def cooling_calculations_of_DC_buildings(locator, master_to_slave_vars, ntwFeat, prices, lca, config, reduced_timesteps_flag):
"""
Computes the parameters for the cooling of the complete DCN
:param locator: path to res folder
:param ntwFeat: network features
:param prices: Prices imported from the database
:type locator: string
:type ntwFeat: class
:type prices: class
:return: costs, co2, prim
:rtype: tuple
"""
############# Recover the cooling needs
# Cooling demands in a neighborhood are divided into three categories currently. They are
# 1. Space Cooling in buildings
# 2. Data center Cooling
# 3. Refrigeration Needs
# Data center cooling can also be done by recovering the heat and heating other demands during the same time
# whereas Space cooling and refrigeration needs are to be provided by District Cooling Network or decentralized cooling
# Currently, all the buildings are assumed to be connected to DCN
# In the following code, the cooling demands of Space cooling and refrigeration are first satisfied by using Lake and VCC
# This is then followed by checking of the Heat recovery from Data Centre, if it is allowed, then the corresponding
# cooling demand is ignored. If not, the corresponding coolind demand is also satisfied by DCN.
t0 = time.time()
DCN_barcode = master_to_slave_vars.DCN_barcode
print ('Cooling Main is Running')
# Space cooling previously aggregated in the substation routine
if master_to_slave_vars.WasteServersHeatRecovery == 1:
df = pd.read_csv(locator.get_optimization_network_data_folder(master_to_slave_vars.network_data_file_cooling),
usecols=["T_DCNf_space_cooling_and_refrigeration_sup_K", "T_DCNf_space_cooling_and_refrigeration_re_K",
"mdot_cool_space_cooling_and_refrigeration_netw_all_kgpers"])
df = df.fillna(0)
T_sup_K = df['T_DCNf_space_cooling_and_refrigeration_sup_K'].values
T_re_K = df['T_DCNf_space_cooling_and_refrigeration_re_K'].values
mdot_kgpers = df['mdot_cool_space_cooling_and_refrigeration_netw_all_kgpers'].values
else:
df = pd.read_csv(locator.get_optimization_network_data_folder(master_to_slave_vars.network_data_file_cooling),
usecols=["T_DCNf_space_cooling_data_center_and_refrigeration_sup_K",
"T_DCNf_space_cooling_data_center_and_refrigeration_re_K",
"mdot_cool_space_cooling_data_center_and_refrigeration_netw_all_kgpers"])
df = df.fillna(0)
T_sup_K = df['T_DCNf_space_cooling_data_center_and_refrigeration_sup_K'].values
T_re_K = df['T_DCNf_space_cooling_data_center_and_refrigeration_re_K'].values
mdot_kgpers = df['mdot_cool_space_cooling_data_center_and_refrigeration_netw_all_kgpers'].values
DCN_operation_parameters = df.fillna(0)
DCN_operation_parameters_array = DCN_operation_parameters.values
Qc_DCN_W = np.array(
pd.read_csv(locator.get_optimization_network_data_folder(master_to_slave_vars.network_data_file_cooling),
usecols=["Q_DCNf_space_cooling_and_refrigeration_W",
"Q_DCNf_space_cooling_data_center_and_refrigeration_W"])) # importing the cooling demands of DCN (space cooling + refrigeration)
# Data center cooling, (treated separately for each building)
df = pd.read_csv(locator.get_total_demand(), usecols=["Name", "Qcdata_sys_MWhyr"])
arrayData = np.array(df)
# total cooling requirements based on the Heat Recovery Flag
Q_cooling_req_W = np.zeros(8760)
if master_to_slave_vars.WasteServersHeatRecovery == 0:
for hour in range(8760): # summing cooling loads of space cooling, refrigeration and data center
Q_cooling_req_W[hour] = Qc_DCN_W[hour][1]
else:
for hour in range(8760): # only including cooling loads of space cooling and refrigeration
Q_cooling_req_W[hour] = Qc_DCN_W[hour][0]
############# Recover the heat already taken from the Lake by the heat pumps
if config.district_heating_network:
try:
dfSlave = pd.read_csv(
locator.get_optimization_slave_heating_activation_pattern(master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["Q_coldsource_HPLake_W"])
Q_Lake_Array_W = np.array(dfSlave)
except:
Q_Lake_Array_W = [0]
else:
Q_Lake_Array_W = [0]
### input parameters
Qc_VCC_max_W = master_to_slave_vars.VCC_cooling_size_W
Qc_ACH_max_W = master_to_slave_vars.Absorption_chiller_size_W
T_ground_K = calculate_ground_temperature(locator, config)
# sizing cold water storage tank
if master_to_slave_vars.Storage_cooling_size_W > 0:
Qc_tank_discharge_peak_W = master_to_slave_vars.Storage_cooling_size_W
Qc_tank_charge_max_W = (Qc_VCC_max_W + Qc_ACH_max_W) * 0.8 # assume reduced capacity when Tsup is lower
peak_hour = np.argmax(Q_cooling_req_W)
area_HEX_tank_discharege_m2, UA_HEX_tank_discharge_WperK, \
area_HEX_tank_charge_m2, UA_HEX_tank_charge_WperK, \
V_tank_m3 = storage_tank.calc_storage_tank_properties(DCN_operation_parameters, Qc_tank_charge_max_W,
Qc_tank_discharge_peak_W, peak_hour, master_to_slave_vars)
else:
Qc_tank_discharge_peak_W = 0
Qc_tank_charge_max_W = 0
area_HEX_tank_discharege_m2 = 0
UA_HEX_tank_discharge_WperK = 0
area_HEX_tank_charge_m2 = 0
UA_HEX_tank_charge_WperK = 0
V_tank_m3 = 0
VCC_cost_data = pd.read_excel(locator.get_supply_systems(config.region), sheetname="Chiller")
VCC_cost_data = VCC_cost_data[VCC_cost_data['code'] == 'CH3']
max_VCC_chiller_size = max(VCC_cost_data['cap_max'].values)
Absorption_chiller_cost_data = pd.read_excel(locator.get_supply_systems(config.region),
sheetname="Absorption_chiller")
Absorption_chiller_cost_data = Absorption_chiller_cost_data[Absorption_chiller_cost_data['type'] == ACH_TYPE_DOUBLE]
max_ACH_chiller_size = max(Absorption_chiller_cost_data['cap_max'].values)
# deciding the number of chillers and the nominal size based on the maximum chiller size
Qc_VCC_max_W = Qc_VCC_max_W * (1 + SIZING_MARGIN)
Qc_ACH_max_W = Qc_ACH_max_W * (1 + SIZING_MARGIN)
Q_peak_load_W = Q_cooling_req_W.max() * (1 + SIZING_MARGIN)
Qc_VCC_backup_max_W = (Q_peak_load_W - Qc_ACH_max_W - Qc_VCC_max_W - Qc_tank_discharge_peak_W)
if Qc_VCC_backup_max_W < 0:
Qc_VCC_backup_max_W = 0
if Qc_VCC_max_W <= max_VCC_chiller_size:
Qnom_VCC_W = Qc_VCC_max_W
number_of_VCC_chillers = 1
else:
number_of_VCC_chillers = int(ceil(Qc_VCC_max_W / max_VCC_chiller_size))
Qnom_VCC_W = Qc_VCC_max_W / number_of_VCC_chillers
if Qc_VCC_backup_max_W <= max_VCC_chiller_size:
Qnom_VCC_backup_W = Qc_VCC_backup_max_W
number_of_VCC_backup_chillers = 1
else:
number_of_VCC_backup_chillers = int(ceil(Qc_VCC_backup_max_W / max_VCC_chiller_size))
Qnom_VCC_backup_W = Qc_VCC_backup_max_W / number_of_VCC_backup_chillers
if Qc_ACH_max_W <= max_ACH_chiller_size:
Qnom_ACH_W = Qc_ACH_max_W
number_of_ACH_chillers = 1
else:
number_of_ACH_chillers = int(ceil(Qc_ACH_max_W / max_ACH_chiller_size))
Qnom_ACH_W = Qc_ACH_max_W / number_of_ACH_chillers
limits = {'Qc_VCC_max_W': Qc_VCC_max_W, 'Qc_ACH_max_W': Qc_ACH_max_W, 'Qc_peak_load_W': Qc_tank_discharge_peak_W,
'Qnom_VCC_W': Qnom_VCC_W, 'number_of_VCC_chillers': number_of_VCC_chillers,
'Qnom_ACH_W': Qnom_ACH_W, 'number_of_ACH_chillers': number_of_ACH_chillers,
'Qnom_VCC_backup_W': Qnom_VCC_backup_W, 'number_of_VCC_backup_chillers': number_of_VCC_backup_chillers,
'Qc_tank_discharge_peak_W': Qc_tank_discharge_peak_W, 'Qc_tank_charge_max_W': Qc_tank_charge_max_W,
'V_tank_m3': V_tank_m3, 'T_tank_fully_charged_K': T_TANK_FULLY_CHARGED_K,
'area_HEX_tank_discharge_m2': area_HEX_tank_discharege_m2,
'UA_HEX_tank_discharge_WperK': UA_HEX_tank_discharge_WperK,
'area_HEX_tank_charge_m2': area_HEX_tank_charge_m2,
'UA_HEX_tank_charge_WperK': UA_HEX_tank_charge_WperK}
### input variables
lake_available_cooling = pd.read_csv(locator.get_lake_potential(), usecols=['lake_potential'])
Qc_available_from_lake_W = np.sum(lake_available_cooling).values[0] + np.sum(Q_Lake_Array_W)
Qc_from_lake_cumulative_W = 0
cooling_resource_potentials = {'T_tank_K': T_TANK_FULLY_DISCHARGED_K,
'Qc_avail_from_lake_W': Qc_available_from_lake_W,
'Qc_from_lake_cumulative_W': Qc_from_lake_cumulative_W}
############# Output results
PipeLifeTime = 40.0 # years, Data from A&W
PipeInterestRate = 0.05 # 5% interest rate
network_costs_USD = ntwFeat.pipesCosts_DCN_USD * DCN_barcode.count('1') / master_to_slave_vars.total_buildings
network_costs_a_USD = network_costs_USD * PipeInterestRate * (1+ PipeInterestRate) ** PipeLifeTime / ((1+PipeInterestRate) ** PipeLifeTime - 1)
costs_a_USD = network_costs_a_USD
CO2_kgCO2 = 0
prim_MJ = 0
nBuild = int(np.shape(arrayData)[0])
if reduced_timesteps_flag == False:
start_t = 0
stop_t = int(np.shape(DCN_operation_parameters)[0])
else:
# timesteps in May
start_t = 2880
stop_t = 3624
timesteps = range(start_t, stop_t)
calfactor_buildings = np.zeros(8760)
TotalCool = 0
Qc_from_Lake_W = np.zeros(8760)
Qc_from_VCC_W = np.zeros(8760)
Qc_from_ACH_W = np.zeros(8760)
Qc_from_storage_tank_W = np.zeros(8760)
Qc_from_VCC_backup_W = np.zeros(8760)
Qc_req_from_CT_W = np.zeros(8760)
Qh_req_from_CCGT_W = np.zeros(8760)
Qh_from_CCGT_W = np.zeros(8760)
E_gen_CCGT_W = np.zeros(8760)
opex_var_Lake_USD = np.zeros(8760)
opex_var_VCC_USD = np.zeros(8760)
opex_var_ACH_USD = np.zeros(8760)
opex_var_VCC_backup_USD = np.zeros(8760)
opex_var_CCGT_USD = np.zeros(8760)
opex_var_CT_USD = np.zeros(8760)
E_used_Lake_W = np.zeros(8760)
E_used_VCC_W = np.zeros(8760)
E_used_VCC_backup_W = np.zeros(8760)
E_used_ACH_W = np.zeros(8760)
E_used_CT_W = np.zeros(8760)
co2_Lake_kgCO2 = np.zeros(8760)
co2_VCC_kgCO2 = np.zeros(8760)
co2_ACH_kgCO2 = np.zeros(8760)
co2_VCC_backup_kgCO2 = np.zeros(8760)
co2_CCGT_kgCO2 = np.zeros(8760)
co2_CT_kgCO2 = np.zeros(8760)
prim_energy_Lake_MJ = np.zeros(8760)
prim_energy_VCC_MJ = np.zeros(8760)
prim_energy_ACH_MJ = np.zeros(8760)
prim_energy_VCC_backup_MJ = np.zeros(8760)
prim_energy_CCGT_MJ = np.zeros(8760)
prim_energy_CT_MJ = np.zeros(8760)
NG_used_CCGT_W = np.zeros(8760)
calfactor_total = 0
for hour in timesteps: # cooling supply for all buildings excluding cooling loads from data centers
performance_indicators_output, \
Qc_supply_to_DCN, calfactor_output, \
Qc_CT_W, Qh_CHP_ACH_W, \
cooling_resource_potentials = cooling_resource_activator(mdot_kgpers[hour], T_sup_K[hour], T_re_K[hour],
limits, cooling_resource_potentials,
T_ground_K[hour], prices, lca, master_to_slave_vars, config, Q_cooling_req_W[hour], locator)
print (hour)
# save results for each time-step
opex_var_Lake_USD[hour] = performance_indicators_output['Opex_var_Lake_USD']
opex_var_VCC_USD[hour] = performance_indicators_output['Opex_var_VCC_USD']
opex_var_ACH_USD[hour] = performance_indicators_output['Opex_var_ACH_USD']
opex_var_VCC_backup_USD[hour] = performance_indicators_output['Opex_var_VCC_backup_USD']
E_used_Lake_W[hour] = performance_indicators_output['E_used_Lake_W']
E_used_VCC_W[hour] = performance_indicators_output['E_used_VCC_W']
E_used_VCC_backup_W[hour] = performance_indicators_output['E_used_VCC_backup_W']
E_used_ACH_W[hour] = performance_indicators_output['E_used_ACH_W']
co2_Lake_kgCO2[hour] = performance_indicators_output['CO2_Lake_kgCO2']
co2_VCC_kgCO2[hour] = performance_indicators_output['CO2_VCC_kgCO2']
co2_ACH_kgCO2[hour] = performance_indicators_output['CO2_ACH_kgCO2']
co2_VCC_backup_kgCO2[hour] = performance_indicators_output['CO2_VCC_backup_kgCO2']
prim_energy_Lake_MJ[hour] = performance_indicators_output['Primary_Energy_Lake_MJ']
prim_energy_VCC_MJ[hour] = performance_indicators_output['Primary_Energy_VCC_MJ']
prim_energy_ACH_MJ[hour] = performance_indicators_output['Primary_Energy_ACH_MJ']
prim_energy_VCC_backup_MJ[hour] = performance_indicators_output['Primary_Energy_VCC_backup_MJ']
calfactor_buildings[hour] = calfactor_output
Qc_from_Lake_W[hour] = Qc_supply_to_DCN['Qc_from_Lake_W']
Qc_from_storage_tank_W[hour] = Qc_supply_to_DCN['Qc_from_Tank_W']
Qc_from_VCC_W[hour] = Qc_supply_to_DCN['Qc_from_VCC_W']
Qc_from_ACH_W[hour] = Qc_supply_to_DCN['Qc_from_ACH_W']
Qc_from_VCC_backup_W[hour] = Qc_supply_to_DCN['Qc_from_backup_VCC_W']
Qc_req_from_CT_W[hour] = Qc_CT_W
Qh_req_from_CCGT_W[hour] = Qh_CHP_ACH_W
if reduced_timesteps_flag:
reduced_costs_USD = np.sum(opex_var_Lake_USD) + np.sum(opex_var_VCC_USD) + np.sum(opex_var_ACH_USD) + np.sum(opex_var_VCC_backup_USD)
reduced_CO2_kgCO2 = np.sum(co2_Lake_kgCO2) + np.sum(co2_Lake_kgCO2) + np.sum(co2_ACH_kgCO2) + np.sum(co2_VCC_backup_kgCO2)
reduced_prim_MJ = np.sum(prim_energy_Lake_MJ) + np.sum(prim_energy_VCC_MJ) + np.sum(prim_energy_ACH_MJ) + np.sum(
prim_energy_VCC_backup_MJ)
costs_a_USD += reduced_costs_USD*(8760/(stop_t-start_t))
CO2_kgCO2 += reduced_CO2_kgCO2*(8760/(stop_t-start_t))
prim_MJ += reduced_prim_MJ*(8760/(stop_t-start_t))
else:
costs_a_USD += np.sum(opex_var_Lake_USD) + np.sum(opex_var_VCC_USD) + np.sum(opex_var_ACH_USD) + np.sum(opex_var_VCC_backup_USD)
CO2_kgCO2 += np.sum(co2_Lake_kgCO2) + np.sum(co2_Lake_kgCO2) + np.sum(co2_ACH_kgCO2) + np.sum(co2_VCC_backup_kgCO2)
prim_MJ += np.sum(prim_energy_Lake_MJ) + np.sum(prim_energy_VCC_MJ) + np.sum(prim_energy_ACH_MJ) + np.sum(
prim_energy_VCC_backup_MJ)
calfactor_total += np.sum(calfactor_buildings)
TotalCool += np.sum(Qc_from_Lake_W) + np.sum(Qc_from_VCC_W) + np.sum(Qc_from_ACH_W) + np.sum(Qc_from_VCC_backup_W) + np.sum(Qc_from_storage_tank_W)
Q_VCC_nom_W = limits['Qnom_VCC_W']
Q_ACH_nom_W = limits['Qnom_ACH_W']
Q_VCC_backup_nom_W = limits['Qnom_VCC_backup_W']
Q_CT_nom_W = np.amax(Qc_req_from_CT_W)
Qh_req_from_CCGT_max_W = np.amax(Qh_req_from_CCGT_W) # the required heat output from CCGT at peak
mdot_Max_kgpers = np.amax(DCN_operation_parameters_array[:, 1]) # sizing of DCN network pumps
Q_GT_nom_W = 0
########## Operation of the cooling tower
if Q_CT_nom_W > 0:
for hour in timesteps:
wdot_CT = CTModel.calc_CT(Qc_req_from_CT_W[hour], Q_CT_nom_W)
opex_var_CT_USD[hour] = (wdot_CT) * lca.ELEC_PRICE
co2_CT_kgCO2[hour] = (wdot_CT) * lca.EL_TO_CO2 * 3600E-6
prim_energy_CT_MJ[hour] = (wdot_CT) * lca.EL_TO_OIL_EQ * 3600E-6
E_used_CT_W[hour] = wdot_CT
if reduced_timesteps_flag:
reduced_costs_USD = np.sum(opex_var_CT_USD)
reduced_CO2_kgCO2 = np.sum(co2_CT_kgCO2)
reduced_prim_MJ = np.sum(prim_energy_CT_MJ)
costs_a_USD += reduced_costs_USD * (8760 / (stop_t - start_t))
CO2_kgCO2 += reduced_CO2_kgCO2 * (8760 / (stop_t - start_t))
prim_MJ += reduced_prim_MJ * (8760 / (stop_t - start_t))
else:
costs_a_USD += np.sum(opex_var_CT_USD)
CO2_kgCO2 += np.sum(co2_CT_kgCO2)
prim_MJ += np.sum(prim_energy_CT_MJ)
########## Operation of the CCGT
if Qh_req_from_CCGT_max_W > 0:
# Sizing of CCGT
GT_fuel_type = 'NG' # assumption for scenarios in SG
Q_GT_nom_sizing_W = Qh_req_from_CCGT_max_W # starting guess for the size of GT
Qh_output_CCGT_max_W = 0 # the heat output of CCGT at currently installed size (Q_GT_nom_sizing_W)
while (Qh_output_CCGT_max_W - Qh_req_from_CCGT_max_W) <= 0:
Q_GT_nom_sizing_W += 1000 # update GT size
# get CCGT performance limits and functions at Q_GT_nom_sizing_W
CCGT_performances = cogeneration.calc_cop_CCGT(Q_GT_nom_sizing_W, ACH_T_IN_FROM_CHP, GT_fuel_type, prices, lca)
Qh_output_CCGT_max_W = CCGT_performances['q_output_max_W']
# unpack CCGT performance functions
Q_GT_nom_W = Q_GT_nom_sizing_W * (1 + SIZING_MARGIN) # installed CCGT capacity
CCGT_performances = cogeneration.calc_cop_CCGT(Q_GT_nom_W, ACH_T_IN_FROM_CHP, GT_fuel_type, prices, lca)
Q_used_prim_W_CCGT_fn = CCGT_performances['q_input_fn_q_output_W']
cost_per_Wh_th_CCGT_fn = CCGT_performances[
'fuel_cost_per_Wh_th_fn_q_output_W'] # gets interpolated cost function
Qh_output_CCGT_min_W = CCGT_performances['q_output_min_W']
Qh_output_CCGT_max_W = CCGT_performances['q_output_max_W']
eta_elec_interpol = CCGT_performances['eta_el_fn_q_input']
for hour in timesteps:
if Qh_req_from_CCGT_W[hour] > Qh_output_CCGT_min_W: # operate above minimal load
if Qh_req_from_CCGT_W[hour] < Qh_output_CCGT_max_W: # Normal operation Possible within partload regime
cost_per_Wh_th = cost_per_Wh_th_CCGT_fn(Qh_req_from_CCGT_W[hour])
Q_used_prim_CCGT_W = Q_used_prim_W_CCGT_fn(Qh_req_from_CCGT_W[hour])
Qh_from_CCGT_W[hour] = Qh_req_from_CCGT_W[hour].copy()
E_gen_CCGT_W[hour] = np.float(eta_elec_interpol(Q_used_prim_CCGT_W)) * Q_used_prim_CCGT_W
else:
raise ValueError('Incorrect CCGT sizing!')
else: # operate at minimum load
cost_per_Wh_th = cost_per_Wh_th_CCGT_fn(Qh_output_CCGT_min_W)
Q_used_prim_CCGT_W = Q_used_prim_W_CCGT_fn(Qh_output_CCGT_min_W)
Qh_from_CCGT_W[hour] = Qh_output_CCGT_min_W
E_gen_CCGT_W[hour] = np.float(eta_elec_interpol(
Qh_output_CCGT_max_W)) * Q_used_prim_CCGT_W
opex_var_CCGT_USD[hour] = cost_per_Wh_th * Qh_from_CCGT_W[hour] - E_gen_CCGT_W[hour] * lca.ELEC_PRICE
co2_CCGT_kgCO2[hour] = Q_used_prim_CCGT_W * lca.NG_CC_TO_CO2_STD * WH_TO_J / 1.0E6 - E_gen_CCGT_W[hour] * lca.EL_TO_CO2 * 3600E-6
prim_energy_CCGT_MJ[hour] = Q_used_prim_CCGT_W * lca.NG_CC_TO_OIL_STD * WH_TO_J / 1.0E6 - E_gen_CCGT_W[hour] * lca.EL_TO_OIL_EQ * 3600E-6
NG_used_CCGT_W[hour] = Q_used_prim_CCGT_W
if reduced_timesteps_flag:
reduced_costs_USD = np.sum(opex_var_CCGT_USD)
reduced_CO2_kgCO2 = np.sum(co2_CCGT_kgCO2)
reduced_prim_MJ = np.sum(prim_energy_CCGT_MJ)
costs_a_USD += reduced_costs_USD * (8760 / (stop_t - start_t))
CO2_kgCO2 += reduced_CO2_kgCO2 * (8760 / (stop_t - start_t))
prim_MJ += reduced_prim_MJ * (8760 / (stop_t - start_t))
else:
costs_a_USD += np.sum(opex_var_CCGT_USD)
CO2_kgCO2 += np.sum(co2_CCGT_kgCO2)
prim_MJ += np.sum(prim_energy_CCGT_MJ)
########## Add investment costs
for i in range(limits['number_of_VCC_chillers']):
Capex_a_VCC_USD, Opex_fixed_VCC_USD, Capex_VCC_USD = VCCModel.calc_Cinv_VCC(Q_VCC_nom_W, locator, config, 'CH3')
costs_a_USD += Capex_a_VCC_USD + Opex_fixed_VCC_USD
for i in range(limits['number_of_VCC_backup_chillers']):
Capex_a_VCC_backup_USD, Opex_fixed_VCC_backup_USD, Capex_VCC_backup_USD = VCCModel.calc_Cinv_VCC(Q_VCC_backup_nom_W, locator, config, 'CH3')
costs_a_USD += Capex_a_VCC_backup_USD + Opex_fixed_VCC_backup_USD
master_to_slave_vars.VCC_backup_cooling_size_W = Q_VCC_backup_nom_W * limits['number_of_VCC_backup_chillers']
for i in range(limits['number_of_ACH_chillers']):
Capex_a_ACH_USD, Opex_fixed_ACH_USD, Capex_ACH_USD = chiller_absorption.calc_Cinv_ACH(Q_ACH_nom_W, locator, ACH_TYPE_DOUBLE, config)
costs_a_USD += Capex_a_ACH_USD + Opex_fixed_ACH_USD
Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD = cogeneration.calc_Cinv_CCGT(Q_GT_nom_W, locator, config)
costs_a_USD += Capex_a_CCGT_USD + Opex_fixed_CCGT_USD
Capex_a_Tank_USD, Opex_fixed_Tank_USD, Capex_Tank_USD = thermal_storage.calc_Cinv_storage(V_tank_m3, locator, config, 'TES2')
costs_a_USD += Capex_a_Tank_USD + Opex_fixed_Tank_USD
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = CTModel.calc_Cinv_CT(Q_CT_nom_W, locator, config, 'CT1')
costs_a_USD += Capex_a_CT_USD + Opex_fixed_CT_USD
Capex_a_pump_USD, Opex_fixed_pump_USD, Opex_var_pump_USD, Capex_pump_USD = PumpModel.calc_Ctot_pump(master_to_slave_vars, ntwFeat, locator, lca, config)
costs_a_USD += Capex_a_pump_USD + Opex_fixed_pump_USD + Opex_var_pump_USD
network_data = pd.read_csv(locator.get_optimization_network_data_folder(master_to_slave_vars.network_data_file_cooling))
date = network_data.DATE.values
results = pd.DataFrame({"DATE": date,
"Q_total_cooling_W": Q_cooling_req_W,
"Opex_var_Lake_USD": opex_var_Lake_USD,
"Opex_var_VCC_USD": opex_var_VCC_USD,
"Opex_var_ACH_USD": opex_var_ACH_USD,
"Opex_var_VCC_backup_USD": opex_var_VCC_backup_USD,
"Opex_var_CT_USD": opex_var_CT_USD,
"Opex_var_CCGT_USD": opex_var_CCGT_USD,
"E_used_Lake_W": E_used_Lake_W,
"E_used_VCC_W": E_used_VCC_W,
"E_used_VCC_backup_W": E_used_VCC_backup_W,
"E_used_ACH_W": E_used_ACH_W,
"E_used_CT_W": E_used_CT_W,
"NG_used_CCGT_W": NG_used_CCGT_W,
"CO2_from_using_Lake": co2_Lake_kgCO2,
"CO2_from_using_VCC": co2_VCC_kgCO2,
"CO2_from_using_ACH": co2_ACH_kgCO2,
"CO2_from_using_VCC_backup": co2_VCC_backup_kgCO2,
"CO2_from_using_CT": co2_CT_kgCO2,
"CO2_from_using_CCGT": co2_CCGT_kgCO2,
"Primary_Energy_from_Lake": prim_energy_Lake_MJ,
"Primary_Energy_from_VCC": prim_energy_VCC_MJ,
"Primary_Energy_from_ACH": prim_energy_ACH_MJ,
"Primary_Energy_from_VCC_backup": prim_energy_VCC_backup_MJ,
"Primary_Energy_from_CT": prim_energy_CT_MJ,
"Primary_Energy_from_CCGT": prim_energy_CCGT_MJ,
"Q_from_Lake_W": Qc_from_Lake_W,
"Q_from_VCC_W": Qc_from_VCC_W,
"Q_from_ACH_W": Qc_from_ACH_W,
"Q_from_VCC_backup_W": Qc_from_VCC_backup_W,
"Q_from_storage_tank_W": Qc_from_storage_tank_W,
"Qc_CT_associated_with_all_chillers_W": Qc_req_from_CT_W,
"Qh_CCGT_associated_with_absorption_chillers_W": Qh_from_CCGT_W,
"E_gen_CCGT_associated_with_absorption_chillers_W": E_gen_CCGT_W
})
results.to_csv(locator.get_optimization_slave_cooling_activation_pattern(master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
index=False)
########### Adjust and add the pumps for filtering and pre-treatment of the water
calibration = calfactor_total / 50976000
extraElec = (127865400 + 85243600) * calibration
costs_a_USD += extraElec * lca.ELEC_PRICE
CO2_kgCO2 += extraElec * lca.EL_TO_CO2 * 3600E-6
prim_MJ += extraElec * lca.EL_TO_OIL_EQ * 3600E-6
# Converting costs into float64 to avoid longer values
costs_a_USD = np.float64(costs_a_USD)
CO2_kgCO2 = np.float64(CO2_kgCO2)
prim_MJ = np.float64(prim_MJ)
# Capex_a and Opex_fixed
results = pd.DataFrame({"Capex_a_VCC_USD": [Capex_a_VCC_USD],
"Opex_fixed_VCC_USD": [Opex_fixed_VCC_USD],
"Capex_a_VCC_backup_USD": [Capex_a_VCC_backup_USD],
"Opex_fixed_VCC_backup_USD": [Opex_fixed_VCC_backup_USD],
"Capex_a_ACH_USD": [Capex_a_ACH_USD],
"Opex_fixed_ACH_USD": [Opex_fixed_ACH_USD],
"Capex_a_CCGT_USD": [Capex_a_CCGT_USD],
"Opex_fixed_CCGT_USD": [Opex_fixed_CCGT_USD],
"Capex_a_Tank_USD": [Capex_a_Tank_USD],
"Opex_fixed_Tank_USD": [Opex_fixed_Tank_USD],
"Capex_a_CT_USD": [Capex_a_CT_USD],
"Opex_fixed_CT_USD": [Opex_fixed_CT_USD],
"Capex_a_pump_USD": [Capex_a_pump_USD],
"Opex_fixed_pump_USD": [Opex_fixed_pump_USD],
"Opex_var_pump_USD": [Opex_var_pump_USD],
"Capex_VCC_USD": [Capex_VCC_USD],
"Capex_VCC_backup_USD": [Capex_VCC_backup_USD],
"Capex_ACH_USD": [Capex_ACH_USD],
"Capex_CCGT_USD": [Capex_CCGT_USD],
"Capex_Tank_USD": [Capex_Tank_USD],
"Capex_CT_USD": [Capex_CT_USD],
"Capex_pump_USD": [Capex_pump_USD]
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed_cooling(master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
index=False)
# print " Cooling main done (", round(time.time()-t0, 1), " seconds used for this task)"
# print ('Cooling costs = ' + str(costs))
# print ('Cooling CO2 = ' + str(CO2))
# print ('Cooling Eprim = ' + str(prim))
return (costs_a_USD, CO2_kgCO2, prim_MJ)
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 calc_network_costs_cooling(locator, master_to_slave_vars, network_features,
lca, network_type):
# Intitialize class
pipesCosts_USD = network_features.pipesCosts_DCN_USD
num_buildings_connected = master_to_slave_vars.number_of_buildings_connected_cooling
num_all_buildings = master_to_slave_vars.num_total_buildings
ratio_connected = num_buildings_connected / num_all_buildings
# Capital costs
Inv_IR = 0.05
Inv_LT = 20
Inv_OM = 0.10
Capex_Network_USD = pipesCosts_USD * ratio_connected
Capex_a_Network_USD = Capex_Network_USD * (Inv_IR) * (
1 + Inv_IR)**Inv_LT / ((1 + Inv_IR)**Inv_LT - 1)
Opex_fixed_Network_USD = Capex_Network_USD * Inv_OM
# costs of pumps
Capex_a_pump_USD, Opex_fixed_pump_USD, Opex_var_pump_USD, Capex_pump_USD, P_motor_tot_W = PumpModel.calc_Ctot_pump(
master_to_slave_vars, network_features, locator, lca, network_type)
# COOLING SUBSTATIONS
DCN_barcode = master_to_slave_vars.DCN_barcode
building_names = master_to_slave_vars.building_names_cooling
Capex_Substations_USD, \
Capex_a_Substations_USD, \
Opex_fixed_Substations_USD, \
Opex_var_Substations_USD = calc_substations_costs_cooling(building_names, master_to_slave_vars, DCN_barcode,
locator)
# summarize
Capex_Network_USD += Capex_pump_USD
Capex_a_Network_USD += Capex_a_pump_USD
Opex_fixed_Network_USD += Opex_fixed_pump_USD
Opex_var_Network_USD = Opex_var_pump_USD
performance = {
'Capex_a_DCN_connected_USD': Capex_a_Network_USD,
"Capex_a_SubstationsCooling_connected_USD": Capex_a_Substations_USD,
"Capex_total_DCN_connected_USD": Capex_Network_USD,
"Capex_total_SubstationsCooling_connected_USD": Capex_Substations_USD,
"Opex_fixed_DCN_connected_USD": Opex_fixed_Network_USD,
"Opex_fixed_SubstationsCooling_connected_USD":
Opex_fixed_Substations_USD,
}
return performance, P_motor_tot_W
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)