Scenario

Scenario class

Description

An instance of this class includes all information required for scheduling, such as the number of time slots, number of chargers, base load, EV configuration.

Creation

Syntax

scenario = Scenario()
scenario = Scenario(Name=Value)

Name-Value Arguments

Name	Type	Description
`Problem_type`	“Lf” (default) \| “Uf”	Type of problem, either “Lf” for load flattening or “Uf” for user-friendly
`N_c`	50 (default) \| positive integer	Number of chargers
`N_ev`	0 (default) \| nonnegative integer	Number of EVs
`N_g`	1 (default) \| positive integer	Number of groups (for analysis purpose)
`T`	24 (default) \| positive integer	Number of time slots comprising a scenario
`Delta`	60 (default) \| positive scalar	Length of time slot [minutes]
`P_max`	200 (default) \| nonnegative scalar	Maximum aggregate charging power
`P_min`	-200 (default) \| nonpositive scalar	Minimum aggregate charging power (i.e., -(maximum aggregate discharging power))
`D`	zeros(T,1) (default) \| T×1 column vector	Base load profile [kW] (required for load flattening problem)
`C`	zeros(T,1) (default) \| T×1 column vector	Electricity price profile [KRW] (required for user-friendly problem)
`EVs`	[] (default) \| 1×N_ev EV array	Array of EV objects
`L`	[] (default) \| (N_c+1)×(N_c+1) symmetric Metzler matrix with zero row sum	Graph Laplacian describing topology of the aggregator and chargers (for load flattening problem, undirected, index N_c+1 corresponds to the aggregatgor)
`W`	[] (default) \| N_c×N_c nonnegative symmetric stochastic matrix	Doubly stochastic matrix describing topology of the chargers (for user-friendly problem, undirected)

Output Arguments

Name	Type	Description
`scenario`	Scenario object	A scenario that can be used for scheduling

Properties

Name	Type	Description
`Problem_type`	“Lf” (default) \| positive integer, scalar	Type of problem, either “Lf” for load flattening or “Uf” for user-friendly
`N_c`	50 (default) \| positive integer	Number of chargers
`N_ev`	0 (default) \| nonnegative integer	Number of EVs
`N_g`	1 (default) \| positive integer	Number of groups (for analysis purpose)
`T`	24 (default) \| positive integer	Number of time slots comprising a scenario
`Delta`	60 (default) \| positive scalar	Length of time slot [minutes]
`P_max`	200 (default) \| nonnegative scalar	Maximum aggregate charging power
`P_min`	-200 (default) \| nonpositive scalar	Minimum aggregate EV load (i.e., -(maximum aggregate discharging power))
`D`	zeros(T,1) (default) \| T×1 column vector	Base load profile [kW] (required for load flattening problem)
`C`	zeros(T,1) (default) \| T×1 column vector	Electricity price profile [KRW] (required for user-friendly problem)
`EVs`	[] (default) \| 1×N_ev EV array	Array of EV objects
`L`	[] (default) \| (N_c+1)×(N_c+1) symmetric Metzler matrix with zero row sum	Graph Laplacian describing topology of the aggregator and chargers (for load flattening problem, undirected, index N_c+1 corresponds to the aggregatgor)
`W`	[] (default) \| N_c×N_c nonnegative symmetric stochastic matrix	Doubly stochastic matrix describing topology of the chargers (for user-friendly problem, undirected)
`valid`	false (default) \| logical	Whether the scenario is validated or not, call validate() to validate a scenario

Object Functions

Name	Description
set	Set properties; this results in `valid = false`
set_ev	Set a specific EV object in property EVs; this results in `valid = false`
validate	Check whether a Scenario object satisfies required conditions
get_subscenario	Return a truncated subscenario, possibly without EVs that is not plugged-in at the initial time slot