def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TLA self.ae = aez.AEZ(solution_name=self.name, cohort=2020, regimes=dd.THERMAL_MOISTURE_REGIMES8) if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None: self.c_tla = tla.CustomTLA( fixed_value=self.ac.custom_tla_fixed_value) custom_world_vals = self.c_tla.get_world_values() elif self.ac.use_custom_tla: self.c_tla = tla.CustomTLA( filename=THISDIR.joinpath('custom_tla_data.csv')) custom_world_vals = self.c_tla.get_world_values() else: custom_world_vals = None self.tla_per_region = tla.tla_per_region( self.ae.get_land_distribution(), custom_world_values=custom_world_vals) adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = {} self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, main_includes_regional=True, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'Constant degradation rate, 100% adoption by 2050, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 100% of the ' 'remaining wetlands in 2050 were protected. This adoption is less than 100% ' 'of the wetlands in 2014. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Constant_degradation_rate_100_adoption_by_2050_linear.csv' ) }, { 'name': 'Constant degradation rate, 80% adoption by 2050, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 80% of the ' 'remaining wetlands in 2050 were protected. This is the same as Scenario 1 ' 'except for a smaller percentage protected by 2050. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Constant_degradation_rate_80_adoption_by_2050_linear.csv' ) }, { 'name': 'Constant degradation rate, 100% adoption by 2030, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 100% of the ' 'remaining wetlands in 2030 were protected. Because there is 100% ' 'protection in 2030, all values after that are constant. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Constant_degradation_rate_100_adoption_by_2030_linear.csv' ) }, { 'name': 'Constant degradation rate, 80% adoption by 2030, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 80% of the ' 'remaining wetlands in 2030 were protected. Because there is only 80% ' 'protection in 2030 linear interpolation was used to bridge the 2014, 2030, ' 'and 2050 values. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Constant_degradation_rate_80_adoption_by_2030_linear.csv' ) }, { 'name': 'Variable degradation rate, 100% adoption by 2050, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 100% of the ' 'remaining wetlands in 2050 were protected. This adoption is less than 100% ' 'of the wetlands in 2014. This is the same as Scenario 1 except an annual ' 'reduction in degradation rate is applied. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Variable_degradation_rate_100_adoption_by_2050_linear.csv' ) }, { 'name': 'Variable degradation rate, 80% adoption by 2050, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 80% of the ' 'remaining wetlands in 2050 were protected. This is the same as Scenario 5 ' 'except for a smaller percentage protected by 2050. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Variable_degradation_rate_80_adoption_by_2050_linear.csv' ) }, { 'name': 'Variable degradation rate, 100% adoption by 2030, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 100% of the ' 'remaining wetlands in 2030 were protected. Because there is 100% ' 'protection in 2030, all values after that are constant. This is the same as ' 'Scenario 3 except a variable degradation rate is used. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Variable_degradation_rate_100_adoption_by_2030_linear.csv' ) }, { 'name': 'Variable degradation rate, 80% adoption by 2030, linear', 'include': True, 'description': ('The TLA_Envelope sheet was used to compute the annual degradation of the ' 'wetland. The annual protection rate was adjusted so that 80% of the ' 'remaining wetlands in 2030 were protected. Because there is only 80% ' 'protection in 2030 linear interpolation was used to bridge the 2014, 2030, ' 'and 2050 values. This scenario is the same as Scenario 4 except a variable ' 'degradation rate is used. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Variable_degradation_rate_80_adoption_by_2030_linear.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=self.tla_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = self.tla_per_region.loc[2050] * ( ht_ref_adoption_initial / self.tla_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region, use_first_pds_datapoint_main=False, adoption_base_year=2018, copy_pds_to_ref=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=self.tla_per_region, pds_total_adoption_units=self.tla_per_region, electricity_unit_factor=1000000.0, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_first_cost_uses_tot_units=True, fc_convert_iunit_factor=land.MHA_TO_HA) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=land.MHA_TO_HA) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2eq_emissions_saved=self.ua. direct_co2eq_emissions_saved_land(), soln_pds_direct_co2_emissions_saved=self.ua. direct_co2_emissions_saved_land(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. direct_n2o_co2_emissions_saved_land(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, tot_red_in_deg_land=self.ua. cumulative_reduction_in_total_degraded_land(), pds_protected_deg_land=self.ua. pds_cumulative_degraded_land_protected(), ref_protected_deg_land=self.ua. ref_cumulative_degraded_land_protected(), regime_distribution=self.ae.get_land_distribution(), regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TLA self.ae = aez.AEZ(solution_name=self.name, cohort=2020, regimes=dd.THERMAL_MOISTURE_REGIMES8) if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None: self.c_tla = tla.CustomTLA( fixed_value=self.ac.custom_tla_fixed_value) custom_world_vals = self.c_tla.get_world_values() elif self.ac.use_custom_tla: self.c_tla = tla.CustomTLA( filename=THISDIR.joinpath('custom_tla_data.csv')) custom_world_vals = self.c_tla.get_world_values() else: custom_world_vals = None self.tla_per_region = tla.tla_per_region( self.ae.get_land_distribution(), custom_world_values=custom_world_vals) adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = {} self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, main_includes_regional=True, adconfig=adconfig) # Custom PDS Data ca_pds_columns = ['Year'] + dd.REGIONS w_2018 = self.ac.ref_base_adoption['World'] w_2050 = self.tla_per_region.loc[2050, 'World'] ca_pds_data_sources = [ { 'name': 'International Pledges, Low Growth (Linear trend)', 'include': True, 'description': ('In the absence of accurate studies predicting future adoption of ' 'multistrata agroforests, Drawdown evaluated current pledges for the ' 'conversion of degraded land to protect land or buffer as part of the Bonn ' 'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- ' 'sheet for a breakdown of calculation). Based on these pledges, projected ' 'low-growth adoption was set at 10%. '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.1 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Medium, linear trend', 'include': True, 'description': ('In the absence of accurate studies predicting future adoption of ' 'multistrata agroforests, Drawdown evaluated current pledges for the ' 'conversion of degraded land to protect land or buffer as part of the Bonn ' 'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- ' 'sheet for a breakdown of calculation). Based on these pledges, projected ' 'low-growth adoption was set at 20%. '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.2 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'High, linear trend', 'include': True, 'description': ('In the absence of accurate studies predicting future adoption of ' 'multistrata agroforests, Drawdown evaluated current pledges for the ' 'conversion of degraded land to protect land or buffer as part of the Bonn ' 'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- ' 'sheet for a breakdown of calculation). Based on these pledges, projected ' 'low-growth adoption was set at 30%. '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.3 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Low, early adoption, linear trend', 'include': True, 'description': ('This is Scenario 1 with 70% adoption by 2030 '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2030, w_2018 + (0.07 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.1 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Medium, early adoption, linear trend', 'include': True, 'description': ('This is Scenario 2 with 70% adoption by 2030 '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2030, w_2018 + (0.14 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.2 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'High, early adoption, linear trend', 'include': True, 'description': ('This is Scenario 3 with 70% adoption by 2030 '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2030, w_2018 + (0.21 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (0.3 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Max growth, linear trend', 'include': False, 'description': ('This is the Drawdown Optimum scenario, assuming 100% adoption of ' 'Multistrata Agroforestry in the allocated TLA. '), 'datapoints': pd.DataFrame([ [ 2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2050, w_2018 + (1.0 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=self.tla_per_region) for s in self.pds_ca.scenarios.values(): df = s['df'] for y in range(2012, 2019): df.loc[y, 'World'] = 0.0001 ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = self.tla_per_region.loc[2050] * ( ht_ref_adoption_initial / self.tla_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region, use_first_pds_datapoint_main=False, adoption_base_year=2018, copy_pds_to_ref=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=self.tla_per_region, pds_total_adoption_units=self.tla_per_region, electricity_unit_factor=1000000.0, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_first_cost_uses_tot_units=True, fc_convert_iunit_factor=land.MHA_TO_HA) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=land.MHA_TO_HA) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2eq_emissions_saved=self.ua. direct_co2eq_emissions_saved_land(), soln_pds_direct_co2_emissions_saved=self.ua. direct_co2_emissions_saved_land(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. direct_n2o_co2_emissions_saved_land(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, annual_land_area_harvested=self.ua. soln_pds_annual_land_area_harvested(), regime_distribution=self.ae.get_land_distribution(), regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') self.tm = tam.TAM( tamconfig=tamconfig, tam_ref_data_sources=rrs.energy_tam_2_ref_data_sources, tam_pds_data_sources=rrs.energy_tam_2_pds_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { 'Baseline Cases': { 'Based on BP Energy Outlook 2019 (Evolving transition Scenario)': THISDIR.joinpath( 'ad', 'ad_based_on_BP_Energy_Outlook_2019_Evolving_transition_Scenario.csv' ), 'Based on IEEJ Outlook - 2019, Ref Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_IEEJ_Outlook_2019_Ref_Scenario.csv'), 'Based on IEA, WEO-2018, Current Policies Scenario (CPS)': THISDIR.joinpath( 'ad', 'ad_based_on_IEA_WEO2018_Current_Policies_Scenario_CPS.csv' ), 'Based on: IEA ETP 2017 Ref Tech': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2017_Ref_Tech.csv'), }, 'Conservative Cases': { 'Based on Equinor (2018), Reform Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_Equinor_2018_Reform_Scenario.csv'), 'Based on IEA, WEO-2018, New Policies Scenario (NPS)': THISDIR.joinpath( 'ad', 'ad_based_on_IEA_WEO2018_New_Policies_Scenario_NPS.csv'), }, 'Ambitious Cases': { 'Based on Equinor (2018), Renewal Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_Equinor_2018_Renewal_Scenario.csv'), 'Based on IEEJ Outlook - 2019, Advanced Tech Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_IEEJ_Outlook_2019_Advanced_Tech_Scenario.csv' ), 'Based on: Grantham Institute and Carbon Tracker (2017), Strong Scenario, Original, Medium': THISDIR.joinpath( 'ad', 'ad_based_on_Grantham_Institute_and_Carbon_Tracker_2017_Strong_Scenario_Original_Medium.csv' ), 'Based on IEA, WEO-2018, SDS Scenario': THISDIR.joinpath('ad', 'ad_based_on_IEA_WEO2018_SDS_Scenario.csv'), 'Based on: IEA ETP 2017 B2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2017_B2DS.csv'), 'Based on: IEA ETP 2017 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2017_2DS.csv'), }, '100% RES2050 Case': { 'Based on average of: LUT/EWG 2019 100% RES, Ecofys 2018 1.5C and Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_average_of_LUTEWG_2019_100_RES_Ecofys_2018_1_5C_and_Greenpeace_2015_Advanced_Revolution.csv' ), }, 'Region: OECD90': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: Eastern Europe': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: Asia (Sans Japan)': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: Middle East and Africa': { 'Baseline Cases': { 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: Latin America': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: China': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: India': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: EU': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, 'Region: USA': { 'Baseline Cases': { 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), }, }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'Project Drawdown High Growth, Ambitious Cases, adjusted', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Project_Drawdown_High_Growth_Ambitious_Cases_adjusted.csv' ) }, { 'name': 'Project Drawdown High Growth, Conservative Cases, adjusted, smoothed curve', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Project_Drawdown_High_Growth_Conservative_Cases_adjusted_smoothed_curve.csv' ) }, { 'name': 'Optimum Nuclear reduces to 0% of TAM by 2050, based on AMPERE RefPol Scenario (2014) till peaking', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Optimum_Nuclear_reduces_to_0_of_TAM_by_2050_based_on_AMPERE_RefPol_Scenario_2014_till_peaking.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) # Custom REF Data ca_ref_data_sources = [ { 'name': 'Custom REF Adoption mirroring decline in nuclear in Plausible SCenario', 'include': False, 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Custom_REF_Adoption_mirroring_decline_in_nuclear_in_Plausible_SCenario.csv' ) }, ] self.ref_ca = customadoption.CustomAdoption( data_sources=ca_ref_data_sources, soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=ref_tam_per_region) if self.ac.soln_ref_adoption_basis == 'Custom': ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region( ) else: ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, ref_adoption_data_per_region=ref_adoption_data_per_region, use_first_pds_datapoint_main=False, adoption_base_year=2018, copy_pds_to_ref=True, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid( ac=self.ac, grid_emissions_version=2) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=rrs.TERAWATT_TO_KILOWATT) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=rrs.TERAWATT_TO_KILOWATT) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ ['param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'], ['source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly'], ['growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium'], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_6DS.csv'), 'ICCT (2012) Global Roadmap Model': THISDIR.joinpath('tam', 'tam_ICCT_2012_Global_Roadmap_Model.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_4DS.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_2DS.csv'), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region=self.tm.ref_tam_per_region() pds_tam_per_region=self.tm.pds_tam_per_region() adconfig_list = [ ['param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'], ['trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly'], ['growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium'], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ {'name': 'PDS1 - Drawdown Projection of Production of Efficient Aircraft of Airbus and Boeing and Third Manufacturer at 13%', 'include': True, 'description': ( 'Taking the production rate of aircraft by the two major suppliers - Airbus ' 'and Boeing, we project the production of "efficient model" aircraft over ' 'the future. We also assume that a third manufacturer enters the market ' '(possibly COMAC or UAC) in 2025 and produces first single aisle then twin ' 'aisle aircraft of competitive quality. 100 aircraft per year are ' 'retrofitted to equivalent new-aircraft efficiency. Each aircraft in the ' 'fleet is assumed to work around an average number of passenger-km per year ' 'according to an estimate for each of single aisle and twin aisle from our ' 'brief schedule calculations including downtime for maintenance checks, and ' 'new models are 1SD below the average estimated efficiency improvement ' '(~13%). We assume that the production rate of the big players remains ' 'constant, but that the newcomer increases production annually. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS1_Drawdown_Projection_of_Production_of_Efficient_Aircraft_of_Airbus_and_Boeing_and_Th_0625227c.csv')}, {'name': 'PDS2 - Drawdown Projection of Production of Efficient Aircraft of Airbus and Boeing and Third Manufacturer at 18%', 'include': True, 'description': ( 'Taking the production rate of aircraft by the two major suppliers - Airbus ' 'and Boeing, we project the production of "efficient model" aircraft over ' 'the future. We also assume that a third manufacturer enters the market ' '(possibly COMAC or UAC) in 2025 and produces first single aisle then twin ' 'aisle aircraft of competitive quality. 100 aircraft per year are ' 'retrofitted to equivalent new-aircraft efficiency. Each aircraft in the ' 'fleet is assumed to work around an average number of passenger-km per year ' 'according to an estimate for each of single aisle and twin aisle from our ' 'brief schedule calculations including downtime for maintenance checks, and ' 'new models are 18% more efficient). We assume that the production rate of ' 'the big players remains constant, but that the newcomer increases ' 'production annually. Global Load factor of solution aircraft increases to ' '83% (current US average). ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2_Drawdown_Projection_of_Production_of_Efficient_Aircraft_of_Airbus_and_Boeing_and_Th_c6a70599.csv')}, {'name': 'PDS3 - Drawdown Projection of Production of Efficient Aircraft of Airbus and Boeing and Third Manufacturer at 20%', 'include': True, 'description': ( 'Taking the production rate of aircraft by the two major suppliers - Airbus ' 'and Boeing, we project the production of "efficient model" aircraft over ' 'the future. We also assume that a third manufacturer enters the market ' '(possibly COMAC or UAC) in 2025 and produces first single aisle then twin ' 'aisle aircraft of competitive quality. 1000 aircraft per year are ' 'retrofitted to equivalent new-aircraft efficiency. Each aircraft in the ' 'fleet is assumed to work around an average number of passenger-km per year ' 'according to an estimate for each of single aisle and twin aisle from our ' 'brief schedule calculations including downtime for maintenance checks, and ' 'new models are 20% more efficient). We assume that the production rate of ' 'the big players remains constant, but that the newcomer increases ' 'production annually. Global Load factor of solution aircraft increases to ' '83%. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS3_Drawdown_Projection_of_Production_of_Efficient_Aircraft_of_Airbus_and_Boeing_and_Th_42d0f286.csv')}, {'name': 'Book Ed.1 Scenario 1', 'include': False, 'description': ( 'Taking the production rate of aircraft by the two major suppliers - Airbus ' 'and Boeing, we project the production of aircraft switching to 100% ' '"efficient models" over the short future. Aircraft older than a certain ' 'number of years (around 25) are retired. Each aircraft in the fleet is ' 'assumed to work around an average number of passenger-km per year ' 'according to an estimate for each of single aisle and twin aisle from our ' 'brief schedule calculations including downtime for maintenance checks. We ' 'assume that the production rate of these players remains constant. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv')}, {'name': 'Book Ed.1 Scenario 2', 'include': False, 'description': ( 'Taking the production rate of aircraft, and the estimated number of orders ' 'for aircraft by the two major suppliers - Airbus and Boeing, we project the ' 'production of aircraft switching to 100% "efficient models" over the short ' 'future. We include a small number of retrofits which would be for engines, ' 'lightweighting and other adjustments to make up the 15% efficiency ' 'improvement expected from a whole new aircraft. Aircraft older than a ' 'certain number of years (around 25) are retired. Each aircraft in the fleet ' 'is assumed to work around an average number of passenger-km per year ' 'according to an estimate for each of single aisle and twin aisle from our ' 'brief schedule calculations including downtime for maintenance checks. We ' 'assume that the production rate of these players remains constant, but that ' 'an additional competitive manufacturer is able to add to production later ' 'in 2025(for single aisle)/2035(for twin aisle) and produce comparable ' 'aircraft. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_2.csv')}, ] self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=self.ac.soln_pds_adoption_custom_high_sd_mult, low_sd_mult=self.ac.soln_pds_adoption_custom_low_sd_mult, total_adoption_limit=pds_tam_per_region) # Custom REF Data ca_ref_data_sources = [ {'name': 'Reference Based on Historical Aircraft Deliveries of Airbus, Boeing', 'include': True, 'description': ( 'Historical deliveries of efficient aircraft have been collected from the ' 'aircraft manufacturers themselves and the delivery data each year are used ' 'to estimate adoption assuming certain work done by each aircraft (single ' 'aisle and twin aisle estimated separately). The historical data from 2014 ' 'and mid-2019 (assumed to apply to 2018) are used and the adoption of the ' 'TAM in 2018 (in percentage terms) is assumed to continue fixed for the ' 'future. ' ), 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Reference_based_on_Historical_Aircraft_Deliveries_of_Airbus_Boeing.csv')}, ] self.ref_ca = customadoption.CustomAdoption(data_sources=ca_ref_data_sources, soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=ref_tam_per_region) if self.ac.soln_ref_adoption_basis == 'Custom': ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region() else: ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region() pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region() pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series( list(self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * (ht_ref_adoption_initial / ref_tam_per_region.loc[2018]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables(ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, ref_adoption_data_per_region=ref_adoption_data_per_region, use_first_pds_datapoint_main=False, copy_pds_to_ref=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption(ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), repeated_cost_for_iunits=False, bug_cfunits_double_count=False) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted() self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs(ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua.soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua.soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua.soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ ['param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'], ['source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly'], ['growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium'], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Based on: IEA ETP 2014 6DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2014_6DS.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2014 4DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2014_4DS.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2014 2DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2014_2DS.csv'), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region=self.tm.ref_tam_per_region() pds_tam_per_region=self.tm.pds_tam_per_region() # Custom PDS Data ca_pds_data_sources = [ {'name': 'PDS1 - Based on ICCT+RMI Freight Work Adoption estimates', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS1_based_on_ICCTRMI_Freight_Work_Adoption_estimates.csv')}, {'name': 'PDS2 - Based on an ICCT Truck Sales extrapolation', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2_based_on_an_ICCT_Truck_Sales_extrapolation.csv')}, {'name': 'PDS3 - Based on IEA Freight Work - 100% Adoption of Trucks by 2035', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS3_based_on_IEA_Freight_Work_100_Adoption_of_Trucks_by_2035.csv')}, {'name': 'Book Ed.1 Scenario 1', 'include': False, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv')}, {'name': 'Book Ed.1 Scenario 3', 'include': False, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_3.csv')}, ] self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) # Custom REF Data ca_ref_data_sources = [ {'name': 'Drawdown Book Reference Scenario', 'include': False, 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Drawdown_Book_Reference_Scenario.csv')}, ] self.ref_ca = customadoption.CustomAdoption(data_sources=ca_ref_data_sources, soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=ref_tam_per_region) ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region() if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region() pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region() pds_adoption_is_single_source = None ht_ref_adoption_initial = pd.Series( [600621.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * (ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip(*self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables(ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, ref_adoption_data_per_region=ref_adoption_data_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption(ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), repeated_cost_for_iunits=False, bug_cfunits_double_count=False) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted() self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs(ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua.soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua.soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua.soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Combined from IEA ETP 2016, ICAO 2014, Boeing 2013, Airbus 2014, Highest Ranges': THISDIR.joinpath( 'tam', 'tam_Combined_from_IEA_ETP_2016_ICAO_2014_Boeing_2013_Airbus_2014_Highest_Ranges.csv' ), }, 'Conservative Cases': { 'Combined from IEA ETP 2016, ICAO 2014, Boeing 2013, Airbus 2014, Middle Ranges': THISDIR.joinpath( 'tam', 'tam_Combined_from_IEA_ETP_2016_ICAO_2014_Boeing_2013_Airbus_2014_Middle_Ranges.csv' ), }, 'Ambitious Cases': { 'Combined from IEA ETP 2016, ICAO 2014, Boeing 2013, Airbus 2014, Lowest Ranges': THISDIR.joinpath( 'tam', 'tam_Combined_from_IEA_ETP_2016_ICAO_2014_Boeing_2013_Airbus_2014_Lowest_Ranges.csv' ), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() # Custom PDS Data wb = xlrd.open_workbook(filename=THISDIR.joinpath('trainsdata.xlsx')) adoption1 = pd.read_excel(io=wb, sheet_name='Adoption1', header=0, index_col=0, usecols='A:C', dtype='float', engine='xlrd', skiprows=12, nrows=47) adoption2 = pd.read_excel(io=wb, sheet_name='Adoption2', header=0, index_col=0, usecols='A:B', dtype='float', engine='xlrd', skiprows=12, nrows=47) adoption3 = pd.read_excel(io=wb, sheet_name='Adoption3', header=0, index_col=0, usecols='A:C', dtype='float', engine='xlrd', skiprows=12, nrows=47) ds1_df = pd.DataFrame(index=range(2012, 2061), columns=dd.REGIONS) ds1_df['World'] = adoption2['Electrified Freight million tonne-km'] ds2_df = pd.DataFrame(index=range(2012, 2061), columns=dd.REGIONS) ds2_df['World'] = adoption1['Electrified'] ds3_df = pd.DataFrame(index=range(2012, 2061), columns=dd.REGIONS) ds3_df['World'] = adoption3['Electrified'] ca_pds_data_sources = [ { 'name': 'PDS2 - Doubling of Historical Electrification Rate (UIC data)', 'include': True, 'description': ('Electrified railway tkms increase at TWICE the historical average annual ' 'rate of track electrification. Using UIC data, we estimate the average ' 'electrification rate at 1.4% annually. We double this and use this rate as ' 'the optimistically plausible electrification rate. This is reasonable since ' "it still remains below the annual rate of many year's growth according to " 'UIC data below. This may come from increased track length and uniform ' 'usage, or higher usage of electrified tracks versus other tracks. ' ), 'dataframe': ds1_df }, { 'name': 'PDS1 - Linear projection of Electricity-powered rail freight from 27% of rail freight in 2014 to 40% in 2050 (IEA 2DS projection)', 'include': True, 'description': ('UIC data for 2014 indicates 27% electrification of rails. The IEA 2DS ' 'scenario projects that 40% of rail freight can be powered by electricity by ' '2050. We linearly interpolate between these two percentages. We also use an ' 'annual average use rate that is 25% higher for the electrified tracks than ' 'the conventional. '), 'dataframe': ds2_df }, { 'name': 'PDS3 - Linear projection of Electricity-powered rail freight from 27% of rail freight in 2014 to 100% in 2050', 'include': True, 'description': ('UIC data for 2014 indicates 27% electrification of rails. The IEA 2DS ' 'scenario projects that 40% of rail freight can be powered by electricity by ' '2050. We examine the impact of making this target 100% in 2050 by linearly ' 'interpolating between these two percentages. We also assume a doubling of ' 'the electrified track usage versus the conventional. '), 'dataframe': ds3_df }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=self.ac.soln_pds_adoption_custom_high_sd_mult, low_sd_mult=self.ac.soln_pds_adoption_custom_low_sd_mult, total_adoption_limit=pds_tam_per_region) # Custom REF Data ca_ref_data_sources = [ { 'name': 'Drawdown Book Reference Scenario', 'include': True, 'description': ('This scenario uses the inputs that were used for the Scenario developed for ' 'the Drawdown Book Edition 1. The scenario assumes a fixed percent of the ' 'TAM is adopted for Efficient trucks as the TAM grows. '), 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Drawdown_Book_Reference_Scenario.csv') }, { 'name': 'Default REF Projection with Adjustment for Recent Historical Adoptions', 'include': True, 'description': ('We take the Default Project Drawdown REF adoption using Average Baseline ' 'TAM data and then adjust the years 2012-2018 to be the estimated historical ' 'adoptions from the Adoption1. '), 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Default_REF_Projection_with_Adjustment_for_Recent_Historical_Adoptions.csv' ) }, ] self.ref_ca = customadoption.CustomAdoption( data_sources=ca_ref_data_sources, soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=ref_tam_per_region) ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region() if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2018]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, ref_adoption_data_per_region=ref_adoption_data_per_region, use_first_pds_datapoint_main=False, copy_pds_to_ref=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid( ac=self.ac, grid_emissions_version=3) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=False) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') self.tm = tam.TAM( tamconfig=tamconfig, tam_ref_data_sources=rrs.energy_tam_2_ref_data_sources, tam_pds_data_sources=rrs.energy_tam_2_pds_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { 'Baseline Cases': { 'Based on: Greenpeace 2015 Energy Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Energy_Revolution.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: Greenpeace 2015 Reference': THISDIR.joinpath('ad', 'ad_based_on_Greenpeace_2015_Reference.csv'), 'Based on: AMPERE 2014 GEM E3 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_GEM_E3_Reference.csv'), 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.csv'), 'Based on: AMPERE 2014 GEM E3 450': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_450.csv'), 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.csv'), 'Based on: AMPERE 2014 GEM E3 550': THISDIR.joinpath('ad', 'ad_based_on_AMPERE_2014_GEM_E3_550.csv'), 'Based on: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), }, '100% RES2050 Case': { 'Based on: Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Advanced_Revolution.csv'), }, 'Region: Middle East and Africa': { 'Conservative Cases': { 'Based on: Greenpeace 2015 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Reference.csv'), }, 'Ambitious Cases': { 'Based on: Greenpeace 2015 Energy Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Energy_Revolution.csv'), }, '100% RES2050 Case': { 'Based on: Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Advanced_Revolution.csv'), }, }, 'Region: India': { 'Baseline Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), }, 'Conservative Cases': { 'Based on: Greenpeace 2015 Reference': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Reference.csv'), 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), }, 'Ambitious Cases': { 'Based on: Greenpeace 2015 Energy Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Energy_Revolution.csv'), 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), }, '100% RES2050 Case': { 'Based on: Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Advanced_Revolution.csv'), }, }, 'Region: EU': { 'Baseline Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), }, }, 'Region: USA': { 'Baseline Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), 'Baeed on: Greenpeace Reference Scenario': THISDIR.joinpath( 'ad', 'ad_Baeed_on_Greenpeace_Reference_Scenario.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: Greenpeace 2015 Energy Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Energy_Revolution.csv'), }, '100% RES2050 Case': { 'Based on: Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Advanced_Revolution.csv'), }, }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'PDS 1 Baseline _Integrated with Waste Model on feedstock availability', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_1_Baseline__Integrated_with_Waste_Model_on_feedstock_availability.csv' ) }, { 'name': 'PDS 2 Baseline _Integrated with Waste Model on feedstock availability', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_2_Baseline__Integrated_with_Waste_Model_on_feedstock_availability.csv' ) }, { 'name': 'PDS 3 Baseline _Integrated with Waste Model on feedstock availability', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_3_Baseline__Integrated_with_Waste_Model_on_feedstock_availability.csv' ) }, { 'name': 'PDS 1 - CONSERVATIVE LANDFILL METHANE IF. CONSERVATIVE W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_1_CONSERVATIVE_LANDFILL_METHANE_IF__CONSERVATIVE_W2E_Integrated_in_Waste_Model.csv' ) }, { 'name': 'PDS 2 - CONSERVATIVE LANDFILL METHANE IF. CONSERVATIVE W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_2_CONSERVATIVE_LANDFILL_METHANE_IF__CONSERVATIVE_W2E_Integrated_in_Waste_Model.csv' ) }, { 'name': 'PDS 3 - CONSERVATIVE LANDFILL METHANE IF. CONSERVATIVE W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_3_CONSERVATIVE_LANDFILL_METHANE_IF__CONSERVATIVE_W2E_Integrated_in_Waste_Model.csv' ) }, { 'name': 'PDS 1 - AMBITIOUS LANDFILL METHANE IF AMBITIOUS W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_1_AMBITIOUS_LANDFILL_METHANE_IF_AMBITIOUS_W2E_Integrated_in_Waste_Model.csv' ) }, { 'name': 'PDS 2 - AMBITIOUS LANDFILL METHANE IF AMBITIOUS W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_2_AMBITIOUS_LANDFILL_METHANE_IF_AMBITIOUS_W2E_Integrated_in_Waste_Model.csv' ) }, { 'name': 'PDS 3 - AMBITIOUS LANDFILL METHANE IF AMBITIOUS W2E (Integrated in Waste Model)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS_3_AMBITIOUS_LANDFILL_METHANE_IF_AMBITIOUS_W2E_Integrated_in_Waste_Model.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, use_first_pds_datapoint_main=False, adoption_base_year=2018, copy_pds_to_ref=True, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid( ac=self.ac, grid_emissions_version=2) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=rrs.TERAWATT_TO_KILOWATT) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=rrs.TERAWATT_TO_KILOWATT) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') self.tm = tam.TAM( tamconfig=tamconfig, tam_ref_data_sources=rrs.energy_tam_1_ref_data_sources, tam_pds_data_sources=rrs.energy_tam_1_pds_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { 'Baseline Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_Reference.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: Greenpeace 2015 Reference': THISDIR.joinpath('ad', 'ad_based_on_Greenpeace_2015_Reference.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_550.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on: Greenpeace 2015 Energy Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Energy_Revolution.csv'), 'Based on: AMPERE 2014 MESSAGE MACRO 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_MESSAGE_MACRO_450.csv'), }, '100% RES2050 Case': { 'Based on: Greenpeace 2015 Advanced Revolution': THISDIR.joinpath( 'ad', 'ad_based_on_Greenpeace_2015_Advanced_Revolution.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'High Ambitious, double growth by 2030 & 2050', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_High_Ambitious_double_growth_by_2030_2050.csv' ) }, { 'name': 'Conservative Growth of 2.5% annum', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Conservative_Growth_of_2_5_annum.csv') }, { 'name': 'Low Ambitious Growth, 10% higher compared to REF case', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Low_Ambitious_Growth_10_higher_compared_to_REF_case.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series([ 547.672, 69.035, 38.758, 403.057, 17.967, 18.856, 383.689, 4.014, 23.027, 3.148 ], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=rrs.TERAWATT_TO_KILOWATT) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=rrs.TERAWATT_TO_KILOWATT) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '2nd Poly', '2nd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Calculations, see sheet "IEA 2006 TAM" for details': THISDIR.joinpath( 'tam', 'tam_Calculations_see_sheet_IEA_2006_TAM_for_details.csv'), 'Calculations on the basis of floor space (m2, Urge-Vorsats et al. 2013 data), average illuminance (lm/m2) and annual operating time (constant 1000 h/a)': THISDIR.joinpath( 'tam', 'tam_Calculations_on_the_basis_of_floor_space_m2_UrgeVorsats_et_al__2013_data_average_illumin_66d3beb0.csv' ), 'ETP2016 6 DS; average efficacy flat at 2014 level; interpolated, 2nd poly; see ETP2016 TAM sheet': THISDIR.joinpath( 'tam', 'tam_ETP2016_6_DS_average_efficacy_flat_at_2014_level_interpolated_2nd_poly_see_ETP2016_TAM_sheet.csv' ), }, 'Region: China': { 'Baseline Cases': { 'Calculations, see sheet "IEA 2006 TAM" for details': THISDIR.joinpath( 'tam', 'tam_Calculations_see_sheet_IEA_2006_TAM_for_details.csv' ), 'Calculations on the basis of floor space (m2, Hong et al. 2014 data), average illuminance (lm/m2) and annual operating time (constant 1000 h/a)': THISDIR.joinpath( 'tam', 'tam_Calculations_on_the_basis_of_floor_space_m2_Hong_et_al__2014_data_average_illuminance_lm_d069a17b.csv' ), 'ETP2016 6 DS; average efficacy flat at 2014 level; interpolated, 2nd poly; see ETP2016 TAM sheet': THISDIR.joinpath( 'tam', 'tam_ETP2016_6_DS_average_efficacy_flat_at_2014_level_interpolated_2nd_poly_see_ETP2016_TAM_sheet.csv' ), }, }, 'Region: EU': { 'Baseline Cases': { 'VITO 2015 Task 7, corrected': THISDIR.joinpath('tam', 'tam_VITO_2015_Task_7_corrected.csv'), 'Calculations, see sheet "IEA 2006 TAM" for details': THISDIR.joinpath( 'tam', 'tam_Calculations_see_sheet_IEA_2006_TAM_for_details.csv' ), 'ETP2016 6 DS; average efficacy flat at 2014 level; interpolated, 2nd poly; see ETP2016 TAM sheet': THISDIR.joinpath( 'tam', 'tam_ETP2016_6_DS_average_efficacy_flat_at_2014_level_interpolated_2nd_poly_see_ETP2016_TAM_sheet.csv' ), }, }, 'Region: USA': { 'Baseline Cases': { 'Navigant Consulting 2010 http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_energy-savings-report_10-30.pdf, growth rate 1.31% in http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/energysavingsforecast14.pdf': THISDIR.joinpath( 'tam', 'tam_Navigant_Consulting_2010_httpapps1_eere_energy_govbuildingspublicationspdfssslssl_energy_95d1ca30.csv' ), 'US DOE 2014 Energy saving forecast (total Tlmh lighting in Figure 3.2) & US DOE 2012 (2010 US Lighting Market) for 8% residential lighting, assumed to be constant': THISDIR.joinpath( 'tam', 'tam_US_DOE_2014_Energy_saving_forecast_total_Tlmh_lighting_in_Figure_3_2_US_DOE_2012_2010_US_0abbe87d.csv' ), 'ETP2016 6 DS; average efficacy flat at 2014 level; interpolated, 2nd poly; see ETP2016 TAM sheet': THISDIR.joinpath( 'tam', 'tam_ETP2016_6_DS_average_efficacy_flat_at_2014_level_interpolated_2nd_poly_see_ETP2016_TAM_sheet.csv' ), }, 'Conservative Cases': { 'Calculations, see sheet "IEA 2006 TAM" for details': THISDIR.joinpath( 'tam', 'tam_Calculations_see_sheet_IEA_2006_TAM_for_details.csv' ), }, }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = {} self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() elif self.ac.soln_pds_adoption_basis == 'Linear': pds_adoption_data_per_region = None pds_adoption_trend_per_region = None pds_adoption_is_single_source = None ht_ref_adoption_initial = pd.Series([ 0.6794492428871898, 0.3191954710617667, 0.10696600912194591, 0.14940816617982913, 0.05052731382746539, 0.028682249324419262, 0.27338382605688477, 0.06226876414656746, 0.08601359398921465, 0.12076301805261236 ], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1000000000000.0) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=1000000000000.0) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TLA self.ae = aez.AEZ(solution_name=self.name, cohort=2020, regimes=dd.THERMAL_MOISTURE_REGIMES8) if self.ac.use_custom_tla: self.c_tla = tla.CustomTLA( filename=THISDIR.joinpath('custom_tla_data.csv')) custom_world_vals = self.c_tla.get_world_values() else: custom_world_vals = None self.tla_per_region = tla.tla_per_region( self.ae.get_land_distribution(), custom_world_values=custom_world_vals) adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { 'Raw Data for ALL LAND TYPES': { 'Sum of regional prognostications below': THISDIR.joinpath( 'ad', 'ad_Sum_of_regional_prognostications_below.csv'), }, 'Region: Asia (Sans Japan)': { 'Raw Data for ALL LAND TYPES': { 'Dara et al. 2018; Kazakshstan recultivation': THISDIR.joinpath( 'ad', 'ad_Dara_et_al__2018_Kazakshstan_recultivation.csv'), }, }, 'Region: China': { 'Tropical-Humid Land': { 'Lin, L, et al 2018, Wenzhou province only': THISDIR.joinpath( 'ad', 'ad_Lin_L_et_al_2018_Wenzhou_province_only.csv'), }, }, 'Region: EU': { 'Tropical-Humid Land': { 'Estel et al. 2015, 2nd Poly, capped at 94.7mha': THISDIR.joinpath( 'ad', 'ad_Estel_et_al__2015_2nd_Poly_capped_at_94_7mha.csv'), }, }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, main_includes_regional=True, adconfig=adconfig) # Custom PDS Data ca_pds_columns = ['Year'] + dd.REGIONS growth_initial = pd.DataFrame( [[2018] + list(self.ac.ref_base_adoption.values())], columns=ca_pds_columns).set_index('Year') tla_init = self.ac.ref_base_adoption['World'] tla_grow = self.tla_per_region.loc[2050, 'World'] - tla_init ca_pds_data_sources = [ { 'name': '1.32% ann, Linear Trend', 'include': True, 'growth_rate': 0.013219212962963, # Limited information on restoration of abandoned farmland is available (see # data interpolation sheet). As abandoned farmlands area a subset of degraded # land, it is asumed that the restoration of abandoned farmland will also follow # similar trends. Refer sheet, "Adoption rates-Deg Area". 'growth_initial': growth_initial }, { 'name': '2.64% annual rate, Linear Trend', 'include': False, # This scenario projects future growth by doubling of historical annual # rate in India 'growth_rate': (2 * 0.013219212962963), 'growth_initial': growth_initial }, { 'name': '63% of TLA, Linear Trend', 'include': True, # It is assumed that by 2050, 63% of the total degraded farmlands will be restored # for cropping. This is the highest reported adoption - from rainfed cultivated # areas in India over several decades: see Adoption trends-Deg Area worksheet. 'datapoints': pd.DataFrame([ [2012] + list(self.ac.ref_base_adoption.values()), [2018] + list(self.ac.ref_base_adoption.values()), [ 2050, tla_init + (tla_grow * 0.63), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Very high: 85% of TLA, Linear Trend', 'include': False, # It is assumed that by 2050, 85% of the total abandoned farmlands will be # restored for cropping. 'datapoints': pd.DataFrame([ [2012] + list(self.ac.ref_base_adoption.values()), [2018] + list(self.ac.ref_base_adoption.values()), [ 2050, tla_init + (tla_grow * 0.85), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Maximum, Linear Trend', 'include': True, # It is assumed that by 2050, 100% of the total abandoned farmlands will be # restored for cropping. 'datapoints': pd.DataFrame([ [2012] + list(self.ac.ref_base_adoption.values()), [2018] + list(self.ac.ref_base_adoption.values()), [ 2050, tla_init + (tla_grow * 1.00), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], ], columns=ca_pds_columns).set_index('Year') }, { 'name': 'Historical linear based on Whenzhou province China reclaimed lands', 'include': True, # Based on low interpolated linear trend from historical data on annual adoption # rate for Whenzhoue province China; Lin, Jia et al 2017 'include': True, 'growth_rate': 0.0696, 'growth_initial': growth_initial }, { 'name': 'Historical linear based on EU -Estel et al 2015', 'include': True, # Based on low interpolated linear trend from historical data on annual adoption # rate for EU, based on Estel et al 2015 'growth_rate': 0.0568, 'growth_initial': growth_initial }, { 'name': 'Historical linear trend based on Kazakstan, Dara et al 2017', 'include': True, # Based on low interpolated linear trend from historical data on annual adoption # rate for Kazahkstan, based on Dara et al 2017 'growth_rate': 0.0512, 'growth_initial': growth_initial }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=self.tla_per_region) for s in self.pds_ca.scenarios.values(): df = s['df'] for year in range(2012, 2019): df.loc[year] = [ 20.029602999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] df.sort_index(inplace=True) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = self.tla_per_region.loc[2050] * ( ht_ref_adoption_initial / self.tla_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region, use_first_pds_datapoint_main=True, adoption_base_year=2018, copy_pds_to_ref=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=self.tla_per_region, pds_total_adoption_units=self.tla_per_region, electricity_unit_factor=1000000.0, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_first_cost_uses_tot_units=True, fc_convert_iunit_factor=land.MHA_TO_HA) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=land.MHA_TO_HA) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2eq_emissions_saved=self.ua. direct_co2eq_emissions_saved_land(), soln_pds_direct_co2_emissions_saved=self.ua. direct_co2_emissions_saved_land(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. direct_n2o_co2_emissions_saved_land(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, annual_land_area_harvested=self.ua. soln_pds_annual_land_area_harvested(), regime_distribution=self.ae.get_land_distribution(), regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TLA self.ae = aez.AEZ(solution_name=self.name) self.tla_per_region = tla.tla_per_region( self.ae.get_land_distribution()) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'Regional linear trend', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Regional_linear_trend.csv') }, { 'name': 'Regional max linear trend', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Regional_max_linear_trend.csv') }, { 'name': '50% of the max annual afforestation rate across the regions', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_50_of_the_max_annual_afforestation_rate_across_the_regions.csv' ) }, { 'name': '100% of the max annual afforestation rate across the regions', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_100_of_the_max_annual_afforestation_rate_across_the_regions.csv' ) }, { 'name': '200% increase for Asia and 100% for the other regions', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_200_increase_for_Asia_and_100_for_the_other_regions.csv' ) }, { 'name': 'Global high - medium early adoption', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Global_high_medium_early_adoption.csv') }, { 'name': 'Global high - high early adoption', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Global_high_high_early_adoption.csv') }, { 'name': 'Global max - max early adoption', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Global_max_max_early_adoption.csv') }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=self.tla_per_region) if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None ht_ref_adoption_initial = pd.Series([ 297.78662238675923, 98.41788764519316, 44.68660278943361, 119.60170249905194, 17.61544828747855, 17.464981165602055, 0.0, 0.0, 0.0, 0.0 ], index=dd.REGIONS) ht_ref_adoption_final = self.tla_per_region.loc[2050] * ( ht_ref_adoption_initial / self.tla_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=self.tla_per_region, pds_total_adoption_units=self.tla_per_region, electricity_unit_factor=1000000.0, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_first_cost_uses_tot_units=True, fc_convert_iunit_factor=land.MHA_TO_HA) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=land.MHA_TO_HA) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2eq_emissions_saved=self.ua. direct_co2eq_emissions_saved_land(), soln_pds_direct_co2_emissions_saved=self.ua. direct_co2_emissions_saved_land(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. direct_n2o_co2_emissions_saved_land(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. direct_ch4_co2_emissions_saved_land(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, annual_land_area_harvested=self.ua. soln_pds_annual_land_area_harvested(), regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Based on: IEA ETP 2016 6DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_6DS.csv'), }, 'Conservative Cases': { 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_4DS.csv'), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('tam', 'tam_based_on_IEA_ETP_2016_2DS.csv'), }, } tam_pds_data_sources = { 'Ambitious Cases': { 'Drawdown TAM: Drawdown Integrated TAM - PDS1': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Drawdown_Integrated_TAM_PDS1.csv'), 'Drawdown TAM: Drawdown Integrated TAM - PDS2': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Drawdown_Integrated_TAM_PDS2.csv'), }, 'Maximum Cases': { 'Drawdown TAM: Drawdown Integrated TAM - PDS3': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Drawdown_Integrated_TAM_PDS3.csv'), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_pds_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = {} self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'Trajectory Adapted from REF Scenario Greenpeace (2015)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Trajectory_Adapted_from_REF_Scenario_Greenpeace_2015.csv' ) }, { 'name': 'Based on: Greenpeace 2015 Energy Revolution', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_based_on_Greenpeace_2015_Energy_Revolution.csv' ) }, { 'name': 'Based on: Greenpeace 2015 Advanced Revolution', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_based_on_Greenpeace_2015_Advanced_Revolution.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series( [1.99921610218339, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), repeated_cost_for_iunits=False, bug_cfunits_double_count=False) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1000000000.0) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=rrs.TERAWATT_TO_KILOWATT) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ ['param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'], ['source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES'], ['trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly'], ['growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium'], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'ETP 2016, URBAN 6 DS + Non-motorized Travel Adjustment': THISDIR.joinpath('tam', 'tam_ETP_2016_URBAN_6_DS_Nonmotorized_Travel_Adjustment.csv'), 'ICCT, 2012, "Global Transportation Roadmap Model" + Non-motorized Travel Adjustment': THISDIR.joinpath('tam', 'tam_ICCT_2012_Global_Transportation_Roadmap_Model_Nonmotorized_Travel_Adjustment.csv'), }, 'Conservative Cases': { 'ETP 2016, URBAN 4 DS + Non-motorized Travel Adjustment': THISDIR.joinpath('tam', 'tam_ETP_2016_URBAN_4_DS_Nonmotorized_Travel_Adjustment.csv'), 'ITDP/UC Davis 2014 Global High Shift Baseline': THISDIR.joinpath('tam', 'tam_ITDPUC_Davis_2014_Global_High_Shift_Baseline.csv'), }, 'Ambitious Cases': { 'ETP 2016, URBAN 2 DS + Non-motorized Travel Adjustment': THISDIR.joinpath('tam', 'tam_ETP_2016_URBAN_2_DS_Nonmotorized_Travel_Adjustment.csv'), 'ITDP/UC Davis 2014 Global High Shift HighShift': THISDIR.joinpath('tam', 'tam_ITDPUC_Davis_2014_Global_High_Shift_HighShift.csv'), }, 'Region: OECD90': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: Eastern Europe': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: Asia (Sans Japan)': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: Middle East and Africa': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: Latin America': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: China': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: India': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: EU': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, 'Region: USA': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('tam', 'tam_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region=self.tm.ref_tam_per_region() pds_tam_per_region=self.tm.pds_tam_per_region() # Custom PDS Data ca_pds_data_sources = [ {'name': 'Book Ed.1 Scenario 1', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv')}, {'name': 'Book Ed.1 Scenario 2', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_2.csv')}, ] self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) # Custom REF Data ca_ref_data_sources = [ {'name': 'Book Reference Scenario', 'include': False, 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Book_Reference_Scenario.csv')}, ] self.ref_ca = customadoption.CustomAdoption(data_sources=ca_ref_data_sources, soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=ref_tam_per_region) ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region() if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region() pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region() pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Linear': pds_adoption_data_per_region = None pds_adoption_trend_per_region = None pds_adoption_is_single_source = None ht_ref_adoption_initial = pd.Series( [561.0, 35.63818652262168, 1.9927024684919843, 441.0159122922099, 1.248023339153054, 3.6014881104912106, 277.7410283849392, 5.674389798859366, 28.00294411913302, 7.901342605929202], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * (ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip(*self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables(ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, ref_adoption_data_per_region=ref_adoption_data_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption(ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted() self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs(ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs(ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua.soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua.soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua.soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Based on IEA (2016), "Energy Technology Perspectives - 6DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_based_on_IEA_2016_Energy_Technology_Perspectives_6DS_IEAOECD.csv' ), 'Based on ICCT (2012) "Global Transport Roadmap Model", http://www.theicct.org/global-transportation-roadmap-model': THISDIR.joinpath( 'tam', 'tam_based_on_ICCT_2012_Global_Transport_Roadmap_Model_httpwww_theicct_orgglobaltransportatio_8916596a.csv' ), }, 'Conservative Cases': { 'Based on IEA (2016), "Energy Technology Perspectives - 4DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_based_on_IEA_2016_Energy_Technology_Perspectives_4DS_IEAOECD.csv' ), }, 'Ambitious Cases': { 'Based on IEA (2016), "Energy Technology Perspectives - 2DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_based_on_IEA_2016_Energy_Technology_Perspectives_2DS_IEAOECD.csv' ), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = { 'Baseline Cases': { 'Based on IEA Reference Tech Scenario- 2017': THISDIR.joinpath( 'ad', 'ad_based_on_IEA_Reference_Tech_Scenario_2017.csv'), }, 'Conservative Cases': { 'Based on OPEC World Energy Outlook 2016': THISDIR.joinpath( 'ad', 'ad_based_on_OPEC_World_Energy_Outlook_2016.csv'), 'Based on The Paris Declaration as Cited in (IEA, 2017- EV Outlook)': THISDIR.joinpath( 'ad', 'ad_based_on_The_Paris_Declaration_as_Cited_in_IEA_2017_EV_Outlook.csv' ), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Based on Bloomberg New Energy Finance - EV Outlook 2017': THISDIR.joinpath( 'ad', 'ad_based_on_Bloomberg_New_Energy_Finance_EV_Outlook_2017.csv' ), 'Based on IEA Beyond 2DS/B2DS Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_IEA_Beyond_2DSB2DS_Scenario.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data # Data about EV Sales comes from sheets from the original Excel implementation wb = xlrd.open_workbook( filename=THISDIR.joinpath('electricvehicledata.xlsx')) raw_sales = pd.read_excel(io=wb, sheet_name='EV Sales', header=0, index_col=0, usecols='A:K', dtype='float', engine='xlrd', skiprows=7, nrows=43) ev_sales = raw_sales.rename( axis='columns', mapper={ 'World ': 'World', 'OECD90 (US, EU Japan, Canada)': 'OECD90', 'Asia sans Japan (China, India & Other.)': 'Asia (Sans Japan)', 'Middle East & Africa': 'Middle East and Africa' }).fillna(0.0) lifetime = int(np.ceil(self.ac.soln_lifetime_replacement)) sales_extended = ev_sales.copy() for year in range(2051, 2061): sales_extended.loc[year, :] = 0.0 vehicle_retirements = sales_extended.shift(periods=lifetime, fill_value=0.0) ev_stock = (ev_sales - vehicle_retirements).cumsum() pass_km_adoption = ev_stock * self.ac.soln_avg_annual_use # Data Source 1 # EVs.xlsm 'Data Interpolation'!H1181, Adoption Data # Based on IEA EV Outlook, 2017 - B2DS Scenario predict = pd.Series([ 16.7014449161593, 35.1749574468086, 49.2449404255312, 635.494231205675, 2239.47229078014, 5072.22886382977 ], index=[2014, 2015, 2016, 2020, 2025, 2030]) pass_km_predicted = interpolation.poly_degree3_trend( predict)['adoption'] pass_km_predicted.update(predict) integration_pds2 = pd.read_csv(THISDIR.joinpath( 'tam', 'integration_PDS2.csv'), skipinitialspace=True, comment='#', index_col=0) tam_limit_pds2 = integration_pds2['URBAN'] + ( 0.3 * integration_pds2['NONURBAN']) world = pd.concat([ pass_km_adoption.loc[2012:2019, 'World'], pass_km_predicted.loc[2020:] ]) ds1_df = pd.DataFrame(0, columns=dd.REGIONS, index=range(2012, 2061)) ds1_df['World'] = world.clip(upper=tam_limit_pds2, axis=0) # Data Source 2 raw = pd.read_excel(io=wb, sheet_name='Vehicle Survival', header=0, index_col=None, usecols='D:AR', dtype='float', engine='xlrd', skiprows=8, nrows=5).T survival_ds2 = pd.concat([ev_stock.loc[:2019, 'World'], raw[3]]) capture_pct_ds2 = raw[4] car_usage = 15765.0 # https://theicct.org/global-transportation-roadmap-model (now 404s) car_occupancy = self.ac.lookup_vma( vma_title='Current Average Car Occupancy') survival_ad_ds2 = survival_ds2 * car_usage * car_occupancy / 1e9 replace_ds2 = (survival_ds2 * capture_pct_ds2).cumsum().subtract( vehicle_retirements['World'], fill_value=0.0) pass_km_potential_ds2 = replace_ds2 * self.ac.conv_avg_annual_use adoption_ds2 = pd.concat( [survival_ad_ds2.loc[:2019], pass_km_potential_ds2.loc[2020:]]) integration_pds3 = pd.read_csv(THISDIR.joinpath( 'tam', 'integration_PDS3.csv'), skipinitialspace=True, comment='#', index_col=0) tam_limit_pds3 = integration_pds3['URBAN'] + ( 0.3 * integration_pds3['NONURBAN']) ds2_df = pd.DataFrame(0, columns=dd.REGIONS, index=range(2012, 2061)) ds2_df['World'] = adoption_ds2.clip(upper=tam_limit_pds3, axis=0) ca_pds_data_sources = [ { 'name': 'PDS2-Based on IEA (2017) B2DS', 'include': True, 'description': ('In this Scenario, we incorporate the highest published stock scenario of ' "EV's currently in the literature: the IEA B2DS scenario of 2017. We take the " "Beyond 2 Degree Scenario projections from the IEA of number of EV's in the " 'global fleet, convert to estimated passenger-km with a fixed factor and we ' 'interpolate and extrapolate to estimate missing years with a 3rd degree ' 'polynomial curve fit. We then limit this to the total projected TAM each ' 'year after higher priority solutions have supplied their full projection in ' 'PDS2 (Higher priority solutions are: Walkable Cities, Bike Infrastructure, ' 'E-Bikes, Mass Transit and Carsharing/Ridesharing) '), 'dataframe': ds1_df }, { 'name': 'PDS3-Based on Replacing All Retired Cars from Survival Analysis', 'include': True, 'description': ('In this Optimum Scenario, to estimate the Fastest that a New Car Technology ' 'can Diffuse into the Global Fleet - assuming that from time of car ' 'replacement, new technology is used. Weibull distributions are assumed ' 'using the Weibull Survival data from ICCT Global Roadmap model v1.0 for 6 ' 'countries (China, USA, Canada, Brazil, Mexico and India). Using these, we ' 'estimate the proportion of cars in each country that are scrapped or ' 'retired X years after purchase date (0 <= X <= 40). Combining this with ' 'vehicle sales data for each of the selected countries (mainly from OICA ' 'database), we estimate how many cars should be retiring each year. Assuming ' 'the average retiring rate of these selected countries applies to entire ' 'world, we scale up the retired cars to global fleet and then convert from ' 'cars to pass-km. We then limit this to the total projected TAM each year ' 'after higher priority solutions have supplied their full projection in PDS3 ' '(Higher priority solutions are: Walkable Cities, Bike Infrastructure, ' 'E-Bikes, Mass Transit and Carsharing/Ridesharing) '), 'dataframe': ds2_df }, { 'name': 'Book Ed.1 Scenario 1', 'include': False, 'description': ('Starting with the IEA 2DS Projection of EV in the Global stock, we ' 'interpolate and apply a fixed car occupancy to 2050. Minor adjustments are ' 'made to early years to ensure smoothness of the adoption curve. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv') }, { 'name': 'Book Ed.1 Scenario 2', 'include': False, 'description': ("Using the IEA's Energy Technology Perspectives 2012 projections of EV " "sales' growth, we project the sales and then global EV stock. Assuming the " "ICCT's global car occupancy average and a 50% growth in this occupancy by " '2050, we estimate the total passenger-km of EV during the period of ' 'analysis. '), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_2.csv') }, { 'name': 'Book Ed.1 Scenario 3', 'include': False, 'description': ("Using the IEA's Energy Technology Perspectives 2012 projections of EV " "sales' growth, we project the sales and then global EV stock. Assuming " "twice the ICCT's global car occupancy average, we estimate the total " "passenger-km of EV during the period of analysis. "), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_3.csv') }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=self.ac.soln_pds_adoption_custom_high_sd_mult, low_sd_mult=self.ac.soln_pds_adoption_custom_low_sd_mult, total_adoption_limit=pds_tam_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series(list( self.ac.ref_base_adoption.values()), index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2018]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_pds_adoption_final_percentage = pd.Series( list(self.ac.pds_adoption_final_percentage.values()), index=list(self.ac.pds_adoption_final_percentage.keys())) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, use_first_pds_datapoint_main=False, copy_pds_to_ref=True, copy_ref_datapoint=False, copy_pds_datapoint=False, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid( ac=self.ac, grid_emissions_version=3) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), repeated_cost_for_iunits=False, bug_cfunits_double_count=False) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)
def __init__(self, scenario=None): if scenario is None: scenario = list(scenarios.keys())[0] self.scenario = scenario self.ac = scenarios[scenario] # TAM tamconfig_list = [ [ 'param', 'World', 'PDS World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'source_until_2014', self.ac.source_until_2014, self.ac.source_until_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'Project Drawdown Estimated based on World Bank and WHO. Water Use Regression against GDP/capita used to estimate GL of water used for populations with at least US$10,000 GDP capita assuming Gini distribution of wealth.', 'ALL SOURCES', 'ALL SOURCES' ], [ 'source_after_2014', self.ac.ref_source_post_2014, self.ac.pds_source_post_2014, 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'ALL SOURCES', 'Project Drawdown Estimated based on World Bank and WHO. Water Use Regression against GDP/capita used to estimate GL of water used for populations with at least US$10,000 GDP capita assuming Gini distribution of wealth.', 'ALL SOURCES', 'ALL SOURCES' ], [ 'trend', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], [ 'low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], [ 'high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ] tamconfig = pd.DataFrame(tamconfig_list[1:], columns=tamconfig_list[0], dtype=np.object).set_index('param') tam_ref_data_sources = { 'Baseline Cases': { 'Project Drawdown Estimated based on World Bank and WHO. Water Use Regression against GDP/capita used to estimate GL of water used for populations with at least US$10,000 GDP capita assuming Gini distribution of wealth.': THISDIR.joinpath( 'tam', 'tam_Project_Drawdown_Estimated_based_on_World_Bank_and_WHO__Water_Use_Regression_against_GDP_f76a56c1.csv' ), }, } self.tm = tam.TAM(tamconfig=tamconfig, tam_ref_data_sources=tam_ref_data_sources, tam_pds_data_sources=tam_ref_data_sources) ref_tam_per_region = self.tm.ref_tam_per_region() pds_tam_per_region = self.tm.pds_tam_per_region() adconfig_list = [ [ 'param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA' ], [ 'trend', self.ac.soln_pds_adoption_prognostication_trend, '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly', '3rd Poly' ], [ 'growth', self.ac.soln_pds_adoption_prognostication_growth, 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium' ], ['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ] adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0], dtype=np.object).set_index('param') ad_data_sources = {} self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'Rapid Conversion of Old Fixtures and 70% Maximum Adoption', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Rapid_Conversion_of_Old_Fixtures_and_70_Maximum_Adoption.csv' ) }, { 'name': 'Very Rapid Conversion of Old Fixtures and 80% Maximum Adoption', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Very_Rapid_Conversion_of_Old_Fixtures_and_80_Maximum_Adoption.csv' ) }, { 'name': 'Very Rapid Conversion of Old Fixtures and 95% Maximum Adoption', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Very_Rapid_Conversion_of_Old_Fixtures_and_95_Maximum_Adoption.csv' ) }, ] self.pds_ca = customadoption.CustomAdoption( data_sources=ca_pds_data_sources, soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name, high_sd_mult=1.0, low_sd_mult=1.0, total_adoption_limit=pds_tam_per_region) ref_adoption_data_per_region = None if False: # One may wonder why this is here. This file was code generated. # This 'if False' allows subsequent conditions to all be elif. pass elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS': pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region( ) pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region( ) pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications': pds_adoption_data_per_region = self.ad.adoption_data_per_region() pds_adoption_trend_per_region = self.ad.adoption_trend_per_region() pds_adoption_is_single_source = self.ad.adoption_is_single_source() ht_ref_adoption_initial = pd.Series([ 86258.8944386277, 47682.03190261271, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], index=dd.REGIONS) ht_ref_adoption_final = ref_tam_per_region.loc[2050] * ( ht_ref_adoption_initial / ref_tam_per_region.loc[2014]) ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0) ht_pds_adoption_initial = ht_ref_adoption_initial ht_regions, ht_percentages = zip( *self.ac.pds_adoption_final_percentage) ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions)) ht_pds_adoption_final = ht_pds_adoption_final_percentage * pds_tam_per_region.loc[ 2050] ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS) ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0) self.ht = helpertables.HelperTables( ac=self.ac, ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints, pds_adoption_data_per_region=pds_adoption_data_per_region, ref_adoption_limits=ref_tam_per_region, pds_adoption_limits=pds_tam_per_region, pds_adoption_trend_per_region=pds_adoption_trend_per_region, pds_adoption_is_single_source=pds_adoption_is_single_source) self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac) self.ua = unitadoption.UnitAdoption( ac=self.ac, ref_total_adoption_units=ref_tam_per_region, pds_total_adoption_units=pds_tam_per_region, soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(), soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(), bug_cfunits_double_count=True) soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd() soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd() conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits() soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted( ) self.fc = firstcost.FirstCost( ac=self.ac, pds_learning_increase_mult=2, ref_learning_increase_mult=2, conv_learning_increase_mult=2, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_tot_iunits=conv_ref_tot_iunits, soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), fc_convert_iunit_factor=1.0) self.oc = operatingcost.OperatingCost( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd, soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd, conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(), soln_pds_annual_world_first_cost=self.fc. soln_pds_annual_world_first_cost(), soln_ref_annual_world_first_cost=self.fc. soln_ref_annual_world_first_cost(), conv_ref_annual_world_first_cost=self.fc. conv_ref_annual_world_first_cost(), single_iunit_purchase_year=2017, soln_pds_install_cost_per_iunit=self.fc. soln_pds_install_cost_per_iunit(), conv_ref_install_cost_per_iunit=self.fc. conv_ref_install_cost_per_iunit(), conversion_factor=1.0) self.c4 = ch4calcs.CH4Calcs( ac=self.ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted) self.c2 = co2calcs.CO2Calcs( ac=self.ac, ch4_ppb_calculator=self.c4.ch4_ppb_calculator(), soln_pds_net_grid_electricity_units_saved=self.ua. soln_pds_net_grid_electricity_units_saved(), soln_pds_net_grid_electricity_units_used=self.ua. soln_pds_net_grid_electricity_units_used(), soln_pds_direct_co2_emissions_saved=self.ua. soln_pds_direct_co2_emissions_saved(), soln_pds_direct_ch4_co2_emissions_saved=self.ua. soln_pds_direct_ch4_co2_emissions_saved(), soln_pds_direct_n2o_co2_emissions_saved=self.ua. soln_pds_direct_n2o_co2_emissions_saved(), soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(), soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(), conv_ref_new_iunits=self.ua.conv_ref_new_iunits(), conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(), conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(), soln_net_annual_funits_adopted=soln_net_annual_funits_adopted, fuel_in_liters=False) self.r2s = rrs.RRS(total_energy_demand=ref_tam_per_region.loc[2014, 'World'], soln_avg_annual_use=self.ac.soln_avg_annual_use, conv_avg_annual_use=self.ac.conv_avg_annual_use)