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- from .consumption import *
- from .production import *
- from .storage import *
- from typing import Tuple
- class Scenario:
- def __init__(
- self,
- yearly_total: float = None,
- sources: dict = {},
- multistorage: dict = {},
- flexibility_power=0,
- flexibility_time=4,
- ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
- """Run a mix scenario.
- :param yearly_total: Annual consumption in GWh, for models with fixed annual consumption. defaults to None
- :type yearly_total: _type_, optional
- :param sources: float, defaults to {}
- :type sources: dict, optional
- :param multistorage: _description_, defaults to {}
- :type multistorage: dict, optional
- :param flexibility_power: _description_, defaults to 0
- :type flexibility_power: int, optional
- :param flexibility_time: _description_, defaults to 8
- :type flexibility_time: int, optional
- :return: Performance of the scenario.
- :rtype: Tuple[float, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
- """
- self.yearly_total = yearly_total
- self.sources = sources
- self.multistorage = multistorage
- self.flexibility_power = flexibility_power
- self.flexibility_time = flexibility_time
- pass
- def run(self, times, intermittent_load_factors):
- # consumption
- consumption_model = FittedConsumptionModel(self.yearly_total)
- load = consumption_model.get(times)
- # intermittent sources (or sources with fixed load factors)
- intermittent_array = IntermittentArray(
- intermittent_load_factors, np.transpose([self.sources["intermittent"]])
- )
- power = intermittent_array.power()
- if self.flexibility_power > 0:
- flexibility_model = ConsumptionFlexibilityModel(
- self.flexibility_power, self.flexibility_time
- )
- load = flexibility_model.run(load, power)
- power_delta = power - load
- # adjust power to load with storage
- storage_model = MultiStorageModel(
- np.array(self.multistorage["capacity"])
- * np.array(self.multistorage["power"]),
- self.multistorage["power"],
- self.multistorage["power"],
- self.multistorage["efficiency"],
- )
- storage, storage_impact = storage_model.run(power_delta)
- gap = load - power - storage_impact.sum(axis=0)
- # further adjust power to load with dispatchable power sources
- dispatchable_model = DispatchableArray(self.sources["dispatchable"])
- dp = dispatchable_model.power(gap)
- gap -= dp.sum(axis=0)
- S = np.maximum(gap, 0).mean()
- return S, load, power, gap, storage, dp
|
