antares-timeseries-generation 0.1.3

Timeseries generation library aiming at creating input data for Antares simulator studies.

antares-timeseries-generation

Timeseries generation library aiming at creating input data for Antares simulator studies.

Install

pip install antares-timeseries-generation

Usage

The generation requires to define a few input data in a ThermalCluster object:

import numpy as np

days = 365
cluster = ThermalCluster(
    unit_count=10,
    nominal_power=100,
    modulation=np.ones(dtype=float, shape=24),
    fo_law=ProbabilityLaw.UNIFORM,
    fo_volatility=0,
    po_law=ProbabilityLaw.UNIFORM,
    po_volatility=0,
    fo_duration=10 * np.ones(dtype=int, shape=days),
    fo_rate=0.2 * np.ones(dtype=float, shape=days),
    po_duration=10 * np.ones(dtype=int, shape=days),
    po_rate=np.zeros(dtype=float, shape=days),
    npo_min=np.zeros(dtype=int, shape=days),
    npo_max=10 * np.ones(dtype=int, shape=days),
)

You then need to provide a random number generator: we provide MersenneTwisterRNG to ensure the same generation as in antares-solver tool.

rng = MersenneTwisterRNG()

Then perform the timeseries generation:

generator = ThermalDataGenerator(rng=rng, days=days)
results = generator.generate_time_series(cluster, 1)

The actual timeseries for the total available power of the cluster are available in the results object as a numpy 2D-array:

print(results.available_power)