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aeromaps.models.impacts.generic_energy_model.bottom_up.abatement_cost

abatement_cost

====================== Module to compute energy abatement costs for different pathways.

EnergyAbatementCost 

EnergyAbatementCost(name, pathway_name, pathways_data, *args, **kwargs)

Bases: AeroMAPSModel

Computes specific abatement cost and generic specific abatement cost for a pathway, based on discounted costs and avoided emissions over the lifespan of each vintage.

Parameters:

Name Type Description Default
name str

Name of the model instance ('f"{pathway_name}_bottom_up_abatement_cost"' by default).

 required
pathway_name str

Name of the energy pathway for which the abatement cost is computed.

 required
pathways_data dict

Dictionary containing data for all pathways, used to complete inputs names necessary.

 required

Attributes:

Name Type Description
input_names dict

Dictionary of input variable names populated at model initialisation before MDA chain creation.
output_names dict

Dictionary of output variable names populated at model initialisation before MDA chain creation.
Source code in aeromaps/models/impacts/generic_energy_model/bottom_up/abatement_cost.py 
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def __init__(self, name, pathway_name, pathways_data, *args, **kwargs):
    super().__init__(
        name=name,
        model_type="custom",
        *args,
        **kwargs,
    )
    self.pathway_name = pathway_name

    # Inputs needed: discounted costs, unitary emissions, discounted emissions
    self.input_names = {
        f"{self.pathway_name}_lifespan_unitary_discounted_costs": pd.Series([0.0]),
        f"{self.pathway_name}_lifespan_unitary_emissions": pd.Series([0.0]),
        f"{self.pathway_name}_lifespan_discounted_unitary_emissions": pd.Series([0.0]),
        f"{self.pathway_name}_mean_mfsp": pd.Series([0.0]),
        f"{self.pathway_name}_mean_co2_emission_factor": pd.Series([0.0]),
        "cac_reference_unitary_discounted_costs": pd.Series([0.0]),
        "cac_reference_unitary_discounted_emissions": pd.Series([0.0]),
        "cac_reference_unitary_emissions": pd.Series([0.0]),
        "cac_reference_co2_emission_factor": pd.Series([0.0]),
        "cac_reference_mfsp": pd.Series([0.0]),
        "social_discount_rate": 0.0,
        "exogenous_carbon_price_trajectory": pd.Series([0.0]),
    }

    if f"{self.pathway_name}_eis_plant_lifespan" in pathways_data:
        self.input_names[f"{self.pathway_name}_eis_plant_lifespan"] = 0.0

    # Outputs: specific abatement cost and generic specific abatement cost

    self.output_names = {
        f"{self.pathway_name}_carbon_abatement_cost": pd.Series([0.0]),
        f"{self.pathway_name}_specific_carbon_abatement_cost": pd.Series([0.0]),
        f"{self.pathway_name}_generic_specific_carbon_abatement_cost": pd.Series([0.0]),
    }

compute 

compute(input_data)

Compute the specific abatement cost and generic specific abatement cost for each vintage.

Parameters:

Name Type Description Default
input_data

Dictionary containing all input data required for the computation, completed at model instantiation with information from yaml files and outputs of other models.

 required

Returns:

Type Description
output_data

Dictionary containing all output data resulting from the computation. Contains outputs defined during model instantiation.
Source code in aeromaps/models/impacts/generic_energy_model/bottom_up/abatement_cost.py 
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def compute(self, input_data) -> dict:
    """
    Compute the specific abatement cost and generic specific abatement cost for each vintage.

    Parameters
    ----------
    input_data
        Dictionary containing all input data required for the computation, completed at model instantiation with information from yaml files and outputs of other models.

    Returns
    -------
    output_data
        Dictionary containing all output data resulting from the computation. Contains outputs defined during model instantiation.
    """
    unitary_discounted_costs = input_data.get(
        f"{self.pathway_name}_lifespan_unitary_discounted_costs", pd.Series([0.0])
    )
    unitary_discounted_emissions = input_data.get(
        f"{self.pathway_name}_lifespan_discounted_unitary_emissions", pd.Series([0.0])
    )
    unitary_emissions = input_data.get(
        f"{self.pathway_name}_lifespan_unitary_emissions", pd.Series([0.0])
    )
    mfsp = input_data.get(f"{self.pathway_name}_mean_mfsp", pd.Series([0.0]))
    co2_emission_factor = input_data.get(
        f"{self.pathway_name}_mean_co2_emission_factor", pd.Series([0.0])
    )
    fossil_mfsp = input_data.get("cac_reference_mfsp", pd.Series([0.0]))
    fossil_ef = input_data.get("cac_reference_co2_emission_factor", pd.Series([0.0]))

    reference_unitary_discounted_costs = input_data.get(
        "cac_reference_unitary_discounted_costs", pd.Series([0.0])
    )
    reference_unitary_discounted_emissions = input_data.get(
        "cac_reference_unitary_discounted_emissions", pd.Series([0.0])
    )
    reference_unitary_emissions = input_data.get(
        "cac_reference_unitary_emissions", pd.Series([0.0])
    )

    generic_specific_carbon_abatement_cost = (
        unitary_discounted_costs - reference_unitary_discounted_costs
    ) / (reference_unitary_discounted_emissions - unitary_discounted_emissions)
    specific_carbon_abatement_cost = (
        unitary_discounted_costs - reference_unitary_discounted_costs
    ) / (reference_unitary_emissions - unitary_emissions)

    carbon_abatement_cost = (mfsp - fossil_mfsp) / (fossil_ef - co2_emission_factor)

    # Unit conversion = > from €/gCO2 to €/tCO2
    specific_carbon_abatement_cost *= 1000000
    generic_specific_carbon_abatement_cost *= 1000000
    carbon_abatement_cost *= 1000000

    output_data = {
        f"{self.pathway_name}_specific_carbon_abatement_cost": specific_carbon_abatement_cost,
        f"{self.pathway_name}_generic_specific_carbon_abatement_cost": generic_specific_carbon_abatement_cost,
        f"{self.pathway_name}_carbon_abatement_cost": carbon_abatement_cost,
    }

    self._store_outputs(output_data)
    return output_data

ReferenceAbatementCost 

ReferenceAbatementCost(name, pathway_name, configuration_data, *args, **kwargs)

Bases: AeroMAPSModel

Computes specific abatement cost and generic specific abatement cost for a reference pathway, based on discounted costs and avoided emissions over the lifespan of each vintage.

Parameters:

Name Type Description Default
name str

Name of the model instance ('f"{pathway_name}_bottom_up_abatement_cost"' by default).

 required
pathway_name str

Name of the energy pathway for which the abatement cost is computed.

 required
pathways_data dict

Dictionary containing data for all pathways, used to complete inputs names necessary.

 required

Attributes:

Name Type Description
input_names dict

Dictionary of input variable names populated at model initialisation before MDA chain creation.
output_names dict

Dictionary of output variable names populated at model initialisation before MDA chain creation.
Source code in aeromaps/models/impacts/generic_energy_model/bottom_up/abatement_cost.py 
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def __init__(self, name, pathway_name, configuration_data, *args, **kwargs):
    super().__init__(
        name=name,
        model_type="custom",
        *args,
        **kwargs,
    )
    self.pathway_name = pathway_name

    # Inputs needed: discounted costs, unitary emissions, discounted emissions
    self.input_names = {
        "social_discount_rate": 0.0,
        "exogenous_carbon_price_trajectory": pd.Series([0.0]),
    }

    # 2 cases: either ref pathway is defined to-down, as before. Weak CAC definition, considering its average price and EF
    # 1:
    if (
        configuration_data["cost_model"] == "bottom-up"
        and configuration_data["cost_model"] == "bottom-up"
    ):
        self.input_names.update(
            {
                f"{self.pathway_name}_lifespan_unitary_discounted_costs": pd.Series([0.0]),
                f"{self.pathway_name}_lifespan_unitary_emissions": pd.Series([0.0]),
                f"{self.pathway_name}_lifespan_discounted_unitary_emissions": pd.Series([0.0]),
                f"{self.pathway_name}_mean_mfsp": pd.Series([0.0]),
                f"{self.pathway_name}_mean_co2_emission_factor": pd.Series([0.0]),
            }
        )
        self.bottom_up_cac = True
    else:
        print(
            f"⚠️ Warning: reference pathway for CAC ({self.pathway_name}) is not defined as bottom-up, "
            f"using top-down values for CAC computation."
        )
        self.input_names.update(
            {
                f"{self.pathway_name}_mean_mfsp": pd.Series([0.0]),
                f"{self.pathway_name}_mean_co2_emission_factor": pd.Series([0.0]),
            }
        )
        self.bottom_up_cac = False

    # Outputs: specific abatement cost and generic specific abatement cost

    self.output_names = {
        "cac_reference_unitary_discounted_costs": pd.Series([0.0]),
        "cac_reference_unitary_discounted_emissions": pd.Series([0.0]),
        "cac_reference_unitary_emissions": pd.Series([0.0]),
        "cac_reference_co2_emission_factor": pd.Series([0.0]),
        "cac_reference_mfsp": pd.Series([0.0]),
    }

compute 

compute(input_data)

Compute the specific abatement cost and generic specific abatement cost for each vintage.

Parameters:

Name Type Description Default
input_data

Dictionary containing all input data required for the computation, completed at model instantiation with information from yaml files and outputs of other models.

 required

Returns:

Type Description
output_data

Dictionary containing all output data resulting from the computation. Contains outputs defined during model instantiation.
Source code in aeromaps/models/impacts/generic_energy_model/bottom_up/abatement_cost.py 
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def compute(self, input_data) -> dict:
    """
    Compute the specific abatement cost and generic specific abatement cost for each vintage.

    Parameters
    ----------
    input_data
        Dictionary containing all input data required for the computation, completed at model instantiation with information from yaml files and outputs of other models.

    Returns
    -------
    output_data
        Dictionary containing all output data resulting from the computation. Contains outputs defined during model instantiation.
    """

    ref_mfsp = input_data.get(f"{self.pathway_name}_mean_mfsp", pd.Series([0.0]))
    ref_ef = input_data.get(f"{self.pathway_name}_mean_co2_emission_factor", pd.Series([0.0]))

    if self.bottom_up_cac:
        reference_unitary_discounted_costs = input_data.get(
            f"{self.pathway_name}_lifespan_unitary_discounted_costs", pd.Series([0.0])
        )
        reference_unitary_discounted_emissions = input_data.get(
            f"{self.pathway_name}_lifespan_discounted_unitary_emissions", pd.Series([0.0])
        )
        reference_unitary_emissions = input_data.get(
            f"{self.pathway_name}_lifespan_unitary_emissions", pd.Series([0.0])
        )

    else:
        reference_lifespan = input_data.get(f"{self.pathway_name}_eis_plant_lifespan", 25.0)
        social_discount_rate = input_data.get("social_discount_rate")
        exogenous_carbon_price_trajectory = input_data.get(
            "exogenous_carbon_price_trajectory", pd.Series([0.0])
        )

        reference_unitary_discounted_costs = self._unit_discounted_cumul_costs(
            ref_mfsp, reference_lifespan, social_discount_rate
        )
        (reference_unitary_emissions, reference_unitary_discounted_emissions) = (
            self._unitary_cumul_emissions_vintage(
                ref_ef,
                exogenous_carbon_price_trajectory,
                reference_lifespan,
                social_discount_rate,
            )
        )

    output_data = {
        "cac_reference_unitary_discounted_costs": reference_unitary_discounted_costs,
        "cac_reference_unitary_discounted_emissions": reference_unitary_discounted_emissions,
        "cac_reference_unitary_emissions": reference_unitary_emissions,
        "cac_reference_co2_emission_factor": ref_ef,
        "cac_reference_mfsp": ref_mfsp,
    }

    self._store_outputs(output_data)
    return output_data