def heating_source_activator(Q_therm_req_W, hour, master_to_slave_vars, mdot_DH_req_kgpers, tdhsup_K, tdhret_req_K, TretsewArray_K,
gv, prices, lca, T_ground):
"""
:param Q_therm_req_W:
:param hour:
:param context:
:type Q_therm_req_W: float
:type hour: int
:type context: list
:return: cost_data_centralPlant_op, source_info, Q_source_data, E_coldsource_data, E_PP_el_data, E_gas_data, E_wood_data, Q_excess
:rtype:
"""
current_source = ACT_FIRST # Start with first source, no cost yet
Q_therm_req_W_copy = Q_therm_req_W
# Initializing resulting values (necessairy as not all of them are over-written):
Q_uncovered_W = 0
cost_HPSew_USD, cost_HPLake_USD, cost_GHP_USD, cost_CHP_USD, cost_Furnace_USD, cost_BaseBoiler_USD, cost_PeakBoiler_USD = 0, 0, 0, 0, 0, 0, 0
# initialize all sources to be off = 0 (turn to "on" with setting to 1)
source_HP_Sewage = 0
source_HP_Lake = 0
source_GHP = 0
source_CHP = 0
source_Furnace = 0
source_BaseBoiler = 0
source_PeakBoiler = 0
Q_excess_W = 0
Q_HPSew_gen_W, Q_HPLake_gen_W, Q_GHP_gen_W, Q_CHP_gen_W, Q_Furnace_gen_W, Q_BaseBoiler_gen_W, Q_PeakBoiler_gen_W = 0, 0, 0, 0, 0, 0, 0
E_HPSew_req_W, E_HPLake_req_W, E_GHP_req_W, E_CHP_gen_W, E_Furnace_gen_W, E_BaseBoiler_req_W, E_PeakBoiler_req_W = 0, 0, 0, 0, 0, 0, 0
Gas_used_HPSew_W, Gas_used_HPLake_W, Gas_used_GHP_W, Gas_used_CHP_W, Gas_used_Furnace_W, Gas_used_BaseBoiler_W, Gas_used_PeakBoiler_W = 0, 0, 0, 0, 0, 0, 0
Wood_used_HPSew_W, Wood_used_HPLake_W, Wood_used_GHP_W, Wood_used_CHP_W, Wood_used_Furnace_W, Wood_used_BaseBoiler_W, Wood_used_PeakBoiler_W = 0, 0, 0, 0, 0, 0, 0
Q_coldsource_HPSew_W, Q_coldsource_HPLake_W, Q_coldsource_GHP_W, Q_coldsource_CHP_W, \
Q_coldsource_Furnace_W, Q_coldsource_BaseBoiler_W, Q_coldsource_PeakBoiler_W = 0, 0, 0, 0, 0, 0, 0
while Q_therm_req_W > 1E-1: # cover demand as long as the supply is lower than demand!
if current_source == 'HP': # use heat pumps available!
if (master_to_slave_vars.HP_Sew_on) == 1 and Q_therm_req_W > 0 and HP_SEW_ALLOWED == 1: # activate if its available
source_HP_Sewage = 0
cost_HPSew_USD = 0.0
Q_HPSew_gen_W = 0.0
E_HPSew_req_W = 0.0
Q_coldsource_HPSew_W = 0.0
if Q_therm_req_W > master_to_slave_vars.HPSew_maxSize_W:
Q_therm_Sew_W = master_to_slave_vars.HPSew_maxSize_W
mdot_DH_to_Sew_kgpers = mdot_DH_req_kgpers * Q_therm_Sew_W / Q_therm_req_W.copy() # scale down the mass flow if the thermal demand is lowered
else:
Q_therm_Sew_W = float(Q_therm_req_W.copy())
mdot_DH_to_Sew_kgpers = float(mdot_DH_req_kgpers.copy())
C_HPSew_el_pure, C_HPSew_per_kWh_th_pure, Q_HPSew_cold_primary_W, Q_HPSew_therm_W, E_HPSew_req_W = HPSew_op_cost(mdot_DH_to_Sew_kgpers, tdhsup_K, tdhret_req_K, TretsewArray_K,
lca, Q_therm_Sew_W)
Q_therm_req_W -= Q_HPSew_therm_W
# Storing data for further processing
if Q_HPSew_therm_W > 0:
source_HP_Sewage = 1
cost_HPSew_USD = float(C_HPSew_el_pure)
Q_HPSew_gen_W = float(Q_HPSew_therm_W)
E_HPSew_req_W = float(E_HPSew_req_W)
Q_coldsource_HPSew_W = float(Q_HPSew_cold_primary_W)
if (master_to_slave_vars.GHP_on) == 1 and hour >= master_to_slave_vars.GHP_SEASON_ON and hour <= master_to_slave_vars.GHP_SEASON_OFF and Q_therm_req_W > 0 and not np.isclose(
tdhsup_K, tdhret_req_K):
# activating GHP plant if possible
source_GHP = 0
cost_GHP_USD = 0.0
Q_GHP_gen_W = 0.0
E_GHP_req_W = 0.0
Q_coldsource_GHP_W = 0.0
Q_max_W, GHP_COP = GHP_Op_max(tdhsup_K, T_ground, master_to_slave_vars.GHP_number)
if Q_therm_req_W > Q_max_W:
mdot_DH_to_GHP_kgpers = Q_max_W / (HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
Q_therm_req_W -= Q_max_W
else: # regular operation possible, demand is covered
mdot_DH_to_GHP_kgpers = Q_therm_req_W.copy() / (HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
Q_therm_req_W = 0
C_GHP_el, E_GHP_req_W, Q_GHP_cold_primary_W, Q_GHP_therm_W = GHP_op_cost(mdot_DH_to_GHP_kgpers, tdhsup_K, tdhret_req_K, GHP_COP, lca)
# Storing data for further processing
source_GHP = 1
cost_GHP_USD = C_GHP_el
Q_GHP_gen_W = Q_GHP_therm_W
E_GHP_req_W = E_GHP_req_W
Q_coldsource_GHP_W = Q_GHP_cold_primary_W
if (master_to_slave_vars.HP_Lake_on) == 1 and Q_therm_req_W > 0 and HP_LAKE_ALLOWED == 1 and not np.isclose(tdhsup_K,
tdhret_req_K): # run Heat Pump Lake
source_HP_Lake = 0
cost_HPLake_USD = 0
Q_HPLake_gen_W = 0
E_HPLake_req_W = 0
Q_coldsource_HPLake_W = 0
if Q_therm_req_W > master_to_slave_vars.HPLake_maxSize_W: # Scale down Load, 100% load achieved
Q_therm_HPL_W = master_to_slave_vars.HPLake_maxSize_W
mdot_DH_to_Lake_kgpers = Q_therm_HPL_W / (
HEAT_CAPACITY_OF_WATER_JPERKGK * (
tdhsup_K - tdhret_req_K)) # scale down the mass flow if the thermal demand is lowered
Q_therm_req_W -= master_to_slave_vars.HPLake_maxSize_W
else: # regular operation possible
Q_therm_HPL_W = Q_therm_req_W.copy()
mdot_DH_to_Lake_kgpers = Q_therm_HPL_W / (HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
Q_therm_req_W = 0
C_HPL_el, E_HPLake_req_W, Q_HPL_cold_primary_W, Q_HPL_therm_W = HPLake_op_cost(mdot_DH_to_Lake_kgpers, tdhsup_K, tdhret_req_K, T_LAKE, lca)
# Storing Data
source_HP_Lake = 1
cost_HPLake_USD = C_HPL_el
Q_HPLake_gen_W = Q_therm_HPL_W
E_HPLake_req_W = E_HPLake_req_W
Q_coldsource_HPLake_W = Q_HPL_cold_primary_W
if current_source == 'CHP' and Q_therm_req_W > 0: # start activating the combined cycles
# By definition, one can either activate the CHP (NG-CC) or ORC (Furnace) BUT NOT BOTH at the same time (not activated by Master)
Cost_CC = 0.0
source_CHP = 0
cost_CHP_USD = 0.0
Q_CHP_gen_W = 0.0
Gas_used_CHP_W = 0.0
E_CHP_gen_W = 0
if (master_to_slave_vars.CC_on) == 1 and Q_therm_req_W > 0 and CC_ALLOWED == 1: # only operate if the plant is available
CC_op_cost_data = calc_cop_CCGT(master_to_slave_vars.CC_GT_SIZE_W, tdhsup_K, master_to_slave_vars.gt_fuel,
prices, lca) # create cost information
Q_used_prim_CC_fn_W = CC_op_cost_data['q_input_fn_q_output_W']
cost_per_Wh_CC_fn = CC_op_cost_data['fuel_cost_per_Wh_th_fn_q_output_W'] # gets interpolated cost function
q_output_CC_min_W = CC_op_cost_data['q_output_min_W']
Q_output_CC_max_W = CC_op_cost_data['q_output_max_W']
eta_elec_interpol = CC_op_cost_data['eta_el_fn_q_input']
if Q_therm_req_W > q_output_CC_min_W: # operation Possible if above minimal load
if Q_therm_req_W < Q_output_CC_max_W: # Normal operation Possible within partload regime
cost_per_Wh_CC = cost_per_Wh_CC_fn(Q_therm_req_W)
Q_used_prim_CC_W = Q_used_prim_CC_fn_W(Q_therm_req_W)
Q_CC_delivered_W = Q_therm_req_W.copy()
Q_therm_req_W = 0
E_CHP_gen_W = np.float(eta_elec_interpol(Q_used_prim_CC_W)) * Q_used_prim_CC_W
else: # Only part of the demand can be delivered as 100% load achieved
cost_per_Wh_CC = cost_per_Wh_CC_fn(Q_output_CC_max_W)
Q_used_prim_CC_W = Q_used_prim_CC_fn_W(Q_output_CC_max_W)
Q_CC_delivered_W = Q_output_CC_max_W
Q_therm_req_W -= Q_output_CC_max_W
E_CHP_gen_W = np.float(eta_elec_interpol(Q_output_CC_max_W)) * Q_used_prim_CC_W
Cost_CC = cost_per_Wh_CC * Q_CC_delivered_W
source_CHP = 1
cost_CHP_USD = Cost_CC
Q_CHP_gen_W = Q_CC_delivered_W
Gas_used_CHP_W = Q_used_prim_CC_W
if (master_to_slave_vars.Furnace_on) == 1 and Q_therm_req_W > 0: # Activate Furnace if its there. By definition, either ORC or NG-CC!
Q_Furn_therm_W = 0
source_Furnace = 0
cost_Furnace_USD = 0.0
Q_Furnace_gen_W = 0.0
Wood_used_Furnace_W = 0.0
Q_Furn_prim_W = 0.0
if Q_therm_req_W > (
gv.Furn_min_Load * master_to_slave_vars.Furnace_Q_max_W): # Operate only if its above minimal load
if Q_therm_req_W > master_to_slave_vars.Furnace_Q_max_W: # scale down if above maximum load, Furnace operates at max. capacity
Furnace_Cost_Data = furnace_op_cost(master_to_slave_vars.Furnace_Q_max_W, master_to_slave_vars.Furnace_Q_max_W, tdhret_req_K,
master_to_slave_vars.Furn_Moist_type, gv)
C_Furn_therm = Furnace_Cost_Data[0]
Q_Furn_prim_W = Furnace_Cost_Data[2]
Q_Furn_therm_W = master_to_slave_vars.Furnace_Q_max_W
Q_therm_req_W -= Q_Furn_therm_W
E_Furnace_gen_W = Furnace_Cost_Data[4]
else: # Normal Operation Possible
Furnace_Cost_Data = furnace_op_cost(Q_therm_req_W, master_to_slave_vars.Furnace_Q_max_W, tdhret_req_K,
master_to_slave_vars.Furn_Moist_type, gv)
Q_Furn_prim_W = Furnace_Cost_Data[2]
C_Furn_therm = Furnace_Cost_Data[0]
Q_Furn_therm_W = Q_therm_req_W.copy()
E_Furnace_gen_W = Furnace_Cost_Data[4]
Q_therm_req_W = 0
source_Furnace = 1
cost_Furnace_USD = C_Furn_therm.copy()
Q_Furnace_gen_W = Q_Furn_therm_W
Wood_used_Furnace_W = Q_Furn_prim_W
if current_source == 'BoilerBase' and Q_therm_req_W > 0:
Q_therm_boiler_W = 0
if (master_to_slave_vars.Boiler_on) == 1:
source_BaseBoiler = 0
cost_BaseBoiler_USD = 0.0
Q_BaseBoiler_gen_W = 0.0
Gas_used_BaseBoiler_W = 0.0
E_BaseBoiler_req_W = 0.0
if Q_therm_req_W >= BOILER_MIN * master_to_slave_vars.Boiler_Q_max_W: # Boiler can be activated?
# Q_therm_boiler = Q_therm_req
if Q_therm_req_W >= master_to_slave_vars.Boiler_Q_max_W: # Boiler above maximum Load?
Q_therm_boiler_W = master_to_slave_vars.Boiler_Q_max_W
else:
Q_therm_boiler_W = Q_therm_req_W.copy()
C_boil_therm, C_boil_per_Wh, Q_primary_W, E_aux_Boiler_req_W = cond_boiler_op_cost(Q_therm_boiler_W, master_to_slave_vars.Boiler_Q_max_W, tdhret_req_K, \
master_to_slave_vars.BoilerType, master_to_slave_vars.EL_TYPE, gv, prices, lca)
source_BaseBoiler = 1
cost_BaseBoiler_USD = C_boil_therm
Q_BaseBoiler_gen_W = Q_therm_boiler_W
Gas_used_BaseBoiler_W = Q_primary_W
E_BaseBoiler_req_W = E_aux_Boiler_req_W
Q_therm_req_W -= Q_therm_boiler_W
if current_source == 'BoilerPeak' and Q_therm_req_W > 0:
if (master_to_slave_vars.BoilerPeak_on) == 1:
source_PeakBoiler = 0
cost_PeakBoiler_USD = 0.0
Q_PeakBoiler_gen_W = 0.0
Gas_used_PeakBoiler_W = 0
E_PeakBoiler_req_W = 0
if Q_therm_req_W > 0: # gv.Boiler_min*master_to_slave_vars.BoilerPeak_Q_max: # Boiler can be activated?
if Q_therm_req_W > master_to_slave_vars.BoilerPeak_Q_max_W: # Boiler above maximum Load?
Q_therm_boilerP_W = master_to_slave_vars.BoilerPeak_Q_max_W
Q_therm_req_W -= Q_therm_boilerP_W
else:
Q_therm_boilerP_W = Q_therm_req_W.copy()
Q_therm_req_W = 0
C_boil_thermP, C_boil_per_WhP, Q_primaryP_W, E_aux_BoilerP_W = cond_boiler_op_cost(Q_therm_boilerP_W, master_to_slave_vars.BoilerPeak_Q_max_W, tdhret_req_K, \
master_to_slave_vars.BoilerPeakType, master_to_slave_vars.EL_TYPE, gv, prices, lca)
source_PeakBoiler = 1
cost_PeakBoiler_USD = C_boil_thermP
Q_PeakBoiler_gen_W = Q_therm_boilerP_W
Gas_used_PeakBoiler_W = Q_primaryP_W
E_PeakBoiler_req_W = E_aux_BoilerP_W
Q_excess_W = 0
if np.floor(Q_therm_req_W) > 0:
if current_source == ACT_FIRST:
current_source = ACT_SECOND
elif current_source == ACT_SECOND:
current_source = ACT_THIRD
elif current_source == ACT_THIRD:
current_source = ACT_FOURTH
else:
Q_uncovered_W = Q_therm_req_W
break
elif round(Q_therm_req_W, 0) != 0:
Q_uncovered_W = 0 # Q_therm_req
Q_excess_W = -Q_therm_req_W
Q_therm_req_W = 0
# break
else:
Q_therm_req_W = 0
source_info = source_HP_Sewage, source_HP_Lake, source_GHP, source_CHP, source_Furnace, source_BaseBoiler, source_PeakBoiler
Q_source_data_W = Q_HPSew_gen_W, Q_HPLake_gen_W, Q_GHP_gen_W, Q_CHP_gen_W, Q_Furnace_gen_W, Q_BaseBoiler_gen_W, Q_PeakBoiler_gen_W, Q_uncovered_W
E_PP_el_data_W = E_HPSew_req_W, E_HPLake_req_W, E_GHP_req_W, E_CHP_gen_W, E_Furnace_gen_W, E_BaseBoiler_req_W, E_PeakBoiler_req_W
E_gas_data_W = Gas_used_HPSew_W, Gas_used_HPLake_W, Gas_used_GHP_W, Gas_used_CHP_W, Gas_used_Furnace_W, Gas_used_BaseBoiler_W, Gas_used_PeakBoiler_W
E_wood_data_W = Wood_used_HPSew_W, Wood_used_HPLake_W, Wood_used_GHP_W, Wood_used_CHP_W, Wood_used_Furnace_W, Wood_used_BaseBoiler_W, Wood_used_PeakBoiler_W
E_coldsource_data_W = Q_coldsource_HPSew_W, Q_coldsource_HPLake_W, Q_coldsource_GHP_W, Q_coldsource_CHP_W, \
Q_coldsource_Furnace_W, Q_coldsource_BaseBoiler_W, Q_coldsource_PeakBoiler_W
opex_output = {'Opex_var_HP_Sewage_USD':cost_HPSew_USD,
'Opex_var_HP_Lake_USD': cost_HPLake_USD,
'Opex_var_GHP_USD': cost_GHP_USD,
'Opex_var_CHP_USD': cost_CHP_USD,
'Opex_var_Furnace_USD': cost_Furnace_USD,
'Opex_var_BaseBoiler_USD': cost_BaseBoiler_USD,
'Opex_var_PeakBoiler_USD': cost_PeakBoiler_USD}
source_output = {'HP_Sewage': source_HP_Sewage,
'HP_Lake': source_HP_Lake,
'GHP': source_GHP,
'CHP': source_CHP,
'Furnace': source_Furnace,
'BaseBoiler': source_BaseBoiler,
'PeakBoiler': source_PeakBoiler}
Q_output = {'Q_HPSew_gen_W': Q_HPSew_gen_W,
'Q_HPLake_gen_W': Q_HPLake_gen_W,
'Q_GHP_gen_W': Q_GHP_gen_W,
'Q_CHP_gen_W': Q_CHP_gen_W,
'Q_Furnace_gen_W': Q_Furnace_gen_W,
'Q_BaseBoiler_gen_W': Q_BaseBoiler_gen_W,
'Q_PeakBoiler_gen_W': Q_PeakBoiler_gen_W,
'Q_uncovered_W': Q_uncovered_W}
E_output = {'E_HPSew_req_W': E_HPSew_req_W,
'E_HPLake_req_W': E_HPLake_req_W,
'E_GHP_req_W': E_GHP_req_W,
'E_CHP_gen_W': E_CHP_gen_W,
'E_Furnace_gen_W': E_Furnace_gen_W,
'E_BaseBoiler_req_W': E_BaseBoiler_req_W,
'E_PeakBoiler_req_W': E_PeakBoiler_req_W}
Gas_output = {'Gas_used_HPSew_W': Gas_used_HPSew_W,
'Gas_used_HPLake_W': Gas_used_HPLake_W,
'Gas_used_GHP_W': Gas_used_GHP_W,
'Gas_used_CHP_W': Gas_used_CHP_W,
'Gas_used_Furnace_W': Gas_used_Furnace_W,
'Gas_used_BaseBoiler_W': Gas_used_BaseBoiler_W,
'Gas_used_PeakBoiler_W': Gas_used_PeakBoiler_W}
Wood_output = {'Wood_used_HPSew_W': Wood_used_HPSew_W,
'Wood_used_HPLake_W': Wood_used_HPLake_W,
'Wood_used_GHP_W': Wood_used_GHP_W,
'Wood_used_CHP_W': Wood_used_CHP_W,
'Wood_used_Furnace_W': Wood_used_Furnace_W,
'Wood_used_BaseBoiler_W': Wood_used_BaseBoiler_W,
'Wood_used_PeakBoiler_W': Wood_used_PeakBoiler_W}
coldsource_output = {'Q_coldsource_HPSew_W': Q_coldsource_HPSew_W,
'Q_coldsource_HPLake_W': Q_coldsource_HPLake_W,
'Q_coldsource_GHP_W': Q_coldsource_GHP_W,
'Q_coldsource_CHP_W': Q_coldsource_CHP_W,
'Q_coldsource_Furnace_W': Q_coldsource_Furnace_W,
'Q_coldsource_BaseBoiler_W': Q_coldsource_BaseBoiler_W,
'Q_coldsource_PeakBoiler_W': Q_coldsource_PeakBoiler_W}
return opex_output, source_output, Q_output, E_output, Gas_output, Wood_output, coldsource_output, Q_excess_W
def heating_calculations_of_DH_buildings(locator, master_to_slave_vars, gv,
config, prices, lca):
"""
Computes the parameters for the heating of the complete DHN
:param locator: locator class
:param master_to_slave_vars: class MastertoSlaveVars containing the value of variables to be passed to the
slave optimization for each individual
:param solar_features: solar features class
:param gv: global variables class
:type locator: class
:type master_to_slave_vars: class
:type solar_features: class
:type gv: class
:return:
- E_oil_eq_MJ: MJ oil Equivalent used during operation
- CO2_kg_eq: kg of CO2-Equivalent emitted during operation
- cost_sum: total cost in CHF used for operation
- Q_source_data[:,7]: uncovered demand
:rtype: float, float, float, array
"""
t = time.time()
# Import data from storage optimization
centralized_plant_data = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_aux_ch_W = np.array(centralized_plant_data['E_aux_ch_W'])
E_aux_dech_W = np.array(centralized_plant_data['E_aux_dech_W'])
Q_missing_W = np.array(centralized_plant_data['Q_missing_W'])
E_aux_solar_and_heat_recovery_W = np.array(
centralized_plant_data['E_aux_solar_and_heat_recovery_Wh'])
Q_SC_ET_gen_Wh = np.array(centralized_plant_data['Q_SC_ET_gen_Wh'])
Q_SC_FP_gen_Wh = np.array(centralized_plant_data['Q_SC_FP_gen_Wh'])
Q_PVT_gen_Wh = np.array(centralized_plant_data['Q_PVT_gen_Wh'])
Q_SCandPVT_gen_Wh = Q_SC_ET_gen_Wh + Q_SC_FP_gen_Wh + Q_PVT_gen_Wh
E_PV_gen_W = np.array(centralized_plant_data['E_PV_Wh'])
E_PVT_gen_W = np.array(centralized_plant_data['E_PVT_Wh'])
E_produced_solar_W = np.array(
centralized_plant_data['E_produced_from_solar_W'])
E_solar_gen_W = np.add(E_PV_gen_W, E_PVT_gen_W)
Q_missing_copy_W = Q_missing_W.copy()
# Import Temperatures from Network Summary:
network_data = pd.read_csv(
locator.get_optimization_network_data_folder(
master_to_slave_vars.network_data_file_heating))
tdhret_K = network_data['T_DHNf_re_K']
mdot_DH_kgpers = network_data['mdot_DH_netw_total_kgpers']
tdhsup_K = network_data['T_DHNf_sup_K']
# import Marginal Cost of PP Data :
# os.chdir(Cost_Maps_Path)
# FIXED ORDER ACTIVATION STARTS
# Import Data - Sewage
if HP_SEW_ALLOWED == 1:
HPSew_Data = pd.read_csv(locator.get_sewage_heat_potential())
QcoldsewArray = np.array(HPSew_Data['Qsw_kW']) * 1E3
TretsewArray_K = np.array(HPSew_Data['ts_C']) + 273
# Initiation of the variables
Opex_var_HP_Sewage_USD = np.zeros(8760)
Opex_var_HP_Lake_USD = np.zeros(8760)
Opex_var_GHP_USD = np.zeros(8760)
Opex_var_CHP_USD = np.zeros(8760)
Opex_var_Furnace_USD = np.zeros(8760)
Opex_var_BaseBoiler_USD = np.zeros(8760)
Opex_var_PeakBoiler_USD = np.zeros(8760)
source_HP_Sewage = np.zeros(8760)
source_HP_Lake = np.zeros(8760)
source_GHP = np.zeros(8760)
source_CHP = np.zeros(8760)
source_Furnace = np.zeros(8760)
source_BaseBoiler = np.zeros(8760)
source_PeakBoiler = np.zeros(8760)
Q_HPSew_gen_W = np.zeros(8760)
Q_HPLake_gen_W = np.zeros(8760)
Q_GHP_gen_W = np.zeros(8760)
Q_CHP_gen_W = np.zeros(8760)
Q_Furnace_gen_W = np.zeros(8760)
Q_BaseBoiler_gen_W = np.zeros(8760)
Q_PeakBoiler_gen_W = np.zeros(8760)
Q_uncovered_W = np.zeros(8760)
E_HPSew_req_W = np.zeros(8760)
E_HPLake_req_W = np.zeros(8760)
E_GHP_req_W = np.zeros(8760)
E_CHP_gen_W = np.zeros(8760)
E_Furnace_gen_W = np.zeros(8760)
E_BaseBoiler_req_W = np.zeros(8760)
E_PeakBoiler_req_W = np.zeros(8760)
NG_used_HPSew_W = np.zeros(8760)
NG_used_HPLake_W = np.zeros(8760)
NG_used_GHP_W = np.zeros(8760)
NG_used_CHP_W = np.zeros(8760)
NG_used_Furnace_W = np.zeros(8760)
NG_used_BaseBoiler_W = np.zeros(8760)
NG_used_PeakBoiler_W = np.zeros(8760)
NG_used_BackupBoiler_W = np.zeros(8760)
BG_used_HPSew_W = np.zeros(8760)
BG_used_HPLake_W = np.zeros(8760)
BG_used_GHP_W = np.zeros(8760)
BG_used_CHP_W = np.zeros(8760)
BG_used_Furnace_W = np.zeros(8760)
BG_used_BaseBoiler_W = np.zeros(8760)
BG_used_PeakBoiler_W = np.zeros(8760)
Wood_used_HPSew_W = np.zeros(8760)
Wood_used_HPLake_W = np.zeros(8760)
Wood_used_GHP_W = np.zeros(8760)
Wood_used_CHP_W = np.zeros(8760)
Wood_used_Furnace_W = np.zeros(8760)
Wood_used_BaseBoiler_W = np.zeros(8760)
Wood_used_PeakBoiler_W = np.zeros(8760)
Q_coldsource_HPSew_W = np.zeros(8760)
Q_coldsource_HPLake_W = np.zeros(8760)
Q_coldsource_GHP_W = np.zeros(8760)
Q_coldsource_CHP_W = np.zeros(8760)
Q_coldsource_Furnace_W = np.zeros(8760)
Q_coldsource_BaseBoiler_W = np.zeros(8760)
Q_coldsource_PeakBoiler_W = np.zeros(8760)
Q_excess_W = np.zeros(8760)
weather_data = epwreader.epw_reader(config.weather)[[
'year', 'drybulb_C', 'wetbulb_C', 'relhum_percent', 'windspd_ms',
'skytemp_C'
]]
ground_temp = calc_ground_temperature(locator,
weather_data['drybulb_C'],
depth_m=10)
weather_data = epwreader.epw_reader(config.weather)[[
'year', 'drybulb_C', 'wetbulb_C', 'relhum_percent', 'windspd_ms',
'skytemp_C'
]]
ground_temp = calc_ground_temperature(locator,
weather_data['drybulb_C'],
depth_m=10)
for hour in range(8760):
Q_therm_req_W = Q_missing_W[hour]
# cost_data_centralPlant_op[hour, :], source_info[hour, :], Q_source_data_W[hour, :], E_coldsource_data_W[hour,
# :], \
# E_PP_el_data_W[hour, :], E_gas_data_W[hour, :], E_wood_data_W[hour, :], Q_excess_W[hour] = source_activator(
# Q_therm_req_W, hour, master_to_slave_vars, mdot_DH_kgpers[hour], tdhsup_K,
# tdhret_K[hour], TretsewArray_K[hour], gv, prices)
opex_output, source_output, Q_output, E_output, Gas_output, Wood_output, coldsource_output, Q_excess_W[
hour] = heating_source_activator(Q_therm_req_W, hour,
master_to_slave_vars,
mdot_DH_kgpers[hour],
tdhsup_K[hour], tdhret_K[hour],
TretsewArray_K[hour], gv, prices,
lca, ground_temp[hour])
Opex_var_HP_Sewage_USD[hour] = opex_output['Opex_var_HP_Sewage_USD']
Opex_var_HP_Lake_USD[hour] = opex_output['Opex_var_HP_Lake_USD']
Opex_var_GHP_USD[hour] = opex_output['Opex_var_GHP_USD']
Opex_var_CHP_USD[hour] = opex_output['Opex_var_CHP_USD']
Opex_var_Furnace_USD[hour] = opex_output['Opex_var_Furnace_USD']
Opex_var_BaseBoiler_USD[hour] = opex_output['Opex_var_BaseBoiler_USD']
Opex_var_PeakBoiler_USD[hour] = opex_output['Opex_var_PeakBoiler_USD']
source_HP_Sewage[hour] = source_output['HP_Sewage']
source_HP_Lake[hour] = source_output['HP_Lake']
source_GHP[hour] = source_output['GHP']
source_CHP[hour] = source_output['CHP']
source_Furnace[hour] = source_output['Furnace']
source_BaseBoiler[hour] = source_output['BaseBoiler']
source_PeakBoiler[hour] = source_output['PeakBoiler']
Q_HPSew_gen_W[hour] = Q_output['Q_HPSew_gen_W']
Q_HPLake_gen_W[hour] = Q_output['Q_HPLake_gen_W']
Q_GHP_gen_W[hour] = Q_output['Q_GHP_gen_W']
Q_CHP_gen_W[hour] = Q_output['Q_CHP_gen_W']
Q_Furnace_gen_W[hour] = Q_output['Q_Furnace_gen_W']
Q_BaseBoiler_gen_W[hour] = Q_output['Q_BaseBoiler_gen_W']
Q_PeakBoiler_gen_W[hour] = Q_output['Q_PeakBoiler_gen_W']
Q_uncovered_W[hour] = Q_output['Q_uncovered_W']
E_HPSew_req_W[hour] = E_output['E_HPSew_req_W']
E_HPLake_req_W[hour] = E_output['E_HPLake_req_W']
E_GHP_req_W[hour] = E_output['E_GHP_req_W']
E_CHP_gen_W[hour] = E_output['E_CHP_gen_W']
E_Furnace_gen_W[hour] = E_output['E_Furnace_gen_W']
E_BaseBoiler_req_W[hour] = E_output['E_BaseBoiler_req_W']
E_PeakBoiler_req_W[hour] = E_output['E_PeakBoiler_req_W']
if master_to_slave_vars.gt_fuel == "NG":
NG_used_HPSew_W[hour] = Gas_output['Gas_used_HPSew_W']
NG_used_HPLake_W[hour] = Gas_output['Gas_used_HPLake_W']
NG_used_GHP_W[hour] = Gas_output['Gas_used_GHP_W']
NG_used_CHP_W[hour] = Gas_output['Gas_used_CHP_W']
NG_used_Furnace_W[hour] = Gas_output['Gas_used_Furnace_W']
NG_used_BaseBoiler_W[hour] = Gas_output['Gas_used_BaseBoiler_W']
NG_used_PeakBoiler_W[hour] = Gas_output['Gas_used_PeakBoiler_W']
elif master_to_slave_vars.gt_fuel == "BG":
BG_used_HPSew_W[hour] = Gas_output['Gas_used_HPSew_W']
BG_used_HPLake_W[hour] = Gas_output['Gas_used_HPLake_W']
BG_used_GHP_W[hour] = Gas_output['Gas_used_GHP_W']
BG_used_CHP_W[hour] = Gas_output['Gas_used_CHP_W']
BG_used_Furnace_W[hour] = Gas_output['Gas_used_Furnace_W']
BG_used_BaseBoiler_W[hour] = Gas_output['Gas_used_BaseBoiler_W']
BG_used_PeakBoiler_W[hour] = Gas_output['Gas_used_PeakBoiler_W']
Wood_used_HPSew_W[hour] = Wood_output['Wood_used_HPSew_W']
Wood_used_HPLake_W[hour] = Wood_output['Wood_used_HPLake_W']
Wood_used_GHP_W[hour] = Wood_output['Wood_used_GHP_W']
Wood_used_CHP_W[hour] = Wood_output['Wood_used_CHP_W']
Wood_used_Furnace_W[hour] = Wood_output['Wood_used_Furnace_W']
Wood_used_BaseBoiler_W[hour] = Wood_output['Wood_used_BaseBoiler_W']
Wood_used_PeakBoiler_W[hour] = Wood_output['Wood_used_PeakBoiler_W']
Q_coldsource_HPSew_W[hour] = coldsource_output['Q_coldsource_HPSew_W']
Q_coldsource_HPLake_W[hour] = coldsource_output[
'Q_coldsource_HPLake_W']
Q_coldsource_GHP_W[hour] = coldsource_output['Q_coldsource_GHP_W']
Q_coldsource_CHP_W[hour] = coldsource_output['Q_coldsource_CHP_W']
Q_coldsource_Furnace_W[hour] = coldsource_output[
'Q_coldsource_Furnace_W']
Q_coldsource_BaseBoiler_W[hour] = coldsource_output[
'Q_coldsource_BaseBoiler_W']
Q_coldsource_PeakBoiler_W[hour] = coldsource_output[
'Q_coldsource_PeakBoiler_W']
# save data
elapsed = time.time() - t
# sum up the uncovered demand, get average and peak load
Q_uncovered_design_W = np.amax(Q_uncovered_W)
Q_uncovered_annual_W = np.sum(Q_uncovered_W)
Opex_var_BackupBoiler_USD = np.zeros(8760)
Q_BackupBoiler_W = np.zeros(8760)
E_BackupBoiler_req_W = np.zeros(8760)
Opex_var_Furnace_wet_USD = np.zeros(8760)
Opex_var_Furnace_dry_USD = np.zeros(8760)
Opex_var_CHP_NG_USD = np.zeros(8760)
Opex_var_CHP_BG_USD = np.zeros(8760)
Opex_var_BaseBoiler_NG_USD = np.zeros(8760)
Opex_var_BaseBoiler_BG_USD = np.zeros(8760)
Opex_var_PeakBoiler_NG_USD = np.zeros(8760)
Opex_var_PeakBoiler_BG_USD = np.zeros(8760)
Opex_var_BackupBoiler_NG_USD = np.zeros(8760)
Opex_var_BackupBoiler_BG_USD = np.zeros(8760)
Opex_var_PV_USD = np.zeros(8760)
Opex_var_PVT_USD = np.zeros(8760)
Opex_var_SC_USD = np.zeros(8760)
if Q_uncovered_design_W != 0:
for hour in range(8760):
tdhret_req_K = tdhret_K[hour]
BoilerBackup_Cost_Data = cond_boiler_op_cost(Q_uncovered_W[hour], Q_uncovered_design_W, tdhret_req_K, \
master_to_slave_vars.BoilerBackupType,
master_to_slave_vars.EL_TYPE, gv, prices, lca)
Opex_var_BackupBoiler_USD[
hour], Opex_var_BackupBoiler_per_Wh_USD, Q_BackupBoiler_W[
hour], E_BackupBoiler_req_W_hour = BoilerBackup_Cost_Data
E_BackupBoiler_req_W[hour] = E_BackupBoiler_req_W_hour
NG_used_BackupBoiler_W[hour] = Q_BackupBoiler_W[hour]
Q_BackupBoiler_sum_W = np.sum(Q_BackupBoiler_W)
Opex_t_var_BackupBoiler_USD = np.sum(Opex_var_BackupBoiler_USD)
else:
Q_BackupBoiler_sum_W = 0.0
Opex_t_var_BackupBoiler_USD = 0.0
if master_to_slave_vars.Furn_Moist_type == "wet":
Opex_var_Furnace_wet_USD = Opex_var_Furnace_USD
elif master_to_slave_vars.Furn_Moist_type == "dry":
Opex_var_Furnace_dry_USD = Opex_var_Furnace_USD
if master_to_slave_vars.gt_fuel == "NG":
Opex_var_CHP_NG_USD = Opex_var_CHP_USD
Opex_var_BaseBoiler_NG_USD = Opex_var_BaseBoiler_USD
Opex_var_PeakBoiler_NG_USD = Opex_var_PeakBoiler_USD
Opex_var_BackupBoiler_NG_USD = Opex_var_BackupBoiler_USD
elif master_to_slave_vars.gt_fuel == "BG":
Opex_var_CHP_BG_USD = Opex_var_CHP_USD
Opex_var_BaseBoiler_BG_USD = Opex_var_BaseBoiler_USD
Opex_var_PeakBoiler_BG_USD = Opex_var_PeakBoiler_USD
Opex_var_BackupBoiler_BG_USD = Opex_var_BackupBoiler_USD
# saving pattern activation to disk
date = network_data.DATE.values
results = pd.DataFrame({
"DATE": date,
"Q_Network_Demand_after_Storage_W": Q_missing_copy_W,
"Opex_var_HP_Sewage": Opex_var_HP_Sewage_USD,
"Opex_var_HP_Lake": Opex_var_HP_Lake_USD,
"Opex_var_GHP": Opex_var_GHP_USD,
"Opex_var_CHP_BG": Opex_var_CHP_BG_USD,
"Opex_var_CHP_NG": Opex_var_CHP_NG_USD,
"Opex_var_Furnace_wet": Opex_var_Furnace_wet_USD,
"Opex_var_Furnace_dry": Opex_var_Furnace_dry_USD,
"Opex_var_BaseBoiler_BG": Opex_var_BaseBoiler_BG_USD,
"Opex_var_BaseBoiler_NG": Opex_var_BaseBoiler_NG_USD,
"Opex_var_PeakBoiler_BG": Opex_var_PeakBoiler_BG_USD,
"Opex_var_PeakBoiler_NG": Opex_var_PeakBoiler_NG_USD,
"Opex_var_BackupBoiler_BG": Opex_var_BackupBoiler_BG_USD,
"Opex_var_BackupBoiler_NG": Opex_var_BackupBoiler_NG_USD,
"HPSew_Status": source_HP_Sewage,
"HPLake_Status": source_HP_Lake,
"GHP_Status": source_GHP,
"CHP_Status": source_CHP,
"Furnace_Status": source_Furnace,
"BoilerBase_Status": source_BaseBoiler,
"BoilerPeak_Status": source_PeakBoiler,
"Q_HPSew_W": Q_HPSew_gen_W,
"Q_HPLake_W": Q_HPLake_gen_W,
"Q_GHP_W": Q_GHP_gen_W,
"Q_CHP_W": Q_CHP_gen_W,
"Q_Furnace_W": Q_Furnace_gen_W,
"Q_BaseBoiler_W": Q_BaseBoiler_gen_W,
"Q_PeakBoiler_W": Q_PeakBoiler_gen_W,
"Q_AddBoiler_W": Q_uncovered_W,
"Q_coldsource_HPLake_W": Q_coldsource_HPLake_W,
"Q_coldsource_HPSew_W": Q_coldsource_HPSew_W,
"Q_coldsource_GHP_W": Q_coldsource_GHP_W,
"Q_coldsource_CHP_W": Q_coldsource_CHP_W,
"Q_coldsource_Furnace_W": Q_coldsource_Furnace_W,
"Q_coldsource_BaseBoiler_W": Q_coldsource_BaseBoiler_W,
"Q_coldsource_PeakBoiler_W": Q_coldsource_PeakBoiler_W,
"Q_excess_W": Q_excess_W,
"E_HPSew_req_W": E_HPSew_req_W,
"E_HPLake_req_W": E_HPLake_req_W,
"E_GHP_req_W": E_GHP_req_W,
"E_CHP_gen_W": E_CHP_gen_W,
"E_Furnace_gen_W": E_Furnace_gen_W,
"E_BaseBoiler_req_W": E_BaseBoiler_req_W,
"E_PeakBoiler_req_W": E_PeakBoiler_req_W,
"E_BackupBoiler_req_W": E_BackupBoiler_req_W,
"NG_used_HPSew_W": NG_used_HPSew_W,
"NG_used_HPLake_W": NG_used_HPLake_W,
"NG_used_GHP_W": NG_used_GHP_W,
"NG_used_CHP_W": NG_used_CHP_W,
"NG_used_Furnace_W": NG_used_Furnace_W,
"NG_used_BaseBoiler_W": NG_used_BaseBoiler_W,
"NG_used_PeakBoiler_W": NG_used_PeakBoiler_W,
"NG_used_BackupBoiler_W": NG_used_BackupBoiler_W,
"BG_used_HPSew_W": BG_used_HPSew_W,
"BG_used_HPLake_W": BG_used_HPLake_W,
"BG_used_GHP_W": BG_used_GHP_W,
"BG_used_CHP_W": BG_used_CHP_W,
"BG_used_Furnace_W": BG_used_Furnace_W,
"BG_used_BaseBoiler_W": BG_used_BaseBoiler_W,
"BG_used_PeakBoiler_W": BG_used_PeakBoiler_W,
"Wood_used_HPSew_W": Wood_used_HPSew_W,
"Wood_used_HPLake_W": Wood_used_HPLake_W,
"Wood_used_GHP_W": Wood_used_GHP_W,
"Wood_used_CHP_": Wood_used_CHP_W,
"Wood_used_Furnace_W": Wood_used_Furnace_W,
"Wood_used_BaseBoiler_W": Wood_used_BaseBoiler_W,
"Wood_used_PeakBoiler_W": Wood_used_PeakBoiler_W
})
results.to_csv(locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
index=False)
if master_to_slave_vars.gt_fuel == "NG":
CO2_emitted, PEN_used = calc_primary_energy_and_CO2(
Q_HPSew_gen_W, Q_HPLake_gen_W, Q_GHP_gen_W, Q_CHP_gen_W,
Q_Furnace_gen_W, Q_BaseBoiler_gen_W, Q_PeakBoiler_gen_W,
Q_uncovered_W, Q_coldsource_HPSew_W, Q_coldsource_HPLake_W,
Q_coldsource_GHP_W, E_CHP_gen_W, E_Furnace_gen_W,
E_BaseBoiler_req_W, E_PeakBoiler_req_W, NG_used_CHP_W,
NG_used_BaseBoiler_W, NG_used_PeakBoiler_W,
Wood_used_Furnace_W, Q_BackupBoiler_sum_W,
np.sum(E_BackupBoiler_req_W), master_to_slave_vars, locator, lca)
elif master_to_slave_vars.gt_fuel == "BG":
CO2_emitted, PEN_used = calc_primary_energy_and_CO2(
Q_HPSew_gen_W, Q_HPLake_gen_W, Q_GHP_gen_W, Q_CHP_gen_W,
Q_Furnace_gen_W, Q_BaseBoiler_gen_W, Q_PeakBoiler_gen_W,
Q_uncovered_W, Q_coldsource_HPSew_W, Q_coldsource_HPLake_W,
Q_coldsource_GHP_W, E_CHP_gen_W, E_Furnace_gen_W,
E_BaseBoiler_req_W, E_PeakBoiler_req_W, BG_used_CHP_W,
BG_used_BaseBoiler_W, BG_used_PeakBoiler_W,
Wood_used_Furnace_W, Q_BackupBoiler_sum_W,
np.sum(E_BackupBoiler_req_W), master_to_slave_vars, locator, lca)
cost_sum = np.sum(Opex_var_HP_Sewage_USD) + np.sum(
Opex_var_HP_Lake_USD) + np.sum(Opex_var_GHP_USD) + np.sum(
Opex_var_CHP_USD) + np.sum(Opex_var_Furnace_USD) + np.sum(
Opex_var_BaseBoiler_USD) + np.sum(
Opex_var_PeakBoiler_USD) + Opex_t_var_BackupBoiler_USD
E_oil_eq_MJ = PEN_used
CO2_kg_eq = CO2_emitted
return E_oil_eq_MJ, CO2_kg_eq, cost_sum, Q_uncovered_design_W, Q_uncovered_annual_W
def least_cost_main(locator, master_to_slave_vars, solar_features, gv, prices, lca, config):
"""
This function runs the least cost optimization code and returns cost, co2 and primary energy required. \
On the go, it saves the operation pattern
:param locator: locator class
:param master_to_slave_vars: class MastertoSlaveVars containing the value of variables to be passed to the
slave optimization for each individual
:param solar_features: solar features class
:param gv: global variables class
:type locator: class
:type master_to_slave_vars: class
:type solar_features: class
:type gv: class
:return:
- E_oil_eq_MJ: MJ oil Equivalent used during operation
- CO2_kg_eq: kg of CO2-Equivalent emitted during operation
- cost_sum: total cost in CHF used for operation
- Q_source_data[:,7]: uncovered demand
:rtype: float, float, float, array
"""
MS_Var = master_to_slave_vars
t = time.time()
# Import data from storage optimization
centralized_plant_data = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(MS_Var.individual_number,
MS_Var.generation_number))
Q_DH_networkload_W = np.array(centralized_plant_data['Q_DH_networkload_W'])
E_aux_ch_W = np.array(centralized_plant_data['E_aux_ch_W'])
E_aux_dech_W = np.array(centralized_plant_data['E_aux_dech_W'])
Q_missing_W = np.array(centralized_plant_data['Q_missing_W'])
Q_storage_content_W = np.array(centralized_plant_data['Q_storage_content_W'])
Q_to_storage_W = np.array(centralized_plant_data['Q_to_storage_W'])
Q_from_storage_W = np.array(centralized_plant_data['Q_from_storage_used_W'])
Q_uncontrollable_W = np.array(centralized_plant_data['Q_uncontrollable_hot_W'])
E_PV_gen_W = np.array(centralized_plant_data['E_PV_Wh'])
E_PVT_gen_W = np.array(centralized_plant_data['E_PVT_Wh'])
E_aux_solar_and_heat_recovery_W = np.array(centralized_plant_data['E_aux_solar_and_heat_recovery_Wh'])
Q_SC_ET_gen_Wh = np.array(centralized_plant_data['Q_SC_ET_gen_Wh'])
Q_SC_FP_gen_Wh = np.array(centralized_plant_data['Q_SC_FP_gen_Wh'])
Q_PVT_gen_Wh = np.array(centralized_plant_data['Q_PVT_gen_Wh'])
Q_SCandPVT_gen_Wh = Q_SC_ET_gen_Wh + Q_SC_FP_gen_Wh + Q_PVT_gen_Wh
E_produced_solar_W = np.array(centralized_plant_data['E_produced_from_solar_W'])
# Q_StorageToDHNpipe_sum = np.sum(E_aux_dech_W) + np.sum(Q_from_storage_W)
#
# HP_operation_Data_sum_array = np.sum(HPServerHeatDesignArray_kWh), \
# np.sum(HPpvt_designArray_Wh), \
# np.sum(HPCompAirDesignArray_kWh), \
# np.sum(HPScDesignArray_Wh)
Q_missing_copy_W = Q_missing_W.copy()
# Import Temperatures from Network Summary:
network_data = pd.read_csv(locator.get_optimization_network_data_folder(MS_Var.network_data_file_heating))
tdhret_K = network_data['T_DHNf_re_K']
mdot_DH_kgpers = network_data['mdot_DH_netw_total_kgpers']
tdhsup_K = network_data['T_DHNf_sup_K']
# import Marginal Cost of PP Data :
# os.chdir(Cost_Maps_Path)
# FIXED ORDER ACTIVATION STARTS
# Import Data - Sewage
if HP_SEW_ALLOWED == 1:
HPSew_Data = pd.read_csv(locator.get_sewage_heat_potential())
QcoldsewArray = np.array(HPSew_Data['Qsw_kW']) * 1E3
TretsewArray_K = np.array(HPSew_Data['ts_C']) + 273
# Initiation of the variables
Opex_var_HP_Sewage = []
Opex_var_HP_Lake = []
Opex_var_GHP = []
Opex_var_CHP = []
Opex_var_Furnace = []
Opex_var_BaseBoiler = []
Opex_var_PeakBoiler = []
source_HP_Sewage = []
source_HP_Lake = []
source_GHP = []
source_CHP = []
source_Furnace = []
source_BaseBoiler = []
source_PeakBoiler = []
Q_HPSew_gen_W = []
Q_HPLake_gen_W = []
Q_GHP_gen_W = []
Q_CHP_gen_W = []
Q_Furnace_gen_W = []
Q_BaseBoiler_gen_W = []
Q_PeakBoiler_gen_W = []
Q_uncovered_W = []
E_HPSew_req_W = []
E_HPLake_req_W = []
E_GHP_req_W = []
E_CHP_gen_W = []
E_Furnace_gen_W = []
E_BaseBoiler_req_W = []
E_PeakBoiler_req_W = []
E_gas_HPSew_W = []
E_gas_HPLake_W = []
E_gas_GHP_W = []
E_gas_CHP_W = []
E_gas_Furnace_W = []
E_gas_BaseBoiler_W = []
E_gas_PeakBoiler_W = []
E_wood_HPSew_W = []
E_wood_HPLake_W = []
E_wood_GHP_W = []
E_wood_CHP_W = []
E_wood_Furnace_W = []
E_wood_BaseBoiler_W = []
E_wood_PeakBoiler_W = []
E_coldsource_HPSew_W = []
E_coldsource_HPLake_W = []
E_coldsource_GHP_W = []
E_coldsource_CHP_W = []
E_coldsource_Furnace_W = []
E_coldsource_BaseBoiler_W = []
E_coldsource_PeakBoiler_W = []
Q_excess_W = np.zeros(8760)
weather_data = epwreader.epw_reader(config.weather)[['year', 'drybulb_C', 'wetbulb_C','relhum_percent',
'windspd_ms', 'skytemp_C']]
ground_temp = calc_ground_temperature(locator, weather_data['drybulb_C'], depth_m=10)
weather_data = epwreader.epw_reader(config.weather)[['year', 'drybulb_C', 'wetbulb_C',
'relhum_percent', 'windspd_ms', 'skytemp_C']]
ground_temp = calc_ground_temperature(locator, weather_data['drybulb_C'], depth_m=10)
for hour in range(8760):
Q_therm_req_W = Q_missing_W[hour]
# cost_data_centralPlant_op[hour, :], source_info[hour, :], Q_source_data_W[hour, :], E_coldsource_data_W[hour,
# :], \
# E_PP_el_data_W[hour, :], E_gas_data_W[hour, :], E_wood_data_W[hour, :], Q_excess_W[hour] = source_activator(
# Q_therm_req_W, hour, master_to_slave_vars, mdot_DH_kgpers[hour], tdhsup_K,
# tdhret_K[hour], TretsewArray_K[hour], gv, prices)
opex_output, source_output, Q_output, E_output, Gas_output, Wood_output, coldsource_output, Q_excess_W[
hour] = heating_source_activator(
Q_therm_req_W, hour, master_to_slave_vars, mdot_DH_kgpers[hour], tdhsup_K[hour],
tdhret_K[hour], TretsewArray_K[hour], gv, prices, lca, ground_temp[hour])
Opex_var_HP_Sewage.append(opex_output['Opex_var_HP_Sewage'])
Opex_var_HP_Lake.append(opex_output['Opex_var_HP_Lake'])
Opex_var_GHP.append(opex_output['Opex_var_GHP'])
Opex_var_CHP.append(opex_output['Opex_var_CHP'])
Opex_var_Furnace.append(opex_output['Opex_var_Furnace'])
Opex_var_BaseBoiler.append(opex_output['Opex_var_BaseBoiler'])
Opex_var_PeakBoiler.append(opex_output['Opex_var_PeakBoiler'])
source_HP_Sewage.append(source_output['HP_Sewage'])
source_HP_Lake.append(source_output['HP_Lake'])
source_GHP.append(source_output['GHP'])
source_CHP.append(source_output['CHP'])
source_Furnace.append(source_output['Furnace'])
source_BaseBoiler.append(source_output['BaseBoiler'])
source_PeakBoiler.append(source_output['PeakBoiler'])
Q_HPSew_gen_W.append(Q_output['Q_HPSew_gen_W'])
Q_HPLake_gen_W.append(Q_output['Q_HPLake_gen_W'])
Q_GHP_gen_W.append(Q_output['Q_GHP_gen_W'])
Q_CHP_gen_W.append(Q_output['Q_CHP_gen_W'])
Q_Furnace_gen_W.append(Q_output['Q_Furnace_gen_W'])
Q_BaseBoiler_gen_W.append(Q_output['Q_BaseBoiler_gen_W'])
Q_PeakBoiler_gen_W.append(Q_output['Q_PeakBoiler_gen_W'])
Q_uncovered_W.append(Q_output['Q_uncovered_W'])
E_HPSew_req_W.append(E_output['E_HPSew_req_W'])
E_HPLake_req_W.append(E_output['E_HPLake_req_W'])
E_GHP_req_W.append(E_output['E_GHP_req_W'])
E_CHP_gen_W.append(E_output['E_CHP_gen_W'])
E_Furnace_gen_W.append(E_output['E_Furnace_gen_W'])
E_BaseBoiler_req_W.append(E_output['E_BaseBoiler_req_W'])
E_PeakBoiler_req_W.append(E_output['E_PeakBoiler_req_W'])
E_gas_HPSew_W.append(Gas_output['E_gas_HPSew_W'])
E_gas_HPLake_W.append(Gas_output['E_gas_HPLake_W'])
E_gas_GHP_W.append(Gas_output['E_gas_GHP_W'])
E_gas_CHP_W.append(Gas_output['E_gas_CHP_W'])
E_gas_Furnace_W.append(Gas_output['E_gas_Furnace_W'])
E_gas_BaseBoiler_W.append(Gas_output['E_gas_BaseBoiler_W'])
E_gas_PeakBoiler_W.append(Gas_output['E_gas_PeakBoiler_W'])
E_wood_HPSew_W.append(Wood_output['E_wood_HPSew_W'])
E_wood_HPLake_W.append(Wood_output['E_wood_HPLake_W'])
E_wood_GHP_W.append(Wood_output['E_wood_GHP_W'])
E_wood_CHP_W.append(Wood_output['E_wood_CHP_W'])
E_wood_Furnace_W.append(Wood_output['E_wood_Furnace_W'])
E_wood_BaseBoiler_W.append(Wood_output['E_wood_BaseBoiler_W'])
E_wood_PeakBoiler_W.append(Wood_output['E_wood_PeakBoiler_W'])
E_coldsource_HPSew_W.append(coldsource_output['E_coldsource_HPSew_W'])
E_coldsource_HPLake_W.append(coldsource_output['E_coldsource_HPLake_W'])
E_coldsource_GHP_W.append(coldsource_output['E_coldsource_GHP_W'])
E_coldsource_CHP_W.append(coldsource_output['E_coldsource_CHP_W'])
E_coldsource_Furnace_W.append(coldsource_output['E_coldsource_Furnace_W'])
E_coldsource_BaseBoiler_W.append(coldsource_output['E_coldsource_BaseBoiler_W'])
E_coldsource_PeakBoiler_W.append(coldsource_output['E_coldsource_PeakBoiler_W'])
# save data
elapsed = time.time() - t
# sum up the uncovered demand, get average and peak load
Q_uncovered_design_W = np.amax(Q_uncovered_W)
Q_uncovered_annual_W = np.sum(Q_uncovered_W)
Opex_var_BackupBoiler = np.zeros(8760)
Q_BackupBoiler_W = np.zeros(8760)
E_aux_AddBoiler_req_W = []
Opex_var_Furnace_wet = np.zeros(8760)
Opex_var_Furnace_dry = np.zeros(8760)
Opex_var_CHP_NG = np.zeros(8760)
Opex_var_CHP_BG = np.zeros(8760)
Opex_var_BaseBoiler_NG = np.zeros(8760)
Opex_var_BaseBoiler_BG = np.zeros(8760)
Opex_var_PeakBoiler_NG = np.zeros(8760)
Opex_var_PeakBoiler_BG = np.zeros(8760)
Opex_var_BackupBoiler_NG = np.zeros(8760)
Opex_var_BackupBoiler_BG = np.zeros(8760)
Opex_var_PV = np.zeros(8760)
Opex_var_PVT = np.zeros(8760)
Opex_var_SC = np.zeros(8760)
if Q_uncovered_design_W != 0:
for hour in range(8760):
tdhret_req_K = tdhret_K[hour]
BoilerBackup_Cost_Data = cond_boiler_op_cost(Q_uncovered_W[hour], Q_uncovered_design_W, tdhret_req_K, \
master_to_slave_vars.BoilerBackupType,
master_to_slave_vars.EL_TYPE, gv, prices, lca)
Opex_var_BackupBoiler[hour], C_boil_per_WhBackup, Q_BackupBoiler_W[
hour], E_aux_AddBoiler_req_W_hour = BoilerBackup_Cost_Data
E_aux_AddBoiler_req_W.append(E_aux_AddBoiler_req_W_hour)
Q_BackupBoiler_sum_W = np.sum(Q_BackupBoiler_W)
Opex_var_BackupBoiler_total = np.sum(Opex_var_BackupBoiler)
else:
for hour in range(8760):
E_aux_AddBoiler_req_W.append(0)
Q_BackupBoiler_sum_W = 0.0
Opex_var_BackupBoiler_total = 0.0
if master_to_slave_vars.Furn_Moist_type == "wet":
Opex_var_Furnace_wet = Opex_var_Furnace
elif master_to_slave_vars.Furn_Moist_type == "dry":
Opex_var_Furnace_dry = Opex_var_Furnace
if master_to_slave_vars.gt_fuel == "NG":
Opex_var_CHP_NG = Opex_var_CHP
Opex_var_BaseBoiler_NG = Opex_var_BaseBoiler
Opex_var_PeakBoiler_NG = Opex_var_PeakBoiler
Opex_var_BackupBoiler_NG = Opex_var_BackupBoiler
elif master_to_slave_vars.gt_fuel == "BG":
Opex_var_CHP_BG = Opex_var_CHP
Opex_var_BaseBoiler_BG = Opex_var_BaseBoiler
Opex_var_PeakBoiler_BG = Opex_var_PeakBoiler
Opex_var_BackupBoiler_BG = Opex_var_BackupBoiler
# Sum up all electricity needs
intermediate_sum_1 = np.add(E_HPSew_req_W, E_HPLake_req_W)
intermediate_sum_2 = np.add(E_GHP_req_W, E_BaseBoiler_req_W)
intermediate_sum_3 = np.add(E_PeakBoiler_req_W, E_aux_AddBoiler_req_W)
intermediate_sum_4 = np.add(intermediate_sum_1, intermediate_sum_2)
E_aux_activation_req_W = np.add(intermediate_sum_3, intermediate_sum_4)
E_aux_storage_solar_and_heat_recovery_req_W = np.add(np.add(E_aux_ch_W, E_aux_dech_W),
E_aux_solar_and_heat_recovery_W)
# Sum up all electricity produced by CHP (CC and Furnace)
# cost already accounted for in System Models (selling electricity --> cheaper thermal energy)
E_CHP_and_Furnace_gen_W = np.add(E_CHP_gen_W, E_Furnace_gen_W)
# price from PV and PVT electricity (both are in E_PV_Wh, see Storage_Design_and..., about Line 133)
E_solar_gen_W = np.add(E_PV_gen_W, E_PVT_gen_W)
E_total_gen_W = np.add(E_produced_solar_W, E_CHP_and_Furnace_gen_W)
E_without_buildingdemand_req_W = np.add(E_aux_storage_solar_and_heat_recovery_req_W, E_aux_activation_req_W)
E_total_req_W = np.add(np.array(network_data['Electr_netw_total_W']), E_without_buildingdemand_req_W)
E_PV_to_grid_W = np.zeros(8760)
E_PVT_to_grid_W = np.zeros(8760)
E_CHP_to_grid_W = np.zeros(8760)
E_Furnace_to_grid_W = np.zeros(8760)
E_PV_directload_W = np.zeros(8760)
E_PVT_directload_W = np.zeros(8760)
E_CHP_directload_W = np.zeros(8760)
E_Furnace_directload_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_PV_gen_W[hour] <= E_hour_W:
E_PV_directload_W[hour] = E_PV_gen_W[hour]
E_hour_W = E_hour_W - E_PV_directload_W[hour]
else:
E_PV_directload_W[hour] = E_hour_W
E_PV_to_grid_W[hour] = E_PV_gen_W[hour] - E_hour_W
E_hour_W = 0
if E_PVT_gen_W[hour] <= E_hour_W:
E_PVT_directload_W[hour] = E_PVT_gen_W[hour]
E_hour_W = E_hour_W - E_PVT_directload_W[hour]
else:
E_PVT_directload_W[hour] = E_hour_W
E_PVT_to_grid_W[hour] = E_PVT_gen_W[hour] - E_hour_W
E_hour_W = 0
if E_CHP_gen_W[hour] <= E_hour_W:
E_CHP_directload_W[hour] = E_CHP_gen_W[hour]
E_hour_W = E_hour_W - E_CHP_directload_W[hour]
else:
E_CHP_directload_W[hour] = E_hour_W
E_CHP_to_grid_W[hour] = E_CHP_gen_W[hour] - E_hour_W
E_hour_W = 0
if E_Furnace_gen_W[hour] <= E_hour_W:
E_Furnace_directload_W[hour] = E_Furnace_gen_W[hour]
E_hour_W = E_hour_W - E_Furnace_directload_W[hour]
else:
E_Furnace_directload_W[hour] = E_hour_W
E_Furnace_to_grid_W[hour] = E_Furnace_gen_W[hour] - E_hour_W
E_hour_W = 0
E_from_grid_W[hour] = E_hour_W
# saving pattern activation to disk
date = network_data.DATE.values
results = pd.DataFrame({"DATE": date,
"Q_Network_Demand_after_Storage_W": Q_missing_copy_W,
"Opex_var_HP_Sewage": Opex_var_HP_Sewage,
"Opex_var_HP_Lake": Opex_var_HP_Lake,
"Opex_var_GHP": Opex_var_GHP,
"Opex_var_CHP_BG": Opex_var_CHP_BG,
"Opex_var_CHP_NG": Opex_var_CHP_NG,
"Opex_var_Furnace_wet": Opex_var_Furnace_wet,
"Opex_var_Furnace_dry": Opex_var_Furnace_dry,
"Opex_var_BaseBoiler_BG": Opex_var_BaseBoiler_BG,
"Opex_var_BaseBoiler_NG": Opex_var_BaseBoiler_NG,
"Opex_var_PeakBoiler_BG": Opex_var_PeakBoiler_BG,
"Opex_var_PeakBoiler_NG": Opex_var_PeakBoiler_NG,
"Opex_var_BackupBoiler_BG": Opex_var_BackupBoiler_BG,
"Opex_var_BackupBoiler_NG": Opex_var_BackupBoiler_NG,
"HPSew_Status": source_HP_Sewage,
"HPLake_Status": source_HP_Lake,
"GHP_Status": source_GHP,
"CHP_Status": source_CHP,
"Furnace_Status": source_Furnace,
"BoilerBase_Status": source_BaseBoiler,
"BoilerPeak_Status": source_PeakBoiler,
"Q_HPSew_W": Q_HPSew_gen_W,
"Q_HPLake_W": Q_HPLake_gen_W,
"Q_GHP_W": Q_GHP_gen_W,
"Q_CHP_W": Q_CHP_gen_W,
"Q_Furnace_W": Q_Furnace_gen_W,
"Q_BaseBoiler_W": Q_BaseBoiler_gen_W,
"Q_PeakBoiler_W": Q_PeakBoiler_gen_W,
"Q_AddBoiler_W": Q_uncovered_W,
"Q_coldsource_HPLake_W": E_coldsource_HPLake_W,
"Q_coldsource_HPSew_W": E_coldsource_HPSew_W,
"Q_coldsource_GHP_W": E_coldsource_GHP_W,
"Q_coldsource_CHP_W": E_coldsource_CHP_W,
"Q_coldsource_Furnace_W": E_coldsource_Furnace_W,
"Q_coldsource_BaseBoiler_W": E_coldsource_BaseBoiler_W,
"Q_coldsource_PeakBoiler_W": E_coldsource_PeakBoiler_W,
"Q_excess_W": Q_excess_W
})
results.to_csv(locator.get_optimization_slave_heating_activation_pattern(MS_Var.individual_number,
MS_Var.generation_number), index=False)
results = pd.DataFrame({"DATE": date,
"E_total_req_W": E_total_req_W,
"E_HPSew_req_W": E_HPSew_req_W,
"E_HPLake_req_W": E_HPLake_req_W,
"E_GHP_req_W": E_GHP_req_W,
"E_CHP_gen_W": E_CHP_gen_W,
"E_Furnace_gen_W": E_Furnace_gen_W,
"E_BaseBoiler_req_W": E_BaseBoiler_req_W,
"E_PeakBoiler_req_W": E_PeakBoiler_req_W,
"E_AddBoiler_req_W": E_aux_AddBoiler_req_W,
"E_PV_gen_W": E_PV_gen_W,
"E_PVT_gen_W": E_PVT_gen_W,
"E_CHP_and_Furnace_gen_W": E_CHP_and_Furnace_gen_W,
"E_gen_total_W": E_total_gen_W,
"E_PV_to_directload_W": E_PV_directload_W,
"E_PVT_to_directload_W": E_PVT_directload_W,
"E_CHP_to_directload_W": E_CHP_directload_W,
"E_Furnace_to_directload_W": E_Furnace_directload_W,
"E_PV_to_grid_W": E_PV_to_grid_W,
"E_PVT_to_grid_W": E_PVT_to_grid_W,
"E_CHP_to_grid_W": E_CHP_to_grid_W,
"E_Furnace_to_grid_W": E_Furnace_to_grid_W,
"E_aux_storage_solar_and_heat_recovery_req_W": E_aux_storage_solar_and_heat_recovery_req_W,
"E_consumed_without_buildingdemand_W": E_without_buildingdemand_req_W,
"E_from_grid_W": E_from_grid_W
})
results.to_csv(locator.get_optimization_slave_electricity_activation_pattern_heating(MS_Var.individual_number,
MS_Var.generation_number), index=False)
E_aux_storage_operation_sum_W = np.sum(E_aux_storage_solar_and_heat_recovery_req_W)
E_aux_solar_and_heat_recovery_W = np.sum(E_aux_solar_and_heat_recovery_W)
CO2_emitted, Eprim_used = calc_primary_energy_and_CO2(Q_HPSew_gen_W, Q_HPLake_gen_W, Q_GHP_gen_W, Q_CHP_gen_W,
Q_Furnace_gen_W, Q_BaseBoiler_gen_W, Q_PeakBoiler_gen_W,
Q_uncovered_W,
E_coldsource_HPSew_W, E_coldsource_HPLake_W,
E_coldsource_GHP_W,
E_CHP_gen_W, E_Furnace_gen_W, E_BaseBoiler_req_W,
E_PeakBoiler_req_W,
E_gas_CHP_W, E_gas_BaseBoiler_W, E_gas_PeakBoiler_W,
E_wood_Furnace_W, Q_BackupBoiler_sum_W,
np.sum(E_aux_AddBoiler_req_W),
np.sum(E_solar_gen_W), np.sum(Q_SCandPVT_gen_Wh),
Q_storage_content_W,
master_to_slave_vars, locator,
E_aux_solar_and_heat_recovery_W,
E_aux_storage_operation_sum_W, gv, lca)
# sum up results from PP Activation
E_consumed_sum_W = np.sum(E_aux_storage_solar_and_heat_recovery_req_W) + np.sum(E_aux_activation_req_W)
# Differenciate between Normal and green electricity for
if MS_Var.EL_TYPE == 'green':
ELEC_PRICE = gv.ELEC_PRICE_GREEN
else:
ELEC_PRICE = lca.ELEC_PRICE
# Area available in NEtwork
Area_AvailablePV_m2 = solar_features.A_PV_m2 * MS_Var.SOLAR_PART_PV
Area_AvailablePVT_m2 = solar_features.A_PVT_m2 * MS_Var.SOLAR_PART_PVT
# import from master
eta_m2_to_kW = ETA_AREA_TO_PEAK # Data from Jimeno
Q_PowerPeakAvailablePV_kW = Area_AvailablePV_m2 * eta_m2_to_kW
Q_PowerPeakAvailablePVT_kW = Area_AvailablePVT_m2 * eta_m2_to_kW
# calculate with conversion factor m'2-kWPeak
KEV_RpPerkWhPVT = calc_Crem_pv(Q_PowerPeakAvailablePVT_kW * 1000.0)
KEV_RpPerkWhPV = calc_Crem_pv(Q_PowerPeakAvailablePV_kW * 1000.0)
KEV_total = KEV_RpPerkWhPVT / 100 * np.sum(E_PVT_gen_W) / 1000 + KEV_RpPerkWhPV / 100 * np.sum(E_PV_gen_W) / 1000
# Units: from Rp/kWh to CHF/Wh
price_obtained_from_KEV_for_PVandPVT = KEV_total
cost_CHP_maintenance = np.sum(E_CHP_gen_W) * prices.CC_MAINTENANCE_PER_KWHEL / 1000.0
# Fill up storage if end-of-season energy is lower than beginning of season
Q_Storage_SeasonEndReheat_W = Q_storage_content_W[-1] - Q_storage_content_W[0]
gas_price = prices.NG_PRICE
if Q_Storage_SeasonEndReheat_W > 0:
cost_Boiler_for_Storage_reHeat_at_seasonend = float(Q_Storage_SeasonEndReheat_W) / 0.8 * gas_price
else:
cost_Boiler_for_Storage_reHeat_at_seasonend = 0
# CHANGED AS THE COST_DATA INCLUDES COST_ELECTRICITY_TOTAL ALREADY! (= double accounting)
cost_HP_aux_uncontrollable = np.sum(E_aux_solar_and_heat_recovery_W) * ELEC_PRICE
cost_HP_storage_operation = np.sum(E_aux_storage_solar_and_heat_recovery_req_W) * ELEC_PRICE
cost_sum = np.sum(Opex_var_HP_Sewage) + np.sum(Opex_var_HP_Lake) + np.sum(Opex_var_GHP) + np.sum(
Opex_var_CHP) + np.sum(Opex_var_Furnace) + np.sum(Opex_var_BaseBoiler) + np.sum(
Opex_var_PeakBoiler) - price_obtained_from_KEV_for_PVandPVT - lca.ELEC_PRICE * np.sum(
E_CHP_gen_W) + Opex_var_BackupBoiler_total + cost_CHP_maintenance + \
cost_Boiler_for_Storage_reHeat_at_seasonend + cost_HP_aux_uncontrollable + cost_HP_storage_operation
save_cost = 1
E_oil_eq_MJ = Eprim_used
CO2_kg_eq = CO2_emitted
# Calculate primary energy from ressources:
E_gas_Primary_W = Q_BackupBoiler_sum_W + np.sum(E_gas_HPSew_W) + np.sum(E_gas_HPLake_W) + np.sum(
E_gas_GHP_W) + np.sum(E_gas_CHP_W) + np.sum(E_gas_Furnace_W) + np.sum(E_gas_BaseBoiler_W) + np.sum(
E_gas_PeakBoiler_W)
E_wood_Primary_W = np.sum(E_wood_HPSew_W) + np.sum(E_wood_HPLake_W) + np.sum(E_wood_GHP_W) + np.sum(
E_wood_CHP_W) + np.sum(E_wood_Furnace_W) + np.sum(E_wood_BaseBoiler_W) + np.sum(E_wood_PeakBoiler_W)
E_Import_Slave_req_W = E_consumed_sum_W + np.sum(E_aux_AddBoiler_req_W)
E_Export_gen_W = np.sum(E_total_gen_W)
E_groundheat_W = np.sum(E_coldsource_HPSew_W) + np.sum(E_coldsource_HPLake_W) + np.sum(E_coldsource_GHP_W) + np.sum(
E_coldsource_CHP_W) + np.sum(E_coldsource_Furnace_W) + np.sum(E_coldsource_BaseBoiler_W) + np.sum(
E_coldsource_PeakBoiler_W)
E_solar_gen_W = np.sum(E_solar_gen_W) + np.sum(Q_SCandPVT_gen_Wh)
intermediate_max_1 = max(np.amax(E_gas_HPSew_W), np.amax(E_gas_HPLake_W))
intermediate_max_2 = max(intermediate_max_1, np.amax(E_gas_GHP_W))
intermediate_max_3 = max(intermediate_max_2, np.amax(E_gas_CHP_W))
intermediate_max_4 = max(intermediate_max_3, np.amax(E_gas_Furnace_W))
intermediate_max_5 = max(intermediate_max_4, np.amax(E_gas_BaseBoiler_W))
intermediate_max_6 = max(intermediate_max_5, np.amax(E_gas_PeakBoiler_W))
E_gas_PrimaryPeakPower_W = intermediate_max_6 + np.amax(Q_BackupBoiler_W)
if save_cost == 1:
results = pd.DataFrame({
"total cost": [cost_sum],
"KEV_Remuneration": [price_obtained_from_KEV_for_PVandPVT],
"costAddBackup_total": [Opex_var_BackupBoiler_total],
"cost_CHP_maintenance": [cost_CHP_maintenance],
"costHPSew_sum": np.sum(Opex_var_HP_Sewage),
"costHPLake_sum": np.sum(Opex_var_HP_Lake),
"costGHP_sum": np.sum(Opex_var_GHP),
"costCHP_sum": np.sum(Opex_var_CHP),
"costFurnace_sum": np.sum(Opex_var_Furnace),
"costBaseBoiler_sum": np.sum(Opex_var_BaseBoiler),
"costPeakBoiler_sum": np.sum(Opex_var_PeakBoiler),
"cost_Boiler_for_Storage_reHeat_at_seasonend": [cost_Boiler_for_Storage_reHeat_at_seasonend],
"cost_HP_aux_uncontrollable": [cost_HP_aux_uncontrollable],
"cost_HP_storage_operation": [cost_HP_storage_operation],
"E_oil_eq_MJ": [E_oil_eq_MJ],
"CO2_kg_eq": [CO2_kg_eq],
"cost_sum": [cost_sum],
"E_gas_Primary_W": [E_gas_Primary_W],
"E_gas_PrimaryPeakPower_W": [E_gas_PrimaryPeakPower_W],
"E_wood_Primary_W": [E_wood_Primary_W],
"E_Import_Slave_req_W": [E_Import_Slave_req_W],
"E_Export_gen_W": [E_Export_gen_W],
"E_groundheat_W": [E_groundheat_W],
"E_solar_gen_Wh": [E_solar_gen_W]
})
results.to_csv(locator.get_optimization_slave_cost_prime_primary_energy_data(MS_Var.individual_number,
MS_Var.generation_number), sep=',')
return E_oil_eq_MJ, CO2_kg_eq, cost_sum, Q_uncovered_design_W, Q_uncovered_annual_W
def heating_source_activator(Q_therm_req_W, master_to_slave_vars,
Q_therm_GHP_W, TretGHPArray_K, TretLakeArray_K,
Q_therm_Lake_W, Q_therm_Sew_W, TretsewArray_K,
tdhsup_K, tdhret_req_K):
"""
:param Q_therm_req_W:
:param hour:
:param context:
:type Q_therm_req_W: float
:type hour: int
:type context: list
:return: cost_data_centralPlant_op, source_info, Q_source_data, E_coldsource_data, E_PP_el_data, E_gas_data, E_wood_data, Q_excess
:rtype:
"""
## initializing unmet heating load
Q_heat_unmet_W = Q_therm_req_W
# ACTIVATE THE COGEN
if master_to_slave_vars.CC_on == 1 and Q_heat_unmet_W > 0.0:
CC_op_cost_data = calc_cop_CCGT(master_to_slave_vars.CCGT_SIZE_W,
tdhsup_K,
"NG") # create cost information
Q_used_prim_CC_fn_W = CC_op_cost_data['q_input_fn_q_output_W']
q_output_CC_min_W = CC_op_cost_data['q_output_min_W']
Q_output_CC_max_W = CC_op_cost_data['q_output_max_W']
eta_elec_interpol = CC_op_cost_data['eta_el_fn_q_input']
if Q_heat_unmet_W >= q_output_CC_min_W:
# operation Possible if above minimal load
if Q_heat_unmet_W <= Q_output_CC_max_W: # Normal operation Possible within partload regime
Q_CHP_gen_W = Q_heat_unmet_W
NG_CHP_req_W = Q_used_prim_CC_fn_W(Q_CHP_gen_W)
E_CHP_gen_W = np.float(
eta_elec_interpol(NG_CHP_req_W)) * NG_CHP_req_W
else: # Only part of the demand can be delivered as 100% load achieved
Q_CHP_gen_W = Q_output_CC_max_W
NG_CHP_req_W = Q_used_prim_CC_fn_W(Q_CHP_gen_W)
E_CHP_gen_W = np.float(
eta_elec_interpol(NG_CHP_req_W)) * NG_CHP_req_W
else:
NG_CHP_req_W = 0.0
E_CHP_gen_W = 0.0
Q_CHP_gen_W = 0.0
Q_heat_unmet_W = Q_heat_unmet_W - Q_CHP_gen_W
else:
NG_CHP_req_W = 0.0
E_CHP_gen_W = 0.0
Q_CHP_gen_W = 0.0
# WET FURNACE
if master_to_slave_vars.Furnace_wet_on == 1 and Q_heat_unmet_W > 0.0: # Activate Furnace if its there.
# Operate only if its above minimal load
if Q_heat_unmet_W > master_to_slave_vars.WBFurnace_Q_max_W:
if Q_heat_unmet_W > master_to_slave_vars.WBFurnace_Q_max_W:
Q_Furnace_wet_gen_W = master_to_slave_vars.WBFurnace_Q_max_W
# scale down if above maximum load, Furnace operates at max. capacity
DryBiomass_Furnace_req_W, E_Furnace_wet_gen_W = furnace_op_cost(
Q_Furnace_wet_gen_W,
master_to_slave_vars.WBFurnace_Q_max_W, tdhret_req_K,
"wet")
else: # Normal Operation Possible
Q_Furnace_wet_gen_W = Q_heat_unmet_W
DryBiomass_Furnace_req_W, E_Furnace_wet_gen_W = furnace_op_cost(
Q_Furnace_wet_gen_W,
master_to_slave_vars.WBFurnace_Q_max_W, tdhret_req_K,
"wet")
else:
E_Furnace_wet_gen_W = 0.0
DryBiomass_Furnace_req_W = 0.0
Q_Furnace_wet_gen_W = 0.0
Q_heat_unmet_W = Q_heat_unmet_W - Q_Furnace_wet_gen_W
else:
E_Furnace_wet_gen_W = 0.0
DryBiomass_Furnace_req_W = 0.0
Q_Furnace_wet_gen_W = 0.0
# DRY FURNACE
if master_to_slave_vars.Furnace_dry_on == 1 and Q_heat_unmet_W > 0.0: # Activate Furnace if its there.
# Operate only if its above minimal load
if Q_heat_unmet_W > master_to_slave_vars.DBFurnace_Q_max_W:
if Q_heat_unmet_W > master_to_slave_vars.DBFurnace_Q_max_W:
Q_Furnace_dry_gen_W = master_to_slave_vars.DBFurnace_Q_max_W
# scale down if above maximum load, Furnace operates at max. capacity
WetBiomass_Furnace_req_W, E_Furnace_dry_gen_W = furnace_op_cost(
Q_Furnace_dry_gen_W,
master_to_slave_vars.DBFurnace_Q_max_W, tdhret_req_K,
"dry")
else: # Normal Operation Possible
Q_Furnace_dry_gen_W = Q_heat_unmet_W
WetBiomass_Furnace_req_W, E_Furnace_dry_gen_W = furnace_op_cost(
Q_Furnace_dry_gen_W,
master_to_slave_vars.DBFurnace_Q_max_W, tdhret_req_K,
"dry")
else:
E_Furnace_dry_gen_W = 0.0
WetBiomass_Furnace_req_W = 0.0
Q_Furnace_dry_gen_W = 0.0
Q_heat_unmet_W = Q_heat_unmet_W - Q_Furnace_dry_gen_W
else:
E_Furnace_dry_gen_W = 0.0
WetBiomass_Furnace_req_W = 0.0
Q_Furnace_dry_gen_W = 0.0
if (master_to_slave_vars.HPSew_on
) == 1 and Q_heat_unmet_W > 0.0 and not np.isclose(
tdhsup_K, tdhret_req_K): # activate if its available
if Q_heat_unmet_W > Q_therm_Sew_W:
Q_HPSew_gen_W = Q_therm_Sew_W
mdot_DH_to_Sew_kgpers = Q_HPSew_gen_W / (
HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
else:
Q_HPSew_gen_W = Q_heat_unmet_W
mdot_DH_to_Sew_kgpers = Q_HPSew_gen_W / (
HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
E_HPSew_req_W, \
Q_coldsource_HPSew_W, \
Q_HPSew_gen_W = HPSew_op_cost(mdot_DH_to_Sew_kgpers,
tdhsup_K,
tdhret_req_K,
TretsewArray_K,
Q_HPSew_gen_W
)
Q_heat_unmet_W = Q_heat_unmet_W - Q_HPSew_gen_W
else:
E_HPSew_req_W = 0.0
Q_HPSew_gen_W = 0.0
if (master_to_slave_vars.HPLake_on
) == 1 and Q_heat_unmet_W > 0.0 and not np.isclose(
tdhsup_K, tdhret_req_K):
if Q_heat_unmet_W > Q_therm_Lake_W: # Scale down Load, 100% load achieved
Q_HPLake_gen_W = Q_therm_Lake_W
else: # regular operation possible
Q_HPLake_gen_W = Q_heat_unmet_W
E_HPLake_req_W, Q_coldsource_HPLake_W, Q_HPLake_gen_W = HPLake_op_cost(
Q_HPLake_gen_W, tdhsup_K, tdhret_req_K, TretLakeArray_K)
E_pump_req_W = calc_water_body_uptake_pumping(Q_HPLake_gen_W,
tdhret_req_K, tdhsup_K)
E_HPLake_req_W += E_pump_req_W
Q_heat_unmet_W = Q_heat_unmet_W - Q_HPLake_gen_W
else:
E_HPLake_req_W = 0.0
Q_HPLake_gen_W = 0.0
if (master_to_slave_vars.GHP_on
) == 1 and Q_heat_unmet_W > 0.0 and not np.isclose(
tdhsup_K, tdhret_req_K):
if Q_heat_unmet_W > Q_therm_GHP_W:
Q_GHP_gen_W = Q_therm_GHP_W
mdot_DH_to_GHP_kgpers = Q_GHP_gen_W / (
HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
else: # regular operation possible, demand is covered
Q_GHP_gen_W = Q_heat_unmet_W
mdot_DH_to_GHP_kgpers = Q_GHP_gen_W / (
HEAT_CAPACITY_OF_WATER_JPERKGK * (tdhsup_K - tdhret_req_K))
E_GHP_req_W, Q_coldsource_GHP_W, Q_GHP_gen_W = GHP_op_cost(
mdot_DH_to_GHP_kgpers, tdhsup_K, tdhret_req_K, TretGHPArray_K,
Q_GHP_gen_W)
Q_heat_unmet_W = Q_heat_unmet_W - Q_GHP_gen_W
else:
E_GHP_req_W = 0.0
Q_GHP_gen_W = 0.0
if (master_to_slave_vars.Boiler_on) == 1 and Q_heat_unmet_W > 0:
if Q_heat_unmet_W >= BOILER_MIN * master_to_slave_vars.Boiler_Q_max_W: # Boiler can be activated?
if Q_heat_unmet_W >= master_to_slave_vars.Boiler_Q_max_W: # Boiler above maximum Load?
Q_BaseBoiler_gen_W = master_to_slave_vars.Boiler_Q_max_W
else:
Q_BaseBoiler_gen_W = Q_heat_unmet_W
NG_BaseBoiler_req_W, E_BaseBoiler_req_W = cond_boiler_op_cost(
Q_BaseBoiler_gen_W, master_to_slave_vars.Boiler_Q_max_W,
tdhret_req_K)
else:
Q_BaseBoiler_gen_W = 0.0
NG_BaseBoiler_req_W = 0.0
E_BaseBoiler_req_W = 0.0
Q_heat_unmet_W = Q_heat_unmet_W - Q_BaseBoiler_gen_W
else:
Q_BaseBoiler_gen_W = 0.0
NG_BaseBoiler_req_W = 0.0
E_BaseBoiler_req_W = 0.0
if master_to_slave_vars.BoilerPeak_on == 1 and Q_heat_unmet_W > 0:
if Q_heat_unmet_W >= BOILER_MIN * master_to_slave_vars.BoilerPeak_Q_max_W: # Boiler can be activated?
if Q_heat_unmet_W > master_to_slave_vars.BoilerPeak_Q_max_W: # Boiler above maximum Load?
Q_PeakBoiler_gen_W = master_to_slave_vars.BoilerPeak_Q_max_W
else:
Q_PeakBoiler_gen_W = Q_heat_unmet_W
NG_PeakBoiler_req_W, E_PeakBoiler_req_W = cond_boiler_op_cost(
Q_PeakBoiler_gen_W, master_to_slave_vars.BoilerPeak_Q_max_W,
tdhret_req_K)
else:
Q_PeakBoiler_gen_W = 0.0
NG_PeakBoiler_req_W = 0
E_PeakBoiler_req_W = 0.0
Q_heat_unmet_W = Q_heat_unmet_W - Q_PeakBoiler_gen_W
else:
Q_PeakBoiler_gen_W = 0.0
NG_PeakBoiler_req_W = 0
E_PeakBoiler_req_W = 0.0
if Q_heat_unmet_W > 1.0E-3:
Q_uncovered_W = Q_heat_unmet_W # this will become the back-up boiler
else:
Q_uncovered_W = 0.0
return Q_HPSew_gen_W, \
Q_HPLake_gen_W, \
Q_GHP_gen_W, \
Q_CHP_gen_W, \
Q_Furnace_dry_gen_W, \
Q_Furnace_wet_gen_W, \
Q_BaseBoiler_gen_W, \
Q_PeakBoiler_gen_W, \
Q_uncovered_W, \
E_HPSew_req_W, \
E_HPLake_req_W, \
E_BaseBoiler_req_W, \
E_PeakBoiler_req_W, \
E_GHP_req_W, \
E_CHP_gen_W, \
E_Furnace_dry_gen_W, \
E_Furnace_wet_gen_W, \
NG_CHP_req_W, \
NG_BaseBoiler_req_W, \
NG_PeakBoiler_req_W, \
WetBiomass_Furnace_req_W, \
DryBiomass_Furnace_req_W
def district_heating_network(locator, master_to_slave_variables, config,
prices, lca, network_features):
"""
Computes the parameters for the heating of the complete DHN
:param locator: locator class
:param master_to_slave_variables: class MastertoSlaveVars containing the value of variables to be passed to the
slave optimization for each individual
:param solar_features: solar features class
:type locator: class
:type master_to_slave_variables: class
:type solar_features: class
:return:
- E_oil_eq_MJ: MJ oil Equivalent used during operation
- CO2_kg_eq: kg of CO2-Equivalent emitted during operation
- cost_sum: total cost in CHF used for operation
- Q_source_data[:,7]: uncovered demand
:rtype: float, float, float, array
"""
# THERMAL STORAGE + NETWORK
print(
"CALCULATING ECOLOGICAL COSTS OF SEASONAL STORAGE - DUE TO OPERATION (IF ANY)"
)
performance_storage, storage_dispatch = storage_main.storage_optimization(
locator, master_to_slave_variables, lca, prices, config)
# Import data from storage optimization
Q_DH_networkload_W = np.array(storage_dispatch['Q_DH_networkload_W'])
Q_thermal_req_W = np.array(storage_dispatch['Q_req_after_storage_W'])
E_PVT_gen_W = storage_dispatch['E_PVT_gen_W']
Q_PVT_to_directload_W = storage_dispatch["Q_PVT_gen_directload_W"]
Q_PVT_to_storage_W = storage_dispatch["Q_PVT_gen_storage_W"]
Q_SC_ET_to_directload_W = storage_dispatch["Q_SC_ET_gen_directload_W"]
Q_SC_ET_to_storage_W = storage_dispatch["Q_SC_ET_gen_storage_W"]
Q_SC_FP_to_directload_W = storage_dispatch["Q_SC_FP_gen_directload_W"]
Q_SC_FP_to_storage_W = storage_dispatch["Q_SC_FP_gen_storage_W"]
Q_HP_Server_to_directload_W = storage_dispatch[
"Q_HP_Server_gen_directload_W"]
Q_HP_Server_to_storage_W = storage_dispatch["Q_HP_Server_gen_storage_W"]
E_Storage_req_charging_W = storage_dispatch["E_Storage_charging_req_W"]
E_Storage_req_discharging_W = storage_dispatch[
"E_Storage_discharging_req_W"]
E_HP_SC_FP_req_W = storage_dispatch["E_HP_SC_FP_req_W"]
E_HP_SC_ET_req_W = storage_dispatch["E_HP_SC_ET_req_W"]
E_HP_PVT_req_W = storage_dispatch["E_HP_PVT_req_W"]
E_HP_Server_req_W = storage_dispatch["E_HP_Server_req_W"]
Q_SC_ET_gen_W = storage_dispatch["Q_SC_ET_gen_W"]
Q_SC_FP_gen_W = storage_dispatch["Q_SC_FP_gen_W"]
Q_PVT_gen_W = storage_dispatch["Q_PVT_gen_W"]
Q_Server_gen_W = storage_dispatch["Q_HP_Server_gen_W"]
Q_Storage_gen_W = storage_dispatch["Q_Storage_gen_W"]
# HEATING PLANT
# Import Temperatures from Network Summary:
mdot_DH_kgpers, T_district_heating_return_K, T_district_heating_supply_K = calc_network_summary_DHN(
locator, master_to_slave_variables)
# FIXED ORDER ACTIVATION STARTS
# Import Data - Sewage heat
if master_to_slave_variables.HPSew_on == 1:
HPSew_Data = pd.read_csv(locator.get_sewage_heat_potential())
Q_therm_Sew = np.array(HPSew_Data['Qsw_kW']) * 1E3
Q_therm_Sew_W = [
x if x < master_to_slave_variables.HPSew_maxSize_W else
master_to_slave_variables.HPSew_maxSize_W for x in Q_therm_Sew
]
T_source_average_sewage_K = np.array(HPSew_Data['Ts_C']) + 273
else:
Q_therm_Sew_W = np.zeros(HOURS_IN_YEAR)
T_source_average_sewage_K = np.zeros(HOURS_IN_YEAR)
# Import Data - lake heat
if master_to_slave_variables.HPLake_on == 1:
HPlake_Data = pd.read_csv(locator.get_lake_potential())
Q_therm_Lake = np.array(HPlake_Data['QLake_kW']) * 1E3
Q_therm_Lake_W = [
x if x < master_to_slave_variables.HPLake_maxSize_W else
master_to_slave_variables.HPLake_maxSize_W 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)
# Import Data - geothermal (shallow)
if master_to_slave_variables.GHP_on == 1:
GHP_Data = pd.read_csv(locator.get_geothermal_potential())
Q_therm_GHP = np.array(GHP_Data['QGHP_kW']) * 1E3
Q_therm_GHP_W = [
x if x < master_to_slave_variables.GHP_maxSize_W else
master_to_slave_variables.GHP_maxSize_W for x in Q_therm_GHP
]
T_source_average_GHP_W = np.array(GHP_Data['Ts_C']) + 273
else:
Q_therm_GHP_W = np.zeros(HOURS_IN_YEAR)
T_source_average_GHP_W = np.zeros(HOURS_IN_YEAR)
# Initiation of the variables
source_HP_Sewage = np.zeros(HOURS_IN_YEAR)
source_HP_Lake = np.zeros(HOURS_IN_YEAR)
source_GHP = np.zeros(HOURS_IN_YEAR)
source_CHP = np.zeros(HOURS_IN_YEAR)
source_Furnace_dry = np.zeros(HOURS_IN_YEAR)
source_Furnace_wet = np.zeros(HOURS_IN_YEAR)
source_BaseBoiler = np.zeros(HOURS_IN_YEAR)
source_PeakBoiler = np.zeros(HOURS_IN_YEAR)
Q_HPSew_gen_W = np.zeros(HOURS_IN_YEAR)
Q_HPLake_gen_W = np.zeros(HOURS_IN_YEAR)
Q_GHP_gen_W = np.zeros(HOURS_IN_YEAR)
Q_CHP_gen_W = np.zeros(HOURS_IN_YEAR)
Q_Furnace_dry_gen_W = np.zeros(HOURS_IN_YEAR)
Q_Furnace_wet_gen_W = np.zeros(HOURS_IN_YEAR)
Q_BaseBoiler_gen_W = np.zeros(HOURS_IN_YEAR)
Q_PeakBoiler_gen_W = np.zeros(HOURS_IN_YEAR)
Q_AddBoiler_gen_W = np.zeros(HOURS_IN_YEAR)
E_HPSew_req_W = np.zeros(HOURS_IN_YEAR)
E_HPLake_req_W = np.zeros(HOURS_IN_YEAR)
E_GHP_req_W = np.zeros(HOURS_IN_YEAR)
E_CHP_gen_W = np.zeros(HOURS_IN_YEAR)
E_Furnace_dry_gen_W = np.zeros(HOURS_IN_YEAR)
E_Furnace_wet_gen_W = np.zeros(HOURS_IN_YEAR)
E_BaseBoiler_req_W = np.zeros(HOURS_IN_YEAR)
E_PeakBoiler_req_W = np.zeros(HOURS_IN_YEAR)
E_BackupBoiler_req_W = np.zeros(HOURS_IN_YEAR)
NG_CHP_req_W = np.zeros(HOURS_IN_YEAR)
NG_BaseBoiler_req_W = np.zeros(HOURS_IN_YEAR)
NG_PeakBoiler_req_W = np.zeros(HOURS_IN_YEAR)
NG_BackupBoiler_req_W = np.zeros(HOURS_IN_YEAR)
WetBiomass_Furnace_req_W = np.zeros(HOURS_IN_YEAR)
DryBiomass_Furnace_req_W = np.zeros(HOURS_IN_YEAR)
for hour in range(HOURS_IN_YEAR):
if Q_thermal_req_W[hour] > 0.0:
activation_output, \
thermal_output, \
electricity_output, \
gas_output, \
biomass_output = heating_source_activator(Q_thermal_req_W[hour],
hour,
master_to_slave_variables,
Q_therm_GHP_W[hour],
T_source_average_GHP_W[hour],
T_source_average_Lake_K[hour],
Q_therm_Lake_W[hour],
Q_therm_Sew_W[hour],
T_source_average_sewage_K[hour],
T_district_heating_supply_K[hour],
T_district_heating_return_K[hour],
prices,
lca)
source_HP_Sewage[hour] = activation_output['Source_HP_Sewage']
source_HP_Lake[hour] = activation_output['Source_HP_Lake']
source_GHP[hour] = activation_output['Source_GHP']
source_CHP[hour] = activation_output['Source_CHP']
source_Furnace_dry[hour] = activation_output['Source_Furnace_dry']
source_Furnace_wet[hour] = activation_output['Source_Furnace_wet']
source_BaseBoiler[hour] = activation_output['Source_BaseBoiler']
source_PeakBoiler[hour] = activation_output['Source_PeakBoiler']
Q_HPSew_gen_W[hour] = thermal_output['Q_HPSew_gen_W']
Q_HPLake_gen_W[hour] = thermal_output['Q_HPLake_gen_W']
Q_GHP_gen_W[hour] = thermal_output['Q_GHP_gen_W']
Q_CHP_gen_W[hour] = thermal_output['Q_CHP_gen_W']
Q_Furnace_dry_gen_W[hour] = thermal_output['Q_Furnace_dry_gen_W']
Q_Furnace_wet_gen_W[hour] = thermal_output['Q_Furnace_wet_gen_W']
Q_BaseBoiler_gen_W[hour] = thermal_output['Q_BaseBoiler_gen_W']
Q_PeakBoiler_gen_W[hour] = thermal_output['Q_PeakBoiler_gen_W']
Q_AddBoiler_gen_W[hour] = thermal_output['Q_AddBoiler_gen_W']
E_HPSew_req_W[hour] = electricity_output['E_HPSew_req_W']
E_HPLake_req_W[hour] = electricity_output['E_HPLake_req_W']
E_GHP_req_W[hour] = electricity_output['E_GHP_req_W']
E_CHP_gen_W[hour] = electricity_output['E_CHP_gen_W']
E_BaseBoiler_req_W[hour] = electricity_output['E_BaseBoiler_req_W']
E_PeakBoiler_req_W[hour] = electricity_output['E_PeakBoiler_req_W']
E_Furnace_dry_gen_W[hour] = electricity_output[
'E_Furnace_dry_gen_W']
E_Furnace_wet_gen_W[hour] = electricity_output[
'E_Furnace_wet_gen_W']
NG_CHP_req_W[hour] = gas_output['Gas_CHP_req_W']
NG_BaseBoiler_req_W[hour] = gas_output['Gas_BaseBoiler_req_W']
NG_PeakBoiler_req_W[hour] = gas_output['Gas_PeakBoiler_req_W']
WetBiomass_Furnace_req_W[hour] = biomass_output[
'Biomass_Furnace_dry_req_W']
DryBiomass_Furnace_req_W[hour] = biomass_output[
'Biomass_Furnace_wet_req_W']
# BACK-UP BOILER
master_to_slave_variables.BackupBoiler_size_W = np.amax(Q_AddBoiler_gen_W)
if master_to_slave_variables.BackupBoiler_size_W != 0:
master_to_slave_variables.BackupBoiler_on = 1
for hour in range(HOURS_IN_YEAR):
tdhret_req_K = T_district_heating_return_K[hour]
_, \
_, \
NG_BackupBoiler_req_W[hour], \
E_BackupBoiler_req_W[hour] = cond_boiler_op_cost(Q_AddBoiler_gen_W[hour],
master_to_slave_variables.BackupBoiler_size_W,
tdhret_req_K,
"NG",
prices, lca, hour)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR) GENERATION UNITS
performance_costs_generation = cost_model.calc_generation_costs_heating(
locator,
master_to_slave_variables,
config,
storage_dispatch,
)
# CAPEX (ANNUAL, TOTAL) AND OPEX (FIXED, VAR) NETWORK
performance_costs_network, \
E_used_district_heating_network_W = cost_model.calc_network_costs_heating(locator,
master_to_slave_variables,
network_features,
lca,
"DH")
# MERGE COSTS AND EMISSIONS IN ONE FILE
district_heating_costs = dict(performance_costs_generation,
**performance_costs_network)
# save data
district_heating_generation_dispatch = {
# demand of the network:
"Q_districtheating_sys_req_W": Q_DH_networkload_W,
# Status of each technology 1 = on, 0 = off in every hour
"HPSew_Status": source_HP_Sewage,
"HPLake_Status": source_HP_Lake,
"GHP_Status": source_GHP,
"CHP_Status": source_CHP,
"Furnace_dry_Status": source_Furnace_dry,
"Furnace_wet_Status": source_Furnace_wet,
"BoilerBase_Status": source_BaseBoiler,
"BoilerPeak_Status": source_PeakBoiler,
# ENERGY GENERATION
# heating
"Q_PVT_gen_storage_W": Q_PVT_to_storage_W,
"Q_SC_ET_gen_storage_W": Q_SC_ET_to_storage_W,
"Q_SC_FP_gen_storage_W": Q_SC_FP_to_storage_W,
"Q_HP_Server_storage_W": Q_HP_Server_to_storage_W,
# this is what is generated out of all the technologies sent to the storage
"Q_Storage_gen_directload_W": Q_Storage_gen_W,
# heating
"Q_PVT_gen_directload_W": Q_PVT_to_directload_W,
"Q_SC_ET_gen_directload_W": Q_SC_ET_to_directload_W,
"Q_SC_FP_gen_directload_W": Q_SC_FP_to_directload_W,
"Q_HP_Server_gen_directload_W": Q_HP_Server_to_directload_W,
"Q_HP_Sew_gen_directload_W": Q_HPSew_gen_W,
"Q_HP_Lake_gen_directload_W": Q_HPLake_gen_W,
"Q_GHP_gen_directload_W": Q_GHP_gen_W,
"Q_CHP_gen_directload_W": Q_CHP_gen_W,
"Q_Furnace_dry_gen_directload_W": Q_Furnace_dry_gen_W,
"Q_Furnace_wet_gen_directload_W": Q_Furnace_wet_gen_W,
"Q_BaseBoiler_gen_directload_W": Q_BaseBoiler_gen_W,
"Q_PeakBoiler_gen_directload_W": Q_PeakBoiler_gen_W,
"Q_BackupBoiler_gen_directload_W": Q_AddBoiler_gen_W,
# electricity
"E_CHP_gen_W": E_CHP_gen_W,
"E_PVT_gen_W": E_PVT_gen_W,
"E_Furnace_dry_gen_W": E_Furnace_dry_gen_W,
"E_Furnace_wet_gen_W": E_Furnace_wet_gen_W,
}
district_heating_electricity_requirements_dispatch = {
# ENERGY REQUIREMENTS
# Electricity
"E_Storage_charging_req_W": E_Storage_req_charging_W,
"E_Storage_discharging_req_W": E_Storage_req_discharging_W,
"E_DHN_req_W": E_used_district_heating_network_W,
"E_HP_SC_FP_req_W": E_HP_SC_FP_req_W,
"E_HP_SC_ET_req_W": E_HP_SC_ET_req_W,
"E_HP_PVT_req_W": E_HP_PVT_req_W,
"E_HP_Server_req_W": E_HP_Server_req_W,
"E_HP_Sew_req_W": E_HPSew_req_W,
"E_HP_Lake_req_W": E_HPLake_req_W,
"E_GHP_req_W": E_GHP_req_W,
"E_BaseBoiler_req_W": E_BaseBoiler_req_W,
"E_PeakBoiler_req_W": E_PeakBoiler_req_W,
"E_BackupBoiler_req_W": E_BackupBoiler_req_W,
}
district_heating_fuel_requirements_dispatch = {
# FUEL REQUIREMENTS
"NG_CHP_req_W": NG_CHP_req_W,
"NG_BaseBoiler_req_W": NG_BaseBoiler_req_W,
"NG_PeakBoiler_req_W": NG_PeakBoiler_req_W,
"NG_BackupBoiler_req_W": NG_BackupBoiler_req_W,
"WB_Furnace_req_W": WetBiomass_Furnace_req_W,
"DB_Furnace_req_W": DryBiomass_Furnace_req_W,
}
return district_heating_costs, \
district_heating_generation_dispatch, \
district_heating_electricity_requirements_dispatch,\
district_heating_fuel_requirements_dispatch