modules

Top-level package for Volte Norway Grid Tariff Calculator.

analysis

monthly_maximums(curve, top_n_averaged=1, single_max_per_day=False)

Function to get the maximum value of each month in the consumption curve. top_n_averaged determines how many of the largest values are averaged per month. single_max_per_day determines if only the maximum value of each day is kept before finding the largest values of each month and averaging.

Expects a list of tuples containing datetimes and corresponding consumption values in kWH during the hour starting at the datetime, an integer for top_n_averaged and a boolean for single_max_per_day.

Source code in grid_tariff_calculator/analysis.py

def monthly_maximums(
    curve: [(datetime.datetime, float)],
    top_n_averaged: int = 1,
    single_max_per_day: bool = False,
) -> pd.DataFrame:
    """Function to get the maximum value of each month in the consumption curve.
    top_n_averaged determines how many of the largest values are averaged per month.
    single_max_per_day determines if only the maximum value of each day is kept before
    finding the largest values of each month and averaging.

    Expects a list of tuples containing datetimes and corresponding consumption values in kWH during the hour starting at the datetime,
    an integer for top_n_averaged and a boolean for single_max_per_day.
    """

    # assert top_n_averaged >= 1
    curve_df = pd.DataFrame(curve, columns=["start_time", "value"])

    with_total_consumption = _get_monthly_totals(curve_df)

    if single_max_per_day:
        curve_df = _get_daily_maximums(curve_df)

    curve_df = _get_top_n_values(curve_df, top_n_averaged)

    curve_df = _average_groups(curve_df)

    curve_df = curve_df.merge(with_total_consumption, on=["year", "month"])

    return curve_df

app

Main module.

grid_tariff_dataclasses

AmsMeter dataclass

Class to create meter objects

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class AmsMeter:
    """Class to create meter objects"""

    identification: str
    distributor_name: str
    estimated_yearly_cons: float

ElFeeTariff dataclass

Class to create el-fee tariff objects Forbruksavgift/el-avgift in norwegian.

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class ElFeeTariff:
    """ Class to create el-fee tariff objects
    Forbruksavgift/el-avgift in norwegian."""

    distributor_name: str
    price_NOK_per_kWh: float
    start_time: datetime.datetime
    end_time: datetime.datetime

EnergyTariff dataclass

Class to create energy tariff objects Energiledd in norwegian.

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class EnergyTariff:
    """ Class to create energy tariff objects
    Energiledd in norwegian.
    """

    distributor_name: str
    price_NOK_per_kWh: float
    start_time: datetime.datetime   # TODO: implement day/night tariff
    end_time: datetime.datetime

EnovaFee dataclass

Class to create objects for the Enova fee

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class EnovaFee:
    """ Class to create objects for the Enova fee"""

    price_NOK_per_year: float
    start_time: datetime.datetime
    end_time: datetime.datetime

FixedTariff dataclass

Class to create objects for fixed yearly fees

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class FixedTariff:
    """ Class to create objects for fixed yearly fees"""

    distributor_name: str
    price_NOK_per_year: float
    start_time: datetime.datetime
    end_time: datetime.datetime

PeakTariffLargeConsumers dataclass

Class to create peak tariff objects for large consumers (above 100 MWh/year) Effektledd in norwegian.

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class PeakTariffLargeConsumers:
    """ Class to create peak tariff objects for large consumers (above 100 MWh/year)
    Effektledd in norwegian.
    """

    distributor_name: str
    price_NOK_per_kWh: float
    start_time: datetime.datetime
    end_time: datetime.datetime

PeakTariffSmallConsumers dataclass

Class to create peak tariff objects for small consumers (below 100 MWh/year) Fastledd/kapasitetsledd in norwegian.

Source code in grid_tariff_calculator/grid_tariff_dataclasses.py

@dataclass
class PeakTariffSmallConsumers:
    """ Class to create peak tariff objects for small consumers (below 100 MWh/year)
    Fastledd/kapasitetsledd in norwegian.
    """

    distributor_name: str
    price_NOK: float
    start_time: datetime.datetime
    end_time: datetime.datetime
    lower_limit: float
    upper_limit: float

tariff_calculator

calculate_costs_over_100k(curve, threshold, load_shifting=True, fixed_tariffs=[], peak_tariffs_large_consumers=[], el_fee=[])

Function to calculate the grid rental costs of a consumption curve. Assumes estimated yearly consumption is over 100k kWh.

Returns a dataframe with columns "year", "month", "maximum", "total_consumption", "peak_cost" and "fixed_cost".

Source code in grid_tariff_calculator/tariff_calculator.py

def calculate_costs_over_100k(
    curve: [(datetime.datetime, float)],
    threshold: float,
    load_shifting: bool = True,
    fixed_tariffs: [FixedTariff] = [],
    peak_tariffs_large_consumers: [PeakTariffLargeConsumers] = [],
    el_fee: [ElFeeTariff] = [],
) -> pd.DataFrame:
    """Function to calculate the grid rental costs of a consumption curve.
    Assumes estimated yearly consumption is over 100k kWh.

    Returns a dataframe with columns "year", "month", "maximum", "total_consumption", "peak_cost" and "fixed_cost".
    """

    monthly_max_df = monthly_maximums(
        curve, top_n_averaged=1, single_max_per_day=False
    )[["year", "month", "maximum", "total_consumption", "maximums_occured_on"]]

    _set_to_threshold(curve, monthly_max_df, threshold, load_shifting)

    if peak_tariffs_large_consumers:
        monthly_max_df["peak_cost"] = _get_peak_costs_large_consumers(
            monthly_max_df, peak_tariffs_large_consumers
        )
    else:
        logger.warning(
            "No peak tariffs for large consumers provided. Peak cost will not be calculated."
        )

    if fixed_tariffs:
        monthly_max_df["fixed_cost"] = _get_fixed_tariff(monthly_max_df, fixed_tariffs)
        monthly_max_df["fixed_cost"] = monthly_max_df.apply(
            _weight_by_nr_days, axis=1, col_name="fixed_cost"
        )
    else:
        logger.warning(
            "No fixed tariffs provided. Fixed cost will not be calculated."
        )

    return monthly_max_df

calculate_costs_under_100k(curve, threshold, load_shifting=True, peak_tariffs_small_consumers=[], el_fee=[])

Function to calculate the grid rental costs of a consumption curve. Assumes estimated yearly consumption is under 100k kWh.

Returns a dataframe with columns "year", "month", "maximum", "total_consumption" and "peak_cost".

Source code in grid_tariff_calculator/tariff_calculator.py

def calculate_costs_under_100k(
    curve: [(datetime.datetime, float)],
    threshold: float,
    load_shifting: bool = True,
    peak_tariffs_small_consumers: [PeakTariffSmallConsumers] = [],
    el_fee: [ElFeeTariff] = [],
) -> pd.DataFrame:
    """Function to calculate the grid rental costs of a consumption curve.
    Assumes estimated yearly consumption is under 100k kWh.

    Returns a dataframe with columns "year", "month", "maximum", "total_consumption" and "peak_cost".
    """

    monthly_max_df = monthly_maximums(curve, top_n_averaged=3, single_max_per_day=True)[
        ["year", "month", "maximum", "total_consumption", "maximums_occured_on"]
    ]

    monthly_max_df = _set_to_threshold(curve, monthly_max_df, threshold, load_shifting)

    if peak_tariffs_small_consumers:
        monthly_max_df["peak_cost"] = _get_peak_tariff_small_consumers(
            monthly_max_df, peak_tariffs_small_consumers
        )
    else:
        logger.warning(
            "No peak tariffs for small consumers provided. Peak cost will not be calculated."
        )

    return monthly_max_df

calculate_grid_rental(curve, load_shifting=True, estimated_yearly_cons=100000, energy_tariffs=[], enova_fee=[], fixed_tariffs=[], peak_tariffs_large_consumers=[], peak_tariffs_small_consumers=[], el_fee=[])

Function to get the grid rental costs of the original consumption curve.

Source code in grid_tariff_calculator/tariff_calculator.py

def calculate_grid_rental(
    curve: [(datetime.datetime, float)],
    load_shifting: bool = True,
    estimated_yearly_cons: float = 100000,
    energy_tariffs: [EnergyTariff] = [],
    enova_fee: [EnovaFee] = [],
    fixed_tariffs: [FixedTariff] = [],
    peak_tariffs_large_consumers: [PeakTariffLargeConsumers] = [],
    peak_tariffs_small_consumers: [PeakTariffSmallConsumers] = [],
    el_fee: [ElFeeTariff] = [],
) -> pd.DataFrame:
    """Function to get the grid rental costs of the original consumption curve."""

    curve_CET = [(t[0].astimezone(pytz.timezone('Europe/Oslo')), t[1]) for t in curve]

    monthly_grid_rental = _calculate_costs(
        curve_CET,
        estimated_yearly_cons,
        threshold=1,
        load_shifting=load_shifting,
        energy_tariffs=energy_tariffs,
        enova_fee=enova_fee,
        fixed_tariffs=fixed_tariffs,
        peak_tariffs_large_consumers=peak_tariffs_large_consumers,
        peak_tariffs_small_consumers=peak_tariffs_small_consumers,
        el_fee=el_fee,
    )
    return monthly_grid_rental

calculate_threshold_grid_rental(curve, threshold, load_shifting=True, estimated_yearly_cons=100000, energy_tariffs=[], enova_fee=[], fixed_tariffs=[], peak_tariffs_large_consumers=[], peak_tariffs_small_consumers=[], el_fee=[])

Function to get the grid rental costs of a consumption curve that's lowered to a threshold.

Source code in grid_tariff_calculator/tariff_calculator.py

def calculate_threshold_grid_rental(
    curve: [(datetime.datetime, float)],
    threshold: float,
    load_shifting: bool = True,
    estimated_yearly_cons: float = 100000,
    energy_tariffs: [EnergyTariff] = [],
    enova_fee: [EnovaFee] = [],
    fixed_tariffs: [FixedTariff] = [],
    peak_tariffs_large_consumers: [PeakTariffLargeConsumers] = [],
    peak_tariffs_small_consumers: [PeakTariffSmallConsumers] = [],
    el_fee: [ElFeeTariff] = [],
) -> pd.DataFrame:
    """Function to get the grid rental costs of a consumption curve that's lowered to a threshold."""

    curve_CET = [(t[0].astimezone(pytz.timezone('Europe/Oslo')), t[1]) for t in curve]

    monthly_grid_rental = _calculate_costs(
        curve_CET,
        estimated_yearly_cons,
        threshold=threshold,
        load_shifting=load_shifting,
        energy_tariffs=energy_tariffs,
        enova_fee=enova_fee,
        fixed_tariffs=fixed_tariffs,
        peak_tariffs_large_consumers=peak_tariffs_large_consumers,
        peak_tariffs_small_consumers=peak_tariffs_small_consumers,
        el_fee=el_fee,
    )

    return monthly_grid_rental