result

Result dataclass

Result(
    constraints: dict[str, DataFrame] = dict(),
    parameters: dict[str, DataFrame] = dict(),
    objectives: dict[str, DataFrame] = dict(),
    sets: dict[str, DataFrame] = dict(),
    variables: dict[str, DataFrame] = dict(),
    postprocessing: dict[str, DataFrame] = dict(),
)

constraints class-attribute instance-attribute

constraints: dict[str, DataFrame] = field(
    default_factory=dict
)

objectives class-attribute instance-attribute

objectives: dict[str, DataFrame] = field(
    default_factory=dict
)

parameters class-attribute instance-attribute

parameters: dict[str, DataFrame] = field(
    default_factory=dict
)

postprocessing class-attribute instance-attribute

postprocessing: dict[str, DataFrame] = field(
    default_factory=dict
)

sets class-attribute instance-attribute

sets: dict[str, DataFrame] = field(default_factory=dict)

variables class-attribute instance-attribute

variables: dict[str, DataFrame] = field(
    default_factory=dict
)

parse_result

parse_result(ampl, id_run=None, results_old=None) -> Result

Source code in src/energyscope/result.py

def parse_result(ampl, id_run=None, results_old=None) -> Result: 
    def _parse_set(ampl, name, set_) -> dict[str, pd.DataFrame]:
        if set_.is_scalar():
            return {name: set_.to_pandas().reset_index().rename(columns={'index': name})}
        else:
            result={name:{index[0]: index[1].get_values().toList() for index in ampl.get_set(name).instances()}}
            return result

    objectives = {name: objective.to_pandas().rename(columns=lambda v: v.rstrip('.val'))
                  for name, objective in ampl.get_objectives()}
    variables = {name: variable.to_pandas().rename(columns=lambda v: v.rstrip('.val'))
                 for name, variable in ampl.get_variables()}
    parameters = {name: parameter.to_pandas() for name, parameter in ampl.get_parameters()}
    sets = {}
    for name, set_ in ampl.get_sets():
        sets = {**sets, **_parse_set(ampl, name, set_)}

# If the solving of the model is not ideal we replace all results by 0 so that the rest of the optimizations continue,
#  to check which optimizations failed check the objectives results, OBJ = 0 means failed optimization
    if ampl.solve_result_num>99:
        for key in variables.keys():
            variables[key].loc[:,:]=0
        for key in parameters.keys():
            parameters[key].loc[:,:]=0
        for key in objectives.keys():
            objectives[key].loc[:,:]=0

    if id_run is not None:
        for _, value in objectives.items():
            value['Run'] = id_run
        for _, value in variables.items():
            value['Run'] = id_run
        for _, value in parameters.items():
            value['Run'] = id_run
        # for _, value in sets.items():
        #     value['Run'] = id_run

    # if results_old is not None:   # TODO implement the option to merge results in the parser
    #     variables = {name: pd.concat([results_old.variables[name], variables[name]]) for name in results_old.variables.keys()}
    #     parameters = {name: pd.concat([results_old.parameters[name], parameters[name]]) for name in results_old.parameters.keys()}

    return Result(
        objectives=objectives,
        variables=variables,
        parameters=parameters,
        sets=sets,
    )

postprocessing

postprocessing(
    Result, df_monthly=True, df_annual=True
) -> Result

Performs post-processing of EnergyScope results by organizing and categorizing key metrics into annual and monthly dataframes.

Parameters:

Result : object The result object containing the outputs of the model's run.

bool, optional

If True, the function processes and stores monthly results, including the flows of different technologies. Defaults to True.

bool, optional

If True, the function processes and stores annual results, including investment costs, maintenance costs, production, and the economic lifetimes (tau) of technologies. Defaults to True.

Returns:

Result : object The updated Result object, containing the processed dataframes

Processing Steps:

• For annual results (df_annual), the function compiles investment costs, maintenance costs, annual production, and flow multipliers. It also calculates annualized investment costs based on the economic lifetime (tau).
• For monthly results (df_monthly), it computes the monthly flows of technologies using flow multipliers, operating time, and layer factors (in/out ratios).
• Both annual and monthly results are categorized into high-level sectors (e.g., Electricity, Mobility, Industry) and assigned Category, Category_2, and Sector labels.
• Technologies that do not fit predefined categories are assigned the "Others" label.

Sector-Technology Mapping:

The function uses predefined mappings between sectors (e.g., Electricity, Mobility, Industry) and their respective technologies to categorize the results accordingly.

Examples:

• Annual Data: The function aggregates and processes investment costs (C_inv), maintenance costs (C_maint), and production (Annual_Prod), calculates annualized costs, and categorizes the data.
• Monthly Data: It merges flow multipliers (F_Mult_t), operating time (t_op), and layer in-out factors (layers_in_out) to compute the monthly flows of technologies and their associated categories.

Source code in src/energyscope/result.py

def postprocessing(Result, df_monthly=True, df_annual=True) -> Result:
    """
        Performs post-processing of EnergyScope results by organizing and categorizing key metrics into annual and monthly dataframes.

        Parameters:
        ----------
        Result : object
            The result object containing the outputs of the model's run.

        df_monthly : bool, optional
            If True, the function processes and stores monthly results, including the flows of different technologies. Defaults to True.

        df_annual : bool, optional
            If True, the function processes and stores annual results, including investment costs, maintenance costs, production, and the economic lifetimes (tau) of technologies. Defaults to True.

        Returns:
        -------
        Result : object
            The updated Result object, containing the processed dataframes

        Processing Steps:
        -----------------
        - For annual results (`df_annual`), the function compiles investment costs, maintenance costs, annual production, and flow multipliers. It also calculates annualized investment costs based on the economic lifetime (`tau`).
        - For monthly results (`df_monthly`), it computes the monthly flows of technologies using flow multipliers, operating time, and layer factors (in/out ratios).
        - Both annual and monthly results are categorized into high-level sectors (e.g., Electricity, Mobility, Industry) and assigned `Category`, `Category_2`, and `Sector` labels.
        - Technologies that do not fit predefined categories are assigned the "Others" label.

        Sector-Technology Mapping:
        --------------------------
        The function uses predefined mappings between sectors (e.g., Electricity, Mobility, Industry) and their respective technologies to categorize the results accordingly.

        Examples:
        ---------
        - Annual Data:
        The function aggregates and processes investment costs (`C_inv`), maintenance costs (`C_maint`), and production (`Annual_Prod`), calculates annualized costs, and categorizes the data.
        - Monthly Data:
        It merges flow multipliers (`F_Mult_t`), operating time (`t_op`), and layer in-out factors (`layers_in_out`) to compute the monthly flows of technologies and their associated categories.
        """

    sector_technologies = {
        "Electricity": [
            "CCGT", "CCGT_CC", "COAL_US", "COAL_IGCC", "COAL_US_CC", "COAL_IGCC_CC", 
            "HYDRO_GAS_CHP", "NUCLEAR"
        ],
        "Mobility": [
            "TRAMWAY", "COACH_CNG_STOICH", "COACH_DIESEL", "COACH_EV", 
            "COACH_FC_HYBRID_H2", "COACH_FC_HYBRID_CH4", "COACH_HY_DIESEL", 
            "COMMUTER_RAIL_DIESEL", "COMMUTER_RAIL_ELEC", "TRAIN_DIESEL", 
            "TRAIN_ELEC", "TRAIN_NG", "TRAIN_H2", "BUS_CNG_STOICH", "BUS_DIESEL", 
            "BUS_FC_HYBRID_H2", "BUS_FC_HYBRID_CH4", "BUS_HY_DIESEL", "BUS_EV", 
            "CAR_BEV_LOWRANGE", "CAR_BEV_MEDRANGE_LOCAL", "CAR_DIESEL_LOCAL", 
            "CAR_DME_D10_LOCAL", "CAR_ETOH_E10_LOCAL", "CAR_ETOH_E85_LOCAL", 
            "CAR_FC_H2_LOCAL", "CAR_FC_CH4_LOCAL", "CAR_GASOLINE_LOCAL", 
            "CAR_HEV_LOCAL", "CAR_MEOH_LOCAL", "CAR_NG_LOCAL", "CAR_PHEV_LOCAL", 
            "CAR_BEV_MEDRANGE_LONGD", "CAR_DIESEL_LONGD", "CAR_DME_D10_LONGD", 
            "CAR_ETOH_E10_LONGD", "CAR_ETOH_E85_LONGD", "CAR_FC_H2_LONGD", 
            "CAR_FC_CH4_LONGD", "CAR_GASOLINE_LONGD", "CAR_HEV_LONGD", "CAR_HEV", 
            "CAR_MEOH_LONGD", "CAR_NG_LONGD", "CAR_PHEV_LONGD", "TRAIN_FREIGHT", 
            "TRAIN_FREIGHT_DIESEL", "TRAIN_FREIGHT_NG", "TRAIN_FREIGHT_H2", 
            "TRUCK", "TRUCK_CO2", "TRUCK_EV", "TRUCK_SNG", "TRUCK_FC", "PLANE",
            "CAR_GASOLINE", "CAR_DIESEL", "CAR_NG", "CAR_PHEV", "CAR_MEOH", "CAR_FC_H2", "CAR_FC_CH4", "CAR_BEV_MEDRANGE", "CAR_ETOH_E10", "CAR_ETOH_E85", "CAR_DME_D10"
        ],
        "Electric Infrastructure": [
            "TRAFO_ML", "TRAFO_LM", "TRAFO_HM", "TRAFO_MH", "TRAFO_EH", "TRAFO_HE",
            "EHV_GRID", "HV_GRID", "MV_GRID", "LV_GRID","GRID"
        ],
        "Gas Infrastructure": [
            "EHP_H2_GRID", "HP_H2_GRID", "MP_H2_GRID", "LP_H2_GRID",
            "EHP_NG_GRID", "HP_NG_GRID", "MP_NG_GRID", "LP_NG_GRID",
            "NG_EXP_EH", "NG_EXP_HM", "NG_EXP_ML", "NG_EXP_EH_COGEN", "NG_EXP_HM_COGEN", "NG_EXP_ML_COGEN",
            "NG_COMP_HE", "NG_COMP_MH", "NG_COMP_LM",
            "H2_EXP_EH", "H2_EXP_HM", "H2_EXP_ML", "H2_EXP_EH_COGEN", "H2_EXP_HM_COGEN", "H2_EXP_ML_COGEN",
            "H2_COMP_HE", "H2_COMP_MH", "H2_COMP_LM"
        ],
        "Wind": ["WIND"],
        "PV": ["PV_LV", "PV_MV", "PV_HV", "PV_EHV"],
        "Geothermal": ["GEOTHERMAL", "DHN_DEEP_GEO", "DEC_DEEP_GEO"],
        "Hydro River & Dam": ["NEW_HYDRO_RIVER", "NEW_HYDRO_DAM", "HYDRO_RIVER", "HYDRO_DAM"],
        "Industry": [
             "AL_MAKING", "AL_MAKING_HR", "CEMENT_PROD", 
            "CEMENT_PROD_HP", "FOOD_PROD", "FOOD_PROD_HP", "FOOD_PROD_HR", "PAPER_MAKING", 
            "PAPER_MAKING_HP", "PAPER_MAKING_HR", "STEEL_MAKING", "STEEL_MAKING_HP", 
            "STEEL_MAKING_HR", "WOOD_METHANOL", "CO2_METHANOL", "METHANOL_FT", 
            "METHANE_TO_METHANOL", "CUMENE_PROCESS", "METHANOL_CARBONYLATION", 
            "ETHANE_OXIDATION", "ETHYLENE_POLYMERIZATION", "PET_FORMATION", 
            "PVC_FORMATION", "POLYPROPYLENE_PP", "STYRENE_POLYMERIZATION",
            "HYDRO_GAS","AN_DIG_SI","BIOMASS_ETHANOL","FT","AN_DIG", "SNG_NG",
            "EFFICIENCY", "METHANATION","GASIFICATION_SNG", "PYROLYSIS","NG_REFORMING",
            "METHANOL_TO_AROMATICS","METHANOL_TO_OLEFINS","CO2-To-Diesel","ETHANE_CRACKING",
            "METATHESIS_PROPYLENE","SMART_PROCESS","CROPS_TO_JETFUELS","CO2_TO_JETFUELS",
            "BIOGAS_BIOMETHANE","CROPS_TO_ETHANOL","ETHANE_TO_ETHYLENE","ETHANOL_TO_JETFUELS"
            ,"GASIFICATION_H2","OTHER_BIOMASS","EOR","DOGR","UNMINEABLE_COAL_SEAM","DEEP_SALINE",
            "MINES_STORAGE","DIRECT_USAGE","CEMENT"
        ],
        "Low Temperature Heat": [
            "DHN_HP_ELEC", "DHN_COGEN_GAS", "DHN_COGEN_WOOD", "DHN_COGEN_WASTE", 
            "DHN_BOILER_GAS", "DHN_BOILER_WOOD", "DHN_BOILER_OIL", "DHN_RENOVATION", 
            "DEC_HP_ELEC", "DEC_THHP_GAS", "DEC_COGEN_GAS", "DEC_COGEN_OIL", 
            "DEC_COGEN_WOOD", "DEC_ADVCOGEN_H2", "DEC_BOILER_GAS", "DEC_BOILER_WOOD", 
            "DEC_BOILER_OIL", "DEC_SOLAR", "DEC_DIRECT_ELEC", "DEC_RENOVATION","DHN",
            "LT_DEC_WH", "LT_DHN_WH", "HT_LT", "HT_LT_DEC",
        ],
        "High Temperature Heat": 
        ["IND_COGEN_GAS", "IND_COGEN_WOOD", "IND_COGEN_WASTE", "IND_BOILER_GAS", 
            "IND_BOILER_WOOD", "IND_BOILER_OIL", "IND_BOILER_COAL", "IND_BOILER_WASTE", 
            "IND_HP_ELEC", "IND_DIRECT_ELEC"],
        "Storage": [
            "DIE_STO", "STO_DIE", "GASO_STO", "STO_GASO", "ELEC_STO", "STO_ELEC", 
            "H2_STO", "STO_H2", "CO2_STO", "STO_CO2", "NG_STO", "STO_NG",
            "DHN_TH_STORAGE", "DEC_TH_STORAGE","BATTERY",""
        ],
        "Electrolysis": [
            "ALKALINE_ELECTROLYSIS", "PEM_ELECTROLYSIS", "SOEC_ELECTROLYSIS"
        ],
        "Carbon Capture": ["CARBON_CAPTURE","DAC_HT", "DAC_LT"]
        #"Car Fuel Types": [ "CAR_GASOLINE", "CAR_DIESEL", "CAR_NG", "CAR_PHEV", "CAR_MEOH", "CAR_FC_H2", "CAR_FC_CH4", "CAR_BEV_MEDRANGE", "CAR_ETOH_E10", "CAR_ETOH_E85", "CAR_DME_D10"]
    }

    if df_annual:
        # df_annual
        df_ = [
        Result.variables['C_inv'].set_index('Run',append=True),
        Result.variables['C_maint'].set_index('Run',append=True),
        Result.variables['Annual_Prod'].set_index('Run',append=True),
        Result.variables['F_Mult'].set_index('Run',append=True),
        Result.parameters['tau'].set_index('Run',append=True),
        ]
        df_ = pd.concat(df_, axis=1, join="inner").loc[:,~pd.concat(df_, axis=1).columns.duplicated()]
        df_.rename(columns={'C_in':'C_inv'},inplace=True)
        df_['C_inv_an'] = df_['C_inv'] * df_['tau']

        ## Add categories
        df_['Category'] = df_.index.to_series().apply(lambda x: next((k for k, v in Result.sets['TECHNOLOGIES_OF_END_USES_TYPE'].items() if x[0] in v), pd.NA))
        df_["Category_2"] = df_.index.get_level_values(0).map({tech: sector for sector, techs in sector_technologies.items() for tech in techs})
        df_['Sector'] = pd.Series(dtype='str')
        df_.loc[df_['Category'].str.contains('MOB_',na=False),'Sector'] = 'Mobility'
        df_.loc[df_['Category'].str.contains('ELECTRICITY_',na=False),'Sector'] = 'Electricity'
        df_.loc[df_['Category'].str.contains('HEAT_HIGH',na=False),'Sector'] = 'Industrial Heat'
        df_.loc[df_['Category'].str.contains('HEAT_LOW',na=False),'Sector'] = 'Domestic Heat'

        Industry_list = ['METHANOL','ALUMINUM','PHENOL','ACETIC_ACID','ACETONE','PE','PET','PVC','PP','PS','CEMENT','FOOD','PAPER','STEEL']
        df_.loc[df_['Category'].isin(Industry_list),'Sector'] = 'Industry'
        df_.loc[df_.loc[:,'Category'].isna(),'Sector'] = 'Others'

        ## Fill missing category with "Others"
        df_.loc[df_['Category'].isna(),'Category'] = 'Others'
        df_.loc[df_['Category_2'].isna(),'Category_2'] = 'Others'

        Result.postprocessing['df_annual'] = df_

    if df_monthly:
        # df_monthly

        F_Mult_t = Result.variables['F_Mult_t'].reset_index().rename(
            columns={"index0": "Technologies", "index1": "Periods"})

        lyrio = Result.parameters['layers_in_out'].reset_index().rename(
            columns={"index0": "Technologies", "index1": "Flow"})
        lyrio = lyrio.loc[lyrio['layers_in_out']!=0,:]  # Drop useless rows, lighten the postprocessing

        t_op = Result.parameters['t_op'].reset_index().rename(columns={'index':'Periods'})

        F_Mult_t = F_Mult_t[F_Mult_t['F_Mult_t']!=0]

        df_ = pd.merge(F_Mult_t,t_op,on=["Periods",'Run'])
        df_ = pd.merge(df_,lyrio,on=["Technologies",'Run'])

        df_['Monthly_flow']=df_['F_Mult_t']*df_['t_op']*df_['layers_in_out']

        # Drop row without information, ie. not production
        df_ = df_.loc[df_['layers_in_out']!=0,:]

        ## Add categories
        df_['Category'] = df_['Technologies'].apply(lambda x: next((k for k, v in Result.sets['TECHNOLOGIES_OF_END_USES_TYPE'].items() if x in v), pd.NA))

        # Assign 'Category_2' based on mapping 'Technologies' to sectors
        df_["Category_2"] = df_['Technologies'].map({tech: sector for sector, techs in sector_technologies.items() for tech in techs})
        df_['Sector'] = pd.Series(dtype='str')
        df_.loc[df_['Flow'].str.contains('MOB_',na=False),'Sector'] = 'Mobility'
        df_.loc[df_['Flow'].str.contains('ELECTRICITY_',na=False),'Sector'] = 'Electricity'
        df_.loc[df_['Flow'].str.contains('HEAT_HIGH',na=False),'Sector'] = 'Industrial Heat'
        df_.loc[df_['Flow'].str.contains('HEAT_LOW',na=False),'Sector'] = 'Domestic Heat'

        ## Fill missing category with "Others"
        df_.loc[df_['Category'].isna(),'Category'] = 'Others'
        df_.loc[df_['Category_2'].isna(),'Category_2'] = 'Others'
        df_.loc[df_['Sector'].isna(),'Sector'] = 'Others'

        Result.postprocessing['df_monthly'] = df_

    return Result