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 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 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 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 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')