def save_regional_summary(summary, res_dir):
"""Saves the summary by region: __regional_residential_ashp_summary.csv
Parameters
----------
summary : DataFrame
compiled regional results
res_dir : path
location to save file
"""
f_name = os.path.join(res_dir, '__regional_' +
COMPONENT_NAME.lower().replace(' ','_').\
replace('(','').replace(')','') + '_summary.csv')
summary.to_csv(f_name, mode='w', index_label='region')
def communities_summary(coms, res_dir):
"""Saves the summary by: community residential_ashp_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
#~ it = coms[c]['community data'].intertie
#~ if it is None:
#~ it = 'parent'
#~ if it == 'child':
#~ continue
if c.find("_intertie") != -1:
continue
try:
ashp = coms[c][COMPONENT_NAME]
kw_excess = ashp.monthly_value_table['kWh consumed'].max()/\
(24 * 31)
try:
peak_monthly_btu_hr_hh = ashp.peak_monthly_btu_hr_hh
except AttributeError:
peak_monthly_btu_hr_hh = 0
try:
#~ ashp.get_electricity_prices()
price = ashp.electricity_prices[0]
#~ print float(ashp.electricity_prices.ix[ashp.start_year])
except AttributeError:
price = 0
intertie = ashp.cd['intertie']
if type(intertie) is list:
intertie = intertie[0]
else:
intertie = ashp.cd['name']
try:
levelized_cost = ashp.levelized_cost_of_energy
except AttributeError:
levelized_cost = 0
try:
break_even = ashp.break_even_cost
except AttributeError:
break_even = 0
ashp.get_diesel_prices()
diesel_price = float(ashp.diesel_prices[0].round(2))
hf_price = diesel_price + ashp.cd['heating fuel premium']
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name, ashp.average_cop, ashp.num_houses,
peak_monthly_btu_hr_hh, price, ashp.electric_consumption,
kw_excess, ashp.heating_oil_saved,
ashp.electric_heat_energy_reduction, diesel_price, hf_price,
break_even, levelized_cost,
ashp.get_NPV_benefits(),
ashp.get_NPV_costs(),
ashp.get_NPV_net_benefit(), ashp.irr,
ashp.get_BC_ratio(), intertie, ashp.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
#~ print c
#~ print e
pass
cols = [
'Community',
"ASHP Residential Average Coefficient of Performance (COP)",
'Number Houses', 'ASHP Residential Peak Household Monthly Btu/hr',
'Electricity Price [$/kWh]', 'ASHP Residential kWh consumed per year',
"ASHP Residential Excess Generation Capacity"
" Needed for Peak Monthly Load (kW)",
"ASHP Residential Displaced Heating Oil [Gal]",
"ASHP Residential Displaced Electricity [kWh]",
"Diesel Price - year 1 [$/gal]", "Heating Fuel Price - year 1 [$/gal]",
'Break Even Heating Fuel Price [$/gal]',
'Levelized Cost Of Energy [$/MMBtu]',
'ASHP Residential NPV benefits [$]', 'ASHP Residential NPV Costs [$]',
'ASHP Residential NPV Net benefit [$]',
'ASHP Residential Internal Rate of Return',
'ASHP Residential Benefit-cost ratio', 'Intertie', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.replace(" ", "_").lower() + '_summary.csv')
fd = open(f_name, 'w')
fd.write(
("# " + COMPONENT_NAME + " summary by community\n"
'# Community: ' + definitions.COMMUNITY + '\n'
'# ASHP Residential Average Coefficient of Performance (COP): '
'ratio of useful heating provided to work required\n'
'# Number Houses: '
'Estimated count of homes that would be heated by ASHP\n'
'# ASHP Residential Peak Household Monthly Btu/hr: \n'
'# Electricity Price [$/kWh]: ' + definitions.PRICE_ELECTRICITY + '\n'
'# ASHP Residential kWh consumed per year: electricity consumed\n'
'# ASHP Residential Excess Generation Capacity '
' Needed for Peak Monthly Load (kW): \n'
'# ASHP Residential Displaced Heating Oil [Gal]:'
' Fuel that would be displaced by ASHP\n'
'# Diesel Price - year 1 [$/gal]: ' + definitions.PRICE_DIESEL + '\n'
'# Heating Fuel Price - year 1 [$/gal]:' + definitions.PRICE_HF + '\n'
'# Break Even Heating Fuel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# ' + COMPONENT_NAME + ' NPV benefits [$]: ' +
definitions.NPV_BENEFITS + '\n'
'# ' + COMPONENT_NAME + ' NPV Costs [$]: ' + definitions.NPV_COSTS +
'\n'
'# ' + COMPONENT_NAME + ' NPV Net benefit [$]: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# ' + COMPONENT_NAME + ' Internal Rate of Return: ' +
definitions.IRR + '\n'
'# ' + COMPONENT_NAME + ' Benefit-cost ratio: ' +
definitions.NPV_BC_RATIO + '\n'
'# Intertie: Intertie community is part of \n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary (coms, res_dir):
"""Saves the summary by: community biomass_cordwood_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
#~ it = coms[c]['community data'].intertie
#~ if it is None:
#~ it = 'parent'
#~ if it == 'child':
#~ continue
if c.find("_intertie") != -1:
continue
try:
biomass = coms[c][COMPONENT_NAME]
biomass.get_diesel_prices()
diesel_price = float(biomass.diesel_prices[0].round(2))
hf_price = diesel_price + biomass.cd['heating fuel premium']
try:
break_even = biomass.break_even_cost
except AttributeError:
break_even = 0
try:
levelized_cost = biomass.levelized_cost_of_energy
except AttributeError:
levelized_cost = 0
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [name,
biomass.max_boiler_output,
biomass.heat_displaced_sqft,
biomass.biomass_fuel_consumed,
biomass.fuel_price_per_unit,
biomass.comp_specs['energy density'],
biomass.heat_diesel_displaced,
hf_price,
break_even,
levelized_cost,
biomass.get_NPV_benefits(),
biomass.get_NPV_costs(),
biomass.get_NPV_net_benefit(),
biomass.irr,
biomass.get_BC_ratio(),
biomass.reason
]
out.append(l)
except (KeyError,AttributeError) as e:
#~ print e
pass
try:
cols = ['Community',
'Maximum Biomass Boiler Output [Btu/hr]',
'Biomass Heat Displacement square footage [Sqft]',
'Proposed ' + biomass.biomass_type + \
" Consumed [" + biomass.units +"]",
'Price [$/' + biomass.units + ']',
"Energy Density [Btu/" + biomass.units + "]",
'Displaced Heating Oil by Biomass [Gal]',
"Heating Fuel Price - year 1 [$/gal]",
'Break Even Heating Fuel Price [$/gal]',
'Levelized Cost Of Energy [$/MMBtu]',
'Biomass Cordwood NPV benefits [$]',
'Biomass Cordwood NPV Costs [$]',
'Biomass Cordwood NPV Net benefit [$]',
'Biomass Cordwood Internal Rate of Return',
'Biomass Cordwood Benefit-cost ratio',
'notes'
]
except UnboundLocalError:
return
data = DataFrame(out,columns = cols).set_index('Community')#.round(2)
f_name = os.path.join(res_dir,
COMPONENT_NAME.lower().replace(' ','_').\
replace('(','').replace(')','') + '_summary.csv')
fd = open(f_name,'w')
fd.write(("# " + COMPONENT_NAME + " summary by community\n"
'# Maximum Biomass Boiler Output [Btu/hr]:\n'
'# Biomass Heat Displacement square footage [Sqft]: Non-residential area to heat with biomass\n'
'# Proposed ' + biomass.biomass_type + ""
" consumed [" + biomass.units +"]: Proposed biomass fuel consumed\n"
'# Price [$/' + biomass.units + ']: Price of biomass fuel\n'
"# Energy Density [Btu/" + biomass.units + "]: Energy Density of fuel\n"
'# Displaced Heating Oil by Biomass [Gal]: Estimated heating fuel displace by biomass heating\n'
'# Heating Fuel Price - year 1 [$/gal]: ' + definitions.PRICE_HF + '\n'
'# Break Even Heating Fuel Price [$/gal]: ' + definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# '+ COMPONENT_NAME +' NPV benefits [$]: '+ definitions.NPV_BENEFITS + '\n'
'# '+ COMPONENT_NAME +' NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# '+ COMPONENT_NAME +' NPV Net benefit [$]: ' + definitions.NPV_NET_BENEFITS + '\n'
'# '+ COMPONENT_NAME +' Internal Rate of Return: ' + definitions.IRR +'\n'
'# '+ COMPONENT_NAME +' Benefit-cost ratio: ' + definitions.NPV_BC_RATIO +'\n'
'# notes: '+ definitions.NOTES +'\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the summary by: community non-residential_building_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
if c.find('+') != -1: # or c.find("_intertie") != -1:
continue
try:
com = coms[c][COMPONENT_NAME]
savings = (com.baseline_HF_consumption -\
com.proposed_HF_consumption ) * constants.mmbtu_to_gal_HF
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
out.append([
name,
com.get_NPV_benefits(),
com.get_NPV_costs(),
com.get_NPV_net_benefit(), com.irr,
com.get_BC_ratio(), com.hoil_price[0], com.elec_price[0],
com.num_buildings, com.total_sqft_to_retrofit,
com.break_even_cost, com.levelized_cost_of_energy['MMBtu'],
com.levelized_cost_of_energy['kWh'],
com.baseline_HF_consumption * constants.mmbtu_to_gal_HF,
com.baseline_kWh_consumption, savings,
com.baseline_kWh_consumption - com.proposed_kWh_consumption
])
except (KeyError, AttributeError) as e:
#~ print c +":"+ str(e)
pass
cols = [
'Community', 'Non-residential Efficiency NPV Benefit',
'Non-residential Efficiency NPV Cost',
'Non-residential Efficiency NPV Net Benefit',
'Non-residential Internal Rate of Return',
'Non-residential Efficiency B/C Ratio', 'Heating Oil Price - year 1',
'$ per kWh - year 1', 'Number Non-residential Buildings',
'Non-residential Total Square Footage',
'Breakeven Heating Fuel Price [$/gal heating oil equiv.]',
'Levelized Cost of Energy [$/MMBtu]',
'Levelized Cost of Energy [$/kWh]',
'Non-residential Heating oil equiv. Consumed(gal) - year 1',
'Non-residential Electricity Consumed (kWh) - year 1',
'Non-residential Efficiency Heating oil equiv. Saved[gal/year]',
'Non-residential Efficiency Electricity Saved [kWh/year]'
]
data = DataFrame(out, columns=cols).set_index('Community').round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.lower().replace(' ', '_') + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# non residential building component summary by community\n"
'# Community: ' + definitions.COMMUNITY + '\n'
'# Non-residential Efficiency NPV Benefit: ' +
definitions.NPV_BENEFITS + '\n'
'# Non-residential Efficiency NPV Cost: ' + definitions.NPV_COSTS +
'\n'
'# Non-residential Efficiency NPV Net Benefit: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# Non-residential Internal Rate of Return: ' + definitions.IRR + '\n'
'# Non-residential Efficiency B/C Ratio: ' + definitions.NPV_BC_RATIO +
'\n'
'# Heating Oil Price - year 1: ' + definitions.PRICE_HF + '\n'
'# $ per kWh - year 1: ' + definitions.PRICE_ELECTRICITY + '\n'
'# Number Non-residential Buildings: Number of buildings in the community.\n'
'# Non-residential Total Square Footage: Total Square footage from buildings.\n'
'# Breakeven Heating Fuel Price [$/gal heating oil equiv.]: ' +
definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost of Energy [$/MMBtu]: ' + definitions.LCOE + '\n'
'# Levelized Cost of Energy [$/kWh]: ' + definitions.LCOE + '\n'
'# Non-residential Heating Oil equiv. Consumed(gal) - year 1: Heating energy consumed in a community with out improvements.\n'
'# Non-residential Electricity Consumed (kWh) - year 1: Electricity consumed in a community without improvements.\n'
'# Non-residential Efficiency Heating oil equiv. Saved[gal/year]: Heating energy saved in a community with improvements.\n'
'# Non-residential Efficiency Electricity Saved [kWh/year]: Electricity consumed in a community with improvements.\n'
))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the summary by: community heat_recovery_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
#~ it = coms[c]['community data'].intertie
#~ if it is None:
#~ it = 'parent'
#~ if it == 'child':
#~ continue
if c.find("_intertie") != -1:
continue
try:
comp = coms[c][COMPONENT_NAME]
comp.get_diesel_prices()
diesel_price = float(comp.diesel_prices[0].round(2))
hfp = comp.cd['heating fuel premium']
proposed_hr = comp.proposed_heat_recovery
#~ eff = comp.cd["diesel generation efficiency"]
try:
break_even_cost = comp.break_even_cost
levelized_cost_of_energy = comp.levelized_cost_of_energy
except AttributeError:
break_even_cost = np.nan
levelized_cost_of_energy = np.nan
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name,
proposed_hr,
diesel_price,
hfp,
diesel_price + hfp,
#~ eff,
break_even_cost,
levelized_cost_of_energy,
comp.get_NPV_benefits(),
comp.get_NPV_costs(),
comp.get_NPV_net_benefit(),
comp.irr,
comp.get_BC_ratio(),
comp.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
#~ print e
pass
cols = [
'Community', 'Proposed Heat Recovery [gallons]',
'Diesel price - year 1 [$/gal]', 'Heating Fuel Premium [$/gal]',
'Heating Fuel Price - year 1 [$/gal]',
'Break Even Heating Fuel Price [$/gal]',
'Levelized Cost of Energy [$/MMBtu]', 'Heat Recovery NPV benefits [$]',
'Heat Recovery NPV Costs [$]', 'Heat Recovery NPV Net benefit [$]',
'Heat Recovery Internal Rate of Return',
'Heat Recovery Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.replace(" ", "_").lower() + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# " + COMPONENT_NAME + " summary by community\n"
'# Community: ' + definitions.COMMUNITY + '\n'
'# Proposed Heat Recovery [gallons]: Proposed gallons of diesel saved \n'
'# Diesel price - year 1 [$/gal]: ' + definitions.PRICE_DIESEL + '\n'
'# Heating Fuel Premium [$/gal]: ' + definitions.PREMIUM + '\n'
'# Heating Fuel Price - year 1 [$/gal]: ' + definitions.PRICE_HF + '\n'
'# Break Even Heating Fuel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Heat Recovery NPV benefits [$]: ' + definitions.NPV_BENEFITS + '\n'
'# Heat Recovery NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Heat Recovery NPV Net benefit [$]: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# Heat Recovery Internal Rate of Return: ' + definitions.IRR + '\n'
'# Heat Recovery Benefit-cost ratio: ' + definitions.NPV_BC_RATIO +
'\n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the component summary by community
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
if c.find('+') != -1 or c.find("_intertie") != -1:
continue
try:
www = coms[c][COMPONENT_NAME]
try:
oil_p, elec_p = www.hoil_price[0], www.elec_price[0]
except AttributeError:
oil_p, elec_p = 0, 0
savings = (www.baseline_HF_consumption -\
www.proposed_HF_consumption) * constants.mmbtu_to_gal_HF
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
out.append([
name,
www.get_NPV_benefits(),
www.get_NPV_costs(),
www.get_NPV_net_benefit(),
www.irr,
www.get_BC_ratio(),
oil_p,
elec_p,
#~ www.num_buildings , www.refit_sqft_total,
www.break_even_cost,
www.levelized_cost_of_energy['MMBtu'],
www.levelized_cost_of_energy['kWh'],
www.baseline_HF_consumption[0] * constants.mmbtu_to_gal_HF,
www.baseline_kWh_consumption[0],
savings[0],
(www.baseline_kWh_consumption -
www.proposed_kWh_consumption)[0]
])
except (KeyError, AttributeError) as e:
#~ print c +":"+ str(e)
pass
cols = [
'Community',
'Water/Wastewater Efficiency NPV Benefit',
'Water/Wastewater Efficiency NPV Cost',
'Water/Wastewater Efficiency NPV Net Benefit',
'Water/Wastewater Internal Rate of Return',
'Water/Wastewater Efficiency B/C Ratio',
'Heating Oil Price - year 1',
'$ per kWh - year 1',
#~ 'Number Water/Wastewater Buildings',
#~ 'Water/Wastewater Total Square Footage',
'Break Even Diesel Price [$/gal heating oil equiv.]',
'Levelized Cost of Energy [$/MMBtu]',
'Levelized Cost of Energy [$/kWh]',
'Water/Wastewater Heating Oil Equiv. Consumed (gal) - year 1',
'Water/Wastewater Electricity Consumed (kWh) - year 1',
'Water/Wastewater Efficiency Heating Oil Equiv. Saved [gal/year]',
'# Water/Wastewater Efficiency Electricity Saved [kWh/year]'
]
data = DataFrame(out, columns=cols).set_index('Community').round(2)
f_name = os.path.join(res_dir,
COMPONENT_NAME.lower().replace(' ','_').\
replace('&','and') + '_summary.csv')
fd = open(f_name, 'w')
fd.write(
("# non residential building component summary by community\n"
'# \n'
'# Community: ' + definitions.COMMUNITY + '\n'
'# Water/Wastewater Efficiency NPV Benefit: ' +
definitions.NPV_BENEFITS + '\n'
'# Water/Wastewater Efficiency NPV Cost: ' + definitions.NPV_COSTS +
'\n'
'# Water/Wastewater Efficiency NPV Net Benefit: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# Water/Wastewater Internal Rate of Return: ' + definitions.IRR +
'\n'
'# Water/Wastewater Efficiency B/C Ratio: ' +
definitions.NPV_BC_RATIO + '\n'
'# Heating Oil Price - year 1:'
' Heating Oil Price year one of project\n'
'# $ per kWh - year 1: Electricity Price year one of project\n'
'# Break Even Diesel Price [$/gal heating oil equiv.]: ' +
definitions.BREAK_EVEN_COST_DIESEL + '\n'
'# Levelized Cost of Energy [$/MMBtu]: \n'
'# Levelized Cost of Energy [$/kWh]: \n'
'# Water/Wastewater Heating Oil Equiv. Consumed (gal) - year 1:'
' heating oil equivalent consumed by water wastewater system\n'
'# Water/Wastewater Electricity Consumed (kWh) - year 1:'
' Electricity consumed by water wastewater system\n'
'# Water/Wastewater Efficiency Heating Oil Equiv. Saved [gal/year]:\n'
'# Water/Wastewater Efficiency Electricity Saved [kWh/year]:\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the component summary by community
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
it = coms[c]['community data'].intertie
if it is None:
it = 'parent'
if it == 'child':
continue
try:
# ??? NPV or year one
it = coms[c][COMPONENT_NAME]
connect_to = it.comp_specs['nearest community with lower price']
if it.reason == 'Not a transmission project.':
continue
try:
if it.connect_to_intertie:
connect_to += 'intertie'
except AttributeError:
pass
start_yr = it.comp_specs['start year']
dist = it.comp_specs['distance to community']
it.get_diesel_prices()
diesel_price = float(it.diesel_prices[0].round(2))
try:
diesel_price_it = float(it.intertie_diesel_prices[0].round(2))
except AttributeError:
diesel_price_it = np.nan
#~ if not it.comp_specs["project details"] is None:
#~ phase = it.comp_specs["project details"]['phase']
#~ else:
phase = "Reconnaissance"
heat_rec_opp = it.cd['heat recovery operational']
try:
generation_displaced = it.pre_intertie_generation[0]
except AttributeError:
generation_displaced = np.nan
try:
generation_conserved = it.intertie_offset_generation[0]
except AttributeError:
generation_conserved = np.nan
try:
lost_heat = it.lost_heat_recovery[0]
except AttributeError:
lost_heat = np.nan
try:
levelized_cost = it.levelized_cost_of_energy
except AttributeError:
levelized_cost = 0
try:
break_even = it.break_even_cost
except AttributeError:
break_even = 0
eff = it.cd["diesel generation efficiency"]
try:
pre_price = it.pre_intertie_generation_fuel_used[0] * \
diesel_price
except AttributeError:
pre_price = np.nan
try:
post_price = it.intertie_offset_generation_fuel_used[0] * \
diesel_price_it
except AttributeError:
post_price = np.nan
try:
eff_it = it.intertie_generation_efficiency
except AttributeError:
eff_it = np.nan
try:
losses = it.annual_transmission_loss
except AttributeError:
losses = np.nan
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name, connect_to, start_yr, phase, dist, generation_displaced,
generation_conserved, lost_heat, heat_rec_opp, eff, eff_it,
diesel_price, diesel_price_it, break_even, losses,
levelized_cost, pre_price, post_price, pre_price - post_price,
it.get_NPV_benefits(),
it.get_NPV_costs(),
it.get_NPV_net_benefit(), it.irr,
it.get_BC_ratio(), it.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
print e
pass
cols = [
'Community to connect', 'Community/Intertie to connect to',
'Start Year', 'Project Phase', 'Miles of Transmission Line',
'Generation Displaced in community to connect [kWh]',
'Electricity Generated, Conserved, or transmitted [kWh]',
'Loss of Recovered Heat from Genset in community to connect [gal]',
'Heat Recovery Operational in community to connect',
'Diesel Generator Efficiency in community to connect',
'Diesel Generator Efficiency in community to connect to',
'Diesel Price - year 1 [$/gal] in community to connect',
'Diesel Price - year 1 [$/gal] in community to connect to',
'Breakeven Diesel Price [$/gal]',
'Annual Transmission loss percentage',
'Levelized Cost Of Energy [$/kWh]',
'Status Quo generation Cost (Year 1)',
'Proposed generation cost (Year 1)',
'Benefit from reduced generation cost (year 1)',
'Transmission NPV benefits [$]', 'Transmission NPV Costs [$]',
'Transmission NPV Net benefit [$]',
'Transmission Internal Rate of Return',
'Transmission Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community to connect')
f_name = os.path.join(
res_dir,
COMPONENT_NAME.replace(" ", "_").lower() + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# Transmission component summary by community\n"
'# Community to connect: name of main community.\n'
'# Community/Intertie to connect to: name of secondary community.\n'
'# Start Year: ' + definitions.START_YEAR + '\n'
'# project phase: ' + definitions.PHASE + '\n'
'# Miles of Transmission Line:'
' Distance transmission line needs to be.\n'
'# Generation Displaced in community to connect [kWh]: Diesel '
'generation displaced in community\n'
'# Electricity Generated, Conserved, or transmitted [kWh]: \n'
'# Loss of Recovered Heat from Genset in community to connect [gal]: \n'
'# Heat Recovery Operational in community to connect: \n'
'# Diesel Generator Efficiency in community to connect: Estimated '
'efficiency of diesel generator in community in kilowatt-'
'hours per gallon.\n'
'# Diesel Generator Efficiency in community to connect to: Estimated '
'efficiency of diesel generator in community connected to in '
'kilowatt-hours per gallon.\n'
'# Diesel Price - year 1 [$/gal] in community to connect: '
'Diesel fuel price in the community during the first year of'
' project operation.\n'
'# Diesel Price - year 1 [$/gal] in community to connect to: '
'Diesel fuel price in the community to connect to during the'
' first year of project operation.\n'
'# Breakeven Diesel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_DIESEL + '\n'
'# Annual Transmission loss percentage: Estimated transmission loss percent.\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Status Quo generation Cost (Year 1): Estimated cost of generation in community if nothing changes\n'
'# Proposed generation cost (Year 1): Estimated cost of generation in community with improvements.\n'
'# Benefit from reduced generation cost (year 1): Difference in base and proposed cost of generation \n'
'# Transmission NPV benefits [$]: ' + definitions.NPV_BENEFITS + '\n'
'# Transmission NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Transmission NPV Net benefit [$]: ' + definitions.NPV_NET_BENEFITS +
'\n'
'# Transmission Internal Rate of Return: ' + definitions.IRR + '\n'
'# Transmission Benefit-cost ratio: ' + definitions.NPV_BC_RATIO + '\n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary (coms, res_dir):
"""Saves the summary by: community residential_building_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
if c.find('+') != -1 or c.find("_intertie") != -1:
continue
try:
res = coms[c][COMPONENT_NAME]
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
out.append([name,
res.get_NPV_benefits(),res.get_NPV_costs(),
res.get_NPV_net_benefit(),res.irr,res.get_BC_ratio(),
res.hoil_price[0], res.init_HH, res.opportunity_HH,
res.break_even_cost, res.levelized_cost_of_energy,
res.baseline_fuel_Hoil_consumption[0]/constants.mmbtu_to_gal_HF,
(res.baseline_fuel_Hoil_consumption[0] - \
res.proposed_fuel_Hoil_consumption[0])/\
constants.mmbtu_to_gal_HF,
round(float(res.fuel_oil_percent)*100,2),
res.baseline_HF_consumption[0],
res.baseline_HF_consumption[0] - \
res.proposed_HF_consumption[0],
])
except (KeyError,AttributeError) as e :
#~ print e
pass
cols = ['Community',
'Residential Efficiency NPV Benefit',
'Residential Efficiency NPV Cost',
'Residential Efficiency NPV Net Benefit',
'Residential Internal Rate of Return',
'Residential Efficiency B/C Ratio',
'Heating Oil Price - year 1',
'Occupied Houses',
'Houses to Retrofit',
'Breakeven Heating Fuel Price [$/gal heating oil equiv.]',
'Levelized Cost of Energy [$/MMBtu]',
'Residential Heating Oil Consumed (MMBtu) - year 1',
'Residential Efficiency Heating Oil Saved (MMBtu/year)',
'Residential Heating Oil as percent of Total Heating Fuels',
'Total Residential Heating Fuels (MMBtu) - year 1',
'Residential Efficiency Total Heating Fuels Saved (MMBtu/year)',
]
data = DataFrame(out,columns = cols).set_index('Community').round(2)
f_name = os.path.join(res_dir,
COMPONENT_NAME.lower().replace(' ','_') + '_summary.csv')
fd = open(f_name,'w')
fd.write(("# resident building component summary by community\n"
'# community: '+ definitions.COMMUNITY + '\n'
'# Residential Efficiency NPV Benefit: ' + definitions.NPV_BENEFITS + '\n'
'# Residential Efficiency NPV Cost: ' + definitions.NPV_COSTS + '\n'
'# Residential Efficiency NPV Net Benefit: ' + definitions.NPV_NET_BENEFITS + '\n'
'# Residential Internal Rate of Return: ' + definitions.IRR +'\n'
'# Residential Efficiency B/C Ratio: ' + definitions.NPV_BC_RATIO + '\n'
'# Heating Oil Price - year 1: ' + definitions.PRICE_HF + '\n'
'# Occupied Houses: Occupied homes in communities.\n'
'# Houses to Retrofit: Houses that are to be retrofit.\n'
'# Breakeven Heating Fuel Price [$/gal heating oil equiv.]: ' + definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost of Energy [$/MMBtu]: ' + definitions.LCOE + '\n'
'# Residential Heating Oil Consumed (MMBtu) - year 1: Heating oil consumed by current systems.\n'
'# Residential Efficiency Heating Oil Saved (MMBtu/year): Heating oil saved by retrofit systems.\n'
'# Residential Heating Oil as percent of Total Heating Fuels: Percentage of heating fuels that is heating oil.\n'
'# Total Residential Heating Fuels (MMBtu) - year 1: Heating fuel consumed by current systems.\n'
'# Residential Efficiency Total Heating Fuels Saved (MMBtu/year): Heating fuel consumed by current systems.\n'
))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the summary by: community Solar_power_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
it = coms[c]['community data'].intertie
if it is None:
it = 'parent'
if it == 'child':
continue
try:
solar = coms[c][COMPONENT_NAME]
start_yr = solar.comp_specs['start year']
solar.get_diesel_prices()
diesel_price = float(solar.diesel_prices[0].round(2))
assumed_out = solar.comp_specs['output per 10kW solar PV']
average_load = solar.average_load
proposed_capacity = solar.proposed_load + 0
existing_capacity = solar.cd['solar capacity']
wind_capacity = solar.cd['wind capacity']
try:
net_gen = solar.generation_proposed[0]
loss_heat = solar.fuel_displaced[0]
hr_op = solar.cd['heat recovery operational']
net_heating = -1 * loss_heat
eff = solar.cd["diesel generation efficiency"]
red_per_year = solar.generation_fuel_used[0]
except AttributeError:
net_gen = 0
loss_heat = 0
hr_op = solar.cd['heat recovery operational']
net_heating = 0
eff = solar.cd["diesel generation efficiency"]
red_per_year = 0
try:
levelized_cost = solar.levelized_cost_of_energy
except AttributeError:
levelized_cost = 0
try:
break_even = solar.break_even_cost
except AttributeError:
break_even = 0
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name, assumed_out, average_load, proposed_capacity,
existing_capacity, wind_capacity, net_gen, loss_heat, hr_op,
net_heating, red_per_year, eff, diesel_price, break_even,
levelized_cost,
solar.get_NPV_benefits(),
solar.get_NPV_costs(),
solar.get_NPV_net_benefit(), solar.irr,
solar.get_BC_ratio(), solar.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
#~ print e
pass
cols = [
'Community', 'Assumed Output per 10kW Solar PV Array',
'Average Diesel Load [kw]', 'Solar Capacity Proposed [kW]',
'Existing Solar Capacity [kW]', 'Existing Wind Capacity [kW]',
'Net Proposed Solar Generation [kWh]',
'Loss of Recovered Heat from Proposed Solar [gal]',
'Heat Recovery Operational',
'Net in Heating Oil Consumption from Proposed Solar [gal]',
'Proposed Solar Reduction in Utility Diesel Consumed per year',
'Diesel Generator Efficiency', 'Diesel Price - year 1 [$/gal]',
'Break Even Diesel Price [$/gal]', 'Levelized Cost Of Energy [$/kWh]',
'Solar NPV benefits [$]', 'Solar NPV Costs [$]',
'Solar NPV Net benefit [$]', 'Solar Internal Rate of Return',
'Solar Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(res_dir,
COMPONENT_NAME.lower().replace(' ','_').\
replace('&','and') + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# solar summary\n"
'# Community: name of community/project.\n'
'# Assumed Output per 10kW Solar PV Array: '
'Assumed power output of 10 kW solar panel\n'
'# Average Diesel Load [kw]: ' + definitions.DIESEL_LOAD + '\n'
'# Solar Capacity Proposed [kW]: '
'Proposed generation offset by solar system\n'
'# Existing Solar Capacity [kW]: '
'Generation capacity of existing solar systems in community\n'
'# Existing Wind Capacity [kW]: '
'Generation capacity of existing wind systems in community\n'
'# Net Proposed Solar Generation [kWh]: '
'Net electric generation from new systems in kilowatt-hours\n'
'# Loss of Recovered Heat from Proposed Solar [gal]: '
'Loss in heat recovery caused by new solar systems.\n'
'# Heat Recovery Operational: ' + definitions.HR_OP + '\n'
'# Net Change in Heating Oil Consumption from Proposed Solar [gal]:'
' Change in heating oil consumption that would be caused by improvements\n'
'# Proposed Solar Reduction in Utility Diesel Consumed per year: '
'Reduction in generation diesel from proposed solar system.\n'
'# Diesel Generator Efficiency: ' + definitions.GEN_EFF + ' \n'
'# Diesel Price - year 1 [$\gal]: ' + definitions.PRICE_DIESEL + '\n'
'# Break Even Diesel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_DIESEL + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Wind power NPV benefits [$]: ' + definitions.NPV_BENEFITS + '\n'
'# Wind power NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Wind power NPV Net benefit [$]: ' + definitions.NPV_NET_BENEFITS +
'\n'
'# Wind power Internal Rate of Return: ' + definitions.IRR + '\n'
'# Wind power Benefit-cost ratio: ' + definitions.NPV_BC_RATIO + '\n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the component summary by community
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
#~ return
out = []
for c in sorted(coms.keys()):
it = coms[c]['community data'].intertie
if it is None:
it = 'parent'
if it == 'child':
continue
try:
# ??? NPV or year one
wind = coms[c][COMPONENT_NAME]
start_yr = wind.comp_specs['start year']
wind.get_diesel_prices()
diesel_price = float(wind.diesel_prices[0].round(2))
phase = wind.comp_specs['phase']
average_load = wind.average_load
existing_load = wind.cd['wind capacity']
existing_solar = wind.cd['solar capacity']
wind_class = float(wind.comp_specs['wind class'])
proposed_load = wind.load_offset_proposed
cap_fac = float(wind.comp_specs['capacity factor'])
heat_rec_opp = wind.cd['heat recovery operational']
try:
#~ offset = wind.load_offset_proposed
net_gen_wind = wind.net_generation_wind
decbb = wind.diesel_equiv_captured
loss_heat = wind.loss_heat_recovery
electric_diesel_reduction = wind.electric_diesel_reduction
diesel_red = wind.reduction_diesel_used
levelized_cost = wind.levelized_cost_of_energy
break_even = wind.break_even_cost
eff = wind.cd["diesel generation efficiency"]
except AttributeError:
offset = 0
net_gen_wind = 0
decbb = 0
electric_diesel_reduction = 0
loss_heat = 0
diesel_red = 0
levelized_cost = 0
break_even = 0
eff = wind.cd["diesel generation efficiency"]
#~ try:
#~ red_per_year = net_gen_wind / eff
#~ except ZeroDivisionError:
#~ red_per_year = 0
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name, start_yr, phase, wind_class, average_load, proposed_load,
existing_load, existing_solar, cap_fac, net_gen_wind, decbb,
loss_heat, heat_rec_opp, diesel_red, electric_diesel_reduction,
eff, diesel_price, break_even, levelized_cost,
wind.get_NPV_benefits(),
wind.get_NPV_costs(),
wind.get_NPV_net_benefit(), wind.irr,
wind.get_BC_ratio(), wind.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
#~ print e
pass
cols = [
'Community', 'Start Year', 'Project phase', 'Assumed Wind Class',
'Average Diesel Load [kW]', 'Wind Capacity Proposed [kW]',
'Existing Wind Capacity [kW]', 'Existing Solar Capacity [kW]',
'Assumed Wind Class Capacity Factor [%]',
'Net Proposed Wind Generation [kWh]',
'Heating Oil Equivalent Captured by Secondary Load [gal]',
'Loss of Recovered Heat from Genset [gal]',
'Heat Recovery Operational', 'Net in Heating Oil Consumption [gal]',
'Wind Power Reduction in Utility Diesel Consumed per year [gal]',
'Diesel Generator Efficiency', 'Diesel Price - year 1 [$/gal]',
'Breakeven Diesel Price [$/gal]', 'Levelized Cost Of Energy [$/kWh]',
'Wind NPV benefits [$]', 'Wind NPV Costs [$]',
'Wind NPV Net benefit [$]', 'Wind Internal Rate of Return',
'Wind Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.lower().replace(' ', '_') + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# wind summary\n"
'# Breakdown by community of potential wind power improvements'
'# \n'
'# Community: ' + definitions.COMMUNITY + '\n'
'# Start Year: ' + definitions.START_YEAR + '\n'
'# Project phase: ' + definitions.PHASE + '\n'
'# Assumed Wind Class: Wind power density class\n'
'# Average Diesel Load [kW]: ' + definitions.DIESEL_LOAD + '\n'
'# Wind Capacity Proposed [kW]: Proposed additional capacity for wind'
' generation in kilowatts.\n'
'# Existing Wind Capacity [kW]: Existing wind generation capacity'
' in kilowatts.\n'
'# Existing Solar Capacity [kW]: Existing solar generation capacity'
' in kilowatts.\n'
'# Assumed Wind Class Capacity Factor [%]:\n'
'# Net Proposed Wind Generation [kWh]: Proposed wind net generation'
' in kilowatt-hours.\n'
'# Heating Oil Equivalent Captured by Secondary Load [gal]: \n'
'# Loss of Recovered Heat from Genset [gal]: \n'
'# Heat Recovery Operational: ' + definitions.HR_OP + '\n'
'# Net in Heating Oil Consumption [gal]: \n'
'# Wind Power Reduction in Utility Diesel Consumed per year [gal]: Estimated '
'Reduction in utility diesel if wind power system is '
'installed/upgraded. In gallons per year\n'
'# Diesel Generator Efficiency: ' + definitions.GEN_EFF + ' \n'
'# Diesel Price - year 1 [$\gal]: ' + definitions.PRICE_DIESEL + '\n'
'# Breakeven Diesel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_DIESEL + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Wind power NPV benefits [$]: ' + definitions.NPV_BENEFITS + '\n'
'# Wind power NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Wind power NPV Net benefit [$]: ' + definitions.NPV_NET_BENEFITS +
'\n'
'# Wind power Internal Rate of Return: ' + definitions.IRR + '\n'
'# Wind power Benefit-cost ratio: ' + definitions.NPV_BC_RATIO + '\n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the summary by: community hydropower_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
it = coms[c]['community data'].intertie
if it is None:
it = 'parent'
if it == 'child':
continue
try:
# ??? NPV or year one
hydro = coms[c]['Hydropower']
start_yr = hydro.comp_specs['start year']
hydro.get_diesel_prices()
diesel_price = float(hydro.diesel_prices[0].round(2))
#~ print hydro.diesel_prices[0]
if hydro.comp_specs["name"] == 'none':
phase = hydro.comp_specs['phase']
else:
phase = "Reconnaissance"
name = hydro.comp_specs['name']
average_load = hydro.average_load
proposed_load = hydro.load_offset_proposed
heat_rec_opp = hydro.cd['heat recovery operational']
net_gen_hydro = hydro.net_generation_proposed
captured_energy = hydro.captured_energy
lost_heat = hydro.lost_heat_recovery
electric_diesel_reduction = hydro.generation_diesel_reduction
diesel_red = hydro.captured_energy - hydro.lost_heat_recovery
eff = hydro.cd["diesel generation efficiency"]
levelized_cost = hydro.levelized_cost_of_energy
break_even = hydro.break_even_cost
#~ except AttributeError:
#~ offset = 0
#~ net_gen_hydro = 0
#~ decbb = 0
#~ electric_diesel_reduction=0
#~ loss_heat = 0
#~ diesel_red = 0
#~ eff = hydro.cd["diesel generation efficiency"]
#~ try:
#~ red_per_year = net_gen_hydro / eff
#~ except ZeroDivisionError:
#~ red_per_year = 0
community = c
if community == 'Barrow':
community = 'Utqiagvik (Barrow)'
l = [
name, name, start_yr, phase, average_load, proposed_load,
net_gen_hydro, captured_energy, lost_heat, heat_rec_opp,
diesel_red, electric_diesel_reduction, eff, diesel_price,
break_even, levelized_cost,
hydro.get_NPV_benefits(),
hydro.get_NPV_costs(),
hydro.get_NPV_net_benefit(), hydro.irr,
hydro.get_BC_ratio(), hydro.reason
]
out.append(l)
except (KeyError, AttributeError, TypeError) as e:
#~ print e
pass
cols = [
'Community', 'Project Name', 'Start Year', 'project phase',
'Average Diesel Load [kw]', 'Hydro Capacity Proposed [kW]:',
'Net Proposed Hydro Generation [kWh]',
'Heating Oil Equivalent Captured by Secondary Load [gal]',
'Loss of Recovered Heat from Genset [gal]',
'Heat Recovery Operational',
'Net Reduction in Heating Oil Consumption [gal]',
'Hydropower Reduction in Utility Diesel Consumed per year',
'Diesel Generator Efficiency', 'Diesel Price - year 1 [$/gal]',
'Break Even Diesel Price [$/gal]', 'Levelized Cost Of Energy [$/kWh]',
'Hydro NPV benefits [$]', 'Hydro NPV Costs [$]',
'Hydro NPV Net benefit [$]', 'Hydro Internal Rate of Return',
'Hydro Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.replace(" ", "_").lower() + '_summary.csv')
with open(f_name, 'w') as summary:
summary.write((
'# Hydropower summary by community.\n'
'# Community: ' + definitions.COMMUNITY + '\n'
'# Project Name: Name of proposed hydropower project \n'
'# Start Year: ' + definitions.START_YEAR + '\n'
'# project phase: ' + definitions.PHASE + '\n'
'# Average Diesel Load [kw]: ' + definitions.DIESEL_LOAD + '\n'
'# Hydro Capacity Proposed [kW]: Proposed generation capacity from Hydropower.\n'
'# Net Proposed Hydro Generation [kWh]: Net generation from proposed hydropower system.\n'
'# Heating Oil Equivalent Captured by Secondary Load [gal]: \n'
'# Loss of Recovered Heat from Genset [gal]: \n'
'# Heat Recovery Operational' + definitions.HR_OP + '\n'
'# Net Reduction in Heating Oil Consumption [gal]: Change in '
'heating oil consumed between base and proposed systems\n'
'# Hydropower Reduction in Utility Diesel Consumed per year: '
'Reduction in diesel used from generation from hydropower project\n'
'# Diesel Generator Efficiency: ' + definitions.GEN_EFF + ' \n'
'# Diesel Price - year 1 [$\gal]: ' + definitions.PRICE_DIESEL +
'\n'
'# Break Even Diesel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_DIESEL + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Hydropower NPV benefits [$]: ' + definitions.NPV_BENEFITS + '\n'
'# Hydropower NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Hydropower NPV Net benefit [$]: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# Hydropower Internal Rate of Return: ' + definitions.IRR + '\n'
'# Hydropower Benefit-cost ratio: ' + definitions.NPV_BC_RATIO +
'\n'
'# notes: ' + definitions.NOTES + '\n'))
data.to_csv(f_name, mode='a')
def communities_summary(coms, res_dir):
"""Saves the summary by: community diesel_efficiency_summary.csv
Parameters
----------
coms : dictionary
results from the model, dictionary with each community or project
as key
res_dir : path
location to save file
"""
out = []
for c in sorted(coms.keys()):
it = coms[c]['community data'].intertie
if it is None:
it = 'parent'
if it == 'child' or c.find("+") != -1:
continue
#~ if c.find("_intertie") != -1:
#~ continue
try:
comp = coms[c][COMPONENT_NAME]
comp.get_diesel_prices()
diesel_price = float(comp.diesel_prices[0].round(2))
try:
average_load = comp.average_load
except AttributeError:
average_load = np.nan
try:
current_capacity = comp.cd['total capacity']
except AttributeError:
current_capacity = np.nan
try:
max_capacity = comp.max_capacity
except AttributeError:
max_capacity = np.nan
try:
generation = comp.generation[0]
except AttributeError:
generation = np.nan
try:
baseline_eff = comp.baseline_diesel_efficiency
except AttributeError:
baseline_eff = np.nan
try:
proposed_eff = comp.proposed_diesel_efficiency
except AttributeError:
proposed_eff = np.nan
try:
baseline_fuel = comp.baseline_generation_fuel_use[0]
except AttributeError:
baseline_fuel = np.nan
try:
proposed_fuel = comp.proposed_generation_fuel_use[0]
except AttributeError:
proposed_fuel = np.nan
try:
break_even_cost = comp.break_even_cost
levelized_cost_of_energy = comp.levelized_cost_of_energy
except AttributeError:
break_even_cost = np.nan
levelized_cost_of_energy = np.nan
name = c
if name == 'Barrow':
name = 'Utqiagvik (Barrow)'
l = [
name, average_load, current_capacity, max_capacity, generation,
baseline_eff, proposed_eff, baseline_fuel, proposed_fuel,
diesel_price, break_even_cost, levelized_cost_of_energy,
comp.get_NPV_benefits(),
comp.get_NPV_costs(),
comp.get_NPV_net_benefit(), comp.irr,
comp.get_BC_ratio(), comp.reason
]
out.append(l)
except (KeyError, AttributeError) as e:
print e
pass
cols = [
'Community', 'Average Load [kW]', 'Current Capacity [kW]',
'Proposed Capacity [kW]', 'Generation - year 1[kWh]',
'Baseline Diesel Generator Efficiency [kWh/gal]',
'Proposed Diesel Generator Efficiency [kWh/gal]',
'Baseline Generation Fuel Consumption [Gal]',
'Proposed Generation Fuel Consumption [Gal]',
'Diesel price - year 1 [$/gal]', 'Break Even Diesel Price [$/gal]',
'Levelized Cost of Energy [$/MMBtu]',
'Diesel Efficiency NPV benefits [$]',
'Diesel Efficiency NPV Costs [$]',
'Diesel Efficiency NPV Net benefit [$]',
'Diesel Efficiency Internal Rate of Return',
'Diesel Efficiency Benefit-cost ratio', 'notes'
]
data = DataFrame(out, columns=cols).set_index('Community') #.round(2)
f_name = os.path.join(
res_dir,
COMPONENT_NAME.replace(" ", "_").lower() + '_summary.csv')
fd = open(f_name, 'w')
fd.write((
"# " + COMPONENT_NAME + " summary by community\n"
'# Community: ' + definitions.COMMUNITY + '\n'
'# Average Load [kW]: ' + definitions.DIESEL_LOAD + '\n'
'# Current Capacity [kW]: \n'
'# Proposed Capacity [kW]: \n'
'# Generation - year 1[kWh]: \n'
'# Baseline Diesel Generator Efficiency [kWh/gal]: Current diesel generator efficiency\n'
'# Proposed Diesel Generator Efficiency [kWh/gal]: Proposed diesel generator efficiency\n'
'# Baseline Generation Fuel Consumption [Gal]: Current diesel used for generation\n'
'# Proposed Generation Fuel Consumption [Gal]: Proposed diesel used for generation\n'
'# Diesel price - year 1 [$/gal]: ' + definitions.PRICE_DIESEL + '\n'
'# Break Even Diesel Price [$/gal]: ' +
definitions.BREAK_EVEN_COST_HF + '\n'
'# Levelized Cost Of Energy [$/kWh]:' + definitions.LCOE + '\n'
'# Diesel Efficiency NPV benefits [$]: ' + definitions.NPV_BENEFITS +
'\n'
'# Diesel Efficiency NPV Costs [$]: ' + definitions.NPV_COSTS + '\n'
'# Diesel Efficiency NPV Net benefit [$]: ' +
definitions.NPV_NET_BENEFITS + '\n'
'# Diesel Efficiency Internal Rate of Return: ' + definitions.IRR +
'\n'
'# Diesel Efficiency Benefit-cost ratio: ' + definitions.NPV_BC_RATIO +
'\n'
'# notes: ' + definitions.NOTES + '\n'))
fd.close()
data.to_csv(f_name, mode='a')