This parameter is there to avoid issues when writing a too large DataFrame at once (instead, we split it into chunks).