Internal API

If the name of an extraneous file that appears in simulation results matches one of these regexes, it is safe to ignore

Abstract type for Device Formulations (a.k.a Models)

Example

import PowerSimulations const PSI = PowerSimulations struct MyCustomDeviceFormulation <: PSI.AbstractDeviceFormulation

Abstract type for Service Formulations (a.k.a Models)

Example

import PowerSimulations const PSI = PowerSimulations struct MyServiceFormulation <: PSI.AbstractServiceFormulation

All events subtyped from this need to be recorded under :simulation_status.

Informs the flusher on what data to keep in cache.

Stores results data for one DecisionModel

Abstract type for Decision Problems

Example

import PowerSimulations const PSI = PowerSimulations struct MyCustomProblem <: PSI.DecisionProblem

Abstract type for models than employ PowerSimulations methods. For custom decision problems use DecisionProblem as the super type.

Abstract type for models than employ PowerSimulations methods. For custom emulation problems use EmulationProblem as the super type.

Stores results data for one EmulationModel

Abstract type for Emulation Problems

Example

import PowerSimulations const PSI = PowerSimulations struct MyCustomEmulator <: PSI.EmulationProblem

Parameter to define Flow From_To limit time series

Parameter to define fuel cost time series

Default PowerSimulations Emulation Problem Type for unspecified problems

Struct to dispatch the creation of HVDC Received Flow at From Bus Variables for PWL formulations

Docs abbreviation: $x$

Struct to dispatch the creation of HVDC Received Flow at To Bus Variables for PWL formulations

Docs abbreviation: $y$

Struct to dispatch the creation of HVDC Piecewise Binary Loss Variables

Docs abbreviation: $z$

Struct to dispatch the creation of HVDC Piecewise Loss Variables

Docs abbreviation: $h$ or $w$

Branch type to represent piecewise lossy power flow on two terminal DC lines

Stores simulation data in an HDF file.

Stores simulation data in memory

Supertype for initial condition chronologies

Stores data to populate initial conditions before the build call

Parameter to define Max Flow limit for interface time series

Parameter to define Min Flow limit for interface time series

Abstract type for Decision Model and Emulation Model. OperationModel structs are parameterized with DecisionProblem or Emulation Problem structs

Cache for a single parameter/variable/dual. Stores arrays chronologically by simulation timestamp.

Cache for all model results

Struct to dispatch the creation of piecewise linear block offer variables for objective function

Docs abbreviation: $\delta$

Struct to create the PieceWiseLinearBlockOfferConstraint associated with a specified variable.

See Piecewise linear cost functions for more information.

Struct to create the PieceWiseLinearUpperBoundConstraint associated with a specified variable.

See Piecewise linear cost functions for more information.

Holds the results of a simulation problem for plotting or exporting.

Provides storage of simulation data

Parameter to define Flow To_From limit time series

empty!(store::PowerSimulations.EmulationModelStore)

Base.empty!(store::EmulationModelStore)

Empty the EmulationModelStore

empty!(cache::PowerSimulations.OptimizationOutputCaches)

Base.empty!(cache::OptimizationOutputCaches)

Empty the OptimizationOutputCaches

empty!(cache::PowerSimulations.OptimizationOutputCache)

Base.empty!(cache::OptimizationOutputCache)

Empty the OptimizationOutputCache

empty!(res::SimulationResults)

Base.empty!(res::SimulationResults)

Empty the SimulationResults

read_results_with_keys(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    result_keys::Vector{<:InfrastructureSystems.Optimization.OptimizationContainerKey};
    start_time,
    len,
    cols
) -> Dict{InfrastructureSystems.Optimization.OptimizationContainerKey, DataFrames.DataFrame}

High-level function to read a DataFrame of results.

Arguments

• res: the results to read.
• result_keys::Vector{<:OptimizationContainerKey}: the keys to read. Output will be a Dict{OptimizationContainerKey, DataFrame} with these as the keys
• start_time::Union{Nothing, Dates.DateTime} = nothing: the time at which the resulting time series should begin; nothing indicates the first time in the results
• len::Union{Int, Nothing} = nothing: the number of steps in the resulting time series; nothing indicates up to the end of the results
• cols::Union{Colon, Vector{String}} = (:): which columns to fetch; defaults to :, i.e., all the columns

_add_pwl_constraint!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    _::InfrastructureSystems.Optimization.VariableType,
    break_points::Vector{Float64},
    period::Int64
)

Implement the constraints for PWL Block Offer variables. That is:

\[\sum_{k\in\mathcal{K}} \delta_{k,t} = p_t \\ \sum_{k\in\mathcal{K}} \delta_{k,t} <= P_{k+1,t}^{max} - P_{k,t}^{max}\]

_add_pwl_constraint!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    _::InfrastructureSystems.Optimization.VariableType,
    break_points::Vector{Float64},
    sos_status::PowerSimulations.SOSStatusVariableModule.SOSStatusVariable,
    period::Int64
)

Implement the constraints for PWL variables. That is:

\[\sum_{k\in\mathcal{K}} P_k^{max} \delta_{k,t} = p_t \\ \sum_{k\in\mathcal{K}} \delta_{k,t} = on_t\]

_add_pwl_constraint!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    _::PowerAboveMinimumVariable,
    break_points::Vector{Float64},
    sos_status::PowerSimulations.SOSStatusVariableModule.SOSStatusVariable,
    period::Int64
)

Implement the constraints for PWL variables for Compact form. That is:

\[\sum_{k\in\mathcal{K}} P_k^{max} \delta_{k,t} = p_t + P_min * u_t \\ \sum_{k\in\mathcal{K}} \delta_{k,t} = on_t\]

_add_pwl_constraint!(
    container::PowerSimulations.OptimizationContainer,
    component::ReserveDemandCurve,
    _::ServiceRequirementVariable,
    break_points::Vector{Float64},
    sos_status::PowerSimulations.SOSStatusVariableModule.SOSStatusVariable,
    period::Int64
)

Implement the constraints for PWL Block Offer variables for ORDC. That is:

\[\sum_{k\in\mathcal{K}} \delta_{k,t} = p_t \\ \sum_{k\in\mathcal{K}} \delta_{k,t} <= P_{k+1,t}^{max} - P_{k,t}^{max}\]

_add_pwl_sos_constraint!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    _::InfrastructureSystems.Optimization.VariableType,
    break_points::Vector{Float64},
    sos_status::PowerSimulations.SOSStatusVariableModule.SOSStatusVariable,
    period::Int64
)

Implement the SOS for PWL variables. That is:

\[\{\delta_{i,t}, ..., \delta_{k,t}\} \in \text{SOS}_2\]

_add_pwl_term!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    cost_function::MarketBidCost,
    _::CostCurve{PiecewiseIncrementalCurve},
    _::InfrastructureSystems.Optimization.VariableType,
    _::PowerSimulations.AbstractDeviceFormulation
) -> Vector{JuMP.AffExpr}

Add PWL cost terms for data coming from the MarketBidCost with a fixed incremental offer curve

_add_pwl_term!(
    container::PowerSimulations.OptimizationContainer,
    component::Component,
    cost_function::Union{CostCurve{PiecewisePointCurve}, FuelCurve{PiecewisePointCurve}},
    _::InfrastructureSystems.Optimization.VariableType,
    _::PowerSimulations.AbstractDeviceFormulation
) -> Vector{JuMP.AffExpr}

Add PWL cost terms for data coming from a PiecewisePointCurve

_add_pwl_term!(
    container::PowerSimulations.OptimizationContainer,
    component::ThermalGen,
    cost_function::Union{CostCurve{PiecewisePointCurve}, FuelCurve{PiecewisePointCurve}},
    _::InfrastructureSystems.Optimization.VariableType,
    _::ThermalDispatchNoMin
) -> Vector{JuMP.AffExpr}

Add PWL cost terms for data coming from a PiecewisePointCurve for ThermalDispatchNoMin formulation

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::CostCurve{LinearCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Adds to the cost function cost terms for sum of variables with common factor to be used for cost expression for optimization_container model.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_component::PSY.CostCurve{PSY.LinearCurve} : container for cost to be associated with variable

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::CostCurve{PiecewisePointCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Creates piecewise linear cost function using a sum of variables and expression with sign and time step included.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_function::PSY.CostCurve{PSY.PiecewisePointCurve}: container for piecewise linear cost

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::CostCurve{QuadraticCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Adds to the cost function cost terms for sum of variables with common factor to be used for cost expression for optimization_container model.

Equation

gen_cost = dt*sign*(sum(variable.^2)*cost_data[1] + sum(variable)*cost_data[2])

LaTeX

$cost = dt\times sign (sum_{i\in I} c_1 v_i^2 + sum_{i\in I} c_2 v_i )$

for quadratic factor large enough. If the first term of the quadratic objective is 0.0, adds a linear cost term sum(variable)*cost_data[2]

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_component::PSY.CostCurve{PSY.QuadraticCurve} : container for quadratic factors

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::FuelCurve{LinearCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Adds to the cost function cost terms for sum of variables with common factor to be used for cost expression for optimization_container model.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_component::PSY.FuelCurve{PSY.LinearCurve} : container for cost to be associated with variable

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::FuelCurve{PiecewisePointCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Creates piecewise linear cost function using a sum of variables and expression with sign and time step included.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_function::PSY.CostCurve{PSY.PiecewisePointCurve}: container for piecewise linear cost

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::FuelCurve{QuadraticCurve},
    _::PowerSimulations.AbstractDeviceFormulation
)

Adds to the cost function cost terms for sum of variables with common factor to be used for cost expression for optimization_container model.

Equation

gen_cost = dt*(sum(variable.^2)*cost_data[1]*fuel_cost + sum(variable)*cost_data[2]*fuel_cost)

LaTeX

$cost = dt\times (sum_{i\in I} c_f c_1 v_i^2 + sum_{i\in I} c_f c_2 v_i )$

for quadratic factor large enough. If the first term of the quadratic objective is 0.0, adds a linear cost term sum(variable)*cost_data[2]

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_component::PSY.FuelCurve{PSY.QuadraticCurve} : container for quadratic factors

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::MarketBidCost,
    _::PowerSimulations.AbstractDeviceFormulation
)

Creates piecewise linear market bid function using a sum of variables and expression for market participants. Decremental offers are not accepted for most components, except Storage systems and loads.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_function::MarketBidCost : container for market bid cost

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::Union{CostCurve{PiecewiseAverageCurve}, CostCurve{PiecewiseIncrementalCurve}},
    _::PowerSimulations.AbstractDeviceFormulation
)

Creates piecewise linear cost function using a sum of variables and expression with sign and time step included.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_function::PSY.Union{PSY.CostCurve{PSY.PiecewiseIncrementalCurve}, PSY.CostCurve{PSY.PiecewiseAverageCurve}}: container for piecewise linear cost

_add_variable_cost_to_objective!(
    container::PowerSimulations.OptimizationContainer,
    _::InfrastructureSystems.Optimization.VariableType,
    component::Component,
    cost_function::Union{FuelCurve{PiecewiseAverageCurve}, FuelCurve{PiecewiseIncrementalCurve}},
    _::PowerSimulations.AbstractDeviceFormulation
)

Creates piecewise linear fuel cost function using a sum of variables and expression with sign and time step included.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• var_key::VariableKey: The variable name
• componentname::String: The componentname of the variable container
• cost_function::PSY.Union{PSY.FuelCurve{PSY.PiecewiseIncrementalCurve}, PSY.FuelCurve{PSY.PiecewiseAverageCurve}}: container for piecewise linear cost

_allocate_execution_order(
    interval_run_counts::Vector{Int64}
) -> Vector{Int64}

Function calculates the total number of problem executions in the simulation and allocates the appropiate vector

_calculate_interval_inner_counts(
    intervals::OrderedDict{Symbol, Dates.Millisecond}
) -> Vector{Int64}

calculateintervalinnercounts(intervals::OrderedDict{String,<:Dates.TimePeriod})

Calculates how many times a problem is executed for every interval of the previous problem

_create_time_series_multiplier_index(
    model,
    _::Type{T<:InfrastructureSystems.Optimization.TimeSeriesParameter}
) -> Union{Nothing, Int64}

Function to create a unique index of time series names for each device model. For example, if two parameters each reference the same time series name, this function will return a different value for each parameter entry

_get_data_for_tdc(
    initial_conditions_on::Array{T<:InitialCondition, 1},
    initial_conditions_off::Array{U<:InitialCondition, 1},
    resolution::Dates.TimePeriod
) -> Tuple{Matrix{InitialCondition}, Vector{@NamedTuple{up::Float64, down::Float64}}}

If the fraction of hours that a generator has a duration constraint is less than the fraction of hours that a single time_step represents then it is not binding.

_get_initial_condition_type(
    _::Type{RampConstraint},
    _::Type{<:ThermalGen},
    _::Type{<:PowerSimulations.AbstractThermalFormulation}
) -> Type{PowerSimulations.DeviceAboveMinPower}

This function gets the data for the generators for ramping constraints of thermal generators

_get_pwl_cost_expression(
    container::PowerSimulations.OptimizationContainer,
    component::ReserveDemandCurve,
    time_period::Int64,
    cost_data::PiecewiseStepData,
    multiplier::Float64
) -> JuMP.AffExpr

Get cost expression for StepwiseCostReserve

_summary_to_dict!(
    optimizer_stats::OptimizerStats,
    jump_model::JuMP.Model
)

Run this function only when getting detailed solver stats

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{NetworkFlowConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{B<:ACBranch},
    model::DeviceModel{B<:ACBranch, <:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{<:PowerSimulations.AbstractPTDFModel}
)

Add network flow constraints for ACBranch and NetworkModel with <: AbstractPTDFModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{RequirementConstraint},
    service::ConstantReserveGroup,
    contributing_services::Vector{<:Service},
    model::ServiceModel{SR<:ConstantReserveGroup, GroupReserve}
)

This function creates the requirement constraint that will be attained by the appropriate services

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    cons_type::Type{RateLimitConstraintFromTo},
    devices::InfrastructureSystems.FlattenIteratorWrapper{B<:ACBranch},
    device_model::DeviceModel{B<:ACBranch, <:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{T<:PowerModels.AbstractPowerModel}
)

Add rate limit from to constraints for ACBranch with AbstractPowerModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    cons_type::Type{RateLimitConstraintToFrom},
    devices::InfrastructureSystems.FlattenIteratorWrapper{B<:ACBranch},
    _::DeviceModel{B<:ACBranch, <:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{T<:PowerModels.AbstractPowerModel}
)

Add rate limit to from constraints for ACBranch with AbstractPowerModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{FlowLimitConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{AreaInterchange},
    model::DeviceModel{AreaInterchange, StaticBranch},
    _::NetworkModel{T<:PowerModels.AbstractActivePowerModel}
)

Add flow constraints for area interchanges

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{NetworkFlowConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:PhaseShiftingTransformer},
    model::DeviceModel{T<:PhaseShiftingTransformer, PhaseAngleControl},
    network_model::NetworkModel{<:PowerSimulations.AbstractPTDFModel}
)

Add network flow constraints for PhaseShiftingTransformer and NetworkModel with <: AbstractPTDFModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{NetworkFlowConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:PhaseShiftingTransformer},
    model::DeviceModel{T<:PhaseShiftingTransformer, PhaseAngleControl},
    _::NetworkModel{DCPPowerModel}
)

Add network flow constraints for PhaseShiftingTransformer and NetworkModel with PM.DCPPowerModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{StartTypeConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:ThermalMultiStart},
    model::DeviceModel{T<:ThermalMultiStart, ThermalMultiStartUnitCommitment},
    _::NetworkModel
)

Constructs contraints that restricts devices to one type of start at a time

Equations

sum(var_starts[name, s, t] for s in starts) = var_start[name, t]

LaTeX

$\sum^{S_g}_{s=1} δ^{s}(t) \eq x^{start}(t)$

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{StartupInitialConditionConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:ThermalMultiStart},
    model::DeviceModel{T<:ThermalMultiStart, ThermalMultiStartUnitCommitment},
    _::NetworkModel
)

Constructs contraints that restricts devices to one type of start at a time

Equations

ub: (time_limits[st+1]-1)*δ^{s}(t) + (1 - δ^{s}(t)) * M_VALUE >= sum(1-varbin[name, i]) for i in 1:t) + initial_condition_offtime lb: (time_limits[st]-1)*δ^{s}(t) =< sum(1-varbin[name, i]) for i in 1:t) + initial_condition_offtime

LaTeX

$TS^{s+1}_{g} δ^{s}(t) + (1-δ^{s}(t)) M_VALUE \geq \sum^{t}_{i=1} x^{status}(i) + DT_{g}^{0} \forall t in \{1, \ldots, TS^{s+1}_{g}$

$TS^{s}_{g} δ^{s}(t) \leq \sum^{t}_{i=1} x^{status}(i) + DT_{g}^{0} \forall t in \{1, \ldots, TS^{s+1}_{g}$

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{StartupTimeLimitTemperatureConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:ThermalMultiStart},
    model::DeviceModel{T<:ThermalMultiStart, ThermalMultiStartUnitCommitment},
    _::NetworkModel
)

Constructs contraints for different types of starts based on generator down-time

Equations

for t in time_limits[s+1]:T

var_starts[name, s, t] <= sum( var_stop[name, t-i] for i in time_limits[s]:(time_limits[s+1]-1)

LaTeX

$δ^{s}(t) \leq \sum_{i=TS^{s}_{g}}^{TS^{s+1}_{g}} x^{stop}(t-i)$

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{PhaseAngleControlLimit},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:PhaseShiftingTransformer},
    model::DeviceModel{T<:PhaseShiftingTransformer, PhaseAngleControl},
    _::NetworkModel{U<:PowerModels.AbstractActivePowerModel}
)

Add phase angle limits for phase shifters

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{FlowLimitConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:Union{MonitoredLine, PhaseShiftingTransformer}},
    model::DeviceModel{T<:Union{MonitoredLine, PhaseShiftingTransformer}, U<:PowerSimulations.AbstractBranchFormulation},
    _::NetworkModel{V<:PowerModels.AbstractDCPModel}
)

Add branch flow constraints for monitored lines with DC Power Model

add_constraints!(
    _::PowerSimulations.OptimizationContainer,
    _::Type{FlowLimitToFromConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:MonitoredLine},
    model::DeviceModel{T<:MonitoredLine, U<:StaticBranchUnbounded},
    _::NetworkModel{V<:PowerModels.AbstractActivePowerModel}
)

Don't add branch flow constraints for monitored lines if formulation is StaticBranchUnbounded

add_constraints!(
    _::PowerSimulations.OptimizationContainer,
    _::Type{RateLimitConstraintFromTo},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:MonitoredLine},
    model::DeviceModel{T<:MonitoredLine, U<:StaticBranchUnbounded},
    _::NetworkModel{V<:PowerModels.AbstractActivePowerModel}
)

Don't add branch flow constraints for monitored lines if formulation is StaticBranchUnbounded

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    cons_type::Type{RateLimitConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:ACBranch},
    device_model::DeviceModel{T<:ACBranch, U<:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{V<:PowerModels.AbstractActivePowerModel}
)

Add branch rate limit constraints for ACBranch with AbstractActivePowerModel

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{RampConstraint},
    devices::InfrastructureSystems.FlattenIteratorWrapper{U<:ThermalGen},
    model::DeviceModel{U<:ThermalGen, V<:PowerSimulations.AbstractThermalUnitCommitment},
    _::NetworkModel{W<:PowerModels.AbstractPowerModel}
)

This function adds the ramping limits of generators when there are CommitmentVariables

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:ActivePowerVariableLimitsConstraint},
    U::Type{<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ThermalMultiStart},
    _::DeviceModel{V<:ThermalMultiStart, W<:ThermalMultiStartUnitCommitment},
    _::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

This function adds range constraint for the first time period. Constraint (10) from PGLIB formulation

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:PowerSimulations.PowerVariableLimitsConstraint},
    U::Type{<:Union{InfrastructureSystems.Optimization.ExpressionType, InfrastructureSystems.Optimization.VariableType}},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ThermalGen},
    model::DeviceModel{V<:ThermalGen, W<:PowerSimulations.AbstractThermalDispatchFormulation},
    _::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Semicontinuous range constraints for thermal dispatch formulations

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:PowerSimulations.PowerVariableLimitsConstraint},
    U::Type{<:Union{InfrastructureSystems.Optimization.ExpressionType, InfrastructureSystems.Optimization.VariableType}},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ThermalGen},
    model::DeviceModel{V<:ThermalGen, W<:PowerSimulations.AbstractThermalUnitCommitment},
    _::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Semicontinuous range constraints for unit commitment formulations

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:PowerSimulations.PowerVariableLimitsConstraint},
    U::Type{<:Union{PowerAboveMinimumVariable, InfrastructureSystems.Optimization.ExpressionType}},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ThermalGen},
    model::DeviceModel{V<:ThermalGen, W<:ThermalCompactDispatch},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Range constraints for thermal compact dispatch

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:ReactivePowerVariableLimitsConstraint},
    U::Type{<:ReactivePowerVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ElectricLoad},
    _::DeviceModel{V<:ElectricLoad, W<:PowerSimulations.AbstractControllablePowerLoadFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Reactive Power Constraints on Controllable Loads Assume Constant power_factor

add_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{<:ReactivePowerVariableLimitsConstraint},
    _::Type{<:ReactivePowerVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:RenewableGen},
    _::DeviceModel{V<:RenewableGen, W<:RenewableConstantPowerFactor},
    _::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Reactive Power Constraints on Renewable Gen Constant power_factor

add_feedforward_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::DeviceModel,
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:Component},
    ff::UpperBoundFeedforward
)

    ub_ff(container::OptimizationContainer,
          cons_name::Symbol,
          constraint_infos,
          param_reference,
          var_key::VariableKey)

Constructs a parameterized upper bound constraint to implement feedforward from other models. The Parameters are initialized using the uppper boundary values of the provided variables.

variable[var_name, t] <= param_reference[var_name]

LaTeX

$x \leq param^{max}$

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• cons_name::Symbol : name of the constraint
• param_reference : Reference to the JuMP.VariableRef used to determine the upperbound
• var_key::VariableKey : the name of the continuous variable

add_feedforward_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::DeviceModel,
    devices::Union{Array{T<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{T<:Component}},
    ff::FixValueFeedforward
)

    add_feedforward_constraints(
        container::OptimizationContainer,
        ::DeviceModel,
        devices::IS.FlattenIteratorWrapper{T},
        ff::FixValueFeedforward,
    ) where {T <: PSY.Component}

Constructs a equality constraint to a fix a variable in one model using the variable value from other model results.

variable[var_name, t] == param[var_name, t]

LaTeX

$x == param$

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• model::DeviceModel : the device model
• devices::IS.FlattenIteratorWrapper{T} : list of devices
• ff::FixValueFeedforward : a instance of the FixValue Feedforward

add_feedforward_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::DeviceModel{T<:Component, U<:PowerSimulations.AbstractDeviceFormulation},
    devices::InfrastructureSystems.FlattenIteratorWrapper{T<:Component},
    ff::LowerBoundFeedforward
)

    lb_ff(container::OptimizationContainer,
          cons_name::Symbol,
          constraint_infos,
          param_reference,
          var_key::VariableKey)

Constructs a parameterized upper bound constraint to implement feedforward from other models. The Parameters are initialized using the uppper boundary values of the provided variables.

variable[var_name, t] <= param_reference[var_name]

LaTeX

$x \leq param^{max}$

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• cons_name::Symbol : name of the constraint
• param_reference : Reference to the JuMP.VariableRef used to determine the upperbound
• var_key::VariableKey : the name of the continuous variable

add_linear_ramp_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:InfrastructureSystems.Optimization.ConstraintType},
    U::Type{S<:Union{ActivePowerVariable, PowerAboveMinimumVariable}},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Component},
    model::DeviceModel{V<:Component, W<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{<:PowerModels.AbstractPowerModel}
)

Constructs allowed rate-of-change constraints from variables, initial condtions, and rate data.

If t = 1:

variable[name, 1] - initial_conditions[ix].value <= rate_data[1][ix].up

initial_conditions[ix].value - variable[name, 1] <= rate_data[1][ix].down

If t > 1:

variable[name, t] - variable[name, t-1] <= rate_data[1][ix].up

variable[name, t-1] - variable[name, t] <= rate_data[1][ix].down

LaTeX

$r^{down} \leq x_1 - x_{init} \leq r^{up}, \text{ for } t = 1$

$r^{down} \leq x_t - x_{t-1} \leq r^{up}, \forall t \geq 2$

add_range_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InfrastructureSystems.Optimization.ConstraintType},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Component},
    model::DeviceModel{V<:Component, W<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{X<:PowerModels.AbstractPowerModel}
)

Constructs min/max range constraint from device variable.

If min and max within an epsilon width:

variable[name, t] == limits.max

Otherwise:

limits.min <= variable[name, t] <= limits.max

where limits in constraint_infos.

LaTeX

$x = limits^{max}, \text{ for } |limits^{max} - limits^{min}| < \varepsilon$

$limits^{min} \leq x \leq limits^{max}, \text{ otherwise }$

add_reserve_range_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InputActivePowerVariableLimitsConstraint},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Component},
    model::DeviceModel{V<:Component, W<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{X<:PowerModels.AbstractPowerModel}
)

Constructs min/max range constraint from device variable and reservation decision variable.

varcts[name, t] <= limits.max * (1 - varbin[name, t])

varcts[name, t] >= limits.min * (1 - varbin[name, t])

where limits in constraint_infos.

LaTeX

$0 \leq x^{cts} \leq limits^{max} (1 - x^{bin}), \text{ for } limits^{min} = 0$

$limits^{min} (1 - x^{bin}) \leq x^{cts} \leq limits^{max} (1 - x^{bin}), \text{ otherwise }$

add_reserve_range_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:Union{OutputActivePowerVariableLimitsConstraint, ReactivePowerVariableLimitsConstraint}},
    _::Type{U<:InfrastructureSystems.Optimization.ExpressionType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{W<:Component},
    model::DeviceModel{W<:Component, X<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{Y<:PowerModels.AbstractPowerModel}
)

Constructs min/max range constraint from device variable and reservation decision variable.

varcts[name, t] <= limits.max * varbin[name, t]

varcts[name, t] >= limits.min * varbin[name, t]

where limits in constraint_infos.

LaTeX

$limits^{min} x^{bin} \leq x^{cts} \leq limits^{max} x^{bin},$

add_reserve_range_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:Union{OutputActivePowerVariableLimitsConstraint, ReactivePowerVariableLimitsConstraint}},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{W<:Component},
    model::DeviceModel{W<:Component, X<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{Y<:PowerModels.AbstractPowerModel}
)

Constructs min/max range constraint from device variable and reservation decision variable.

varcts[name, t] <= limits.max * varbin[name, t]

varcts[name, t] >= limits.min * varbin[name, t]

where limits in constraint_infos.

LaTeX

$limits^{min} x^{bin} \leq x^{cts} \leq limits^{max} x^{bin},$

add_result!(
    cache::PowerSimulations.OptimizationOutputCache,
    timestamp::Dates.DateTime,
    array::Array{Float64},
    system_cache_is_full::Bool
) -> Int64

Add result to the cache.

add_semicontinuous_ramp_constraints!(
    container::PowerSimulations.OptimizationContainer,
    T::Type{<:InfrastructureSystems.Optimization.ConstraintType},
    U::Type{S<:Union{ActivePowerVariable, PowerAboveMinimumVariable}},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Component},
    _::DeviceModel{V<:Component, W<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{<:PowerModels.AbstractPowerModel}
)

Constructs allowed rate-of-change constraints from variables, initial condtions, start/stop status, and rate data

Equations

If t = 1:

variable[name, 1] - initial_conditions[ix].value <= rate_data[1][ix].up + rate_data[2][ix].max*varstart[name, 1]

initial_conditions[ix].value - variable[name, 1] <= rate_data[1][ix].down + rate_data[2][ix].min*varstop[name, 1]

If t > 1:

variable[name, t] - variable[name, t-1] <= rate_data[1][ix].up + rate_data[2][ix].max*varstart[name, t]

variable[name, t-1] - variable[name, t] <= rate_data[1][ix].down + rate_data[2][ix].min*varstop[name, t]

LaTeX

$r^{down} + r^{min} x^{stop}_1 \leq x_1 - x_{init} \leq r^{up} + r^{max} x^{start}_1, \text{ for } t = 1$

$r^{down} + r^{min} x^{stop}_t \leq x_t - x_{t-1} \leq r^{up} + r^{max} x^{start}_t, \forall t \geq 2$

add_semicontinuous_range_constraints!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InfrastructureSystems.Optimization.ConstraintType},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Component},
    model::DeviceModel{V<:Component, W<:PowerSimulations.AbstractDeviceFormulation},
    _::Type{X<:PowerModels.AbstractPowerModel}
)

Constructs min/max range constraint from device variable and on/off decision variable.

If device min = 0:

varcts[name, t] <= limits.max*varbin[name, t])

varcts[name, t] >= 0.0

Otherwise:

varcts[name, t] <= limits.max*varbin[name, t]

varcts[name, t] >= limits.min*varbin[name, t]

where limits in constraint_infos.

LaTeX

$0 \leq x^{cts} \leq limits^{max} x^{bin}, \text{ for } limits^{min} = 0$

$limits^{min} x^{bin} \leq x^{cts} \leq limits^{max} x^{bin}, \text{ otherwise }$

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:PhaseShifterAngle},
    devices::InfrastructureSystems.FlattenIteratorWrapper{PhaseShiftingTransformer},
    _::DeviceModel{PhaseShiftingTransformer, V<:PhaseAngleControl},
    network_model::NetworkModel{<:PowerSimulations.AbstractPTDFModel}
)

Implementation of addtoexpression! for lossless branch/network models

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ACBranch},
    _::DeviceModel{V<:ACBranch, W<:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{CopperPlatePowerModel}
)

Implementation of addtoexpression! for lossless branch/network models

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:StaticInjection},
    device_model::DeviceModel{V<:StaticInjection, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{CopperPlatePowerModel}
)

Default implementation to add variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerToFromVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.AbstractTwoTerminalDCLineFormulation},
    network_model::NetworkModel{PTDFPowerModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ACBranch},
    _::DeviceModel{V<:ACBranch, W<:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{PTDFPowerModel}
)

Implementation of addtoexpression! for lossless branch/network models

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:StaticInjection},
    device_model::DeviceModel{V<:StaticInjection, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{PTDFPowerModel}
)

Default implementation to add variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerFromToVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Branch},
    _::DeviceModel{V<:Branch, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerFromToVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.AbstractTwoTerminalDCLineFormulation},
    network_model::NetworkModel{X<:CopperPlatePowerModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerFromToVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.AbstractTwoTerminalDCLineFormulation},
    network_model::NetworkModel{X<:PowerSimulations.AbstractPTDFModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerToFromVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ACBranch},
    _::DeviceModel{V<:ACBranch, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerToFromVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.AbstractTwoTerminalDCLineFormulation},
    network_model::NetworkModel{X<:CopperPlatePowerModel}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:FlowActivePowerVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:ACBranch},
    _::DeviceModel{V<:ACBranch, W<:PowerSimulations.AbstractBranchFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractActivePowerModel}
)

Implementation of addtoexpression! for lossless branch/network models

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:HVDCLosses},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:HVDCTwoTerminalDispatch},
    network_model::NetworkModel{X<:Union{CopperPlatePowerModel, PTDFPowerModel}}
)

Default implementation to add branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:PowerSimulations.HVDCActivePowerReceivedFromVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.HVDCTwoTerminalPiecewiseLoss},
    network_model::NetworkModel{X<:PowerSimulations.AbstractPTDFModel}
)

PWL implementation to add FromTo branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:ActivePowerBalance},
    _::Type{U<:PowerSimulations.HVDCActivePowerReceivedToVariable},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}},
    _::DeviceModel{V<:Union{TwoTerminalHVDCLine, TwoTerminalVSCDCLine}, W<:PowerSimulations.HVDCTwoTerminalPiecewiseLoss},
    network_model::NetworkModel{X<:PowerSimulations.AbstractPTDFModel}
)

PWL implementation to add FromTo branch variables to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:SystemBalanceExpressions},
    _::Type{U<:InfrastructureSystems.Optimization.TimeSeriesParameter},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:Device},
    model::DeviceModel{V<:Device, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Default implementation to add parameters to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:SystemBalanceExpressions},
    _::Type{U<:InfrastructureSystems.Optimization.TimeSeriesParameter},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:StaticInjection},
    device_model::DeviceModel{V<:StaticInjection, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:CopperPlatePowerModel}
)

Default implementation to add parameters to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:SystemBalanceExpressions},
    _::Type{U<:InfrastructureSystems.Optimization.TimeSeriesParameter},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:StaticInjection},
    device_model::DeviceModel{V<:StaticInjection, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:PowerSimulations.AbstractPTDFModel}
)

Default implementation to add parameters to SystemBalanceExpressions

add_to_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:SystemBalanceExpressions},
    _::Type{U<:InfrastructureSystems.Optimization.VariableType},
    devices::InfrastructureSystems.FlattenIteratorWrapper{V<:StaticInjection},
    _::DeviceModel{V<:StaticInjection, W<:PowerSimulations.AbstractDeviceFormulation},
    network_model::NetworkModel{X<:PowerModels.AbstractPowerModel}
)

Default implementation to add device variables to SystemBalanceExpressions

add_variable!(
    container::PowerSimulations.OptimizationContainer,
    variable_type::ServiceRequirementVariable,
    service::ReserveDemandCurve,
    formulation
)

Add variables for ServiceRequirementVariable for StepWiseCostReserve

add_variable!(
    container::PowerSimulations.OptimizationContainer,
    var_type::InfrastructureSystems.Optimization.AuxVariableType,
    devices::Union{Array{D<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{D<:Component}},
    formulation
)

Default implementation of adding auxiliary variable to the model.

add_variable!(
    container::PowerSimulations.OptimizationContainer,
    variable_type::InfrastructureSystems.Optimization.VariableType,
    devices::Union{Array{D<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{D<:Component}},
    formulation
)

Adds a variable to the optimization model and to the affine expressions contained in the optimization_container model according to the specified sign. Based on the inputs, the variable can be specified as binary.

Bounds

lb_value_function <= varstart[name, t] <= ub_value_function

If binary = true:

varstart[name, t] in {0,1}

LaTeX

$lb \ge x^{device}_t \le ub \forall t$

$x^{device}_t \in {0,1} \forall t iff \text{binary = true}$

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• devices : Vector or Iterator with the devices
• var_key::VariableKey : Base Name for the variable
• binary::Bool : Select if the variable is binary
• expressionname::Symbol : Expressionname name stored in container.expressions to add the variable
• sign::Float64 : sign of the addition of the variable to the expression_name. Default Value is 1.0

Accepted Keyword Arguments

• ubvalue : Provides the function over device to obtain the value for a upperbound
• lbvalue : Provides the function over device to obtain the value for a lowerbound. If the variable is meant to be positive define lb = x -> 0.0
• initial_value : Provides the function over device to obtain the warm start value

add_variable!(
    container::PowerSimulations.OptimizationContainer,
    variable_type::Union{OnVariable, StartVariable, StopVariable},
    devices::Union{Array{D<:ThermalGen, 1}, InfrastructureSystems.FlattenIteratorWrapper{D<:ThermalGen}},
    formulation::PowerSimulations.AbstractThermalFormulation
)

Adds a variable to the optimization model for the OnVariable of Thermal Units

add_variables!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InfrastructureSystems.Optimization.AuxVariableType},
    devices::Union{Array{U<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{U<:Component}},
    formulation::Union{PowerSimulations.AbstractDeviceFormulation, PowerSimulations.AbstractServiceFormulation}
)

Add variables to the OptimizationContainer for any component.

add_variables!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InfrastructureSystems.Optimization.VariableType},
    devices::Union{Array{U<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{U<:Component}},
    formulation::Union{PowerSimulations.AbstractDeviceFormulation, PowerSimulations.AbstractServiceFormulation}
)

Add variables to the OptimizationContainer for any component.

add_variables!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{T<:InfrastructureSystems.Optimization.VariableType},
    service::AbstractReserve,
    contributing_devices::Union{Array{V<:Component, 1}, InfrastructureSystems.FlattenIteratorWrapper{V<:Component}},
    formulation::PowerSimulations.AbstractReservesFormulation
)

Add variables to the OptimizationContainer for a service.

build_model!(
    model::DecisionModel{<:PowerSimulations.DefaultDecisionProblem}
)

Default implementation of build method for Operational Problems for models conforming with DecisionProblem specification. Overload this function to implement a custom build method

build_model!(model::EmulationModel)

Default implementation of build method for Emulation Problems for models conforming with DecisionProblem specification. Overload this function to implement a custom build method

check_activeservice_variables(
    container::PowerSimulations.OptimizationContainer,
    contributing_services::Array{T<:Service, 1}
)

This function checks if the variables for reserves were created

check_file_integrity(path::String)

check_file_integrity(path::String)

Checks the hash value for each file made with the file is written with the new hash_value to verify the file hasn't been tampered with since written

Arguments

• path::String: this is the folder path that contains the results and the check.sha256 file

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, D<:PowerSimulations.AbstractStandardUnitCommitment},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the arguments model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, D<:PowerSimulations.AbstractStandardUnitCommitment},
    network_model::NetworkModel
)

This function creates the arguments for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalBasicUnitCommitment},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the arguments for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalBasicUnitCommitment},
    network_model::NetworkModel
)

This function creates the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalStandardDispatch},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the arguments for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalStandardDispatch},
    network_model::NetworkModel
)

This function creates the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, <:PowerSimulations.AbstractStandardUnitCommitment},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the constraints for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, <:PowerSimulations.AbstractStandardUnitCommitment},
    network_model::NetworkModel
)

This function creates the constraints for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalBasicUnitCommitment},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the constraints for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalBasicUnitCommitment},
    network_model::NetworkModel
)

This function creates the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalStandardDispatch},
    network_model::NetworkModel{<:PowerModels.AbstractActivePowerModel}
)

This function creates the constraints for the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_device!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ModelConstructStage,
    model::DeviceModel{T<:ThermalGen, ThermalStandardDispatch},
    network_model::NetworkModel
)

This function creates the model for a full thermal dispatch formulation depending on combination of devices, deviceformulation and systemformulation

construct_service!(
    container::PowerSimulations.OptimizationContainer,
    sys::System,
    _::InfrastructureSystems.Optimization.ArgumentConstructStage,
    model::ServiceModel{SR<:ConstantReserveGroup, GroupReserve},
    _::Dict{Symbol, DeviceModel},
    _::Set{<:DataType},
    _::NetworkModel
)

Constructs a service for ConstantReserveGroup.

container_spec(
    _::Type{Float64},
    axs...
) -> JuMP.Containers.DenseAxisArray

Returns the correct container specification for the selected type of JuMP Model

container_spec(
    _::Type{T},
    axs...
) -> JuMP.Containers.DenseAxisArray

Returns the correct container specification for the selected type of JuMP Model

device_duration_compact_retrospective!(
    container::PowerSimulations.OptimizationContainer,
    duration_data::Vector{@NamedTuple{up::Float64, down::Float64}},
    initial_duration::Matrix{InitialCondition},
    cons_type::InfrastructureSystems.Optimization.ConstraintType,
    var_types::Tuple{InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType},
    _::Type{T<:Component}
)

This formulation of the duration constraints adds over the start times looking backwards.

LaTeX

• Minimum up-time constraint:

$\sum_{i=t-min(d_{min}^{up}, T)+ 1}^t x_i^{start} - x_t^{on} \leq 0$

for i in the set of time steps.

• Minimum down-time constraint:

$\sum_{i=t-min(d_{min}^{down}, T) + 1}^t x_i^{stop} + x_t^{on} \leq 1$

for i in the set of time steps.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• duration_data::Vector{UpDown} : gives how many time steps variable needs to be up or down
• initial_duration::Matrix{InitialCondition} : gives initial conditions for up (column 1) and down (column 2)
• cons_name::Symbol : name of the constraint
• var_keys::Tuple{VariableKey, VariableKey, VariableKey}) : names of the variables
• : var_keys[1] : varon
• : var_keys[2] : varstart
• : var_keys[3] : varstop

device_duration_look_ahead!(
    container::PowerSimulations.OptimizationContainer,
    duration_data::Vector{@NamedTuple{up::Float64, down::Float64}},
    initial_duration::Matrix{InitialCondition},
    cons_type_up::InfrastructureSystems.Optimization.ConstraintType,
    cons_type_down::InfrastructureSystems.Optimization.ConstraintType,
    var_types::Tuple{InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType},
    _::Type{T<:Component}
)

This formulation of the duration constraints looks ahead in the time frame of the model.

LaTeX

• Minimum up-time constraint:

If $t \leq d_{min}^{up}$

$d_{min}^{down}x_t^{stop} - \sum_{i=t-d_{min}^{up} + 1}^t x_i^{on} - x_{init}^{up} \leq 0$

for i in the set of time steps. Otherwise:

$d_{min}^{down}x_t^{stop} - \sum_{i=t-d_{min}^{up} + 1}^t x_i^{on} \leq 0$

for i in the set of time steps.

• Minimum down-time constraint:

If $t \leq d_{min}^{down}$

$d_{min}^{up}x_t^{start} - \sum_{i=t-d_{min^{down} + 1}^t (1 - x_i^{on}) - x_{init}^{down} \leq 0$

for i in the set of time steps. Otherwise:

$d_{min}^{up}x_t^{start} - \sum_{i=t-d_{min^{down} + 1}^t (1 - x_i^{on}) \leq 0$

for i in the set of time steps.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• duration_data::Vector{UpDown} : gives how many time steps variable needs to be up or down
• initial_duration::Matrix{InitialCondition} : gives initial conditions for up (column 1) and down (column 2)
• cons_name::Symbol : name of the constraint
• var_keys::Tuple{VariableKey, VariableKey, VariableKey}) : names of the variables
• : var_keys[1] : varon
• : var_keys[2] : varstart
• : var_keys[3] : varstop

device_duration_parameters!(
    container::PowerSimulations.OptimizationContainer,
    duration_data::Vector{@NamedTuple{up::Float64, down::Float64}},
    initial_duration::Matrix{InitialCondition},
    cons_type::InfrastructureSystems.Optimization.ConstraintType,
    var_types::Tuple{InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType},
    _::Type{T<:Component}
)

This formulation of the duration constraints considers parameters.

LaTeX

• Minimum up-time constraint:

If $t \leq d_{min}^{up}$

$d_{min}^{down}x_t^{stop} - \sum_{i=t-d_{min}^{up} + 1}^t x_i^{on} - x_{init}^{up} \leq 0$

for i in the set of time steps. Otherwise:

$\sum_{i=t-d_{min}^{up} + 1}^t x_i^{start} - x_t^{on} \leq 0$

for i in the set of time steps.

• Minimum down-time constraint:

If $t \leq d_{min}^{down}$

$d_{min}^{up}x_t^{start} - \sum_{i=t-d_{min^{down} + 1}^t (1 - x_i^{on}) - x_{init}^{down} \leq 0$

for i in the set of time steps. Otherwise:

$\sum_{i=t-d_{min}^{down} + 1}^t x_i^{stop} + x_t^{on} \leq 1$

for i in the set of time steps.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• duration_data::Vector{UpDown} : gives how many time steps variable needs to be up or down
• initialdurationon::Vector{InitialCondition} : gives initial number of time steps variable is up
• initialdurationoff::Vector{InitialCondition} : gives initial number of time steps variable is down
• cons_name::Symbol : name of the constraint
• var_keys::Tuple{VariableKey, VariableKey, VariableKey}) : names of the variables
• : var_keys[1] : varon
• : var_keys[2] : varstart
• : var_keys[3] : varstop

device_duration_retrospective!(
    container::PowerSimulations.OptimizationContainer,
    duration_data::Vector{@NamedTuple{up::Float64, down::Float64}},
    initial_duration::Matrix{InitialCondition},
    cons_type::InfrastructureSystems.Optimization.ConstraintType,
    var_types::Tuple{InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType, InfrastructureSystems.Optimization.VariableType},
    _::Type{T<:Component}
)

This formulation of the duration constraints adds over the start times looking backwards.

LaTeX

• Minimum up-time constraint:

If $t \leq d_{min}^{up} - d_{init}^{up}$ and $d_{init}^{up} > 0$

$1 + \sum_{i=t-d_{min}^{up} + 1}^t x_i^{start} - x_t^{on} \leq 0$

for i in the set of time steps. Otherwise:

$\sum_{i=t-d_{min}^{up} + 1}^t x_i^{start} - x_t^{on} \leq 0$

for i in the set of time steps.

• Minimum down-time constraint:

If $t \leq d_{min}^{down} - d_{init}^{down}$ and $d_{init}^{down} > 0$

$1 + \sum_{i=t-d_{min}^{down} + 1}^t x_i^{stop} + x_t^{on} \leq 1$

for i in the set of time steps. Otherwise:

$\sum_{i=t-d_{min}^{down} + 1}^t x_i^{stop} + x_t^{on} \leq 1$

for i in the set of time steps.

Arguments

• container::OptimizationContainer : the optimization_container model built in PowerSimulations
• duration_data::Vector{UpDown} : gives how many time steps variable needs to be up or down
• initial_duration::Matrix{InitialCondition} : gives initial conditions for up (column 1) and down (column 2)
• cons_name::Symbol : name of the constraint
• var_keys::Tuple{VariableKey, VariableKey, VariableKey}) : names of the variables
• : var_keys[1] : varon
• : var_keys[2] : varstart
• : var_keys[3] : varstop

find_key_with_value(d, value) -> Any

Return the key for the given value

find_timestamp_index(
    dates::Union{StepRange{Dates.DateTime, Dates.Millisecond}, Vector{Dates.DateTime}},
    date::Dates.DateTime
) -> Int64

calculates the index in the time series corresponding to the data. Assumes that the dates vector is sorted.

generate_formulation_combinations(

) -> Dict{String, Vector{Any}}
generate_formulation_combinations(
    sys
) -> Dict{String, Vector{Any}}

Generate valid combinations of devicetype/formulation and servicetype/formulation. Return vectors of dictionaries with Julia types.

Arguments

• sys::Union{Nothing, System}: If set, only include component types present in the system.

get_absolute_step_range(
    partitions::SimulationPartitions,
    index::Int64
) -> UnitRange{Int64}

Return a UnitRange for the steps in the partition with the given index. Includes overlap.

get_dirty_data_to_flush!(
    cache::PowerSimulations.OptimizationOutputCache
) -> Tuple{Vector{Dates.DateTime}, Any}

Return all dirty data from the cache. Mark the timestamps as clean.

get_enum_value(enum, value::String) -> Any

Get the enum value for the string. Case insensitive.

get_last_updated_timestamp(
    container::PowerSimulations.DatasetContainer,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey
) -> Dates.DateTime

Return the timestamp from most recent data row updated in the dataset. This value may not be the same as the result from get_update_timestamp

get_last_updated_timestamp(
    s::PowerSimulations.HDF5Dataset
) -> Dates.DateTime

Return the timestamp from most recent data row updated in the dataset. This value may not be the same as the result from get_update_timestamp

get_last_updated_timestamp(
    s::PowerSimulations.InMemoryDataset
) -> Dates.DateTime

Return the timestamp from most recent data row updated in the dataset. This value may not be the same as the result from get_update_timestamp

get_min_max_limits(
    device::ACBranch,
    _::Type{<:InfrastructureSystems.Optimization.ConstraintType},
    _::Type{<:PowerSimulations.AbstractBranchFormulation}
) -> @NamedTuple{min::Float64, max::Float64}

Min and max limits for Abstract Branch Formulation

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractCompactUnitCommitment}
) -> Any

Min and Max active power limits for Compact Unit Commitment

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractThermalDispatchFormulation}
) -> Any

Min and max active power limits of generators for thermal dispatch formulations

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractThermalUnitCommitment}
) -> NamedTuple{(:min, :max), <:Tuple{Float64, Any}}

Min and max active power limits of generators for thermal unit commitment formulations

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{ThermalCompactDispatch}
) -> NamedTuple{(:min, :max), <:Tuple{Float64, Any}}

Min and max active power limits of generators for thermal dispatch compact formulations

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{ThermalDispatchNoMin}
) -> NamedTuple{(:min, :max), <:Tuple{Float64, Any}}

Min and max active power limits of generators for thermal dispatch no minimum formulations

get_min_max_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{ThermalMultiStartUnitCommitment}
) -> NamedTuple{(:min, :max), <:Tuple{Float64, Any}}

Min and max active power limits for multi-start unit commitment formulations

get_min_max_limits(
    device,
    _::Type{ReactivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractThermalDispatchFormulation}
) -> Any

Reactive power limits of generators for all dispatch formulations

get_min_max_limits(
    device,
    _::Type{ReactivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractThermalUnitCommitment}
) -> Any

Reactive power limits of generators when there CommitmentVariables

get_min_max_limits(
    device::MonitoredLine,
    _::Type{<:InfrastructureSystems.Optimization.ConstraintType},
    _::Type{<:PowerSimulations.AbstractBranchFormulation}
) -> NamedTuple{(:min, :max), <:Tuple{Any, Any}}

Min and max limits for monitored line

get_min_max_limits(
    device::MonitoredLine,
    _::Type{FlowLimitFromToConstraint},
    _::Type{<:PowerSimulations.AbstractBranchFormulation}
) -> NamedTuple{(:min, :max), <:Tuple{Any, Any}}

Min and max limits for flow limit from-to constraint

get_min_max_limits(
    device::MonitoredLine,
    _::Type{FlowLimitToFromConstraint},
    _::Type{<:PowerSimulations.AbstractBranchFormulation}
) -> NamedTuple{(:min, :max), <:Tuple{Any, Any}}

Min and max limits for flow limit to-from constraint

get_min_max_limits(
    _::PhaseShiftingTransformer,
    _::Type{PhaseAngleControlLimit},
    _::Type{PhaseAngleControl}
) -> @NamedTuple{min::Float64, max::Float64}

Min and max limits for Abstract Branch Formulation

get_piecewise_incrementalcurve_per_system_unit(
    cost_component::PiecewiseStepData,
    unit_system::UnitSystem,
    system_base_power::Float64,
    device_base_power::Float64
) -> PiecewiseStepData

Obtain the normalized PiecewiseStep cost data in system base per unit depending on the specified power units.

Note that the costs (y-axis) are in /MWh, /(sys pu h) or /(device pu h), so they also require transformation.

get_piecewise_pointcurve_per_system_unit(
    cost_component::PiecewiseLinearData,
    unit_system::UnitSystem,
    system_base_power::Float64,
    device_base_power::Float64
) -> PiecewiseLinearData

Obtain the normalized PiecewiseLinear cost data in system base per unit depending on the specified power units.

Note that the costs (y-axis) are always in /h so they do not require transformation

get_proportional_cost_per_system_unit(
    cost_term::Float64,
    unit_system::UnitSystem,
    system_base_power::Float64,
    device_base_power::Float64
) -> Float64

Obtain proportional (marginal or slope) cost data in system base per unit depending on the specified power units

get_quadratic_cost_per_system_unit(
    cost_term::Float64,
    unit_system::UnitSystem,
    system_base_power::Float64,
    device_base_power::Float64
) -> Float64

Obtain quadratic cost data in system base per unit depending on the specified power units

get_startup_shutdown_limits(
    device,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{<:PowerSimulations.AbstractCompactUnitCommitment}
) -> NamedTuple{(:startup, :shutdown), <:Tuple{Any, Any}}

Startup shutdown limits for Compact Unit Commitment

get_startup_shutdown_limits(
    device::ThermalMultiStart,
    _::Type{ActivePowerVariableLimitsConstraint},
    _::Type{ThermalMultiStartUnitCommitment}
) -> NamedTuple{(:startup, :shutdown), <:Tuple{Any, Any}}

Startup and shutdown active power limits for Compact Unit Commitment

get_update_timestamp(
    s::PowerSimulations.AbstractDataset
) -> Any

Return the timestamp from the data used in the last update

get_update_timestamp(
    container::PowerSimulations.DatasetContainer,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey
) -> Any

Return the timestamp from the data used in the last update

get_valid_step_length(
    partitions::SimulationPartitions,
    index::Int64
) -> Int64

Return the length of valid data at the given index.

get_valid_step_offset(
    partitions::SimulationPartitions,
    index::Int64
) -> Int64

Return the step offset for valid data at the given index.

has_dirty(
    cache::PowerSimulations.OptimizationOutputCaches
) -> Bool

Return true if the cache has data that has not been flushed to storage.

is_cached(
    cache::PowerSimulations.OptimizationOutputCaches,
    model_name,
    key,
    index
) -> Bool

Return true if the data for timestamp is stored in cache.

join_simulation(path::AbstractString)

Combine all partition simulation files.

list_decision_model_keys(
    store::PowerSimulations.HdfSimulationStore,
    model::Symbol,
    container_type::Symbol
) -> Vector

Return the fields stored for the problem and container_type (duals/parameters/variables).

list_decision_models(
    store::PowerSimulations.HdfSimulationStore
) -> Base.KeySet{Symbol, OrderedDict{Symbol, PowerSimulations.DatasetContainer{PowerSimulations.HDF5Dataset}}}

Return the problem names in order of execution.

log_cache_hit_percentages(
    cache::PowerSimulations.OptimizationOutputCaches
)

Log the cache hit percentages for all caches.

onvar_cost(
    container::PowerSimulations.OptimizationContainer,
    cost::ThermalGenerationCost,
    S::OnVariable,
    d::ThermalGen,
    U::PowerSimulations.AbstractThermalFormulation,
    t::Int64
) -> Any

Theoretical Cost at power output zero. Mathematically is the intercept with the y-axis

open_store(
    ::Type{PowerSimulations.HdfSimulationStore},
    directory::AbstractString;
    ...
) -> PowerSimulations.HdfSimulationStore
open_store(
    ::Type{PowerSimulations.HdfSimulationStore},
    directory::AbstractString,
    mode;
    filename
) -> PowerSimulations.HdfSimulationStore

Construct and open an HdfSimulationStore.

When reading or writing results in a program you should use the method that accepts a function in order to guarantee that the file handle gets closed.

Arguments

• directory::AbstractString: Directory containing the store file
• mode::AbstractString: Mode to use to open the store file
• filename::AbstractString: Base name of the store file

Examples

# Assumes a simulation has been executed in the './rts' directory with these parameters.
path = "./rts"
problem = :ED
var_name = :P__ThermalStandard
timestamp = DateTime("2020-01-01T05:00:00")
store = open_store(HdfSimulationStore, path)
df = PowerSimulations.read_result(DataFrame, store, model, :variables, var_name, timestamp)

read_dataframe(
    filename::AbstractString
) -> DataFrames.DataFrame

Return a DataFrame from a CSV file.

read_json(
    filename::AbstractString
) -> Union{Nothing, Bool, Float64, Int64, String, JSON3.Array, JSON3.Object}

Return a decoded JSON file.

read_result(
    cache::PowerSimulations.OptimizationOutputCaches,
    model_name,
    key,
    timestamp
) -> Array

Read the result from cache. Callers must first call is_cached to check if the timestamp is present.

read_result(
    _::Type{DataFrames.DataFrame},
    store::PowerSimulations.HdfSimulationStore,
    model_name::Symbol,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey,
    index::Union{Int64, Dates.DateTime}
) -> DataFrames.DataFrame

Return DataFrame, DenseAxisArray, or Array for a model result at a timestamp.

serialize_formulation_combinations(

) -> Dict{String, Vector{Any}}
serialize_formulation_combinations(
    sys
) -> Dict{String, Vector{Any}}

Generate valid combinations of devicetype/formulation and servicetype/formulation. Return vectors of dictionaries with Julia types encoded as strings.

Arguments

• sys::Union{Nothing, System}: If set, only include component types present in the system.

serialize_jump_optimization_model(
    jump_model::JuMP.Model,
    save_path::String
)

Exports the JuMP object in MathOptFormat

serialize_simulation(sim::Simulation; path, force) -> String

serialize_simulation(sim::Simulation, path = ".")

Serialize the simulation to a directory in path.

Return the serialized simulation directory name that is created.

Arguments

• sim::Simulation: simulation to serialize
• path = ".": path in which to create the serialzed directory
• force = false: If true, delete the directory if it already exists. Otherwise, it will throw an exception.

set_expression!(
    container::PowerSimulations.OptimizationContainer,
    _::Type{S<:CostExpressions},
    cost_expression::JuMP.AbstractJuMPScalar,
    component::Component,
    time_period::Int64
)

Replaces an expression value in the expression container if the key exists

set_ic_quantity!(
    ic::InitialCondition{T<:InfrastructureSystems.Optimization.InitialConditionType, Float64},
    var_value::Float64
)

Default implementation of setinitialcondition_value

set_ic_quantity!(
    ic::InitialCondition{T<:InfrastructureSystems.Optimization.InitialConditionType, JuMP.VariableRef},
    var_value::Float64
)

Default implementation of setinitialcondition_value

solve_impl!(
    container::PowerSimulations.OptimizationContainer,
    system::System
) -> Any

Default solve method for OptimizationContainer

sparse_container_spec(
    _::Type{T<:JuMP.AbstractJuMPScalar},
    axs...
) -> JuMP.Containers.SparseAxisArray

Returns the correct container specification for the selected type of JuMP Model

to_dataframe(
    array::JuMP.Containers.DenseAxisArray{T<:Number, 2, Ax, L} where {Ax, L<:Tuple{JuMP.Containers._AxisLookup, JuMP.Containers._AxisLookup}},
    key::InfrastructureSystems.Optimization.OptimizationContainerKey
) -> DataFrames.DataFrame

Creates a DataFrame from a JuMP DenseAxisArray or SparseAxisArray.

Arguments

• array: JuMP DenseAxisArray or SparseAxisArray to convert
• key::OptimizationContainerKey:

update_container_parameter_values!(
    optimization_container::PowerSimulations.OptimizationContainer,
    model::PowerSimulations.OperationModel,
    key::InfrastructureSystems.Optimization.ParameterKey{T<:InfrastructureSystems.Optimization.ParameterType, U<:Component},
    input::PowerSimulations.DatasetContainer{PowerSimulations.InMemoryDataset}
)

Update parameter function an OperationModel

update_model!(
    model::PowerSimulations.OperationModel,
    sim::Simulation
)

Default problem update function for most problems with no customization

update_parameter_values!(
    model::EmulationModel,
    key::InfrastructureSystems.Optimization.ParameterKey{T<:InfrastructureSystems.Optimization.ParameterType, U<:Component},
    input::PowerSimulations.DatasetContainer{PowerSimulations.InMemoryDataset}
)

Update parameter function an OperationModel

update_parameter_values!(
    model::PowerSimulations.OperationModel,
    key::InfrastructureSystems.Optimization.ParameterKey{T<:InfrastructureSystems.Optimization.ParameterType, U<:Component},
    simulation_state::PowerSimulations.SimulationState
)

Update parameter function an OperationModel

variable_reactive_net_injection(
    pm::PowerModels.AbstractActivePowerModel;
    kwargs...
)

active power only models ignore reactive power variables

write_formulation_combinations(filename::AbstractString)
write_formulation_combinations(
    filename::AbstractString,
    sys
)

Generate valid combinations of devicetype/formulation and servicetype/formulation and write the result to a JSON file.

Arguments

• sys::Union{Nothing, System}: If set, only include component types present in the system.

write_result!(
    store::PowerSimulations.HdfSimulationStore,
    model_name::Symbol,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey,
    index::Dates.DateTime,
    _::Dates.DateTime,
    data::JuMP.Containers.DenseAxisArray{Float64, 3, var"#s549", L} where {var"#s549"<:Tuple{Any, Any, Any}, L<:Tuple{JuMP.Containers._AxisLookup, JuMP.Containers._AxisLookup, JuMP.Containers._AxisLookup}}
)

Write a decision model result for a timestamp to the store.

write_result!(
    store::PowerSimulations.HdfSimulationStore,
    _::Symbol,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey,
    index::Int64,
    simulation_time::Dates.DateTime,
    data::JuMP.Containers.DenseAxisArray
)

Write an emulation model result for an execution index value and the timestamp of the update

write_result!(
    store::PowerSimulations.HdfSimulationStore,
    model_name::Symbol,
    key::InfrastructureSystems.Optimization.OptimizationContainerKey,
    index::Dates.DateTime,
    _::Dates.DateTime,
    data::JuMP.Containers.DenseAxisArray{Float64, N, var"#s549", L} where {var"#s549"<:NTuple{N, Any}, L<:NTuple{N, JuMP.Containers._AxisLookup}}
)

Write a decision model result for a timestamp to the store.