def cost_breakdown(self):
"""Calculate costs breakdown for the solar power plant"""
eng_t = self.mat.abscissa('E_elec', valuesOnly=True) # Time [s]
eng_v = self.mat.data('E_elec') # Cumulative electricity generated [J]
disc_v = self.mat.data('r_disc') # Discount rate [-]
life_v = self.mat.data('t_life') # Plant lifetime [year]
C_field_v = self.mat.data('C_field') # Solar field capital cost [$]
C_tower_v = self.mat.data('C_tower') # Tower capital cost [$]
C_receiver_v = self.mat.data('C_receiver') # Receiver capital cost [$]
C_storage_v = self.mat.data(
'C_storage') # Storage tanks capital cost [$]
C_block_v = self.mat.data('C_block') # Power block capital cost [$]
C_bop_v = self.mat.data('C_bop') # Balance of plant cost [$]
C_land_v = self.mat.data('C_land') # Land cos [$]
C_cap_v = self.mat.data('C_cap') # Capital costs [$]
om_y_v = self.mat.data('C_year') # Fixed O&M costs per year [$/year]
om_p_v = self.mat.data(
'C_prod') # Variable O&M costs per production per year [$/J/year]
dur = eng_t[-1] - eng_t[0] # Time duration [s]
epy = fin.energy_per_year(dur,
eng_v[-1]) # Energy expected in a year [J]
C_cap = C_cap_v[0] * 1e-3 # Total capital investment (TCI) [k$]
C_cap_ann = fin.annualised_capital_cost(C_cap, disc_v[0], int(
life_v[0])) # Annualised TCI [k$/year]
C_year = (om_y_v[0] +
om_p_v[0] * epy) * 1e-3 # Total operational costs [k$/year]
C_cap_bd_n = [
'Solar field', 'Tower', 'Receiver', 'Storage', 'PB', 'BOP', 'Land'
] # Capital cost components name
C_cap_bd_u = 'k$' # Capital cost components unit
C_cap_bd_v = [
C_field_v[0] * 1e-3, C_tower_v[0] * 1e-3, C_receiver_v[0] * 1e-3,
C_storage_v[0] * 1e-3, C_block_v[0] * 1e-3, C_bop_v[0] * 1e-3,
C_land_v[0] * 1e-3
] # Capital cost breakdown [k$]
C_op_bd_n = ['Fixed O&M',
'variable O&M'] # Operational cost components name
C_op_bd_u = 'k$/year' # Operational cost components unit
C_op_bd_v = [om_y_v[0] * 1e-3, om_p_v[0] * epy * 1e-3
] # Operational cost breakdown [k$/year]
C_ann_bd_n = ['Total capital investment',
'Operational'] # Annualised cost breakdown names
C_ann_bd_u = 'k$/year' # Annualised cost breakdown unit
C_ann_bd_v = [C_cap_ann, C_year] # Annualised cost breakdown [k$/year]
return C_cap_bd_n, C_cap_bd_u, C_cap_bd_v, C_op_bd_n, C_op_bd_u, C_op_bd_v, C_ann_bd_n, C_ann_bd_u, C_ann_bd_v
def calc_perf(self):
"""Calculate plant performance.
Some of the metrics will be returned as none if simulation runtime is
not a multiple of a year.
"""
eng_t = self.mat.abscissa('E_elec', valuesOnly=True)
eng_v = self.mat.data('E_elec') # cumulative electricity generated
cap_v = self.mat.data('C_cap') # capital costs
om_y_v = self.mat.data('C_year') # O&M costs per year
om_p_v = self.mat.data('C_prod') # O&M costs per production per year
disc_v = self.mat.data('r_disc') # discount factor
life_v = self.mat.data('t_life') # plant lifetime
cons_v = self.mat.data('t_cons') # construction time
name_v = self.mat.data('P_name') # generator nameplate
rev_v = self.mat.data('R_spot') # cumulative revenue
dur = eng_t[-1] - eng_t[0]
years = dur / 31536000
# Only provide certain metrics if runtime is a multiple of a year
close_to_year = years > 0.5 and abs(years - round(years)) <= 0.01
epy = fin.energy_per_year(dur, eng_v[-1]) # energy expected in a year
srev = rev_v[-1] # spot market revenue
lcoe = None # levelised cost of electricity
capf = None # capacity factor
if close_to_year:
lcoe = fin.lcoe(cap_v[0], om_y_v[0] + om_p_v[0] * epy, disc_v[0],
int(life_v[0]), int(cons_v[0]), epy)
capf = fin.capacity_factor(name_v[0], epy)
# Convert to useful units
epy = epy / (1e6 * 3600) # convert from J/year to MWh/year
if close_to_year:
lcoe = lcoe * 1e6 * 3600 # convert from $/J to $/MWh
capf = 100 * capf
return [
epy,
lcoe,
capf,
srev,
]
def calc_perf(self):
"""Calculate the solar power plant performance.
Some of the metrics will be returned as none if simulation runtime is
not a multiple of a year.
"""
var_names = self.get_names()
assert('E_elec' in var_names), "For a levelised cost of electricity calculation, It is expected to see E_elec variable in the results file!"
eng_t = self.mat.abscissa('E_elec', valuesOnly=True) # Time [s]
eng_v = self.mat.data('E_elec') # Cumulative electricity generated [J]
cap_v = self.mat.data('C_cap') # Capital costs [$]
om_y_v = self.mat.data('C_year') # O&M costs per year [$/year]
om_p_v = self.mat.data('C_prod') # O&M costs per production per year [$/J/year]
disc_v = self.mat.data('r_disc') # Discount rate [-]
life_v = self.mat.data('t_life') # Plant lifetime [year]
cons_v = self.mat.data('t_cons') # Construction time [year]
name_v = self.mat.data('P_name') # Generator nameplate [W]
rev_v = self.mat.data('R_spot') # Cumulative revenue [$]
dur = eng_t[-1] - eng_t[0] # Time duration [s]
years = dur/31536000 # number of years of simulation [year]
# Only provide certain metrics if runtime is a multiple of a year
close_to_year = years > 0.5 and abs(years - round(years)) <= 0.01
epy = fin.energy_per_year(dur, eng_v[-1]) # Energy expected in a year [J]
srev = rev_v[-1] # spot market revenue [$]
lcoe = None # Levelised cost of electricity
capf = None # Capacity factor
if close_to_year:
lcoe = fin.lcoe_r(cap_v[0], om_y_v[0] + om_p_v[0]*epy, disc_v[0],
int(life_v[0]), int(cons_v[0]), epy)
capf = fin.capacity_factor(name_v[0], epy)
# Convert to useful units
epy = epy/(1e6*3600) # Convert from J/year to MWh/year
if close_to_year:
lcoe = lcoe*1e6*3600 # Convert from $/J to $/MWh
capf = 100*capf
return [epy, lcoe, capf, srev,]
def calc_perf(self):
"""Calculate plant performance.
Some of the metrics will be returned as none if simulation runtime is
not a multiple of a year.
"""
eng_t = self.mat.abscissa("E_elec", valuesOnly=True)
eng_v = self.mat.data("E_elec") # cumulative electricity generated
cap_v = self.mat.data("C_cap") # capital costs
om_y_v = self.mat.data("C_year") # O&M costs per year
om_p_v = self.mat.data("C_prod") # O&M costs per production per year
disc_v = self.mat.data("r_disc") # discount factor
life_v = self.mat.data("t_life") # plant lifetime
cons_v = self.mat.data("t_cons") # construction time
name_v = self.mat.data("P_name") # generator nameplate
rev_v = self.mat.data("R_spot") # cumulative revenue
dur = eng_t[-1] - eng_t[0]
years = dur / 31536000
# Only provide certain metrics if runtime is a multiple of a year
close_to_year = years > 0.5 and abs(years - round(years)) <= 0.01
epy = fin.energy_per_year(dur, eng_v[-1]) # energy expected in a year
srev = rev_v[-1] # spot market revenue
lcoe = None # levelised cost of electricity
capf = None # capacity factor
if close_to_year:
lcoe = fin.lcoe(cap_v[0], om_y_v[0] + om_p_v[0] * epy, disc_v[0], int(life_v[0]), int(cons_v[0]), epy)
capf = fin.capacity_factor(name_v[0], epy)
# Convert to useful units
epy = epy / (1e6 * 3600) # convert from J/year to MWh/year
if close_to_year:
lcoe = lcoe * 1e6 * 3600 # convert from $/J to $/MWh
capf = 100 * capf
return [epy, lcoe, capf, srev]
def calc_perf(self, peaker=False):
"""Calculate the solar power plant performance.
Some of the metrics will be returned as none if simulation runtime is
not a multiple of a year.
peaker: bool, True: to calculate performance of a peaker plant
"""
var_names = self.get_names()
assert (
'E_elec' in var_names
), "For a levelised cost of electricity calculation, It is expected to see E_elec variable in the results file!"
eng_t = self.mat.abscissa('E_elec', valuesOnly=True) # Time [s]
eng_v = self.mat.data('E_elec') # Cumulative electricity generated [J]
cap_v = self.mat.data('C_cap') # Capital costs [$]
om_y_v = self.mat.data('C_year') # O&M costs per year [$/year]
om_p_v = self.mat.data(
'C_prod') # O&M costs per production per year [$/J/year]
disc_v = self.mat.data('r_disc') # Discount rate [-]
life_v = self.mat.data('t_life') # Plant lifetime [year]
cons_v = self.mat.data('t_cons') # Construction time [year]
name_v = self.mat.data('P_name') # Generator nameplate [W]
rev_v = self.mat.data('R_spot') # Cumulative revenue [$]
dur = eng_t[-1] - eng_t[0] # Time duration [s]
years = dur / 31536000 # number of years of simulation [year]
# Only provide certain metrics if runtime is a multiple of a year
close_to_year = years > 0.5 and abs(years - round(years)) <= 0.01
epy = fin.energy_per_year(dur,
eng_v[-1]) # Energy expected in a year [J]
srev = rev_v[-1] # spot market revenue [$]
lcoe = None # Levelised cost of electricity
capf = None # Capacity factor
if close_to_year:
if peaker:
tod_v = self.mat.data('TOD_W')
tod_factor = tod_v[-1] / eng_v[-1]
lcoe = fin.lcoe_p(cap_v[0],
om_y_v[0] + om_p_v[0] * epy, disc_v[0],
int(life_v[0]), int(cons_v[0]), epy,
tod_factor)
capf = fin.capacity_factor(name_v[0], tod_v[-1])
else:
lcoe = fin.lcoe_r(cap_v[0],
om_y_v[0] + om_p_v[0] * epy, disc_v[0],
int(life_v[0]), int(cons_v[0]), epy)
capf = fin.capacity_factor(name_v[0], epy)
# Convert to useful units
epy = epy / (1e6 * 3600) # Convert from J/year to MWh/year
if close_to_year:
lcoe = lcoe * 1e6 * 3600 # Convert from $/J to $/MWh
capf = 100 * capf
'''
#dump param
SM = self.mat.data('SM')[0]
H_tower = self.mat.data("H_tower")[0]
t_storage = self.mat.data("t_storage")[0]
fb = self.mat.data("fb")[0]
R1 = self.mat.data("R1")[0]
if peaker:
fn = "/home/philgun/Documents/PhD/report/dispatch-optimisation-report/new-run-for-epri/withglpk/result.csv"
import os
if not os.path.exists(fn):
f = open(fn,'w')
f.write("SM,H_tower,t_storage,fb,R1,lcoe_peaker,capf_peaker,t_bar,TOD_W\n")
f.close()
f = open(fn,"a")
f.write("%s,%s,%s,%s,%s,%s,%s,%s,%s\n"%(SM,H_tower,t_storage,fb,R1,lcoe,capf,tod_factor,tod_v[-1]))
f.close()
'''
return [
epy,
lcoe,
capf,
srev,
]
