def test_bass_diffusion_regions_NaN():
# From Bioplastics "S Curve Adoption"!B119:K128
sconfig = pd.DataFrame(
[[
'World', 2014, 2050, 0.00536977491961415, 1.67, 791.974792264998,
0.00112096, 0.10333344
], ['OECD90', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['Eastern Europe', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['Asia (Sans Japan)', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['Middle East and Africa', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['Latin America', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['China', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['India', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['EU', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0],
['USA', 2014, 2050, 0.0, 0.0, 0.0, 0.0, 0.0]],
columns=[
'region', 'base_year', 'last_year', 'base_percent',
'base_adoption', 'pds_tam_2050', 'innovation', 'imitation'
]).set_index('region')
sc = s_curve.SCurve(transition_period=None, sconfig=sconfig)
result = sc.bass_diffusion_adoption()
expected = pd.DataFrame(
bass_diffusion_adoption_regions_NaN_list[1:],
columns=bass_diffusion_adoption_regions_NaN_list[0]).set_index('Year')
expected.name = 'bass_diffusion_adoption'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_bass_diffusion():
# From Water Efficiency Measures "S Curve Adoption"!B119:K128
sconfig = pd.DataFrame(
[[
'World', 2014, 2050, 0.5, 86258.8944386277, 499769.3997737280,
0.0011209600, 0.1033334410
],
[
'OECD90', 2014, 2050, 0.5558684888, 47682.0319026127,
165775.7429560840, 0.0011209600, 0.1033334410
],
[
'Eastern Europe', 2014, 2050, 0.0, 0.0, 21474.3917595734,
0.0011209600, 0.1033334410
],
[
'Asia (Sans Japan)', 2014, 2050, 0.0, 0.0, 188212.6206588630,
0.0011209600, 0.1033334410
],
[
'Middle East and Africa', 2014, 2050, 0.0, 0.0, 62742.1062748580,
0.0011209600, 0.1033334410
],
[
'Latin America', 2014, 2050, 0.0, 0.0, 45343.3516130736,
0.0011209600, 0.1033334410
],
[
'China', 2014, 2050, 0.0, 0.0, 77612.1430465617, 0.0011209600,
0.1033334410
],
[
'India', 2014, 2050, 0.0, 0.0, 45491.8656042331, 0.0011209600,
0.1033334410
],
[
'EU', 2014, 2050, 0.0, 0.0, 59700.4155768868, 0.0011209600,
0.1033334410
],
[
'USA', 2014, 2050, 0.0, 0.0, 64756.7863759130, 0.0011209600,
0.1033334410
]],
columns=[
'region', 'base_year', 'last_year', 'base_percent',
'base_adoption', 'pds_tam_2050', 'innovation', 'imitation'
]).set_index('region')
sc = s_curve.SCurve(transition_period=None, sconfig=sconfig)
result = sc.bass_diffusion_adoption()
expected = pd.DataFrame(
bass_diffusion_adoption_list[1:],
columns=bass_diffusion_adoption_list[0]).set_index('Year')
expected.name = 'bass_diffusion_adoption'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_sigmoid_logistic():
"""Test logistic sigmoid, simple case."""
sc = s_curve.SCurve(transition_period=None, sconfig=None)
# values from Building Automation System "S Curve Adoption"!AH17:AH22
result = sc._sigmoid_logistic(base_year=2014,
last_year=2050,
base_percent=0.346959145052,
last_percent=0.95,
base_adoption=16577.8259167003,
pds_tam_2050=77969.4257883872)
expected = pd.DataFrame(
world_sigmoid_logistic_list[1:],
columns=world_sigmoid_logistic_list[0]).set_index('Year')
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_logistic_adoption():
# From Building Automation System "S Curve Adoption"!AJ17:AJ22
sconfig = pd.DataFrame(
[[
'World', 2014, 2050, 0.346959145052, 0.95, 16577.8259167003,
77969.4257883872
],
[
'OECD90', 2014, 2050, 0.677494504097, 1.0, 14915.99,
30578.7612542884
],
[
'Eastern Europe', 2014, 2050, 0.0, 0.212709603444,
325.933926458798, 1532.2953039347
],
[
'Asia (Sans Japan)', 2014, 2050, 0.074153999059, 0.447707349074,
1087.77094452167, 25358.3339750411
],
[
'Middle East and Africa', 2014, 2050, 0.0, 0.085083841378, 0.0,
4140.6610709308
],
[
'Latin America', 2014, 2050, 0.0, 0.223853674537, 0.0,
1021.9329224435
],
['China', 2014, 2050, 0.0848, 0.0, 1087.77094452167, 18965.135056084],
['India', 2014, 2050, 0.0, 0.0, 0.0, 8804.235498036],
['EU', 2014, 2050, 0.482603137947, 1.0, 3622.85, 11003.3574757203],
['USA', 2014, 2050, 0.445634302889, 1.0, 11293.14, 36879.9583966390]],
columns=[
'region', 'base_year', 'last_year', 'base_percent', 'last_percent',
'base_adoption', 'pds_tam_2050'
]).set_index('region')
sc = s_curve.SCurve(transition_period=16, sconfig=sconfig)
result = sc.logistic_adoption()
expected = pd.DataFrame(
logistic_s_curve_adoption_list[1:],
columns=logistic_s_curve_adoption_list[0]).set_index('Year')
expected.name = 'logistic_adoption'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_sigmoid_logistic_100_percent_final_adoption():
"""Test logistic sigmoid, with 100% last_percent adoption."""
sc = s_curve.SCurve(transition_period=None, sconfig=None)
# values from Building Automation System "S Curve Adoption"!AJ17:AJ22
result = sc._sigmoid_logistic(base_year=2014,
last_year=2050,
base_percent=0.677494504097,
last_percent=1.0,
base_adoption=14915.990000000000,
pds_tam_2050=30578.7612542884)
expected = pd.DataFrame(
OECD90_sigmoid_logistic_list[1:],
columns=OECD90_sigmoid_logistic_list[0]).set_index('Year')
pd.testing.assert_frame_equal(result, expected, check_exact=False)
result = sc._sigmoid_logistic(base_year=2014,
last_year=2050,
base_percent=0.677494504097,
last_percent=0.999999999999,
base_adoption=14915.990000000000,
pds_tam_2050=30578.7612542884)
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_sigmoid_logistic_divide_by_zero():
"""Test logistic sigmoid, with 100% last_percent adoption."""
sc = s_curve.SCurve(transition_period=None, sconfig=None)
result = sc._sigmoid_logistic(base_year=2014,
last_year=2050,
base_percent=0.1,
last_percent=0.0,
base_adoption=1000.0,
pds_tam_2050=1000.0)
expected = pd.DataFrame(
nan_sigmoid_logistic_list[1:],
columns=nan_sigmoid_logistic_list[0]).set_index('Year')
pd.testing.assert_frame_equal(result, expected, check_exact=False)
with pytest.warns(None) as warnings:
result = sc._sigmoid_logistic(base_year=2014,
last_year=2050,
base_percent=np.float64(0.1),
last_percent=np.float64(0.0),
base_adoption=1000.0,
pds_tam_2050=1000.0)
assert len(warnings) == 0
pd.testing.assert_frame_equal(result, expected, check_exact=False)
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', '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', 'High', 'High', 'High', 'High'
],
[
'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 = {
'Ambitious 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: China': {
'Ambitious Cases': {
'IEA, 2013, "Transition to Sustainable Buildings" – see TAM Factoring':
THISDIR.joinpath(
'tam',
'tam_IEA_2013_Transition_to_Sustainable_Buildings_see_TAM_Factoring.csv'
),
'Hong et al. (2014) – see TAM Factoring':
THISDIR.joinpath(
'tam', 'tam_Hong_et_al__2014_see_TAM_Factoring.csv'),
},
},
'Region: India': {
'Ambitious Cases': {
'IEA, 2013, "Transition to Sustainable Buildings" – see TAM Factoring':
THISDIR.joinpath(
'tam',
'tam_IEA_2013_Transition_to_Sustainable_Buildings_see_TAM_Factoring.csv'
),
'Chaturvedi et al (2014) – see TAM Factoring':
THISDIR.joinpath(
'tam',
'tam_Chaturvedi_et_al_2014_see_TAM_Factoring.csv'),
},
},
'Region: EU': {
'Ambitious Cases': {
'IEA, 2013, "Transition to Sustainable Buildings" – see TAM Factoring':
THISDIR.joinpath(
'tam',
'tam_IEA_2013_Transition_to_Sustainable_Buildings_see_TAM_Factoring.csv'
),
'Boermans et al. (2012); BPIE (2014) – see TAM Factoring':
THISDIR.joinpath(
'tam',
'tam_Boermans_et_al__2012_BPIE_2014_see_TAM_Factoring.csv'
),
},
},
'Region: USA': {
'Baseline Cases': {
'EIA, 2016, "Annual Energy Outlook 2016" – Reference Case':
THISDIR.joinpath(
'tam',
'tam_EIA_2016_Annual_Energy_Outlook_2016_Reference_Case.csv'
),
'EIA, 2016, "Annual Energy Outlook 2016" – Reference Case w/o CPP':
THISDIR.joinpath(
'tam',
'tam_EIA_2016_Annual_Energy_Outlook_2016_Reference_Case_wo_CPP.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()
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([
16577.82591670033, 14915.99, 0.0, 1087.7709445216651, 0.0, 0.0,
1087.7709445216651, 0.0, 3622.85, 11293.14
],
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=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)