Interface¶

This module contains methods for directly interacting with the _simulator.

class acnportal.acnsim.interface.Interface(simulator)¶

Interface between algorithms and the ACN Simulation Environment.

active_evs¶

Returns a list of active EVs for use by the algorithm.

Returns:	List of EVs currently plugged in and not finished charging
Return type:	List[EV]

allowable_pilot_signals(station_id)¶

Returns the allowable pilot signal levels for the specified EVSE. One may assume an EVSE pilot signal of 0 is allowed regardless of this function’s return values.

Parameters:	station_id (str) – The ID of the station for which the allowable rates should be returned.
Returns:	If the range is continuous or not list[float]: The sorted set of acceptable pilot signals. If continuous this range will have 2 values the min and the max acceptable values. [A]
Return type:	bool

current_time¶

Get the current time (the current _iteration) of the simulator.

Returns:	The current _iteration of the simulator.
Return type:	int

evse_phase(station_id)¶

Returns the phase angle of the EVSE.

Parameters:	station_id (str) – The ID of the station for which the allowable rates should be returned.
Returns:	phase angle of the EVSE. [degrees]
Return type:	float

evse_voltage(station_id)¶

Returns the voltage of the EVSE.

Parameters:	station_id (str) – The ID of the station for which the allowable rates should be returned.
Returns:	voltage of the EVSE. [V]
Return type:	float

get_constraints()¶

Get the constraint matrix and corresponding EVSE ids for the network.

Returns:	Matrix representing the constraints of the network. Each row is a constraint and each
Return type:	np.ndarray

get_demand_charge(start=None)¶

Get the demand charge for the given period. ($/kW)

Parameters:	start (int) – Time step of the simulation where price vector should begin. If None, uses the current timestep of the simulation. Default None.
Returns:	Demand charge for the given period. ($/kW)
Return type:	float

get_prev_peak()¶

Get the highest aggregate peak demand so far in the simulation.

Returns:	Peak demand so far in the simulation. (A)
Return type:	float

get_prices(length, start=None)¶

Get a vector of prices beginning at time start and continuing for length periods. ($/kWh)

Parameters:	length (int) – Number of elements in the prices vector. One entry per period. start (int) – Time step of the simulation where price vector should begin. If None, uses the current timestep of the simulation. Default None.
Returns:	Array of floats where each entry is the price for the corresponding period. ($/kWh)
Return type:	np.ndarray[float]

is_feasible(load_currents, linear=False, violation_tolerance=None, relative_tolerance=None)¶

Return if a set of current magnitudes for each load are feasible.

Wraps Network’s is_feasible method.

For a given constraint, the larger of the violation_tolerance and relative_tolerance is used to evaluate feasibility.

Parameters:	load_currents (Dict[str, List[number]]) – Dictionary mapping load_ids to schedules of charging rates. linear (bool) – If True, linearize all constraints to a more conservative but easier to compute constraint by ignoring the phase angle and taking the absolute value of all load coefficients. Default False. violation_tolerance (float) – Absolute amount by which schedule may violate network constraints. Default None, in which case the network’s violation_tolerance attribute is used. relative_tolerance (float) – Relative amount by which schedule may violate network constraints. Default None, in which case the network’s relative_tolerance attribute is used.
Returns:	If load_currents is feasible at time t according to this set of constraints.
Return type:	bool

last_actual_charging_rate¶

Return the actual charging rates in the last period for all active EVs.

Returns:	A dictionary with the session ID as key and actual charging rate as value.
Return type:	Dict[str, number]

last_applied_pilot_signals¶

Return the pilot signals that were applied in the last _iteration of the simulation for all active EVs.

Does not include EVs that arrived in the current _iteration.

Returns:	A dictionary with the session ID as key and the pilot signal as value.
Return type:	Dict[str, number]

max_pilot_signal(station_id)¶

Returns the maximum allowable pilot signal level for the specified EVSE.

Parameters:	station_id (str) – The ID of the station for which the allowable rates should be returned.
Returns:	the maximum pilot signal supported by this EVSE. [A]
Return type:	float

max_recompute_time¶

Return the maximum recompute time of the simulator.

Returns:	Maximum recompute time of the simulator in number of periods. [periods]
Return type:	int

min_pilot_signal(station_id)¶

Returns the minimum allowable pilot signal level for the specified EVSE.

Parameters:	station_id (str) – The ID of the station for which the allowable rates should be returned.
Returns:	the minimum pilot signal supported by this EVSE. [A]
Return type:	float

period¶

Return the length of each timestep in the simulation.

Returns:	Length of each time interval in the simulation. [minutes]
Return type:	int

remaining_amp_periods(ev)¶

Return the EV’s remaining demand in A*periods.

Returns:	the EV’s remaining demand in A*periods.
Return type:	float

exception acnportal.acnsim.interface.InvalidScheduleError¶: Raised when the schedule passed to the simulator is invalid.