def calc_Cinv_CCGT(CC_size_W, locator, config, technology=0):
"""
Annualized investment costs for the Combined cycle
:type CC_size_W : float
:param CC_size_W: Electrical size of the CC
:rtype InvCa : float
:returns InvCa: annualized investment costs in CHF
..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including
polygeneration energy conversion technologies., PhD Thesis, EPFL
"""
CCGT_cost_data = pd.read_excel(locator.get_database_supply_systems(),
sheet_name="CCGT")
technology_code = list(set(CCGT_cost_data['code']))
CCGT_cost_data[CCGT_cost_data['code'] == technology_code[technology]]
# if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least
# capacity for the corresponding technology from the database
if CC_size_W < CCGT_cost_data['cap_min'][0]:
CC_size_W = CCGT_cost_data['cap_min'][0]
CCGT_cost_data = CCGT_cost_data[(CCGT_cost_data['cap_min'] <= CC_size_W)
& (CCGT_cost_data['cap_max'] > CC_size_W)]
#costs of connection
connection_costs = ngas.calc_Cinv_gas(CC_size_W)
Inv_a = CCGT_cost_data.iloc[0]['a']
Inv_b = CCGT_cost_data.iloc[0]['b']
Inv_c = CCGT_cost_data.iloc[0]['c']
Inv_d = CCGT_cost_data.iloc[0]['d']
Inv_e = CCGT_cost_data.iloc[0]['e']
Inv_IR = (CCGT_cost_data.iloc[0]['IR_%']) / 100
Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']
Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100
InvC = Inv_a + Inv_b * (CC_size_W)**Inv_c + (
Inv_d + Inv_e * CC_size_W) * log(CC_size_W)
Capex_a_CCGT_USD = (InvC + connection_costs) * (Inv_IR) * (
1 + Inv_IR)**Inv_LT / ((1 + Inv_IR)**Inv_LT - 1)
Opex_fixed_CCGT_USD = InvC * Inv_OM
Capex_CCGT_USD = InvC
return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD
def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):
"""
Annualized investment costs for the Combined cycle
:type CC_size_W : float
:param CC_size_W: Electrical size of the CC
:rtype InvCa : float
:returns InvCa: annualized investment costs in CHF
..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including
polygeneration energy conversion technologies., PhD Thesis, EPFL
"""
# if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least
# capacity for the corresponding technology from the database
if CC_size_W < CCGT_cost_data['cap_min'][0]:
CC_size_W = CCGT_cost_data['cap_min'][0]
CCGT_cost_data = CCGT_cost_data[(CCGT_cost_data['cap_min'] <= CC_size_W)
& (CCGT_cost_data['cap_max'] > CC_size_W)]
#costs of connection
connection_costs = ngas.calc_Cinv_gas(CC_size_W)
Inv_a = CCGT_cost_data.iloc[0]['a']
Inv_b = CCGT_cost_data.iloc[0]['b']
Inv_c = CCGT_cost_data.iloc[0]['c']
Inv_d = CCGT_cost_data.iloc[0]['d']
Inv_e = CCGT_cost_data.iloc[0]['e']
Inv_IR = CCGT_cost_data.iloc[0]['IR_%']
Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']
Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100
InvC = Inv_a + Inv_b * (CC_size_W)**Inv_c + (
Inv_d + Inv_e * CC_size_W) * log(CC_size_W)
Capex_a_CCGT_USD = calc_capex_annualized((InvC + connection_costs), Inv_IR,
Inv_LT)
Opex_fixed_CCGT_USD = InvC * Inv_OM
Capex_CCGT_USD = InvC
return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD
def addCosts(indCombi, buildList, locator, dicoSupply, Q_uncovered_design_W,
Q_uncovered_annual_W, solarFeat, ntwFeat, gv):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param indCombi: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param dicoSupply: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solarFeat: solar features
:param ntwFeat: network features
:param gv: global variables
:type indCombi: string
:type buildList: list
:type locator: string
:type dicoSupply: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solarFeat: class
:type ntwFeat: class
:type gv: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
addCosts = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
print "\n COSTS FROM DISCONNECTED BUILDINGS"
os.chdir(locator.get_optimization_disconnected_folder())
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
FurnaceInvCost = 0
CCInvCost = 0
BoilerBInvCost = 0
BoilerPInvCost = 0
HPLakeInvC = 0
HPSewInvC = 0
GHPInvC = 0
PVInvC = 0
SCInvC = 0
PVTInvC = 0
BoilerAddInvC = 0
StorageHEXCost = 0
StorageHPCost = 0
StorageInvC = 0
NetworkCost = 0
SubstHEXCost = 0
PVTHEXCost = 0
SCHEXCost = 0
pumpCosts = 0
GasConnectionInvCost = 0
for (index, building_name) in zip(indCombi, buildList):
if index == "0":
discFileName = "DiscOp_" + building_name + "_result.csv"
df = pd.read_csv(discFileName)
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest["Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
print dfBest["Total Costs [CHF]"].iloc[
0], building_name, "disconnected"
else:
nBuildinNtw += 1
addCosts += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
# Add the features for the distribution
if indCombi.count("1") > 0:
os.chdir(locator.get_optimization_slave_results_folder())
print " \n MACHINERY COSTS"
# Add the investment costs of the energy systems
# Furnace
if dicoSupply.Furnace_on == 1:
P_design_W = dicoSupply.Furnace_Q_max
fNameSlavePP = locator.get_optimization_slave_pp_activation_pattern(
dicoSupply.configKey)
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace_W"])
arrayFurnace_W = np.array(dfFurnace)
Q_annual_W = 0
for i in range(int(np.shape(arrayFurnace_W)[0])):
Q_annual_W += arrayFurnace_W[i][0]
FurnaceInvCost = furnace.calc_Cinv_furnace(P_design_W, Q_annual_W,
gv)
addCosts += FurnaceInvCost
print furnace.calc_Cinv_furnace(P_design_W, Q_annual_W,
gv), " Furnace"
# CC
if dicoSupply.CC_on == 1:
CC_size_W = dicoSupply.CC_GT_SIZE
CCInvCost = chp.calc_Cinv_CCT(CC_size_W, gv)
addCosts += CCInvCost
print chp.calc_Cinv_CCT(CC_size_W, gv), " CC"
# Boiler Base
if dicoSupply.Boiler_on == 1:
Q_design_W = dicoSupply.Boiler_Q_max
fNameSlavePP = locator.get_optimization_slave_pp_activation_pattern(
dicoSupply.configKey)
dfBoilerBase = pd.read_csv(fNameSlavePP,
usecols=["Q_BoilerBase_W"])
arrayBoilerBase_W = np.array(dfBoilerBase)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerBase_W)[0])):
Q_annual_W += arrayBoilerBase_W[i][0]
BoilerBInvCost = boiler.calc_Cinv_boiler(Q_design_W, Q_annual_W,
gv)
addCosts += BoilerBInvCost
print boiler.calc_Cinv_boiler(Q_design_W, Q_annual_W,
gv), " Boiler Base "
# Boiler Peak
if dicoSupply.BoilerPeak_on == 1:
Q_design_W = dicoSupply.BoilerPeak_Q_max
fNameSlavePP = locator.get_optimization_slave_pp_activation_pattern(
dicoSupply.configKey)
dfBoilerPeak = pd.read_csv(fNameSlavePP,
usecols=["Q_BoilerPeak_W"])
arrayBoilerPeak_W = np.array(dfBoilerPeak)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerPeak_W)[0])):
Q_annual_W += arrayBoilerPeak_W[i][0]
BoilerPInvCost = boiler.calc_Cinv_boiler(Q_design_W, Q_annual_W,
gv)
addCosts += BoilerPInvCost
print boiler.calc_Cinv_boiler(Q_design_W, Q_annual_W,
gv), " Boiler Peak"
# HP Lake
if dicoSupply.HP_Lake_on == 1:
HP_Size_W = dicoSupply.HPLake_maxSize
HPLakeInvC = hp.calc_Cinv_HP(HP_Size_W, gv)
addCosts += HPLakeInvC
print hp.calc_Cinv_HP(HP_Size_W, gv), " HP Lake"
# HP Sewage
if dicoSupply.HP_Sew_on == 1:
HP_Size_W = dicoSupply.HPSew_maxSize
HPSewInvC = hp.calc_Cinv_HP(HP_Size_W, gv)
addCosts += HPSewInvC
print hp.calc_Cinv_HP(HP_Size_W, gv), "HP Sewage"
# GHP
if dicoSupply.GHP_on == 1:
fNameSlavePP = locator.get_optimization_slave_pp_activation_pattern(
dicoSupply.configKey)
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_GHP_req_W"])
arrayGHP_W = np.array(dfGHP)
GHP_Enom_W = np.amax(arrayGHP_W)
GHPInvC = hp.calc_Cinv_GHP(GHP_Enom_W, gv) * gv.EURO_TO_CHF
addCosts += GHPInvC
print hp.calc_Cinv_GHP(GHP_Enom_W, gv) * gv.EURO_TO_CHF, " GHP"
# Solar technologies
PV_peak_kW = dicoSupply.SOLAR_PART_PV * solarFeat.A_PV_m2 * gv.nPV #kW
PVInvC = pv.calc_Cinv_pv(PV_peak_kW)
addCosts += PVInvC
print pv.calc_Cinv_pv(PV_peak_kW), "PV peak"
SC_area_m2 = dicoSupply.SOLAR_PART_SC * solarFeat.A_SC_m2
SCInvC = stc.calc_Cinv_SC(SC_area_m2, gv)
addCosts += SCInvC
print stc.calc_Cinv_SC(SC_area_m2, gv), "SC area"
PVT_peak_kW = dicoSupply.SOLAR_PART_PVT * solarFeat.A_PVT_m2 * gv.nPVT #kW
PVTInvC = pvt.calc_Cinv_PVT(PVT_peak_kW, gv)
addCosts += PVTInvC
print pvt.calc_Cinv_PVT(PVT_peak_kW, gv), "PVT peak"
# Back-up boiler
BoilerAddInvC = boiler.calc_Cinv_boiler(Q_uncovered_design_W,
Q_uncovered_annual_W, gv)
addCosts += BoilerAddInvC
print boiler.calc_Cinv_boiler(Q_uncovered_design_W,
Q_uncovered_annual_W,
gv), "backup boiler"
# Hex and HP for Heat recovery
print "\n STORAGE PART COSTS"
if dicoSupply.WasteServersHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
dicoSupply.NETWORK_DATA_FILE),
usecols=["Qcdata_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(Q_HEX_max_kWh, gv)
print hex.calc_Cinv_HEX(Q_HEX_max_kWh, gv), "Hex for data center"
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
dicoSupply.configKey),
usecols=["HPServerHeatDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(Q_HP_max_kWh, gv)
print hp.calc_Cinv_HP(Q_HP_max_kWh, gv), "HP for data center"
if dicoSupply.WasteCompressorHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
dicoSupply.NETWORK_DATA_FILE),
usecols=["Ecaf_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(Q_HEX_max_kWh, gv)
print hex.calc_Cinv_HEX(Q_HEX_max_kWh,
gv), "Hex for compressed air"
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
dicoSupply.configKey),
usecols=["HPCompAirDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(Q_HP_max_kWh, gv)
print hp.calc_Cinv_HP(Q_HP_max_kWh, gv), "HP for compressed air"
addCosts += StorageHEXCost
# Heat pump solar to storage
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
dicoSupply.configKey),
usecols=["HPScDesignArray_Wh", "HPpvt_designArray_Wh"])
array = np.array(df)
Q_HP_max_PVT_Wh = np.amax(array[:, 1])
QhpMax_SC_Wh = np.amax(array[:, 0])
StorageHPCost += hp.calc_Cinv_HP(Q_HP_max_PVT_Wh, gv)
print hp.calc_Cinv_HP(Q_HP_max_PVT_Wh, gv), "HP for PVT"
StorageHPCost += hp.calc_Cinv_HP(QhpMax_SC_Wh, gv)
print hp.calc_Cinv_HP(QhpMax_SC_Wh, gv), "HP for SC"
# HP for storage operation
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
dicoSupply.configKey),
usecols=[
"E_aux_ch_W", "E_aux_dech_W",
"Q_from_storage_used_W", "Q_to_storage_W"
])
array = np.array(df)
Q_HP_max_storage_W = 0
for i in range(gv.DAYS_IN_YEAR * gv.HOURS_IN_DAY):
if array[i][0] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][3] + array[i][0])
elif array[i][1] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][2] + array[i][1])
StorageHPCost += hp.calc_Cinv_HP(Q_HP_max_storage_W, gv)
addCosts += StorageHPCost
print hp.calc_Cinv_HP(Q_HP_max_storage_W, gv), "HP for storage"
# Storage
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
dicoSupply.configKey),
usecols=["Storage_Size_m3"],
nrows=1)
StorageVol_m3 = np.array(df)[0][0]
StorageInvC += storage.calc_Cinv_storage(StorageVol_m3, gv)
addCosts += StorageInvC
print storage.calc_Cinv_storage(StorageVol_m3, gv), "Storage Costs"
# Costs from distribution configuration
print "\n COSTS FROM NETWORK CONFIGURATION"
if gv.ZernezFlag == 1:
NetworkCost += network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv) * nBuildinNtw / len(buildList)
else:
NetworkCost += ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList)
addCosts += NetworkCost
print ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList), "Pipes Costs"
# HEX (1 per building in ntw)
for (index, building_name) in zip(indCombi, buildList):
if index == "1":
df = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name),
usecols=["Q_dhw_W", "Q_heating_W"])
subsArray = np.array(df)
Q_max_W = np.amax(subsArray[:, 0] + subsArray[:, 1])
SubstHEXCost += hex.calc_Cinv_HEX(Q_max_W, gv)
print hex.calc_Cinv_HEX(Q_max_W, gv), "Hex", building_name
addCosts += SubstHEXCost
# HEX for solar
roof_area_m2 = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(indCombi)):
index = indCombi[i]
if index == "1":
areaAvail += roof_area_m2[i][0]
for i in range(len(indCombi)):
index = indCombi[i]
if index == "1":
share = roof_area_m2[i][0] / areaAvail
#print share, "solar area share", buildList[i]
Q_max_SC_Wh = solarFeat.Q_nom_SC_Wh * dicoSupply.SOLAR_PART_SC * share
SCHEXCost += hex.calc_Cinv_HEX(Q_max_SC_Wh, gv)
print hex.calc_Cinv_HEX(Q_max_SC_Wh,
gv), "Hex SC", buildList[i]
Q_max_PVT_Wh = solarFeat.Q_nom_PVT_Wh * dicoSupply.SOLAR_PART_PVT * share
PVTHEXCost += hex.calc_Cinv_HEX(Q_max_PVT_Wh, gv)
print hex.calc_Cinv_HEX(Q_max_PVT_Wh,
gv), "Hex PVT", buildList[i]
addCosts += SCHEXCost
addCosts += PVTHEXCost
print addCosts, "addCosts in extraCostsMain"
# Pump operation costs
pumpCosts = pumps.calc_Ctot_pump(
dicoSupply, buildList,
locator.get_optimization_network_results_folder(), ntwFeat, gv)
addCosts += pumpCosts
print pumpCosts, "Pump Operation costs in extraCostsMain\n"
# import gas consumption data from:
if indCombi.count("1") > 0:
# import gas consumption data from:
E_gas_PrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_primary_energy_by_source(
dicoSupply.configKey),
usecols=["E_gas_PrimaryPeakPower_W"])
E_gas_primary_peak_power_W = float(np.array(E_gas_PrimaryDataframe_W))
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
else:
GasConnectionInvCost = 0.0
addCosts += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"SCInvC": [SCInvC],
"PVTInvC": [PVTInvC],
"BoilerAddInvC": [BoilerAddInvC],
"StorageHEXCost": [StorageHEXCost],
"StorageHPCost": [StorageHPCost],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + StorageHPCost + StorageHEXCost],
"NetworkCost": [NetworkCost],
"SubstHEXCost": [SubstHEXCost],
"DHNInvestCost": [addCosts - CostDiscBuild],
"PVTHEXCost": [PVTHEXCost],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"FurnaceInvCost": [FurnaceInvCost],
"BoilerBInvCost": [BoilerBInvCost],
"BoilerPInvCost": [BoilerPInvCost],
"HPLakeInvC": [HPLakeInvC],
"HPSewInvC": [HPSewInvC],
"SCHEXCost": [SCHEXCost],
"pumpCosts": [pumpCosts],
"SumInvestCost": [addCosts],
"GasConnectionInvCa": [GasConnectionInvCost]
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed(
dicoSupply.configKey),
sep=',')
return (addCosts, addCO2, addPrim)
def addCosts(indCombi, buildList, locator, dicoSupply, QUncoveredDesign, QUncoveredAnnual, solarFeat, ntwFeat, gv):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
Parameters
----------
indCombi : string
with 0 if disconnected building, 1 if connected
buildList : list
list of buildings in the district
locator : string
path to folders
dicoSupply : class context
with the features of the specific individual
QuncoveredDesign : float
hourly max of the heating uncovered demand
QuncoveredAnnual : float
total heating uncovered
solarFeat / ntwFeat : class solarFeatures / ntwFeatures
Returns
-------
(addCosts, addCO2, addPrim) : tuple
"""
addCosts = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
print "\n COSTS FROM DISCONNECTED BUILDINGS"
os.chdir(locator.pathDiscRes)
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
FurnaceInvCost = 0
CCInvCost = 0
BoilerBInvCost = 0
BoilerPInvCost = 0
HPLakeInvC = 0
HPSewInvC = 0
GHPInvC = 0
PVInvC = 0
SCInvC = 0
PVTInvC = 0
BoilerAddInvC = 0
StorageHEXCost = 0
StorageHPCost = 0
StorageInvC = 0
NetworkCost = 0
SubstHEXCost = 0
PVTHEXCost = 0
SCHEXCost = 0
pumpCosts = 0
GasConnectionInvCost = 0
for (index, buildName) in zip(indCombi, buildList):
if index == "0":
discFileName = "DiscOp_" + buildName + "_result.csv"
df = pd.read_csv(discFileName)
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[0] # [kg CO2]
PrimDiscBuild += dfBest["Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
print dfBest["Total Costs [CHF]"].iloc[0], buildName, "disconnected"
else:
nBuildinNtw += 1
addCosts += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
# Add the features for the network
if indCombi.count("1") > 0:
os.chdir(locator.pathSlaveRes)
print " \n MACHINERY COSTS"
# Add the investment costs of the energy systems
# Furnace
if dicoSupply.Furnace_on == 1:
P_design = dicoSupply.Furnace_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace"])
arrayFurnace = np.array(dfFurnace)
Q_annual = 0
for i in range(int(np.shape(arrayFurnace)[0])):
Q_annual += arrayFurnace[i][0]
FurnaceInvCost = furnace.calc_Cinv_furnace(P_design, Q_annual, gv)
addCosts += FurnaceInvCost
print furnace.calc_Cinv_furnace(P_design, Q_annual, gv), " Furnace"
# CC
if dicoSupply.CC_on == 1:
CC_size = dicoSupply.CC_GT_SIZE
CCInvCost = chp.calc_Cinv_CCT(CC_size, gv)
addCosts += CCInvCost
print chp.calc_Cinv_CCT(CC_size, gv), " CC"
# Boiler Base
if dicoSupply.Boiler_on == 1:
Q_design = dicoSupply.Boiler_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfBoilerBase = pd.read_csv(fNameSlavePP, usecols=["Q_BoilerBase"])
arrayBoilerBase = np.array(dfBoilerBase)
Q_annual = 0
for i in range(int(np.shape(arrayBoilerBase)[0])):
Q_annual += arrayBoilerBase[i][0]
BoilerBInvCost = boiler.calc_Cinv_boiler(Q_design, Q_annual, gv)
addCosts += BoilerBInvCost
print boiler.calc_Cinv_boiler(Q_design, Q_annual, gv), " Boiler Base "
# Boiler Peak
if dicoSupply.BoilerPeak_on == 1:
Q_design = dicoSupply.BoilerPeak_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfBoilerPeak = pd.read_csv(fNameSlavePP, usecols=["Q_BoilerPeak"])
arrayBoilerPeak = np.array(dfBoilerPeak)
Q_annual = 0
for i in range(int(np.shape(arrayBoilerPeak)[0])):
Q_annual += arrayBoilerPeak[i][0]
BoilerPInvCost = boiler.calc_Cinv_boiler(Q_design, Q_annual, gv)
addCosts += BoilerPInvCost
print boiler.calc_Cinv_boiler(Q_design, Q_annual, gv), " Boiler Peak"
# HP Lake
if dicoSupply.HP_Lake_on == 1:
HP_Size = dicoSupply.HPLake_maxSize
HPLakeInvC = hp.calc_Cinv_HP(HP_Size, gv)
addCosts += HPLakeInvC
print hp.calc_Cinv_HP(HP_Size, gv), " HP Lake"
# HP Sewage
if dicoSupply.HP_Sew_on == 1:
HP_Size = dicoSupply.HPSew_maxSize
HPSewInvC = hp.calc_Cinv_HP(HP_Size, gv)
addCosts += HPSewInvC
print hp.calc_Cinv_HP(HP_Size, gv), "HP Sewage"
# GHP
if dicoSupply.GHP_on == 1:
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_GHP"])
arrayGHP = np.array(dfGHP)
GHP_Enom = np.amax(arrayGHP)
GHPInvC = hp.calc_Cinv_GHP(GHP_Enom, gv) * gv.EURO_TO_CHF
addCosts += GHPInvC
print hp.calc_Cinv_GHP(GHP_Enom, gv) * gv.EURO_TO_CHF, " GHP"
# Solar technologies
PV_peak = dicoSupply.SOLAR_PART_PV * solarFeat.SolarAreaPV * gv.nPV #kW
PVInvC = pv.calc_Cinv_PV(PV_peak)
addCosts += PVInvC
print pv.calc_Cinv_PV(PV_peak), "PV peak"
SC_area = dicoSupply.SOLAR_PART_SC * solarFeat.SolarAreaSC
SCInvC = stc.calc_Cinv_SC(SC_area)
addCosts += SCInvC
print stc.calc_Cinv_SC(SC_area), "SC area"
PVT_peak = dicoSupply.SOLAR_PART_PVT * solarFeat.SolarAreaPVT * gv.nPVT #kW
PVTInvC = pvt.calc_Cinv_PVT(PVT_peak)
addCosts += PVTInvC
print pvt.calc_Cinv_PVT(PVT_peak), "PVT peak"
# Back-up boiler
BoilerAddInvC = boiler.calc_Cinv_boiler(QUncoveredDesign, QUncoveredAnnual, gv)
addCosts += BoilerAddInvC
print boiler.calc_Cinv_boiler(QUncoveredDesign, QUncoveredAnnual, gv), "backup boiler"
# Hex and HP for Heat recovery
print "\n STORAGE PART COSTS"
if dicoSupply.WasteServersHeatRecovery == 1:
df = pd.read_csv(locator.pathNtwRes + "/" + dicoSupply.NETWORK_DATA_FILE, usecols = ["Qcdata_netw_total"])
array = np.array(df)
QhexMax = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(QhexMax, gv)
print hex.calc_Cinv_HEX(QhexMax, gv), "Hex for data center"
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey + "StorageOperationData.csv", usecols = ["HPServerHeatDesignArray"])
array = np.array(df)
QhpMax = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(QhpMax, gv)
print hp.calc_Cinv_HP(QhpMax, gv), "HP for data center"
if dicoSupply.WasteCompressorHeatRecovery == 1:
df = pd.read_csv(locator.pathNtwRes + "/" + dicoSupply.NETWORK_DATA_FILE, usecols = ["Ecaf_netw_total"])
array = np.array(df)
QhexMax = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(QhexMax, gv)
print hex.calc_Cinv_HEX(QhexMax, gv), "Hex for compressed air"
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey + "StorageOperationData.csv", usecols = ["HPCompAirDesignArray"])
array = np.array(df)
QhpMax = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(QhpMax, gv)
print hp.calc_Cinv_HP(QhpMax, gv), "HP for compressed air"
addCosts += StorageHEXCost
# Heat pump solar to storage
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey + "StorageOperationData.csv", usecols = ["HPScDesignArray", "HPpvt_designArray"])
array = np.array(df)
QhpMax_PVT = np.amax(array[:,1])
QhpMax_SC = np.amax(array[:,0])
StorageHPCost += hp.calc_Cinv_HP(QhpMax_PVT, gv)
print hp.calc_Cinv_HP(QhpMax_PVT, gv), "HP for PVT"
StorageHPCost += hp.calc_Cinv_HP(QhpMax_SC, gv)
print hp.calc_Cinv_HP(QhpMax_SC, gv), "HP for SC"
# HP for storage operation
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey + "StorageOperationData.csv", usecols = ["E_aux_ch", "E_aux_dech", "Q_from_storage_used", "Q_to_storage"])
array = np.array(df)
QmaxHPStorage = 0
for i in range(gv.DAYS_IN_YEAR * gv.HOURS_IN_DAY):
if array[i][0] > 0:
QmaxHPStorage = max(QmaxHPStorage, array[i][3] + array[i][0])
elif array[i][1] > 0:
QmaxHPStorage = max(QmaxHPStorage, array[i][2] + array[i][1])
StorageHPCost += hp.calc_Cinv_HP(QmaxHPStorage, gv)
addCosts += StorageHPCost
print hp.calc_Cinv_HP(QmaxHPStorage, gv), "HP for storage"
# Storage
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey + "StorageOperationData.csv", usecols = ["Storage_Size"], nrows = 1)
StorageVol = np.array(df)[0][0]
StorageInvC += storage.calc_Cinv_storage(StorageVol, gv)
addCosts += StorageInvC
print storage.calc_Cinv_storage(StorageVol, gv), "Storage Costs"
# Costs from network configuration
print "\n COSTS FROM NETWORK CONFIGURATION"
if gv.ZernezFlag == 1:
NetworkCost += network.calc_Cinv_network_linear(gv.NetworkLengthZernez, gv) * nBuildinNtw / len(buildList)
else:
NetworkCost += ntwFeat.pipesCosts_DHN * nBuildinNtw / len(buildList)
addCosts += NetworkCost
print ntwFeat.pipesCosts_DHN * nBuildinNtw / len(buildList), "Pipes Costs"
# HEX (1 per building in ntw)
for (index, buildName) in zip(indCombi, buildList):
if index == "1":
subsFileName = buildName + "_result.csv"
df = pd.read_csv(locator.pathSubsRes + "/" + subsFileName, usecols = ["Q_dhw", "Q_heating"])
subsArray = np.array(df)
Qmax = np.amax( subsArray[:,0] + subsArray[:,1] )
SubstHEXCost += hex.calc_Cinv_HEX(Qmax, gv)
print hex.calc_Cinv_HEX(Qmax, gv), "Hex", buildName
addCosts += SubstHEXCost
# HEX for solar
roof_area = np.array(pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range( len(indCombi) ):
index = indCombi[i]
if index == "1":
areaAvail += roof_area[i][0]
for i in range( len(indCombi) ):
index = indCombi[i]
if index == "1":
share = roof_area[i][0] / areaAvail
#print share, "solar area share", buildList[i]
SC_Qmax = solarFeat.SC_Qnom * dicoSupply.SOLAR_PART_SC * share
SCHEXCost += hex.calc_Cinv_HEX(SC_Qmax, gv)
print hex.calc_Cinv_HEX(SC_Qmax, gv), "Hex SC", buildList[i]
PVT_Qmax = solarFeat.PVT_Qnom * dicoSupply.SOLAR_PART_PVT * share
PVTHEXCost += hex.calc_Cinv_HEX(PVT_Qmax, gv)
print hex.calc_Cinv_HEX(PVT_Qmax, gv), "Hex PVT", buildList[i]
addCosts += SCHEXCost
addCosts += PVTHEXCost
print addCosts,"addCosts in extraCostsMain"
# Pump operation costs
pumpCosts = pumps.calc_Ctot_pump(dicoSupply, buildList, locator.pathNtwRes, ntwFeat, gv)
addCosts += pumpCosts
print pumpCosts, "Pump Operation costs in extraCostsMain\n"
# import gas consumption data from:
if indCombi.count("1") > 0:
# import gas consumption data from:
FileName = locator.pathSlaveRes + "/" + dicoSupply.configKey + "PrimaryEnergyBySource.csv"
colName = "EgasPrimaryPeakPower"
EgasPrimaryDataframe = pd.read_csv(FileName, usecols=[colName])
#print EgasPrimaryDataframe
#print np.array(EgasPrimaryDataframe)
#print float(np.array(EgasPrimaryDataframe))
EgasPrimaryPeakPower = float(np.array(EgasPrimaryDataframe))
GasConnectionInvCost = ngas.calc_Cinv_gas(EgasPrimaryPeakPower, gv)
else:
GasConnectionInvCost = 0.0
addCosts += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"SCInvC":[SCInvC],
"PVTInvC":[PVTInvC],
"BoilerAddInvC":[BoilerAddInvC],
"StorageHEXCost":[StorageHEXCost],
"StorageHPCost":[StorageHPCost],
"StorageInvC":[StorageInvC],
"StorageCostSum":[StorageInvC+StorageHPCost+StorageHEXCost],
"NetworkCost":[NetworkCost],
"SubstHEXCost":[SubstHEXCost],
"DHNInvestCost":[addCosts - CostDiscBuild],
"PVTHEXCost":[PVTHEXCost],
"CostDiscBuild":[CostDiscBuild],
"CO2DiscBuild":[CO2DiscBuild],
"PrimDiscBuild":[PrimDiscBuild],
"FurnaceInvCost":[FurnaceInvCost],
"BoilerBInvCost":[BoilerBInvCost],
"BoilerPInvCost":[BoilerPInvCost],
"HPLakeInvC":[HPLakeInvC],
"HPSewInvC":[HPSewInvC],
"SCHEXCost":[SCHEXCost],
"pumpCosts":[pumpCosts],
"SumInvestCost":[addCosts],
"GasConnectionInvCa":[GasConnectionInvCost]
})
Name = "/" + dicoSupply.configKey + "_InvestmentCostDetailed.csv"
results.to_csv(locator.pathSlaveRes + Name, sep=',')
return (addCosts, addCO2, addPrim)
def addCosts(indCombi, buildList, locator, dicoSupply, QUncoveredDesign,
QUncoveredAnnual, solarFeat, ntwFeat, gv):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
Parameters
----------
indCombi : string
with 0 if disconnected building, 1 if connected
buildList : list
list of buildings in the district
locator : string
path to folders
dicoSupply : class context
with the features of the specific individual
QuncoveredDesign : float
hourly max of the heating uncovered demand
QuncoveredAnnual : float
total heating uncovered
solarFeat / ntwFeat : class solarFeatures / ntwFeatures
Returns
-------
(addCosts, addCO2, addPrim) : tuple
"""
addCosts = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
print "\n COSTS FROM DISCONNECTED BUILDINGS"
os.chdir(locator.pathDiscRes)
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
FurnaceInvCost = 0
CCInvCost = 0
BoilerBInvCost = 0
BoilerPInvCost = 0
HPLakeInvC = 0
HPSewInvC = 0
GHPInvC = 0
PVInvC = 0
SCInvC = 0
PVTInvC = 0
BoilerAddInvC = 0
StorageHEXCost = 0
StorageHPCost = 0
StorageInvC = 0
NetworkCost = 0
SubstHEXCost = 0
PVTHEXCost = 0
SCHEXCost = 0
pumpCosts = 0
GasConnectionInvCost = 0
for (index, buildName) in zip(indCombi, buildList):
if index == "0":
discFileName = "DiscOp_" + buildName + "_result.csv"
df = pd.read_csv(discFileName)
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest["Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
print dfBest["Total Costs [CHF]"].iloc[
0], buildName, "disconnected"
else:
nBuildinNtw += 1
addCosts += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
# Add the features for the network
if indCombi.count("1") > 0:
os.chdir(locator.pathSlaveRes)
print " \n MACHINERY COSTS"
# Add the investment costs of the energy systems
# Furnace
if dicoSupply.Furnace_on == 1:
P_design = dicoSupply.Furnace_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace"])
arrayFurnace = np.array(dfFurnace)
Q_annual = 0
for i in range(int(np.shape(arrayFurnace)[0])):
Q_annual += arrayFurnace[i][0]
FurnaceInvCost = furnace.calc_Cinv_furnace(P_design, Q_annual, gv)
addCosts += FurnaceInvCost
print furnace.calc_Cinv_furnace(P_design, Q_annual, gv), " Furnace"
# CC
if dicoSupply.CC_on == 1:
CC_size = dicoSupply.CC_GT_SIZE
CCInvCost = chp.calc_Cinv_CCT(CC_size, gv)
addCosts += CCInvCost
print chp.calc_Cinv_CCT(CC_size, gv), " CC"
# Boiler Base
if dicoSupply.Boiler_on == 1:
Q_design = dicoSupply.Boiler_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfBoilerBase = pd.read_csv(fNameSlavePP, usecols=["Q_BoilerBase"])
arrayBoilerBase = np.array(dfBoilerBase)
Q_annual = 0
for i in range(int(np.shape(arrayBoilerBase)[0])):
Q_annual += arrayBoilerBase[i][0]
BoilerBInvCost = boiler.calc_Cinv_boiler(Q_design, Q_annual, gv)
addCosts += BoilerBInvCost
print boiler.calc_Cinv_boiler(Q_design, Q_annual,
gv), " Boiler Base "
# Boiler Peak
if dicoSupply.BoilerPeak_on == 1:
Q_design = dicoSupply.BoilerPeak_Q_max
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfBoilerPeak = pd.read_csv(fNameSlavePP, usecols=["Q_BoilerPeak"])
arrayBoilerPeak = np.array(dfBoilerPeak)
Q_annual = 0
for i in range(int(np.shape(arrayBoilerPeak)[0])):
Q_annual += arrayBoilerPeak[i][0]
BoilerPInvCost = boiler.calc_Cinv_boiler(Q_design, Q_annual, gv)
addCosts += BoilerPInvCost
print boiler.calc_Cinv_boiler(Q_design, Q_annual,
gv), " Boiler Peak"
# HP Lake
if dicoSupply.HP_Lake_on == 1:
HP_Size = dicoSupply.HPLake_maxSize
HPLakeInvC = hp.calc_Cinv_HP(HP_Size, gv)
addCosts += HPLakeInvC
print hp.calc_Cinv_HP(HP_Size, gv), " HP Lake"
# HP Sewage
if dicoSupply.HP_Sew_on == 1:
HP_Size = dicoSupply.HPSew_maxSize
HPSewInvC = hp.calc_Cinv_HP(HP_Size, gv)
addCosts += HPSewInvC
print hp.calc_Cinv_HP(HP_Size, gv), "HP Sewage"
# GHP
if dicoSupply.GHP_on == 1:
fNameSlavePP = dicoSupply.configKey + "PPActivationPattern.csv"
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_GHP"])
arrayGHP = np.array(dfGHP)
GHP_Enom = np.amax(arrayGHP)
GHPInvC = hp.calc_Cinv_GHP(GHP_Enom, gv) * gv.EURO_TO_CHF
addCosts += GHPInvC
print hp.calc_Cinv_GHP(GHP_Enom, gv) * gv.EURO_TO_CHF, " GHP"
# Solar technologies
PV_peak = dicoSupply.SOLAR_PART_PV * solarFeat.SolarAreaPV * gv.nPV #kW
PVInvC = pv.calc_Cinv_PV(PV_peak)
addCosts += PVInvC
print pv.calc_Cinv_PV(PV_peak), "PV peak"
SC_area = dicoSupply.SOLAR_PART_SC * solarFeat.SolarAreaSC
SCInvC = stc.calc_Cinv_SC(SC_area)
addCosts += SCInvC
print stc.calc_Cinv_SC(SC_area), "SC area"
PVT_peak = dicoSupply.SOLAR_PART_PVT * solarFeat.SolarAreaPVT * gv.nPVT #kW
PVTInvC = pvt.calc_Cinv_PVT(PVT_peak)
addCosts += PVTInvC
print pvt.calc_Cinv_PVT(PVT_peak), "PVT peak"
# Back-up boiler
BoilerAddInvC = boiler.calc_Cinv_boiler(QUncoveredDesign,
QUncoveredAnnual, gv)
addCosts += BoilerAddInvC
print boiler.calc_Cinv_boiler(QUncoveredDesign, QUncoveredAnnual,
gv), "backup boiler"
# Hex and HP for Heat recovery
print "\n STORAGE PART COSTS"
if dicoSupply.WasteServersHeatRecovery == 1:
df = pd.read_csv(locator.pathNtwRes + "/" +
dicoSupply.NETWORK_DATA_FILE,
usecols=["Qcdata_netw_total"])
array = np.array(df)
QhexMax = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(QhexMax, gv)
print hex.calc_Cinv_HEX(QhexMax, gv), "Hex for data center"
df = pd.read_csv(locator.pathSlaveRes + "/" +
dicoSupply.configKey + "StorageOperationData.csv",
usecols=["HPServerHeatDesignArray"])
array = np.array(df)
QhpMax = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(QhpMax, gv)
print hp.calc_Cinv_HP(QhpMax, gv), "HP for data center"
if dicoSupply.WasteCompressorHeatRecovery == 1:
df = pd.read_csv(locator.pathNtwRes + "/" +
dicoSupply.NETWORK_DATA_FILE,
usecols=["Ecaf_netw_total"])
array = np.array(df)
QhexMax = np.amax(array)
StorageHEXCost += hex.calc_Cinv_HEX(QhexMax, gv)
print hex.calc_Cinv_HEX(QhexMax, gv), "Hex for compressed air"
df = pd.read_csv(locator.pathSlaveRes + "/" +
dicoSupply.configKey + "StorageOperationData.csv",
usecols=["HPCompAirDesignArray"])
array = np.array(df)
QhpMax = np.amax(array)
StorageHEXCost += hp.calc_Cinv_HP(QhpMax, gv)
print hp.calc_Cinv_HP(QhpMax, gv), "HP for compressed air"
addCosts += StorageHEXCost
# Heat pump solar to storage
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey +
"StorageOperationData.csv",
usecols=["HPScDesignArray", "HPpvt_designArray"])
array = np.array(df)
QhpMax_PVT = np.amax(array[:, 1])
QhpMax_SC = np.amax(array[:, 0])
StorageHPCost += hp.calc_Cinv_HP(QhpMax_PVT, gv)
print hp.calc_Cinv_HP(QhpMax_PVT, gv), "HP for PVT"
StorageHPCost += hp.calc_Cinv_HP(QhpMax_SC, gv)
print hp.calc_Cinv_HP(QhpMax_SC, gv), "HP for SC"
# HP for storage operation
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey +
"StorageOperationData.csv",
usecols=[
"E_aux_ch", "E_aux_dech", "Q_from_storage_used",
"Q_to_storage"
])
array = np.array(df)
QmaxHPStorage = 0
for i in range(gv.DAYS_IN_YEAR * gv.HOURS_IN_DAY):
if array[i][0] > 0:
QmaxHPStorage = max(QmaxHPStorage, array[i][3] + array[i][0])
elif array[i][1] > 0:
QmaxHPStorage = max(QmaxHPStorage, array[i][2] + array[i][1])
StorageHPCost += hp.calc_Cinv_HP(QmaxHPStorage, gv)
addCosts += StorageHPCost
print hp.calc_Cinv_HP(QmaxHPStorage, gv), "HP for storage"
# Storage
df = pd.read_csv(locator.pathSlaveRes + "/" + dicoSupply.configKey +
"StorageOperationData.csv",
usecols=["Storage_Size"],
nrows=1)
StorageVol = np.array(df)[0][0]
StorageInvC += storage.calc_Cinv_storage(StorageVol, gv)
addCosts += StorageInvC
print storage.calc_Cinv_storage(StorageVol, gv), "Storage Costs"
# Costs from network configuration
print "\n COSTS FROM NETWORK CONFIGURATION"
if gv.ZernezFlag == 1:
NetworkCost += network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv) * nBuildinNtw / len(buildList)
else:
NetworkCost += ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList)
addCosts += NetworkCost
print ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList), "Pipes Costs"
# HEX (1 per building in ntw)
for (index, buildName) in zip(indCombi, buildList):
if index == "1":
subsFileName = buildName + "_result.csv"
df = pd.read_csv(locator.pathSubsRes + "/" + subsFileName,
usecols=["Q_dhw", "Q_heating"])
subsArray = np.array(df)
Qmax = np.amax(subsArray[:, 0] + subsArray[:, 1])
SubstHEXCost += hex.calc_Cinv_HEX(Qmax, gv)
print hex.calc_Cinv_HEX(Qmax, gv), "Hex", buildName
addCosts += SubstHEXCost
# HEX for solar
roof_area = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(indCombi)):
index = indCombi[i]
if index == "1":
areaAvail += roof_area[i][0]
for i in range(len(indCombi)):
index = indCombi[i]
if index == "1":
share = roof_area[i][0] / areaAvail
#print share, "solar area share", buildList[i]
SC_Qmax = solarFeat.SC_Qnom * dicoSupply.SOLAR_PART_SC * share
SCHEXCost += hex.calc_Cinv_HEX(SC_Qmax, gv)
print hex.calc_Cinv_HEX(SC_Qmax, gv), "Hex SC", buildList[i]
PVT_Qmax = solarFeat.PVT_Qnom * dicoSupply.SOLAR_PART_PVT * share
PVTHEXCost += hex.calc_Cinv_HEX(PVT_Qmax, gv)
print hex.calc_Cinv_HEX(PVT_Qmax, gv), "Hex PVT", buildList[i]
addCosts += SCHEXCost
addCosts += PVTHEXCost
print addCosts, "addCosts in extraCostsMain"
# Pump operation costs
pumpCosts = pumps.calc_Ctot_pump(dicoSupply, buildList,
locator.pathNtwRes, ntwFeat, gv)
addCosts += pumpCosts
print pumpCosts, "Pump Operation costs in extraCostsMain\n"
# import gas consumption data from:
if indCombi.count("1") > 0:
# import gas consumption data from:
FileName = locator.pathSlaveRes + "/" + dicoSupply.configKey + "PrimaryEnergyBySource.csv"
colName = "EgasPrimaryPeakPower"
EgasPrimaryDataframe = pd.read_csv(FileName, usecols=[colName])
#print EgasPrimaryDataframe
#print np.array(EgasPrimaryDataframe)
#print float(np.array(EgasPrimaryDataframe))
EgasPrimaryPeakPower = float(np.array(EgasPrimaryDataframe))
GasConnectionInvCost = ngas.calc_Cinv_gas(EgasPrimaryPeakPower, gv)
else:
GasConnectionInvCost = 0.0
addCosts += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"SCInvC": [SCInvC],
"PVTInvC": [PVTInvC],
"BoilerAddInvC": [BoilerAddInvC],
"StorageHEXCost": [StorageHEXCost],
"StorageHPCost": [StorageHPCost],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + StorageHPCost + StorageHEXCost],
"NetworkCost": [NetworkCost],
"SubstHEXCost": [SubstHEXCost],
"DHNInvestCost": [addCosts - CostDiscBuild],
"PVTHEXCost": [PVTHEXCost],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"FurnaceInvCost": [FurnaceInvCost],
"BoilerBInvCost": [BoilerBInvCost],
"BoilerPInvCost": [BoilerPInvCost],
"HPLakeInvC": [HPLakeInvC],
"HPSewInvC": [HPSewInvC],
"SCHEXCost": [SCHEXCost],
"pumpCosts": [pumpCosts],
"SumInvestCost": [addCosts],
"GasConnectionInvCa": [GasConnectionInvCost]
})
Name = "/" + dicoSupply.configKey + "_InvestmentCostDetailed.csv"
results.to_csv(locator.pathSlaveRes + Name, sep=',')
return (addCosts, addCO2, addPrim)
def addCosts(buildList, locator, master_to_slave_vars, Q_uncovered_design_W,
Q_uncovered_annual_W, solar_features, network_features, gv,
config, prices, lca):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param DHN_barcode: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param master_to_slave_vars: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solar_features: solar features
:param network_features: network features
:param gv: global variables
:type indCombi: string
:type buildList: list
:type locator: string
:type master_to_slave_vars: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solar_features: class
:type network_features: class
:type gv: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
DHN_barcode = master_to_slave_vars.DHN_barcode
DCN_barcode = master_to_slave_vars.DCN_barcode
addcosts_Capex_a_USD = 0
addcosts_Opex_fixed_USD = 0
addcosts_Capex_USD = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
Capex_Disconnected = 0
Opex_Disconnected = 0
Capex_a_furnace_USD = 0
Capex_a_CHP_USD = 0
Capex_a_Boiler_USD = 0
Capex_a_Boiler_peak_USD = 0
Capex_a_Lake_USD = 0
Capex_a_Sewage_USD = 0
Capex_a_GHP_USD = 0
Capex_a_PV_USD = 0
Capex_a_SC_ET_USD = 0
Capex_a_SC_FP_USD = 0
Capex_a_PVT_USD = 0
Capex_a_Boiler_backup_USD = 0
Capex_a_HEX = 0
Capex_a_storage_HP = 0
Capex_a_HP_storage_USD = 0
Opex_fixed_SC = 0
Opex_fixed_PVT_USD = 0
Opex_fixed_HP_PVT_USD = 0
Opex_fixed_furnace_USD = 0
Opex_fixed_CHP_USD = 0
Opex_fixed_Boiler_USD = 0
Opex_fixed_Boiler_peak_USD = 0
Opex_fixed_Boiler_backup_USD = 0
Opex_fixed_Lake_USD = 0
Opex_fixed_wasteserver_HEX_USD = 0
Opex_fixed_wasteserver_HP_USD = 0
Opex_fixed_PV_USD = 0
Opex_fixed_GHP_USD = 0
Opex_fixed_storage_USD = 0
Opex_fixed_Sewage_USD = 0
Opex_fixed_HP_storage_USD = 0
StorageInvC = 0
NetworkCost_a_USD = 0
SubstHEXCost_capex = 0
SubstHEXCost_opex = 0
PVTHEXCost_Capex = 0
PVTHEXCost_Opex = 0
SCHEXCost_Capex = 0
SCHEXCost_Opex = 0
pumpCosts = 0
GasConnectionInvCost = 0
cost_PV_disconnected = 0
CO2_PV_disconnected = 0
Eprim_PV_disconnected = 0
Capex_furnace_USD = 0
Capex_CHP_USD = 0
Capex_Boiler_USD = 0
Capex_Boiler_peak_USD = 0
Capex_Lake_USD = 0
Capex_Sewage_USD = 0
Capex_GHP = 0
Capex_PV_USD = 0
Capex_SC = 0
Capex_PVT_USD = 0
Capex_Boiler_backup_USD = 0
Capex_HEX = 0
Capex_storage_HP = 0
Capex_HP_storage = 0
Capex_SC_ET_USD = 0
Capex_SC_FP_USD = 0
Capex_PVT_USD = 0
Capex_Boiler_backup_USD = 0
Capex_HP_storage_USD = 0
Capex_storage_HP = 0
Capex_CHP_USD = 0
Capex_furnace_USD = 0
Capex_Boiler_USD = 0
Capex_Boiler_peak_USD = 0
Capex_Lake_USD = 0
Capex_Sewage_USD = 0
Capex_pump_USD = 0
if config.district_heating_network:
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "0":
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_heating(
building_name))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
else:
nBuildinNtw += 1
if config.district_cooling_network:
PV_barcode = ''
for (index, building_name) in zip(DCN_barcode, buildList):
if index == "0": # choose the best decentralized configuration
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, configuration='AHU_ARU_SCU'))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (FP)"].iloc[0] == 1:
to_PV = 0
else: # adding costs for buildings in which the centralized plant provides a part of the load requirements
DCN_unit_configuration = master_to_slave_vars.DCN_supplyunits
if DCN_unit_configuration == 1: # corresponds to AHU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU_SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 2: # corresponds to ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 3: # corresponds to SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_ARU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 4: # corresponds to AHU + ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 5: # corresponds to AHU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 6: # corresponds to ARU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 7: # corresponds to AHU + ARU + SCU from central plant
to_PV = 1
nBuildinNtw += 1
PV_barcode = PV_barcode + str(to_PV)
addcosts_Capex_a_USD += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
# Solar technologies
PV_installed_area_m2 = master_to_slave_vars.SOLAR_PART_PV * solar_features.A_PV_m2 # kW
Capex_a_PV_USD, Opex_fixed_PV_USD, Capex_PV_USD = pv.calc_Cinv_pv(
PV_installed_area_m2, locator, config)
addcosts_Capex_a_USD += Capex_a_PV_USD
addcosts_Opex_fixed_USD += Opex_fixed_PV_USD
addcosts_Capex_USD += Capex_PV_USD
SC_ET_area_m2 = master_to_slave_vars.SOLAR_PART_SC_ET * solar_features.A_SC_ET_m2
Capex_a_SC_ET_USD, Opex_fixed_SC_ET_USD, Capex_SC_ET_USD = stc.calc_Cinv_SC(
SC_ET_area_m2, locator, config, 'ET')
addcosts_Capex_a_USD += Capex_a_SC_ET_USD
addcosts_Opex_fixed_USD += Opex_fixed_SC_ET_USD
addcosts_Capex_USD += Capex_SC_ET_USD
SC_FP_area_m2 = master_to_slave_vars.SOLAR_PART_SC_FP * solar_features.A_SC_FP_m2
Capex_a_SC_FP_USD, Opex_fixed_SC_FP_USD, Capex_SC_FP_USD = stc.calc_Cinv_SC(
SC_FP_area_m2, locator, config, 'FP')
addcosts_Capex_a_USD += Capex_a_SC_FP_USD
addcosts_Opex_fixed_USD += Opex_fixed_SC_FP_USD
addcosts_Capex_USD += Capex_SC_FP_USD
PVT_peak_kW = master_to_slave_vars.SOLAR_PART_PVT * solar_features.A_PVT_m2 * N_PVT # kW
Capex_a_PVT_USD, Opex_fixed_PVT_USD, Capex_PVT_USD = pvt.calc_Cinv_PVT(
PVT_peak_kW, locator, config)
addcosts_Capex_a_USD += Capex_a_PVT_USD
addcosts_Opex_fixed_USD += Opex_fixed_PVT_USD
addcosts_Capex_USD += Capex_PVT_USD
# Add the features for the distribution
if DHN_barcode.count("1") > 0 and config.district_heating_network:
os.chdir(
locator.get_optimization_slave_results_folder(
master_to_slave_vars.generation_number))
# Add the investment costs of the energy systems
# Furnace
if master_to_slave_vars.Furnace_on == 1:
P_design_W = master_to_slave_vars.Furnace_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern_heating(
master_to_slave_vars.configKey,
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace_W"])
arrayFurnace_W = np.array(dfFurnace)
Q_annual_W = 0
for i in range(int(np.shape(arrayFurnace_W)[0])):
Q_annual_W += arrayFurnace_W[i][0]
Capex_a_furnace_USD, Opex_fixed_furnace_USD, Capex_furnace_USD = furnace.calc_Cinv_furnace(
P_design_W, Q_annual_W, config, locator, 'FU1')
addcosts_Capex_a_USD += Capex_a_furnace_USD
addcosts_Opex_fixed_USD += Opex_fixed_furnace_USD
addcosts_Capex_USD += Capex_furnace_USD
# CC
if master_to_slave_vars.CC_on == 1:
CC_size_W = master_to_slave_vars.CC_GT_SIZE_W
Capex_a_CHP_USD, Opex_fixed_CHP_USD, Capex_CHP_USD = chp.calc_Cinv_CCGT(
CC_size_W, locator, config)
addcosts_Capex_a_USD += Capex_a_CHP_USD
addcosts_Opex_fixed_USD += Opex_fixed_CHP_USD
addcosts_Capex_USD += Capex_CHP_USD
# Boiler Base
if master_to_slave_vars.Boiler_on == 1:
Q_design_W = master_to_slave_vars.Boiler_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerBase = pd.read_csv(fNameSlavePP,
usecols=["Q_BaseBoiler_W"])
arrayBoilerBase_W = np.array(dfBoilerBase)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerBase_W)[0])):
Q_annual_W += arrayBoilerBase_W[i][0]
Capex_a_Boiler_USD, Opex_fixed_Boiler_USD, Capex_Boiler_USD = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_USD
addcosts_Capex_USD += Capex_Boiler_USD
# Boiler Peak
if master_to_slave_vars.BoilerPeak_on == 1:
Q_design_W = master_to_slave_vars.BoilerPeak_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerPeak = pd.read_csv(fNameSlavePP,
usecols=["Q_PeakBoiler_W"])
arrayBoilerPeak_W = np.array(dfBoilerPeak)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerPeak_W)[0])):
Q_annual_W += arrayBoilerPeak_W[i][0]
Capex_a_Boiler_peak_USD, Opex_fixed_Boiler_peak_USD, Capex_Boiler_peak_USD = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_peak_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_peak_USD
addcosts_Capex_USD += Capex_Boiler_peak_USD
# HP Lake
if master_to_slave_vars.HP_Lake_on == 1:
HP_Size_W = master_to_slave_vars.HPLake_maxSize_W
Capex_a_Lake_USD, Opex_fixed_Lake_USD, Capex_Lake_USD = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a_USD += Capex_a_Lake_USD
addcosts_Opex_fixed_USD += Opex_fixed_Lake_USD
addcosts_Capex_USD += Capex_Lake_USD
# HP Sewage
if master_to_slave_vars.HP_Sew_on == 1:
HP_Size_W = master_to_slave_vars.HPSew_maxSize_W
Capex_a_Sewage_USD, Opex_fixed_Sewage_USD, Capex_Sewage_USD = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a_USD += Capex_a_Sewage_USD
addcosts_Opex_fixed_USD += Opex_fixed_Sewage_USD
addcosts_Capex_USD += Capex_Sewage_USD
# GHP
if master_to_slave_vars.GHP_on == 1:
fNameSlavePP = locator.get_optimization_slave_electricity_activation_pattern_heating(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_used_GHP_W"])
arrayGHP_W = np.array(dfGHP)
GHP_Enom_W = np.amax(arrayGHP_W)
Capex_a_GHP_USD, Opex_fixed_GHP_USD, Capex_GHP_USD = hp.calc_Cinv_GHP(
GHP_Enom_W, locator, config)
addcosts_Capex_a_USD += Capex_a_GHP_USD * prices.EURO_TO_CHF
addcosts_Opex_fixed_USD += Opex_fixed_GHP_USD * prices.EURO_TO_CHF
addcosts_Capex_USD += Capex_GHP_USD
# Back-up boiler
Capex_a_Boiler_backup_USD, Opex_fixed_Boiler_backup_USD, Capex_Boiler_backup_USD = boiler.calc_Cinv_boiler(
Q_uncovered_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_backup_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_backup_USD
addcosts_Capex_USD += Capex_Boiler_backup_USD
master_to_slave_vars.BoilerBackup_Q_max_W = Q_uncovered_design_W
# Hex and HP for Heat recovery
if master_to_slave_vars.WasteServersHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
master_to_slave_vars.network_data_file_heating),
usecols=["Qcdata_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
Capex_a_wasteserver_HEX_USD, Opex_fixed_wasteserver_HEX_USD, Capex_wasteserver_HEX_USD = hex.calc_Cinv_HEX(
Q_HEX_max_kWh, locator, config, 'HEX1')
addcosts_Capex_a_USD += (Capex_a_wasteserver_HEX_USD)
addcosts_Opex_fixed_USD += Opex_fixed_wasteserver_HEX_USD
addcosts_Capex_USD += Capex_wasteserver_HEX_USD
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPServerHeatDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
Capex_a_wasteserver_HP_USD, Opex_fixed_wasteserver_HP_USD, Capex_wasteserver_HP_USD = hp.calc_Cinv_HP(
Q_HP_max_kWh, locator, config, 'HP2')
addcosts_Capex_a_USD += (Capex_a_wasteserver_HP_USD)
addcosts_Opex_fixed_USD += Opex_fixed_wasteserver_HP_USD
addcosts_Capex_USD += Capex_wasteserver_HP_USD
# Heat pump from solar to DH
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPScDesignArray_Wh", "HPpvt_designArray_Wh"])
array = np.array(df)
Q_HP_max_PVT_wh = np.amax(array[:, 1])
Q_HP_max_SC_Wh = np.amax(array[:, 0])
Capex_a_HP_PVT_USD, Opex_fixed_HP_PVT_USD, Capex_HP_PVT_USD = hp.calc_Cinv_HP(
Q_HP_max_PVT_wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_PVT_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_PVT_USD
addcosts_Capex_USD += Capex_HP_PVT_USD
Capex_a_HP_SC_USD, Opex_fixed_HP_SC_USD, Capex_HP_SC_USD = hp.calc_Cinv_HP(
Q_HP_max_SC_Wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_SC_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_SC_USD
addcosts_Capex_USD += Capex_HP_SC_USD
# HP for storage operation for charging from solar and discharging to DH
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=[
"E_aux_ch_W", "E_aux_dech_W",
"Q_from_storage_used_W", "Q_to_storage_W"
])
array = np.array(df)
Q_HP_max_storage_W = 0
for i in range(DAYS_IN_YEAR * HOURS_IN_DAY):
if array[i][0] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][3] + array[i][0])
elif array[i][1] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][2] + array[i][1])
Capex_a_HP_storage_USD, Opex_fixed_HP_storage_USD, Capex_HP_storage_USD = hp.calc_Cinv_HP(
Q_HP_max_storage_W, locator, config, 'HP2')
addcosts_Capex_a_USD += (Capex_a_HP_storage_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_storage_USD
addcosts_Capex_USD += Capex_HP_storage_USD
# Storage
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["Storage_Size_m3"],
nrows=1)
StorageVol_m3 = np.array(df)[0][0]
Capex_a_storage_USD, Opex_fixed_storage_USD, Capex_storage_USD = storage.calc_Cinv_storage(
StorageVol_m3, locator, config, 'TES2')
addcosts_Capex_a_USD += Capex_a_storage_USD
addcosts_Opex_fixed_USD += Opex_fixed_storage_USD
addcosts_Capex_USD += Capex_storage_USD
# Costs from distribution configuration
if gv.ZernezFlag == 1:
NetworkCost_a_USD, NetworkCost_USD = network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv)
NetworkCost_a_USD = NetworkCost_a_USD * nBuildinNtw / len(
buildList)
NetworkCost_USD = NetworkCost_USD * nBuildinNtw / len(buildList)
else:
NetworkCost_USD = network_features.pipesCosts_DHN_USD
NetworkCost_USD = NetworkCost_USD * nBuildinNtw / len(buildList)
NetworkCost_a_USD = NetworkCost_USD * gv.PipeInterestRate * (
1 + gv.PipeInterestRate)**gv.PipeLifeTime / (
(1 + gv.PipeInterestRate)**gv.PipeLifeTime - 1)
addcosts_Capex_a_USD += NetworkCost_a_USD
addcosts_Capex_USD += NetworkCost_USD
# HEX (1 per building in ntw)
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "1":
df = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name),
usecols=["Q_dhw_W", "Q_heating_W"])
subsArray = np.array(df)
Q_max_W = np.amax(subsArray[:, 0] + subsArray[:, 1])
Capex_a_HEX_building_USD, Opex_fixed_HEX_building_USD, Capex_HEX_building_USD = hex.calc_Cinv_HEX(
Q_max_W, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_building_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_building_USD
addcosts_Capex_USD += Capex_HEX_building_USD
# HEX for solar
roof_area_m2 = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
areaAvail += roof_area_m2[i][0]
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
share = roof_area_m2[i][0] / areaAvail
#print share, "solar area share", buildList[i]
Q_max_SC_ET_Wh = solar_features.Q_nom_SC_ET_Wh * master_to_slave_vars.SOLAR_PART_SC_ET * share
Capex_a_HEX_SC_ET_USD, Opex_fixed_HEX_SC_ET_USD, Capex_HEX_SC_ET_USD = hex.calc_Cinv_HEX(
Q_max_SC_ET_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_SC_ET_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_SC_ET_USD
addcosts_Capex_USD += Capex_HEX_SC_ET_USD
Q_max_SC_FP_Wh = solar_features.Q_nom_SC_FP_Wh * master_to_slave_vars.SOLAR_PART_SC_FP * share
Capex_a_HEX_SC_FP_USD, Opex_fixed_HEX_SC_FP_USD, Capex_HEX_SC_FP_USD = hex.calc_Cinv_HEX(
Q_max_SC_FP_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_SC_FP_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_SC_FP_USD
addcosts_Capex_USD += Capex_HEX_SC_FP_USD
Q_max_PVT_Wh = solar_features.Q_nom_PVT_Wh * master_to_slave_vars.SOLAR_PART_PVT * share
Capex_a_HEX_PVT_USD, Opex_fixed_HEX_PVT_USD, Capex_HEX_PVT_USD = hex.calc_Cinv_HEX(
Q_max_PVT_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_PVT_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_PVT_USD
addcosts_Capex_USD += Capex_HEX_PVT_USD
# Pump operation costs
Capex_a_pump_USD, Opex_fixed_pump_USD, Opex_var_pump_USD, Capex_pump_USD = pumps.calc_Ctot_pump(
master_to_slave_vars, network_features, gv, locator, lca, config)
addcosts_Capex_a_USD += Capex_a_pump_USD
addcosts_Opex_fixed_USD += Opex_fixed_pump_USD
addcosts_Capex_USD += Capex_pump_USD
# import gas consumption data from:
if DHN_barcode.count("1") > 0 and config.district_heating_network:
# import gas consumption data from:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_natural_gas_imports(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_gas_primary_peak_power_W = np.amax(
EgasPrimaryDataframe_W['NG_total_W'])
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
elif DCN_barcode.count("1") > 0 and config.district_cooling_network:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_natural_gas_imports(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_gas_primary_peak_power_W = np.amax(
EgasPrimaryDataframe_W['NG_total_W'])
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
else:
GasConnectionInvCost = 0.0
addcosts_Capex_a_USD += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"Capex_a_SC_ET_USD": [Capex_a_SC_ET_USD],
"Capex_a_SC_FP_USD": [Capex_a_SC_FP_USD],
"Opex_fixed_SC": [Opex_fixed_SC],
"Capex_a_PVT": [Capex_a_PVT_USD],
"Opex_fixed_PVT": [Opex_fixed_PVT_USD],
"Capex_a_Boiler_backup": [Capex_a_Boiler_backup_USD],
"Opex_fixed_Boiler_backup": [Opex_fixed_Boiler_backup_USD],
"Capex_a_storage_HEX": [Capex_a_HP_storage_USD],
"Opex_fixed_storage_HEX": [Opex_fixed_HP_storage_USD],
"Capex_a_storage_HP": [Capex_a_storage_HP],
"Capex_a_CHP": [Capex_a_CHP_USD],
"Opex_fixed_CHP": [Opex_fixed_CHP_USD],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + Capex_a_storage_HP + Capex_a_HEX],
"NetworkCost": [NetworkCost_a_USD],
"SubstHEXCost": [SubstHEXCost_capex],
"DHNInvestCost": [addcosts_Capex_a_USD - CostDiscBuild],
"PVTHEXCost_Capex": [PVTHEXCost_Capex],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"Capex_a_furnace": [Capex_a_furnace_USD],
"Opex_fixed_furnace": [Opex_fixed_furnace_USD],
"Capex_a_Boiler": [Capex_a_Boiler_USD],
"Opex_fixed_Boiler": [Opex_fixed_Boiler_USD],
"Capex_a_Boiler_peak": [Capex_a_Boiler_peak_USD],
"Opex_fixed_Boiler_peak": [Opex_fixed_Boiler_peak_USD],
"Capex_Disconnected": [Capex_Disconnected],
"Opex_Disconnected": [Opex_Disconnected],
"Capex_a_Lake": [Capex_a_Lake_USD],
"Opex_fixed_Lake": [Opex_fixed_Lake_USD],
"Capex_a_Sewage": [Capex_a_Sewage_USD],
"Opex_fixed_Sewage": [Opex_fixed_Sewage_USD],
"SCHEXCost_Capex": [SCHEXCost_Capex],
"Capex_a_pump": [Capex_a_pump_USD],
"Opex_fixed_pump": [Opex_fixed_pump_USD],
"Opex_var_pump": [Opex_var_pump_USD],
"Sum_CAPEX": [addcosts_Capex_a_USD],
"Sum_OPEX_fixed": [addcosts_Opex_fixed_USD],
"GasConnectionInvCa": [GasConnectionInvCost],
"CO2_PV_disconnected": [CO2_PV_disconnected],
"cost_PV_disconnected": [cost_PV_disconnected],
"Eprim_PV_disconnected": [Eprim_PV_disconnected],
"Capex_SC_ET_USD": [Capex_SC_ET_USD],
"Capex_SC_FP_USD": [Capex_SC_FP_USD],
"Capex_PVT": [Capex_PVT_USD],
"Capex_Boiler_backup": [Capex_Boiler_backup_USD],
"Capex_storage_HEX": [Capex_HP_storage_USD],
"Capex_storage_HP": [Capex_storage_HP],
"Capex_CHP": [Capex_CHP_USD],
"Capex_furnace": [Capex_furnace_USD],
"Capex_Boiler_base": [Capex_Boiler_USD],
"Capex_Boiler_peak": [Capex_Boiler_peak_USD],
"Capex_Lake": [Capex_Lake_USD],
"Capex_Sewage": [Capex_Sewage_USD],
"Capex_pump": [Capex_pump_USD],
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
sep=',')
return (addcosts_Capex_a_USD + addcosts_Opex_fixed_USD, addCO2, addPrim)
def addCosts(DHN_barcode, DCN_barcode, buildList, locator,
master_to_slave_vars, Q_uncovered_design_W, Q_uncovered_annual_W,
solarFeat, ntwFeat, gv, config, prices, lca):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param DHN_barcode: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param master_to_slave_vars: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solarFeat: solar features
:param ntwFeat: network features
:param gv: global variables
:type indCombi: string
:type buildList: list
:type locator: string
:type master_to_slave_vars: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solarFeat: class
:type ntwFeat: class
:type gv: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
addcosts_Capex_a = 0
addcosts_Opex_fixed = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
Capex_Disconnected = 0
Opex_Disconnected = 0
Capex_a_furnace = 0
Capex_a_CHP = 0
Capex_a_Boiler = 0
Capex_a_Boiler_peak = 0
Capex_a_Lake = 0
Capex_a_Sewage = 0
Capex_a_GHP = 0
Capex_a_PV = 0
Capex_a_SC = 0
Capex_a_PVT = 0
Capex_a_Boiler_backup = 0
Capex_a_HEX = 0
Capex_a_storage_HP = 0
Capex_a_HP_storage = 0
Opex_fixed_SC = 0
Opex_fixed_PVT = 0
Opex_fixed_HP_PVT = 0
Opex_fixed_furnace = 0
Opex_fixed_CHP = 0
Opex_fixed_Boiler = 0
Opex_fixed_Boiler_peak = 0
Opex_fixed_Boiler_backup = 0
Opex_fixed_Lake = 0
Opex_fixed_wasteserver_HEX = 0
Opex_fixed_wasteserver_HP = 0
Opex_fixed_PV = 0
Opex_fixed_GHP = 0
Opex_fixed_storage = 0
Opex_fixed_Sewage = 0
Opex_fixed_HP_storage = 0
StorageInvC = 0
NetworkCost = 0
SubstHEXCost_capex = 0
SubstHEXCost_opex = 0
PVTHEXCost_Capex = 0
PVTHEXCost_Opex = 0
SCHEXCost_Capex = 0
SCHEXCost_Opex = 0
pumpCosts = 0
GasConnectionInvCost = 0
cost_PV_disconnected = 0
CO2_PV_disconnected = 0
Eprim_PV_disconnected = 0
if config.optimization.isheating:
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "0":
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_heating(
building_name))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
else:
nBuildinNtw += 1
if config.optimization.iscooling:
PV_barcode = ''
for (index, building_name) in zip(DCN_barcode, buildList):
if index == "0": # choose the best decentralized configuration
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, configuration='AHU_ARU_SCU'))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (FP)"].iloc[0] == 1:
to_PV = 0
else: # adding costs for buildings in which the centralized plant provides a part of the load requirements
DCN_unit_configuration = master_to_slave_vars.DCN_supplyunits
if DCN_unit_configuration == 1: # corresponds to AHU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU_SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 2: # corresponds to ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 3: # corresponds to SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_ARU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 4: # corresponds to AHU + ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 5: # corresponds to AHU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 6: # corresponds to ARU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 7: # corresponds to AHU + ARU + SCU from central plant
to_PV = 1
nBuildinNtw += 1
PV_barcode = PV_barcode + str(to_PV)
addcosts_Capex_a += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
if not config.optimization.isheating:
if PV_barcode.count("1") > 0:
df1 = pd.DataFrame({'A': []})
for (i, index) in enumerate(PV_barcode):
if index == str(1):
if df1.empty:
data = pd.read_csv(locator.PV_results(buildList[i]))
df1 = data
else:
data = pd.read_csv(locator.PV_results(buildList[i]))
df1 = df1 + data
if not df1.empty:
df1.to_csv(locator.PV_network(PV_barcode),
index=True,
float_format='%.2f')
solar_data = pd.read_csv(locator.PV_network(PV_barcode),
usecols=['E_PV_gen_kWh', 'Area_PV_m2'],
nrows=8760)
E_PV_sum_kW = np.sum(solar_data['E_PV_gen_kWh'])
E_PV_W = solar_data['E_PV_gen_kWh'] * 1000
Area_AvailablePV_m2 = np.max(solar_data['Area_PV_m2'])
Q_PowerPeakAvailablePV_kW = Area_AvailablePV_m2 * ETA_AREA_TO_PEAK
KEV_RpPerkWhPV = calc_Crem_pv(Q_PowerPeakAvailablePV_kW * 1000.0)
KEV_total = KEV_RpPerkWhPV / 100 * np.sum(E_PV_sum_kW)
addcosts_Capex_a = addcosts_Capex_a - KEV_total
addCO2 = addCO2 - (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_CO2 - lca.EL_TO_CO2_GREEN) *
WH_TO_J / 1.0E6)
addPrim = addPrim - (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_OIL_EQ - lca.EL_TO_OIL_EQ_GREEN)
* WH_TO_J / 1.0E6)
cost_PV_disconnected = KEV_total
CO2_PV_disconnected = (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_CO2 - lca.EL_TO_CO2_GREEN) *
WH_TO_J / 1.0E6)
Eprim_PV_disconnected = (
E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_OIL_EQ - lca.EL_TO_OIL_EQ_GREEN) * WH_TO_J /
1.0E6)
network_data = pd.read_csv(
locator.get_optimization_network_data_folder(
master_to_slave_vars.network_data_file_cooling))
E_total_req_W = np.array(network_data['Electr_netw_total_W'])
cooling_data = pd.read_csv(
locator.get_optimization_slave_cooling_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_from_CHP_W = np.array(cooling_data[
'E_gen_CCGT_associated_with_absorption_chillers_W'])
E_CHP_to_directload_W = np.zeros(8760)
E_CHP_to_grid_W = np.zeros(8760)
E_PV_to_directload_W = np.zeros(8760)
E_PV_to_grid_W = np.zeros(8760)
E_from_grid_W = np.zeros(8760)
for hour in range(8760):
E_hour_W = E_total_req_W[hour]
if E_hour_W > 0:
if E_PV_W[hour] > E_hour_W:
E_PV_to_directload_W[hour] = E_hour_W
E_PV_to_grid_W[
hour] = E_PV_W[hour] - E_total_req_W[hour]
E_hour_W = 0
else:
E_hour_W = E_hour_W - E_PV_W[hour]
E_PV_to_directload_W[hour] = E_PV_W[hour]
if E_from_CHP_W[hour] > E_hour_W:
E_CHP_to_directload_W[hour] = E_hour_W
E_CHP_to_grid_W[hour] = E_from_CHP_W[hour] - E_hour_W
E_hour_W = 0
else:
E_hour_W = E_hour_W - E_from_CHP_W[hour]
E_CHP_to_directload_W[hour] = E_from_CHP_W[hour]
E_from_grid_W[hour] = E_hour_W
date = network_data.DATE.values
results = pd.DataFrame({
"DATE":
date,
"E_total_req_W":
E_total_req_W,
"E_PV_W":
solar_data['E_PV_gen_kWh'] * 1000,
"Area_PV_m2":
solar_data['Area_PV_m2'],
"KEV":
KEV_RpPerkWhPV / 100 * solar_data['E_PV_gen_kWh'],
"E_from_grid_W":
E_from_grid_W,
"E_PV_to_directload_W":
E_PV_to_directload_W,
"E_CHP_to_directload_W":
E_CHP_to_directload_W,
"E_CHP_to_grid_W":
E_CHP_to_grid_W,
"E_PV_to_grid_W":
E_PV_to_grid_W
})
results.to_csv(
locator.
get_optimization_slave_electricity_activation_pattern_cooling(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
index=False)
# Add the features for the distribution
if DHN_barcode.count("1") > 0 and config.optimization.isheating:
os.chdir(
locator.get_optimization_slave_results_folder(
master_to_slave_vars.generation_number))
# Add the investment costs of the energy systems
# Furnace
if master_to_slave_vars.Furnace_on == 1:
P_design_W = master_to_slave_vars.Furnace_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern_heating(
master_to_slave_vars.configKey,
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace_W"])
arrayFurnace_W = np.array(dfFurnace)
Q_annual_W = 0
for i in range(int(np.shape(arrayFurnace_W)[0])):
Q_annual_W += arrayFurnace_W[i][0]
Capex_a_furnace, Opex_fixed_furnace = furnace.calc_Cinv_furnace(
P_design_W, Q_annual_W, config, locator, 'FU1')
addcosts_Capex_a += Capex_a_furnace
addcosts_Opex_fixed += Opex_fixed_furnace
# CC
if master_to_slave_vars.CC_on == 1:
CC_size_W = master_to_slave_vars.CC_GT_SIZE
Capex_a_CHP, Opex_fixed_CHP = chp.calc_Cinv_CCGT(
CC_size_W, locator, config)
addcosts_Capex_a += Capex_a_CHP
addcosts_Opex_fixed += Opex_fixed_CHP
# Boiler Base
if master_to_slave_vars.Boiler_on == 1:
Q_design_W = master_to_slave_vars.Boiler_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerBase = pd.read_csv(fNameSlavePP,
usecols=["Q_BaseBoiler_W"])
arrayBoilerBase_W = np.array(dfBoilerBase)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerBase_W)[0])):
Q_annual_W += arrayBoilerBase_W[i][0]
Capex_a_Boiler, Opex_fixed_Boiler = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler
addcosts_Opex_fixed += Opex_fixed_Boiler
# Boiler Peak
if master_to_slave_vars.BoilerPeak_on == 1:
Q_design_W = master_to_slave_vars.BoilerPeak_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerPeak = pd.read_csv(fNameSlavePP,
usecols=["Q_PeakBoiler_W"])
arrayBoilerPeak_W = np.array(dfBoilerPeak)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerPeak_W)[0])):
Q_annual_W += arrayBoilerPeak_W[i][0]
Capex_a_Boiler_peak, Opex_fixed_Boiler_peak = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler_peak
addcosts_Opex_fixed += Opex_fixed_Boiler_peak
# HP Lake
if master_to_slave_vars.HP_Lake_on == 1:
HP_Size_W = master_to_slave_vars.HPLake_maxSize
Capex_a_Lake, Opex_fixed_Lake = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a += Capex_a_Lake
addcosts_Opex_fixed += Opex_fixed_Lake
# HP Sewage
if master_to_slave_vars.HP_Sew_on == 1:
HP_Size_W = master_to_slave_vars.HPSew_maxSize
Capex_a_Sewage, Opex_fixed_Sewage = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a += Capex_a_Sewage
addcosts_Opex_fixed += Opex_fixed_Sewage
# GHP
if master_to_slave_vars.GHP_on == 1:
fNameSlavePP = locator.get_optimization_slave_electricity_activation_pattern_heating(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_GHP_req_W"])
arrayGHP_W = np.array(dfGHP)
GHP_Enom_W = np.amax(arrayGHP_W)
Capex_a_GHP, Opex_fixed_GHP = hp.calc_Cinv_GHP(
GHP_Enom_W, locator, config)
addcosts_Capex_a += Capex_a_GHP * prices.EURO_TO_CHF
addcosts_Opex_fixed += Opex_fixed_GHP * prices.EURO_TO_CHF
# Solar technologies
PV_installed_area_m2 = master_to_slave_vars.SOLAR_PART_PV * solarFeat.A_PV_m2 #kW
Capex_a_PV, Opex_fixed_PV = pv.calc_Cinv_pv(PV_installed_area_m2,
locator, config)
addcosts_Capex_a += Capex_a_PV
addcosts_Opex_fixed += Opex_fixed_PV
SC_ET_area_m2 = master_to_slave_vars.SOLAR_PART_SC_ET * solarFeat.A_SC_ET_m2
Capex_a_SC_ET, Opex_fixed_SC_ET = stc.calc_Cinv_SC(
SC_ET_area_m2, locator, config, 'ET')
addcosts_Capex_a += Capex_a_SC_ET
addcosts_Opex_fixed += Opex_fixed_SC_ET
SC_FP_area_m2 = master_to_slave_vars.SOLAR_PART_SC_FP * solarFeat.A_SC_FP_m2
Capex_a_SC_FP, Opex_fixed_SC_FP = stc.calc_Cinv_SC(
SC_FP_area_m2, locator, config, 'FP')
addcosts_Capex_a += Capex_a_SC_FP
addcosts_Opex_fixed += Opex_fixed_SC_FP
PVT_peak_kW = master_to_slave_vars.SOLAR_PART_PVT * solarFeat.A_PVT_m2 * N_PVT #kW
Capex_a_PVT, Opex_fixed_PVT = pvt.calc_Cinv_PVT(
PVT_peak_kW, locator, config)
addcosts_Capex_a += Capex_a_PVT
addcosts_Opex_fixed += Opex_fixed_PVT
# Back-up boiler
Capex_a_Boiler_backup, Opex_fixed_Boiler_backup = boiler.calc_Cinv_boiler(
Q_uncovered_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler_backup
addcosts_Opex_fixed += Opex_fixed_Boiler_backup
# Hex and HP for Heat recovery
if master_to_slave_vars.WasteServersHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
master_to_slave_vars.network_data_file_heating),
usecols=["Qcdata_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
Capex_a_wasteserver_HEX, Opex_fixed_wasteserver_HEX = hex.calc_Cinv_HEX(
Q_HEX_max_kWh, locator, config, 'HEX1')
addcosts_Capex_a += (Capex_a_wasteserver_HEX)
addcosts_Opex_fixed += Opex_fixed_wasteserver_HEX
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPServerHeatDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
Capex_a_wasteserver_HP, Opex_fixed_wasteserver_HP = hp.calc_Cinv_HP(
Q_HP_max_kWh, locator, config, 'HP2')
addcosts_Capex_a += (Capex_a_wasteserver_HP)
addcosts_Opex_fixed += Opex_fixed_wasteserver_HP
# if master_to_slave_vars.WasteCompressorHeatRecovery == 1:
# df = pd.read_csv(
# os.path.join(locator.get_optimization_network_results_folder(), master_to_slave_vars.network_data_file_heating),
# usecols=["Ecaf_netw_total_kWh"])
# array = np.array(df)
# Q_HEX_max_kWh = np.amax(array)
#
# Capex_a_wastecompressor_HEX, Opex_fixed_wastecompressor_HEX = hex.calc_Cinv_HEX(Q_HEX_max_kWh, locator,
# config, 'HEX1')
# addcosts_Capex_a += (Capex_a_wastecompressor_HEX)
# addcosts_Opex_fixed += Opex_fixed_wastecompressor_HEX
# df = pd.read_csv(
# locator.get_optimization_slave_storage_operation_data(master_to_slave_vars.individual_number,
# master_to_slave_vars.generation_number),
# usecols=["HPCompAirDesignArray_kWh"])
# array = np.array(df)
# Q_HP_max_kWh = np.amax(array)
# Capex_a_wastecompressor_HP, Opex_fixed_wastecompressor_HP = hp.calc_Cinv_HP(Q_HP_max_kWh, locator, config, 'HP2')
# addcosts_Capex_a += (Capex_a_wastecompressor_HP)
# addcosts_Opex_fixed += Opex_fixed_wastecompressor_HP
# Heat pump from solar to DH
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPScDesignArray_Wh", "HPpvt_designArray_Wh"])
array = np.array(df)
Q_HP_max_PVT_wh = np.amax(array[:, 1])
Q_HP_max_SC_Wh = np.amax(array[:, 0])
Capex_a_HP_PVT, Opex_fixed_HP_PVT = hp.calc_Cinv_HP(
Q_HP_max_PVT_wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_PVT)
addcosts_Opex_fixed += Opex_fixed_HP_PVT
Capex_a_HP_SC, Opex_fixed_HP_SC = hp.calc_Cinv_HP(
Q_HP_max_SC_Wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_SC)
addcosts_Opex_fixed += Opex_fixed_HP_SC
# HP for storage operation for charging from solar and discharging to DH
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=[
"E_aux_ch_W", "E_aux_dech_W",
"Q_from_storage_used_W", "Q_to_storage_W"
])
array = np.array(df)
Q_HP_max_storage_W = 0
for i in range(DAYS_IN_YEAR * HOURS_IN_DAY):
if array[i][0] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][3] + array[i][0])
elif array[i][1] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][2] + array[i][1])
Capex_a_HP_storage, Opex_fixed_HP_storage = hp.calc_Cinv_HP(
Q_HP_max_storage_W, locator, config, 'HP2')
addcosts_Capex_a += (Capex_a_HP_storage)
addcosts_Opex_fixed += Opex_fixed_HP_storage
# Storage
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["Storage_Size_m3"],
nrows=1)
StorageVol_m3 = np.array(df)[0][0]
Capex_a_storage, Opex_fixed_storage = storage.calc_Cinv_storage(
StorageVol_m3, locator, config, 'TES2')
addcosts_Capex_a += Capex_a_storage
addcosts_Opex_fixed += Opex_fixed_storage
# Costs from distribution configuration
if gv.ZernezFlag == 1:
NetworkCost += network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv) * nBuildinNtw / len(buildList)
else:
NetworkCost += ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList)
addcosts_Capex_a += NetworkCost
# HEX (1 per building in ntw)
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "1":
df = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name),
usecols=["Q_dhw_W", "Q_heating_W"])
subsArray = np.array(df)
Q_max_W = np.amax(subsArray[:, 0] + subsArray[:, 1])
Capex_a_HEX_building, Opex_fixed_HEX_building = hex.calc_Cinv_HEX(
Q_max_W, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_building
addcosts_Opex_fixed += Opex_fixed_HEX_building
# HEX for solar
roof_area_m2 = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
areaAvail += roof_area_m2[i][0]
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
share = roof_area_m2[i][0] / areaAvail
#print share, "solar area share", buildList[i]
Q_max_SC_ET_Wh = solarFeat.Q_nom_SC_ET_Wh * master_to_slave_vars.SOLAR_PART_SC_ET * share
Capex_a_HEX_SC_ET, Opex_fixed_HEX_SC_ET = hex.calc_Cinv_HEX(
Q_max_SC_ET_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_SC_ET
addcosts_Opex_fixed += Opex_fixed_HEX_SC_ET
Q_max_SC_FP_Wh = solarFeat.Q_nom_SC_FP_Wh * master_to_slave_vars.SOLAR_PART_SC_FP * share
Capex_a_HEX_SC_FP, Opex_fixed_HEX_SC_FP = hex.calc_Cinv_HEX(
Q_max_SC_FP_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_SC_FP
addcosts_Opex_fixed += Opex_fixed_HEX_SC_FP
Q_max_PVT_Wh = solarFeat.Q_nom_PVT_Wh * master_to_slave_vars.SOLAR_PART_PVT * share
Capex_a_HEX_PVT, Opex_fixed_HEX_PVT = hex.calc_Cinv_HEX(
Q_max_PVT_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_PVT
addcosts_Opex_fixed += Opex_fixed_HEX_PVT
# Pump operation costs
Capex_a_pump, Opex_fixed_pump, Opex_var_pump = pumps.calc_Ctot_pump(
master_to_slave_vars, ntwFeat, gv, locator, lca, config)
addcosts_Capex_a += Capex_a_pump
addcosts_Opex_fixed += Opex_fixed_pump
# import gas consumption data from:
if DHN_barcode.count("1") > 0 and config.optimization.isheating:
# import gas consumption data from:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_cost_prime_primary_energy_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["E_gas_PrimaryPeakPower_W"])
E_gas_primary_peak_power_W = float(np.array(EgasPrimaryDataframe_W))
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
else:
GasConnectionInvCost = 0.0
addcosts_Capex_a += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"Capex_a_SC": [Capex_a_SC],
"Opex_fixed_SC": [Opex_fixed_SC],
"Capex_a_PVT": [Capex_a_PVT],
"Opex_fixed_PVT": [Opex_fixed_PVT],
"Capex_a_Boiler_backup": [Capex_a_Boiler_backup],
"Opex_fixed_Boiler_backup": [Opex_fixed_Boiler_backup],
"Capex_a_storage_HEX": [Capex_a_HP_storage],
"Opex_fixed_storage_HEX": [Opex_fixed_HP_storage],
"Capex_a_storage_HP": [Capex_a_storage_HP],
"Capex_a_CHP": [Capex_a_CHP],
"Opex_fixed_CHP": [Opex_fixed_CHP],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + Capex_a_storage_HP + Capex_a_HEX],
"NetworkCost": [NetworkCost],
"SubstHEXCost": [SubstHEXCost_capex],
"DHNInvestCost": [addcosts_Capex_a - CostDiscBuild],
"PVTHEXCost_Capex": [PVTHEXCost_Capex],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"Capex_a_furnace": [Capex_a_furnace],
"Opex_fixed_furnace": [Opex_fixed_furnace],
"Capex_a_Boiler": [Capex_a_Boiler],
"Opex_fixed_Boiler": [Opex_fixed_Boiler],
"Capex_a_Boiler_peak": [Capex_a_Boiler_peak],
"Opex_fixed_Boiler_peak": [Opex_fixed_Boiler_peak],
"Capex_Disconnected": [Capex_Disconnected],
"Opex_Disconnected": [Opex_Disconnected],
"Capex_a_Lake": [Capex_a_Lake],
"Opex_fixed_Lake": [Opex_fixed_Lake],
"Capex_a_Sewage": [Capex_a_Sewage],
"Opex_fixed_Sewage": [Opex_fixed_Sewage],
"SCHEXCost_Capex": [SCHEXCost_Capex],
"Capex_a_pump": [Capex_a_pump],
"Opex_fixed_pump": [Opex_fixed_pump],
"Opex_var_pump": [Opex_var_pump],
"Sum_CAPEX": [addcosts_Capex_a],
"Sum_OPEX_fixed": [addcosts_Opex_fixed],
"GasConnectionInvCa": [GasConnectionInvCost],
"CO2_PV_disconnected": [CO2_PV_disconnected],
"cost_PV_disconnected": [cost_PV_disconnected],
"Eprim_PV_disconnected": [Eprim_PV_disconnected]
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
sep=',')
return (addcosts_Capex_a + addcosts_Opex_fixed, addCO2, addPrim)