API Reference

DeviceModel(
    ::Type{D},
    ::Type{B},
    feedforwards::Vector{<:AbstractAffectFeedforward}
    use_slacks::Bool,
    duals::Vector{DataType},
    services::Vector{ServiceModel}
    attributes::Dict{String, Any}
)

Establishes the model for a particular device specified by type. Uses the keyword argument feedforward to enable passing values between operation model at simulation time

Arguments

• ::Type{D} where D<:PSY.Device: Power System Device Type
• ::Type{B} where B<:AbstractDeviceFormulation: Abstract Device Formulation
• feedforward::Array{<:AbstractAffectFeedforward} = Vector{AbstractAffectFeedforward}() : use to pass parameters between models
• use_slacks::Bool = false : Add slacks to the device model. Implementation is model dependent and not all models feature slacks
• duals::Vector{DataType} = Vector{DataType}(): use to pass constraint type to calculate the duals. The DataType needs to be a valid ConstraintType
• time_series_names::Dict{Type{<:TimeSeriesParameter}, String} = get_default_time_series_names(D, B) : use to specify time series names associated to the device`
• attributes::Dict{String, Any} = get_default_attributes(D, B) : use to specify attributes to the device

Example

thermal_gens = DeviceModel(ThermalStandard, ThermalBasicUnitCommitment)

Transport Lossless HVDC network model. No DC voltage variables are added and DC lines are modeled as lossless power transport elements

DC Voltage HVDC network model, where currents are solved based on DC voltage difference between DC buses

Quadratic Loss InterconnectingConverter Model

Lossy Line Abstract Model

ProblemTemplate(::Type{T}) where {T<:PM.AbstractPowerFormulation}

Creates a model reference of the PowerSimulations Optimization Problem.

Arguments

• model::Type{T<:PM.AbstractPowerFormulation}:

Example

template = ProblemTemplate(CopperPlatePowerModel)

set_device_model!(
    template::ProblemTemplate,
    model::DeviceModel
)

Sets the device model in a template using a DeviceModel instance

set_device_model!(
    template::ProblemTemplate,
    component_type::Type{<:Device},
    formulation::Type{<:PowerSimulations.AbstractDeviceFormulation}
)

Sets the device model in a template using the component type and formulation. Builds a default DeviceModel

set_hvdc_network_model!(
    template::ProblemTemplate,
    model::Union{Nothing, PowerSimulations.AbstractHVDCNetworkModel}
)

Sets the network model in a template.

set_hvdc_network_model!(
    template::ProblemTemplate,
    model::Type{U<:PowerSimulations.AbstractHVDCNetworkModel}
)

Sets the network model in a template.

set_network_model!(
    template::ProblemTemplate,
    model::NetworkModel
)

Sets the network model in a template.

set_service_model!(
    template::ProblemTemplate,
    service_name::String,
    service_type::Type{<:Service},
    formulation::Type{<:PowerSimulations.AbstractServiceFormulation}
)

Sets the service model in a template using a name and the service type and formulation. Builds a default ServiceModel with useservicename set to true.

set_service_model!(
    template::ProblemTemplate,
    service_type::Type{<:Service},
    formulation::Type{<:PowerSimulations.AbstractServiceFormulation}
)

Sets the service model in a template using a ServiceModel instance.

template_economic_dispatch(; kwargs...) -> ProblemTemplate

template_economic_dispatch(; kwargs...)

Creates a ProblemTemplate with default DeviceModels for an Economic Dispatch problem.

Example

template = templateeconomicdispatch()

# Accepted Key Words
- `network::Type{<:PM.AbstractPowerModel}` : override default network model settings
- `devices::Vector{DeviceModel}` : override default `DeviceModel` settings
- `services::Vector{ServiceModel}` : override default `ServiceModel` settings

template_unit_commitment(; kwargs...) -> ProblemTemplate

template_unit_commitment(; kwargs...)

Creates a ProblemTemplate with default DeviceModels for a Unit Commitment problem.

Example

template = templateunitcommitment()

# Accepted Key Words
- `network::Type{<:PM.AbstractPowerModel}` : override default network model settings
- `devices::Vector{DeviceModel}` : override default `DeviceModel` settings
- `services::Vector{ServiceModel}` : override default `ServiceModel` settings

DecisionModel(
    directory::AbstractString,
    optimizer::MathOptInterface.OptimizerWithAttributes;
    jump_model,
    system
) -> Any

Construct an DecisionProblem from a serialized file.

Arguments

• directory::AbstractString: Directory containing a serialized model
• jump_model::Union{Nothing, JuMP.Model} = nothing: The JuMP model does not get serialized. Callers should pass whatever they passed to the original problem.
• optimizer::Union{Nothing,MOI.OptimizerWithAttributes} = nothing: The optimizer does not get serialized. Callers should pass whatever they passed to the original problem.
• system::Union{Nothing, PSY.System}: Optionally, the system used for the model. If nothing and systofile was set to true when the model was created, the system will be deserialized from a file.

DecisionModel{M}(
    template::AbstractProblemTemplate,
    sys::PSY.System,
    jump_model::Union{Nothing, JuMP.Model}=nothing;
    kwargs...) where {M<:DecisionProblem}

Build the optimization problem of type M with the specific system and template.

Arguments

• ::Type{M} where M<:DecisionProblem: The abstract operation model type
• template::AbstractProblemTemplate: The model reference made up of transmission, devices, branches, and services.
• sys::PSY.System: the system created using Power Systems
• jump_model::Union{Nothing, JuMP.Model}: Enables passing a custom JuMP model. Use with care
• name = nothing: name of model, string or symbol; defaults to the type of template converted to a symbol.
• optimizer::Union{Nothing,MOI.OptimizerWithAttributes} = nothing : The optimizer does not get serialized. Callers should pass whatever they passed to the original problem.
• horizon::Dates.Period = UNSET_HORIZON: Manually specify the length of the forecast Horizon
• resolution::Dates.Period = UNSET_RESOLUTION: Manually specify the model's resolution
• warm_start::Bool = true: True will use the current operation point in the system to initialize variable values. False initializes all variables to zero. Default is true
• system_to_file::Bool = true:: True to create a copy of the system used in the model.
• initialize_model::Bool = true: Option to decide to initialize the model or not.
• initialization_file::String = "": This allows to pass pre-existing initialization values to avoid the solution of an optimization problem to find feasible initial conditions.
• deserialize_initial_conditions::Bool = false: Option to deserialize conditions
• export_pwl_vars::Bool = false: True to export all the pwl intermediate variables. It can slow down significantly the build and solve time.
• allow_fails::Bool = false: True to allow the simulation to continue even if the optimization step fails. Use with care.
• optimizer_solve_log_print::Bool = false: Uses JuMP.unset_silent() to print the optimizer's log. By default all solvers are set to MOI.Silent()
• detailed_optimizer_stats::Bool = false: True to save detailed optimizer stats log.
• calculate_conflict::Bool = false: True to use solver to calculate conflicts for infeasible problems. Only specific solvers are able to calculate conflicts.
• direct_mode_optimizer::Bool = false: True to use the solver in direct mode. Creates a JuMP.direct_model.
• store_variable_names::Bool = false: to store variable names in optimization model. Decreases the build times.
• rebuild_model::Bool = false: It will force the rebuild of the underlying JuMP model with each call to update the model. It increases solution times, use only if the model can't be updated in memory.
• initial_time::Dates.DateTime = UNSET_INI_TIME: Initial Time for the model solve.
• time_series_cache_size::Int = IS.TIME_SERIES_CACHE_SIZE_BYTES: Size in bytes to cache for each time array. Default is 1 MiB. Set to 0 to disable.

Example

template = ProblemTemplate(CopperPlatePowerModel, devices, branches, services)
OpModel = DecisionModel(MockOperationProblem, template, system)

DecisionModel(
    ::Type{M<:PowerSimulations.DecisionProblem},
    template::PowerSimulations.AbstractProblemTemplate,
    sys::System;
    ...
) -> DecisionModel
DecisionModel(
    ::Type{M<:PowerSimulations.DecisionProblem},
    template::PowerSimulations.AbstractProblemTemplate,
    sys::System,
    jump_model::Union{Nothing, JuMP.Model};
    kwargs...
) -> DecisionModel

Build the optimization problem of type M with the specific system and template

Arguments

• ::Type{M} where M<:DecisionProblem: The abstract operation model type
• template::AbstractProblemTemplate: The model reference made up of transmission, devices, branches, and services.
• sys::PSY.System: the system created using Power Systems
• jump_model::Union{Nothing, JuMP.Model} = nothing: Enables passing a custom JuMP model. Use with care.

Example

template = ProblemTemplate(CopperPlatePowerModel, devices, branches, services)
problem = DecisionModel(MyOpProblemType, template, system, optimizer)

Builds an empty decision model. This constructor is used for the implementation of custom decision models that do not require a template.

Arguments

• ::Type{M} where M<:DecisionProblem: The abstract operation model type
• sys::PSY.System: the system created using Power Systems
• jump_model::Union{Nothing, JuMP.Model} = nothing: Enables passing a custom JuMP model. Use with care.

Example

problem = DecisionModel(system, optimizer)

Generic PowerSimulations Operation Problem Type for unspecified models

build!(
    model::DecisionModel;
    output_dir,
    recorders,
    console_level,
    file_level,
    disable_timer_outputs
)

Build the Decision Model based on the specified DecisionProblem.

Arguments

• model::DecisionModel{<:DecisionProblem}: DecisionModel object
• output_dir::String: Output directory for results
• recorders::Vector{Symbol} = []: recorder names to register
• console_level = Logging.Error:
• file_level = Logging.Info:
• disable_timer_outputs = false : Enable/Disable timing outputs

solve!(
    model::DecisionModel;
    export_problem_results,
    console_level,
    file_level,
    disable_timer_outputs,
    export_optimization_problem,
    kwargs...
) -> InfrastructureSystems.Simulation.RunStatusModule.RunStatus

Default solve method for models that conform to the requirements of DecisionModel{<: DecisionProblem}.

This will call build! on the model if it is not already built. It will forward all keyword arguments to that function.

Arguments

• model::OperationModel = model: operation model
• export_problem_results::Bool = false: If true, export OptimizationProblemResults DataFrames to CSV files. Reduces solution times during simulation.
• console_level = Logging.Error:
• file_level = Logging.Info:
• disable_timer_outputs = false : Enable/Disable timing outputs
• export_optimization_problem::Bool = true: If true, serialize the model to a file to allow re-execution later.

Examples

results = solve!(OpModel)
results = solve!(OpModel, export_problem_results = true)

solve!(
    step::Int64,
    model::DecisionModel,
    start_time::Dates.DateTime,
    store::PowerSimulations.SimulationStore;
    exports
) -> InfrastructureSystems.Simulation.RunStatusModule.RunStatus

Default solve method for a DecisionModel used inside of a Simulation. Solves problems that conform to the requirements of DecisionModel{<: DecisionProblem}

Arguments

• step::Int: Simulation Step
• model::OperationModel: operation model
• start_time::Dates.DateTime: Initial Time of the simulation step in Simulation time.
• store::SimulationStore: Simulation output store

Accepted Key Words

• exports: realtime export of output. Use wisely, it can have negative impacts in the simulation times

EmulationModel{M}(
    template::AbstractProblemTemplate,
    sys::PSY.System,
    jump_model::Union{Nothing, JuMP.Model}=nothing;
    kwargs...) where {M<:EmulationProblem}

Build the optimization problem of type M with the specific system and template.

Arguments

• ::Type{M} where M<:EmulationProblem: The abstract Emulation model type
• template::AbstractProblemTemplate: The model reference made up of transmission, devices, branches, and services.
• sys::PSY.System: the system created using Power Systems
• jump_model::Union{Nothing, JuMP.Model}: Enables passing a custom JuMP model. Use with care
• name = nothing: name of model, string or symbol; defaults to the type of template converted to a symbol.
• optimizer::Union{Nothing,MOI.OptimizerWithAttributes} = nothing : The optimizer does not get serialized. Callers should pass whatever they passed to the original problem.
• warm_start::Bool = true: True will use the current operation point in the system to initialize variable values. False initializes all variables to zero. Default is true
• system_to_file::Bool = true:: True to create a copy of the system used in the model.
• initialize_model::Bool = true: Option to decide to initialize the model or not.
• initialization_file::String = "": This allows to pass pre-existing initialization values to avoid the solution of an optimization problem to find feasible initial conditions.
• deserialize_initial_conditions::Bool = false: Option to deserialize conditions
• export_pwl_vars::Bool = false: True to export all the pwl intermediate variables. It can slow down significantly the build and solve time.
• allow_fails::Bool = false: True to allow the simulation to continue even if the optimization step fails. Use with care.
• calculate_conflict::Bool = false: True to use solver to calculate conflicts for infeasible problems. Only specific solvers are able to calculate conflicts.
• optimizer_solve_log_print::Bool = false: Uses JuMP.unset_silent() to print the optimizer's log. By default all solvers are set to MOI.Silent()
• detailed_optimizer_stats::Bool = false: True to save detailed optimizer stats log.
• direct_mode_optimizer::Bool = false: True to use the solver in direct mode. Creates a JuMP.direct_model.
• store_variable_names::Bool = false: True to store variable names in optimization model.
• rebuild_model::Bool = false: It will force the rebuild of the underlying JuMP model with each call to update the model. It increases solution times, use only if the model can't be updated in memory.
• initial_time::Dates.DateTime = UNSET_INI_TIME: Initial Time for the model solve.
• time_series_cache_size::Int = IS.TIME_SERIES_CACHE_SIZE_BYTES: Size in bytes to cache for each time array. Default is 1 MiB. Set to 0 to disable.

Example

template = ProblemTemplate(CopperPlatePowerModel, devices, branches, services)
OpModel = EmulationModel(MockEmulationProblem, template, system)

EmulationModel(
    ::Type{M<:PowerSimulations.EmulationProblem},
    template::PowerSimulations.AbstractProblemTemplate,
    sys::System;
    ...
) -> EmulationModel
EmulationModel(
    ::Type{M<:PowerSimulations.EmulationProblem},
    template::PowerSimulations.AbstractProblemTemplate,
    sys::System,
    jump_model::Union{Nothing, JuMP.Model};
    kwargs...
) -> EmulationModel

Build the optimization problem of type M with the specific system and template

Arguments

• ::Type{M} where M<:EmulationProblem: The abstract Emulation model type
• template::AbstractProblemTemplate: The model reference made up of transmission, devices, branches, and services.
• sys::PSY.System: the system created using Power Systems
• jump_model::Union{Nothing, JuMP.Model}: Enables passing a custom JuMP model. Use with care

Example

template = ProblemTemplate(CopperPlatePowerModel, devices, branches, services)
problem = EmulationModel(MyEmProblemType, template, system, optimizer)

EmulationModel(
    directory::AbstractString,
    optimizer::MathOptInterface.OptimizerWithAttributes;
    jump_model,
    system,
    kwargs...
) -> Any

Construct an EmulationProblem from a serialized file.

Arguments

• directory::AbstractString: Directory containing a serialized model.
• optimizer::MOI.OptimizerWithAttributes: The optimizer does not get serialized. Callers should pass whatever they passed to the original problem.
• jump_model::Union{Nothing, JuMP.Model} = nothing: The JuMP model does not get serialized. Callers should pass whatever they passed to the original problem.
• system::Union{Nothing, PSY.System}: Optionally, the system used for the model. If nothing and systofile was set to true when the model was created, the system will be deserialized from a file.

build!(
    model::EmulationModel;
    executions,
    output_dir,
    recorders,
    console_level,
    file_level,
    disable_timer_outputs
)

Implementation of build for any EmulationProblem

run!(
    model::EmulationModel;
    export_problem_results,
    console_level,
    file_level,
    disable_timer_outputs,
    export_optimization_model,
    kwargs...
) -> InfrastructureSystems.Simulation.RunStatusModule.RunStatus

Default run method for problems that conform to the requirements of EmulationModel{<: EmulationProblem}

This will call build! on the model if it is not already built. It will forward all keyword arguments to that function.

Arguments

• model::EmulationModel = model: Emulation model
• optimizer::MOI.OptimizerWithAttributes: The optimizer that is used to solve the model
• executions::Int: Number of executions for the emulator run
• export_problem_results::Bool: If true, export OptimizationProblemResults DataFrames to CSV files.
• output_dir::String: Required if the model is not already built, otherwise ignored
• enable_progress_bar::Bool: Enables/Disable progress bar printing
• export_optimization_model::Bool: If true, serialize the model to a file to allow re-execution later.

Examples

status = run!(model; optimizer = HiGHS.Optimizer, executions = 10)
status = run!(model; output_dir = ./model_output, optimizer = HiGHS.Optimizer, executions = 10)

solve!(
    step::Int64,
    model::EmulationModel,
    start_time::Dates.DateTime,
    store::PowerSimulations.SimulationStore;
    exports
) -> InfrastructureSystems.Simulation.RunStatusModule.RunStatus

Default solve method for an EmulationModel used inside of a Simulation. Solves problems that conform to the requirements of DecisionModel{<: DecisionProblem}

Arguments

• step::Int: Simulation Step
• model::OperationModel: operation model
• start_time::Dates.DateTime: Initial Time of the simulation step in Simulation time.
• store::SimulationStore: Simulation output store
• exports = nothing: realtime export of output. Use wisely, it can have negative impacts in the simulation times

Establishes the model for a particular services specified by type. Uses the keyword argument use_service_name to assign the model to a service with the same name as the name in the template. Uses the keyword argument feedforward to enable passing values between operation model at simulation time

Arguments

-::Type{D}: Power System Service Type -::Type{B}: Abstract Service Formulation

Accepted Key Words

• feedforward::Array{<:AbstractAffectFeedforward} : use to pass parameters between models
• use_service_name::Bool : use the name as the name for the service

Example

reserves = ServiceModel(PSY.VariableReserve{PSY.ReserveUp}, RangeReserve)

Container for the initial condition data

SimulationModels(
    decision_models::Vector{<:DecisionModel},
    emulation_models::Union{Nothing, EmulationModel}
)

Stores the OperationProblem definitions to be used in the simulation. When creating the SimulationModels, the order in which the models are created determines the order on which the simulation is executed.

Arguments

• decision_models::Vector{<:DecisionModel}: Vector of decision models.
• emulation_models::Union{Nothing, EmulationModel}: Optional argument to include

an EmulationModel in the Simulation

Example

template_uc = template_unit_commitment()
template_ed = template_economic_dispatch()
my_decision_model_uc = DecisionModel(template_1, sys_uc, optimizer, name = "UC")
my_decision_model_ed = DecisionModel(template_ed, sys_ed, optimizer, name = "ED")
models = SimulationModels(
    decision_models = [
        my_decision_model_uc,
        my_decision_model_ed
    ]
)

SimulationSequence(
    models::SimulationModels,
    feedforward::Dict{String, Vector{<:AbstractAffectFeedforward}}
    ini_cond_chronology::InitialConditionChronology
)

Construct the simulation sequence between decision and emulation models.

Arguments

• models::SimulationModels: Vector of decisions and emulation models.
• feedforward = Dict{String, Vector{<:AbstractAffectFeedforward}}(): Optional dictionary to specify how information and variables are exchanged between decision and emulation models.
• ini_cond_chronology::InitialConditionChronology = InterProblemChronology(): Define information sharing model between stages with InterProblemChronology

Example

template_uc = template_unit_commitment()
template_ed = template_economic_dispatch()
my_decision_model_uc = DecisionModel(template_1, sys_uc, optimizer, name = "UC")
my_decision_model_ed = DecisionModel(template_ed, sys_ed, optimizer, name = "ED")
models = SimulationModels(
    decision_models = [
        my_decision_model_uc,
        my_decision_model_ed
    ]
)
# The following sequence set the commitment variables (`OnVariable`) for `ThermalStandard` units from UC to ED.
sequence = SimulationSequence(;
    models = models,
    feedforwards = Dict(
        "ED" => [
            SemiContinuousFeedforward(;
                component_type = ThermalStandard,
                source = OnVariable,
                affected_values = [ActivePowerVariable],
            ),
        ],
    ),
)

Simulation(
    sequence::SimulationSequence,
    name::String,
    steps::Int
    models::SimulationModels,
    simulation_folder::String,
    initial_time::Union{Nothing, Dates.DateTime}
)

Construct the Simulation structure to run the sequence of decision and emulation models specified.

Arguments

• sequence::SimulationSequence: Simulation sequence that specify how the decision and emulation models will be executed.
• name::String: Name of the Simulation
• steps::Int: Number of steps on which the sequence of models will be executed
• models::SimulationModels: List of Decision and Emulation Models
• simulation_folder::String: Folder on which results will be stored
• initial_time::Union{Nothing, Dates.DateTime} = nothing: Initial time of which the simulation starts. If nothing it will default to the first timestamp of time series of the system.

Example

template_uc = template_unit_commitment()
template_ed = template_economic_dispatch()
my_decision_model_uc = DecisionModel(template_1, sys_uc, optimizer, name = "UC")
my_decision_model_ed = DecisionModel(template_ed, sys_ed, optimizer, name = "ED")
models = SimulationModels(
    decision_models = [
        my_decision_model_uc,
        my_decision_model_ed
    ]
)
# The following sequence set the commitment variables (`OnVariable`) for `ThermalStandard` units from UC to ED.
sequence = SimulationSequence(;
    models = models,
    feedforwards = Dict(
        "ED" => [
            SemiContinuousFeedforward(;
                component_type = ThermalStandard,
                source = OnVariable,
                affected_values = [ActivePowerVariable],
            ),
        ],
    ),
)

sim = Simulation(
    sequence = sequence,
    name = "Sim",
    steps = 5,
    models = models,
    simulation_folder = mktempdir(cleanup=true),
)

Simulation(directory::AbstractString, model_info::Dict)

Constructs Simulation from a serialized directory. Callers should pass any kwargs here that they passed to the original Simulation.

Arguments

• directory::AbstractString: the directory returned from the call to serialize
• model_info::Dict: Two-level dictionary containing model parameters that cannot be serialized. The outer dict should be keyed by the problem name. The inner dict must contain 'optimizer' and may contain 'jump_model'. These should be the same values used for the original simulation.

build!(
    sim::Simulation;
    recorders,
    console_level,
    file_level,
    serialize,
    partitions,
    index
) -> InfrastructureSystems.Simulation.SimulationBuildStatusModule.SimulationBuildStatus

Build the Simulation, problems and the related folder structure.

Arguments

• sim::Simulation: simulation object
• recorders::Vector{Symbol} = []: recorder names to register
• serialize::Bool = true: serializes the simulation objects in the simulation
• console_level = Logging.Error:
• file_level = Logging.Info:

execute!(
    sim::Simulation;
    kwargs...
) -> InfrastructureSystems.Simulation.RunStatusModule.RunStatus

Solves the simulation model for sequential Simulations.

Arguments

• sim::Simulation=sim: simulation object created by Simulation()

The optional keyword argument exports controls exporting of results to CSV files as the simulation runs.

Example

sim = Simulation("Test", 7, problems, "/Users/folder")
execute!(sim::Simulation; kwargs...)

Defines how a simulation can be partition into partitions and run in parallel.

get_num_partitions(x::SimulationPartitions) -> Int64

Return the number of partitions in the simulation.

run_parallel_simulation(
    build_function,
    execute_function;
    script,
    output_dir,
    name,
    num_steps,
    period,
    num_overlap_steps,
    num_parallel_processes,
    exeflags,
    force
)

Run a partitioned simulation in parallel on a local computer.

Arguments

• build_function: Function reference that returns a built Simulation.
• execute_function: Function reference that executes a Simulation.
• script::AbstractString: Path to script that includes $build_function$ and $execute_function$.
• output_dir::AbstractString: Path for simulation outputs
• name::AbstractString: Simulation name
• num_steps::Integer: Total number of steps in the simulation
• period::Integer: Number of steps in each simulation partition
• num_overlap_steps::Integer: Number of steps that each partition overlaps with the previous partition
• num_parallel_processes: Number of partitions to run in parallel. If nothing, use the number of cores.
• exeflags: Path to Julia project. Forwarded to Distributed.addprocs.
• force: Overwrite the output directory if it already exists.

InterProblemChronology()

Type struct to select an information sharing model between stages that uses results from the most recent stage executed to calculate the initial conditions. This model takes into account solutions from stages defined with finer temporal resolutions

See also: IntraProblemChronology

IntraProblemChronology()

Type struct to select an information sharing model between stages that uses results from the same recent stage to calculate the initial conditions. This model ignores solutions from stages defined with finer temporal resolutions.

See also: InterProblemChronology

Struct to dispatch the creation of Active Power Variables

Docs abbreviation: $p$

Struct to dispatch the creation of Reactive Power Variables

Docs abbreviation: $q$

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

Docs abbreviation: $\delta$

Struct to dispatch the creation of Slack variables for rate of change constraints up limits

Docs abbreviation: $p^\text{sl,up}$

Struct to dispatch the creation of Slack variables for rate of change constraints down limits

Docs abbreviation: $p^\text{sl,dn}$

Struct to dispatch the creation of Post-Contingency Active Power Change Variables.

Docs abbreviation: $\Delta p_{g,c}$

Struct to dispatch the creation of a binary commitment status variable

Docs abbreviation: $u$

Struct to dispatch the creation of Binary Start Variables

Docs abbreviation: $v$

Struct to dispatch the creation of Binary Stop Variables

Docs abbreviation: $w$

Struct to dispatch the creation of Hot Start Variable for Thermal units with temperature considerations

Docs abbreviation: $z^\text{th}$

Struct to dispatch the creation of Warm Start Variable for Thermal units with temperature considerations

Docs abbreviation: $y^\text{th}$

Struct to dispatch the creation of Cold Start Variable for Thermal units with temperature considerations

Docs abbreviation: $x^\text{th}$

Struct to dispatch the creation of Active Power Variables above minimum power for Thermal Compact formulations

Docs abbreviation: $\Delta p$

Struct to dispatch the creation of binary storage charge reservation variable

Docs abbreviation: $u^\text{st}$

Struct to dispatch the creation of a variable for energy storage level (state of charge)

Docs abbreviation: $e$

Struct to dispatch the creation of Active Power Output Variables for 2-directional devices. For instance storage or pump-hydro

Docs abbreviation: $p^\text{out}$

Struct to dispatch the creation of Active Power Input Variables for 2-directional devices. For instance storage or pump-hydro

Docs abbreviation: $p^\text{in}$

Struct to dispatch the creation of bidirectional Active Power Flow Variables

Docs abbreviation: $f$

Struct to dispatch the creation of active power flow upper bound slack variables. Used when there is not enough flow through the branch in the forward direction.

Docs abbreviation: $f^\text{sl,up}$

Struct to dispatch the creation of active power flow lower bound slack variables. Used when there is not enough flow through the branch in the reverse direction.

Docs abbreviation: $f^\text{sl,lo}$

Struct to dispatch the creation of unidirectional Active Power Flow Variables

Docs abbreviation: $f^\text{from-to}$

Struct to dispatch the creation of unidirectional Active Power Flow Variables

Docs abbreviation: $f^\text{to-from}$

Struct to dispatch the creation of unidirectional Reactive Power Flow Variables

Docs abbreviation: $f^\text{q,from-to}$

Struct to dispatch the creation of unidirectional Reactive Power Flow Variables

Docs abbreviation: $f^\text{q,to-from}$

Struct to dispatch the creation of Phase Shifters Variables

Docs abbreviation: $\theta^\text{shift}$

Struct to dispatch the creation of HVDC Losses Auxiliary Variables

Docs abbreviation: $\ell$

Struct to dispatch the creation of HVDC Flow Direction Auxiliary Variables

Docs abbreviation: $u^\text{dir}$

Struct to dispatch the creation of Voltage Magnitude Variables for AC formulations

Docs abbreviation: $v$

Struct to dispatch the creation of Voltage Angle Variables for AC/DC formulations

Docs abbreviation: $\theta$

Struct to dispatch the creation of Binary Interpolation Variable for Squared Converter Current

Docs abbreviation: $z_c^{i}$

Struct to dispatch the creation of Squared Voltage Variables for DC formulations Docs abbreviation: $v^{sq,dc}$

Struct to dispatch the variable of DC Current Variables for DC Lines formulations Docs abbreviation: $i_l^{dc}$

Struct to dispatch the creation of Continuous Interpolation Variable for Squared Converter Voltage

Docs abbreviation: $\delta_c^{v}$

Struct to dispatch the creation of Binary Interpolation Variable for Squared Converter Voltage

Docs abbreviation: $z_c^{v}$

Struct to dispatch the creation of Auxiliary Variable for Converter Bilinear term: v * i Docs abbreviation: $\gamma_c^{dc}$

Struct to dispatch the creation of Auxiliary Variable for Squared Converter Bilinear term: v * i

Docs abbreviation: $\gamma_c^{sq,dc}$

Struct to dispatch the creation of Continuous Interpolation Variable for Squared Converter AuxVar

Docs abbreviation: $\delta_c^{\gamma}$

Struct to dispatch the creation of Binary Interpolation Variable for Squared Converter AuxVar

Docs abbreviation: $z_c^{\gamma}$

Struct to dispatch the creation of Continuous Interpolation Variable for Squared Converter Current

Docs abbreviation: $\delta_c^{i}$

Struct to dispatch the creation of Voltage Variables for DC formulations Docs abbreviation: $v^{dc}$

Struct to dispatch the creation of DC Converter Current Variables for DC formulations Docs abbreviation: $i_c^{dc}$

Struct to dispatch the creation of Squared DC Converter Current Variables for DC formulations Docs abbreviation: $i_c^{sq,dc}$

Struct to dispatch the creation of DC Converter Positive Term Current Variables for DC formulations Docs abbreviation: $i_c^{+,dc}$

Struct to dispatch the creation of DC Converter Negative Term Current Variables for DC formulations Docs abbreviation: $i_c^{-,dc}$

Struct to dispatch the creation of Binary Variable for Converter Power Direction Docs abbreviation: $\kappa_c^{dc}$

Struct to dispatch the creation of Active Power Reserve Variables

Docs abbreviation: $r$

Struct to dispatch the creation of Service Requirement Variables

Docs abbreviation: $\text{req}$

Struct to dispatch the creation of System-wide slack up variables. Used when there is not enough generation.

Docs abbreviation: $p^\text{sl,up}$

Struct to dispatch the creation of System-wide slack down variables. Used when there is not enough load curtailment.

Docs abbreviation: $p^\text{sl,dn}$

Struct to dispatch the creation of Reserve requirement slack variables. Used when there is not reserves in the system to satisfy the requirement.

Docs abbreviation: $r^\text{sl}$

Struct to dispatch the creation of Interface Flow Slack Up variables

Docs abbreviation: $f^\text{sl,up}$

Struct to dispatch the creation of Interface Flow Slack Down variables

Docs abbreviation: $f^\text{sl,dn}$

Struct to dispatch the creation of Post-Contingency Active Power Deployment Variable for mapping reserves deployment under contingencies.

Docs abbreviation: $\Delta rsv_{r,g,c}$

Struct to dispatch the creation of Slack variables for UpperBoundFeedforward

Docs abbreviation: $p^\text{ff,ubsl}$

Struct to dispatch the creation of Slack variables for LowerBoundFeedforward

Docs abbreviation: $p^\text{ff,lbsl}$

Auxiliary Variable for Thermal Generation Models to keep track of time elapsed on

Auxiliary Variable for Thermal Generation Models to keep track of time elapsed off

Auxiliary Variable for Thermal Generation Models that solve for power above min

Auxiliary Variable for the bus angle results from power flow evaluation

Auxiliary Variable for the bus voltage magnitued results from power flow evaluation

Auxiliary Variable for the loss factors from AC power flow evaluation that are calculated using the Jacobian matrix

Auxiliary Variable for the voltage stability factors from AC power flow evaluation that are calculated using the Jacobian matrix

Auxiliary Variable for the line reactive flow in the from -> to direction from power flow evaluation

Auxiliary Variable for the line reactive flow in the to -> from direction from power flow evaluation

Auxiliary Variable for the line active flow in the from -> to direction from power flow evaluation

Auxiliary Variable for the line active flow in the to -> from direction from power flow evaluation

Struct to create the PiecewiseLinearCostConstraint associated with a specified variable.

See Piecewise linear cost functions for more information.

Struct to create the constraint to balance power in the copperplate model. For more information check Network Formulations.

The specified constraint is generally formulated as:

\[\sum_{c \in \text{components}} p_t^c = 0, \quad \forall t \in \{1, \dots, T\}\]

Struct to create the constraint to balance active power in nodal formulation. For more information check Network Formulations.

The specified constraint depends on the network model chosen.

Struct to create the constraint to balance reactive power in nodal formulation. For more information check Network Formulations.

The specified constraint depends on the network model chosen.

Struct to create the constraint to balance power across specified areas. For more information check Network Formulations.

The specified constraint is generally formulated as:

\[\sum_{c \in \text{components}_a} p_t^c = 0, \quad \forall a\in \{1,\dots, A\}, t \in \{1, \dots, T\}\]

Struct to create the constraint to limit active power expressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[P^\text{min} \le p_t \le P^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit reactive power expressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[Q^\text{min} \le q_t \le Q^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit active power expressions by a time series parameter. For more information check Device Formulations.

The specified constraint depends on the UpperBound expressions, but in its most basic formulation is of the form:

\[p_t \le \text{ActivePowerTimeSeriesParameter}_t, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit active power input expressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[P^\text{min} \le p_t^\text{in} \le P^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit active power output expressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[P^\text{min} \le p_t^\text{out} \le P^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit active power expressions by a time series parameter. For more information check Device Formulations.

The specified constraint depends on the UpperBound expressions, but in its most basic formulation is of the form:

\[p_t^{in} \le \text{ActivePowerTimeSeriesParameter}_t, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit active power expressions by a time series parameter. For more information check Device Formulations.

The specified constraint depends on the UpperBound expressions, but in its most basic formulation is of the form:

\[p_t^{out} \le \text{ActivePowerTimeSeriesParameter}_t, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint for satisfying active power reserve requirements. For more information check Service Formulations.

The constraint is as follows:

\[\sum_{d\in\mathcal{D}_s} r_{d,t} + r_t^\text{sl} \ge \text{Req},\quad \forall t\in \{1,\dots, T\} \quad \text{(for a ConstantReserve)} \\ \sum_{d\in\mathcal{D}_s} r_{d,t} + r_t^\text{sl} \ge \text{RequirementTimeSeriesParameter}_{t},\quad \forall t\in \{1,\dots, T\} \quad \text{(for a VariableReserve)}\]

Struct to create the constraint to participation assignments limits in the active power reserves. For more information check Service Formulations.

The constraint is as follows:

\[r_{d,t} \le \text{Req} \cdot \text{PF} ,\quad \forall d\in \mathcal{D}_s, \forall t\in \{1,\dots, T\} \quad \text{(for a ConstantReserve)} \\ r_{d,t} \le \text{RequirementTimeSeriesParameter}_{t} \cdot \text{PF}\quad \forall d\in \mathcal{D}_s, \forall t\in \{1,\dots, T\}, \quad \text{(for a VariableReserve)}\]

Struct to create the constraint for ensuring that NonSpinning Reserve can be delivered from turn-off thermal units.

For more information check Service Formulations for NonSpinningReserve.

The constraint is as follows:

\[r_{d,t} \le (1 - u_{d,t}^\text{th}) \cdot R^\text{limit}_d, \quad \forall d \in \mathcal{D}_s, \forall t \in \{1,\dots, T\}\]

Struct to create the constraint for starting up ThermalMultiStart units. For more information check ThermalGen Formulations for ThermalMultiStartUnitCommitment.

The specified constraint is formulated as:

\[\max\{P^\text{th,max} - P^\text{th,shdown}, 0\} \cdot w_1^\text{th} \le u^\text{th,init} (P^\text{th,max} - P^\text{th,min}) - P^\text{th,init}\]

Struct to create the commitment constraint between the on, start, and stop variables. For more information check ThermalGen Formulations.

The specified constraints are formulated as:

\[u_1^\text{th} = u^\text{th,init} + v_1^\text{th} - w_1^\text{th} \\ u_t^\text{th} = u_{t-1}^\text{th} + v_t^\text{th} - w_t^\text{th}, \quad \forall t \in \{2,\dots,T\} \\ v_t^\text{th} + w_t^\text{th} \le 1, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the duration constraint for commitment formulations, i.e. min-up and min-down.

For more information check ThermalGen Formulations.

Struct to create the RampConstraint associated with a specified thermal device or reserve service.

For thermal units, see more information in Thermal Formulations. The constraint is as follows:

\[-R^\text{th,dn} \le p_t^\text{th} - p_{t-1}^\text{th} \le R^\text{th,up}, \quad \forall t\in \{1, \dots, T\}\]

For Ramp Reserve, see more information in Service Formulations. The constraint is as follows:

\[r_{d,t} \le R^\text{th,up} \cdot \text{TF}\quad \forall d\in \mathcal{D}_s, \forall t\in \{1,\dots, T\}, \quad \text{(for ReserveUp)} \\ r_{d,t} \le R^\text{th,dn} \cdot \text{TF}\quad \forall d\in \mathcal{D}_s, \forall t\in \{1,\dots, T\}, \quad \text{(for ReserveDown)}\]

Struct to create the start-up initial condition constraints for ThermalMultiStart.

For more information check ThermalGen Formulations for ThermalMultiStartUnitCommitment.

Struct to create the start-up time limit constraints for ThermalMultiStart.

For more information check ThermalGen Formulations for ThermalMultiStartUnitCommitment.

Struct to create the constraint that sets the reactive power to the power factor in the RenewableConstantPowerFactor formulation for renewable units.

For more information check RenewableGen Formulations.

The specified constraint is formulated as:

\[q_t^\text{re} = \text{pf} \cdot p_t^\text{re}, \quad \forall t \in \{1,\dots, T\}\]

Struct to create the constraint to limit the import and exports in a determined period. For more information check Device Formulations.

Struct to create the constraint that set the flow limits through a PhaseShiftingTransformer.

For more information check Branch Formulations.

The specified constraint is formulated as:

\[-R^\text{max} \le f_t \le R^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraint that set the AC flow limits through AC branches and HVDC two-terminal branches.

For more information check Branch Formulations.

The specified constraint is formulated as:

\[\begin{align*} & f_t - f_t^\text{sl,up} \le R^\text{max},\quad \forall t \in \{1,\dots, T\} \\ & f_t + f_t^\text{sl,lo} \ge -R^\text{max},\quad \forall t \in \{1,\dots, T\} \end{align*}\]

Struct to create the constraint for branch flow rate limits from the 'from' bus to the 'to' bus. For more information check Branch Formulations.

Struct to create the constraint for branch flow rate limits from the 'to' bus to the 'from' bus. For more information check Branch Formulations.

Struct to create the constraints that set the power balance across a lossy HVDC two-terminal line.

For more information check Branch Formulations.

The specified constraints are formulated as:

\[\begin{align*} & f_t^\text{to-from} - f_t^\text{from-to} \le L_1 \cdot f_t^\text{to-from} - L_0,\quad \forall t \in \{1,\dots, T\} \\ & f_t^\text{from-to} - f_t^\text{to-from} \ge L_1 \cdot f_t^\text{from-to} + L_0,\quad \forall t \in \{1,\dots, T\} \\ & f_t^\text{from-to} - f_t^\text{to-from} \ge - M^\text{big} (1 - u^\text{dir}_t),\quad \forall t \in \{1,\dots, T\} \\ & f_t^\text{to-from} - f_t^\text{from-to} \ge - M^\text{big} u^\text{dir}_t,\quad \forall t \in \{1,\dots, T\} \\ \end{align*}\]

Struct to create the constraint the AC branch flows depending on the network model. For more information check Branch Formulations.

The specified constraint depends on the network model chosen. The most common application is the StaticBranch in a PTDF Network Model:

\[f_t = \sum_{i=1}^N \text{PTDF}_{i,b} \cdot \text{Bal}_{i,t}, \quad \forall t \in \{1,\dots, T\}\]

Struct to create the constraint that set the angle limits through a PhaseShiftingTransformer.

For more information check Branch Formulations.

The specified constraint is formulated as:

\[\Theta^\text{min} \le \theta^\text{shift}_t \le \Theta^\text{max}, \quad \forall t \in \{1,\dots,T\}\]

Struct to create the constraints that decide the balance of AC and DC power of the converter.

The specified constraints are formulated as:

\[\begin{align*} & p_ac = p_dc - loss_t \quad \forall t \in \{1,\dots, T\} \\ & loss_t = a i_c^2 + b i_c + c \\ \end{align*}\]

Struct to create the Quadratic PWL interpolation constraints that decide square value of the voltage. In this case x = voltage and y = squared_voltage. The specified constraints are formulated as:

\[\begin{align*} & x = x_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & y = y_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & z_k \le \delta_k, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ & z_k \ge \delta_{k+1}, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ \end{align*}\]

Struct to create the Quadratic PWL interpolation constraints that decide square value of the current. In this case x = current and y = squared_current. The specified constraints are formulated as:

\[\begin{align*} & x = x_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & y = y_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & z_k \le \delta_k, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ & z_k \ge \delta_{k+1}, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ \end{align*}\]

Struct to create the Quadratic PWL interpolation constraints that decide square value of the bilinear variable γ. In this case x = γ and y = squared_γ. The specified constraints are formulated as:

\[\begin{align*} & x = x_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & y = y_0 + \sum_{k=1}^K (x_{k} - x_{k-1}) \delta_k, \quad \forall t \in \{1,\dots, T\} \\ & z_k \le \delta_k, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ & z_k \ge \delta_{k+1}, \quad \forall t \in \{1,\dots, T\}, \forall k \in \{1,\dots, K-1\} \\ \end{align*}\]

Struct to create the constraints that set the absolute value for the current to use in losses through a lossy Interconnecting Power Converter. The specified constraint is formulated as:

\[\begin{align*} & i_c^{dc} = i_c^+ - i_c^-, \quad \forall t \in \{1,\dots, T\} \\ & i_c^+ \le I_{max} \cdot \nu_c, \quad \forall t \in \{1,\dots, T\} \\ & i_c^+ \le I_{max} \cdot (1 - \nu_c), \quad \forall t \in \{1,\dots, T\} \end{align*}\]

Struct to create the constraints that compute the converter DC power based on current and voltage.

The specified constraints are formulated as:

\[\begin{align*} & p_c = 0.5 * (γ^sq - v^sq - i^sq), \quad \forall t \in \{1,\dots, T\} \\ & γ_c = v_c + i_c, \quad \forall t \in \{1,\dots, T\} \\ \end{align*}\]

Struct to create the McCormick envelopes constraints that decide the bounds on the DC active power.

The specified constraints are formulated as:

\[\begin{align*} & p_c >= V^{min} i_c + v_c I^{min} - I^{min}V^{min}, \quad \forall t \in \{1,\dots, T\} \\ & p_c >= V^{max} i_c + v_c I^{max} - I^{max}V^{max}, \quad \forall t \in \{1,\dots, T\} \\ & p_c <= V^{max} i_c + v_c I^{min} - I^{min}V^{max}, \quad \forall t \in \{1,\dots, T\} \\ & p_c <= V^{min} i_c + v_c I^{max} - I^{max}V^{min}, \quad \forall t \in \{1,\dots, T\} \\ \end{align*}\]

Struct to create the constraints that set the current flowing through a DC line.

\[\begin{align*} & i_l^{dc} = \frac{1}{r_l} (v_{from,l} - v_{to,l}), \quad \forall t \in \{1,\dots, T\} \end{align*}\]

Struct for DC current balance in multi-terminal DC networks

Struct to create the constraint to balance active power. For more information check ThermalGen Formulations.

The specified constraint is generally formulated as:

\[\sum_{g \in \mathcal{G}_c} p_{g,t} &= \sum_{g \in \mathcal{G}} \Delta p_{g, c, t} &\quad \forall c \in \mathcal{C} \ \forall t \in \{1, \dots, T\}\]

Struct to create the constraint to limit post-contingency active power expressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[P^\text{min} \le p_t + \Delta p_{c, t} \le P^\text{max}, \quad \forall c \in \mathcal{C} \ \forall t \in \{1,\dots,T\}\]

Struct to create the constraint to limit post-contingency active power reserve deploymentexpressions. For more information check Device Formulations.

The specified constraint depends on the UpperBound and LowerBound expressions, but in its most basic formulation is of the form:

\[\Delta rsv_{r, c, t} \le rsv_{r, c, t}, \quad \forall r \in \mathcal{R} \ \forall c \in \mathcal{C} \ \forall t \in \{1,\dots,T\}\]

Struct to create the PiecewiseLinearBlockIncrementalOfferConstraint associated with a specified variable.

See Piecewise linear cost functions for more information.

Struct to create the PiecewiseLinearBlockDecrementalOfferConstraint associated with a specified variable.

See Piecewise linear cost functions for more information.

Struct to create the constraint for semicontinuous feedforward limits.

For more information check Feedforward Formulations.

The specified constraint is formulated as:

\[\begin{align*} & \text{ActivePowerRangeExpressionUB}_t := p_t^\text{th} - \text{on}_t^\text{th}P^\text{th,max} \le 0, \quad \forall t\in \{1, \dots, T\} \\ & \text{ActivePowerRangeExpressionLB}_t := p_t^\text{th} - \text{on}_t^\text{th}P^\text{th,min} \ge 0, \quad \forall t\in \{1, \dots, T\} \end{align*}\]

Struct to create the constraint for upper bound feedforward limits.

For more information check Feedforward Formulations.

The specified constraint is formulated as:

\[\begin{align*} & \text{AffectedVariable}_t - p_t^\text{ff,ubsl} \le \text{SourceVariableParameter}_t, \quad \forall t \in \{1,\dots, T\} \end{align*}\]

Struct to create the constraint for lower bound feedforward limits.

For more information check Feedforward Formulations.

The specified constraint is formulated as:

\[\begin{align*} & \text{AffectedVariable}_t + p_t^\text{ff,lbsl} \ge \text{SourceVariableParameter}_t, \quad \forall t \in \{1,\dots, T\} \end{align*}\]

Parameter to define active power time series

Parameter to define reactive power time series

Parameter to define requirement time series

Parameter to define reactive power offset during an event.

Parameter to define active power out time series

Parameter to define active power in time series

Parameter to define fuel cost time series

Parameter to define Flow From_To limit time series

Parameter to define Flow To_From limit time series

Parameter to define variable upper bound

Parameter to define variable lower bound

Parameter to define unit commitment status updated from the system state

Parameter to FixValueParameter

Parameter to define cost function coefficient

Parameter to record that the component changed in the availability status

Parameter to define component availability status updated from the system state

Parameter to define active power offset during an event.

Parameter to define the dynamic rating time series of a branch

Parameter to define the dynamic ratings time series of an AC branch for post-contingency condition

serialize_optimization_model(
    container::PowerSimulations.OptimizationContainer,
    save_path::String
)

Exports the OpModel JuMP object in MathOptFormat

get_all_constraint_index(
    model::PowerSimulations.OperationModel
) -> Vector{Tuple{InfrastructureSystems.Optimization.ConstraintKey, Int64, Int64}}

Each Tuple corresponds to (conname, internalindex, moi_index)

get_all_variable_index(
    model::PowerSimulations.OperationModel
) -> Vector{Tuple{Symbol, Int64, Int64}}

Each Tuple corresponds to (conname, internalindex, moi_index)

OptimizationProblemResults(
    model::DecisionModel
) -> OptimizationProblemResults

Construct OptimizationProblemResults from a solved DecisionModel.

OptimizationProblemResults(
    model::EmulationModel
) -> OptimizationProblemResults

Construct OptimizationProblemResults from a solved EmulationModel.

SimulationResults(
    path::AbstractString,
    name::AbstractString;
    ...
) -> SimulationResults
SimulationResults(
    path::AbstractString,
    name::AbstractString,
    execution;
    ignore_status
) -> SimulationResults

Construct SimulationResults from a simulation output directory.

Arguments

• path::AbstractString: Simulation output directory
• name::AbstractString: Simulation name
• execution::AbstractString: Execution number. Default is the most recent.
• ignore_status::Bool: If true, return results even if the simulation failed.

SimulationResults(
    sim::Simulation;
    ignore_status,
    kwargs...
) -> SimulationResults

Construct SimulationResults from a simulation.

export_results(results::SimulationResults, exports)

Export results to files in the results directory.

Arguments

• results::SimulationResults: simulation results
• exports: SimulationResultsExport or anything that can be passed to its constructor. (such as Dict or path to JSON file)

An example JSON file demonstrating possible options is below. Note that start_time, end_time, path, and format are optional.

{
  "decision_models": [
    {
      "name": "ED",
      "variables": [
        "P__ThermalStandard",
      ],
      "parameters": [
        "all"
      ]
    },
    {
      "name": "UC",
      "variables": [
        "On__ThermalStandard"
      ],
      "parameters": [
        "all"
      ],
      "duals": [
        "all"
      ]
    }
  ],
  "start_time": "2020-01-01T04:00:00",
  "end_time": null,
  "path": null,
  "format": "csv"
}

read_aux_variable(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    args...;
    initial_time,
    count,
    store,
    table_format
) -> SortedDict{_A, _B, Base.Order.ForwardOrdering} where {_A, _B}

Return the values for the requested auxillary variables. It keeps requests when performing multiple retrievals.

Arguments

• args: Can be a string returned from list_aux_variable_names or args that can be splatted into a AuxVarKey.
• initial_time::Dates.DateTime : initial of the requested results
• count::Int: Number of results

read_dual(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    args...;
    initial_time,
    count,
    store,
    table_format
) -> SortedDict{_A, _B, Base.Order.ForwardOrdering} where {_A, _B}

Return the values for the requested dual. It keeps requests when performing multiple retrievals.

Arguments

• args: Can be a string returned from list_dual_names or args that can be splatted into a ConstraintKey.
• initial_time::Dates.DateTime : initial of the requested results
• count::Int: Number of results
• store::SimulationStore: a store that has been opened for reading
• table_format::TableFormat: Format of the table to be returned. Default is TableFormat.LONG where the columns are DateTime, name, and value when the data has two dimensions and DateTime, name, name2, and value when the data has three dimensions. Set to it TableFormat.WIDE to pivot the names as columns. Note: TableFormat.WIDE is not supported when the data has three dimensions.

read_expression(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    args...;
    initial_time,
    count,
    store,
    table_format
) -> SortedDict{_A, _B, Base.Order.ForwardOrdering} where {_A, _B}

Return the values for the requested auxillary variables. It keeps requests when performing multiple retrievals.

Arguments

• args: Can be a string returned from list_expression_names or args that can be splatted into a ExpressionKey.
• initial_time::Dates.DateTime : initial of the requested results
• count::Int: Number of results

read_parameter(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    args...;
    initial_time,
    count,
    store,
    table_format
) -> SortedDict{_A, _B, Base.Order.ForwardOrdering} where {_A, _B}

Return the values for the requested parameter. It keeps requests when performing multiple retrievals.

Arguments

• args: Can be a string returned from list_parameter_names or args that can be splatted into a ParameterKey.
• initial_time::Dates.DateTime : initial of the requested results
• count::Int: Number of results
• table_format::TableFormat: Format of the table to be returned. Default is TableFormat.LONG where the columns are DateTime, name, and value when the data has two dimensions and DateTime, name, name2, and value when the data has three dimensions. Set to it TableFormat.WIDE to pivot the names as columns. Note: TableFormat.WIDE is not supported when the data has three dimensions.

read_variable(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    args...;
    initial_time,
    count,
    store,
    table_format
) -> SortedDict{_A, _B, Base.Order.ForwardOrdering} where {_A, _B}

Return the values for the requested variable. It keeps requests when performing multiple retrievals.

Arguments

• args: Can be a string returned from list_variable_names or args that can be splatted into a VariableKey.
• initial_time::Dates.DateTime : initial of the requested results
• count::Int: Number of results
• store::SimulationStore: a store that has been opened for reading
• table_format::TableFormat: Format of the table to be returned. Default is TableFormat.LONG where the columns are DateTime, name, and value when the data has two dimensions and DateTime, name, name2, and value when the data has three dimensions. Set to it TableFormat.WIDE to pivot the names as columns. Note: TableFormat.WIDE is not supported when the data has three dimensions.

Examples

read_variable(results, ActivePowerVariable, ThermalStandard)
read_variable(results, "ActivePowerVariable__ThermalStandard")
read_variable(results, "ActivePowerVariable__ThermalStandard", table_format = TableFormat.WIDE)

get_decision_problem_results(
    results::SimulationResults,
    problem::String;
    populate_system,
    populate_units
) -> PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults}

Return SimulationProblemResults corresponding to a SimulationResults

Arguments

• sim_results::PSI.SimulationResults: the simulation results to read from
• problem::String: the name of the problem (e.g., "UC", "ED")
• populate_system::Bool = true: whether to set the results' system as if using get_system!
• populate_units::Union{IS.UnitSystem, String, Nothing} = IS.UnitSystem.NATURAL_UNITS: the units system with which to populate the results' system, if any (requires populate_system=true)

get_emulation_problem_results(
    results::SimulationResults;
    populate_system,
    populate_units
) -> PowerSimulations.SimulationProblemResults{PowerSimulations.EmulationModelSimulationResults}

Return SimulationProblemResults corresponding to a SimulationResults

Arguments

• sim_results::PSI.SimulationResults: the simulation results to read from
• populate_system::Bool = true: whether to set the results' system as if using get_system!
• populate_units::Union{IS.UnitSystem, String, Nothing} = IS.UnitSystem.NATURAL_UNITS: the units system with which to populate the results' system, if any (requires populate_system=true)

list_decision_problems(
    results::SimulationResults
) -> Vector{String}

Return the problem names in the simulation.

load_results!(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.DecisionModelSimulationResults},
    count::Int64;
    initial_time,
    variables,
    duals,
    parameters,
    aux_variables,
    expressions,
    store
)

Load the simulation results into memory for repeated reads. This is useful when loading results from remote locations over network connections, when reading the same data very many times, etc. Multiple calls augment the cache according to these rules, where "variable" means "variable, expression, etc.":

• Requests for an already cached variable at a lesser count than already cached do not decrease the count of the cached variable
• Requests for an already cached variable at a greater count than already cached do increase the count of the cached variable
• Requests for new variables are fulfilled without evicting existing variables

Note that count is global across all variables, so increasing the count re-reads already cached variables. For each variable, each element must be the name encoded as a string, like "ActivePowerVariable__ThermalStandard" or a Tuple with its constituent types, like (ActivePowerVariable, ThermalStandard). To clear the cache, use Base.empty!.

Arguments

• count::Int: Number of windows to load.
• initial_time::Dates.DateTime : Initial time of first window to load. Defaults to first.
• aux_variables::Vector{Union{String, Tuple}}: Optional list of aux variables to load.
• duals::Vector{Union{String, Tuple}}: Optional list of duals to load.
• expressions::Vector{Union{String, Tuple}}: Optional list of expressions to load.
• parameters::Vector{Union{String, Tuple}}: Optional list of parameters to load.
• variables::Vector{Union{String, Tuple}}: Optional list of variables to load.

load_results!(
    res::PowerSimulations.SimulationProblemResults{PowerSimulations.EmulationModelSimulationResults};
    aux_variables,
    duals,
    expressions,
    parameters,
    variables
)

Load the simulation results into memory for repeated reads. This is useful when loading results from remote locations over network connections.

For each variable/parameter/dual, etc., each element must be the name encoded as a string, like "ActivePowerVariable__ThermalStandard"or a Tuple with its constituent types, like(ActivePowerVariable, ThermalStandard)`.

Arguments

• aux_variables::Vector{Union{String, Tuple}}: Optional list of aux variables to load.
• duals::Vector{Union{String, Tuple}}: Optional list of duals to load.
• expressions::Vector{Union{String, Tuple}}: Optional list of expressions to load.
• parameters::Vector{Union{String, Tuple}}: Optional list of parameters to load.
• variables::Vector{Union{String, Tuple}}: Optional list of variables to load.

get_timestamps(
    result::PowerSimulations.SimulationProblemResults
) -> StepRange{Dates.DateTime, Dates.Millisecond}

Return a reference to a StepRange of available timestamps.

list_aux_variable_names(
    res::PowerSimulations.SimulationProblemResults
) -> Vector{String}

Return an array of auxillary variable names (strings) that are available for reads.

list_dual_names(
    res::PowerSimulations.SimulationProblemResults
) -> Vector{String}

Return an array of dual names (strings) that are available for reads.

list_expression_names(
    res::PowerSimulations.SimulationProblemResults
) -> Vector{String}

Return an array of expression names (strings) that are available for reads.

list_parameter_names(
    res::PowerSimulations.SimulationProblemResults
) -> Vector{String}

Return an array of parmater names (strings) that are available for reads.

list_variable_names(
    res::PowerSimulations.SimulationProblemResults
) -> Vector{String}

Return an array of variable names (strings) that are available for reads.

read_optimizer_stats(
    res::PowerSimulations.SimulationProblemResults;
    store
) -> Any

Return the optimizer stats for the problem as a DataFrame.

Accepted keywords

• store::SimulationStore: a store that has been opened for reading

get_system!(
    results::Union{OptimizationProblemResults, PowerSimulations.SimulationProblemResults};
    kwargs...
) -> Union{Nothing, InfrastructureSystems.InfrastructureSystemsType}

Return the system used for the problem. If the system hasn't already been deserialized or set with set_system! then deserialize and store it.

If the simulation was configured to serialize all systems to file then the returned system will include all data. If that was not configured then the returned system will include all data except time series data.

read_realized_aux_variable(
    res::PowerSimulations.SimulationProblemResults,
    aux_variable::AbstractString;
    kwargs...
) -> Any

Return the final values for the requested auxiliary variable for each time step for a problem.

Refer to read_realized_variable for help and examples.

read_realized_aux_variables(
    res::PowerSimulations.SimulationProblemResults;
    kwargs...
) -> Dict

Return the final values for the requested auxiliary variables for each time step for a problem.

Refer to read_realized_aux_variables for help and examples.

read_realized_dual(
    res::PowerSimulations.SimulationProblemResults,
    dual::AbstractString;
    kwargs...
) -> Any

Return the final values for the requested dual for each time step for a problem.

Refer to read_realized_variable for help and examples.

read_realized_duals(
    res::PowerSimulations.SimulationProblemResults;
    kwargs...
) -> Dict

Return the final values for the requested duals for each time step for a problem.

Refer to read_realized_duals for help and examples.

read_realized_expression(
    res::PowerSimulations.SimulationProblemResults,
    expression::AbstractString;
    kwargs...
) -> Any

Return the final values for the requested expression for each time step for a problem.

Refer to read_realized_variable for help and examples.

read_realized_expressions(
    res::PowerSimulations.SimulationProblemResults;
    kwargs...
) -> Dict

Return the final values for the requested expressions for each time step for a problem.

Refer to read_realized_expressions for help and examples.

read_realized_parameter(
    res::PowerSimulations.SimulationProblemResults,
    parameter::AbstractString;
    kwargs...
) -> Any

Return the final values for the requested parameter for each time step for a problem.

Refer to read_realized_variable for help and examples.

read_realized_parameters(
    res::PowerSimulations.SimulationProblemResults;
    kwargs...
) -> Dict

Return the final values for the requested parameters for each time step for a problem.

Refer to read_realized_parameters for help and examples.

read_realized_variable(
    res::PowerSimulations.SimulationProblemResults,
    variable::AbstractString;
    kwargs...
) -> Any

Return the final values for the requested variable for each time step for a problem.

Decision problem results are returned in a Dict{DateTime, DataFrame}.

Emulation problem results are returned in a DataFrame.

Limit the data sizes returned by specifying initial_time and count for decision problems or start_time and len for emulation problems.

See also load_results! to preload data into memory.

Arguments

• variable::Union{String, Tuple}: Variable name as a string or a Tuple with variable type and device type.
• initial_time::Dates.DateTime: Initial time of the requested results. Decision problems only.
• count::Int: Number of results. Decision problems only.
• start_time::Dates.DateTime: Start time of the requested results. Emulation problems only.
• len::Int: Number of rows in each DataFrame. Emulation problems only.
• table_format::TableFormat: Format of the table to be returned. Default is TableFormat.LONG where the columns are DateTime, name, and value when the data has two dimensions and DateTime, name, name2, and value when the data has three dimensions. Set to it TableFormat.WIDE to pivot the names as columns. Note: TableFormat.WIDE is not supported when the data has three dimensions.

Examples

julia > read_realized_variable(results, "ActivePowerVariable__ThermalStandard")
julia > read_realized_variable(results, (ActivePowerVariable, ThermalStandard))
julia > read_realized_variable(results, (ActivePowerVariable, ThermalStandard), table_format = TableFormat.WIDE)

read_realized_variables(
    res::PowerSimulations.SimulationProblemResults;
    kwargs...
) -> Dict

Return the final values for the requested variables for each time step for a problem.

Decision problem results are returned in a Dict{String, Dict{DateTime, DataFrame}}.

Emulation problem results are returned in a Dict{String, DataFrame}.

Limit the data sizes returned by specifying initial_time and count for decision problems or start_time and len for emulation problems.

If the Julia process is started with multiple threads, the code will read the variables in parallel.

See also load_results! to preload data into memory.

Arguments

• variables::Vector{Union{String, Tuple}}: Variable name as a string or a Tuple with variable type and device type. If not provided then return all variables.
• initial_time::Dates.DateTime: Initial time of the requested results. Decision problems only.
• count::Int: Number of results. Decision problems only.
• start_time::Dates.DateTime: Start time of the requested results. Emulation problems only.
• len::Int: Number of rows in each DataFrame. Emulation problems only.
• table_format::TableFormat: Format of the table to be returned. Default is TableFormat.LONG where the columns are DateTime, name, and value when the data has two dimensions and DateTime, name, name2, and value when the data has three dimensions. Set to it TableFormat.WIDE to pivot the names as columns, matching earlier versions of PowerSimulations.jl. Note: TableFormat.WIDE is not supported when the data has three dimensions.

Examples

julia> variables_as_strings =
    ["ActivePowerVariable__ThermalStandard", "ActivePowerVariable__RenewableDispatch"]
julia> variables_as_types =
    [(ActivePowerVariable, ThermalStandard), (ActivePowerVariable, RenewableDispatch)]
julia> df_long =read_realized_variables(results, variables_as_strings)
julia> df_long = read_realized_variables(results, variables_as_types)
julia> df_wide = read_realized_variables(results, variables_as_types, table_format = TableFormat.WIDE)
julia> using DataFramesMeta
julia> df_agg_generators = @chain df_long begin
    @groupby(:DateTime)
    @combine(:value = sum(:value))
end

set_system!(
    results::PowerSimulations.SimulationProblemResults,
    system::AbstractString
)

Set the system in the results instance.

Throws InvalidValue if the system UUID is incorrect.

Arguments

• results::SimulationProblemResults: Results object
• system::AbstractString: Path to the system json file

Examples

julia > set_system!(res, "my_path/system_data.json")

Handles merging of simulation partitions

read_optimizer_stats(
    store::PowerSimulations.HdfSimulationStore,
    model_name
) -> Any

Return the optimizer stats for a problem as a DataFrame.

read_optimizer_stats(
    store::PowerSimulations.HdfSimulationStore,
    simulation_step::Int64,
    model_name::Symbol,
    execution_index::Int64
) -> Any

Read the optimizer stats for a problem execution.

list_simulation_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    output_dir::AbstractString;
    ...
) -> Vector{T} where T<:PowerSimulations.AbstractSimulationStatusEvent
list_simulation_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    output_dir::AbstractString,
    filter_func::Union{Nothing, Function};
    step,
    model_name
) -> Vector{T} where T<:PowerSimulations.AbstractSimulationStatusEvent

list_simulation_events(
    ::Type{T},
    output_dir::AbstractString,
    filter_func::Union{Nothing, Function} = nothing;
    step = nothing,
    model = nothing,
) where {T <: IS.AbstractRecorderEvent}

List simulation events of type T in a simulation output directory.

Arguments

• output_dir::AbstractString: Simulation output directory
• filter_func::Union{Nothing, Function} = nothing: Refer to show_simulation_events.
• step::Int = nothing: Filter events by step. Required if model is passed.
• model::Int = nothing: Filter events by model.

show_recorder_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    filename::AbstractString;
    ...
)
show_recorder_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    filename::AbstractString,
    filter_func::Union{Nothing, Function};
    wall_time,
    kwargs...
)

show_recorder_events(
    ::Type{T},
    filename::AbstractString,
    filter_func::Union{Nothing, Function} = nothing;
    wall_time = false,
    kwargs...,
) where {T <: IS.AbstractRecorderEvent}

Show the events of type T in a recorder file.

Arguments

• ::Type{T}: Recorder event type
• filename::AbstractString: recorder filename
• filter_func::Union{Nothing, Function} = nothing: Optional function that accepts an event of type T and returns a Bool. Apply this function to each event and only return events where the result is true.
• wall_time = false: If true, show the wall_time timestamp.

show_simulation_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    output_dir::AbstractString;
    ...
)
show_simulation_events(
    ::Type{T<:InfrastructureSystems.AbstractRecorderEvent},
    output_dir::AbstractString,
    filter_func::Union{Nothing, Function};
    step,
    model,
    wall_time,
    kwargs...
)

show_simulation_events(
    ::Type{T},
    output_dir::AbstractString,
    filter_func::Union{Nothing,Function} = nothing;
    step = nothing,
    model = nothing,
    wall_time = false,
    kwargs...,
) where { T <: IS.AbstractRecorderEvent}

Show all simulation events of type T in a simulation output directory.

Arguments

• ::Type{T}: Recorder event type
• output_dir::AbstractString: Simulation output directory
• filter_func::Union{Nothing, Function} = nothing: Refer to show_recorder_events.
• step::Int = nothing: Filter events by step. Required if model is passed.
• model::Int = nothing: Filter events by model.
• wall_time = false: If true, show the wall_time timestamp.