Simulation Utilities

NEMStorageUnderUncertainty.calculate_actual_revenue — Method

calculate_actual_revenue(
    sim_results::DataFrames.DataFrame,
    actual_price_data::DataFrames.DataFrame,
    τ::Float64
) -> Any

Summary

Calculates actual revenue and adds it as a column to sim_results.

Revenue is calculated for binding decisions
non binding decisions have missing revenue

Arguments:

sim_results: DataFrame of simulation results
actual_price_data: DataFrame with actual price data

(with SETTLEMENTDATE column and prices covering simulation period)

tau: Interval length in hours

Returns

sim_results with revenue column.

Methods

calculate_actual_revenue(sim_results, actual_price_data, τ)

defined at /home/runner/work/NEMStorageUnderUncertainty/NEMStorageUnderUncertainty/src/simulation_utils.jl:18.

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NEMStorageUnderUncertainty.exponential_discounting — Method

exponential_discounting(
    times::Vector{Float64},
    r::Float64
) -> Vector{Float64}

Summary

Exponential discounting

\[DF(r, t) = e^{-rt}\]

Arguments

times: Vector that describes ahead times in hours ahead
r: Discount rate

Returns

Discount factor vector

Methods

exponential_discounting(times, r)

defined at /home/runner/work/NEMStorageUnderUncertainty/NEMStorageUnderUncertainty/src/simulation_utils.jl:138.

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NEMStorageUnderUncertainty.hyperbolic_discounting — Method

hyperbolic_discounting(
    times::Vector{Float64},
    r::Float64
) -> Vector{Float64}

Summary

Hyperbolic discounting

\[DF(r, t) = \frac{1}{1+rt}\]

Arguments

times: Vector that describes ahead times in hours ahead
r: Discount rate

Returns

Discount factor vector

Methods

hyperbolic_discounting(times, r)

defined at /home/runner/work/NEMStorageUnderUncertainty/NEMStorageUnderUncertainty/src/simulation_utils.jl:158.

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NEMStorageUnderUncertainty.results_to_jld2 — Method

results_to_jld2(
    results_file::String,
    group::String,
    key::String,
    data::DataFrames.DataFrame
)

Summary

Saves simulation results to a JLD2 (HDF5) data file

Simulation results (data) are saved in results_file/group/key

Arguments

results_file: Path to file, including .jld2 extension
group: Data group - actual or forecast
key: Dataset key - storage power capacity
data: Simulation results DataFrame

Methods

results_to_jld2(results_file, group, key, data)

defined at /home/runner/work/NEMStorageUnderUncertainty/NEMStorageUnderUncertainty/src/simulation_utils.jl:114.

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NEMStorageUnderUncertainty.run_perfect_foresight — Method

run_perfect_foresight(
    optimizer::MathOptInterface.OptimizerWithAttributes,
    storage::NEMStorageUnderUncertainty.StorageDevice,
    actual_data::NEMStorageUnderUncertainty.ActualData,
    formulation::NEMStorageUnderUncertainty.StorageModelFormulation,
    degradation::NEMStorageUnderUncertainty.DegradationModel;
    silent,
    time_limit_sec,
    string_names
) -> Any

Summary

Runs a perfect foresight model across the period of an ActualData instance.

Perfect foresight entails:

Perfect knowledge of future price (hence use of actual price data)
Complete horizon lookahead

Arguments

optimizer: A solver optimizer
storage: StorageDevice
actual_data: ActualData
formulation: A model formulation (StorageModelFormulation)
degradation: A degradation model (DegradationModel)
silent: default false. true to suppress solver output
time_limit_sec: default nothing. Float64 to impose solver time limit in seconds
string_names: default true. false to disable JuMP string names

Returns

Simulation results for the one binding decision point (i.e. at start of simulation period)

Methods

run_perfect_foresight(
    optimizer,
    storage,
    actual_data,
    formulation,
    degradation;
    silent,
    time_limit_sec,
    string_names
)

defined at /home/runner/work/NEMStorageUnderUncertainty/NEMStorageUnderUncertainty/src/simulation_utils.jl:68.

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