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() 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': { 'ITDP and UCD (2015) "A Global Highshift Cycling Scenario" - Baseline Scenario': THISDIR.joinpath('ad', 'ad_ITDP_and_UCD_2015_A_Global_Highshift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP and UCD (2015) "A Global Highshift Cycling Scenario" - Highshift Scenario': THISDIR.joinpath('ad', 'ad_ITDP_and_UCD_2015_A_Global_Highshift_Cycling_Scenario_Highshift_Scenario.csv'), }, 'Region: OECD90': { 'Baseline Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, Baseline Scenario': THISDIR.joinpath('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('ad', 'ad_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('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('ad', 'ad_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('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('ad', 'ad_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('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('ad', 'ad_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('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_Baseline_Scenario.csv'), }, 'Conservative Cases': { 'Drawdown Team based on Data from Navigant, Bloomberg and other Sources': THISDIR.joinpath('ad', 'ad_Drawdown_Team_based_on_Data_from_Navigant_Bloomberg_and_other_Sources.csv'), }, 'Ambitious Cases': { 'ITDP, UC Davis (2015) A Global High Shift Cycling Scenario, High shift Scenario': THISDIR.joinpath('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_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('ad', 'ad_ITDP_UC_Davis_2015_A_Global_High_Shift_Cycling_Scenario_High_shift_Scenario.csv'), }, }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ {'name': 'Book Ed.1 Scenario 1', 'include': True, 'description': ( 'Using estimated projections of e-bike sales from Navigant, and Bloomberg, ' 'along with estimated growth rates of sales for missing years, and assumed ' 'bike lifetimes (really battery lifetimes as these dominate), estimated ' 'e-bike stocks are developed for each Drawdown region (OECD90, Eastern ' 'Europe, Asia sans Japan, Middle East and Africa and Latin America). These ' 'are assumed to each have a fixed number of passenger-km per year based on ' '10 km per workday and 5km per weekend day with a ridership of 1.05 ' 'considering the popularity of multiple riders on Chinese e-bikes. the ' 'resulting passenger-km are summed for global results. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv')}, {'name': 'Book Ed.1 Scenario 2', 'include': True, 'description': ( 'Using estimated projections of e-bike pass-km of ITDP/UCD, best fit curves ' 'were developed using 3rd degree polynomial functions. ' ), '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': 'Book Reference Scenario', 'include': True, 'description': ( 'The previously developed Reference scenario, as with most Drawdown models, ' 'is based on the TAM data and modeling. Therefore as thse inputs have ' 'changed in the new model, the Reference adoption is also different. The ' 'previous reference adoption is recorded here for the Book Scenarios. ' ), 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_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 Modeshare URBAN tab. ' ), '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 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( 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_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, copy_ref_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(), 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': { 'Custom calculated from (GBPN, Urge-Vorsatz Factored by IEA Building Data)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_UrgeVorsatz_Factored_by_IEA_Building_Data.csv'), 'IEA 6DS (2016), Residential & Commercial Water Heating': THISDIR.joinpath('tam', 'tam_IEA_6DS_2016_Residential_Commercial_Water_Heating.csv'), 'GBPN Energy for water heating, Urban & Rural / All buildings, All Vintages, Frozen efficiency (Water Heating Thermal energy use in TWHth)': THISDIR.joinpath('tam', 'tam_GBPN_Energy_for_water_heating_Urban_Rural_All_buildings_All_Vintages_Frozen_efficiency_W_e86b69eb.csv'), }, 'Conservative Cases': { 'Custom calculated from (GBPN and Urge-Vorsatz)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_and_UrgeVorsatz.csv'), 'IEA 4DS (2016), Residential & Commercial Water Heating': THISDIR.joinpath('tam', 'tam_IEA_4DS_2016_Residential_Commercial_Water_Heating.csv'), }, 'Region: OECD90': { 'Baseline Cases': { 'Custom calculated from (GBPN, Urge-Vorsatz Factored by IEA Building Data)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_UrgeVorsatz_Factored_by_IEA_Building_Data.csv'), 'Custom calculated from (GBPN and Urge-Vorsatz)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_and_UrgeVorsatz.csv'), 'GBPN Energy for water heating, Urban & Rural / All buildings, All Vintages, Frozen efficiency (Water Heating Thermal energy use in TWHth)': THISDIR.joinpath('tam', 'tam_GBPN_Energy_for_water_heating_Urban_Rural_All_buildings_All_Vintages_Frozen_efficiency_W_e86b69eb.csv'), }, }, 'Region: Eastern Europe': { 'Baseline Cases': { 'Custom calculated from (GBPN, Urge-Vorsatz Factored by IEA Building Data)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_UrgeVorsatz_Factored_by_IEA_Building_Data.csv'), 'Custom calculated from (GBPN and Urge-Vorsatz)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_and_UrgeVorsatz.csv'), 'GBPN Energy for water heating, Urban & Rural / All buildings, All Vintages, Frozen efficiency (Water Heating Thermal energy use in TWHth)': THISDIR.joinpath('tam', 'tam_GBPN_Energy_for_water_heating_Urban_Rural_All_buildings_All_Vintages_Frozen_efficiency_W_e86b69eb.csv'), }, }, 'Region: Asia (Sans Japan)': { 'Baseline Cases': { 'Custom calculated from (GBPN, Urge-Vorsatz Factored by IEA Building Data)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_UrgeVorsatz_Factored_by_IEA_Building_Data.csv'), 'Custom calculated from (GBPN and Urge-Vorsatz)': THISDIR.joinpath('tam', 'tam_Custom_calculated_from_GBPN_and_UrgeVorsatz.csv'), 'GBPN Energy for water heating, Urban & Rural / All buildings, All Vintages, Frozen efficiency (Water Heating Thermal energy use in TWHth)': THISDIR.joinpath('tam', 'tam_GBPN_Energy_for_water_heating_Urban_Rural_All_buildings_All_Vintages_Frozen_efficiency_W_e86b69eb.csv'), }, }, } tam_pds_data_sources = { 'Baseline Cases': { 'Drawdown TAM: PDS1 - post-Low-Flow': THISDIR.joinpath('tam', 'tam_pds_Drawdown_TAM_PDS1_postLowFlow.csv'), }, 'Conservative Cases': { 'Drawdown TAM: PDS2 - post-Low-Flow': THISDIR.joinpath('tam', 'tam_pds_Drawdown_TAM_PDS2_postLowFlow.csv'), }, 'Ambitious Cases': { 'Drawdown TAM: PDS3 - post-Low-Flow': THISDIR.joinpath('tam', 'tam_pds_Drawdown_TAM_PDS3_postLowFlow.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 = { 'Conservative Cases': { 'IEA (2012) Technology Roadmap Solar Heating and Cooling - Cons': THISDIR.joinpath('ad', 'ad_IEA_2012_Technology_Roadmap_Solar_Heating_and_Cooling_Cons.csv'), }, 'Ambitious Cases': { 'Solar Heat Worldwide http://www.iea-shc.org/solar-heat-worldwide': THISDIR.joinpath('ad', 'ad_Solar_Heat_Worldwide_httpwww_ieashc_orgsolarheatworldwide.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ {'name': 'Conservative, based on IEA 2012', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Conservative_based_on_IEA_2012.csv')}, {'name': 'Aggressive, High Growth, early', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_High_Growth_early.csv')}, {'name': 'Aggressive, High Growth, based on IEA', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_High_Growth_based_on_IEA.csv')}, {'name': 'Aggressive, High Growth, late', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_High_Growth_late.csv')}, {'name': 'Aggressive, V. High Growth, late', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_V__High_Growth_late.csv')}, {'name': 'Aggressive, V. High Growth', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_V__High_Growth.csv')}, {'name': 'Conservative Growth, late', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Conservative_Growth_late.csv')}, {'name': 'Conservative Growth, early', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Conservative_Growth_early.csv')}, {'name': 'Low Growth', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Low_Growth.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( [335.463, 56.493, 2.374, 240.305, 9.948, 9.113, 231.838, 6.4350000000000005, 23.777, 17.233], 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=1000000000.0) 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_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: AMPERE 2014 IMAGE TIMER Reference': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_Reference.csv'), '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: IEA ETP 2016 6DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_6DS.csv'), }, 'Conservative Cases': { 'Based on: AMPERE 2014 IMAGE TIMER 550': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_550.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: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on: Greenpeace Reference (2015)': THISDIR.joinpath('ad', 'ad_based_on_Greenpeace_Reference_2015.csv'), }, 'Ambitious Cases': { 'Based on: AMPERE 2014 IMAGE TIMER 450': THISDIR.joinpath( 'ad', 'ad_based_on_AMPERE_2014_IMAGE_TIMER_450.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: 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) 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() ht_ref_adoption_initial = pd.Series([ 75.43696666666665, 50.234754444444434, 0.22261666666666663, 14.113495555555552, 1.0549222222222219, 9.81238111111111, 10.027777777777775, 1.8406988888888887, 37.01894555555555, 8.790349999999998 ], 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', '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 = { 'Conservative Cases': { 'Navigant Research': THISDIR.joinpath('ad', 'ad_Navigant_Research.csv'), 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on Clean Energy Manufacturing Analysis Center': THISDIR.joinpath( 'ad', 'ad_based_on_Clean_Energy_Manufacturing_Analysis_Center.csv' ), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Interpolation Based on World Energy Council 2011 - Global Transport Scenarios 2050': THISDIR.joinpath( 'ad', 'ad_Interpolation_based_on_World_Energy_Council_2011_Global_Transport_Scenarios_2050.csv' ), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data wb = xlrd.open_workbook( filename=THISDIR.joinpath('hybridcarsdata.xlsx')) raw_sales = pd.read_excel(io=wb, sheet_name='HEV Sales', header=0, index_col=0, usecols='A:K', dtype='float', engine='xlrd', skiprows=7, nrows=43) hev_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 = hev_sales.copy() for year in range(2019, 2061): sales_extended.loc[year, :] = 0.0 vehicle_retirements = sales_extended.shift(periods=lifetime, fill_value=0.0) hev_stock = (hev_sales - vehicle_retirements).cumsum() pass_km_adoption = hev_stock * self.ac.soln_avg_annual_use # HybridCars.xlsm 'Data Interpolator'!H1582, Adoption Data # Project Drawdown Analysis based on Market Reports and a Drop in HEV # in later years (replaced by EVs) - PDS2 predict = pd.read_csv(THISDIR.joinpath('ca_pds_data', 'pass_km_datapoints_PDS2.csv'), skipinitialspace=True, comment='#', index_col=0, squeeze=True) pass_km_predicted = interpolation.poly_degree3_trend( predict)['adoption'] pass_km_predicted.update( predict.loc[:2018]) # Early years adjusted to be actual values integration_pds2 = pd.read_csv(THISDIR.joinpath( 'tam', 'integration_PDS2.csv'), skipinitialspace=True, comment='#', index_col=0) tam_limit_pds2 = 0.95 * (integration_pds2['URBAN'] + integration_pds2['NONURBAN']) * 1e9 world = pd.concat([ pass_km_adoption.loc[2012:2016, 'World'], pass_km_predicted.loc[2017:] ]) ds1_df = pd.DataFrame(0, columns=dd.REGIONS, index=range(2012, 2061)) ds1_df['World'] = world.clip(upper=tam_limit_pds2, lower=0.0, axis=0) # Data Source 2 predict = pd.read_csv(THISDIR.joinpath('ca_pds_data', 'pass_km_datapoints_PDS3.csv'), skipinitialspace=True, comment='#', index_col=0, squeeze=True) pass_km_predicted = interpolation.poly_degree3_trend( predict)['adoption'] pass_km_predicted.update( predict.loc[:2018]) # Early years adjusted to be actual values integration_pds3 = pd.read_csv(THISDIR.joinpath( 'tam', 'integration_PDS3.csv'), skipinitialspace=True, comment='#', index_col=0) intg_limit = (integration_pds3['URBAN'] + integration_pds3['NONURBAN']) * 1e9 tam_limit_pds3 = pd.concat([(intg_limit.loc[:2035] * 0.95), (intg_limit.loc[2036:] * 0.9)]) world = pd.concat([ pass_km_adoption.loc[2012:2016, 'World'], pass_km_predicted.loc[2017:] ]) ds2_df = pd.DataFrame(0, columns=dd.REGIONS, index=range(2012, 2061)) ds2_df['World'] = world.clip(upper=tam_limit_pds3, lower=0.0, axis=0) ca_pds_data_sources = [ { 'name': 'PDS2-Transition to EVs in Cities', 'include': True, 'description': ('Considering that Electric Vehicles (BEV or PHEV) are a better technology ' 'from a lifetime emissions perspective, HEV are considered as a transition ' 'technology in the PDS2 where the target is drawdown by 2050 particularly ' 'within cities where there is minimal range anxiety. In this Drawdown ' 'scenario, then, the focus is on growing EV after all higher priority ' 'solutions (like non-motorized transportation) in cities are grown to their ' "maximum potential. For HEV's then, the adoption is projected to only occur " 'where BEV or PHEV cars cannot easily be used, such as for long distance ' 'intercity trips until perhaps around 2025 when EV battery technology can be ' 'assumed to be adequate enough to eliminate all range anxiety. The HEV ' 'adoption is projected to continue its growth until around 2025 when it ' 'starts to decline and trend to zero by or before 2050. Sales data for ' 'multiple key countries and regions were used to estimate the actual global ' "sales. Using the model's lifetime data, the older HEVs are removed from the " 'fleet while aggregating the total sales to get the total stock per year. ' 'With these, the projected sales from IEA are used to project increments to ' 'the existing stock to 2050 (latest data vailable). Stock data are converted ' "to usage with model's Advanced Controls input. All scenarios are limited " 'by integrated TAM after removing adoptions of higher priority solutions. ' ), 'dataframe': ds1_df }, { 'name': 'PDS3-Transition to EVs', 'include': True, 'description': ('Considering that Electric Vehicles (BEV or PHEV) are a better technology ' 'from a lifetime emissions perspective, HEV are considered as a transition ' 'technology in the PDS3 where the target is maximizing emissions reduction. ' 'In this scenario, then, the focus is on growing EV after all higher ' 'priority solutions (like non-motorized transportation) are grown to their ' 'maximum potential. As soon as possible, HEV sales will rapidly decline. ' 'Sales data for multiple key countries and regions were used to estimate the ' "actual global sales. Using the model's lifetime data, the older HEVs are " 'removed from the fleet while aggregating the total sales to get the total ' "stock per year. Stock data are converted to usage with model's Advanced " 'Controls input. All scenarios are limited by integrated TAM after removing ' 'adoptions of higher priority solutions. '), 'dataframe': ds2_df }, { 'name': 'Drawdown Book - Edition 1- Quick Doubling of Hybrid Car Occupancy', 'include': True, 'description': ('We take the Average of two Ambitious adoption scenarios (on Adoption Data ' 'tab): Interpolation of IEA 2016 ETP 2DS(2016), and World Energy Council ' '(2011) (both with annual use of ICCT Roadmap Model). We then double the HEV ' 'car occupancy from 2017 and interpolate back to current adoption for 2014. ' ), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_Quick_Doubling_of_Hybrid_Car_Occupancy.csv' ) }, { 'name': 'PDS1 - Aggressive Growth from Existing Stock based on IEA 2DS', 'include': True, 'description': ('Sales data for multiple key countries and regions were used to estimate the ' "global sales. Using the model's lifetime data, the older HEVs are removed " 'from the fleet while aggregating the total sales to get the total stock per ' 'year. With these, the projected sales from IEA are used to project ' 'increments to the existing stock to 2050 (latest data vailable). Stock data ' "are converted to usage with model's Advanced Controls input. All scenarios " 'are limited by integrated TAM after removing adoptions of higher priority ' 'solutions. '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS1_Aggressive_Growth_from_Existing_Stock_based_on_IEA_2DS.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': '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 HEV Pass-Km tab. '), '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) 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, 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(), 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 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 wb = xlrd.open_workbook(filename=THISDIR.joinpath('trucksdata.xlsx')) adoption1 = pd.read_excel(io=wb, sheet_name='AdoptionFactoring1', header=0, index_col=0, usecols='B:C', dtype='float', engine='xlrd', skiprows=11, nrows=51) adoption2 = pd.read_excel(io=wb, sheet_name='AdoptionFactoring2', header=0, index_col=0, usecols='B:H,J:M', dtype='float', engine='xlrd', skiprows=10, nrows=50) adoption3 = pd.read_excel(io=wb, sheet_name='AdoptionFactoring3', header=0, index_col=0, usecols='B:D', dtype='float', engine='xlrd', skiprows=9, nrows=51) ds1_df = pd.DataFrame(index=range(2012, 2061), columns=dd.REGIONS) ds1_df['World'] = adoption1.loc[2014:, 'Global'] ds2_df = adoption2.loc[2012:2060].dropna(axis=0).rename( axis='columns', mapper={ 'Asia (sans Japan)': 'Asia (Sans Japan)', 'Middle East & Africa': 'Middle East and Africa', 'OECD': 'OECD90', 'EU27': 'EU' }).fillna(0.0) ds2_df.index = ds2_df.index.astype(int) ds3_df = pd.DataFrame(index=range(2012, 2061), columns=dd.REGIONS) ds3_df['World'] = adoption3['Adoption tonne-km'] # Excel Custom PDS Adoption overrides these with real data for df in [ds1_df, ds2_df, ds3_df]: df.loc[2014, 'World'] = 304732.461811978 df.loc[2015, 'World'] = 475099.277763475 df.loc[2016, 'World'] = 660286.809851347 df.loc[2017, 'World'] = 853343.225192547 df.loc[2018, 'World'] = 1055789.45303526 ca_pds_data_sources = [ { 'name': 'PDS1 - Based on ICCT+RMI Freight Work Adoption estimates', 'include': True, 'description': ('ICCT estimates the total freight work each five years, and RMI estimates ' 'that by 2050 50% of trucks globally may have efficient technologies. We ' "therefore interpolate and extrapolate ICCT's freight work data and project " 'linear adoption globally from current adoption of truck freight work ' "(approximately 5%) to RMI's projection in 2050. "), 'dataframe': ds1_df }, { 'name': 'PDS2 - Based on an ICCT Truck Sales extrapolation', 'include': True, 'description': ("We use the number of truck sales estimated for each of ICCT's 16 regions " '(interpolated and extrapolated for each) to estimate the number of truck ' 'with efficiency packages installed. ICCT has an estimate of the year when ' 'truck fuel efficiency legislation becomes mandatory for each region. ' ), 'dataframe': ds2_df }, { 'name': 'PDS3 - Based on IEA Freight Work - 100% Adoption of Trucks by 2035', 'include': True, 'description': ("IEA's estimated Truck freight work data are interpolated and extrapolated " 'and we apply an increasing fraction of adoption rising to 100% in 2050. ' ), 'dataframe': ds3_df }, { 'name': 'Book Ed.1 Scenario 1', 'include': False, 'description': ('ICCT estimates the total freight work each five years, and RMI estimates ' 'that by 2050 50% of trucks globally may have efficient technologies. We ' "therefore interpolate and extrapolate ICCT's freight work data and project " 'linear adoption globally from current adoption of truck freight work ' "(approximately 5%) to RMI's projection in 2050. "), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Book_Ed_1_Scenario_1.csv') }, { 'name': 'Book Ed.1 Scenario 3', 'include': False, 'description': ('IEA"s estimated Truck freight work data are interpolated and extrapolated ' 'and we apply an increasing fraction of adoption rising to 100% (of global ' 'truck freight market only) in 2050. '), '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) # 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': 'Efficient Truck Share of Market is Fixed', 'include': True, 'description': ('Drawdown calculations for REF adoption of Efficient Trucks based on fixed ' 'percent of growth of trucking. The 2014 - 2018 values are taken from ' 'estimates of historical data '), 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Efficient_Truck_Share_of_Market_is_Fixed.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[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 * 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=True, copy_ref_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', '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': { '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'), }, } 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 = { 'Ambitious Cases': { 'ITDP/UCD (2015) A Global High Shift Cycling Scenario - High Shift Scenario - Early Years replaced with Recent Historical Data': THISDIR.joinpath('ad', 'ad_ITDPUCD_2015_A_Global_High_Shift_Cycling_Scenario_High_Shift_Scenario_Early_Years_replac_b131f9f6.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ {'name': 'PDS2 - Density remains Constant and is the key driver of walking in cities.', 'include': True, 'description': ( 'We project the amount of walking that would happen in 1,737 cities ' 'worldwide representing 57% of the global urban population then we scale the ' 'total amount of walking to 100% of the world’s urban population. The ' 'background calculations of this scenario use the population and density of ' 'each of the 1737 cities from Demographia’s report and projects the fraction ' 'of each country’s urban population in that city. We assume that that ' 'fraction is constant and then project the city’s population each year to ' '2050 using UN projections of each country’s urban population to 2050. With ' 'the TAM and total urban population projections, we estimate the mobility ' 'per urban resident each year and then apply 6.5% to walking in each city in ' 'each year when the density is over the "dense city threshold" (~3,000 ' 'p/sqkm) and 2% otherwise. Each year is scaled to the global urban ' 'population. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2_Density_remains_Constant_and_is_the_key_driver_of_walking_in_cities_.csv')}, {'name': 'PDS2 - Increasing Urban Density with Density Driving Urban Walking (Book Ed.1)', 'include': True, 'description': ( 'We project the amount of walking that would happen in 1,737 cities ' 'worldwide representing 57% of the global urban population then we scale the ' 'total amount of walking to 100% of the world’s urban population. The ' 'background calculations of this scenario use the population and density of ' 'each of the 1737 cities from Demographia’s report and projects the fraction ' 'of each country’s urban population in that city. We assume that that ' 'fraction is constant and then project the city’s population each year to ' '2050 using UN projections of each country’s urban population to 2050. With ' 'the TAM and total urban population projections, we estimate the mobility ' 'per urban resident each year and then apply 7% to walking in each city in ' 'each year when the density is over the "dense city threshold" (~3,000 ' 'p/sqkm) and 2% otherwise. Each year is scaled to the global urban ' 'population. City densities are assumed to increased by around 2.4% annually ' 'which reverses historical declines of around 2%. This scenario was ' 'calculated for the Drawdown book Edition 1. Some variables may have been ' 'updated. ' ), 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2_Increasing_Urban_Density_with_Density_Driving_Urban_Walking_Book_Ed_1.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': 'Custom REF Scenario 1: Fixed Passenger-km Annual after 2014', 'include': True, 'description': ( 'Taking the estimated passenger-km adoption value from 2014, we hold that ' 'constant out to 2050 which assumes that the total amount of walking remains ' 'constant despite increasing populations. The rapid rise in populations ' 'generally happens in developing countries, and as these countries urbanise ' 'and get wealthier, there is a large trend towards increased motorization ' 'following the historical patterns of Western Nations. This then, although a ' 'pessimistic case, is not unrealistic. ' ), 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Custom_REF_Scenario_1_Fixed_Passengerkm_Annual_after_2018.csv')}, {'name': 'Reference Growth in Walking', 'include': True, 'description': ( 'Here the Drawdown Model of Urban density for 1,737 cities across the world ' 'in the Demographia Dataset was used to develop a reasonable Reference ' 'Scenario for Walking out to the end of the analysis period. While full ' 'details are described in the [Mobility Output] sheet, here is the summary: ' 'The urban density of these cities (representing 57% of the world urban ' 'population), was obtained from the data source and projected out to 2050 ' 'using an assumed global urban density change as listed below. Research on ' 'walking in cities indicates that higher density is correlated with more ' 'walking, so an assumed walking mode share in cities with at least the ' 'minimum threshold urban density, and another assumed walking mode share for ' 'cities below this threshold were together used to estimate the average ' 'walking for each city in each year (considering that density change is ' 'assumed). The total walking mobility in these cities was summed and then ' 'scaled linearly to 100% of the global urban population. This projection to ' '2050 was then interpolated and extrapolated to develop a smooth curve for ' 'the entire analysis period. Also taken into account: increasing urban ' 'population, assumed fixed total mobility per capita, recent historical ' 'walking estimates have been used (2012-2018) instead of curve fit. ' ), 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Reference_Growth_in_Walking.csv')}, {'name': 'ITDP/ UCDavis (2015) Global Highshift Cycling Scenario - Baseline Case for Walking', 'include': True, 'description': ( 'The Source listed below estimated a Baseline Walking Case which generally ' 'shows an increae in walking, but not in line with total mobility increase, ' 'so the walking mode share declines over time. The source published data in ' '5-year increments, these have been interpolated for missing years (on Data- ' 'Interpolator) and pasted here. ' ), 'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_ITDP_UCDavis_2015_Global_Highshift_Cycling_Scenario_Baseline_Case_for_Walking.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 == '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( 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_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, copy_ref_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(), 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': { 'Project Drawdown Analysis of Several Sources.Click to see source.': THISDIR.joinpath( 'tam', 'tam_Project_Drawdown_Analysis_of_Several_Sources_Click_to_see_source_.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': 'Halfway to Passive House', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Halfway_to_Passive_House.csv') }, { 'name': 'Almost Passive House', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Almost_Passive_House.csv') }, { 'name': 'Passive House', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Passive_House.csv') }, { 'name': 'Drawdown Book Edition 1 PDS 1 Scenario', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_PDS_1_Scenario.csv') }, { 'name': 'Drawdown Book Edition 1 PDS 2 Scenario', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_PDS_2_Scenario.csv') }, { 'name': 'Drawdown Book Edition 1 PDS 3 Scenario', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_PDS_3_Scenario.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': 'Frozen Efficiency - Natural Rate of Insulation (1.4%)', 'include': False, 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Frozen_Efficiency_Natural_Rate_of_Insulation_1_4.csv' ) }, { 'name': 'Drawdown Book Edition 1 REF Scenario', 'include': False, 'filename': THISDIR.joinpath( 'ca_ref_data', 'custom_ref_ad_Drawdown_Book_Edition_1_REF_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( [35739.10972659552, 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(), 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 ca_pds_data_sources = [ { 'name': 'PDS1 - Doubling of Historical Electrification Rate (UIC data)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS1_Doubling_of_Historical_Electrification_Rate_UIC_data.csv' ) }, { 'name': 'PDS2 - Linear projection of Electricity-powered rail freight from 27% of rail freight in 2014 to 40% in 2050 (IEA 2DS projection)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS2_Linear_projection_of_Electricitypowered_rail_freight_from_27_of_rail_freight_in_201_37e3af9a.csv' ) }, { 'name': 'PDS3 - Linear projection of Electricity-powered rail freight from 27% of rail freight in 2014 to 100% in 2050', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS3_Linear_projection_of_Electricitypowered_rail_freight_from_27_of_rail_freight_in_201_bdba7429.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( [2751916.5073962263, 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(), 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', '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': { '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', 'Low', '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': { 'No Standards Case (David Siap, 2016, based on US Federal Rulemakings, 2016)': THISDIR.joinpath( 'ad', 'ad_No_Standards_Case_David_Siap_2016_based_on_US_Federal_Rulemakings_2016.csv' ), }, 'Conservative Cases': { 'Standards Case (David Siap, 2016, based on US Federal Rulemakings, 2016)': THISDIR.joinpath( 'ad', 'ad_Standards_Case_David_Siap_2016_based_on_US_Federal_Rulemakings_2016.csv' ), }, 'Ambitious Cases': { 'Aggressive Standards Case (David Siap, 2016, based on US Federal Rulemakings, 2016)': THISDIR.joinpath( 'ad', 'ad_Aggressive_Standards_Case_David_Siap_2016_based_on_US_Federal_Rulemakings_2016.csv' ), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) sconfig_list = [['region', 'base_year', 'last_year'], ['World', 2014, 2050], ['OECD90', 2014, 2050], ['Eastern Europe', 2014, 2050], ['Asia (Sans Japan)', 2014, 2050], ['Middle East and Africa', 2014, 2050], ['Latin America', 2014, 2050], ['China', 2014, 2050], ['India', 2014, 2050], ['EU', 2014, 2050], ['USA', 2014, 2050]] sconfig = pd.DataFrame(sconfig_list[1:], columns=sconfig_list[0], dtype=np.object).set_index('region') sconfig['pds_tam_2050'] = pds_tam_per_region.loc[[2050]].T sc_regions, sc_percentages = zip(*self.ac.pds_base_adoption) sconfig['base_adoption'] = pd.Series(list(sc_percentages), index=list(sc_regions)) sconfig['base_percent'] = sconfig[ 'base_adoption'] / pds_tam_per_region.loc[2014] sc_regions, sc_percentages = zip( *self.ac.pds_adoption_final_percentage) sconfig['last_percent'] = pd.Series(list(sc_percentages), index=list(sc_regions)) if self.ac.pds_adoption_s_curve_innovation is not None: sc_regions, sc_percentages = zip( *self.ac.pds_adoption_s_curve_innovation) sconfig['innovation'] = pd.Series(list(sc_percentages), index=list(sc_regions)) if self.ac.pds_adoption_s_curve_imitation is not None: sc_regions, sc_percentages = zip( *self.ac.pds_adoption_s_curve_imitation) sconfig['imitation'] = pd.Series(list(sc_percentages), index=list(sc_regions)) self.sc = s_curve.SCurve(transition_period=16, sconfig=sconfig) 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 == 'Logistic S-Curve': pds_adoption_data_per_region = None pds_adoption_trend_per_region = self.sc.logistic_adoption() pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Bass Diffusion S-Curve': pds_adoption_data_per_region = None pds_adoption_trend_per_region = self.sc.bass_diffusion_adoption() 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( [2.0, 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, 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=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, 1000000000.0)) 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': { 'Drawdown Calculations - High Projection Based on Data from Gschrey, B., & Schwarz, W. (2009). Projections of global emissions of fluorinated greenhouse gases in 2050. Öko-Recherche.': THISDIR.joinpath('tam', 'tam_Drawdown_Calculations_High_Projection_based_on_Data_from_Gschrey_B__Schwarz_W__2009__Pro_3a9477d3.csv'), }, 'Conservative Cases': { 'Drawdown Calculations - Medium Projection Based on Data from Gschrey, B., & Schwarz, W. (2009). Projections of global emissions of fluorinated greenhouse gases in 2050. Öko-Recherche.': THISDIR.joinpath('tam', 'tam_Drawdown_Calculations_Medium_Projection_based_on_Data_from_Gschrey_B__Schwarz_W__2009__P_557e4efd.csv'), }, 'Ambitious Cases': { 'Drawdown Calculations - Low Projection Based on Data from Gschrey, B., & Schwarz, W. (2009). Projections of global emissions of fluorinated greenhouse gases in 2050. Öko-Recherche.': THISDIR.joinpath('tam', 'tam_Drawdown_Calculations_Low_Projection_based_on_Data_from_Gschrey_B__Schwarz_W__2009__Proj_5ee67131.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 - Projected Based on Published and Estimated Rates of Refrigerant Recovery and Destruction (With HFC Sectors Summed)', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS1_Projected_based_on_Published_and_Estimated_Rates_of_Refrigerant_Recovery_and_Destru_80b1ed21.csv')}, {'name': 'PDS2 - Projected Based on Published and Estimated Rates of Refrigerant Recovery and Destruction (With Averages for HFC Banks used)', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2_Projected_based_on_Published_and_Estimated_Rates_of_Refrigerant_Recovery_and_Destru_ee48c99e.csv')}, {'name': 'PDS3 - Same as PDS2 (Maximum Adoption Obtained)', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS3_Same_as_PDS2_Maximum_Adoption_Obtained.csv')}, {'name': 'Drawdown Book Edition 1 Scenario 1', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_Scenario_1.csv')}, {'name': 'Drawdown Book Edition 1 Scenario 2 and Scenario 3', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_Scenario_2_and_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) 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( [29.609913664850783, 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(), 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': { 'Project Drawdown extrapolated Biochar data, based on Lal 2005 and Woolf et al 2010 methods. Alpha Scenario': THISDIR.joinpath( 'tam', 'tam_Project_Drawdown_extrapolated_Biochar_data_based_on_Lal_2005_and_Woolf_et_al_2010_method_144eca81.csv' ), }, 'Conservative Cases': { 'Project Drawdown extrapolated Biochar data, based on Lal 2005 and Woolf et al 2010 methods. Beta Scenario': THISDIR.joinpath( 'tam', 'tam_Project_Drawdown_extrapolated_Biochar_data_based_on_Lal_2005_and_Woolf_et_al_2010_method_2a78e935.csv' ), }, 'Maximum Cases': { 'Project Drawdown extrapolated Biochar data, based on Lal 2005 and Woolf et al 2010 methods. MSTP Scenario': THISDIR.joinpath( 'tam', 'tam_Project_Drawdown_extrapolated_Biochar_data_based_on_Lal_2005_and_Woolf_et_al_2010_method_a5bf52aa.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': 'Linear, low growth', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Linear_low_growth.csv') }, { 'name': 'High Growth, 2nd Poly, based on International Biochar Initiative (2015)', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_High_Growth_2nd_Poly_based_on_International_Biochar_Initiative_2015.csv' ) }, { 'name': 'Linear, high growth', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Linear_high_growth.csv') }, { 'name': 'Linear, max growth', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Linear_max_growth.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( [7457.0, 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, 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=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': { 'Custom TAM based on PlasticsEurope (2015) & World Economic Forum (2016)': THISDIR.joinpath( 'tam', 'tam_Custom_TAM_based_on_PlasticsEurope_2015_World_Economic_Forum_2016.csv' ), 'Custom TAM based on PlasticsEurope (2015) & 3.6% growth rate': THISDIR.joinpath( 'tam', 'tam_Custom_TAM_based_on_PlasticsEurope_2015_3_6_growth_rate.csv' ), }, 'Ambitious Cases': { 'Custom TAM based on Mosko (2012) assuming 5.3% growth from 2013-2020': THISDIR.joinpath( 'tam', 'tam_Custom_TAM_based_on_Mosko_2012_assuming_5_3_growth_from_20132020.csv' ), 'PlasticsEurope (PEMRG) (2015), for historic values / Mosko (2012) est. 385MMt in 2050': THISDIR.joinpath( 'tam', 'tam_PlasticsEurope_PEMRG_2015_for_historic_values_Mosko_2012_est__385MMt_in_2050.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 = { 'Ambitious Cases': { 'European Bioplastics (2013), 2nd Poly extrapolation': THISDIR.joinpath( 'ad', 'ad_European_Bioplastics_2013_2nd_Poly_extrapolation.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'ConservativeLow Based on CAGR 29.3% with continued trend to 2060', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_ConservativeLow_based_on_CAGR_29_3_with_continued_trend_to_2060.csv' ) }, { 'name': 'ConservativeHigh, continued 3rd poly trend to 2060', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_ConservativeHigh_continued_3rd_poly_trend_to_2060.csv' ) }, { 'name': 'AggressiveMed, 40% by 2050, 3rd Poly', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_AggressiveMed_40_by_2050_3rd_Poly.csv') }, { 'name': 'ConservativeHigh, 75% by 2045, 3rd Poly', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_ConservativeHigh_75_by_2045_3rd_Poly.csv') }, { 'name': 'ConservativeLow, 25% by 2050, 3rd Poly', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_ConservativeLow_25_by_2050_3rd_Poly.csv') }, { 'name': 'AggressiveLow, 50% by 2050, 3rd Poly', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_AggressiveLow_50_by_2050_3rd_Poly.csv') }, { 'name': 'AggressiveMax, 30% by 2030, 3rd Poly', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_AggressiveMax_30_by_2030_3rd_Poly.csv') }, { 'name': 'AggressiveMax, 90 % by 2030, 90% by 2050', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_AggressiveMax_90_by_2030_90_by_2050.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( [1.67, 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=True, 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 ETP 2016, URBAN 6 DS + Non-motorized Travel Adjustment': THISDIR.joinpath( 'tam', 'tam_based_on_ETP_2016_URBAN_6_DS_Nonmotorized_Travel_Adjustment.csv' ), 'Based on ICCT, 2012, "Global Transportation Roadmap Model" + Non-motorized Travel Adjustment': THISDIR.joinpath( 'tam', 'tam_based_on_ICCT_2012_Global_Transportation_Roadmap_Model_Nonmotorized_Travel_Adjustment.csv' ), }, 'Conservative Cases': { 'Based on ETP 2016, URBAN 4 DS + Non-motorized Travel Adjustment': THISDIR.joinpath( 'tam', 'tam_based_on_ETP_2016_URBAN_4_DS_Nonmotorized_Travel_Adjustment.csv' ), 'Based on ITDP/UC Davis (2014) A Global High Shift Scenario Updated Report Data - Baseline Scenario': THISDIR.joinpath( 'tam', 'tam_based_on_ITDPUC_Davis_2014_A_Global_High_Shift_Scenario_Updated_Report_Data_Baseline_Scenario.csv' ), }, 'Ambitious Cases': { 'Based on ETP 2016, URBAN 2 DS + Non-motorized Travel Adjustment': THISDIR.joinpath( 'tam', 'tam_based_on_ETP_2016_URBAN_2_DS_Nonmotorized_Travel_Adjustment.csv' ), 'Based on ITDP/UC Davis (2014) A Global High Shift Scenario Updated Report Data - HighShift Scenario': THISDIR.joinpath( 'tam', 'tam_based_on_ITDPUC_Davis_2014_A_Global_High_Shift_Scenario_Updated_Report_Data_HighShift_Scenario.csv' ), }, } tam_pds_data_sources = { 'Ambitious Cases': { 'Drawdown TAM: Integrated Urban TAM post Non-Car Solutions for PDS1': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Integrated_Urban_TAM_post_NonCar_Solutions_for_PDS1.csv' ), 'Drawdown TAM: Integrated Urban TAM post Non-Car Solutions for PDS2': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Integrated_Urban_TAM_post_NonCar_Solutions_for_PDS2.csv' ), }, 'Maximum Cases': { 'Drawdown TAM: Integrated Urban TAM post Non-Car Solutions for PDS3': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Integrated_Urban_TAM_post_NonCar_Solutions_for_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_columns = ['Year'] + dd.REGIONS car_occ = self.ac.lookup_vma(vma_title='Current Average Car Occupancy') ride_occ = self.ac.lookup_vma( vma_title='Average Ridesharing Car Occupancy') ad_2018 = (car_occ - 1) / (ride_occ - 1) def global_load_df(ad_2018, ad_2050): """Compute increasing car occupancy over time as a percentage of TAM.""" occ = pd.DataFrame(columns=dd.REGIONS, dtype='float') coeff = np.polyfit(x=[2018, 2050], y=[ad_2018, ad_2050], deg=1) for year in range(2017, 2011, -1): occ.loc[year, 'World'] = np.polyval(p=coeff, x=year) for year in range(2018, 2061): occ.loc[year, 'World'] = np.polyval(p=coeff, x=year) df = occ.fillna(0.0) * pds_tam_per_region # Hard-coded values in Excel for these years. df.loc[2014, 'World'] = 3146198294784.37 df.loc[2015, 'World'] = 3237108104808.58 df.loc[2016, 'World'] = 3330046150206.16 df.loc[2017, 'World'] = 3422733823378.58 df.loc[2018, 'World'] = 3515221717452.8 return df ds4_ad_2050 = (1.75 - 1) / (ride_occ - 1) ds4_df = global_load_df(ad_2018=ad_2018, ad_2050=ds4_ad_2050) ds5_ad_2050 = (2.0 - 1) / (ride_occ - 1) ds5_df = global_load_df(ad_2018=ad_2018, ad_2050=ds5_ad_2050) ds6_ad_2050 = (3.0 - 1) / (ride_occ - 1) ds6_df = global_load_df(ad_2018=ad_2018, ad_2050=ds6_ad_2050) ca_pds_data_sources = [ { 'name': 'PDS1 (15%) - Drawdown Book Edition 1', 'include': True, 'description': ('PDS1 - Drawdown Team Calculations based on: 15% adoption by Car commuters ' 'in 2050 '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS1_15_Drawdown_Book_Edition_1.csv') }, { 'name': 'PDS2 (20%) - Drawdown Book Edition 1', 'include': True, 'description': ('PDS2 - Drawdown Team Calculations based on: 20% adoption by Car commuters ' 'in 2050 '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS2_20_Drawdown_Book_Edition_1.csv') }, { 'name': 'PDS3 (30%) - Drawdown Book Edition 1', 'include': True, 'description': ('PDS3 - Drawdown Team Calculations based on: 30% adoption by Car commuters ' 'in 2050 '), 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS3_30_Drawdown_Book_Edition_1.csv') }, { 'name': 'PDS1 - With Global Load Factor of 1.75 person per vehicle per trip by 2050', 'include': True, 'description': ('We take a relatively high average car load factor from data from several ' 'countries and assume that it can be the 2050 global average load factor. We ' 'assume that that figure is out of a maximum as entered on Advanced Controls ' '(~3 persons per trip) and estimate what effective adoption share the target ' 'load factor represents (assuming that all trips are either single occupancy ' 'or the maximum entered. This load factor in 2050 and that in 2014 (current ' 'value) are interpolated to get the load factor each year which is used to ' 'estimate the adoption. Recent Historical adoptions were estimated by ' 'assuming that the average load factors calculated from the weighted ' 'available data are applied to the total urban mobility each year after ' 'applying the car mode share (assumed fixed) '), 'dataframe': ds4_df }, { 'name': 'PDS2 - With Global Load Factor of 2 person per vehicle per trip by 2050', 'include': True, 'description': ('We take a relatively high average car load factor from data from several ' 'countries and assume that it can be the 2050 global average load factor. We ' 'assume that that figure is out of a maximum as entered on Advanced Controls ' '(~3 persons per trip) and estimate what effective adoption share the target ' 'load factor represents (assuming that all trips are either single occupancy ' 'or the maximum entered. This load factor in 2050 and that in 2014 (current ' 'value) are interpolated to get the load factor each year which is used to ' 'estimate the adoption. Recent Historical adoptions were estimated by ' 'assuming that the average load factors calculated from the weighted ' 'available data are applied to the total urban mobility each year after ' 'applying the car mode share (assumed fixed) '), 'dataframe': ds5_df }, { 'name': 'PDS3- With Global Load Factor of 3 person per vehicle per trip by 2050', 'include': True, 'description': ('We take a very high load factor average, which is close to the maximum and ' 'assume that it can be the 2050 global average load factor. We assume that ' 'that figure is out of a maximum as entered on Advanced Controls (~3 persons ' 'per trip) and estimate what effective adoption share the target load factor ' 'represents (assuming that all trips are either single occupancy or the ' 'maximum entered. This load factor in 2050 and that in 2014 (current value) ' 'are interpolated to get the load factor each year which is used to estimate ' 'the adoption. Recent Historical adoptions were estimated by assuming that ' 'the average load factors calculated from the weighted available data are ' 'applied to the total urban mobility each year after applying the car mode ' 'share (assumed fixed) '), 'dataframe': ds6_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) 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() 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(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=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') 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', '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)
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') 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', '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': { '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': { 'IEA (2016), "Energy Technology Perspectives - 6DS", IEA/OECD': THISDIR.joinpath('tam', 'tam_IEA_2016_Energy_Technology_Perspectives_6DS_IEAOECD.csv'), 'ICCT (2012) "Global Transport Roadmap Model", http://www.theicct.org/global-transportation-roadmap-model': THISDIR.joinpath('tam', 'tam_ICCT_2012_Global_Transport_Roadmap_Model_httpwww_theicct_orgglobaltransportationroadmapmodel.csv'), }, 'Conservative Cases': { 'IEA (2016), "Energy Technology Perspectives - 4DS", IEA/OECD': THISDIR.joinpath('tam', 'tam_IEA_2016_Energy_Technology_Perspectives_4DS_IEAOECD.csv'), }, 'Ambitious Cases': { 'IEA (2016), "Energy Technology Perspectives - 2DS", IEA/OECD': THISDIR.joinpath('tam', 'tam_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'), }, 'Maximum Cases': { 'Double EV occupancy on PDS2 = double pass-km': THISDIR.joinpath('ad', 'ad_Double_EV_occupancy_on_PDS2_double_passkm.csv'), 'Drawdown Projections based on adjusted IEA data (ETP 2012) on projected growth in each year, and recent sales Data (IEA - ETP 2016)': THISDIR.joinpath('ad', 'ad_Drawdown_Projections_based_on_adjusted_IEA_data_ETP_2012_on_projected_growth_in_each_yea_72fb5617.csv'), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ {'name': 'PDS2-Based on IEA (2017) B2DS+50% Occupancy Increase', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS2based_on_IEA_2017_B2DS50_Occupancy_Increase.csv')}, {'name': 'PDS3-Based on IEA B2DS with 100% Increase in Car Occupancy', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_PDS3based_on_IEA_B2DS_with_100_Increase_in_Car_Occupancy.csv')}, {'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')}, {'name': 'Book Ed.1 Scenario 3', 'include': True, '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) 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( [16.701444916159307, 14.405959906291683, 8.68248837596512e-05, 2.7858260096477, 8.68248837596512e-05, 8.68248837596512e-05, 2.6861138324271523, 0.09962535233678836, 4.508096807458965, 6.7600117997589235], 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=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 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': { 'IEA, 2013, "Transition to Sustainable Buildings" – see TAM Factoring': THISDIR.joinpath( 'tam', 'tam_IEA_2013_Transition_to_Sustainable_Buildings_see_TAM_Factoring.csv' ), 'Ürge-Vorsatz et al. (2015) – see TAM Factoring': THISDIR.joinpath( 'tam', 'tam_ÜrgeVorsatz_et_al__2015_see_TAM_Factoring.csv'), }, 'Region: USA': { 'Baseline Cases': { 'Annual Energy Outlook 2016, U.S. Energy Information Administration, 2016.': THISDIR.joinpath( 'tam', 'tam_Annual_Energy_Outlook_2016_U_S__Energy_Information_Administration_2016_.csv' ), }, }, } tam_pds_data_sources = { 'Baseline Cases': { 'Drawdown TAM: Adjusted GBPN Data - Commercial Floor Area': THISDIR.joinpath( 'tam', 'tam_pds_Drawdown_TAM_Adjusted_GBPN_Data_Commercial_Floor_Area.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': 'PDS1 - Adoption based on Navigant Sales and World Green Buildings Council Targets', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS1_Adoption_based_on_Navigant_Sales_and_World_Green_Buildings_Council_Targets.csv' ) }, { 'name': 'PDS2 - Adoption based on Navigant Sales and World Green Buildings Council Targets', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS2_Adoption_based_on_Navigant_Sales_and_World_Green_Buildings_Council_Targets.csv' ) }, { 'name': 'PDS3 - Adoption based on Navigant Sales and World Green Buildings Council Targets', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS3_Adoption_based_on_Navigant_Sales_and_World_Green_Buildings_Council_Targets.csv' ) }, { 'name': 'Drawdown Book (Edition 1) Scenario 3', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_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) 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( [1.9734999131249993, 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=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', '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] # 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 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'), 'Based on IEEJ Outlook - 2019, Ref Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_IEEJ_Outlook_2019_Ref_Scenario.csv'), 'Based on IRENA (2018), Roadmap-2050, Reference Case': THISDIR.joinpath( 'ad', 'ad_based_on_IRENA_2018_Roadmap2050_Reference_Case.csv'), }, 'Conservative Cases': { 'Based on IEA, WEO-2018, New Policies Scenario (NPS)': THISDIR.joinpath( 'ad', 'ad_based_on_IEA_WEO2018_New_Policies_Scenario_NPS.csv'), 'Based on IEEJ Outlook - 2019, Advanced Tech Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_IEEJ_Outlook_2019_Advanced_Tech_Scenario.csv' ), }, 'Ambitious Cases': { '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 Grantham Institute and Carbon Tracker (2017) Strong PV Scenario': THISDIR.joinpath( 'ad', 'ad_based_on_Grantham_Institute_and_Carbon_Tracker_2017_Strong_PV_Scenario.csv' ), 'Based on IRENA. 2018) Roadmap-2050, REmap Case': THISDIR.joinpath( 'ad', 'ad_based_on_IRENA__2018_Roadmap2050_REmap_Case.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' ), }, } 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() 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=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=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_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') tam_ref_data_sources = { 'Baseline Cases': { 'Custom (See TAM Factoring) based on http://www.gbpn.org/databases-tools/mrv-tool/methodology.': THISDIR.joinpath('tam', 'tam_Custom_See_TAM_Factoring_based_on_httpwww_gbpn_orgdatabasestoolsmrvtoolmethodology_.csv'), 'Based on GBPN - BEST PRACTICE POLICIES FOR LOW CARBON & ENERGY BUILDINGS BASED ON SCENARIO ANALYSIS May 2012': THISDIR.joinpath('tam', 'tam_based_on_GBPN_BEST_PRACTICE_POLICIES_FOR_LOW_CARBON_ENERGY_BUILDINGS_BASED_ON_SCENARIO_A_c7e92439.csv'), 'IEA (2013)': THISDIR.joinpath('tam', 'tam_IEA_2013.csv'), }, 'Conservative Cases': { 'McKinsey': THISDIR.joinpath('tam', 'tam_McKinsey.csv'), 'Navigant (2014)': THISDIR.joinpath('tam', 'tam_Navigant_2014.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) sconfig_list = [['region', 'base_year', 'last_year'], ['World', 2014, 2050], ['OECD90', 2014, 2050], ['Eastern Europe', 2014, 2050], ['Asia (Sans Japan)', 2014, 2050], ['Middle East and Africa', 2014, 2050], ['Latin America', 2014, 2050], ['China', 2014, 2050], ['India', 2014, 2050], ['EU', 2014, 2050], ['USA', 2014, 2050]] sconfig = pd.DataFrame(sconfig_list[1:], columns=sconfig_list[0], dtype=np.object).set_index('region') sconfig['pds_tam_2050'] = pds_tam_per_region.loc[[2050]].T sc_regions, sc_percentages = zip(*self.ac.pds_base_adoption) sconfig['base_adoption'] = pd.Series(list(sc_percentages), index=list(sc_regions)) sconfig['base_percent'] = sconfig['base_adoption'] / pds_tam_per_region.loc[2014] sc_regions, sc_percentages = zip(*self.ac.pds_adoption_final_percentage) sconfig['last_percent'] = pd.Series(list(sc_percentages), index=list(sc_regions)) if self.ac.pds_adoption_s_curve_innovation is not None: sc_regions, sc_percentages = zip(*self.ac.pds_adoption_s_curve_innovation) sconfig['innovation'] = pd.Series(list(sc_percentages), index=list(sc_regions)) if self.ac.pds_adoption_s_curve_imitation is not None: sc_regions, sc_percentages = zip(*self.ac.pds_adoption_s_curve_imitation) sconfig['imitation'] = pd.Series(list(sc_percentages), index=list(sc_regions)) self.sc = s_curve.SCurve(transition_period=16, sconfig=sconfig) 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 == 'Logistic S-Curve': pds_adoption_data_per_region = None pds_adoption_trend_per_region = self.sc.logistic_adoption() pds_adoption_is_single_source = None elif self.ac.soln_pds_adoption_basis == 'Bass Diffusion S-Curve': pds_adoption_data_per_region = None pds_adoption_trend_per_region = self.sc.bass_diffusion_adoption() 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( [165284837.0, 153214036.0, 2001574.0, 10001070.0, 1759.0, 66398.0, 10000000.0, 1070.0, 129000000.0, 21532448.0], 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)
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', '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: CES ITU AMPERE Baseline': THISDIR.joinpath('tam', 'tam_based_on_CES_ITU_AMPERE_Baseline.csv'), 'Based on: CES ITU AMPERE 550': THISDIR.joinpath('tam', 'tam_based_on_CES_ITU_AMPERE_550.csv'), 'Based on: CES ITU AMPERE 450': THISDIR.joinpath('tam', 'tam_based_on_CES_ITU_AMPERE_450.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': 'Aggressive, Low', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_Low.csv') }, { 'name': 'Conservative, Low', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Conservative_Low.csv') }, { 'name': 'Aggressive, high', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_high.csv') }, { 'name': 'Aggressive, high, early', 'include': True, 'filename': THISDIR.joinpath('ca_pds_data', 'custom_pds_ad_Aggressive_high_early.csv') }, { 'name': 'Drawdown Book Ed.1 Scenario 1', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Ed_1_Scenario_1.csv') }, { 'name': 'Drawdown Book Ed.1 Scenario 2', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Ed_1_Scenario_2.csv') }, { 'name': 'Drawdown Book Ed.1 Scenario 3', 'include': False, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_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) 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( [3.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7, 2.5], 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=1000000.0) 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': { 'IEA (2016), "Energy Technology Perspectives - 6DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_IEA_2016_Energy_Technology_Perspectives_6DS_IEAOECD.csv' ), 'ICCT (2012) "Global Transport Roadmap Model", http://www.theicct.org/global-transportation-roadmap-model': THISDIR.joinpath( 'tam', 'tam_ICCT_2012_Global_Transport_Roadmap_Model_httpwww_theicct_orgglobaltransportationroadmapmodel.csv' ), }, 'Conservative Cases': { 'IEA (2016), "Energy Technology Perspectives - 4DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_IEA_2016_Energy_Technology_Perspectives_4DS_IEAOECD.csv' ), }, 'Ambitious Cases': { 'IEA (2016), "Energy Technology Perspectives - 2DS", IEA/OECD': THISDIR.joinpath( 'tam', 'tam_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 = { 'Conservative Cases': { 'Navigant Research': THISDIR.joinpath('ad', 'ad_Navigant_Research.csv'), 'Based on: IEA ETP 2016 4DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_4DS.csv'), 'Based on Clean Energy Manufacturing Analysis Center': THISDIR.joinpath( 'ad', 'ad_based_on_Clean_Energy_Manufacturing_Analysis_Center.csv' ), }, 'Ambitious Cases': { 'Based on: IEA ETP 2016 2DS': THISDIR.joinpath('ad', 'ad_based_on_IEA_ETP_2016_2DS.csv'), 'Interpolation Based on World Energy Council 2011 - Global Transport Scenarios 2050': THISDIR.joinpath( 'ad', 'ad_Interpolation_based_on_World_Energy_Council_2011_Global_Transport_Scenarios_2050.csv' ), }, } self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources, adconfig=adconfig) # Custom PDS Data ca_pds_data_sources = [ { 'name': 'PDS2 - Project Drawdown based on data from IEA, ICCT and World Energy Council.', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS2_Project_Drawdown_based_on_data_from_IEA_ICCT_and_World_Energy_Council_.csv' ) }, { 'name': 'PDS3- Quick Doubling of Hybrid Car Occupancy', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_PDS3_Quick_Doubling_of_Hybrid_Car_Occupancy.csv' ) }, { 'name': 'Drawdown Book - Edition 1- Quick Doubling of Hybrid Car Occupancy', 'include': True, 'filename': THISDIR.joinpath( 'ca_pds_data', 'custom_pds_ad_Drawdown_Book_Edition_1_Quick_Doubling_of_Hybrid_Car_Occupancy.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([ 57650630795.526825, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 78638537576.59837 ], 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)