diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 0b5d270..b721cdf 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.3","generation_timestamp":"2025-01-29T15:10:05","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.3","generation_timestamp":"2025-01-30T18:06:57","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/api/internal/index.html b/dev/api/internal/index.html index 8ab79a8..ae94abb 100644 --- a/dev/api/internal/index.html +++ b/dev/api/internal/index.html @@ -5,13 +5,13 @@ samples = storavail["MyStorage123", ZonedDateTime(2020, 1, 1, 0, tz"UTC")] @assert samples isa Vector{Bool} -@assert length(samples) == 10source
SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE — Constant
FILE with restrictions on CC units in PRAS
source
SiennaPRASInterface.HVDCLineTypes — Type
Supported DC branch Types
source
SiennaPRASInterface.OUTAGE_INFO_FILE — Constant
DEFAULT outage data which is used when outage_flag is set to FALSE

Based on ERCOT historical data

source
SiennaPRASInterface.TransformerTypes — Constant
Filtered Transformer Types

These transformers are not modeled as lines in PRAS.

source
SiennaPRASInterface.S2P_metadata — Type
S2P_metadata

Struct to store metadata for the Sienna to PRAS conversion

source
SiennaPRASInterface.outage_data — Type
outage_data

Struct to store the outage information for a component.

source
SiennaPRASInterface.check_file — Method
check_file(loc::String)

Check if the file exists and is openable.

source
SiennaPRASInterface.get_available_components_in_aggregation_topology — Method
get_available_components_in_aggregation_topology(
+@assert length(samples) == 10
source
SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE — Constant
FILE with restrictions on CC units in PRAS
source
SiennaPRASInterface.HVDCLineTypes — Type
Supported DC branch Types
source
SiennaPRASInterface.OUTAGE_INFO_FILE — Constant
DEFAULT outage data which is used when outage_flag is set to FALSE

Based on ERCOT historical data

source
SiennaPRASInterface.TransformerTypes — Constant
Filtered Transformer Types

These transformers are not modeled as lines in PRAS.

source
SiennaPRASInterface.S2P_metadata — Type
S2P_metadata

Struct to store metadata for the Sienna to PRAS conversion

source
SiennaPRASInterface.outage_data — Type
outage_data

Struct to store the outage information for a component.

source
SiennaPRASInterface.check_file — Method
check_file(loc::String)

Check if the file exists and is openable.

source
SiennaPRASInterface.get_available_components_in_aggregation_topology — Method
get_available_components_in_aggregation_topology(
     type::Type{<:PSY.StaticInjection},
     sys::PSY.System,
     region::PSY.AggregationTopology,
-)

Get available components in the AggregationTopology region of the given type.

source
SiennaPRASInterface.get_first_ts — Method
get_first_ts(ts::TS)

Get time series and handle components with no time series (such as unavailable)

source
SiennaPRASInterface.get_generator_category — Method
get_generator_category(gen::StaticInjection)

Get the category of the generator.

Arguments

  • gen::StaticInjection: Generator

Returns

  • String: Category of the generator
source
SiennaPRASInterface.get_sorted_lines — Method
get_sorted_lines(lines::Vector{PSY.Branch}, region_names::Vector{String})

Get sorted lines, interface region indices, and interface line indices.

Arguments

  • lines::Vector{PSY.Branch}: Lines
  • region_names::Vector{String}: Region names

Returns

  • sorted_lines::Vector{PSY.Branch}: Sorted lines
  • interface_reg_idxs::Vector{Tuple{Int, Int}}: Interface region indices
  • interface_line_idxs::Vector{UnitRange{Int}}: Interface line indices
source
SiennaPRASInterface.get_sorted_region_tuples — Method
get_sorted_region_tuples(lines::Vector{PSY.Branch}, region_names::Vector{String})

Get sorted (regfrom, regto) tuples of inter-regional lines.

source
SiennaPRASInterface.get_timestamps — Method
get_timestamps(initial_time, resolution, steps, horizon)

Return a vector of timestamps starting from initial_time with resolution and steps for horizon steps

source
SiennaPRASInterface.get_ts_values — Method
get_ts_values(ts::PSY.TimeSeriesData)

Get the time series values.

Arguments

  • ts::TimeSeries: Time series

Returns

  • Array{Float64}: Time series values
source
SiennaPRASInterface.initialize_availability! — Method

initializeavailability!(rng, availability, nexttransition, devices, tlast)

Arguments

  • rng::Random.AbstractRNG: Random number generator
  • availability::Vector{Bool}: Vector of availability status
  • nexttransition::Vector{Int}: Vector of next transition time
  • devices::Vector{PSY.Generator}: Vector of devices with outage SupplementalAttributes
  • t_last::Int: Last time step

Returns

Modifies input arguments and rereturns

  • availability::Vector{Bool}: Vector of availability status
  • nexttransition::Vector{Int}: Vector of next transition time
source
SiennaPRASInterface.line_rating — Method
line_rating(line::Branch)

Get the line rating.

Arguments

  • line::Branch: Line

Returns

  • Tuple{forward_capacity::Float64, backward_capacity::Float64}: Line rating
source
SiennaPRASInterface.make_pras_interfaces — Method
make_pras_interfaces(
+)

Get available components in the AggregationTopology region of the given type.

source
SiennaPRASInterface.get_first_ts — Method
get_first_ts(ts::TS)

Get time series and handle components with no time series (such as unavailable)

source
SiennaPRASInterface.get_generator_category — Method
get_generator_category(gen::StaticInjection)

Get the category of the generator.

Arguments

  • gen::StaticInjection: Generator

Returns

  • String: Category of the generator
source
SiennaPRASInterface.get_sorted_lines — Method
get_sorted_lines(lines::Vector{PSY.Branch}, region_names::Vector{String})

Get sorted lines, interface region indices, and interface line indices.

Arguments

  • lines::Vector{PSY.Branch}: Lines
  • region_names::Vector{String}: Region names

Returns

  • sorted_lines::Vector{PSY.Branch}: Sorted lines
  • interface_reg_idxs::Vector{Tuple{Int, Int}}: Interface region indices
  • interface_line_idxs::Vector{UnitRange{Int}}: Interface line indices
source
SiennaPRASInterface.get_sorted_region_tuples — Method
get_sorted_region_tuples(lines::Vector{PSY.Branch}, region_names::Vector{String})

Get sorted (regfrom, regto) tuples of inter-regional lines.

source
SiennaPRASInterface.get_timestamps — Method
get_timestamps(initial_time, resolution, steps, horizon)

Return a vector of timestamps starting from initial_time with resolution and steps for horizon steps

source
SiennaPRASInterface.get_ts_values — Method
get_ts_values(ts::PSY.TimeSeriesData)

Get the time series values.

Arguments

  • ts::TimeSeries: Time series

Returns

  • Array{Float64}: Time series values
source
SiennaPRASInterface.initialize_availability! — Method

initializeavailability!(rng, availability, nexttransition, devices, tlast)

Arguments

  • rng::Random.AbstractRNG: Random number generator
  • availability::Vector{Bool}: Vector of availability status
  • nexttransition::Vector{Int}: Vector of next transition time
  • devices::Vector{PSY.Generator}: Vector of devices with outage SupplementalAttributes
  • t_last::Int: Last time step

Returns

Modifies input arguments and rereturns

  • availability::Vector{Bool}: Vector of availability status
  • nexttransition::Vector{Int}: Vector of next transition time
source
SiennaPRASInterface.line_rating — Method
line_rating(line::Branch)

Get the line rating.

Arguments

  • line::Branch: Line

Returns

  • Tuple{forward_capacity::Float64, backward_capacity::Float64}: Line rating
source
SiennaPRASInterface.line_type — Method
line_type(line::Branch)

Get the line type.

Arguments

  • line::Branch: Line

Returns

  • String: Line type
source
SiennaPRASInterface.make_pras_interfaces — Method
make_pras_interfaces(
     sorted_lines::Vector{PSY.Branch},
     interface_reg_idxs::Vector{Tuple{Int64, Int64}},
     interface_line_idxs::Vector{UnitRange{Int64}},
     s2p_meta::S2P_metadata,
-)

Converts PSY branches and interaces indices into PRAS Lines and Interfaces.

Returns

  • new_lines::PRASCore.Lines: PRAS Lines
  • new_interfaces::PRASCore.Interfaces: PRAS Interfaces
source
SiennaPRASInterface.rate_to_probability — Method
rate_to_probability(for_gen::Float64, mttr::Int64)

Converts the forced outage rate and mean time to repair to the λ and μ parameters

Arguments

  • for_gen::Float64: Forced outage rate [1/T]
  • mttr::Int64: Mean time to repair [T]

Returns

  • λ::Float64: Transition probability from online to offline [1/T]
  • μ::Float64: Transition rate from offline to online [1/T]

Reference

https://core.ac.uk/download/pdf/13643059.pdf

source
SiennaPRASInterface.runchecks — Method
runchecks(sys_location::String)

Check if the System JSON file is serialized as well as other files required.

source
SiennaPRASInterface.update_availability! — Method
update_availability!(rng, availability, nexttransition, devices, t_now, t_last)

Return availability and next transition with new randomness

source
+)

Converts PSY branches and interaces indices into PRAS Lines and Interfaces.

Returns

source
SiennaPRASInterface.rate_to_probability — Method
rate_to_probability(for_gen::Float64, mttr::Int64)

Converts the forced outage rate and mean time to repair to the λ and μ parameters

Arguments

  • for_gen::Float64: Forced outage rate [1/T]
  • mttr::Int64: Mean time to repair [T]

Returns

  • λ::Float64: Transition probability from online to offline [1/T]
  • μ::Float64: Transition rate from offline to online [1/T]

Reference

https://core.ac.uk/download/pdf/13643059.pdf

source
SiennaPRASInterface.runchecks — Method
runchecks(sys_location::String)

Check if the System JSON file is serialized as well as other files required.

source
SiennaPRASInterface.update_availability! — Method
update_availability!(rng, availability, nexttransition, devices, t_now, t_last)

Return availability and next transition with new randomness

source
diff --git a/dev/api/public/index.html b/dev/api/public/index.html index c0f19a1..cf09172 100644 --- a/dev/api/public/index.html +++ b/dev/api/public/index.html @@ -1,11 +1,11 @@ -Public API Reference · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub

Public API Reference

SiennaPRASInterface.generate_pras_system — Function
generate_pras_system(sys::PSY.System, aggregation; kwargs...)

Sienna/Data PowerSystems.jl System is the input and an object of PRAS SystemModel is returned. ...

Arguments

  • sys::PSY.System: Sienna/Data PowerSystems.jl System
  • aggregation<:PSY.AggregationTopology: "PSY.Area" (or) "PSY.LoadZone" {Optional}
  • lump_region_renewable_gens::Bool: Whether to lumps PV and Wind generators in a region because usually these generators don't have FOR data {Optional}
  • export_location::String: Export location of the .pras file ...

Returns

- `PRASCore.SystemModel`: PRAS SystemModel object

Examples

julia> generate_pras_system(psy_sys)
-PRAS SystemModel
source
generate_pras_system(sys_location::String, aggregation; kwargs...)

Generate a PRAS SystemModel from a Sienna/Data PowerSystems System JSON file.

Arguments

  • sys_location::String: Location of the Sienna/Data PowerSystems System JSON file
  • aggregation::Type{AT}: Aggregation topology type
  • lump_region_renewable_gens::Bool: Lumping of region renewable generators
  • export_location::Union{Nothing, String}: Export location of the .pras file

Returns

  • PRASCore.SystemModel: PRAS SystemModel
source
PRASCore.Simulations.assess — Function
assess(system::SystemModel, method::SequentialMonteCarlo, resultspecs::ResultSpec...)

Run a Sequential Monte Carlo simulation on a system using the method data and return resultspecs.

Arguments

  • system::SystemModel: PRAS data structure
  • method::SequentialMonteCarlo: method for PRAS analysis
  • resultspecs::ResultSpec...: PRAS metric for metrics like Shortfall missing generation

Returns

  • results::Tuple{Vararg{ResultAccumulator{SequentialMonteCarlo}}}: PRAS metric results
source
assess(
+Public API Reference · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub
Public API Reference
SiennaPRASInterface.generate_pras_system — Function
generate_pras_system(sys::PSY.System, aggregation; kwargs...)
Sienna/Data PowerSystems.jl System is the input and an object of PRAS SystemModel is returned. ...
Arguments
  • sys::PSY.System: Sienna/Data PowerSystems.jl System
  • aggregation<:PSY.AggregationTopology: "PSY.Area" (or) "PSY.LoadZone" {Optional}
  • lump_region_renewable_gens::Bool: Whether to lumps PV and Wind generators in a region because usually these generators don't have FOR data {Optional}
  • export_location::String: Export location of the .pras file ...
Returns
- `PRASCore.SystemModel`: PRAS SystemModel object
Examples
julia> generate_pras_system(psy_sys)
+PRAS SystemModel
source
generate_pras_system(sys_location::String, aggregation; kwargs...)
Generate a PRAS SystemModel from a Sienna/Data PowerSystems System JSON file.
Arguments
  • sys_location::String: Location of the Sienna/Data PowerSystems System JSON file
  • aggregation::Type{AT}: Aggregation topology type
  • lump_region_renewable_gens::Bool: Lumping of region renewable generators
  • export_location::Union{Nothing, String}: Export location of the .pras file
Returns
  • PRASCore.SystemModel: PRAS SystemModel
source
PRASCore.Simulations.assess — Function
assess(system::SystemModel, method::SequentialMonteCarlo, resultspecs::ResultSpec...)
Run a Sequential Monte Carlo simulation on a system using the method data and return resultspecs.
Arguments
  • system::SystemModel: PRAS data structure
  • method::SequentialMonteCarlo: method for PRAS analysis
  • resultspecs::ResultSpec...: PRAS metric for metrics like Shortfall missing generation
Returns
  • results::Tuple{Vararg{ResultAccumulator{SequentialMonteCarlo}}}: PRAS metric results
source
assess(
     sys::PSY.System,
     aggregation::Type{AT},
     method::PRASCore.SequentialMonteCarlo,
     resultsspecs::PRASCore.Results.ResultSpec...,
-) where {AT <: PSY.AggregationTopology}
Estimate resource adequacy using Monte Carlo simulation.
Arguments
  • sys::PSY.System: PowerSystems.jl system model
  • aggregation::Type{AT}: Aggregation topology to use in translating to PRAS
  • method::PRASCore.SequentialMonteCarlo: Simulation method to use
  • resultsspec::PRASCore.Results.ResultSpec...: Results to compute
Returns
source
PRASCore.Simulations.SequentialMonteCarlo — Type
SequentialMonteCarlo(;
+) where {AT <: PSY.AggregationTopology}
Estimate resource adequacy using Monte Carlo simulation.
Arguments
  • sys::PSY.System: PowerSystems.jl system model
  • aggregation::Type{AT}: Aggregation topology to use in translating to PRAS
  • method::PRASCore.SequentialMonteCarlo: Simulation method to use
  • resultsspec::PRASCore.Results.ResultSpec...: Results to compute
Returns
source
PRASCore.Results.LOLE — Type
LOLE
LOLE reports loss of load expectation over a particular time period and regional extent. When the reporting period is a single simulation timestep, the metric is equivalent to loss of load probability (LOLP).
Contains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.
source
PRASCore.Results.EUE — Type
EUE
EUE reports expected unserved energy over a particular time period and regional extent.
Contains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.
source
SiennaPRASInterface.generate_outage_profile — Function
generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)
Process the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).
...
Arguments
  • pras_system::PRASCore.SystemModel: PRAS System
  • num_runs::Int64: Number of PRAS runs
  • psy_sys::PSY.System: PSY System
  • num_scenarios::Int64: Number of scenarios of user interest.
  • location::String: Location to store outage profile. ...
Examples
julia> generate_outage_profile(results, pras_sys, psy_sys, 1)
-PSY System exported using to_json method in InfrastructureSystems
source
SiennaPRASInterface.generate_csv_outage_profile — Function
generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)
Process the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).
...
Arguments
  • pras_system::PRASCore.SystemModel: PRAS System
  • num_runs::Int64: Number of PRAS runs
  • psy_sys::PSY.System: PSY System
  • num_scenarios::Int64: Number of scenarios of user interest.
  • location::String: Location to store outage profile. ...
Examples
julia> generate_outage_profile(results, pras_sys, psy_sys, 1)
-PSY System exported using to_json method in InfrastructureSystems
source
SiennaPRASInterface.generate_csv_outage_profile — Function
generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)
Process the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).
...
Arguments
  • pras_system::PRASCore.SystemModel: PRAS System
  • num_runs::Int64: Number of PRAS runs
  • psy_sys::PSY.System: PSY System
  • num_scenarios::Int64: Number of scenarios of user interest.
  • location::String: Location to store outage profile. ...
Examples
julia> generate_outage_profile(results, pras_sys, psy_sys, 1)
+PSY System exported using to_json method in InfrastructureSystems
source
SiennaPRASInterface.add_csv_time_series! — Function
add_csv_time_series!(
     sys_DA,
     sys_RT,
     outage_csv_location::String;
     days_of_interest::Union{Nothing, UnitRange}=nothing,
     add_scenario::Union{Nothing, Int}=nothing,
-)
Generates outage profile for two stage PowerSimulation and adds availability time series data to Generators in PSY System from CSV files.
source
SiennaPRASInterface.add_csv_time_series_single_stage! — Function
add_csv_time_series_single_stage!(
     sys_DA,
     outage_csv_location::String;
     days_of_interest::Union{Nothing, UnitRange}=nothing,
     add_scenario::Union{Nothing, Int}=nothing,
-)
Generates outage profile for single stage PowerSimulation and adds availability time series.
source
SiennaPRASInterface.make_generator_outage_draws! — Function
make_generator_outage_draws!(
     sys,
     initial_time::Dates.DateTime=nothing,
     resolution::TIMEPERIOD=nothing,
     steps::Int=nothing,
     horizon::Int=nothing,
-) where {TIMEPERIOD <: Dates.TimePeriod}
Adds availability time series to the generators in the system.
Main function to make generator outage draws.
source
PRASCore.Results.ShortfallSamples — Type
ShortfallSamples
The ShortfallSamples result specification reports sample-level unserved energy outcomes, producing a ShortfallSamplesResult.
A ShortfallSamplesResult can be directly indexed by a region name and a timestamp to retrieve a vector of sample-level unserved energy results in that region and timestep. EUE and LOLE constructors can also be used to retrieve standard risk metrics.
Example:
shortfall, =
+) where {TIMEPERIOD <: Dates.TimePeriod}
Adds availability time series to the generators in the system.
Main function to make generator outage draws.
source
PRASCore.Results.ShortfallSamples — Type
ShortfallSamples
The ShortfallSamples result specification reports sample-level unserved energy outcomes, producing a ShortfallSamplesResult.
A ShortfallSamplesResult can be directly indexed by a region name and a timestamp to retrieve a vector of sample-level unserved energy results in that region and timestep. EUE and LOLE constructors can also be used to retrieve standard risk metrics.
Example:
shortfall, =
     assess(sys, SequentialMonteCarlo(samples=10), ShortfallSamples())
 
 period = ZonedDateTime(2020, 1, 1, 0, tz"UTC")
@@ -161,4 +161,4 @@
 samples = lineavail["MyLine123", ZonedDateTime(2020, 1, 1, 0, tz"UTC")]
 
 @assert samples isa Vector{Bool}
-@assert length(samples) == 10
source

+@assert length(samples) == 10
source
diff --git a/dev/explanations/default_outage_values/index.html b/dev/explanations/default_outage_values/index.html index 616e861..8a16064 100644 --- a/dev/explanations/default_outage_values/index.html +++ b/dev/explanations/default_outage_values/index.html @@ -1,2 +1,2 @@ -Default outage values · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub

Default values used for outage statistics

When a system does not contain any GeometricDistributionForcedOutage supplemental attributes attached to any components, then the outage rates default to a set of defaults defined in the Default Outage Rates CSV based off of rates in ERCOT.

For any remaining components not captured by the CSV defaults, such as lines, renewables, and storage, the outage rates are 0 and will never fail.

+Default outage values · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub

Default values used for outage statistics

When a system does not contain any GeometricDistributionForcedOutage supplemental attributes attached to any components, then the outage rates default to a set of defaults defined in the Default Outage Rates CSV based off of rates in ERCOT.

For any remaining components not captured by the CSV defaults, such as lines, renewables, and storage, the outage rates are 0 and will never fail.

diff --git a/dev/how_to_guides/how_do_i_add_outage_data/index.html b/dev/how_to_guides/how_do_i_add_outage_data/index.html index 7838d09..c6ca688 100644 --- a/dev/how_to_guides/how_do_i_add_outage_data/index.html +++ b/dev/how_to_guides/how_do_i_add_outage_data/index.html @@ -5,7 +5,7 @@ mean_time_to_recovery=10, # Units of hours outage_transition_probability=0.005, # Probability for outage per hour ) -transition_data
GeometricDistributionForcedOutage: 4edce8db-0c39-4902-89c4-3441d6744931:
+transition_data
GeometricDistributionForcedOutage: 936fb649-c0e1-49a6-97aa-9f8d869a349f:
    mean_time_to_recovery: 10.0
    outage_transition_probability: 0.005
    has_time_series: false

Step 2 : Attaching Data to Components

Once you have a GeometricDistributionForcedOutage object, then you can add it to any components with that data:

component = get_component(Generator, sys, "101_CT_1")
@@ -55,10 +55,10 @@
 # Here we assume you have a system named sys
 add_time_series!(sys, transition_data, outage_time_series)
 add_time_series!(sys, transition_data, recovery_time_series)
-transition_data
GeometricDistributionForcedOutage: 4edce8db-0c39-4902-89c4-3441d6744931:
+transition_data
GeometricDistributionForcedOutage: 936fb649-c0e1-49a6-97aa-9f8d869a349f:
    mean_time_to_recovery: 10.0
    outage_transition_probability: 0.005
    has_time_series: true

Step 4 : Run simulations and verify result

using SiennaPRASInterface
 method = SequentialMonteCarlo(samples=10, seed=1)
 shortfalls, = assess(sys, PowerSystems.Area, method, Shortfall())
-eue = EUE(shortfalls)
EUE = 0.00000 MWh/43920min
+eue = EUE(shortfalls)
EUE = 0.00000 MWh/43920min
diff --git a/dev/how_to_guides/intro_page/index.html b/dev/how_to_guides/intro_page/index.html index 77abdf7..0326a92 100644 --- a/dev/how_to_guides/intro_page/index.html +++ b/dev/how_to_guides/intro_page/index.html @@ -1,2 +1,2 @@ -Examples · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub
+Examples · github.com/NREL-Sienna/SiennaPRASInterface.jl
GitHub
diff --git a/dev/index.html b/dev/index.html index f8598a7..ad4d6ec 100644 --- a/dev/index.html +++ b/dev/index.html @@ -6,6 +6,6 @@ ) component = get_component(Generator, sys, "test_generator") add_supplemental_attribute!(sys, component, transition_data)

3. Calculate Shortfalls and Expected Unserved Energy on System

using SiennaPRASInterface
-method = SequentialMonteCarlo(samples=10_000, seed=1)
+sequential_monte_carlo = SequentialMonteCarlo(samples=10_000, seed=1)
 shortfalls, = assess(sys, PowerSystems.Area, sequential_monte_carlo, Shortfall())
-eue = EUE(shortfalls)

Documentation

SiennaPRASInterface has been developed as part of the Transmission Planning Tools Maintenance project at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) funded by DOE Grid Deployment Office (GDO).

+eue = EUE(shortfalls)

Documentation

SiennaPRASInterface has been developed as part of the Transmission Planning Tools Maintenance project at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) funded by DOE Grid Deployment Office (GDO).

diff --git a/dev/objects.inv b/dev/objects.inv index c709af1..9381013 100644 Binary files a/dev/objects.inv and b/dev/objects.inv differ diff --git a/dev/search_index.js b/dev/search_index.js index 569b717..253e3ed 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"how_to_guides/intro_page/#Examples","page":"Examples","title":"Examples","text":"","category":"section"},{"location":"how_to_guides/intro_page/","page":"Examples","title":"Examples","text":"Tutorials to be created soon.","category":"page"},{"location":"api/internal/#Internal-API","page":"Internal API Reference","title":"Internal API","text":"","category":"section"},{"location":"api/internal/","page":"Internal API Reference","title":"Internal API Reference","text":"Simulations.DispatchProblem\nResults.StorageAvailability","category":"page"},{"location":"api/internal/#PRASCore.Simulations.DispatchProblem","page":"Internal API Reference","title":"PRASCore.Simulations.DispatchProblem","text":"DispatchProblem(sys::SystemModel)\n\nCreate a min-cost flow problem for the multi-region max power delivery problem with generation and storage discharging in decreasing order of priority, and storage charging with excess capacity. Storage and GeneratorStorage devices within a region are represented individually on the network.\n\nThis involves injections/withdrawals at one node (regional capacity surplus/shortfall) for each modelled region, as well as two/three nodes associated with each Storage/GeneratorStorage device, and a supplementary \"slack\" node in the network that can absorb undispatched power or pass unserved energy or unused charging capability through to satisfy power balance constraints.\n\nFlows from the generation nodes are free, while flows to charging and from discharging nodes are costed or rewarded according to the time-to-discharge of the storage device, ensuring efficient coordination across units, while enforcing that storage is only discharged once generation capacity is exhausted (implying an operational strategy that prioritizes resource adequacy over economic arbitrage). This is based on the storage dispatch strategy of Evans, Tindemans, and Angeli, as outlined in \"Minimizing Unserved Energy Using Heterogenous Storage Units\" (IEEE Transactions on Power Systems, 2019).\n\nFlows to the charging node have an attenuated negative cost (reward), incentivizing immediate storage charging if generation and transmission allows it, while avoiding charging by discharging other storage (since that would incur an overall positive cost).\n\nFlows to the slack node (representing unused generation or storage discharge capacity) are free, but flows from the slack node to serve load incur the lost load penalty of 9999. Flows from the slack node in lieu of storage charging or discharging are free.\n\nFlows on transmission interfaces assume a hurdle rate of 1 to keep unserved energy close to the source of the shortage and eliminate loop flows. This has the side-effect of disincentivising wheeling power across multiple regions for charging purposes, however.\n\nNodes in the problem are ordered as:\n\nRegions generation surplus/shortfall (Regions order)\nStorage discharge capacity (Storage order)\nStorage charge capacity (Storage order)\nGenerationStorage inflow capacity (GeneratorStorage order)\nGenerationStorage discharge capacity (GeneratorStorage order)\nGenerationStorage grid injection (GeneratorStorage order)\nGenerationStorage charge capacity (GeneratorStorage order)\nSlack node\n\nEdges are ordered as:\n\nRegions demand unserved (Regions order)\nRegions generation unused (Regions order)\nInterfaces forward flow (Interfaces order)\nInterfaces reverse flow (Interfaces order)\nStorage discharge to grid (Storage order)\nStorage discharge unused (Storage order)\nStorage charge from grid (Storage order)\nStorage charge unused (Storage order)\nGenerationStorage discharge to grid (GeneratorStorage order)\nGenerationStorage discharge unused (GeneratorStorage order)\nGenerationStorage inflow to grid (GenerationStorage order)\nGenerationStorage total to grid (GeneratorStorage order)\nGenerationStorage charge from grid (GeneratorStorage order)\nGenerationStorage charge from inflow (GeneratorStorage order)\nGenerationStorage charge unused (GeneratorStorage order)\nGenerationStorage inflow unused (GeneratorStorage order)\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#PRASCore.Results.StorageAvailability","page":"Internal API Reference","title":"PRASCore.Results.StorageAvailability","text":"StorageAvailability\n\nThe StorageAvailability result specification reports the sample-level discrete availability of Storages, producing a StorageAvailabilityResult.\n\nA StorageAvailabilityResult can be indexed by storage device name and a timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\nstoravail, =\n assess(sys, SequentialMonteCarlo(samples=10), StorageAvailability())\n\nsamples = storavail[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/internal/","page":"Internal API Reference","title":"Internal API Reference","text":"Modules = [SiennaPRASInterface, SiennaPRASInterface.PRASCore]\nPublic = false","category":"page"},{"location":"api/internal/#SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE","page":"Internal API Reference","title":"SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE","text":"FILE with restrictions on CC units in PRAS\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.HVDCLineTypes","page":"Internal API Reference","title":"SiennaPRASInterface.HVDCLineTypes","text":"Supported DC branch Types\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.OUTAGE_INFO_FILE","page":"Internal API Reference","title":"SiennaPRASInterface.OUTAGE_INFO_FILE","text":"DEFAULT outage data which is used when outage_flag is set to FALSE\n\nBased on ERCOT historical data\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.TransformerTypes","page":"Internal API Reference","title":"SiennaPRASInterface.TransformerTypes","text":"Filtered Transformer Types\n\nThese transformers are not modeled as lines in PRAS.\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.S2P_metadata","page":"Internal API Reference","title":"SiennaPRASInterface.S2P_metadata","text":"S2P_metadata\n\nStruct to store metadata for the Sienna to PRAS conversion\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.outage_data","page":"Internal API Reference","title":"SiennaPRASInterface.outage_data","text":"outage_data\n\nStruct to store the outage information for a component.\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.check_file-Tuple{String}","page":"Internal API Reference","title":"SiennaPRASInterface.check_file","text":"check_file(loc::String)\n\nCheck if the file exists and is openable.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_available_components_in_aggregation_topology-Tuple{Type{<:PowerSystems.StaticInjection}, PowerSystems.System, PowerSystems.AggregationTopology}","page":"Internal API Reference","title":"SiennaPRASInterface.get_available_components_in_aggregation_topology","text":"get_available_components_in_aggregation_topology(\n type::Type{<:PSY.StaticInjection},\n sys::PSY.System,\n region::PSY.AggregationTopology,\n)\n\nGet available components in the AggregationTopology region of the given type.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_first_ts-Tuple{TS} where TS<:Channel{Any}","page":"Internal API Reference","title":"SiennaPRASInterface.get_first_ts","text":"get_first_ts(ts::TS)\n\nGet time series and handle components with no time series (such as unavailable)\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_generator_category-Tuple{PowerSystems.StaticInjection}","page":"Internal API Reference","title":"SiennaPRASInterface.get_generator_category","text":"get_generator_category(gen::StaticInjection)\n\nGet the category of the generator.\n\nArguments\n\ngen::StaticInjection: Generator\n\nReturns\n\nString: Category of the generator\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_sorted_lines-Tuple{Vector{PowerSystems.Branch}, Vector{String}}","page":"Internal API Reference","title":"SiennaPRASInterface.get_sorted_lines","text":"get_sorted_lines(lines::Vector{PSY.Branch}, region_names::Vector{String})\n\nGet sorted lines, interface region indices, and interface line indices.\n\nArguments\n\nlines::Vector{PSY.Branch}: Lines\nregion_names::Vector{String}: Region names\n\nReturns\n\nsorted_lines::Vector{PSY.Branch}: Sorted lines\ninterface_reg_idxs::Vector{Tuple{Int, Int}}: Interface region indices\ninterface_line_idxs::Vector{UnitRange{Int}}: Interface line indices\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_sorted_region_tuples-Tuple{Vector{PowerSystems.Branch}, Vector{String}}","page":"Internal API Reference","title":"SiennaPRASInterface.get_sorted_region_tuples","text":"get_sorted_region_tuples(lines::Vector{PSY.Branch}, region_names::Vector{String})\n\nGet sorted (regfrom, regto) tuples of inter-regional lines.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_timestamps-Union{Tuple{TIMEPERIOD}, Tuple{Dates.DateTime, TIMEPERIOD, Int64, Int64}} where TIMEPERIOD<:Dates.TimePeriod","page":"Internal API Reference","title":"SiennaPRASInterface.get_timestamps","text":"get_timestamps(initial_time, resolution, steps, horizon)\n\nReturn a vector of timestamps starting from initial_time with resolution and steps for horizon steps\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_ts_values-Tuple{InfrastructureSystems.TimeSeriesData}","page":"Internal API Reference","title":"SiennaPRASInterface.get_ts_values","text":"get_ts_values(ts::PSY.TimeSeriesData)\n\nGet the time series values.\n\nArguments\n\nts::TimeSeries: Time series\n\nReturns\n\nArray{Float64}: Time series values\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.initialize_availability!-Tuple{Random.AbstractRNG, Vector{Bool}, Vector{Int64}, Vector{PowerSystems.Generator}, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.initialize_availability!","text":"initializeavailability!(rng, availability, nexttransition, devices, tlast)\n\nArguments\n\nrng::Random.AbstractRNG: Random number generator\navailability::Vector{Bool}: Vector of availability status\nnexttransition::Vector{Int}: Vector of next transition time\ndevices::Vector{PSY.Generator}: Vector of devices with outage SupplementalAttributes\nt_last::Int: Last time step\n\nReturns\n\nModifies input arguments and rereturns\n\navailability::Vector{Bool}: Vector of availability status\nnexttransition::Vector{Int}: Vector of next transition time\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.line_rating-Tuple{PowerSystems.Branch}","page":"Internal API Reference","title":"SiennaPRASInterface.line_rating","text":"line_rating(line::Branch)\n\nGet the line rating.\n\nArguments\n\nline::Branch: Line\n\nReturns\n\nTuple{forward_capacity::Float64, backward_capacity::Float64}: Line rating\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.make_pras_interfaces-Tuple{Vector{PowerSystems.Branch}, Vector{Tuple{Int64, Int64}}, Vector{UnitRange{Int64}}, SiennaPRASInterface.S2P_metadata}","page":"Internal API Reference","title":"SiennaPRASInterface.make_pras_interfaces","text":"make_pras_interfaces(\n sorted_lines::Vector{PSY.Branch},\n interface_reg_idxs::Vector{Tuple{Int64, Int64}},\n interface_line_idxs::Vector{UnitRange{Int64}},\n s2p_meta::S2P_metadata,\n)\n\nConverts PSY branches and interaces indices into PRAS Lines and Interfaces.\n\nReturns\n\nnew_lines::PRASCore.Lines: PRAS Lines\nnew_interfaces::PRASCore.Interfaces: PRAS Interfaces\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.rate_to_probability-Tuple{Float64, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.rate_to_probability","text":"rate_to_probability(for_gen::Float64, mttr::Int64)\n\nConverts the forced outage rate and mean time to repair to the λ and μ parameters\n\nArguments\n\nfor_gen::Float64: Forced outage rate [1/T]\nmttr::Int64: Mean time to repair [T]\n\nReturns\n\nλ::Float64: Transition probability from online to offline [1/T]\nμ::Float64: Transition rate from offline to online [1/T]\n\nReference\n\nhttps://core.ac.uk/download/pdf/13643059.pdf\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.runchecks-Tuple{String}","page":"Internal API Reference","title":"SiennaPRASInterface.runchecks","text":"runchecks(sys_location::String)\n\nCheck if the System JSON file is serialized as well as other files required.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.update_availability!-Tuple{Random.AbstractRNG, Vector{Bool}, Vector{Int64}, Vector{PowerSystems.Generator}, Int64, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.update_availability!","text":"update_availability!(rng, availability, nexttransition, devices, t_now, t_last)\n\nReturn availability and next transition with new randomness\n\n\n\n\n\n","category":"method"},{"location":"explanations/default_outage_values/#Default-values-used-for-outage-statistics","page":"Default outage values","title":"Default values used for outage statistics","text":"","category":"section"},{"location":"explanations/default_outage_values/","page":"Default outage values","title":"Default outage values","text":"When a system does not contain any GeometricDistributionForcedOutage supplemental attributes attached to any components, then the outage rates default to a set of defaults defined in the Default Outage Rates CSV based off of rates in ERCOT.","category":"page"},{"location":"explanations/default_outage_values/","page":"Default outage values","title":"Default outage values","text":"For any remaining components not captured by the CSV defaults, such as lines, renewables, and storage, the outage rates are 0 and will never fail.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#How-do-I-add-outage-data-to-Sienna?","page":"How do I add outage data?","title":"How do I add outage data to Sienna?","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"You can attach outage data to PowerSystems Components by using the supplemental attribute GeometricDistributionForcedOutage.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-1-:-Parse-your-outage-data-into-Sienna","page":"How do I add outage data?","title":"Step 1 : Parse your outage data into Sienna","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"SiennaPRASInterface.jl uses outage information in the form of independent mean_time_to_recovery in units of hours and outage_transition_probability in probability of outage per hour. A simple Markov model models the transitions between out and active using these parameters.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"We support data either being fixed and specified in the GeometricDistributionForcedOutage object or attached as time-series to the GeometricDistributionForcedOutage struct.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Creating-a-GeometricDistributionForcedOutage-from-fixed-data","page":"How do I add outage data?","title":"Creating a GeometricDistributionForcedOutage from fixed data","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using PowerSystems\nimport PowerSystemCaseBuilder # hide\nconst PSCB = PowerSystemCaseBuilder # hide\nsys = PSCB.build_system(PSCB.PSISystems, \"RTS_GMLC_DA_sys\") # hide\nset_units_base_system!(sys, \"natural_units\") # hide\n\ntransition_data = GeometricDistributionForcedOutage(;\n mean_time_to_recovery=10, # Units of hours\n outage_transition_probability=0.005, # Probability for outage per hour\n)\ntransition_data","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-2-:-Attaching-Data-to-Components","page":"How do I add outage data?","title":"Step 2 : Attaching Data to Components","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"Once you have a GeometricDistributionForcedOutage object, then you can add it to any components with that data:","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"component = get_component(Generator, sys, \"101_CT_1\")\nadd_supplemental_attribute!(sys, component, transition_data)\ncomponent","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-3-(Optional)-:-Adding-time-series-data-to-GeometricDistributionForcedOutage-from-time-series-data","page":"How do I add outage data?","title":"Step 3 (Optional) : Adding time-series data to GeometricDistributionForcedOutage from time series data","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"Time series should be attached to a GeometricDistributionForcedOutage object under the keys recovery_probability (1/mean_time_to_recovery) and outage_probability.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"See the Sienna time-series documentation on working with time-series.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using Dates\nusing TimeSeries\n\noutage_probability = [0.1, 0.1, 0.2, 0.3, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]\nrecovery_probability = [0.1, 0.1, 0.2, 0.3, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]\n\n# Your resolution and length must match the other SingleTimeSeries in your System.\nresolution = Dates.Minute(5)\ntimestamps = range(DateTime(\"2020-01-01T08:00:00\"); step=resolution, length=8784)\noutage_timearray = TimeArray(timestamps, repeat(outage_probability, inner=div(8784, 12)))\noutage_time_series = SingleTimeSeries(; name=\"outage_probability\", data=outage_timearray)\n\nrecovery_timearray =\n TimeArray(timestamps, repeat(recovery_probability, inner=div(8784, 12)))\nrecovery_time_series =\n SingleTimeSeries(; name=\"recovery_probability\", data=recovery_timearray)\n\n# Here we assume you have a system named sys\nadd_time_series!(sys, transition_data, outage_time_series)\nadd_time_series!(sys, transition_data, recovery_time_series)\ntransition_data","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-4-:-Run-simulations-and-verify-result","page":"How do I add outage data?","title":"Step 4 : Run simulations and verify result","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using SiennaPRASInterface\nmethod = SequentialMonteCarlo(samples=10, seed=1)\nshortfalls, = assess(sys, PowerSystems.Area, method, Shortfall())\neue = EUE(shortfalls)","category":"page"},{"location":"#SiennaPRASInterface.jl","page":"Welcome Page","title":"SiennaPRASInterface.jl","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"CurrentModule = SiennaPRASInterface","category":"page"},{"location":"#About","page":"Welcome Page","title":"About","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"SiennaPRASInterface.jl is a Julia package that provides an interface to PRAS.jl from Sienna's PowerSystem.jl's System data model.","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"The Probabilistic Resource Adequacy Suite (PRAS) analyzes the resource adequacy of a bulk power system using Monte Carlo methods.","category":"page"},{"location":"#Getting-Started","page":"Welcome Page","title":"Getting Started","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"To use SiennaPRASInterface.jl, you first need a System from PowerSystems.jl","category":"page"},{"location":"#1.-Install","page":"Welcome Page","title":"1. Install","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"] add SiennaPRASInterface","category":"page"},{"location":"#2.-Add-Data","page":"Welcome Page","title":"2. Add Data","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"Add outage information to generators using the supplemental attribute GeometricDistributionForcedOutage.","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"using PowerSystems\ntransition_data = GeometricDistributionForcedOutage(;\n mean_time_to_recovery=10, # Units of hours\n outage_transition_probability=0.005, # Probability for outage per hour\n)\ncomponent = get_component(Generator, sys, \"test_generator\")\nadd_supplemental_attribute!(sys, component, transition_data)","category":"page"},{"location":"#3.-Calculate-Shortfalls-and-Expected-Unserved-Energy-on-System","page":"Welcome Page","title":"3. Calculate Shortfalls and Expected Unserved Energy on System","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"using SiennaPRASInterface\nmethod = SequentialMonteCarlo(samples=10_000, seed=1)\nshortfalls, = assess(sys, PowerSystems.Area, sequential_monte_carlo, Shortfall())\neue = EUE(shortfalls)","category":"page"},{"location":"#Documentation","page":"Welcome Page","title":"Documentation","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"PRAS Documentation","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"Pages = [\"api/public.md\", \"tutorials\"]\nDepth = 2","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"SiennaPRASInterface has been developed as part of the Transmission Planning Tools Maintenance project at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) funded by DOE Grid Deployment Office (GDO).","category":"page"},{"location":"api/public/#Public-API-Reference","page":"Public API Reference","title":"Public API Reference","text":"","category":"section"},{"location":"api/public/","page":"Public API Reference","title":"Public API Reference","text":"SiennaPRASInterface\ngenerate_pras_system\nSystemModel\nassess\nSequentialMonteCarlo\nShortfall\nSurplus\nFlow\nUtilization\nStorageEnergy\nGeneratorStorageEnergy\nLOLE\nEUE\ngenerate_outage_profile\ngenerate_csv_outage_profile\nadd_csv_time_series!\nadd_csv_time_series_single_stage!\nmake_generator_outage_draws!\nShortfallSamples\nSurplusSamples\nFlowSamples\nUtilizationSamples\nStorageEnergySamples\nGeneratorStorageEnergySamples\nGeneratorAvailability\nGeneratorStorageAvailability\nLineAvailability","category":"page"},{"location":"api/public/#SiennaPRASInterface","page":"Public API Reference","title":"SiennaPRASInterface","text":"PowerSystems Interface for Probabilistic Resource Adequacy Studies (PRAS)\n\nKey Functions\n\ngenerate_pras_system: convert PSY to PRAS model\nassess: assess PRAS model\n\nKey PRAS Types\n\nSystemModel: PRAS data structure\nSequentialMonteCarlo: method for PRAS analysis\nShortfall: PRAS metric for missing generation\nLOLE: PRAS metric for loss of load expectation\nEUE: PRAS metric for energy unserved expectation\n\n\n\n\n\n","category":"module"},{"location":"api/public/#SiennaPRASInterface.generate_pras_system","page":"Public API Reference","title":"SiennaPRASInterface.generate_pras_system","text":"generate_pras_system(sys::PSY.System, aggregation; kwargs...)\n\nSienna/Data PowerSystems.jl System is the input and an object of PRAS SystemModel is returned. ...\n\nArguments\n\nsys::PSY.System: Sienna/Data PowerSystems.jl System\naggregation<:PSY.AggregationTopology: \"PSY.Area\" (or) \"PSY.LoadZone\" {Optional}\nlump_region_renewable_gens::Bool: Whether to lumps PV and Wind generators in a region because usually these generators don't have FOR data {Optional}\nexport_location::String: Export location of the .pras file ...\n\nReturns\n\n- `PRASCore.SystemModel`: PRAS SystemModel object\n\nExamples\n\njulia> generate_pras_system(psy_sys)\nPRAS SystemModel\n\n\n\n\n\ngenerate_pras_system(sys_location::String, aggregation; kwargs...)\n\nGenerate a PRAS SystemModel from a Sienna/Data PowerSystems System JSON file.\n\nArguments\n\nsys_location::String: Location of the Sienna/Data PowerSystems System JSON file\naggregation::Type{AT}: Aggregation topology type\nlump_region_renewable_gens::Bool: Lumping of region renewable generators\nexport_location::Union{Nothing, String}: Export location of the .pras file\n\nReturns\n\nPRASCore.SystemModel: PRAS SystemModel\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Systems.SystemModel","page":"Public API Reference","title":"PRASCore.Systems.SystemModel","text":"SystemModel\n\nA SystemModel contains a representation of a power system to be studied with PRAS.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Simulations.assess","page":"Public API Reference","title":"PRASCore.Simulations.assess","text":"assess(system::SystemModel, method::SequentialMonteCarlo, resultspecs::ResultSpec...)\n\nRun a Sequential Monte Carlo simulation on a system using the method data and return resultspecs.\n\nArguments\n\nsystem::SystemModel: PRAS data structure\nmethod::SequentialMonteCarlo: method for PRAS analysis\nresultspecs::ResultSpec...: PRAS metric for metrics like Shortfall missing generation\n\nReturns\n\nresults::Tuple{Vararg{ResultAccumulator{SequentialMonteCarlo}}}: PRAS metric results\n\n\n\n\n\nassess(\n sys::PSY.System,\n aggregation::Type{AT},\n method::PRASCore.SequentialMonteCarlo,\n resultsspecs::PRASCore.Results.ResultSpec...,\n) where {AT <: PSY.AggregationTopology}\n\nEstimate resource adequacy using Monte Carlo simulation.\n\nArguments\n\nsys::PSY.System: PowerSystems.jl system model\naggregation::Type{AT}: Aggregation topology to use in translating to PRAS\nmethod::PRASCore.SequentialMonteCarlo: Simulation method to use\nresultsspec::PRASCore.Results.ResultSpec...: Results to compute\n\nReturns\n\nTuple of results from resultsspec: default is (ShortfallResult,)\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Simulations.SequentialMonteCarlo","page":"Public API Reference","title":"PRASCore.Simulations.SequentialMonteCarlo","text":"SequentialMonteCarlo(;\n samples::Int=10_000,\n seed::Integer=rand(UInt64),\n verbose::Bool=false,\n threaded::Bool=true\n)\n\nSequential Monte Carlo simulation parameters for PRAS analysis\n\nIt it recommended that you fix the random seed for reproducibility.\n\nArguments\n\nsamples::Int=10_000: Number of samples\nseed::Integer=rand(UInt64): Random seed\nverbose::Bool=false: Print progress\nthreaded::Bool=true: Use multi-threading\n\nReturns\n\nSequentialMonteCarlo: PRAS simulation specification\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Shortfall","page":"Public API Reference","title":"PRASCore.Results.Shortfall","text":"Shortfall\n\nThe Shortfall result specification reports expectation-based resource adequacy risk metrics such as EUE and LOLE, producing a ShortfallResult.\n\nA ShortfallResult can be directly indexed by a region name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average unserved energy in that region and timestep. However, in most cases it's simpler to use EUE and LOLE constructors to directly retrieve standard risk metrics.\n\nExample:\n\nshortfall, =\n assess(sys, SequentialMonteCarlo(samples=1000), Shortfall())\n\nperiod = ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")\n\n# Unserved energy mean and standard deviation\nsf_mean, sf_std = shortfall[\"Region A\", period]\n\n# System-wide risk metrics\neue = EUE(shortfall)\nlole = LOLE(shortfall)\n\n# Regional risk metrics\nregional_eue = EUE(shortfall, \"Region A\")\nregional_lole = LOLE(shortfall, \"Region A\")\n\n# Period-specific risk metrics\nperiod_eue = EUE(shortfall, period)\nperiod_lolp = LOLE(shortfall, period)\n\n# Region- and period-specific risk metrics\nperiod_eue = EUE(shortfall, \"Region A\", period)\nperiod_lolp = LOLE(shortfall, \"Region A\", period)\n\nSee ShortfallSamples for recording sample-level shortfall results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Surplus","page":"Public API Reference","title":"PRASCore.Results.Surplus","text":"Surplus\n\nThe Surplus result specification reports unused generation and storage discharge capability of Regions, producing a SurplusResult.\n\nA SurplusResult can be indexed by region name and timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average unused capacity in that region and timestep.\n\nExample:\n\nsurplus, =\n assess(sys, SequentialMonteCarlo(samples=1000), Surplus())\n\nsurplus_mean, surplus_std =\n surplus[\"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee SurplusSamples for sample-level surplus results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Flow","page":"Public API Reference","title":"PRASCore.Results.Flow","text":"Flow\n\nThe Flow result specification reports the estimated average flow across transmission Interfaces, producing a FlowResult.\n\nA FlowResult can be indexed by a directional Pair of region names and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average net flow magnitude and direction relative to the given directed interface in that timestep. For a query of \"Region A\" => \"Region B\", if estimated average flow was from A to B, the reported value would be positive, while if average flow was in the reverse direction, from B to A, the value would be negative.\n\nExample:\n\nflows, =\n assess(sys, SequentialMonteCarlo(samples=1000), Flow())\n\nflow_mean, flow_std =\n flows[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\nflow2_mean, flow2_std =\n flows[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n@assert flow_mean == -flow2_mean\n\nSee FlowSamples for sample-level flow results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Utilization","page":"Public API Reference","title":"PRASCore.Results.Utilization","text":"Utilization\n\nThe Utilization result specification reports the estimated average absolute utilization of Interfaces, producing a UtilizationResult.\n\nWhereas Flow reports the average directional power transfer across an interface, Utilization reports the absolute value of flow relative to the interface's transfer capability (counting the effects of line outages). For example, a symmetrically-constrained interface which is fully congested with max power flowing in one direction in half of the samples, and the other direction in the remaining samples, would have an average flow of 0 MW, but an average utilization of 100%.\n\nA UtilizationResult can be indexed by a Pair of region names and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average utilization of the interface. Given the absolute value nature of the outcome, results are independent of direction. Querying \"Region A\" => \"Region B\" will yield the same result as \"Region B\" => \"Region A\".\n\nExample:\n\nutils, =\n assess(sys, SequentialMonteCarlo(samples=1000), Utilization())\n\nutil_mean, util_std =\n utils[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nutil2_mean, util2_std =\n utils[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert util_mean == util2_mean\n\nSee UtilizationSamples for sample-level utilization results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.StorageEnergy","page":"Public API Reference","title":"PRASCore.Results.StorageEnergy","text":"StorageEnergy\n\nThe StorageEnergy result specification reports the average state of charge of Storages, producing a StorageEnergyResult.\n\nA StorageEnergyResult can be indexed by storage device name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average energy level for the given storage device in that timestep.\n\nExample:\n\nstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=1000), StorageEnergy())\n\nsoc_mean, soc_std =\n storenergy[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee StorageEnergySamples for sample-level storage states of charge.\n\nSee GeneratorStorageEnergy for average generator-storage states of charge.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageEnergy","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageEnergy","text":"GeneratorStorageEnergy\n\nThe GeneratorStorageEnergy result specification reports the average state of charge of GeneratorStorages, producing a GeneratorStorageEnergyResult.\n\nA GeneratorStorageEnergyResult can be indexed by generator-storage device name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average energy level for the given generator-storage device in that timestep.\n\nExample:\n\ngenstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=1000), GeneratorStorageEnergy())\n\nsoc_mean, soc_std =\n genstorenergy[\"MyGeneratorStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee GeneratorStorageEnergySamples for sample-level generator-storage states of charge.\n\nSee StorageEnergy for average storage states of charge.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.LOLE","page":"Public API Reference","title":"PRASCore.Results.LOLE","text":"LOLE\n\nLOLE reports loss of load expectation over a particular time period and regional extent. When the reporting period is a single simulation timestep, the metric is equivalent to loss of load probability (LOLP).\n\nContains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.EUE","page":"Public API Reference","title":"PRASCore.Results.EUE","text":"EUE\n\nEUE reports expected unserved energy over a particular time period and regional extent.\n\nContains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#SiennaPRASInterface.generate_outage_profile","page":"Public API Reference","title":"SiennaPRASInterface.generate_outage_profile","text":"generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)\n\nProcess the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).\n\n...\n\nArguments\n\npras_system::PRASCore.SystemModel: PRAS System\nnum_runs::Int64: Number of PRAS runs\npsy_sys::PSY.System: PSY System\nnum_scenarios::Int64: Number of scenarios of user interest.\nlocation::String: Location to store outage profile. ...\n\nExamples\n\njulia> generate_outage_profile(results, pras_sys, psy_sys, 1)\nPSY System exported using to_json method in InfrastructureSystems\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.generate_csv_outage_profile","page":"Public API Reference","title":"SiennaPRASInterface.generate_csv_outage_profile","text":"generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)\n\nProcess the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).\n\n...\n\nArguments\n\npras_system::PRASCore.SystemModel: PRAS System\nnum_runs::Int64: Number of PRAS runs\npsy_sys::PSY.System: PSY System\nnum_scenarios::Int64: Number of scenarios of user interest.\nlocation::String: Location to store outage profile. ...\n\nExamples\n\njulia> generate_outage_profile(results, pras_sys, psy_sys, 1)\nPSY System exported using to_json method in InfrastructureSystems\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.add_csv_time_series!","page":"Public API Reference","title":"SiennaPRASInterface.add_csv_time_series!","text":"add_csv_time_series!(\n sys_DA,\n sys_RT,\n outage_csv_location::String;\n days_of_interest::Union{Nothing, UnitRange}=nothing,\n add_scenario::Union{Nothing, Int}=nothing,\n)\n\nGenerates outage profile for two stage PowerSimulation and adds availability time series data to Generators in PSY System from CSV files.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.add_csv_time_series_single_stage!","page":"Public API Reference","title":"SiennaPRASInterface.add_csv_time_series_single_stage!","text":"add_csv_time_series_single_stage!(\n sys_DA,\n outage_csv_location::String;\n days_of_interest::Union{Nothing, UnitRange}=nothing,\n add_scenario::Union{Nothing, Int}=nothing,\n)\n\nGenerates outage profile for single stage PowerSimulation and adds availability time series.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.make_generator_outage_draws!","page":"Public API Reference","title":"SiennaPRASInterface.make_generator_outage_draws!","text":"make_generator_outage_draws!(\n sys,\n initial_time::Dates.DateTime=nothing,\n resolution::TIMEPERIOD=nothing,\n steps::Int=nothing,\n horizon::Int=nothing,\n) where {TIMEPERIOD <: Dates.TimePeriod}\n\nAdds availability time series to the generators in the system.\n\nMain function to make generator outage draws.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Results.ShortfallSamples","page":"Public API Reference","title":"PRASCore.Results.ShortfallSamples","text":"ShortfallSamples\n\nThe ShortfallSamples result specification reports sample-level unserved energy outcomes, producing a ShortfallSamplesResult.\n\nA ShortfallSamplesResult can be directly indexed by a region name and a timestamp to retrieve a vector of sample-level unserved energy results in that region and timestep. EUE and LOLE constructors can also be used to retrieve standard risk metrics.\n\nExample:\n\nshortfall, =\n assess(sys, SequentialMonteCarlo(samples=10), ShortfallSamples())\n\nperiod = ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")\n\nsamples = shortfall[\"Region A\", period]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\n# System-wide risk metrics\neue = EUE(shortfall)\nlole = LOLE(shortfall)\n\n# Regional risk metrics\nregional_eue = EUE(shortfall, \"Region A\")\nregional_lole = LOLE(shortfall, \"Region A\")\n\n# Period-specific risk metrics\nperiod_eue = EUE(shortfall, period)\nperiod_lolp = LOLE(shortfall, period)\n\n# Region- and period-specific risk metrics\nperiod_eue = EUE(shortfall, \"Region A\", period)\nperiod_lolp = LOLE(shortfall, \"Region A\", period)\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Shortfall for average shortfall outcomes when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.SurplusSamples","page":"Public API Reference","title":"PRASCore.Results.SurplusSamples","text":"SurplusSamples\n\nThe SurplusSamples result specification reports sample-level unused generation and storage discharge capability of Regions, producing a SurplusSamplesResult.\n\nA SurplusSamplesResult can be indexed by region name and timestamp to retrieve a vector of sample-level surplus values in that region and timestep.\n\nExample:\n\nsurplus, =\n assess(sys, SequentialMonteCarlo(samples=10), SurplusSamples())\n\nsamples = surplus[\"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Surplus for estimated average surplus values when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.FlowSamples","page":"Public API Reference","title":"PRASCore.Results.FlowSamples","text":"FlowSamples\n\nThe FlowSamples result specification reports the sample-level magnitude and direction of power flows across Interfaces, producing a FlowSamplesResult.\n\nA FlowSamplesResult can be indexed by a directional Pair of region names and a timestamp to retrieve a vector of sample-level net flow magnitudes and directions relative to the given directed interface in that timestep. For a query of \"Region A\" => \"Region B\", if flow in one sample was from A to B, the reported value would be positive, while if flow was in the reverse direction, from B to A, the value would be negative.\n\nExample:\n\nflows, =\n assess(sys, SequentialMonteCarlo(samples=10), FlowSamples())\n\nsamples = flows[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nsamples2 = flows[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples == -samples2\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Flow for estimated average flow results when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.UtilizationSamples","page":"Public API Reference","title":"PRASCore.Results.UtilizationSamples","text":"UtilizationSamples\n\nThe UtilizationSamples result specification reports the sample-level absolute utilization of Interfaces, producing a UtilizationSamplesResult.\n\nWhereas FlowSamples reports the directional power transfer across an interface, UtilizationSamples reports the absolute value of flow relative to the interface's transfer capability (counting the effects of line outages). For example, a 100 MW symmetrically-constrained interface which is fully congested may have a flow of +100 or -100 MW, but in both cases the utilization will be 100%. If a 50 MW line in the interface went on outage, flow may drop to +50 or -50 MW, but utilization would remain at 100%.\n\nA UtilizationSamplesResult can be indexed by a Pair of region names and a timestamp to retrieve a vector of sample-level utilizations of the interface in that timestep. Given the absolute value nature of the outcome, results are independent of direction. Querying \"Region A\" => \"Region B\" will yield the same result as \"Region B\" => \"Region A\".\n\nExample:\n\nutils, =\n assess(sys, SequentialMonteCarlo(samples=10), UtilizationSamples())\n\nsamples =\n utils[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nsamples2 =\n utils[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples == samples2\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Utilization for sample-averaged utilization results when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.StorageEnergySamples","page":"Public API Reference","title":"PRASCore.Results.StorageEnergySamples","text":"StorageEnergySamples\n\nThe StorageEnergySamples result specification reports the sample-level state of charge of Storages, producing a StorageEnergySamplesResult.\n\nA StorageEnergySamplesResult can be indexed by storage device name and a timestamp to retrieve a vector of sample-level charge states for the device in the given timestep.\n\nExample:\n\nstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=10), StorageEnergySamples())\n\nsamples = storenergy[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See StorageEnergy for estimated average storage state of charge when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageEnergySamples","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageEnergySamples","text":"GeneratorStorageEnergySamples\n\nThe GeneratorStorageEnergySamples result specification reports the sample-level state of charge of GeneratorStorages, producing a GeneratorStorageEnergySamplesResult.\n\nA GeneratorStorageEnergySamplesResult can be indexed by generator-storage device name and a timestamp to retrieve a vector of sample-level charge states for the device in the given timestep.\n\nExample:\n\ngenstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorStorageEnergySamples())\n\nsamples = genstorenergy[\"MyGeneratorStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See GeneratorStorageEnergy for estimated average generator-storage state of charge when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorAvailability","page":"Public API Reference","title":"PRASCore.Results.GeneratorAvailability","text":"GeneratorAvailability\n\nThe GeneratorAvailability result specification reports the sample-level discrete availability of Generators, producing a GeneratorAvailabilityResult.\n\nA GeneratorAvailabilityResult can be indexed by generator name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\ngenavail, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorAvailability())\n\nsamples = genavail[\"MyGenerator123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageAvailability","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageAvailability","text":"GeneratorStorageAvailability\n\nThe GeneratorStorageAvailability result specification reports the sample-level discrete availability of GeneratorStorages, producing a GeneratorStorageAvailabilityResult.\n\nA GeneratorStorageAvailabilityResult can be indexed by generator-storage name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\ngenstoravail, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorStorageAvailability())\n\nsamples = genstoravail[\"MyGenerator123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.LineAvailability","page":"Public API Reference","title":"PRASCore.Results.LineAvailability","text":"LineAvailability\n\nThe LineAvailability result specification reports the sample-level discrete availability of Lines, producing a LineAvailabilityResult.\n\nA LineAvailabilityResult can be indexed by line name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\nlineavail, =\n assess(sys, SequentialMonteCarlo(samples=10), LineAvailability())\n\nsamples = lineavail[\"MyLine123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"}] +[{"location":"how_to_guides/intro_page/#Examples","page":"Examples","title":"Examples","text":"","category":"section"},{"location":"how_to_guides/intro_page/","page":"Examples","title":"Examples","text":"Tutorials to be created soon.","category":"page"},{"location":"api/internal/#Internal-API","page":"Internal API Reference","title":"Internal API","text":"","category":"section"},{"location":"api/internal/","page":"Internal API Reference","title":"Internal API Reference","text":"Simulations.DispatchProblem\nResults.StorageAvailability","category":"page"},{"location":"api/internal/#PRASCore.Simulations.DispatchProblem","page":"Internal API Reference","title":"PRASCore.Simulations.DispatchProblem","text":"DispatchProblem(sys::SystemModel)\n\nCreate a min-cost flow problem for the multi-region max power delivery problem with generation and storage discharging in decreasing order of priority, and storage charging with excess capacity. Storage and GeneratorStorage devices within a region are represented individually on the network.\n\nThis involves injections/withdrawals at one node (regional capacity surplus/shortfall) for each modelled region, as well as two/three nodes associated with each Storage/GeneratorStorage device, and a supplementary \"slack\" node in the network that can absorb undispatched power or pass unserved energy or unused charging capability through to satisfy power balance constraints.\n\nFlows from the generation nodes are free, while flows to charging and from discharging nodes are costed or rewarded according to the time-to-discharge of the storage device, ensuring efficient coordination across units, while enforcing that storage is only discharged once generation capacity is exhausted (implying an operational strategy that prioritizes resource adequacy over economic arbitrage). This is based on the storage dispatch strategy of Evans, Tindemans, and Angeli, as outlined in \"Minimizing Unserved Energy Using Heterogenous Storage Units\" (IEEE Transactions on Power Systems, 2019).\n\nFlows to the charging node have an attenuated negative cost (reward), incentivizing immediate storage charging if generation and transmission allows it, while avoiding charging by discharging other storage (since that would incur an overall positive cost).\n\nFlows to the slack node (representing unused generation or storage discharge capacity) are free, but flows from the slack node to serve load incur the lost load penalty of 9999. Flows from the slack node in lieu of storage charging or discharging are free.\n\nFlows on transmission interfaces assume a hurdle rate of 1 to keep unserved energy close to the source of the shortage and eliminate loop flows. This has the side-effect of disincentivising wheeling power across multiple regions for charging purposes, however.\n\nNodes in the problem are ordered as:\n\nRegions generation surplus/shortfall (Regions order)\nStorage discharge capacity (Storage order)\nStorage charge capacity (Storage order)\nGenerationStorage inflow capacity (GeneratorStorage order)\nGenerationStorage discharge capacity (GeneratorStorage order)\nGenerationStorage grid injection (GeneratorStorage order)\nGenerationStorage charge capacity (GeneratorStorage order)\nSlack node\n\nEdges are ordered as:\n\nRegions demand unserved (Regions order)\nRegions generation unused (Regions order)\nInterfaces forward flow (Interfaces order)\nInterfaces reverse flow (Interfaces order)\nStorage discharge to grid (Storage order)\nStorage discharge unused (Storage order)\nStorage charge from grid (Storage order)\nStorage charge unused (Storage order)\nGenerationStorage discharge to grid (GeneratorStorage order)\nGenerationStorage discharge unused (GeneratorStorage order)\nGenerationStorage inflow to grid (GenerationStorage order)\nGenerationStorage total to grid (GeneratorStorage order)\nGenerationStorage charge from grid (GeneratorStorage order)\nGenerationStorage charge from inflow (GeneratorStorage order)\nGenerationStorage charge unused (GeneratorStorage order)\nGenerationStorage inflow unused (GeneratorStorage order)\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#PRASCore.Results.StorageAvailability","page":"Internal API Reference","title":"PRASCore.Results.StorageAvailability","text":"StorageAvailability\n\nThe StorageAvailability result specification reports the sample-level discrete availability of Storages, producing a StorageAvailabilityResult.\n\nA StorageAvailabilityResult can be indexed by storage device name and a timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\nstoravail, =\n assess(sys, SequentialMonteCarlo(samples=10), StorageAvailability())\n\nsamples = storavail[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/internal/","page":"Internal API Reference","title":"Internal API Reference","text":"Modules = [SiennaPRASInterface, SiennaPRASInterface.PRASCore]\nPublic = false","category":"page"},{"location":"api/internal/#SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE","page":"Internal API Reference","title":"SiennaPRASInterface.CC_RESTRICTIONS_UTIL_FILE","text":"FILE with restrictions on CC units in PRAS\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.HVDCLineTypes","page":"Internal API Reference","title":"SiennaPRASInterface.HVDCLineTypes","text":"Supported DC branch Types\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.OUTAGE_INFO_FILE","page":"Internal API Reference","title":"SiennaPRASInterface.OUTAGE_INFO_FILE","text":"DEFAULT outage data which is used when outage_flag is set to FALSE\n\nBased on ERCOT historical data\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.TransformerTypes","page":"Internal API Reference","title":"SiennaPRASInterface.TransformerTypes","text":"Filtered Transformer Types\n\nThese transformers are not modeled as lines in PRAS.\n\n\n\n\n\n","category":"constant"},{"location":"api/internal/#SiennaPRASInterface.S2P_metadata","page":"Internal API Reference","title":"SiennaPRASInterface.S2P_metadata","text":"S2P_metadata\n\nStruct to store metadata for the Sienna to PRAS conversion\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.outage_data","page":"Internal API Reference","title":"SiennaPRASInterface.outage_data","text":"outage_data\n\nStruct to store the outage information for a component.\n\n\n\n\n\n","category":"type"},{"location":"api/internal/#SiennaPRASInterface.check_file-Tuple{String}","page":"Internal API Reference","title":"SiennaPRASInterface.check_file","text":"check_file(loc::String)\n\nCheck if the file exists and is openable.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_available_components_in_aggregation_topology-Tuple{Type{<:PowerSystems.StaticInjection}, PowerSystems.System, PowerSystems.AggregationTopology}","page":"Internal API Reference","title":"SiennaPRASInterface.get_available_components_in_aggregation_topology","text":"get_available_components_in_aggregation_topology(\n type::Type{<:PSY.StaticInjection},\n sys::PSY.System,\n region::PSY.AggregationTopology,\n)\n\nGet available components in the AggregationTopology region of the given type.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_first_ts-Tuple{TS} where TS<:Channel{Any}","page":"Internal API Reference","title":"SiennaPRASInterface.get_first_ts","text":"get_first_ts(ts::TS)\n\nGet time series and handle components with no time series (such as unavailable)\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_generator_category-Tuple{PowerSystems.StaticInjection}","page":"Internal API Reference","title":"SiennaPRASInterface.get_generator_category","text":"get_generator_category(gen::StaticInjection)\n\nGet the category of the generator.\n\nArguments\n\ngen::StaticInjection: Generator\n\nReturns\n\nString: Category of the generator\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_sorted_lines-Tuple{Vector{PowerSystems.Branch}, Vector{String}}","page":"Internal API Reference","title":"SiennaPRASInterface.get_sorted_lines","text":"get_sorted_lines(lines::Vector{PSY.Branch}, region_names::Vector{String})\n\nGet sorted lines, interface region indices, and interface line indices.\n\nArguments\n\nlines::Vector{PSY.Branch}: Lines\nregion_names::Vector{String}: Region names\n\nReturns\n\nsorted_lines::Vector{PSY.Branch}: Sorted lines\ninterface_reg_idxs::Vector{Tuple{Int, Int}}: Interface region indices\ninterface_line_idxs::Vector{UnitRange{Int}}: Interface line indices\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_sorted_region_tuples-Tuple{Vector{PowerSystems.Branch}, Vector{String}}","page":"Internal API Reference","title":"SiennaPRASInterface.get_sorted_region_tuples","text":"get_sorted_region_tuples(lines::Vector{PSY.Branch}, region_names::Vector{String})\n\nGet sorted (regfrom, regto) tuples of inter-regional lines.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_timestamps-Union{Tuple{TIMEPERIOD}, Tuple{Dates.DateTime, TIMEPERIOD, Int64, Int64}} where TIMEPERIOD<:Dates.TimePeriod","page":"Internal API Reference","title":"SiennaPRASInterface.get_timestamps","text":"get_timestamps(initial_time, resolution, steps, horizon)\n\nReturn a vector of timestamps starting from initial_time with resolution and steps for horizon steps\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.get_ts_values-Tuple{InfrastructureSystems.TimeSeriesData}","page":"Internal API Reference","title":"SiennaPRASInterface.get_ts_values","text":"get_ts_values(ts::PSY.TimeSeriesData)\n\nGet the time series values.\n\nArguments\n\nts::TimeSeries: Time series\n\nReturns\n\nArray{Float64}: Time series values\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.initialize_availability!-Tuple{Random.AbstractRNG, Vector{Bool}, Vector{Int64}, Vector{PowerSystems.Generator}, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.initialize_availability!","text":"initializeavailability!(rng, availability, nexttransition, devices, tlast)\n\nArguments\n\nrng::Random.AbstractRNG: Random number generator\navailability::Vector{Bool}: Vector of availability status\nnexttransition::Vector{Int}: Vector of next transition time\ndevices::Vector{PSY.Generator}: Vector of devices with outage SupplementalAttributes\nt_last::Int: Last time step\n\nReturns\n\nModifies input arguments and rereturns\n\navailability::Vector{Bool}: Vector of availability status\nnexttransition::Vector{Int}: Vector of next transition time\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.line_rating-Tuple{PowerSystems.Branch}","page":"Internal API Reference","title":"SiennaPRASInterface.line_rating","text":"line_rating(line::Branch)\n\nGet the line rating.\n\nArguments\n\nline::Branch: Line\n\nReturns\n\nTuple{forward_capacity::Float64, backward_capacity::Float64}: Line rating\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.line_type-Tuple{PowerSystems.Branch}","page":"Internal API Reference","title":"SiennaPRASInterface.line_type","text":"line_type(line::Branch)\n\nGet the line type.\n\nArguments\n\nline::Branch: Line\n\nReturns\n\nString: Line type\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.make_pras_interfaces-Tuple{Vector{PowerSystems.Branch}, Vector{Tuple{Int64, Int64}}, Vector{UnitRange{Int64}}, Vector{String}, PowerSystems.System, SiennaPRASInterface.S2P_metadata}","page":"Internal API Reference","title":"SiennaPRASInterface.make_pras_interfaces","text":"make_pras_interfaces(\n sorted_lines::Vector{PSY.Branch},\n interface_reg_idxs::Vector{Tuple{Int64, Int64}},\n interface_line_idxs::Vector{UnitRange{Int64}},\n s2p_meta::S2P_metadata,\n)\n\nConverts PSY branches and interaces indices into PRAS Lines and Interfaces.\n\nReturns\n\nnew_lines::PRASCore.Lines: PRAS Lines\nnew_interfaces::PRASCore.Interfaces: PRAS Interfaces\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.rate_to_probability-Tuple{Float64, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.rate_to_probability","text":"rate_to_probability(for_gen::Float64, mttr::Int64)\n\nConverts the forced outage rate and mean time to repair to the λ and μ parameters\n\nArguments\n\nfor_gen::Float64: Forced outage rate [1/T]\nmttr::Int64: Mean time to repair [T]\n\nReturns\n\nλ::Float64: Transition probability from online to offline [1/T]\nμ::Float64: Transition rate from offline to online [1/T]\n\nReference\n\nhttps://core.ac.uk/download/pdf/13643059.pdf\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.runchecks-Tuple{String}","page":"Internal API Reference","title":"SiennaPRASInterface.runchecks","text":"runchecks(sys_location::String)\n\nCheck if the System JSON file is serialized as well as other files required.\n\n\n\n\n\n","category":"method"},{"location":"api/internal/#SiennaPRASInterface.update_availability!-Tuple{Random.AbstractRNG, Vector{Bool}, Vector{Int64}, Vector{PowerSystems.Generator}, Int64, Int64}","page":"Internal API Reference","title":"SiennaPRASInterface.update_availability!","text":"update_availability!(rng, availability, nexttransition, devices, t_now, t_last)\n\nReturn availability and next transition with new randomness\n\n\n\n\n\n","category":"method"},{"location":"explanations/default_outage_values/#Default-values-used-for-outage-statistics","page":"Default outage values","title":"Default values used for outage statistics","text":"","category":"section"},{"location":"explanations/default_outage_values/","page":"Default outage values","title":"Default outage values","text":"When a system does not contain any GeometricDistributionForcedOutage supplemental attributes attached to any components, then the outage rates default to a set of defaults defined in the Default Outage Rates CSV based off of rates in ERCOT.","category":"page"},{"location":"explanations/default_outage_values/","page":"Default outage values","title":"Default outage values","text":"For any remaining components not captured by the CSV defaults, such as lines, renewables, and storage, the outage rates are 0 and will never fail.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#How-do-I-add-outage-data-to-Sienna?","page":"How do I add outage data?","title":"How do I add outage data to Sienna?","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"You can attach outage data to PowerSystems Components by using the supplemental attribute GeometricDistributionForcedOutage.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-1-:-Parse-your-outage-data-into-Sienna","page":"How do I add outage data?","title":"Step 1 : Parse your outage data into Sienna","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"SiennaPRASInterface.jl uses outage information in the form of independent mean_time_to_recovery in units of hours and outage_transition_probability in probability of outage per hour. A simple Markov model models the transitions between out and active using these parameters.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"We support data either being fixed and specified in the GeometricDistributionForcedOutage object or attached as time-series to the GeometricDistributionForcedOutage struct.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Creating-a-GeometricDistributionForcedOutage-from-fixed-data","page":"How do I add outage data?","title":"Creating a GeometricDistributionForcedOutage from fixed data","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using PowerSystems\nimport PowerSystemCaseBuilder # hide\nconst PSCB = PowerSystemCaseBuilder # hide\nsys = PSCB.build_system(PSCB.PSISystems, \"RTS_GMLC_DA_sys\") # hide\nset_units_base_system!(sys, \"natural_units\") # hide\n\ntransition_data = GeometricDistributionForcedOutage(;\n mean_time_to_recovery=10, # Units of hours\n outage_transition_probability=0.005, # Probability for outage per hour\n)\ntransition_data","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-2-:-Attaching-Data-to-Components","page":"How do I add outage data?","title":"Step 2 : Attaching Data to Components","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"Once you have a GeometricDistributionForcedOutage object, then you can add it to any components with that data:","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"component = get_component(Generator, sys, \"101_CT_1\")\nadd_supplemental_attribute!(sys, component, transition_data)\ncomponent","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-3-(Optional)-:-Adding-time-series-data-to-GeometricDistributionForcedOutage-from-time-series-data","page":"How do I add outage data?","title":"Step 3 (Optional) : Adding time-series data to GeometricDistributionForcedOutage from time series data","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"Time series should be attached to a GeometricDistributionForcedOutage object under the keys recovery_probability (1/mean_time_to_recovery) and outage_probability.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"See the Sienna time-series documentation on working with time-series.","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using Dates\nusing TimeSeries\n\noutage_probability = [0.1, 0.1, 0.2, 0.3, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]\nrecovery_probability = [0.1, 0.1, 0.2, 0.3, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]\n\n# Your resolution and length must match the other SingleTimeSeries in your System.\nresolution = Dates.Minute(5)\ntimestamps = range(DateTime(\"2020-01-01T08:00:00\"); step=resolution, length=8784)\noutage_timearray = TimeArray(timestamps, repeat(outage_probability, inner=div(8784, 12)))\noutage_time_series = SingleTimeSeries(; name=\"outage_probability\", data=outage_timearray)\n\nrecovery_timearray =\n TimeArray(timestamps, repeat(recovery_probability, inner=div(8784, 12)))\nrecovery_time_series =\n SingleTimeSeries(; name=\"recovery_probability\", data=recovery_timearray)\n\n# Here we assume you have a system named sys\nadd_time_series!(sys, transition_data, outage_time_series)\nadd_time_series!(sys, transition_data, recovery_time_series)\ntransition_data","category":"page"},{"location":"how_to_guides/how_do_i_add_outage_data/#Step-4-:-Run-simulations-and-verify-result","page":"How do I add outage data?","title":"Step 4 : Run simulations and verify result","text":"","category":"section"},{"location":"how_to_guides/how_do_i_add_outage_data/","page":"How do I add outage data?","title":"How do I add outage data?","text":"using SiennaPRASInterface\nmethod = SequentialMonteCarlo(samples=10, seed=1)\nshortfalls, = assess(sys, PowerSystems.Area, method, Shortfall())\neue = EUE(shortfalls)","category":"page"},{"location":"#SiennaPRASInterface.jl","page":"Welcome Page","title":"SiennaPRASInterface.jl","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"CurrentModule = SiennaPRASInterface","category":"page"},{"location":"#About","page":"Welcome Page","title":"About","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"SiennaPRASInterface.jl is a Julia package that provides an interface to PRAS.jl from Sienna's PowerSystem.jl's System data model.","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"The Probabilistic Resource Adequacy Suite (PRAS) analyzes the resource adequacy of a bulk power system using Monte Carlo methods.","category":"page"},{"location":"#Getting-Started","page":"Welcome Page","title":"Getting Started","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"To use SiennaPRASInterface.jl, you first need a System from PowerSystems.jl","category":"page"},{"location":"#1.-Install","page":"Welcome Page","title":"1. Install","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"] add SiennaPRASInterface","category":"page"},{"location":"#2.-Add-Data","page":"Welcome Page","title":"2. Add Data","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"Add outage information to generators using the supplemental attribute GeometricDistributionForcedOutage.","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"using PowerSystems\ntransition_data = GeometricDistributionForcedOutage(;\n mean_time_to_recovery=10, # Units of hours\n outage_transition_probability=0.005, # Probability for outage per hour\n)\ncomponent = get_component(Generator, sys, \"test_generator\")\nadd_supplemental_attribute!(sys, component, transition_data)","category":"page"},{"location":"#3.-Calculate-Shortfalls-and-Expected-Unserved-Energy-on-System","page":"Welcome Page","title":"3. Calculate Shortfalls and Expected Unserved Energy on System","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"using SiennaPRASInterface\nsequential_monte_carlo = SequentialMonteCarlo(samples=10_000, seed=1)\nshortfalls, = assess(sys, PowerSystems.Area, sequential_monte_carlo, Shortfall())\neue = EUE(shortfalls)","category":"page"},{"location":"#Documentation","page":"Welcome Page","title":"Documentation","text":"","category":"section"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"PRAS Documentation","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"Pages = [\"api/public.md\", \"tutorials\"]\nDepth = 2","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"","category":"page"},{"location":"","page":"Welcome Page","title":"Welcome Page","text":"SiennaPRASInterface has been developed as part of the Transmission Planning Tools Maintenance project at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) funded by DOE Grid Deployment Office (GDO).","category":"page"},{"location":"api/public/#Public-API-Reference","page":"Public API Reference","title":"Public API Reference","text":"","category":"section"},{"location":"api/public/","page":"Public API Reference","title":"Public API Reference","text":"SiennaPRASInterface\ngenerate_pras_system\nSystemModel\nassess\nSequentialMonteCarlo\nShortfall\nSurplus\nFlow\nUtilization\nStorageEnergy\nGeneratorStorageEnergy\nLOLE\nEUE\ngenerate_outage_profile\ngenerate_csv_outage_profile\nadd_csv_time_series!\nadd_csv_time_series_single_stage!\nmake_generator_outage_draws!\nShortfallSamples\nSurplusSamples\nFlowSamples\nUtilizationSamples\nStorageEnergySamples\nGeneratorStorageEnergySamples\nGeneratorAvailability\nGeneratorStorageAvailability\nLineAvailability","category":"page"},{"location":"api/public/#SiennaPRASInterface","page":"Public API Reference","title":"SiennaPRASInterface","text":"PowerSystems Interface for Probabilistic Resource Adequacy Studies (PRAS)\n\nKey Functions\n\ngenerate_pras_system: convert PSY to PRAS model\nassess: assess PRAS model\n\nKey PRAS Types\n\nSystemModel: PRAS data structure\nSequentialMonteCarlo: method for PRAS analysis\nShortfall: PRAS metric for missing generation\nLOLE: PRAS metric for loss of load expectation\nEUE: PRAS metric for energy unserved expectation\n\n\n\n\n\n","category":"module"},{"location":"api/public/#SiennaPRASInterface.generate_pras_system","page":"Public API Reference","title":"SiennaPRASInterface.generate_pras_system","text":"generate_pras_system(sys::PSY.System, aggregation; kwargs...)\n\nSienna/Data PowerSystems.jl System is the input and an object of PRAS SystemModel is returned. ...\n\nArguments\n\nsys::PSY.System: Sienna/Data PowerSystems.jl System\naggregation<:PSY.AggregationTopology: \"PSY.Area\" (or) \"PSY.LoadZone\" {Optional}\nlump_region_renewable_gens::Bool: Whether to lumps PV and Wind generators in a region because usually these generators don't have FOR data {Optional}\nexport_location::String: Export location of the .pras file ...\n\nReturns\n\n- `PRASCore.SystemModel`: PRAS SystemModel object\n\nExamples\n\njulia> generate_pras_system(psy_sys)\nPRAS SystemModel\n\n\n\n\n\ngenerate_pras_system(sys_location::String, aggregation; kwargs...)\n\nGenerate a PRAS SystemModel from a Sienna/Data PowerSystems System JSON file.\n\nArguments\n\nsys_location::String: Location of the Sienna/Data PowerSystems System JSON file\naggregation::Type{AT}: Aggregation topology type\nlump_region_renewable_gens::Bool: Lumping of region renewable generators\nexport_location::Union{Nothing, String}: Export location of the .pras file\n\nReturns\n\nPRASCore.SystemModel: PRAS SystemModel\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Systems.SystemModel","page":"Public API Reference","title":"PRASCore.Systems.SystemModel","text":"SystemModel\n\nA SystemModel contains a representation of a power system to be studied with PRAS.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Simulations.assess","page":"Public API Reference","title":"PRASCore.Simulations.assess","text":"assess(system::SystemModel, method::SequentialMonteCarlo, resultspecs::ResultSpec...)\n\nRun a Sequential Monte Carlo simulation on a system using the method data and return resultspecs.\n\nArguments\n\nsystem::SystemModel: PRAS data structure\nmethod::SequentialMonteCarlo: method for PRAS analysis\nresultspecs::ResultSpec...: PRAS metric for metrics like Shortfall missing generation\n\nReturns\n\nresults::Tuple{Vararg{ResultAccumulator{SequentialMonteCarlo}}}: PRAS metric results\n\n\n\n\n\nassess(\n sys::PSY.System,\n aggregation::Type{AT},\n method::PRASCore.SequentialMonteCarlo,\n resultsspecs::PRASCore.Results.ResultSpec...,\n) where {AT <: PSY.AggregationTopology}\n\nEstimate resource adequacy using Monte Carlo simulation.\n\nArguments\n\nsys::PSY.System: PowerSystems.jl system model\naggregation::Type{AT}: Aggregation topology to use in translating to PRAS\nmethod::PRASCore.SequentialMonteCarlo: Simulation method to use\nresultsspec::PRASCore.Results.ResultSpec...: Results to compute\n\nReturns\n\nTuple of results from resultsspec: default is (ShortfallResult,)\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Simulations.SequentialMonteCarlo","page":"Public API Reference","title":"PRASCore.Simulations.SequentialMonteCarlo","text":"SequentialMonteCarlo(;\n samples::Int=10_000,\n seed::Integer=rand(UInt64),\n verbose::Bool=false,\n threaded::Bool=true\n)\n\nSequential Monte Carlo simulation parameters for PRAS analysis\n\nIt it recommended that you fix the random seed for reproducibility.\n\nArguments\n\nsamples::Int=10_000: Number of samples\nseed::Integer=rand(UInt64): Random seed\nverbose::Bool=false: Print progress\nthreaded::Bool=true: Use multi-threading\n\nReturns\n\nSequentialMonteCarlo: PRAS simulation specification\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Shortfall","page":"Public API Reference","title":"PRASCore.Results.Shortfall","text":"Shortfall\n\nThe Shortfall result specification reports expectation-based resource adequacy risk metrics such as EUE and LOLE, producing a ShortfallResult.\n\nA ShortfallResult can be directly indexed by a region name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average unserved energy in that region and timestep. However, in most cases it's simpler to use EUE and LOLE constructors to directly retrieve standard risk metrics.\n\nExample:\n\nshortfall, =\n assess(sys, SequentialMonteCarlo(samples=1000), Shortfall())\n\nperiod = ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")\n\n# Unserved energy mean and standard deviation\nsf_mean, sf_std = shortfall[\"Region A\", period]\n\n# System-wide risk metrics\neue = EUE(shortfall)\nlole = LOLE(shortfall)\n\n# Regional risk metrics\nregional_eue = EUE(shortfall, \"Region A\")\nregional_lole = LOLE(shortfall, \"Region A\")\n\n# Period-specific risk metrics\nperiod_eue = EUE(shortfall, period)\nperiod_lolp = LOLE(shortfall, period)\n\n# Region- and period-specific risk metrics\nperiod_eue = EUE(shortfall, \"Region A\", period)\nperiod_lolp = LOLE(shortfall, \"Region A\", period)\n\nSee ShortfallSamples for recording sample-level shortfall results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Surplus","page":"Public API Reference","title":"PRASCore.Results.Surplus","text":"Surplus\n\nThe Surplus result specification reports unused generation and storage discharge capability of Regions, producing a SurplusResult.\n\nA SurplusResult can be indexed by region name and timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average unused capacity in that region and timestep.\n\nExample:\n\nsurplus, =\n assess(sys, SequentialMonteCarlo(samples=1000), Surplus())\n\nsurplus_mean, surplus_std =\n surplus[\"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee SurplusSamples for sample-level surplus results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Flow","page":"Public API Reference","title":"PRASCore.Results.Flow","text":"Flow\n\nThe Flow result specification reports the estimated average flow across transmission Interfaces, producing a FlowResult.\n\nA FlowResult can be indexed by a directional Pair of region names and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average net flow magnitude and direction relative to the given directed interface in that timestep. For a query of \"Region A\" => \"Region B\", if estimated average flow was from A to B, the reported value would be positive, while if average flow was in the reverse direction, from B to A, the value would be negative.\n\nExample:\n\nflows, =\n assess(sys, SequentialMonteCarlo(samples=1000), Flow())\n\nflow_mean, flow_std =\n flows[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\nflow2_mean, flow2_std =\n flows[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n@assert flow_mean == -flow2_mean\n\nSee FlowSamples for sample-level flow results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.Utilization","page":"Public API Reference","title":"PRASCore.Results.Utilization","text":"Utilization\n\nThe Utilization result specification reports the estimated average absolute utilization of Interfaces, producing a UtilizationResult.\n\nWhereas Flow reports the average directional power transfer across an interface, Utilization reports the absolute value of flow relative to the interface's transfer capability (counting the effects of line outages). For example, a symmetrically-constrained interface which is fully congested with max power flowing in one direction in half of the samples, and the other direction in the remaining samples, would have an average flow of 0 MW, but an average utilization of 100%.\n\nA UtilizationResult can be indexed by a Pair of region names and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average utilization of the interface. Given the absolute value nature of the outcome, results are independent of direction. Querying \"Region A\" => \"Region B\" will yield the same result as \"Region B\" => \"Region A\".\n\nExample:\n\nutils, =\n assess(sys, SequentialMonteCarlo(samples=1000), Utilization())\n\nutil_mean, util_std =\n utils[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nutil2_mean, util2_std =\n utils[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert util_mean == util2_mean\n\nSee UtilizationSamples for sample-level utilization results.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.StorageEnergy","page":"Public API Reference","title":"PRASCore.Results.StorageEnergy","text":"StorageEnergy\n\nThe StorageEnergy result specification reports the average state of charge of Storages, producing a StorageEnergyResult.\n\nA StorageEnergyResult can be indexed by storage device name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average energy level for the given storage device in that timestep.\n\nExample:\n\nstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=1000), StorageEnergy())\n\nsoc_mean, soc_std =\n storenergy[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee StorageEnergySamples for sample-level storage states of charge.\n\nSee GeneratorStorageEnergy for average generator-storage states of charge.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageEnergy","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageEnergy","text":"GeneratorStorageEnergy\n\nThe GeneratorStorageEnergy result specification reports the average state of charge of GeneratorStorages, producing a GeneratorStorageEnergyResult.\n\nA GeneratorStorageEnergyResult can be indexed by generator-storage device name and a timestamp to retrieve a tuple of sample mean and standard deviation, estimating the average energy level for the given generator-storage device in that timestep.\n\nExample:\n\ngenstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=1000), GeneratorStorageEnergy())\n\nsoc_mean, soc_std =\n genstorenergy[\"MyGeneratorStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\nSee GeneratorStorageEnergySamples for sample-level generator-storage states of charge.\n\nSee StorageEnergy for average storage states of charge.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.LOLE","page":"Public API Reference","title":"PRASCore.Results.LOLE","text":"LOLE\n\nLOLE reports loss of load expectation over a particular time period and regional extent. When the reporting period is a single simulation timestep, the metric is equivalent to loss of load probability (LOLP).\n\nContains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.EUE","page":"Public API Reference","title":"PRASCore.Results.EUE","text":"EUE\n\nEUE reports expected unserved energy over a particular time period and regional extent.\n\nContains both the estimated value itself as well as the standard error of that estimate, which can be extracted with val and stderror, respectively.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#SiennaPRASInterface.generate_outage_profile","page":"Public API Reference","title":"SiennaPRASInterface.generate_outage_profile","text":"generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)\n\nProcess the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).\n\n...\n\nArguments\n\npras_system::PRASCore.SystemModel: PRAS System\nnum_runs::Int64: Number of PRAS runs\npsy_sys::PSY.System: PSY System\nnum_scenarios::Int64: Number of scenarios of user interest.\nlocation::String: Location to store outage profile. ...\n\nExamples\n\njulia> generate_outage_profile(results, pras_sys, psy_sys, 1)\nPSY System exported using to_json method in InfrastructureSystems\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.generate_csv_outage_profile","page":"Public API Reference","title":"SiennaPRASInterface.generate_csv_outage_profile","text":"generate_outage_profile(pras_system,num_runs,psy_sys,num_scenarios,location)\n\nProcess the assess results to get timeseries of generator status and include this timeseries data to the corresponding component in PSY System and exported using to_json method (serializing the PSY System).\n\n...\n\nArguments\n\npras_system::PRASCore.SystemModel: PRAS System\nnum_runs::Int64: Number of PRAS runs\npsy_sys::PSY.System: PSY System\nnum_scenarios::Int64: Number of scenarios of user interest.\nlocation::String: Location to store outage profile. ...\n\nExamples\n\njulia> generate_outage_profile(results, pras_sys, psy_sys, 1)\nPSY System exported using to_json method in InfrastructureSystems\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.add_csv_time_series!","page":"Public API Reference","title":"SiennaPRASInterface.add_csv_time_series!","text":"add_csv_time_series!(\n sys_DA,\n sys_RT,\n outage_csv_location::String;\n days_of_interest::Union{Nothing, UnitRange}=nothing,\n add_scenario::Union{Nothing, Int}=nothing,\n)\n\nGenerates outage profile for two stage PowerSimulation and adds availability time series data to Generators in PSY System from CSV files.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.add_csv_time_series_single_stage!","page":"Public API Reference","title":"SiennaPRASInterface.add_csv_time_series_single_stage!","text":"add_csv_time_series_single_stage!(\n sys_DA,\n outage_csv_location::String;\n days_of_interest::Union{Nothing, UnitRange}=nothing,\n add_scenario::Union{Nothing, Int}=nothing,\n)\n\nGenerates outage profile for single stage PowerSimulation and adds availability time series.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#SiennaPRASInterface.make_generator_outage_draws!","page":"Public API Reference","title":"SiennaPRASInterface.make_generator_outage_draws!","text":"make_generator_outage_draws!(\n sys,\n initial_time::Dates.DateTime=nothing,\n resolution::TIMEPERIOD=nothing,\n steps::Int=nothing,\n horizon::Int=nothing,\n) where {TIMEPERIOD <: Dates.TimePeriod}\n\nAdds availability time series to the generators in the system.\n\nMain function to make generator outage draws.\n\n\n\n\n\n","category":"function"},{"location":"api/public/#PRASCore.Results.ShortfallSamples","page":"Public API Reference","title":"PRASCore.Results.ShortfallSamples","text":"ShortfallSamples\n\nThe ShortfallSamples result specification reports sample-level unserved energy outcomes, producing a ShortfallSamplesResult.\n\nA ShortfallSamplesResult can be directly indexed by a region name and a timestamp to retrieve a vector of sample-level unserved energy results in that region and timestep. EUE and LOLE constructors can also be used to retrieve standard risk metrics.\n\nExample:\n\nshortfall, =\n assess(sys, SequentialMonteCarlo(samples=10), ShortfallSamples())\n\nperiod = ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")\n\nsamples = shortfall[\"Region A\", period]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\n# System-wide risk metrics\neue = EUE(shortfall)\nlole = LOLE(shortfall)\n\n# Regional risk metrics\nregional_eue = EUE(shortfall, \"Region A\")\nregional_lole = LOLE(shortfall, \"Region A\")\n\n# Period-specific risk metrics\nperiod_eue = EUE(shortfall, period)\nperiod_lolp = LOLE(shortfall, period)\n\n# Region- and period-specific risk metrics\nperiod_eue = EUE(shortfall, \"Region A\", period)\nperiod_lolp = LOLE(shortfall, \"Region A\", period)\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Shortfall for average shortfall outcomes when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.SurplusSamples","page":"Public API Reference","title":"PRASCore.Results.SurplusSamples","text":"SurplusSamples\n\nThe SurplusSamples result specification reports sample-level unused generation and storage discharge capability of Regions, producing a SurplusSamplesResult.\n\nA SurplusSamplesResult can be indexed by region name and timestamp to retrieve a vector of sample-level surplus values in that region and timestep.\n\nExample:\n\nsurplus, =\n assess(sys, SequentialMonteCarlo(samples=10), SurplusSamples())\n\nsamples = surplus[\"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Surplus for estimated average surplus values when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.FlowSamples","page":"Public API Reference","title":"PRASCore.Results.FlowSamples","text":"FlowSamples\n\nThe FlowSamples result specification reports the sample-level magnitude and direction of power flows across Interfaces, producing a FlowSamplesResult.\n\nA FlowSamplesResult can be indexed by a directional Pair of region names and a timestamp to retrieve a vector of sample-level net flow magnitudes and directions relative to the given directed interface in that timestep. For a query of \"Region A\" => \"Region B\", if flow in one sample was from A to B, the reported value would be positive, while if flow was in the reverse direction, from B to A, the value would be negative.\n\nExample:\n\nflows, =\n assess(sys, SequentialMonteCarlo(samples=10), FlowSamples())\n\nsamples = flows[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nsamples2 = flows[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples == -samples2\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Flow for estimated average flow results when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.UtilizationSamples","page":"Public API Reference","title":"PRASCore.Results.UtilizationSamples","text":"UtilizationSamples\n\nThe UtilizationSamples result specification reports the sample-level absolute utilization of Interfaces, producing a UtilizationSamplesResult.\n\nWhereas FlowSamples reports the directional power transfer across an interface, UtilizationSamples reports the absolute value of flow relative to the interface's transfer capability (counting the effects of line outages). For example, a 100 MW symmetrically-constrained interface which is fully congested may have a flow of +100 or -100 MW, but in both cases the utilization will be 100%. If a 50 MW line in the interface went on outage, flow may drop to +50 or -50 MW, but utilization would remain at 100%.\n\nA UtilizationSamplesResult can be indexed by a Pair of region names and a timestamp to retrieve a vector of sample-level utilizations of the interface in that timestep. Given the absolute value nature of the outcome, results are independent of direction. Querying \"Region A\" => \"Region B\" will yield the same result as \"Region B\" => \"Region A\".\n\nExample:\n\nutils, =\n assess(sys, SequentialMonteCarlo(samples=10), UtilizationSamples())\n\nsamples =\n utils[\"Region A\" => \"Region B\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nsamples2 =\n utils[\"Region B\" => \"Region A\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples == samples2\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See Utilization for sample-averaged utilization results when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.StorageEnergySamples","page":"Public API Reference","title":"PRASCore.Results.StorageEnergySamples","text":"StorageEnergySamples\n\nThe StorageEnergySamples result specification reports the sample-level state of charge of Storages, producing a StorageEnergySamplesResult.\n\nA StorageEnergySamplesResult can be indexed by storage device name and a timestamp to retrieve a vector of sample-level charge states for the device in the given timestep.\n\nExample:\n\nstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=10), StorageEnergySamples())\n\nsamples = storenergy[\"MyStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See StorageEnergy for estimated average storage state of charge when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageEnergySamples","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageEnergySamples","text":"GeneratorStorageEnergySamples\n\nThe GeneratorStorageEnergySamples result specification reports the sample-level state of charge of GeneratorStorages, producing a GeneratorStorageEnergySamplesResult.\n\nA GeneratorStorageEnergySamplesResult can be indexed by generator-storage device name and a timestamp to retrieve a vector of sample-level charge states for the device in the given timestep.\n\nExample:\n\ngenstorenergy, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorStorageEnergySamples())\n\nsamples = genstorenergy[\"MyGeneratorStorage123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Float64}\n@assert length(samples) == 10\n\nNote that this result specification requires large amounts of memory for larger sample sizes. See GeneratorStorageEnergy for estimated average generator-storage state of charge when sample-level granularity isn't required.\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorAvailability","page":"Public API Reference","title":"PRASCore.Results.GeneratorAvailability","text":"GeneratorAvailability\n\nThe GeneratorAvailability result specification reports the sample-level discrete availability of Generators, producing a GeneratorAvailabilityResult.\n\nA GeneratorAvailabilityResult can be indexed by generator name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\ngenavail, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorAvailability())\n\nsamples = genavail[\"MyGenerator123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.GeneratorStorageAvailability","page":"Public API Reference","title":"PRASCore.Results.GeneratorStorageAvailability","text":"GeneratorStorageAvailability\n\nThe GeneratorStorageAvailability result specification reports the sample-level discrete availability of GeneratorStorages, producing a GeneratorStorageAvailabilityResult.\n\nA GeneratorStorageAvailabilityResult can be indexed by generator-storage name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\ngenstoravail, =\n assess(sys, SequentialMonteCarlo(samples=10), GeneratorStorageAvailability())\n\nsamples = genstoravail[\"MyGenerator123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"},{"location":"api/public/#PRASCore.Results.LineAvailability","page":"Public API Reference","title":"PRASCore.Results.LineAvailability","text":"LineAvailability\n\nThe LineAvailability result specification reports the sample-level discrete availability of Lines, producing a LineAvailabilityResult.\n\nA LineAvailabilityResult can be indexed by line name and timestamp to retrieve a vector of sample-level availability states for the unit in the given timestep. States are provided as a boolean with true indicating that the unit is available and false indicating that it's unavailable.\n\nExample:\n\nlineavail, =\n assess(sys, SequentialMonteCarlo(samples=10), LineAvailability())\n\nsamples = lineavail[\"MyLine123\", ZonedDateTime(2020, 1, 1, 0, tz\"UTC\")]\n\n@assert samples isa Vector{Bool}\n@assert length(samples) == 10\n\n\n\n\n\n","category":"type"}] }