datafev.routines.smart_reservation package

Submodules

datafev.routines.smart_reservation.arrival module

src.datafev.routines.smart_reservation.arrival.arrival_routine(ts, tdelta, fleet)[source]

This routine is executed upon arrival of EVs that have smart reservations.

Parameters:

  • ts (datetime) – Current time.

  • tdelta (timedelta) – Resolution of scheduling.

  • fleet (data_handling.fleet) – EV fleet object.

Return type:

None.

datafev.routines.smart_reservation.reservation module

src.datafev.routines.smart_reservation.reservation.reservation_routine(ts, tdelta, system, fleet, solver, traffic_forecast, f_discount=0.001, f_markup=0.001, arbitrage_coeff=0.0)[source]

This routine is executed to reserve chargers for the EVs approaching a multi-cluster system.

The smart reservations specify:

  • which cluster and which charger the approaching EVs must connect to,

  • optimal charging schedule of EVs,

  • and the payment for agreed charging service.

Parameters:

  • ts (datetime) – Current time.

  • tdelta (timedelta) – Resolution of scheduling.

  • system (data_handling.multi_cluster) – Multi-cluster system object.

  • fleet (data_handling.fleet) – EV fleet object.

  • solver (pyomo.SolverFactory) – Optimization solver.

  • traffic_forecast (dict of dict) – Traffic forecast data.

  • f_discount (dict of float, optional) – Discount factor (to motivate load increase) in dynamic pricing. The default is 0.05.

  • f_markup (dict of float, optional) – Markup factor (to motivate load decrease) in dynamic pricing. The default is 0.05.

  • arbitrage_coeff (float, optional) – Arbitrage coefficient to distinguish G2V/V2G prices. The default is 0.0.

Return type:

None.