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 district_cooling_network(locator,
master_to_slave_variables,
config,
prices,
network_features):
"""
Computes the parameters for the cooling of the complete DCN
:param cea.inputlocator.InputLocator locator: path to res folder
:param network_features: network features
:param prices: Prices imported from the database
:type network_features: class
:type prices: class
:return: costs, co2, prim
:rtype: tuple
"""
if master_to_slave_variables.DCN_exists:
# THERMAL STORAGE + NETWORK
# Import Temperatures from Network Summary:
Q_thermal_req_W, \
T_district_cooling_return_K, \
T_district_cooling_supply_K, \
mdot_kgpers = calc_network_summary_DCN(master_to_slave_variables)
# Initialize daily storage calss
T_ground_K = calculate_ground_temperature(locator)
daily_storage = LoadLevelingDailyStorage(master_to_slave_variables.Storage_cooling_on,
master_to_slave_variables.Storage_cooling_size_W,
min(T_district_cooling_supply_K) - DT_COOL,
max(T_district_cooling_return_K) - DT_COOL,
T_TANK_FULLY_DISCHARGED_K,
np.mean(T_ground_K)
)
# Import Data - potentials lake heat
if master_to_slave_variables.WS_BaseVCC_on == 1 or master_to_slave_variables.WS_PeakVCC_on == 1:
HPlake_Data = pd.read_csv(locator.get_water_body_potential())
Q_therm_Lake = np.array(HPlake_Data['QLake_kW']) * 1E3
total_WS_VCC_installed = master_to_slave_variables.WS_BaseVCC_size_W + master_to_slave_variables.WS_PeakVCC_size_W
Q_therm_Lake_W = [x if x < total_WS_VCC_installed else total_WS_VCC_installed for x in Q_therm_Lake]
T_source_average_Lake_K = np.array(HPlake_Data['Ts_C']) + 273
else:
Q_therm_Lake_W = np.zeros(HOURS_IN_YEAR)
T_source_average_Lake_K = np.zeros(HOURS_IN_YEAR)
# get properties of technology used in this script
absorption_chiller = AbsorptionChiller(pd.read_excel(locator.get_database_conversion_systems(), sheet_name="Absorption_chiller"), 'double')
CCGT_prop = calc_cop_CCGT(master_to_slave_variables.NG_Trigen_ACH_size_W, ACH_T_IN_FROM_CHP_K, "NG")
VCC_database = pd.read_excel(locator.get_database_conversion_systems(), sheet_name="Chiller")
technology_type = VCC_CODE_CENTRALIZED
VCC_database = VCC_database[VCC_database['code'] == technology_type]
max_VCC_capacity = int(VCC_database['cap_max'])
min_VCC_capacity = int(VCC_database['cap_min'])
# G_VALUE = VCC_database['ISENTROPIC_EFFICIENCY'] # create vessel to carry down gvalue and max_VCC_capacity, min_VCC_capacity to VCC module
# initialize variables
Q_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_DailyStorage_gen_directload_W = np.zeros(HOURS_IN_YEAR)
E_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
NG_Trigen_req_W = np.zeros(HOURS_IN_YEAR)
Q_BackupVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_Trigen_NG_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_WS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_WS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_AS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_AS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BackupVCC_AS_directload_W = np.zeros(HOURS_IN_YEAR)
for hour in range(HOURS_IN_YEAR): # cooling supply for all buildings excluding cooling loads from data centers
if Q_thermal_req_W[hour] > 0.0: # only if there is a cooling load!
daily_storage, \
thermal_output, \
electricity_output, \
gas_output = cooling_resource_activator(Q_thermal_req_W[hour],
T_district_cooling_supply_K[hour],
T_district_cooling_return_K[hour],
Q_therm_Lake_W[hour],
T_source_average_Lake_K[hour],
daily_storage,
T_ground_K[hour],
master_to_slave_variables,
absorption_chiller,
CCGT_prop,
min_VCC_capacity,
max_VCC_capacity)
Q_DailyStorage_gen_directload_W[hour] = thermal_output['Q_DailyStorage_gen_directload_W']
Q_Trigen_NG_gen_directload_W[hour] = thermal_output['Q_Trigen_NG_gen_directload_W']
Q_BaseVCC_WS_gen_directload_W[hour] = thermal_output['Q_BaseVCC_WS_gen_directload_W']
Q_PeakVCC_WS_gen_directload_W[hour] = thermal_output['Q_PeakVCC_WS_gen_directload_W']
Q_BaseVCC_AS_gen_directload_W[hour] = thermal_output['Q_BaseVCC_AS_gen_directload_W']
Q_PeakVCC_AS_gen_directload_W[hour] = thermal_output['Q_PeakVCC_AS_gen_directload_W']
Q_BackupVCC_AS_directload_W[hour] = thermal_output['Q_BackupVCC_AS_directload_W']
Q_Trigen_NG_gen_W[hour] = thermal_output['Q_Trigen_NG_gen_W']
Q_BaseVCC_WS_gen_W[hour] = thermal_output['Q_BaseVCC_WS_gen_W']
Q_PeakVCC_WS_gen_W[hour] = thermal_output['Q_PeakVCC_WS_gen_W']
Q_BaseVCC_AS_gen_W[hour] = thermal_output['Q_BaseVCC_AS_gen_W']
Q_PeakVCC_AS_gen_W[hour] = thermal_output['Q_PeakVCC_AS_gen_W']
Q_BackupVCC_AS_gen_W[hour] = thermal_output['Q_BackupVCC_AS_gen_W']
E_BaseVCC_WS_req_W[hour] = electricity_output['E_BaseVCC_WS_req_W']
E_PeakVCC_WS_req_W[hour] = electricity_output['E_PeakVCC_WS_req_W']
E_BaseVCC_AS_req_W[hour] = electricity_output['E_BaseVCC_AS_req_W']
E_PeakVCC_AS_req_W[hour] = electricity_output['E_PeakVCC_AS_req_W']
E_Trigen_NG_gen_W[hour] = electricity_output['E_Trigen_NG_gen_W']
NG_Trigen_req_W[hour] = gas_output['NG_Trigen_req_W']
#calculate the electrical capacity as a function of the peak produced by the turbine
master_to_slave_variables.NG_Trigen_CCGT_size_electrical_W = E_Trigen_NG_gen_W.max()
# BACK-UPP VCC - AIR SOURCE
scale = 'DISTRICT'
master_to_slave_variables.AS_BackupVCC_size_W = np.amax(Q_BackupVCC_AS_gen_W)
size_chiller_CT = master_to_slave_variables.AS_BackupVCC_size_W
if master_to_slave_variables.AS_BackupVCC_size_W != 0.0:
master_to_slave_variables.AS_BackupVCC_on = 1
Q_BackupVCC_AS_gen_W, E_BackupVCC_AS_req_W = np.vectorize(calc_vcc_CT_operation)(Q_BackupVCC_AS_gen_W,
T_district_cooling_return_K,
T_district_cooling_supply_K,
VCC_T_COOL_IN,
size_chiller_CT,
min_VCC_capacity,
max_VCC_capacity,
scale)
else:
E_BackupVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) GENERATION UNITS
supply_systems = SupplySystemsDatabase(locator)
mdotnMax_kgpers = np.amax(mdot_kgpers)
performance_costs_generation, \
district_cooling_capacity_installed = cost_model.calc_generation_costs_capacity_installed_cooling(locator,
master_to_slave_variables,
supply_systems,
mdotnMax_kgpers
)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) STORAGE UNITS
performance_costs_storage = cost_model.calc_generation_costs_cooling_storage(locator,
master_to_slave_variables,
config,
daily_storage
)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) NETWORK
performance_costs_network, \
E_used_district_cooling_network_W = cost_model.calc_network_costs_cooling(locator,
master_to_slave_variables,
network_features,
"DC",
prices)
# MERGE COSTS AND EMISSIONS IN ONE FILE
performance = dict(performance_costs_generation, **performance_costs_storage)
district_cooling_costs = dict(performance, **performance_costs_network)
else:
Q_thermal_req_W = np.zeros(HOURS_IN_YEAR)
Q_DailyStorage_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_Trigen_NG_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_WS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_WS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_AS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_AS_gen_directload_W = np.zeros(HOURS_IN_YEAR)
Q_BackupVCC_AS_directload_W = np.zeros(HOURS_IN_YEAR)
Q_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BackupVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
E_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
E_used_district_cooling_network_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_BackupVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
NG_Trigen_req_W = np.zeros(HOURS_IN_YEAR)
district_cooling_costs = {}
district_cooling_capacity_installed = {}
# SAVE
district_cooling_generation_dispatch = {
# demand of the network
"Q_districtcooling_sys_req_W": Q_thermal_req_W,
# ENERGY GENERATION TO DIRECT LOAD
# from storage
"Q_DailyStorage_gen_directload_W": Q_DailyStorage_gen_directload_W,
# cooling
"Q_Trigen_NG_gen_directload_W": Q_Trigen_NG_gen_directload_W,
"Q_BaseVCC_WS_gen_directload_W": Q_BaseVCC_WS_gen_directload_W,
"Q_PeakVCC_WS_gen_directload_W": Q_PeakVCC_WS_gen_directload_W,
"Q_BaseVCC_AS_gen_directload_W": Q_BaseVCC_AS_gen_directload_W,
"Q_PeakVCC_AS_gen_directload_W": Q_PeakVCC_AS_gen_directload_W,
"Q_BackupVCC_AS_directload_W": Q_BackupVCC_AS_directload_W,
# ENERGY GENERATION TOTAL
# cooling
"Q_Trigen_NG_gen_W": Q_Trigen_NG_gen_W,
"Q_BaseVCC_WS_gen_W": Q_BaseVCC_WS_gen_W,
"Q_PeakVCC_WS_gen_W": Q_PeakVCC_WS_gen_W,
"Q_BaseVCC_AS_gen_W": Q_BaseVCC_AS_gen_W,
"Q_PeakVCC_AS_gen_W": Q_PeakVCC_AS_gen_W,
"Q_BackupVCC_AS_W": Q_BackupVCC_AS_gen_W,
# electricity
"E_Trigen_NG_gen_W": E_Trigen_NG_gen_W
}
district_cooling_electricity_requirements_dispatch = {
# ENERGY REQUIREMENTS
# Electricity
"E_DCN_req_W": E_used_district_cooling_network_W,
"E_BaseVCC_WS_req_W": E_BaseVCC_WS_req_W,
"E_PeakVCC_WS_req_W": E_PeakVCC_WS_req_W,
"E_BaseVCC_AS_req_W": E_BaseVCC_AS_req_W,
"E_PeakVCC_AS_req_W": E_PeakVCC_AS_req_W,
"E_BackupVCC_AS_req_W": E_BackupVCC_AS_req_W,
}
district_cooling_fuel_requirements_dispatch = {
# fuels
"NG_Trigen_req_W": NG_Trigen_req_W
}
# PLOT RESULTS
return district_cooling_costs, \
district_cooling_generation_dispatch, \
district_cooling_electricity_requirements_dispatch, \
district_cooling_fuel_requirements_dispatch, \
district_cooling_capacity_installed
def district_cooling_network(locator, master_to_slave_variables, config,
prices, lca, network_features):
"""
Computes the parameters for the cooling of the complete DCN
:param locator: path to res folder
:param network_features: network features
:param prices: Prices imported from the database
:type locator: string
:type network_features: class
:type prices: class
:return: costs, co2, prim
:rtype: tuple
"""
# THERMAL STORAGE + NETWORK
# Import Temperatures from Network Summary:
Q_thermal_req_W, \
T_district_cooling_return_K, \
T_district_cooling_supply_K,\
mdot_kgpers = calc_network_summary_DCN(locator, master_to_slave_variables)
print(
"CALCULATING ECOLOGICAL COSTS OF DAILY COOLING STORAGE - DUE TO OPERATION (IF ANY)"
)
# Initialize daily storage calss
T_ground_K = calculate_ground_temperature(locator, config)
daily_storage = LoadLevelingDailyStorage(
master_to_slave_variables.Storage_cooling_on,
master_to_slave_variables.Storage_cooling_size_W,
min(T_district_cooling_supply_K) - DT_COOL,
max(T_district_cooling_return_K) - DT_COOL, T_TANK_FULLY_DISCHARGED_K,
np.mean(T_ground_K))
# Import Data - potentials lake heat
if master_to_slave_variables.WS_BaseVCC_on == 1 or master_to_slave_variables.WS_PeakVCC_on == 1:
HPlake_Data = pd.read_csv(locator.get_lake_potential())
Q_therm_Lake = np.array(HPlake_Data['QLake_kW']) * 1E3
total_WS_VCC_installed = master_to_slave_variables.WS_BaseVCC_size_W + master_to_slave_variables.WS_PeakVCC_size_W
Q_therm_Lake_W = [
x if x < total_WS_VCC_installed else total_WS_VCC_installed
for x in Q_therm_Lake
]
T_source_average_Lake_K = np.array(HPlake_Data['Ts_C']) + 273
else:
Q_therm_Lake_W = np.zeros(HOURS_IN_YEAR)
T_source_average_Lake_K = np.zeros(HOURS_IN_YEAR)
Q_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_WS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BackupVCC_AS_gen_W = np.zeros(HOURS_IN_YEAR)
Q_DailyStorage_gen_W = np.zeros(HOURS_IN_YEAR)
opex_var_Trigen_NG_USDhr = np.zeros(HOURS_IN_YEAR)
opex_var_BaseVCC_WS_USDhr = np.zeros(HOURS_IN_YEAR)
opex_var_PeakVCC_WS_USDhr = np.zeros(HOURS_IN_YEAR)
opex_var_BaseVCC_AS_USDhr = np.zeros(HOURS_IN_YEAR)
opex_var_PeakVCC_AS_USDhr = np.zeros(HOURS_IN_YEAR)
opex_var_BackupVCC_AS_USDhr = np.zeros(HOURS_IN_YEAR)
E_Trigen_NG_gen_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
E_BaseVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakVCC_WS_req_W = np.zeros(HOURS_IN_YEAR)
E_BackupVCC_AS_req_W = np.zeros(HOURS_IN_YEAR)
NG_Trigen_req_W = np.zeros(HOURS_IN_YEAR)
source_Trigen_NG = np.zeros(HOURS_IN_YEAR)
source_BaseVCC_WS = np.zeros(HOURS_IN_YEAR)
source_PeakVCC_WS = np.zeros(HOURS_IN_YEAR)
source_BaseVCC_AS = np.zeros(HOURS_IN_YEAR)
source_PeakVCC_AS = np.zeros(HOURS_IN_YEAR)
for hour in range(
HOURS_IN_YEAR
): # cooling supply for all buildings excluding cooling loads from data centers
if Q_thermal_req_W[hour] > 0.0: # only if there is a cooling load!
daily_storage, \
activation_output, \
thermal_output, \
electricity_output, \
gas_output = cooling_resource_activator(Q_thermal_req_W[hour],
T_district_cooling_supply_K[hour],
T_district_cooling_return_K[hour],
Q_therm_Lake_W[hour],
T_source_average_Lake_K[hour],
daily_storage,
T_ground_K[hour],
lca,
master_to_slave_variables,
hour,
prices,
locator)
source_Trigen_NG[hour] = activation_output["source_Trigen_NG"]
source_BaseVCC_WS[hour] = activation_output["source_BaseVCC_WS"]
source_PeakVCC_WS[hour] = activation_output["source_PeakVCC_WS"]
source_BaseVCC_AS[hour] = activation_output["source_BaseVCC_AS"]
source_PeakVCC_AS[hour] = activation_output["source_PeakVCC_AS"]
Q_Trigen_NG_gen_W[hour] = thermal_output['Q_Trigen_NG_gen_W']
Q_BaseVCC_WS_gen_W[hour] = thermal_output['Q_BaseVCC_WS_gen_W']
Q_PeakVCC_WS_gen_W[hour] = thermal_output['Q_PeakVCC_WS_gen_W']
Q_BaseVCC_AS_gen_W[hour] = thermal_output['Q_BaseVCC_AS_gen_W']
Q_PeakVCC_AS_gen_W[hour] = thermal_output['Q_PeakVCC_AS_gen_W']
Q_BackupVCC_AS_gen_W[hour] = thermal_output['Q_BackupVCC_AS_gen_W']
Q_DailyStorage_gen_W[hour] = thermal_output[
'Q_DailyStorage_WS_gen_W']
E_BaseVCC_WS_req_W[hour] = electricity_output['E_BaseVCC_WS_req_W']
E_PeakVCC_WS_req_W[hour] = electricity_output['E_PeakVCC_WS_req_W']
E_BaseVCC_AS_req_W[hour] = electricity_output['E_BaseVCC_AS_req_W']
E_PeakVCC_AS_req_W[hour] = electricity_output['E_PeakVCC_AS_req_W']
E_BackupVCC_AS_req_W[hour] = electricity_output[
'E_BackupVCC_AS_req_W']
E_Trigen_NG_gen_W[hour] = electricity_output['E_Trigen_NG_gen_W']
NG_Trigen_req_W = gas_output['NG_Trigen_req_W']
# BACK-UPP VCC - AIR SOURCE
master_to_slave_variables.AS_BackupVCC_size_W = np.amax(
Q_BackupVCC_AS_gen_W)
if master_to_slave_variables.AS_BackupVCC_size_W != 0:
master_to_slave_variables.AS_BackupVCC_on = 1
for hour in range(HOURS_IN_YEAR):
opex_var_BackupVCC_AS_USDhr[hour], \
Q_BackupVCC_AS_gen_W[hour], \
E_BackupVCC_AS_req_W[hour] = calc_vcc_CT_operation(Q_BackupVCC_AS_gen_W[hour],
T_district_cooling_return_K[hour],
T_district_cooling_supply_K[hour],
VCC_T_COOL_IN,
lca)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) GENERATION UNITS
performance_costs_generation = cost_model.calc_generation_costs_cooling(
locator, master_to_slave_variables, config)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) STORAGE UNITS
performance_costs_storage = cost_model.calc_generation_costs_cooling_storage(
locator, master_to_slave_variables, config, daily_storage)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR, ANNUAL) NETWORK
performance_costs_network, \
E_used_district_cooling_network_W = cost_model.calc_network_costs_cooling(locator,
master_to_slave_variables,
network_features,
lca,
"DC")
# MERGE COSTS AND EMISSIONS IN ONE FILE
performance = dict(performance_costs_generation,
**performance_costs_storage)
district_cooling_costs = dict(performance, **performance_costs_network)
# SAVE
district_cooling_generation_dispatch = {
# demand of the network
"Q_districtcooling_sys_req_W": Q_thermal_req_W,
# Status of each technology 1 = on, 0 = off in every hour
"Trigen_NG_Status": source_Trigen_NG,
"BaseVCC_WS_Status": source_BaseVCC_WS,
"PeakVCC_WS_Status": source_PeakVCC_WS,
"BaseVCC_AS_Status": source_BaseVCC_AS,
"PeakVCC_AS_Status": source_PeakVCC_AS,
# ENERGY GENERATION
# from storage
"Q_DailyStorage_gen_directload_W": Q_DailyStorage_gen_W,
# cooling
"Q_Trigen_NG_gen_directload_W": Q_Trigen_NG_gen_W,
"Q_BaseVCC_WS_gen_directload_W": Q_BaseVCC_WS_gen_W,
"Q_PeakVCC_WS_gen_directload_W": Q_PeakVCC_WS_gen_W,
"Q_BaseVCC_AS_gen_directload_W": Q_BaseVCC_AS_gen_W,
"Q_PeakVCC_AS_gen_directload_W": Q_PeakVCC_AS_gen_W,
"Q_BackupVCC_AS_directload_W": Q_BackupVCC_AS_gen_W,
# electricity
"E_Trigen_NG_gen_W": E_Trigen_NG_gen_W
}
district_cooling_electricity_requirements_dispatch = {
# ENERGY REQUIREMENTS
# Electricity
"E_DCN_req_W": E_used_district_cooling_network_W,
"E_BaseVCC_WS_req_W": E_BaseVCC_WS_req_W,
"E_PeakVCC_WS_req_W": E_PeakVCC_WS_req_W,
"E_BaseVCC_AS_req_W": E_BaseVCC_AS_req_W,
"E_PeakVCC_AS_req_W": E_PeakVCC_AS_req_W,
"E_BackupVCC_AS_req_W": E_BackupVCC_AS_req_W,
}
district_cooling_fuel_requirements_dispatch = {
# fuels
"NG_Trigen_req_W": NG_Trigen_req_W
}
# PLOT RESULTS
return district_cooling_costs, \
district_cooling_generation_dispatch, \
district_cooling_electricity_requirements_dispatch,\
district_cooling_fuel_requirements_dispatch