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)
