def calc_pareto_Qhp(locator, total_demand, prices, lca):
"""
This function calculates the contribution to the pareto optimal results of process heating,
:param locator: locator class
:param total_demand: dataframe with building demand
:type locator: class
:type total_demand: class
:return: hpCosts, hpCO2, hpPrim
:rtype: tuple
"""
hpCosts = 0
hpCO2 = 0
hpPrim = 0
boiler_cost_data = pd.read_excel(locator.get_database_conversion_systems(),
sheet_name="Boiler")
if total_demand["Qhpro_sys_MWhyr"].sum() > 0:
df = total_demand[total_demand.Qhpro_sys_MWhyr != 0]
for name in df.Name:
# Extract process heat needs
Qhpro_sys_kWh = pd.read_csv(locator.get_demand_results_file(name),
usecols=["Qhpro_sys_kWh"
]).Qhpro_sys_kWh.values
Qnom_Wh = 0
Qannual_Wh = 0
# Operation costs / CO2 / Prim
for i in range(HOURS_IN_YEAR):
Qgas_Wh = Qhpro_sys_kWh[
i] * 1E3 / BOILER_ETA_HP # [Wh] Assumed 0.9 efficiency
if Qgas_Wh < Qnom_Wh:
Qnom_Wh = Qgas_Wh
Qannual_Wh += Qgas_Wh
hpCosts += Qgas_Wh * prices.NG_PRICE # [CHF]
hpCO2 += Qgas_Wh * WH_TO_J / 1.0E6 * lca.NG_BACKUPBOILER_TO_CO2_STD / 1E3 # [ton CO2]
hpPrim += Qgas_Wh * WH_TO_J / 1.0E6 * lca.NG_BACKUPBOILER_TO_OIL_STD # [MJ-oil-eq]
# Investment costs
Capex_a_hp_USD, Opex_fixed_hp_USD, Capex_hp_USD = boiler.calc_Cinv_boiler(
Qnom_Wh, 'BO1', boiler_cost_data)
hpCosts += (Capex_a_hp_USD + Opex_fixed_hp_USD)
else:
hpCosts = hpCO2 = hpPrim = 0
return hpCosts, hpCO2, hpPrim
def calc_pareto_Qhp(locator, total_demand, prices, lca, config):
"""
This function calculates the contribution to the pareto optimal results of process heating,
:param locator: locator class
:param total_demand: dataframe with building demand
:param gv: global variables
:type locator: class
:type total_demand: class
:type gv: class
:return: hpCosts, hpCO2, hpPrim
:rtype: tuple
"""
hpCosts = 0
hpCO2 = 0
hpPrim = 0
if total_demand["Qhpro_sys_MWhyr"].sum() > 0:
df = total_demand[total_demand.Qhpro_sys_MWhyr != 0]
for name in df.Name:
# Extract process heat needs
Qhpro_sys = pd.read_csv(locator.get_demand_results_file(name),
usecols=["Qhpro_sys_kWh"
]).Qhpro_sys_kWh.values
Qnom = 0
Qannual = 0
# Operation costs / CO2 / Prim
for i in range(8760):
Qgas = Qhpro_sys[
i] * 1E3 / BOILER_ETA_HP # [Wh] Assumed 0.9 efficiency
if Qgas < Qnom:
Qnom = Qgas * (1 + SIZING_MARGIN)
Qannual += Qgas
hpCosts += Qgas * prices.NG_PRICE # [CHF]
hpCO2 += Qgas * 3600E-6 * lca.NG_BACKUPBOILER_TO_CO2_STD # [kg CO2]
hpPrim += Qgas * 3600E-6 * lca.NG_BACKUPBOILER_TO_OIL_STD # [MJ-oil-eq]
# Investment costs
Capex_a_hp, Opex_fixed_hp = boiler.calc_Cinv_boiler(
Qnom, locator, config, 'BO1')
hpCosts += (Capex_a_hp + Opex_fixed_hp)
else:
hpCosts = hpCO2 = hpPrim = 0
return hpCosts, hpCO2, hpPrim
def disconnected_buildings_heating_main(locator, building_names, config,
prices, lca):
"""
Computes the parameters for the operation of disconnected buildings
output results in csv files.
There is no optimization at this point. The different technologies are calculated and compared 1 to 1 to
each technology. it is a classical combinatorial problem.
:param locator: locator class
:param building_names: list with names of buildings
:type locator: class
:type building_names: list
:return: results of operation of buildings located in locator.get_optimization_disconnected_folder
:rtype: Nonetype
"""
t0 = time.clock()
prop_geometry = Gdf.from_file(locator.get_zone_geometry())
restrictions = Gdf.from_file(locator.get_building_restrictions())
geometry = pd.DataFrame({
'Name': prop_geometry.Name,
'Area': prop_geometry.area
})
geothermal_potential_data = dbf.dbf_to_dataframe(
locator.get_building_supply())
geothermal_potential_data = pd.merge(geothermal_potential_data,
geometry,
on='Name').merge(restrictions,
on='Name')
geothermal_potential_data['Area_geo'] = (
1 - geothermal_potential_data['GEOTHERMAL']
) * geothermal_potential_data['Area']
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)
BestData = {}
total_demand = pd.read_csv(locator.get_total_demand())
def calc_new_load(mdot, TsupDH, Tret):
"""
This function calculates the load distribution side of the district heating distribution.
:param mdot: mass flow
:param TsupDH: supply temeperature
:param Tret: return temperature
:param gv: global variables class
:type mdot: float
:type TsupDH: float
:type Tret: float
:type gv: class
:return: Qload: load of the distribution
:rtype: float
"""
Qload = mdot * HEAT_CAPACITY_OF_WATER_JPERKGK * (TsupDH - Tret) * (
1 + Q_LOSS_DISCONNECTED)
if Qload < 0:
Qload = 0
return Qload
for building_name in building_names:
print building_name
substation.substation_main(locator,
total_demand,
building_names=[building_name],
heating_configuration=7,
cooling_configuration=7,
Flag=False)
loads = pd.read_csv(
locator.get_optimization_substations_results_file(building_name),
usecols=[
"T_supply_DH_result_K", "T_return_DH_result_K",
"mdot_DH_result_kgpers"
])
Qload = np.vectorize(calc_new_load)(loads["mdot_DH_result_kgpers"],
loads["T_supply_DH_result_K"],
loads["T_return_DH_result_K"])
Qannual = Qload.sum()
Qnom = Qload.max() * (1 + SIZING_MARGIN
) # 1% reliability margin on installed capacity
# Create empty matrices
result = np.zeros((13, 7))
result[0][0] = 1
result[1][1] = 1
result[2][2] = 1
InvCosts = np.zeros((13, 1))
resourcesRes = np.zeros((13, 4))
QannualB_GHP = np.zeros(
(10, 1)) # For the investment costs of the boiler used with GHP
Wel_GHP = np.zeros((10, 1)) # For the investment costs of the GHP
# Supply with the Boiler / FC / GHP
Tret = loads["T_return_DH_result_K"].values
TsupDH = loads["T_supply_DH_result_K"].values
mdot = loads["mdot_DH_result_kgpers"].values
for hour in range(8760):
if Tret[hour] == 0:
Tret[hour] = TsupDH[hour]
# Boiler NG
BoilerEff = Boiler.calc_Cop_boiler(Qload[hour], Qnom, Tret[hour])
Qgas = Qload[hour] / BoilerEff
result[0][4] += prices.NG_PRICE * Qgas # CHF
result[0][
5] += lca.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][
6] += lca.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[0][0] += Qload[hour]
if DISC_BIOGAS_FLAG == 1:
result[0][4] += prices.BG_PRICE * Qgas # CHF
result[0][
5] += lca.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][
6] += lca.BG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
# Boiler BG
result[1][4] += prices.BG_PRICE * Qgas # CHF
result[1][
5] += lca.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[1][
6] += lca.BG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[1][1] += Qload[hour]
# FC
(FC_Effel, FC_Effth) = FC.calc_eta_FC(Qload[hour], Qnom, 1, "B")
Qgas = Qload[hour] / (FC_Effth + FC_Effel)
Qelec = Qgas * FC_Effel
result[2][
4] += prices.NG_PRICE * Qgas - lca.ELEC_PRICE * Qelec # CHF, extra electricity sold to grid
result[2][
5] += 0.0874 * Qgas * 3600E-6 + 773 * 0.45 * Qelec * 1E-6 - lca.EL_TO_CO2 * Qelec * 3600E-6 # kgCO2
# Bloom box emissions within the FC: 773 lbs / MWh_el (and 1 lbs = 0.45 kg)
# http://www.carbonlighthouse.com/2011/09/16/bloom-box/
result[2][
6] += 1.51 * Qgas * 3600E-6 - lca.EL_TO_OIL_EQ * Qelec * 3600E-6 # MJ-oil-eq
resourcesRes[2][0] += Qload[hour]
resourcesRes[2][2] += Qelec
# GHP
for i in range(10):
QnomBoiler = i / 10 * Qnom
QnomGHP = Qnom - QnomBoiler
if Qload[hour] <= QnomGHP:
(wdot_el, qcolddot, qhotdot_missing,
tsup2) = HP.calc_Cop_GHP(ground_temp[hour], mdot[hour],
TsupDH[hour], Tret[hour])
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3 + i][4] += lca.ELEC_PRICE * wdot_el # CHF
result[3 + i][
5] += lca.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3 + i][
6] += lca.SMALL_GHP_TO_OIL_STD * wdot_el * 3600E-6 # MJ-oil-eq
resourcesRes[3 + i][2] -= wdot_el
resourcesRes[3 + i][3] += Qload[hour] - qhotdot_missing
if qhotdot_missing > 0:
print "GHP unable to cover the whole demand, boiler activated!"
BoilerEff = Boiler.calc_Cop_boiler(
qhotdot_missing, QnomBoiler, tsup2)
Qgas = qhotdot_missing / BoilerEff
result[3 + i][4] += prices.NG_PRICE * Qgas # CHF
result[3 + i][
5] += lca.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3 + i][
6] += lca.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
QannualB_GHP[i][0] += qhotdot_missing
resourcesRes[3 + i][0] += qhotdot_missing
else:
# print "Boiler activated to compensate GHP", i
# if gv.DiscGHPFlag == 0:
# print QnomGHP
# QnomGHP = 0
# print "GHP not allowed 2, set QnomGHP to zero"
TexitGHP = QnomGHP / (
mdot[hour] *
HEAT_CAPACITY_OF_WATER_JPERKGK) + Tret[hour]
(wdot_el, qcolddot, qhotdot_missing,
tsup2) = HP.calc_Cop_GHP(ground_temp[hour], mdot[hour],
TexitGHP, Tret[hour])
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3 + i][4] += lca.ELEC_PRICE * wdot_el # CHF
result[3 + i][
5] += lca.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3 + i][
6] += lca.SMALL_GHP_TO_OIL_STD * wdot_el * 3600E-6 # MJ-oil-eq
resourcesRes[3 + i][2] -= wdot_el
resourcesRes[3 + i][3] += QnomGHP - qhotdot_missing
if qhotdot_missing > 0:
print "GHP unable to cover the whole demand, boiler activated!"
BoilerEff = Boiler.calc_Cop_boiler(
qhotdot_missing, QnomBoiler, tsup2)
Qgas = qhotdot_missing / BoilerEff
result[3 + i][4] += prices.NG_PRICE * Qgas # CHF
result[3 + i][
5] += lca.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3 + i][
6] += lca.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
QannualB_GHP[i][0] += qhotdot_missing
resourcesRes[3 + i][0] += qhotdot_missing
QtoBoiler = Qload[hour] - QnomGHP
QannualB_GHP[i][0] += QtoBoiler
BoilerEff = Boiler.calc_Cop_boiler(QtoBoiler, QnomBoiler,
TexitGHP)
Qgas = QtoBoiler / BoilerEff
result[3 + i][4] += prices.NG_PRICE * Qgas # CHF
result[3 + i][
5] += lca.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3 + i][
6] += lca.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[3 + i][0] += QtoBoiler
# Investment Costs / CO2 / Prim
Capex_a_Boiler, Opex_Boiler = Boiler.calc_Cinv_boiler(
Qnom, locator, config, 'BO1')
InvCosts[0][0] = Capex_a_Boiler + Opex_Boiler
InvCosts[1][0] = Capex_a_Boiler + Opex_Boiler
Capex_a_FC, Opex_FC = FC.calc_Cinv_FC(Qnom, locator, config)
InvCosts[2][0] = Capex_a_FC + Opex_FC
for i in range(10):
result[3 + i][0] = i / 10
result[3 + i][3] = 1 - i / 10
QnomBoiler = i / 10 * Qnom
Capex_a_Boiler, Opex_Boiler = Boiler.calc_Cinv_boiler(
QnomBoiler, locator, config, 'BO1')
InvCosts[3 + i][0] = Capex_a_Boiler + Opex_Boiler
Capex_a_GHP, Opex_GHP = HP.calc_Cinv_GHP(Wel_GHP[i][0], locator,
config)
InvCaGHP = Capex_a_GHP + Opex_GHP
InvCosts[3 + i][0] += InvCaGHP * prices.EURO_TO_CHF
# Best configuration
Best = np.zeros((13, 1))
indexBest = 0
TotalCosts = np.zeros((13, 2))
TotalCO2 = np.zeros((13, 2))
TotalPrim = np.zeros((13, 2))
for i in range(13):
TotalCosts[i][0] = TotalCO2[i][0] = TotalPrim[i][0] = i
TotalCosts[i][1] = InvCosts[i][0] + result[i][4]
TotalCO2[i][1] = result[i][5]
TotalPrim[i][1] = result[i][6]
CostsS = TotalCosts[np.argsort(TotalCosts[:, 1])]
CO2S = TotalCO2[np.argsort(TotalCO2[:, 1])]
PrimS = TotalPrim[np.argsort(TotalPrim[:, 1])]
el = len(CostsS)
rank = 0
Bestfound = False
optsearch = np.empty(el)
optsearch.fill(3)
indexBest = 0
geothermal_potential = geothermal_potential_data.set_index('Name')
# Check the GHP area constraint
for i in range(10):
QGHP = (1 - i / 10) * Qnom
areaAvail = geothermal_potential.ix[building_name, 'Area_geo']
Qallowed = np.ceil(areaAvail / GHP_A) * GHP_HMAX_SIZE # [W_th]
if Qallowed < QGHP:
optsearch[i + 3] += 1
Best[i + 3][0] = -1
while not Bestfound and rank < el:
optsearch[int(CostsS[rank][0])] -= 1
optsearch[int(CO2S[rank][0])] -= 1
optsearch[int(PrimS[rank][0])] -= 1
if np.count_nonzero(optsearch) != el:
Bestfound = True
indexBest = np.where(optsearch == 0)[0][0]
rank += 1
# get the best option according to the ranking.
Best[indexBest][0] = 1
Qnom_array = np.ones(len(Best[:, 0])) * Qnom
# Save results in csv file
dico = {}
dico["BoilerNG Share"] = result[:, 0]
dico["BoilerBG Share"] = result[:, 1]
dico["FC Share"] = result[:, 2]
dico["GHP Share"] = result[:, 3]
dico["Operation Costs [CHF]"] = result[:, 4]
dico["CO2 Emissions [kgCO2-eq]"] = result[:, 5]
dico["Primary Energy Needs [MJoil-eq]"] = result[:, 6]
dico["Annualized Investment Costs [CHF]"] = InvCosts[:, 0]
dico["Total Costs [CHF]"] = TotalCosts[:, 1]
dico["Best configuration"] = Best[:, 0]
dico["Nominal Power"] = Qnom_array
dico["QfromNG"] = resourcesRes[:, 0]
dico["QfromBG"] = resourcesRes[:, 1]
dico["EforGHP"] = resourcesRes[:, 2]
dico["QfromGHP"] = resourcesRes[:, 3]
results_to_csv = pd.DataFrame(dico)
fName_result = locator.get_optimization_disconnected_folder_building_result_heating(
building_name)
results_to_csv.to_csv(fName_result, sep=',')
BestComb = {}
BestComb["BoilerNG Share"] = result[indexBest, 0]
BestComb["BoilerBG Share"] = result[indexBest, 1]
BestComb["FC Share"] = result[indexBest, 2]
BestComb["GHP Share"] = result[indexBest, 3]
BestComb["Operation Costs [CHF]"] = result[indexBest, 4]
BestComb["CO2 Emissions [kgCO2-eq]"] = result[indexBest, 5]
BestComb["Primary Energy Needs [MJoil-eq]"] = result[indexBest, 6]
BestComb["Annualized Investment Costs [CHF]"] = InvCosts[indexBest, 0]
BestComb["Total Costs [CHF]"] = TotalCosts[indexBest, 1]
BestComb["Best configuration"] = Best[indexBest, 0]
BestComb["Nominal Power"] = Qnom
BestData[building_name] = BestComb
if 0:
fName = locator.get_optimization_disconnected_folder_disc_op_summary_heating(
)
results_to_csv = pd.DataFrame(BestData)
results_to_csv.to_csv(fName, sep=',')
print time.clock(
) - t0, "seconds process time for the Disconnected Building Routine \n"
def disconnected_heating_for_building(building_name, supply_systems,
T_ground_K, geothermal_potential_data,
lca, locator, prices):
print('{building_name} disconnected heating supply system simulations...'.
format(building_name=building_name))
GHP_cost_data = supply_systems.HP
BH_cost_data = supply_systems.BH
boiler_cost_data = supply_systems.Boiler
# run substation model to derive temperatures of the building
substation_results = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name, "DH", ""))
q_load_Wh = np.vectorize(calc_new_load)(
substation_results["mdot_DH_result_kgpers"],
substation_results["T_supply_DH_result_K"],
substation_results["T_return_DH_result_K"])
Qnom_W = q_load_Wh.max()
# Create empty matrices
Opex_a_var_USD = np.zeros((13, 7))
Capex_total_USD = np.zeros((13, 7))
Capex_a_USD = np.zeros((13, 7))
Opex_a_fixed_USD = np.zeros((13, 7))
Capex_opex_a_fixed_only_USD = np.zeros((13, 7))
Opex_a_USD = np.zeros((13, 7))
GHG_tonCO2 = np.zeros((13, 7))
# indicate supply technologies for each configuration
Opex_a_var_USD[0][0] = 1 # Boiler NG
Opex_a_var_USD[1][1] = 1 # Boiler BG
Opex_a_var_USD[2][2] = 1 # Fuel Cell
resourcesRes = np.zeros((13, 4))
Q_Boiler_for_GHP_W = np.zeros(
(10, 1)) # Save peak capacity of GHP Backup Boilers
GHP_el_size_W = np.zeros((10, 1)) # Save peak capacity of GHP
# save supply system activation of all supply configurations
heating_dispatch = {}
# Supply with the Boiler / FC / GHP
Tret_K = substation_results["T_return_DH_result_K"].values
Tsup_K = substation_results["T_supply_DH_result_K"].values
mdot_kgpers = substation_results["mdot_DH_result_kgpers"].values
## Start Hourly calculation
print(building_name,
' decentralized heating supply systems simulations...')
Tret_K = np.where(Tret_K > 0.0, Tret_K, Tsup_K)
## 0: Boiler NG
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(q_load_Wh, Qnom_W, Tret_K)
Qgas_to_Boiler_Wh = np.divide(q_load_Wh,
BoilerEff,
out=np.zeros_like(q_load_Wh),
where=BoilerEff != 0.0)
Boiler_Status = np.where(Qgas_to_Boiler_Wh > 0.0, 1, 0)
# add costs
Opex_a_var_USD[0][4] += sum(prices.NG_PRICE * Qgas_to_Boiler_Wh) # CHF
GHG_tonCO2[0][5] += sum(
calc_emissions_Whyr_to_tonCO2yr(Qgas_to_Boiler_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
resourcesRes[0][0] += sum(q_load_Wh) # q from NG
heating_dispatch[0] = {
'Q_Boiler_gen_directload_W': q_load_Wh,
'Boiler_Status': Boiler_Status,
'NG_Boiler_req_W': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': np.zeros(8760)
}
## 1: Boiler BG
# add costs
Opex_a_var_USD[1][4] += sum(prices.BG_PRICE * Qgas_to_Boiler_Wh) # CHF
GHG_tonCO2[1][5] += sum(
calc_emissions_Whyr_to_tonCO2yr(Qgas_to_Boiler_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
resourcesRes[1][1] += sum(q_load_Wh) # q from BG
heating_dispatch[1] = {
'Q_Boiler_gen_directload_W': q_load_Wh,
'Boiler_Status': Boiler_Status,
'BG_Boiler_req_W': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': np.zeros(8760)
}
## 2: Fuel Cell
(FC_Effel, FC_Effth) = np.vectorize(FC.calc_eta_FC)(q_load_Wh, Qnom_W, 1,
"B")
Qgas_to_FC_Wh = q_load_Wh / (FC_Effth + FC_Effel
) # FIXME: should be q_load_Wh/FC_Effth?
el_from_FC_Wh = Qgas_to_FC_Wh * FC_Effel
FC_Status = np.where(Qgas_to_FC_Wh > 0.0, 1, 0)
# add variable costs, emissions and primary energy
Opex_a_var_USD[2][4] += sum(
prices.NG_PRICE * Qgas_to_FC_Wh - prices.ELEC_PRICE_EXPORT *
el_from_FC_Wh) # CHF, extra electricity sold to grid
GHG_tonCO2_from_FC = (0.0874 * Qgas_to_FC_Wh * 3600E-6 +
773 * 0.45 * el_from_FC_Wh * 1E-6 -
lca.EL_TO_CO2_EQ * el_from_FC_Wh * 3600E-6) / 1E3
GHG_tonCO2[2][5] += sum(GHG_tonCO2_from_FC) # tonCO2
# Bloom box emissions within the FC: 773 lbs / MWh_el (and 1 lbs = 0.45 kg)
# http://www.carbonlighthouse.com/2011/09/16/bloom-box/
# add activation
resourcesRes[2][0] = sum(q_load_Wh) # q from NG
resourcesRes[2][2] = sum(el_from_FC_Wh) # el for GHP # FIXME: el from FC
heating_dispatch[2] = {
'Q_Fuelcell_gen_directload_W': q_load_Wh,
'Fuelcell_Status': FC_Status,
'NG_FuelCell_req_W': Qgas_to_FC_Wh,
'E_Fuelcell_gen_export_W': el_from_FC_Wh,
'E_hs_ww_req_W': np.zeros(8760)
}
# 3-13: Boiler NG + GHP
for i in range(10):
# set nominal size for Boiler and GHP
QnomBoiler_W = i / 10.0 * Qnom_W
QnomGHP_W = Qnom_W - QnomBoiler_W
# GHP operation
Texit_GHP_nom_K = QnomGHP_W / (mdot_kgpers *
HEAT_CAPACITY_OF_WATER_JPERKGK) + Tret_K
el_GHP_Wh, q_load_NG_Boiler_Wh, \
qhot_missing_Wh, \
Texit_GHP_K, q_from_GHP_Wh = np.vectorize(calc_GHP_operation)(QnomGHP_W, T_ground_K, Texit_GHP_nom_K,
Tret_K, Tsup_K, mdot_kgpers, q_load_Wh)
GHP_el_size_W[i][0] = max(el_GHP_Wh)
GHP_Status = np.where(q_from_GHP_Wh > 0.0, 1, 0)
# GHP Backup Boiler operation
if max(qhot_missing_Wh) > 0.0:
print("GHP unable to cover the whole demand, boiler activated!")
Qnom_GHP_Backup_Boiler_W = max(qhot_missing_Wh)
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(
qhot_missing_Wh, Qnom_GHP_Backup_Boiler_W, Texit_GHP_K)
Qgas_to_GHPBoiler_Wh = np.divide(
qhot_missing_Wh,
BoilerEff,
out=np.zeros_like(qhot_missing_Wh),
where=BoilerEff != 0.0)
else:
Qgas_to_GHPBoiler_Wh = np.zeros(q_load_Wh.shape[0])
Qnom_GHP_Backup_Boiler_W = 0.0
Q_Boiler_for_GHP_W[i][0] = Qnom_GHP_Backup_Boiler_W
GHPbackupBoiler_Status = np.where(qhot_missing_Wh > 0.0, 1, 0)
# NG Boiler operation
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(q_load_NG_Boiler_Wh,
QnomBoiler_W,
Texit_GHP_K)
Qgas_to_Boiler_Wh = np.divide(q_load_NG_Boiler_Wh,
BoilerEff,
out=np.zeros_like(q_load_NG_Boiler_Wh),
where=BoilerEff != 0.0)
Boiler_Status = np.where(q_load_NG_Boiler_Wh > 0.0, 1, 0)
# add costs
# electricity
el_total_Wh = el_GHP_Wh
Opex_a_var_USD[3 + i][4] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
GHG_tonCO2[3 + i][5] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# gas
Q_gas_total_Wh = Qgas_to_GHPBoiler_Wh + Qgas_to_Boiler_Wh
Opex_a_var_USD[3 + i][4] += sum(prices.NG_PRICE *
Q_gas_total_Wh) # CHF
GHG_tonCO2[3 + i][5] += sum(
calc_emissions_Whyr_to_tonCO2yr(Q_gas_total_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
resourcesRes[3 + i][0] = sum(qhot_missing_Wh + q_load_NG_Boiler_Wh)
resourcesRes[3 + i][2] = sum(el_GHP_Wh)
resourcesRes[3 + i][3] = sum(q_from_GHP_Wh)
heating_dispatch[3 + i] = {
'Q_GHP_gen_directload_W': q_from_GHP_Wh,
'Q_BackupBoiler_gen_directload_W': qhot_missing_Wh,
'Q_Boiler_gen_directload_W': q_load_NG_Boiler_Wh,
'GHP_Status': GHP_Status,
'BackupBoiler_Status': GHPbackupBoiler_Status,
'Boiler_Status': Boiler_Status,
'NG_BackupBoiler_req_Wh': Qgas_to_GHPBoiler_Wh,
'NG_Boiler_req_Wh': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': el_GHP_Wh
}
# Add all costs
# 0: Boiler NG
Capex_a_Boiler_USD, Opex_a_fixed_Boiler_USD, Capex_Boiler_USD = Boiler.calc_Cinv_boiler(
Qnom_W, 'BO1', boiler_cost_data)
Capex_total_USD[0][0] = Capex_Boiler_USD
Capex_a_USD[0][0] = Capex_a_Boiler_USD
Opex_a_fixed_USD[0][0] = Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[0][
0] = Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# 1: Boiler BG
Capex_total_USD[1][0] = Capex_Boiler_USD
Capex_a_USD[1][0] = Capex_a_Boiler_USD
Opex_a_fixed_USD[1][0] = Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[1][
0] = Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# 2: Fuel Cell
Capex_a_FC_USD, Opex_fixed_FC_USD, Capex_FC_USD = FC.calc_Cinv_FC(
Qnom_W, supply_systems.FC)
Capex_total_USD[2][0] = Capex_FC_USD
Capex_a_USD[2][0] = Capex_a_FC_USD
Opex_a_fixed_USD[2][0] = Opex_fixed_FC_USD
Capex_opex_a_fixed_only_USD[2][
0] = Capex_a_FC_USD + Opex_fixed_FC_USD # TODO:variable price?
# 3-13: BOILER + GHP
for i in range(10):
Opex_a_var_USD[3 + i][0] = i / 10.0 # Boiler share
Opex_a_var_USD[3 + i][3] = 1 - i / 10.0 # GHP share
# Get boiler costs
QnomBoiler_W = i / 10.0 * Qnom_W
Capex_a_Boiler_USD, Opex_a_fixed_Boiler_USD, Capex_Boiler_USD = Boiler.calc_Cinv_boiler(
QnomBoiler_W, 'BO1', boiler_cost_data)
Capex_total_USD[3 + i][0] += Capex_Boiler_USD
Capex_a_USD[3 + i][0] += Capex_a_Boiler_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[3 + i][
0] += Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# Get back up boiler costs
Qnom_Backup_Boiler_W = Q_Boiler_for_GHP_W[i][0]
Capex_a_GHPBoiler_USD, Opex_a_fixed_GHPBoiler_USD, Capex_GHPBoiler_USD = Boiler.calc_Cinv_boiler(
Qnom_Backup_Boiler_W, 'BO1', boiler_cost_data)
Capex_total_USD[3 + i][0] += Capex_GHPBoiler_USD
Capex_a_USD[3 + i][0] += Capex_a_GHPBoiler_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_GHPBoiler_USD
Capex_opex_a_fixed_only_USD[3 + i][
0] += Capex_a_GHPBoiler_USD + Opex_a_fixed_GHPBoiler_USD # TODO:variable price?
# Get ground source heat pump costs
Capex_a_GHP_USD, Opex_a_fixed_GHP_USD, Capex_GHP_USD = HP.calc_Cinv_GHP(
GHP_el_size_W[i][0], GHP_cost_data, BH_cost_data)
Capex_total_USD[3 + i][0] += Capex_GHP_USD
Capex_a_USD[3 + i][0] += Capex_a_GHP_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_GHP_USD
Capex_opex_a_fixed_only_USD[3 + i][
0] += Capex_a_GHP_USD + Opex_a_fixed_GHP_USD # TODO:variable price?
# Best configuration
Best = np.zeros((13, 1))
indexBest = 0
TAC_USD = np.zeros((13, 2))
TotalCO2 = np.zeros((13, 2))
for i in range(13):
TAC_USD[i][0] = TotalCO2[i][0] = i
Opex_a_USD[i][1] = Opex_a_fixed_USD[i][0] + +Opex_a_var_USD[i][4]
TAC_USD[i][
1] = Capex_opex_a_fixed_only_USD[i][0] + Opex_a_var_USD[i][4]
TotalCO2[i][1] = GHG_tonCO2[i][5]
CostsS = TAC_USD[np.argsort(TAC_USD[:, 1])]
CO2S = TotalCO2[np.argsort(TotalCO2[:, 1])]
el = len(CostsS)
rank = 0
Bestfound = False
optsearch = np.empty(el)
optsearch.fill(3)
indexBest = 0
geothermal_potential = geothermal_potential_data.set_index('Name')
# Check the GHP area constraint
for i in range(10):
QGHP = (1 - i / 10.0) * Qnom_W
areaAvail = geothermal_potential.loc[building_name, 'Area_geo']
Qallowed = np.ceil(areaAvail / GHP_A) * GHP_HMAX_SIZE # [W_th]
if Qallowed < QGHP:
optsearch[i + 3] += 1
Best[i + 3][0] = -1
while not Bestfound and rank < el:
optsearch[int(CostsS[rank][0])] -= 1
optsearch[int(CO2S[rank][0])] -= 1
if np.count_nonzero(optsearch) != el:
Bestfound = True
indexBest = np.where(optsearch == 0)[0][0]
rank += 1
# get the best option according to the ranking.
Best[indexBest][0] = 1
# Save results in csv file
performance_results = {
"Nominal heating load": Qnom_W,
"Capacity_BaseBoiler_NG_W": Qnom_W * Opex_a_var_USD[:, 0],
"Capacity_FC_NG_W": Qnom_W * Opex_a_var_USD[:, 2],
"Capacity_GS_HP_W": Qnom_W * Opex_a_var_USD[:, 3],
"TAC_USD": TAC_USD[:, 1],
"Capex_a_USD": Capex_a_USD[:, 0],
"Capex_total_USD": Capex_total_USD[:, 0],
"Opex_fixed_USD": Opex_a_fixed_USD[:, 0],
"Opex_var_USD": Opex_a_var_USD[:, 4],
"GHG_tonCO2": GHG_tonCO2[:, 5],
"Best configuration": Best[:, 0]
}
results_to_csv = pd.DataFrame(performance_results)
fName_result = locator.get_optimization_decentralized_folder_building_result_heating(
building_name)
results_to_csv.to_csv(fName_result, sep=',', index=False)
# save activation for the best supply system configuration
best_activation_df = pd.DataFrame.from_dict(heating_dispatch[indexBest]) #
best_activation_df.to_csv(
locator.
get_optimization_decentralized_folder_building_result_heating_activation(
building_name),
index=False)
def disconnected_buildings_heating_main(locator, total_demand, building_names, config, prices, lca):
"""
Computes the parameters for the operation of disconnected buildings
output results in csv files.
There is no optimization at this point. The different technologies are calculated and compared 1 to 1 to
each technology. it is a classical combinatorial problem.
:param locator: locator class
:param building_names: list with names of buildings
:type locator: class
:type building_names: list
:return: results of operation of buildings located in locator.get_optimization_decentralized_folder
:rtype: Nonetype
"""
t0 = time.clock()
prop_geometry = Gdf.from_file(locator.get_zone_geometry())
geometry = pd.DataFrame({'Name': prop_geometry.Name, 'Area': prop_geometry.area})
geothermal_potential_data = dbf.dbf_to_dataframe(locator.get_building_supply())
geothermal_potential_data = pd.merge(geothermal_potential_data, geometry, on='Name')
geothermal_potential_data['Area_geo'] = geothermal_potential_data['Area']
weather_path = locator.get_weather_file()
weather_data = epwreader.epw_reader(weather_path)[['year', 'drybulb_C', 'wetbulb_C',
'relhum_percent', 'windspd_ms', 'skytemp_C']]
T_ground_K = calc_ground_temperature(locator, weather_data['drybulb_C'], depth_m=10)
# This will calculate the substation state if all buildings where connected(this is how we study this)
substation.substation_main_heating(locator, total_demand, building_names)
for building_name in building_names:
print('running for building %s') %(building_name)
# run substation model to derive temperatures of the building
substation_results = pd.read_csv(locator.get_optimization_substations_results_file(building_name, "DH", ""))
q_load_Wh = np.vectorize(calc_new_load)(substation_results["mdot_DH_result_kgpers"],
substation_results["T_supply_DH_result_K"],
substation_results["T_return_DH_result_K"])
Qnom_W = q_load_Wh.max()
# Create empty matrices
Opex_a_var_USD = np.zeros((13, 7))
Capex_total_USD = np.zeros((13, 7))
Capex_a_USD = np.zeros((13, 7))
Opex_a_fixed_USD = np.zeros((13, 7))
Capex_opex_a_fixed_only_USD = np.zeros((13, 7))
Opex_a_USD = np.zeros((13, 7))
GHG_tonCO2 = np.zeros((13, 7))
PEN_MJoil = np.zeros((13, 7))
# indicate supply technologies for each configuration
Opex_a_var_USD[0][0] = 1 # Boiler NG
Opex_a_var_USD[1][1] = 1 # Boiler BG
Opex_a_var_USD[2][2] = 1 # Fuel Cell
resourcesRes = np.zeros((13, 4))
Q_Boiler_for_GHP_W = np.zeros((10, 1)) # Save peak capacity of GHP Backup Boilers
GHP_el_size_W = np.zeros((10, 1)) # Save peak capacity of GHP
# save supply system activation of all supply configurations
heating_dispatch = {}
# Supply with the Boiler / FC / GHP
Tret_K = substation_results["T_return_DH_result_K"].values
Tsup_K = substation_results["T_supply_DH_result_K"].values
mdot_kgpers = substation_results["mdot_DH_result_kgpers"].values
## Start Hourly calculation
print building_name, ' decentralized heating supply systems simulations...'
Tret_K = np.where(Tret_K > 0.0, Tret_K, Tsup_K)
## 0: Boiler NG
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(q_load_Wh, Qnom_W, Tret_K)
Qgas_to_Boiler_Wh = np.divide(q_load_Wh, BoilerEff, out=np.zeros_like(q_load_Wh), where=BoilerEff != 0.0)
Boiler_Status = np.where(Qgas_to_Boiler_Wh > 0.0, 1, 0)
# add costs
Opex_a_var_USD[0][4] += sum(prices.NG_PRICE * Qgas_to_Boiler_Wh) # CHF
GHG_tonCO2[0][5] += calc_emissions_Whyr_to_tonCO2yr(sum(Qgas_to_Boiler_Wh), lca.NG_TO_CO2_EQ) # ton CO2
PEN_MJoil[0][6] += calc_pen_Whyr_to_MJoilyr(sum(Qgas_to_Boiler_Wh), lca.NG_TO_OIL_EQ) # MJ-oil-eq
# add activation
resourcesRes[0][0] += sum(q_load_Wh) # q from NG
heating_dispatch[0] = {'Q_Boiler_gen_directload_W': q_load_Wh,
'Boiler_Status': Boiler_Status,
'NG_Boiler_req_W': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': np.zeros(8760)}
## 1: Boiler BG
# add costs
Opex_a_var_USD[1][4] += sum(prices.BG_PRICE * Qgas_to_Boiler_Wh) # CHF
GHG_tonCO2[1][5] += calc_emissions_Whyr_to_tonCO2yr(sum(Qgas_to_Boiler_Wh), lca.NG_TO_CO2_EQ) # ton CO2
PEN_MJoil[1][6] += calc_pen_Whyr_to_MJoilyr(sum(Qgas_to_Boiler_Wh), lca.NG_TO_OIL_EQ) # MJ-oil-eq
# add activation
resourcesRes[1][1] += sum(q_load_Wh) # q from BG
heating_dispatch[1] = {'Q_Boiler_gen_directload_W': q_load_Wh,
'Boiler_Status': Boiler_Status,
'BG_Boiler_req_W': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': np.zeros(8760)}
## 2: Fuel Cell
(FC_Effel, FC_Effth) = np.vectorize(FC.calc_eta_FC)(q_load_Wh, Qnom_W, 1, "B")
Qgas_to_FC_Wh = q_load_Wh / (FC_Effth + FC_Effel) # FIXME: should be q_load_Wh/FC_Effth?
el_from_FC_Wh = Qgas_to_FC_Wh * FC_Effel
FC_Status = np.where(Qgas_to_FC_Wh > 0.0, 1, 0)
# add variable costs, emissions and primary energy
Opex_a_var_USD[2][4] += sum(prices.NG_PRICE * Qgas_to_FC_Wh - prices.ELEC_PRICE_EXPORT * el_from_FC_Wh) # CHF, extra electricity sold to grid
GHG_tonCO2_from_FC = (0.0874 * Qgas_to_FC_Wh * 3600E-6 + 773 * 0.45 * el_from_FC_Wh * 1E-6 -
lca.EL_TO_CO2_EQ * el_from_FC_Wh * 3600E-6) / 1E3
GHG_tonCO2[2][5] += sum(GHG_tonCO2_from_FC) # tonCO2
# Bloom box emissions within the FC: 773 lbs / MWh_el (and 1 lbs = 0.45 kg)
# http://www.carbonlighthouse.com/2011/09/16/bloom-box/
PEN_MJoil_from_FC = 1.51 * Qgas_to_FC_Wh * 3600E-6 - lca.EL_TO_OIL_EQ * el_from_FC_Wh * 3600E-6
PEN_MJoil[2][6] += sum(PEN_MJoil_from_FC) # MJ-oil-eq
# add activation
resourcesRes[2][0] = sum(q_load_Wh) # q from NG
resourcesRes[2][2] = sum(el_from_FC_Wh) # el for GHP # FIXME: el from FC
heating_dispatch[2] = {'Q_Fuelcell_gen_directload_W': q_load_Wh,
'Fuelcell_Status': FC_Status,
'NG_FuelCell_req_W': Qgas_to_FC_Wh,
'E_Fuelcell_gen_export_W': el_from_FC_Wh,
'E_hs_ww_req_W': np.zeros(8760)}
# 3-13: Boiler NG + GHP
for i in range(10):
# set nominal size for Boiler and GHP
QnomBoiler_W = i / 10.0 * Qnom_W
mdot_kgpers_Boiler = i / 10.0 * mdot_kgpers
QnomGHP_W = Qnom_W - QnomBoiler_W
# GHP operation
Texit_GHP_nom_K = QnomGHP_W / (mdot_kgpers * HEAT_CAPACITY_OF_WATER_JPERKGK) + Tret_K
el_GHP_Wh, q_load_NG_Boiler_Wh, \
qhot_missing_Wh, \
Texit_GHP_K, q_from_GHP_Wh = np.vectorize(calc_GHP_operation)(QnomGHP_W, T_ground_K, Texit_GHP_nom_K,
Tret_K, Tsup_K, mdot_kgpers, q_load_Wh)
GHP_el_size_W[i][0] = max(el_GHP_Wh)
GHP_Status = np.where(q_from_GHP_Wh > 0.0, 1, 0)
# GHP Backup Boiler operation
if max(qhot_missing_Wh) > 0.0:
print "GHP unable to cover the whole demand, boiler activated!"
Qnom_GHP_Backup_Boiler_W = max(qhot_missing_Wh)
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(qhot_missing_Wh, Qnom_GHP_Backup_Boiler_W, Texit_GHP_K)
Qgas_to_GHPBoiler_Wh = np.divide(qhot_missing_Wh, BoilerEff,
out=np.zeros_like(qhot_missing_Wh), where=BoilerEff != 0.0)
else:
Qgas_to_GHPBoiler_Wh = np.zeros(q_load_Wh.shape[0])
Qnom_GHP_Backup_Boiler_W = 0.0
Q_Boiler_for_GHP_W[i][0] = Qnom_GHP_Backup_Boiler_W
GHPbackupBoiler_Status = np.where(qhot_missing_Wh > 0.0, 1, 0)
# NG Boiler operation
BoilerEff = np.vectorize(Boiler.calc_Cop_boiler)(q_load_NG_Boiler_Wh, QnomBoiler_W, Texit_GHP_K)
Qgas_to_Boiler_Wh = np.divide(q_load_NG_Boiler_Wh, BoilerEff,
out=np.zeros_like(q_load_NG_Boiler_Wh), where=BoilerEff != 0.0)
Boiler_Status = np.where(q_load_NG_Boiler_Wh > 0.0, 1, 0)
# add costs
# electricity
el_total_Wh = el_GHP_Wh
Opex_a_var_USD[3 + i][4] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
GHG_tonCO2[3 + i][5] += calc_emissions_Whyr_to_tonCO2yr(sum(el_total_Wh), lca.EL_TO_CO2_EQ) # ton CO2
PEN_MJoil[3 + i][6] += calc_pen_Whyr_to_MJoilyr(sum(el_total_Wh), lca.EL_TO_OIL_EQ) # MJ-oil-eq
# gas
Q_gas_total_Wh = Qgas_to_GHPBoiler_Wh + Qgas_to_Boiler_Wh
Opex_a_var_USD[3 + i][4] += sum(prices.NG_PRICE * Q_gas_total_Wh) # CHF
GHG_tonCO2[3 + i][5] += calc_emissions_Whyr_to_tonCO2yr(sum(Q_gas_total_Wh), lca.NG_TO_CO2_EQ) # ton CO2
PEN_MJoil[3 + i][6] += calc_pen_Whyr_to_MJoilyr(sum(Q_gas_total_Wh), lca.NG_TO_OIL_EQ) # MJ-oil-eq
# add activation
resourcesRes[3 + i][0] = sum(qhot_missing_Wh + q_load_NG_Boiler_Wh)
resourcesRes[3 + i][2] = sum(el_GHP_Wh)
resourcesRes[3 + i][3] = sum(q_from_GHP_Wh)
heating_dispatch[3 + i] = {'Q_GHP_gen_directload_W': q_from_GHP_Wh,
'Q_BackupBoiler_gen_directload_W': qhot_missing_Wh,
'Q_Boiler_gen_directload_W': q_load_NG_Boiler_Wh,
'GHP_Status': GHP_Status,
'BackupBoiler_Status': GHPbackupBoiler_Status,
'Boiler_Status': Boiler_Status,
'NG_BackupBoiler_req_Wh': Qgas_to_GHPBoiler_Wh,
'NG_Boiler_req_Wh': Qgas_to_Boiler_Wh,
'E_hs_ww_req_W': el_GHP_Wh}
# Add all costs
# 0: Boiler NG
Capex_a_Boiler_USD, Opex_a_fixed_Boiler_USD, Capex_Boiler_USD = Boiler.calc_Cinv_boiler(Qnom_W, locator, config,
'BO1')
Capex_total_USD[0][0] = Capex_Boiler_USD
Capex_a_USD[0][0] = Capex_a_Boiler_USD
Opex_a_fixed_USD[0][0] = Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[0][0] = Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# 1: Boiler BG
Capex_total_USD[1][0] = Capex_Boiler_USD
Capex_a_USD[1][0] = Capex_a_Boiler_USD
Opex_a_fixed_USD[1][0] = Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[1][0] = Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# 2: Fuel Cell
Capex_a_FC_USD, Opex_fixed_FC_USD, Capex_FC_USD = FC.calc_Cinv_FC(Qnom_W, locator, config)
Capex_total_USD[2][0] = Capex_FC_USD
Capex_a_USD[2][0] = Capex_a_FC_USD
Opex_a_fixed_USD[2][0] = Opex_fixed_FC_USD
Capex_opex_a_fixed_only_USD[2][0] = Capex_a_FC_USD + Opex_fixed_FC_USD # TODO:variable price?
# 3-13: BOILER + GHP
for i in range(10):
Opex_a_var_USD[3 + i][0] = i / 10.0 # Boiler share
Opex_a_var_USD[3 + i][3] = 1 - i / 10.0 # GHP share
# Get boiler costs
QnomBoiler_W = i / 10.0 * Qnom_W
Capex_a_Boiler_USD, Opex_a_fixed_Boiler_USD, Capex_Boiler_USD = Boiler.calc_Cinv_boiler(QnomBoiler_W,
locator,
config, 'BO1')
Capex_total_USD[3 + i][0] += Capex_Boiler_USD
Capex_a_USD[3 + i][0] += Capex_a_Boiler_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_Boiler_USD
Capex_opex_a_fixed_only_USD[3 + i][
0] += Capex_a_Boiler_USD + Opex_a_fixed_Boiler_USD # TODO:variable price?
# Get back up boiler costs
Qnom_Backup_Boiler_W = Q_Boiler_for_GHP_W[i][0]
Capex_a_GHPBoiler_USD, Opex_a_fixed_GHPBoiler_USD, Capex_GHPBoiler_USD = Boiler.calc_Cinv_boiler(
Qnom_Backup_Boiler_W, locator,
config, 'BO1')
Capex_total_USD[3 + i][0] += Capex_GHPBoiler_USD
Capex_a_USD[3 + i][0] += Capex_a_GHPBoiler_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_GHPBoiler_USD
Capex_opex_a_fixed_only_USD[3 + i][
0] += Capex_a_GHPBoiler_USD + Opex_a_fixed_GHPBoiler_USD # TODO:variable price?
# Get ground source heat pump costs
Capex_a_GHP_USD, Opex_a_fixed_GHP_USD, Capex_GHP_USD = HP.calc_Cinv_GHP(GHP_el_size_W[i][0], locator,
config)
Capex_total_USD[3 + i][0] += Capex_GHP_USD
Capex_a_USD[3 + i][0] += Capex_a_GHP_USD
Opex_a_fixed_USD[3 + i][0] += Opex_a_fixed_GHP_USD
Capex_opex_a_fixed_only_USD[3 + i][0] += Capex_a_GHP_USD + Opex_a_fixed_GHP_USD # TODO:variable price?
# Best configuration
Best = np.zeros((13, 1))
indexBest = 0
TAC_USD = np.zeros((13, 2))
TotalCO2 = np.zeros((13, 2))
TotalPrim = np.zeros((13, 2))
for i in range(13):
TAC_USD[i][0] = TotalCO2[i][0] = TotalPrim[i][0] = i
Opex_a_USD[i][1] = Opex_a_fixed_USD[i][0] + + Opex_a_var_USD[i][4]
TAC_USD[i][1] = Capex_opex_a_fixed_only_USD[i][0] + Opex_a_var_USD[i][4]
TotalCO2[i][1] = GHG_tonCO2[i][5]
TotalPrim[i][1] = PEN_MJoil[i][6]
CostsS = TAC_USD[np.argsort(TAC_USD[:, 1])]
CO2S = TotalCO2[np.argsort(TotalCO2[:, 1])]
PrimS = TotalPrim[np.argsort(TotalPrim[:, 1])]
el = len(CostsS)
rank = 0
Bestfound = False
optsearch = np.empty(el)
optsearch.fill(3)
indexBest = 0
geothermal_potential = geothermal_potential_data.set_index('Name')
# Check the GHP area constraint
for i in range(10):
QGHP = (1 - i / 10.0) * Qnom_W
areaAvail = geothermal_potential.ix[building_name, 'Area_geo']
Qallowed = np.ceil(areaAvail / GHP_A) * GHP_HMAX_SIZE # [W_th]
if Qallowed < QGHP:
optsearch[i + 3] += 1
Best[i + 3][0] = - 1
while not Bestfound and rank < el:
optsearch[int(CostsS[rank][0])] -= 1
optsearch[int(CO2S[rank][0])] -= 1
optsearch[int(PrimS[rank][0])] -= 1
if np.count_nonzero(optsearch) != el:
Bestfound = True
indexBest = np.where(optsearch == 0)[0][0]
rank += 1
# get the best option according to the ranking.
Best[indexBest][0] = 1
# Save results in csv file
performance_results = {
"Nominal heating load": Qnom_W,
"Capacity_BaseBoiler_NG_W": Qnom_W * Opex_a_var_USD[:, 0],
"Capacity_FC_NG_W": Qnom_W * Opex_a_var_USD[:, 2],
"Capacity_GS_HP_W": Qnom_W * Opex_a_var_USD[:, 3],
"TAC_USD": TAC_USD[:, 1],
"Capex_a_USD": Capex_a_USD[:, 0],
"Capex_total_USD": Capex_total_USD[:, 0],
"Opex_fixed_USD": Opex_a_fixed_USD[:, 0],
"Opex_var_USD": Opex_a_var_USD[:, 4],
"GHG_tonCO2": GHG_tonCO2[:, 5],
"PEN_MJoil": PEN_MJoil[:, 6],
"Best configuration": Best[:, 0]}
results_to_csv = pd.DataFrame(performance_results)
fName_result = locator.get_optimization_decentralized_folder_building_result_heating(building_name)
results_to_csv.to_csv(fName_result, sep=',')
# save activation for the best supply system configuration
best_activation_df = pd.DataFrame.from_dict(heating_dispatch[indexBest]) #
best_activation_df.to_csv(
locator.get_optimization_decentralized_folder_building_result_heating_activation(building_name))
print time.clock() - t0, "seconds process time for the Disconnected Building Routine \n"
def disconnected_cooling_for_building(building_name, supply_systems, lca,
locator, prices, total_demand):
chiller_prop = supply_systems.Absorption_chiller
boiler_cost_data = supply_systems.Boiler
scale = 'BUILDING'
VCC_chiller = chiller_vapor_compression.VaporCompressionChiller(
locator, scale)
## Calculate cooling loads for different combinations
# SENSIBLE COOLING UNIT
Qc_nom_SCU_W, \
T_re_SCU_K, \
T_sup_SCU_K, \
mdot_SCU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['scu'])
# AIR HANDLING UNIT + AIR RECIRCULATION UNIT
Qc_nom_AHU_ARU_W, \
T_re_AHU_ARU_K, \
T_sup_AHU_ARU_K, \
mdot_AHU_ARU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['ahu', 'aru'])
# SENSIBLE COOLING UNIT + AIR HANDLING UNIT + AIR RECIRCULATION UNIT
Qc_nom_AHU_ARU_SCU_W, \
T_re_AHU_ARU_SCU_K, \
T_sup_AHU_ARU_SCU_K, \
mdot_AHU_ARU_SCU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['ahu', 'aru', 'scu'])
## Get hourly hot water supply condition of Solar Collectors (SC)
# Flate Plate Solar Collectors
SC_FP_data, T_hw_in_FP_C, el_aux_SC_FP_Wh, q_sc_gen_FP_Wh = get_SC_data(
building_name, locator, panel_type="FP")
Capex_a_SC_FP_USD, Opex_SC_FP_USD, Capex_SC_FP_USD = solar_collector.calc_Cinv_SC(
SC_FP_data['Area_SC_m2'][0], locator, panel_type="FP")
# Evacuated Tube Solar Collectors
SC_ET_data, T_hw_in_ET_C, el_aux_SC_ET_Wh, q_sc_gen_ET_Wh = get_SC_data(
building_name, locator, panel_type="ET")
Capex_a_SC_ET_USD, Opex_SC_ET_USD, Capex_SC_ET_USD = solar_collector.calc_Cinv_SC(
SC_ET_data['Area_SC_m2'][0], locator, panel_type="ET")
## Calculate ground temperatures to estimate cold water supply temperatures for absorption chiller
T_ground_K = calculate_ground_temperature(
locator) # FIXME: change to outlet temperature from the cooling towers
## Initialize table to save results
# save costs of all supply configurations
operation_results = initialize_result_tables_for_supply_configurations(
Qc_nom_SCU_W)
# save supply system activation of all supply configurations
cooling_dispatch = {}
## HOURLY OPERATION
print('{building_name} decentralized cooling supply system simulations...'.
format(building_name=building_name))
T_re_AHU_ARU_SCU_K = np.where(T_re_AHU_ARU_SCU_K > 0.0, T_re_AHU_ARU_SCU_K,
T_sup_AHU_ARU_SCU_K)
## 0. DX operation
print('{building_name} Config 0: Direct Expansion Units -> AHU,ARU,SCU'.
format(building_name=building_name))
el_DX_hourly_Wh, \
q_DX_chw_Wh = np.vectorize(dx.calc_DX)(mdot_AHU_ARU_SCU_kgpers, T_sup_AHU_ARU_SCU_K, T_re_AHU_ARU_SCU_K)
DX_Status = np.where(q_DX_chw_Wh > 0.0, 1, 0)
# add electricity costs, CO2, PE
operation_results[0][7] += sum(prices.ELEC_PRICE * el_DX_hourly_Wh)
operation_results[0][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_DX_hourly_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# activation
cooling_dispatch[0] = {
'Q_DX_AS_gen_directload_W': q_DX_chw_Wh,
'E_DX_AS_req_W': el_DX_hourly_Wh,
'E_cs_cre_cdata_req_W': el_DX_hourly_Wh,
}
# capacity of cooling technologies
operation_results[0][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[0][1] = Qc_nom_AHU_ARU_SCU_W # 1: DX_AS
system_COP = np.nanmedian(
np.divide(q_DX_chw_Wh[None, :], el_DX_hourly_Wh[None, :]).flatten())
operation_results[0][9] += system_COP
## 1. VCC (AHU + ARU + SCU) + CT
print(
'{building_name} Config 1: Vapor Compression Chillers -> AHU,ARU,SCU'.
format(building_name=building_name))
# VCC operation
el_VCC_Wh, q_VCC_cw_Wh, q_VCC_chw_Wh = calc_VCC_operation(
T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K, mdot_AHU_ARU_SCU_kgpers,
VCC_chiller)
VCC_Status = np.where(q_VCC_chw_Wh > 0.0, 1, 0)
# CT operation
q_CT_VCC_to_AHU_ARU_SCU_Wh = q_VCC_cw_Wh
Q_nom_CT_VCC_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(
q_CT_VCC_to_AHU_ARU_SCU_Wh)
# add costs
el_total_Wh = el_VCC_Wh + el_CT_Wh
operation_results[1][7] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
operation_results[1][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
system_COP_list = np.divide(q_VCC_chw_Wh[None, :],
el_total_Wh[None, :]).flatten()
system_COP = np.nansum(
q_VCC_chw_Wh[None, :] * system_COP_list) / np.nansum(
q_VCC_chw_Wh[None, :]) # weighted average of the system efficiency
operation_results[1][9] += system_COP
cooling_dispatch[1] = {
'Q_BaseVCC_AS_gen_directload_W': q_VCC_chw_Wh,
'E_BaseVCC_AS_req_W': el_VCC_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
}
# capacity of cooling technologies
operation_results[1][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[1][2] = Qc_nom_AHU_ARU_SCU_W # 2: BaseVCC_AS
## 2: SC_FP + single-effect ACH (AHU + ARU + SCU) + CT + Boiler + SC_FP
print(
'{building_name} Config 2: Flat-plate Solar Collectors + Single-effect Absorption chillers -> AHU,ARU,SCU'
.format(building_name=building_name))
# ACH operation
T_hw_out_single_ACH_K, \
el_single_ACH_Wh, \
q_cw_single_ACH_Wh, \
q_hw_single_ACH_Wh, \
q_chw_single_ACH_Wh = calc_ACH_operation(T_ground_K, T_hw_in_FP_C, T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K,
chiller_prop, mdot_AHU_ARU_SCU_kgpers, ACH_TYPE_SINGLE)
ACH_Status = np.where(q_chw_single_ACH_Wh > 0.0, 1, 0)
# CT operation
q_CT_FP_to_single_ACH_to_AHU_ARU_SCU_Wh = q_cw_single_ACH_Wh
Q_nom_CT_FP_to_single_ACH_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(
q_CT_FP_to_single_ACH_to_AHU_ARU_SCU_Wh)
# boiler operation
q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh, \
Q_nom_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_W, \
q_load_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh = calc_boiler_operation(Qc_nom_AHU_ARU_SCU_W,
T_hw_out_single_ACH_K,
q_hw_single_ACH_Wh,
q_sc_gen_FP_Wh)
# add electricity costs
el_total_Wh = el_single_ACH_Wh + el_aux_SC_FP_Wh + el_CT_Wh
operation_results[2][7] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
operation_results[2][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# add gas costs
q_gas_total_Wh = q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh
operation_results[2][7] += sum(prices.NG_PRICE * q_gas_total_Wh) # CHF
operation_results[2][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(q_gas_total_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
cooling_dispatch[2] = {
'Q_ACH_gen_directload_W': q_chw_single_ACH_Wh,
'Q_Boiler_NG_ACH_W': q_load_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh,
'Q_SC_FP_ACH_W': q_sc_gen_FP_Wh,
'E_ACH_req_W': el_single_ACH_Wh,
'E_CT_req_W': el_CT_Wh,
'E_SC_FP_req_W': el_aux_SC_FP_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'NG_Boiler_req': q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh,
}
# capacity of cooling technologies
operation_results[2][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[2][4] = Qc_nom_AHU_ARU_SCU_W # 4: ACH_SC_FP
q_total_load = q_chw_single_ACH_Wh[None, :] + q_sc_gen_FP_Wh[
None, :] + q_load_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh[None, :]
system_COP_list = np.divide(
q_total_load,
(el_total_Wh[None, :] +
q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh[None, :])).flatten()
system_COP = np.nansum(q_total_load * system_COP_list) / np.nansum(
q_total_load) # weighted average of the system efficiency
operation_results[2][9] += system_COP
# 3: SC_ET + single-effect ACH (AHU + ARU + SCU) + CT + Boiler + SC_ET
print(
'{building_name} Config 3: Evacuated Tube Solar Collectors + Single-effect Absorption chillers -> AHU,ARU,SCU'
.format(building_name=building_name))
# ACH operation
T_hw_out_single_ACH_K, \
el_single_ACH_Wh, \
q_cw_single_ACH_Wh, \
q_hw_single_ACH_Wh, \
q_chw_single_ACH_Wh = calc_ACH_operation(T_ground_K, T_hw_in_ET_C, T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K,
chiller_prop, mdot_AHU_ARU_SCU_kgpers, ACH_TYPE_SINGLE)
# CT operation
q_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W = q_cw_single_ACH_Wh
Q_nom_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(
q_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W)
# burner operation
q_gas_for_burner_Wh, \
Q_nom_Burner_ET_to_single_ACH_to_AHU_ARU_SCU_W, \
q_burner_load_Wh = calc_burner_operation(Qc_nom_AHU_ARU_SCU_W, q_hw_single_ACH_Wh, q_sc_gen_ET_Wh)
# add electricity costs
el_total_Wh = el_single_ACH_Wh + el_aux_SC_ET_Wh + el_CT_Wh
operation_results[3][7] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
operation_results[3][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# add gas costs
operation_results[3][7] += sum(prices.NG_PRICE *
q_gas_for_burner_Wh) # CHF
operation_results[3][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(q_gas_for_burner_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
cooling_dispatch[3] = {
'Q_ACH_gen_directload_W': q_chw_single_ACH_Wh,
'Q_Burner_NG_ACH_W': q_burner_load_Wh,
'Q_SC_ET_ACH_W': q_sc_gen_ET_Wh,
'E_ACH_req_W': el_single_ACH_Wh,
'E_CT_req_W': el_CT_Wh,
'E_SC_ET_req_W': el_aux_SC_ET_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'NG_Burner_req': q_gas_for_burner_Wh,
}
# capacity of cooling technologies
operation_results[3][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[3][5] = Qc_nom_AHU_ARU_SCU_W
q_total_load = (q_burner_load_Wh[None, :] + q_chw_single_ACH_Wh[None, :] +
q_sc_gen_ET_Wh[None, :])
system_COP_list = np.divide(
q_total_load,
(el_total_Wh[None, :] + q_gas_for_burner_Wh[None, :])).flatten()
system_COP = np.nansum(q_total_load * system_COP_list) / np.nansum(
q_total_load) # weighted average of the system efficiency
operation_results[3][9] += system_COP
# these two configurations are only activated when SCU is in use
if Qc_nom_SCU_W > 0.0:
# 4: VCC (AHU + ARU) + VCC (SCU) + CT
print(
'{building_name} Config 4: Vapor Compression Chillers(HT) -> SCU & Vapor Compression Chillers(LT) -> AHU,ARU'
.format(building_name=building_name))
# VCC (AHU + ARU) operation
el_VCC_to_AHU_ARU_Wh, \
q_cw_VCC_to_AHU_ARU_Wh, \
q_chw_VCC_to_AHU_ARU_Wh = calc_VCC_operation(T_re_AHU_ARU_K, T_sup_AHU_ARU_K, mdot_AHU_ARU_kgpers, VCC_chiller)
VCC_LT_Status = np.where(q_chw_VCC_to_AHU_ARU_Wh > 0.0, 1, 0)
# VCC(SCU) operation
el_VCC_to_SCU_Wh, \
q_cw_VCC_to_SCU_Wh, \
q_chw_VCC_to_SCU_Wh = calc_VCC_operation(T_re_SCU_K, T_sup_SCU_K, mdot_SCU_kgpers, VCC_chiller)
VCC_HT_Status = np.where(q_chw_VCC_to_AHU_ARU_Wh > 0.0, 1, 0)
# CT operation
q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W = q_cw_VCC_to_AHU_ARU_Wh + q_cw_VCC_to_SCU_Wh
Q_nom_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W, el_CT_Wh = calc_CT_operation(
q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W)
# add el costs
el_total_Wh = el_VCC_to_AHU_ARU_Wh + el_VCC_to_SCU_Wh + el_CT_Wh
operation_results[4][7] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
operation_results[4][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# add activation
cooling_dispatch[4] = {
'Q_BaseVCC_AS_gen_directload_W': q_chw_VCC_to_AHU_ARU_Wh,
'Q_BaseVCCHT_AS_gen_directload_W': q_chw_VCC_to_SCU_Wh,
'E_BaseVCC_req_W': el_VCC_to_AHU_ARU_Wh,
'E_VCC_HT_req_W': el_VCC_to_SCU_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh
}
# capacity of cooling technologies
operation_results[4][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[4][2] = Qc_nom_AHU_ARU_W # 2: BaseVCC_AS
operation_results[4][3] = Qc_nom_SCU_W # 3: VCCHT_AS
system_COP_list = np.divide(
q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W[None, :],
el_total_Wh[None, :]).flatten()
system_COP = np.nansum(
q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W[None, :] *
system_COP_list) / np.nansum(q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W[
None, :]) # weighted average of the system efficiency
operation_results[4][9] += system_COP
# 5: VCC (AHU + ARU) + ACH (SCU) + CT
print(
'{building_name} Config 5: Vapor Compression Chillers(LT) -> AHU,ARU & Flate-place SC + Absorption Chillers(HT) -> SCU'
.format(building_name=building_name))
# ACH (SCU) operation
T_hw_FP_ACH_to_SCU_K, \
el_FP_ACH_to_SCU_Wh, \
q_cw_FP_ACH_to_SCU_Wh, \
q_hw_FP_ACH_to_SCU_Wh, \
q_chw_FP_ACH_to_SCU_Wh = calc_ACH_operation(T_ground_K, T_hw_in_FP_C, T_re_SCU_K, T_sup_SCU_K, chiller_prop,
mdot_SCU_kgpers, ACH_TYPE_SINGLE)
ACH_HT_Status = np.where(q_chw_FP_ACH_to_SCU_Wh > 0.0, 1, 0)
# boiler operation
q_gas_for_boiler_Wh, \
Q_nom_boiler_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, \
q_load_from_boiler_Wh = calc_boiler_operation(Qc_nom_SCU_W, T_hw_FP_ACH_to_SCU_K,
q_hw_FP_ACH_to_SCU_Wh, q_sc_gen_FP_Wh)
# CT operation
q_CT_VCC_to_AHU_ARU_and_single_ACH_to_SCU_Wh = q_cw_VCC_to_AHU_ARU_Wh + q_cw_FP_ACH_to_SCU_Wh
Q_nom_CT_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, \
el_CT_Wh = calc_CT_operation(q_CT_VCC_to_AHU_ARU_and_single_ACH_to_SCU_Wh)
# add electricity costs
el_total_Wh = el_VCC_to_AHU_ARU_Wh + el_FP_ACH_to_SCU_Wh + el_aux_SC_FP_Wh + el_CT_Wh
operation_results[5][7] += sum(prices.ELEC_PRICE * el_total_Wh) # CHF
operation_results[5][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(el_total_Wh,
lca.EL_TO_CO2_EQ)) # ton CO2
# add gas costs
q_gas_total_Wh = q_gas_for_boiler_Wh
operation_results[5][7] += sum(prices.NG_PRICE * q_gas_total_Wh) # CHF
operation_results[5][8] += sum(
calc_emissions_Whyr_to_tonCO2yr(q_gas_total_Wh,
lca.NG_TO_CO2_EQ)) # ton CO2
# add activation
cooling_dispatch[5] = {
'Q_BaseVCC_AS_gen_directload_W': q_chw_VCC_to_AHU_ARU_Wh,
'Q_ACHHT_AS_gen_directload_W': q_chw_FP_ACH_to_SCU_Wh,
'E_BaseVCC_req_W': el_VCC_to_AHU_ARU_Wh,
'E_ACHHT_req_W': el_FP_ACH_to_SCU_Wh,
'E_SC_FP_ACH_req_W': el_aux_SC_FP_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'Q_BaseBoiler_NG_req': q_gas_for_boiler_Wh,
}
# capacity of cooling technologies
operation_results[5][0] = Qc_nom_AHU_ARU_SCU_W
operation_results[5][2] = Qc_nom_AHU_ARU_W # 2: BaseVCC_AS
operation_results[5][6] = Qc_nom_SCU_W # 6: ACHHT_SC_FP
q_total_load = q_CT_VCC_to_AHU_ARU_and_single_ACH_to_SCU_Wh[
None, :] + q_gas_for_boiler_Wh[None, :]
system_COP_list = np.divide(q_total_load,
el_total_Wh[None, :]).flatten()
system_COP = np.nansum(q_total_load * system_COP_list) / np.nansum(
q_total_load) # weighted average of the system efficiency
operation_results[5][9] += system_COP
## Calculate Capex/Opex
# Initialize arrays
number_of_configurations = len(operation_results)
Capex_a_USD = np.zeros((number_of_configurations, 1))
Capex_total_USD = np.zeros((number_of_configurations, 1))
Opex_a_fixed_USD = np.zeros((number_of_configurations, 1))
print('{building_name} Cost calculation...'.format(
building_name=building_name))
# 0: DX
Capex_a_DX_USD, Opex_fixed_DX_USD, Capex_DX_USD = dx.calc_Cinv_DX(
Qc_nom_AHU_ARU_SCU_W)
# add costs
Capex_a_USD[0][0] = Capex_a_DX_USD
Capex_total_USD[0][0] = Capex_DX_USD
Opex_a_fixed_USD[0][0] = Opex_fixed_DX_USD
# 1: VCC + CT
Capex_a_VCC_USD, Opex_fixed_VCC_USD, Capex_VCC_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_AHU_ARU_SCU_W, locator, 'CH3')
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_SCU_W, locator, 'CT1')
# add costs
Capex_a_USD[1][0] = Capex_a_CT_USD + Capex_a_VCC_USD
Capex_total_USD[1][0] = Capex_CT_USD + Capex_VCC_USD
Opex_a_fixed_USD[1][0] = Opex_fixed_CT_USD + Opex_fixed_VCC_USD
# 2: single effect ACH + CT + Boiler + SC_FP
Capex_a_ACH_USD, Opex_fixed_ACH_USD, Capex_ACH_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_AHU_ARU_SCU_W, supply_systems.Absorption_chiller,
ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_FP_to_single_ACH_to_AHU_ARU_SCU_W, locator, 'CT1')
Capex_a_boiler_USD, Opex_fixed_boiler_USD, Capex_boiler_USD = boiler.calc_Cinv_boiler(
Q_nom_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_W, 'BO1',
boiler_cost_data)
Capex_a_USD[2][
0] = Capex_a_CT_USD + Capex_a_ACH_USD + Capex_a_boiler_USD + Capex_a_SC_FP_USD
Capex_total_USD[2][
0] = Capex_CT_USD + Capex_ACH_USD + Capex_boiler_USD + Capex_SC_FP_USD
Opex_a_fixed_USD[2][
0] = Opex_fixed_CT_USD + Opex_fixed_ACH_USD + Opex_fixed_boiler_USD + Opex_SC_FP_USD
# 3: double effect ACH + CT + Boiler + SC_ET
Capex_a_ACH_USD, Opex_fixed_ACH_USD, Capex_ACH_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_AHU_ARU_SCU_W, supply_systems.Absorption_chiller,
ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W, locator, 'CT1')
Capex_a_burner_USD, Opex_fixed_burner_USD, Capex_burner_USD = burner.calc_Cinv_burner(
Q_nom_Burner_ET_to_single_ACH_to_AHU_ARU_SCU_W, boiler_cost_data,
'BO1')
Capex_a_USD[3][
0] = Capex_a_CT_USD + Capex_a_ACH_USD + Capex_a_burner_USD + Capex_a_SC_ET_USD
Capex_total_USD[3][
0] = Capex_CT_USD + Capex_ACH_USD + Capex_burner_USD + Capex_SC_ET_USD
Opex_a_fixed_USD[3][
0] = Opex_fixed_CT_USD + Opex_fixed_ACH_USD + Opex_fixed_burner_USD + Opex_SC_ET_USD
# these two configurations are only activated when SCU is in use
if Qc_nom_SCU_W > 0.0:
# 4: VCC (AHU + ARU) + VCC (SCU) + CT
Capex_a_VCC_AA_USD, Opex_VCC_AA_USD, Capex_VCC_AA_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_AHU_ARU_W, locator, 'CH3')
Capex_a_VCC_S_USD, Opex_VCC_S_USD, Capex_VCC_S_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_SCU_W, locator, 'CH3')
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W, locator, 'CT1')
Capex_a_USD[4][
0] = Capex_a_CT_USD + Capex_a_VCC_AA_USD + Capex_a_VCC_S_USD
Capex_total_USD[4][
0] = Capex_CT_USD + Capex_VCC_AA_USD + Capex_VCC_S_USD
Opex_a_fixed_USD[4][
0] = Opex_fixed_CT_USD + Opex_VCC_AA_USD + Opex_VCC_S_USD
# 5: VCC (AHU + ARU) + ACH (SCU) + CT + Boiler + SC_FP
Capex_a_ACH_S_USD, Opex_fixed_ACH_S_USD, Capex_ACH_S_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_SCU_W, supply_systems.Absorption_chiller, ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, locator,
'CT1')
Capex_a_boiler_USD, Opex_fixed_boiler_USD, Capex_boiler_USD = boiler.calc_Cinv_boiler(
Q_nom_boiler_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, 'BO1',
boiler_cost_data)
Capex_a_USD[5][0] = Capex_a_CT_USD + Capex_a_VCC_AA_USD + Capex_a_ACH_S_USD + \
Capex_a_SC_FP_USD + Capex_a_boiler_USD
Capex_total_USD[5][0] = Capex_CT_USD + Capex_VCC_AA_USD + Capex_ACH_S_USD + \
Capex_SC_FP_USD + Capex_boiler_USD
Opex_a_fixed_USD[5][0] = Opex_fixed_CT_USD + Opex_VCC_AA_USD + Opex_fixed_ACH_S_USD + \
Opex_SC_FP_USD + Opex_fixed_boiler_USD
## write all results from the configurations into TotalCosts, TotalCO2, TotalPrim
Opex_a_USD, TAC_USD, TotalCO2, TotalPrim = compile_TAC_CO2_Prim(
Capex_a_USD, Opex_a_fixed_USD, number_of_configurations,
operation_results)
## Determine the best configuration
Best, indexBest = rank_results(TAC_USD, TotalCO2, TotalPrim,
number_of_configurations)
# Save results in csv file
performance_results = {
"Nominal heating load": operation_results[:, 0],
"Capacity_DX_AS_W": operation_results[:, 1],
"Capacity_BaseVCC_AS_W": operation_results[:, 2],
"Capacity_VCCHT_AS_W": operation_results[:, 3],
"Capacity_ACH_SC_FP_W": operation_results[:, 4],
"Capaticy_ACH_SC_ET_W": operation_results[:, 5],
"Capacity_ACHHT_FP_W": operation_results[:, 6],
"Capex_a_USD": Capex_a_USD[:, 0],
"Capex_total_USD": Capex_total_USD[:, 0],
"Opex_fixed_USD": Opex_a_fixed_USD[:, 0],
"Opex_var_USD": operation_results[:, 7],
"GHG_tonCO2": operation_results[:, 8],
"TAC_USD": TAC_USD[:, 1],
"Best configuration": Best[:, 0],
"system_COP": operation_results[:, 9],
}
performance_results_df = pd.DataFrame(performance_results)
performance_results_df.to_csv(
locator.get_optimization_decentralized_folder_building_result_cooling(
building_name),
index=False)
# save activation for the best supply system configuration
best_activation_df = pd.DataFrame.from_dict(cooling_dispatch[indexBest]) #
best_activation_df.to_csv(
locator.
get_optimization_decentralized_folder_building_cooling_activation(
building_name),
index=False)
def addCosts(buildList, locator, master_to_slave_vars, Q_uncovered_design_W,
Q_uncovered_annual_W, solar_features, network_features, gv,
config, prices, lca):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param DHN_barcode: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param master_to_slave_vars: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solar_features: solar features
:param network_features: network features
:param gv: global variables
:type indCombi: string
:type buildList: list
:type locator: string
:type master_to_slave_vars: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solar_features: class
:type network_features: class
:type gv: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
DHN_barcode = master_to_slave_vars.DHN_barcode
DCN_barcode = master_to_slave_vars.DCN_barcode
addcosts_Capex_a_USD = 0
addcosts_Opex_fixed_USD = 0
addcosts_Capex_USD = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
Capex_Disconnected = 0
Opex_Disconnected = 0
Capex_a_furnace_USD = 0
Capex_a_CHP_USD = 0
Capex_a_Boiler_USD = 0
Capex_a_Boiler_peak_USD = 0
Capex_a_Lake_USD = 0
Capex_a_Sewage_USD = 0
Capex_a_GHP_USD = 0
Capex_a_PV_USD = 0
Capex_a_SC_ET_USD = 0
Capex_a_SC_FP_USD = 0
Capex_a_PVT_USD = 0
Capex_a_Boiler_backup_USD = 0
Capex_a_HEX = 0
Capex_a_storage_HP = 0
Capex_a_HP_storage_USD = 0
Opex_fixed_SC = 0
Opex_fixed_PVT_USD = 0
Opex_fixed_HP_PVT_USD = 0
Opex_fixed_furnace_USD = 0
Opex_fixed_CHP_USD = 0
Opex_fixed_Boiler_USD = 0
Opex_fixed_Boiler_peak_USD = 0
Opex_fixed_Boiler_backup_USD = 0
Opex_fixed_Lake_USD = 0
Opex_fixed_wasteserver_HEX_USD = 0
Opex_fixed_wasteserver_HP_USD = 0
Opex_fixed_PV_USD = 0
Opex_fixed_GHP_USD = 0
Opex_fixed_storage_USD = 0
Opex_fixed_Sewage_USD = 0
Opex_fixed_HP_storage_USD = 0
StorageInvC = 0
NetworkCost_a_USD = 0
SubstHEXCost_capex = 0
SubstHEXCost_opex = 0
PVTHEXCost_Capex = 0
PVTHEXCost_Opex = 0
SCHEXCost_Capex = 0
SCHEXCost_Opex = 0
pumpCosts = 0
GasConnectionInvCost = 0
cost_PV_disconnected = 0
CO2_PV_disconnected = 0
Eprim_PV_disconnected = 0
Capex_furnace_USD = 0
Capex_CHP_USD = 0
Capex_Boiler_USD = 0
Capex_Boiler_peak_USD = 0
Capex_Lake_USD = 0
Capex_Sewage_USD = 0
Capex_GHP = 0
Capex_PV_USD = 0
Capex_SC = 0
Capex_PVT_USD = 0
Capex_Boiler_backup_USD = 0
Capex_HEX = 0
Capex_storage_HP = 0
Capex_HP_storage = 0
Capex_SC_ET_USD = 0
Capex_SC_FP_USD = 0
Capex_PVT_USD = 0
Capex_Boiler_backup_USD = 0
Capex_HP_storage_USD = 0
Capex_storage_HP = 0
Capex_CHP_USD = 0
Capex_furnace_USD = 0
Capex_Boiler_USD = 0
Capex_Boiler_peak_USD = 0
Capex_Lake_USD = 0
Capex_Sewage_USD = 0
Capex_pump_USD = 0
if config.district_heating_network:
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "0":
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_heating(
building_name))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
else:
nBuildinNtw += 1
if config.district_cooling_network:
PV_barcode = ''
for (index, building_name) in zip(DCN_barcode, buildList):
if index == "0": # choose the best decentralized configuration
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, configuration='AHU_ARU_SCU'))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (FP)"].iloc[0] == 1:
to_PV = 0
else: # adding costs for buildings in which the centralized plant provides a part of the load requirements
DCN_unit_configuration = master_to_slave_vars.DCN_supplyunits
if DCN_unit_configuration == 1: # corresponds to AHU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU_SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 2: # corresponds to ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 3: # corresponds to SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_ARU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 4: # corresponds to AHU + ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'SCU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 5: # corresponds to AHU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 6: # corresponds to ARU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU'
df = pd.read_csv(
locator.
get_optimization_decentralized_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 7: # corresponds to AHU + ARU + SCU from central plant
to_PV = 1
nBuildinNtw += 1
PV_barcode = PV_barcode + str(to_PV)
addcosts_Capex_a_USD += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
# Solar technologies
PV_installed_area_m2 = master_to_slave_vars.SOLAR_PART_PV * solar_features.A_PV_m2 # kW
Capex_a_PV_USD, Opex_fixed_PV_USD, Capex_PV_USD = pv.calc_Cinv_pv(
PV_installed_area_m2, locator, config)
addcosts_Capex_a_USD += Capex_a_PV_USD
addcosts_Opex_fixed_USD += Opex_fixed_PV_USD
addcosts_Capex_USD += Capex_PV_USD
SC_ET_area_m2 = master_to_slave_vars.SOLAR_PART_SC_ET * solar_features.A_SC_ET_m2
Capex_a_SC_ET_USD, Opex_fixed_SC_ET_USD, Capex_SC_ET_USD = stc.calc_Cinv_SC(
SC_ET_area_m2, locator, config, 'ET')
addcosts_Capex_a_USD += Capex_a_SC_ET_USD
addcosts_Opex_fixed_USD += Opex_fixed_SC_ET_USD
addcosts_Capex_USD += Capex_SC_ET_USD
SC_FP_area_m2 = master_to_slave_vars.SOLAR_PART_SC_FP * solar_features.A_SC_FP_m2
Capex_a_SC_FP_USD, Opex_fixed_SC_FP_USD, Capex_SC_FP_USD = stc.calc_Cinv_SC(
SC_FP_area_m2, locator, config, 'FP')
addcosts_Capex_a_USD += Capex_a_SC_FP_USD
addcosts_Opex_fixed_USD += Opex_fixed_SC_FP_USD
addcosts_Capex_USD += Capex_SC_FP_USD
PVT_peak_kW = master_to_slave_vars.SOLAR_PART_PVT * solar_features.A_PVT_m2 * N_PVT # kW
Capex_a_PVT_USD, Opex_fixed_PVT_USD, Capex_PVT_USD = pvt.calc_Cinv_PVT(
PVT_peak_kW, locator, config)
addcosts_Capex_a_USD += Capex_a_PVT_USD
addcosts_Opex_fixed_USD += Opex_fixed_PVT_USD
addcosts_Capex_USD += Capex_PVT_USD
# Add the features for the distribution
if DHN_barcode.count("1") > 0 and config.district_heating_network:
os.chdir(
locator.get_optimization_slave_results_folder(
master_to_slave_vars.generation_number))
# Add the investment costs of the energy systems
# Furnace
if master_to_slave_vars.Furnace_on == 1:
P_design_W = master_to_slave_vars.Furnace_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern_heating(
master_to_slave_vars.configKey,
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace_W"])
arrayFurnace_W = np.array(dfFurnace)
Q_annual_W = 0
for i in range(int(np.shape(arrayFurnace_W)[0])):
Q_annual_W += arrayFurnace_W[i][0]
Capex_a_furnace_USD, Opex_fixed_furnace_USD, Capex_furnace_USD = furnace.calc_Cinv_furnace(
P_design_W, Q_annual_W, config, locator, 'FU1')
addcosts_Capex_a_USD += Capex_a_furnace_USD
addcosts_Opex_fixed_USD += Opex_fixed_furnace_USD
addcosts_Capex_USD += Capex_furnace_USD
# CC
if master_to_slave_vars.CC_on == 1:
CC_size_W = master_to_slave_vars.CC_GT_SIZE_W
Capex_a_CHP_USD, Opex_fixed_CHP_USD, Capex_CHP_USD = chp.calc_Cinv_CCGT(
CC_size_W, locator, config)
addcosts_Capex_a_USD += Capex_a_CHP_USD
addcosts_Opex_fixed_USD += Opex_fixed_CHP_USD
addcosts_Capex_USD += Capex_CHP_USD
# Boiler Base
if master_to_slave_vars.Boiler_on == 1:
Q_design_W = master_to_slave_vars.Boiler_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerBase = pd.read_csv(fNameSlavePP,
usecols=["Q_BaseBoiler_W"])
arrayBoilerBase_W = np.array(dfBoilerBase)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerBase_W)[0])):
Q_annual_W += arrayBoilerBase_W[i][0]
Capex_a_Boiler_USD, Opex_fixed_Boiler_USD, Capex_Boiler_USD = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_USD
addcosts_Capex_USD += Capex_Boiler_USD
# Boiler Peak
if master_to_slave_vars.BoilerPeak_on == 1:
Q_design_W = master_to_slave_vars.BoilerPeak_Q_max_W
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerPeak = pd.read_csv(fNameSlavePP,
usecols=["Q_PeakBoiler_W"])
arrayBoilerPeak_W = np.array(dfBoilerPeak)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerPeak_W)[0])):
Q_annual_W += arrayBoilerPeak_W[i][0]
Capex_a_Boiler_peak_USD, Opex_fixed_Boiler_peak_USD, Capex_Boiler_peak_USD = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_peak_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_peak_USD
addcosts_Capex_USD += Capex_Boiler_peak_USD
# HP Lake
if master_to_slave_vars.HP_Lake_on == 1:
HP_Size_W = master_to_slave_vars.HPLake_maxSize_W
Capex_a_Lake_USD, Opex_fixed_Lake_USD, Capex_Lake_USD = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a_USD += Capex_a_Lake_USD
addcosts_Opex_fixed_USD += Opex_fixed_Lake_USD
addcosts_Capex_USD += Capex_Lake_USD
# HP Sewage
if master_to_slave_vars.HP_Sew_on == 1:
HP_Size_W = master_to_slave_vars.HPSew_maxSize_W
Capex_a_Sewage_USD, Opex_fixed_Sewage_USD, Capex_Sewage_USD = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a_USD += Capex_a_Sewage_USD
addcosts_Opex_fixed_USD += Opex_fixed_Sewage_USD
addcosts_Capex_USD += Capex_Sewage_USD
# GHP
if master_to_slave_vars.GHP_on == 1:
fNameSlavePP = locator.get_optimization_slave_electricity_activation_pattern_heating(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_used_GHP_W"])
arrayGHP_W = np.array(dfGHP)
GHP_Enom_W = np.amax(arrayGHP_W)
Capex_a_GHP_USD, Opex_fixed_GHP_USD, Capex_GHP_USD = hp.calc_Cinv_GHP(
GHP_Enom_W, locator, config)
addcosts_Capex_a_USD += Capex_a_GHP_USD * prices.EURO_TO_CHF
addcosts_Opex_fixed_USD += Opex_fixed_GHP_USD * prices.EURO_TO_CHF
addcosts_Capex_USD += Capex_GHP_USD
# Back-up boiler
Capex_a_Boiler_backup_USD, Opex_fixed_Boiler_backup_USD, Capex_Boiler_backup_USD = boiler.calc_Cinv_boiler(
Q_uncovered_design_W, locator, config, 'BO1')
addcosts_Capex_a_USD += Capex_a_Boiler_backup_USD
addcosts_Opex_fixed_USD += Opex_fixed_Boiler_backup_USD
addcosts_Capex_USD += Capex_Boiler_backup_USD
master_to_slave_vars.BoilerBackup_Q_max_W = Q_uncovered_design_W
# Hex and HP for Heat recovery
if master_to_slave_vars.WasteServersHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
master_to_slave_vars.network_data_file_heating),
usecols=["Qcdata_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
Capex_a_wasteserver_HEX_USD, Opex_fixed_wasteserver_HEX_USD, Capex_wasteserver_HEX_USD = hex.calc_Cinv_HEX(
Q_HEX_max_kWh, locator, config, 'HEX1')
addcosts_Capex_a_USD += (Capex_a_wasteserver_HEX_USD)
addcosts_Opex_fixed_USD += Opex_fixed_wasteserver_HEX_USD
addcosts_Capex_USD += Capex_wasteserver_HEX_USD
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPServerHeatDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
Capex_a_wasteserver_HP_USD, Opex_fixed_wasteserver_HP_USD, Capex_wasteserver_HP_USD = hp.calc_Cinv_HP(
Q_HP_max_kWh, locator, config, 'HP2')
addcosts_Capex_a_USD += (Capex_a_wasteserver_HP_USD)
addcosts_Opex_fixed_USD += Opex_fixed_wasteserver_HP_USD
addcosts_Capex_USD += Capex_wasteserver_HP_USD
# Heat pump from solar to DH
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPScDesignArray_Wh", "HPpvt_designArray_Wh"])
array = np.array(df)
Q_HP_max_PVT_wh = np.amax(array[:, 1])
Q_HP_max_SC_Wh = np.amax(array[:, 0])
Capex_a_HP_PVT_USD, Opex_fixed_HP_PVT_USD, Capex_HP_PVT_USD = hp.calc_Cinv_HP(
Q_HP_max_PVT_wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_PVT_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_PVT_USD
addcosts_Capex_USD += Capex_HP_PVT_USD
Capex_a_HP_SC_USD, Opex_fixed_HP_SC_USD, Capex_HP_SC_USD = hp.calc_Cinv_HP(
Q_HP_max_SC_Wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_SC_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_SC_USD
addcosts_Capex_USD += Capex_HP_SC_USD
# HP for storage operation for charging from solar and discharging to DH
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=[
"E_aux_ch_W", "E_aux_dech_W",
"Q_from_storage_used_W", "Q_to_storage_W"
])
array = np.array(df)
Q_HP_max_storage_W = 0
for i in range(DAYS_IN_YEAR * HOURS_IN_DAY):
if array[i][0] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][3] + array[i][0])
elif array[i][1] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][2] + array[i][1])
Capex_a_HP_storage_USD, Opex_fixed_HP_storage_USD, Capex_HP_storage_USD = hp.calc_Cinv_HP(
Q_HP_max_storage_W, locator, config, 'HP2')
addcosts_Capex_a_USD += (Capex_a_HP_storage_USD)
addcosts_Opex_fixed_USD += Opex_fixed_HP_storage_USD
addcosts_Capex_USD += Capex_HP_storage_USD
# Storage
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["Storage_Size_m3"],
nrows=1)
StorageVol_m3 = np.array(df)[0][0]
Capex_a_storage_USD, Opex_fixed_storage_USD, Capex_storage_USD = storage.calc_Cinv_storage(
StorageVol_m3, locator, config, 'TES2')
addcosts_Capex_a_USD += Capex_a_storage_USD
addcosts_Opex_fixed_USD += Opex_fixed_storage_USD
addcosts_Capex_USD += Capex_storage_USD
# Costs from distribution configuration
if gv.ZernezFlag == 1:
NetworkCost_a_USD, NetworkCost_USD = network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv)
NetworkCost_a_USD = NetworkCost_a_USD * nBuildinNtw / len(
buildList)
NetworkCost_USD = NetworkCost_USD * nBuildinNtw / len(buildList)
else:
NetworkCost_USD = network_features.pipesCosts_DHN_USD
NetworkCost_USD = NetworkCost_USD * nBuildinNtw / len(buildList)
NetworkCost_a_USD = NetworkCost_USD * gv.PipeInterestRate * (
1 + gv.PipeInterestRate)**gv.PipeLifeTime / (
(1 + gv.PipeInterestRate)**gv.PipeLifeTime - 1)
addcosts_Capex_a_USD += NetworkCost_a_USD
addcosts_Capex_USD += NetworkCost_USD
# HEX (1 per building in ntw)
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "1":
df = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name),
usecols=["Q_dhw_W", "Q_heating_W"])
subsArray = np.array(df)
Q_max_W = np.amax(subsArray[:, 0] + subsArray[:, 1])
Capex_a_HEX_building_USD, Opex_fixed_HEX_building_USD, Capex_HEX_building_USD = hex.calc_Cinv_HEX(
Q_max_W, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_building_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_building_USD
addcosts_Capex_USD += Capex_HEX_building_USD
# HEX for solar
roof_area_m2 = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
areaAvail += roof_area_m2[i][0]
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
share = roof_area_m2[i][0] / areaAvail
#print share, "solar area share", buildList[i]
Q_max_SC_ET_Wh = solar_features.Q_nom_SC_ET_Wh * master_to_slave_vars.SOLAR_PART_SC_ET * share
Capex_a_HEX_SC_ET_USD, Opex_fixed_HEX_SC_ET_USD, Capex_HEX_SC_ET_USD = hex.calc_Cinv_HEX(
Q_max_SC_ET_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_SC_ET_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_SC_ET_USD
addcosts_Capex_USD += Capex_HEX_SC_ET_USD
Q_max_SC_FP_Wh = solar_features.Q_nom_SC_FP_Wh * master_to_slave_vars.SOLAR_PART_SC_FP * share
Capex_a_HEX_SC_FP_USD, Opex_fixed_HEX_SC_FP_USD, Capex_HEX_SC_FP_USD = hex.calc_Cinv_HEX(
Q_max_SC_FP_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_SC_FP_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_SC_FP_USD
addcosts_Capex_USD += Capex_HEX_SC_FP_USD
Q_max_PVT_Wh = solar_features.Q_nom_PVT_Wh * master_to_slave_vars.SOLAR_PART_PVT * share
Capex_a_HEX_PVT_USD, Opex_fixed_HEX_PVT_USD, Capex_HEX_PVT_USD = hex.calc_Cinv_HEX(
Q_max_PVT_Wh, locator, config, 'HEX1')
addcosts_Capex_a_USD += Capex_a_HEX_PVT_USD
addcosts_Opex_fixed_USD += Opex_fixed_HEX_PVT_USD
addcosts_Capex_USD += Capex_HEX_PVT_USD
# Pump operation costs
Capex_a_pump_USD, Opex_fixed_pump_USD, Opex_var_pump_USD, Capex_pump_USD = pumps.calc_Ctot_pump(
master_to_slave_vars, network_features, gv, locator, lca, config)
addcosts_Capex_a_USD += Capex_a_pump_USD
addcosts_Opex_fixed_USD += Opex_fixed_pump_USD
addcosts_Capex_USD += Capex_pump_USD
# import gas consumption data from:
if DHN_barcode.count("1") > 0 and config.district_heating_network:
# import gas consumption data from:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_natural_gas_imports(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_gas_primary_peak_power_W = np.amax(
EgasPrimaryDataframe_W['NG_total_W'])
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
elif DCN_barcode.count("1") > 0 and config.district_cooling_network:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_natural_gas_imports(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_gas_primary_peak_power_W = np.amax(
EgasPrimaryDataframe_W['NG_total_W'])
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
else:
GasConnectionInvCost = 0.0
addcosts_Capex_a_USD += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"Capex_a_SC_ET_USD": [Capex_a_SC_ET_USD],
"Capex_a_SC_FP_USD": [Capex_a_SC_FP_USD],
"Opex_fixed_SC": [Opex_fixed_SC],
"Capex_a_PVT": [Capex_a_PVT_USD],
"Opex_fixed_PVT": [Opex_fixed_PVT_USD],
"Capex_a_Boiler_backup": [Capex_a_Boiler_backup_USD],
"Opex_fixed_Boiler_backup": [Opex_fixed_Boiler_backup_USD],
"Capex_a_storage_HEX": [Capex_a_HP_storage_USD],
"Opex_fixed_storage_HEX": [Opex_fixed_HP_storage_USD],
"Capex_a_storage_HP": [Capex_a_storage_HP],
"Capex_a_CHP": [Capex_a_CHP_USD],
"Opex_fixed_CHP": [Opex_fixed_CHP_USD],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + Capex_a_storage_HP + Capex_a_HEX],
"NetworkCost": [NetworkCost_a_USD],
"SubstHEXCost": [SubstHEXCost_capex],
"DHNInvestCost": [addcosts_Capex_a_USD - CostDiscBuild],
"PVTHEXCost_Capex": [PVTHEXCost_Capex],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"Capex_a_furnace": [Capex_a_furnace_USD],
"Opex_fixed_furnace": [Opex_fixed_furnace_USD],
"Capex_a_Boiler": [Capex_a_Boiler_USD],
"Opex_fixed_Boiler": [Opex_fixed_Boiler_USD],
"Capex_a_Boiler_peak": [Capex_a_Boiler_peak_USD],
"Opex_fixed_Boiler_peak": [Opex_fixed_Boiler_peak_USD],
"Capex_Disconnected": [Capex_Disconnected],
"Opex_Disconnected": [Opex_Disconnected],
"Capex_a_Lake": [Capex_a_Lake_USD],
"Opex_fixed_Lake": [Opex_fixed_Lake_USD],
"Capex_a_Sewage": [Capex_a_Sewage_USD],
"Opex_fixed_Sewage": [Opex_fixed_Sewage_USD],
"SCHEXCost_Capex": [SCHEXCost_Capex],
"Capex_a_pump": [Capex_a_pump_USD],
"Opex_fixed_pump": [Opex_fixed_pump_USD],
"Opex_var_pump": [Opex_var_pump_USD],
"Sum_CAPEX": [addcosts_Capex_a_USD],
"Sum_OPEX_fixed": [addcosts_Opex_fixed_USD],
"GasConnectionInvCa": [GasConnectionInvCost],
"CO2_PV_disconnected": [CO2_PV_disconnected],
"cost_PV_disconnected": [cost_PV_disconnected],
"Eprim_PV_disconnected": [Eprim_PV_disconnected],
"Capex_SC_ET_USD": [Capex_SC_ET_USD],
"Capex_SC_FP_USD": [Capex_SC_FP_USD],
"Capex_PVT": [Capex_PVT_USD],
"Capex_Boiler_backup": [Capex_Boiler_backup_USD],
"Capex_storage_HEX": [Capex_HP_storage_USD],
"Capex_storage_HP": [Capex_storage_HP],
"Capex_CHP": [Capex_CHP_USD],
"Capex_furnace": [Capex_furnace_USD],
"Capex_Boiler_base": [Capex_Boiler_USD],
"Capex_Boiler_peak": [Capex_Boiler_peak_USD],
"Capex_Lake": [Capex_Lake_USD],
"Capex_Sewage": [Capex_Sewage_USD],
"Capex_pump": [Capex_pump_USD],
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
sep=',')
return (addcosts_Capex_a_USD + addcosts_Opex_fixed_USD, addCO2, addPrim)
def calc_generation_costs_heating(
locator,
master_to_slave_vars,
config,
storage_activation_data,
):
"""
Computes costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param DHN_barcode: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param master_to_slave_vars: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solar_features: solar features
:param thermal_network: network features
:type indCombi: string
:type buildList: list
:type locator: string
:type master_to_slave_vars: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solar_features: class
:type thermal_network: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
thermal_network = pd.read_csv(
locator.get_optimization_thermal_network_data_file(
master_to_slave_vars.network_data_file_heating))
# CCGT
if master_to_slave_vars.CC_on == 1:
CC_size_W = master_to_slave_vars.CCGT_SIZE_W
Capex_a_CHP_NG_USD, Opex_fixed_CHP_NG_USD, Capex_CHP_NG_USD = chp.calc_Cinv_CCGT(
CC_size_W, locator, config)
else:
Capex_a_CHP_NG_USD = 0.0
Opex_fixed_CHP_NG_USD = 0.0
Capex_CHP_NG_USD = 0.0
# DRY BIOMASS
if master_to_slave_vars.Furnace_dry_on == 1:
Dry_Furnace_size_W = master_to_slave_vars.DBFurnace_Q_max_W
Capex_a_furnace_dry_USD, \
Opex_fixed_furnace_dry_USD, \
Capex_furnace_dry_USD = furnace.calc_Cinv_furnace(Dry_Furnace_size_W, locator, 'FU1')
else:
Capex_furnace_dry_USD = 0.0
Capex_a_furnace_dry_USD = 0.0
Opex_fixed_furnace_dry_USD = 0.0
# WET BIOMASS
if master_to_slave_vars.Furnace_wet_on == 1:
Wet_Furnace_size_W = master_to_slave_vars.WBFurnace_Q_max_W
Capex_a_furnace_wet_USD, \
Opex_fixed_furnace_wet_USD, \
Capex_furnace_wet_USD = furnace.calc_Cinv_furnace(Wet_Furnace_size_W, locator, 'FU1')
else:
Capex_a_furnace_wet_USD = 0.0
Opex_fixed_furnace_wet_USD = 0.0
Capex_furnace_wet_USD = 0.0
# BOILER BASE LOAD
if master_to_slave_vars.Boiler_on == 1:
Q_design_W = master_to_slave_vars.Boiler_Q_max_W
Capex_a_BaseBoiler_NG_USD, \
Opex_fixed_BaseBoiler_NG_USD, \
Capex_BaseBoiler_NG_USD = boiler.calc_Cinv_boiler(Q_design_W, locator, config, 'BO1')
else:
Capex_a_BaseBoiler_NG_USD = 0.0
Opex_fixed_BaseBoiler_NG_USD = 0.0
Capex_BaseBoiler_NG_USD = 0.0
# BOILER PEAK LOAD
if master_to_slave_vars.BoilerPeak_on == 1:
Q_design_W = master_to_slave_vars.BoilerPeak_Q_max_W
Capex_a_PeakBoiler_NG_USD, \
Opex_fixed_PeakBoiler_NG_USD, \
Capex_PeakBoiler_NG_USD = boiler.calc_Cinv_boiler(Q_design_W, locator, config, 'BO1')
else:
Capex_a_PeakBoiler_NG_USD = 0.0
Opex_fixed_PeakBoiler_NG_USD = 0.0
Capex_PeakBoiler_NG_USD = 0.0
# HEATPUMP LAKE
if master_to_slave_vars.HPLake_on == 1:
HP_Size_W = master_to_slave_vars.HPLake_maxSize_W
Capex_a_Lake_USD, \
Opex_fixed_Lake_USD, \
Capex_Lake_USD = hp.calc_Cinv_HP(HP_Size_W, locator, 'HP2')
else:
Capex_a_Lake_USD = 0.0
Opex_fixed_Lake_USD = 0.0
Capex_Lake_USD = 0.0
# HEATPUMP_SEWAGE
if master_to_slave_vars.HPSew_on == 1:
HP_Size_W = master_to_slave_vars.HPSew_maxSize_W
Capex_a_Sewage_USD, \
Opex_fixed_Sewage_USD, \
Capex_Sewage_USD = hp.calc_Cinv_HP(HP_Size_W, locator, 'HP2')
else:
Capex_a_Sewage_USD = 0.0
Opex_fixed_Sewage_USD = 0.0
Capex_Sewage_USD = 0.0
# GROUND HEAT PUMP
if master_to_slave_vars.GHP_on == 1:
GHP_Enom_W = master_to_slave_vars.GHP_maxSize_W
Capex_a_GHP_USD, \
Opex_fixed_GHP_USD, \
Capex_GHP_USD = hp.calc_Cinv_GHP(GHP_Enom_W, locator, config)
else:
Capex_a_GHP_USD = 0.0
Opex_fixed_GHP_USD = 0.0
Capex_GHP_USD = 0.0
# BACK-UP BOILER
if master_to_slave_vars.BackupBoiler_on != 0:
Q_backup_W = master_to_slave_vars.BackupBoiler_size_W
Capex_a_BackupBoiler_NG_USD, \
Opex_fixed_BackupBoiler_NG_USD, \
Capex_BackupBoiler_NG_USD = boiler.calc_Cinv_boiler(Q_backup_W, locator, config, 'BO1')
else:
Capex_a_BackupBoiler_NG_USD = 0.0
Opex_fixed_BackupBoiler_NG_USD = 0.0
Capex_BackupBoiler_NG_USD = 0.0
# DATA CENTRE SOURCE HEAT PUMP
if master_to_slave_vars.WasteServersHeatRecovery == 1:
Q_HEX_max_Wh = thermal_network["Qcdata_netw_total_kWh"].max(
) * 1000 # convert to Wh
Capex_a_wasteserver_HEX_USD, Opex_fixed_wasteserver_HEX_USD, Capex_wasteserver_HEX_USD = hex.calc_Cinv_HEX(
Q_HEX_max_Wh, locator, config, 'HEX1')
Q_HP_max_Wh = storage_activation_data["Q_HP_Server_W"].max()
Capex_a_wasteserver_HP_USD, Opex_fixed_wasteserver_HP_USD, Capex_wasteserver_HP_USD = hp.calc_Cinv_HP(
Q_HP_max_Wh, locator, 'HP2')
else:
Capex_a_wasteserver_HEX_USD = 0.0
Opex_fixed_wasteserver_HEX_USD = 0.0
Capex_wasteserver_HEX_USD = 0.0
Capex_a_wasteserver_HP_USD = 0.0
Opex_fixed_wasteserver_HP_USD = 0.0
Capex_wasteserver_HP_USD = 0.0
# SOLAR TECHNOLOGIES
# ADD COSTS AND EMISSIONS DUE TO SOLAR TECHNOLOGIES
SC_ET_area_m2 = master_to_slave_vars.A_SC_ET_m2
Capex_a_SC_ET_USD, \
Opex_fixed_SC_ET_USD, \
Capex_SC_ET_USD = stc.calc_Cinv_SC(SC_ET_area_m2, locator,
'ET')
SC_FP_area_m2 = master_to_slave_vars.A_SC_FP_m2
Capex_a_SC_FP_USD, \
Opex_fixed_SC_FP_USD, \
Capex_SC_FP_USD = stc.calc_Cinv_SC(SC_FP_area_m2, locator,
'FP')
PVT_peak_kW = master_to_slave_vars.A_PVT_m2 * N_PVT # kW
Capex_a_PVT_USD, \
Opex_fixed_PVT_USD, \
Capex_PVT_USD = pvt.calc_Cinv_PVT(PVT_peak_kW, locator, config)
# HEATPUMP FOR SOLAR UPGRADE TO DISTRICT HEATING
Q_HP_max_PVT_wh = storage_activation_data["Q_HP_PVT_W"].max()
Capex_a_HP_PVT_USD, \
Opex_fixed_HP_PVT_USD, \
Capex_HP_PVT_USD = hp.calc_Cinv_HP(Q_HP_max_PVT_wh, locator, 'HP2')
# hack split into two technologies
Q_HP_max_SC_ET_Wh = storage_activation_data["Q_HP_SC_ET_W"].max()
Capex_a_HP_SC_ET_USD, \
Opex_fixed_HP_SC_ET_USD, \
Capex_HP_SC_ET_USD = hp.calc_Cinv_HP(Q_HP_max_SC_ET_Wh, locator, 'HP2')
Q_HP_max_SC_FP_Wh = storage_activation_data["Q_HP_SC_FP_W"].max()
Capex_a_HP_SC_FP_USD, \
Opex_fixed_HP_SC_FP_USD, \
Capex_HP_SC_FP_USD = hp.calc_Cinv_HP(Q_HP_max_SC_FP_Wh, locator, 'HP2')
# HEAT EXCHANGER FOR SOLAR COLLECTORS
Q_max_SC_ET_Wh = (storage_activation_data["Q_SC_ET_gen_directload_W"] +
storage_activation_data["Q_SC_ET_gen_storage_W"]).max()
Capex_a_HEX_SC_ET_USD, \
Opex_fixed_HEX_SC_ET_USD, \
Capex_HEX_SC_ET_USD = hex.calc_Cinv_HEX(Q_max_SC_ET_Wh, locator, config, 'HEX1')
Q_max_SC_FP_Wh = (storage_activation_data["Q_SC_FP_gen_directload_W"] +
storage_activation_data["Q_SC_FP_gen_storage_W"]).max()
Capex_a_HEX_SC_FP_USD, \
Opex_fixed_HEX_SC_FP_USD, \
Capex_HEX_SC_FP_USD = hex.calc_Cinv_HEX(Q_max_SC_FP_Wh, locator, config, 'HEX1')
Q_max_PVT_Wh = (storage_activation_data["Q_PVT_gen_directload_W"] +
storage_activation_data["Q_PVT_gen_storage_W"]).max()
Capex_a_HEX_PVT_USD, \
Opex_fixed_HEX_PVT_USD, \
Capex_HEX_PVT_USD = hex.calc_Cinv_HEX(Q_max_PVT_Wh, locator, config, 'HEX1')
performance_costs = {
"Capex_a_SC_ET_connected_USD":
Capex_a_SC_ET_USD + Capex_a_HP_SC_ET_USD + Capex_a_HEX_SC_ET_USD,
"Capex_a_SC_FP_connected_USD":
Capex_a_SC_FP_USD + Capex_a_HP_SC_FP_USD + Capex_a_HEX_SC_FP_USD,
"Capex_a_PVT_connected_USD":
Capex_a_PVT_USD + Capex_a_HP_PVT_USD + Capex_a_HEX_PVT_USD,
"Capex_a_HP_Server_connected_USD":
Capex_a_wasteserver_HP_USD + Capex_a_wasteserver_HEX_USD,
"Capex_a_HP_Sewage_connected_USD": Capex_a_Sewage_USD,
"Capex_a_HP_Lake_connected_USD": Capex_a_Lake_USD,
"Capex_a_GHP_connected_USD": Capex_a_GHP_USD,
"Capex_a_CHP_NG_connected_USD": Capex_a_CHP_NG_USD,
"Capex_a_Furnace_wet_connected_USD": Capex_a_furnace_wet_USD,
"Capex_a_Furnace_dry_connected_USD": Capex_a_furnace_dry_USD,
"Capex_a_BaseBoiler_NG_connected_USD": Capex_a_BaseBoiler_NG_USD,
"Capex_a_PeakBoiler_NG_connected_USD": Capex_a_PeakBoiler_NG_USD,
"Capex_a_BackupBoiler_NG_connected_USD": Capex_a_BackupBoiler_NG_USD,
# total_capex
"Capex_total_SC_ET_connected_USD":
Capex_SC_ET_USD + Capex_HP_SC_ET_USD + Capex_HEX_SC_ET_USD,
"Capex_total_SC_FP_connected_USD":
Capex_SC_FP_USD + Capex_HP_SC_FP_USD + Capex_HEX_SC_FP_USD,
"Capex_total_PVT_connected_USD":
Capex_PVT_USD + Capex_HP_PVT_USD + Capex_HEX_PVT_USD,
"Capex_total_HP_Server_connected_USD":
Capex_wasteserver_HP_USD + Capex_wasteserver_HEX_USD,
"Capex_total_HP_Sewage_connected_USD": Capex_Sewage_USD,
"Capex_total_HP_Lake_connected_USD": Capex_Lake_USD,
"Capex_total_GHP_connected_USD": Capex_GHP_USD,
"Capex_total_CHP_NG_connected_USD": Capex_CHP_NG_USD,
"Capex_total_Furnace_wet_connected_USD": Capex_furnace_wet_USD,
"Capex_total_Furnace_dry_connected_USD": Capex_furnace_dry_USD,
"Capex_total_BaseBoiler_NG_connected_USD": Capex_BaseBoiler_NG_USD,
"Capex_total_PeakBoiler_NG_connected_USD": Capex_PeakBoiler_NG_USD,
"Capex_total_BackupBoiler_NG_connected_USD": Capex_BackupBoiler_NG_USD,
# opex fixed costs
"Opex_fixed_SC_ET_connected_USD": Opex_fixed_SC_ET_USD,
"Opex_fixed_SC_FP_connected_USD": Opex_fixed_SC_FP_USD,
"Opex_fixed_PVT_connected_USD": Opex_fixed_PVT_USD,
"Opex_fixed_HP_Server_connected_USD":
Opex_fixed_wasteserver_HP_USD + Opex_fixed_wasteserver_HEX_USD,
"Opex_fixed_HP_Sewage_connected_USD": Opex_fixed_Sewage_USD,
"Opex_fixed_HP_Lake_connected_USD": Opex_fixed_Lake_USD,
"Opex_fixed_GHP_connected_USD": Opex_fixed_GHP_USD,
"Opex_fixed_CHP_NG_connected_USD": Opex_fixed_CHP_NG_USD,
"Opex_fixed_Furnace_wet_connected_USD": Opex_fixed_furnace_wet_USD,
"Opex_fixed_Furnace_dry_connected_USD": Opex_fixed_furnace_dry_USD,
"Opex_fixed_BaseBoiler_NG_connected_USD": Opex_fixed_BaseBoiler_NG_USD,
"Opex_fixed_PeakBoiler_NG_connected_USD": Opex_fixed_PeakBoiler_NG_USD,
"Opex_fixed_BackupBoiler_NG_connected_USD":
Opex_fixed_BackupBoiler_NG_USD,
}
return performance_costs
def disconnected_buildings_cooling_main(locator, building_names, total_demand, config, prices, lca):
"""
Computes the parameters for the operation of disconnected buildings output results in csv files.
There is no optimization at this point. The different cooling energy supply system configurations are calculated
and compared 1 to 1 to each other. it is a classical combinatorial problem.
The six supply system configurations include:
(VCC: Vapor Compression Chiller, ACH: Absorption Chiller, CT: Cooling Tower, Boiler)
(AHU: Air Handling Units, ARU: Air Recirculation Units, SCU: Sensible Cooling Units)
- config 0: Direct Expansion / Mini-split units (NOTE: this configuration is not fully built yet)
- config 1: VCC_to_AAS (AHU + ARU + SCU) + CT
- config 2: FP + single-effect ACH_to_AAS (AHU + ARU + SCU) + Boiler + CT
- config 3: ET + single-effect ACH_to_AAS (AHU + ARU + SCU) + Boiler + CT
- config 4: VCC_to_AA (AHU + ARU) + VCC_to_S (SCU) + CT
- config 5: VCC_to_AA (AHU + ARU) + single effect ACH_S (SCU) + CT + Boiler
Note:
1. Only cooling supply configurations are compared here. The demand for electricity is supplied from the grid,
and the demand for domestic hot water is supplied from electric boilers.
2. Single-effect chillers are coupled with flat-plate solar collectors, and the double-effect chillers are coupled
with evacuated tube solar collectors.
:param locator: locator class with paths to input/output files
:param building_names: list with names of buildings
:param config: cea.config
:param prices: prices class
:return: one .csv file with results of operations of disconnected buildings; one .csv file with operation of the
best configuration (Cost, CO2, Primary Energy)
"""
t0 = time.clock()
for building_name in building_names:
## Calculate cooling loads for different combinations
# SENSIBLE COOLING UNIT
Qc_nom_SCU_W, \
T_re_SCU_K, \
T_sup_SCU_K, \
mdot_SCU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['scu'])
# AIR HANDLING UNIT + AIR RECIRCULATION UNIT
Qc_nom_AHU_ARU_W, \
T_re_AHU_ARU_K, \
T_sup_AHU_ARU_K, \
mdot_AHU_ARU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['ahu', 'aru'])
# SENSIBLE COOLING UNIT + AIR HANDLING UNIT + AIR RECIRCULATION UNIT
Qc_nom_AHU_ARU_SCU_W, \
T_re_AHU_ARU_SCU_K, \
T_sup_AHU_ARU_SCU_K, \
mdot_AHU_ARU_SCU_kgpers = calc_combined_cooling_loads(building_name, locator, total_demand,
cooling_configuration=['ahu', 'aru', 'scu'])
## Get hourly hot water supply condition of Solar Collectors (SC)
# Flate Plate Solar Collectors
SC_FP_data, T_hw_in_FP_C, el_aux_SC_FP_Wh, q_sc_gen_FP_Wh = get_SC_data(building_name, locator, panel_type="FP")
Capex_a_SC_FP_USD, Opex_SC_FP_USD, Capex_SC_FP_USD = solar_collector.calc_Cinv_SC(SC_FP_data['Area_SC_m2'][0],
locator,
panel_type="FP")
# Evacuated Tube Solar Collectors
SC_ET_data, T_hw_in_ET_C, el_aux_SC_ET_Wh, q_sc_gen_ET_Wh = get_SC_data(building_name, locator, panel_type="ET")
Capex_a_SC_ET_USD, Opex_SC_ET_USD, Capex_SC_ET_USD = solar_collector.calc_Cinv_SC(SC_ET_data['Area_SC_m2'][0],
locator,
panel_type="ET")
## Calculate ground temperatures to estimate cold water supply temperatures for absorption chiller
T_ground_K = calculate_ground_temperature(locator,
config) # FIXME: change to outlet temperature from the cooling towers
## Initialize table to save results
# save costs of all supply configurations
operation_results = initialize_result_tables_for_supply_configurations(Qc_nom_SCU_W)
# save supply system activation of all supply configurations
cooling_dispatch = {}
## HOURLY OPERATION
print building_name, ' decentralized cooling supply system simulations...'
T_re_AHU_ARU_SCU_K = np.where(T_re_AHU_ARU_SCU_K > 0.0, T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K)
## 0. DX operation
print 'Config 0: Direct Expansion Units -> AHU,ARU,SCU'
el_DX_hourly_Wh,\
q_DX_chw_Wh = np.vectorize(dx.calc_DX)(mdot_AHU_ARU_SCU_kgpers, T_sup_AHU_ARU_SCU_K, T_re_AHU_ARU_SCU_K)
DX_Status = np.where(q_DX_chw_Wh > 0.0, 1, 0)
# add electricity costs, CO2, PE
operation_results[0][7] += sum(lca.ELEC_PRICE * el_DX_hourly_Wh)
operation_results[0][8] += sum(el_DX_hourly_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[0][9] += sum(el_DX_hourly_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ oil
# activation
cooling_dispatch[0] = {'Q_DX_gen_directload_W': q_DX_chw_Wh,
'E_DX_req_W': el_DX_hourly_Wh,
'DX_Status': DX_Status,
'E_cs_cre_cdata_req_W': el_DX_hourly_Wh,
}
## 1. VCC (AHU + ARU + SCU) + CT
print 'Config 1: Vapor Compression Chillers -> AHU,ARU,SCU'
# VCC operation
el_VCC_Wh, q_VCC_cw_Wh, q_VCC_chw_Wh = calc_VCC_operation(T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K,
mdot_AHU_ARU_SCU_kgpers)
VCC_Status = np.where(q_VCC_chw_Wh > 0.0, 1, 0)
# CT operation
q_CT_VCC_to_AHU_ARU_SCU_Wh = q_VCC_cw_Wh
Q_nom_CT_VCC_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(q_CT_VCC_to_AHU_ARU_SCU_Wh)
# add costs
el_total_Wh = el_VCC_Wh + el_CT_Wh
operation_results[1][7] += sum(lca.ELEC_PRICE * el_total_Wh) # CHF
operation_results[1][8] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[1][9] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ-oil-eq
cooling_dispatch[1] = {'Q_VCC_gen_directload_W': q_VCC_chw_Wh,
'E_VCC_req_W': el_VCC_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'VCC_Status': VCC_Status
}
## 2: SC_FP + single-effect ACH (AHU + ARU + SCU) + CT + Boiler + SC_FP
print 'Config 2: Flat-plate Solar Collectors + Single-effect Absorption chillers -> AHU,ARU,SCU'
# ACH operation
T_hw_out_single_ACH_K, \
el_single_ACH_Wh, \
q_cw_single_ACH_Wh, \
q_hw_single_ACH_Wh,\
q_chw_single_ACH_Wh = calc_ACH_operation(T_ground_K, T_hw_in_FP_C, T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K,
locator, mdot_AHU_ARU_SCU_kgpers, ACH_TYPE_SINGLE)
ACH_Status = np.where(q_chw_single_ACH_Wh > 0.0, 1, 0)
# CT operation
q_CT_FP_to_single_ACH_to_AHU_ARU_SCU_Wh = q_cw_single_ACH_Wh
Q_nom_CT_FP_to_single_ACH_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(
q_CT_FP_to_single_ACH_to_AHU_ARU_SCU_Wh)
# boiler operation
q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh, \
Q_nom_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_W, \
q_load_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh = calc_boiler_operation(Qc_nom_AHU_ARU_SCU_W,
T_hw_out_single_ACH_K,
q_hw_single_ACH_Wh,
q_sc_gen_FP_Wh)
# add electricity costs
el_total_Wh = el_single_ACH_Wh + el_aux_SC_FP_Wh + el_CT_Wh
operation_results[2][7] += sum(lca.ELEC_PRICE * el_total_Wh) # CHF
operation_results[2][8] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[2][9] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ-oil-eq
# add gas costs
q_gas_total_Wh = q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh
operation_results[2][7] += sum(prices.NG_PRICE * q_gas_total_Wh) # CHF
operation_results[2][8] += sum(q_gas_total_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_CO2_STD / 1E3) # ton CO2
operation_results[2][9] += sum(q_gas_total_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_OIL_STD) # MJ-oil-eq
# add activation
cooling_dispatch[2] = {'Q_ACH_gen_directload_W': q_chw_single_ACH_Wh,
'ACH_Status': ACH_Status,
'Q_Boiler_ACH_W': q_load_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh,
'Q_SC_FP_ACH_W': q_sc_gen_FP_Wh,
'E_ACH_req_W': el_single_ACH_Wh,
'E_CT_req_W': el_CT_Wh,
'E_SC_FP_req_W': el_aux_SC_FP_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'NG_Boiler_req': q_gas_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_Wh,
}
# 3: SC_ET + single-effect ACH (AHU + ARU + SCU) + CT + Boiler + SC_ET
print 'Config 3: Evacuated Tube Solar Collectors + Single-effect Absorption chillers -> AHU,ARU,SCU'
# ACH operation
T_hw_out_single_ACH_K, \
el_single_ACH_Wh, \
q_cw_single_ACH_Wh, \
q_hw_single_ACH_Wh,\
q_chw_single_ACH_Wh = calc_ACH_operation(T_ground_K, T_hw_in_ET_C, T_re_AHU_ARU_SCU_K, T_sup_AHU_ARU_SCU_K,
locator, mdot_AHU_ARU_SCU_kgpers, ACH_TYPE_SINGLE)
# CT operation
q_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W = q_cw_single_ACH_Wh
Q_nom_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W, el_CT_Wh = calc_CT_operation(q_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W)
# burner operation
q_gas_for_burner_Wh, \
Q_nom_Burner_ET_to_single_ACH_to_AHU_ARU_SCU_W,\
q_burner_load_Wh = calc_burner_operation(Qc_nom_AHU_ARU_SCU_W, q_hw_single_ACH_Wh, q_sc_gen_ET_Wh)
# add electricity costs
el_total_Wh = el_single_ACH_Wh + el_aux_SC_ET_Wh + el_CT_Wh
operation_results[3][7] += sum(lca.ELEC_PRICE * el_total_Wh) # CHF
operation_results[3][8] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[3][9] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ-oil-eq
# add gas costs
operation_results[3][7] += sum(prices.NG_PRICE * q_gas_for_burner_Wh) # CHF
operation_results[3][8] += sum(
q_gas_for_burner_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_CO2_STD / 1E3) # ton CO2
operation_results[3][9] += sum(
q_gas_for_burner_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_OIL_STD) # MJ-oil-eq
# add activation
cooling_dispatch[3] = {'Q_ACH_gen_directload_W': q_chw_single_ACH_Wh,
'ACH_Status': ACH_Status,
'Q_Burner_ACH_W': q_burner_load_Wh,
'Q_SC_ET_ACH_W': q_sc_gen_ET_Wh,
'E_ACH_req_W': el_single_ACH_Wh,
'E_CT_req_W': el_CT_Wh,
'E_SC_FP_req_W': el_aux_SC_ET_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'NG_Burner_req': q_gas_for_burner_Wh,
}
# these two configurations are only activated when SCU is in use
if Qc_nom_SCU_W > 0.0:
# 4: VCC (AHU + ARU) + VCC (SCU) + CT
print 'Config 4: Vapor Compression Chillers(HT) -> SCU & Vapor Compression Chillers(LT) -> AHU,ARU'
# VCC (AHU + ARU) operation
el_VCC_to_AHU_ARU_Wh, \
q_cw_VCC_to_AHU_ARU_Wh,\
q_chw_VCC_to_AHU_ARU_Wh = calc_VCC_operation(T_re_AHU_ARU_K, T_sup_AHU_ARU_K, mdot_AHU_ARU_kgpers)
VCC_LT_Status = np.where(q_chw_VCC_to_AHU_ARU_Wh > 0.0, 1, 0)
# VCC(SCU) operation
el_VCC_to_SCU_Wh, \
q_cw_VCC_to_SCU_Wh,\
q_chw_VCC_to_SCU_Wh = calc_VCC_operation(T_re_SCU_K, T_sup_SCU_K, mdot_SCU_kgpers)
VCC_HT_Status = np.where(q_chw_VCC_to_AHU_ARU_Wh > 0.0, 1, 0)
# CT operation
q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W = q_cw_VCC_to_AHU_ARU_Wh + q_cw_VCC_to_SCU_Wh
Q_nom_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W, el_CT_Wh = calc_CT_operation(q_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W)
# add el costs
el_total_Wh = el_VCC_to_AHU_ARU_Wh + el_VCC_to_SCU_Wh + el_CT_Wh
operation_results[4][7] += sum(lca.ELEC_PRICE * el_total_Wh) # CHF
operation_results[4][8] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[4][9] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ-oil-eq
# add activation
cooling_dispatch[4] = {'Q_VCC_LT_gen_directload_W': q_chw_VCC_to_AHU_ARU_Wh,
'Q_VCC_HT_gen_directload_W': q_chw_VCC_to_SCU_Wh,
'VCC_LT_Status': VCC_LT_Status,
'VCC_HT_Status': VCC_HT_Status,
'E_VCC_LT_req_W': el_VCC_to_AHU_ARU_Wh,
'E_VCC_HT_req_W': el_VCC_to_SCU_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh
}
# 5: VCC (AHU + ARU) + ACH (SCU) + CT
print 'Config 5: Vapor Compression Chillers(LT) -> AHU,ARU & Flate-place SC + Absorption Chillers(HT) -> SCU'
# ACH (SCU) operation
T_hw_FP_ACH_to_SCU_K, \
el_FP_ACH_to_SCU_Wh, \
q_cw_FP_ACH_to_SCU_Wh, \
q_hw_FP_ACH_to_SCU_Wh,\
q_chw_FP_ACH_to_SCU_Wh = calc_ACH_operation(T_ground_K, T_hw_in_FP_C, T_re_SCU_K, T_sup_SCU_K, locator,
mdot_SCU_kgpers, ACH_TYPE_SINGLE)
ACH_HT_Status = np.where(q_chw_FP_ACH_to_SCU_Wh > 0.0, 1, 0)
# boiler operation
q_gas_for_boiler_Wh, \
Q_nom_boiler_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W,\
q_load_from_boiler_Wh = calc_boiler_operation(Qc_nom_SCU_W, T_hw_FP_ACH_to_SCU_K,
q_hw_FP_ACH_to_SCU_Wh, q_sc_gen_FP_Wh)
# CT operation
q_CT_VCC_to_AHU_ARU_and_single_ACH_to_SCU_Wh = q_cw_VCC_to_AHU_ARU_Wh + q_cw_FP_ACH_to_SCU_Wh
Q_nom_CT_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, \
el_CT_Wh = calc_CT_operation(q_CT_VCC_to_AHU_ARU_and_single_ACH_to_SCU_Wh)
# add electricity costs
el_total_Wh = el_VCC_to_AHU_ARU_Wh + el_FP_ACH_to_SCU_Wh + el_aux_SC_FP_Wh + el_CT_Wh
operation_results[5][7] += sum(lca.ELEC_PRICE * el_total_Wh) # CHF
operation_results[5][8] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_CO2 / 1E3) # ton CO2
operation_results[5][9] += sum(el_total_Wh * WH_TO_J / 1E6 * lca.EL_TO_OIL_EQ) # MJ-oil-eq
# add gas costs
q_gas_total_Wh = q_gas_for_boiler_Wh
operation_results[5][7] += sum(prices.NG_PRICE * q_gas_total_Wh) # CHF
operation_results[5][8] += sum(
q_gas_total_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_CO2_STD / 1E3) # ton CO2
operation_results[5][9] += sum(q_gas_total_Wh * WH_TO_J / 1E6 * lca.NG_BACKUPBOILER_TO_OIL_STD) # MJ-oil-eq
# add activation
cooling_dispatch[5] = {'Q_VCC_LT_gen_directload_W': q_chw_VCC_to_AHU_ARU_Wh,
'Q_ACH_HT_gen_directload_W': q_chw_FP_ACH_to_SCU_Wh,
'VCC_LT_Status': VCC_LT_Status,
'ACH_HT_Status': ACH_HT_Status,
'E_VCC_LT_req_W': el_VCC_to_AHU_ARU_Wh,
'E_ACH_HT_req_W': el_FP_ACH_to_SCU_Wh,
'E_SC_FP_req_W': el_aux_SC_FP_Wh,
'E_CT_req_W': el_CT_Wh,
'E_cs_cre_cdata_req_W': el_total_Wh,
'NG_Boiler_req': q_gas_for_boiler_Wh,
}
## Calculate Capex/Opex
# Initialize arrays
number_of_configurations = len(operation_results)
Capex_a_USD = np.zeros((number_of_configurations, 1))
Capex_total_USD = np.zeros((number_of_configurations, 1))
Opex_a_fixed_USD = np.zeros((number_of_configurations, 1))
print 'Cost calculation...'
# 0: DX
Capex_a_DX_USD, Opex_fixed_DX_USD, Capex_DX_USD = dx.calc_Cinv_DX(Qc_nom_AHU_ARU_SCU_W)
# add costs
Capex_a_USD[0][0] = Capex_a_DX_USD
Capex_total_USD[0][0] = Capex_DX_USD
Opex_a_fixed_USD[0][0] = Opex_fixed_DX_USD
# 1: VCC + CT
Capex_a_VCC_USD, Opex_fixed_VCC_USD, Capex_VCC_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_AHU_ARU_SCU_W, locator, config, 'CH3')
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_SCU_W, locator, 'CT1')
# add costs
Capex_a_USD[1][0] = Capex_a_CT_USD + Capex_a_VCC_USD
Capex_total_USD[1][0] = Capex_CT_USD + Capex_VCC_USD
Opex_a_fixed_USD[1][0] = Opex_fixed_CT_USD + Opex_fixed_VCC_USD
# 2: single effect ACH + CT + Boiler + SC_FP
Capex_a_ACH_USD, Opex_fixed_ACH_USD, Capex_ACH_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_AHU_ARU_SCU_W, locator, ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_FP_to_single_ACH_to_AHU_ARU_SCU_W, locator, 'CT1')
Capex_a_boiler_USD, Opex_fixed_boiler_USD, Capex_boiler_USD = boiler.calc_Cinv_boiler(
Q_nom_Boiler_FP_to_single_ACH_to_AHU_ARU_SCU_W, locator, config, 'BO1')
Capex_a_USD[2][0] = Capex_a_CT_USD + Capex_a_ACH_USD + Capex_a_boiler_USD + Capex_a_SC_FP_USD
Capex_total_USD[2][0] = Capex_CT_USD + Capex_ACH_USD + Capex_boiler_USD + Capex_SC_FP_USD
Opex_a_fixed_USD[2][
0] = Opex_fixed_CT_USD + Opex_fixed_ACH_USD + Opex_fixed_boiler_USD + Opex_SC_FP_USD
# 3: double effect ACH + CT + Boiler + SC_ET
Capex_a_ACH_USD, Opex_fixed_ACH_USD, Capex_ACH_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_AHU_ARU_SCU_W, locator, ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_ET_to_single_ACH_to_AHU_ARU_SCU_W, locator, 'CT1')
Capex_a_burner_USD, Opex_fixed_burner_USD, Capex_burner_USD = burner.calc_Cinv_burner(
Q_nom_Burner_ET_to_single_ACH_to_AHU_ARU_SCU_W, locator, config, 'BO1')
Capex_a_USD[3][0] = Capex_a_CT_USD + Capex_a_ACH_USD + Capex_a_burner_USD + Capex_a_SC_ET_USD
Capex_total_USD[3][0] = Capex_CT_USD + Capex_ACH_USD + Capex_burner_USD + Capex_SC_ET_USD
Opex_a_fixed_USD[3][
0] = Opex_fixed_CT_USD + Opex_fixed_ACH_USD + Opex_fixed_burner_USD + Opex_SC_ET_USD
# these two configurations are only activated when SCU is in use
if Qc_nom_SCU_W > 0.0:
# 4: VCC (AHU + ARU) + VCC (SCU) + CT
Capex_a_VCC_AA_USD, Opex_VCC_AA_USD, Capex_VCC_AA_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_AHU_ARU_W, locator, config, 'CH3')
Capex_a_VCC_S_USD, Opex_VCC_S_USD, Capex_VCC_S_USD = chiller_vapor_compression.calc_Cinv_VCC(
Qc_nom_SCU_W, locator, config, 'CH3')
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_and_VCC_to_SCU_W, locator, 'CT1')
Capex_a_USD[4][0] = Capex_a_CT_USD + Capex_a_VCC_AA_USD + Capex_a_VCC_S_USD
Capex_total_USD[4][0] = Capex_CT_USD + Capex_VCC_AA_USD + Capex_VCC_S_USD
Opex_a_fixed_USD[4][0] = Opex_fixed_CT_USD + Opex_VCC_AA_USD + Opex_VCC_S_USD
# 5: VCC (AHU + ARU) + ACH (SCU) + CT + Boiler + SC_FP
Capex_a_ACH_S_USD, Opex_fixed_ACH_S_USD, Capex_ACH_S_USD = chiller_absorption.calc_Cinv_ACH(
Qc_nom_SCU_W, locator, ACH_TYPE_SINGLE)
Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD = cooling_tower.calc_Cinv_CT(
Q_nom_CT_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, locator, 'CT1')
Capex_a_boiler_USD, Opex_fixed_boiler_USD, Capex_boiler_USD = boiler.calc_Cinv_boiler(
Q_nom_boiler_VCC_to_AHU_ARU_and_FP_to_single_ACH_to_SCU_W, locator, config, 'BO1')
Capex_a_USD[5][0] = Capex_a_CT_USD + Capex_a_VCC_AA_USD + Capex_a_ACH_S_USD + \
Capex_a_SC_FP_USD + Capex_a_boiler_USD
Capex_total_USD[5][0] = Capex_CT_USD + Capex_VCC_AA_USD + Capex_ACH_S_USD + \
Capex_SC_FP_USD + Capex_boiler_USD
Opex_a_fixed_USD[5][0] = Opex_fixed_CT_USD + Opex_VCC_AA_USD + Opex_fixed_ACH_S_USD + \
Opex_SC_FP_USD + Opex_fixed_boiler_USD
## write all results from the configurations into TotalCosts, TotalCO2, TotalPrim
Opex_a_USD, TAC_USD, TotalCO2, TotalPrim = compile_TAC_CO2_Prim(Capex_a_USD, Opex_a_fixed_USD,
number_of_configurations, operation_results)
## Determine the best configuration
Best, indexBest = rank_results(TAC_USD, TotalCO2, TotalPrim, number_of_configurations)
# Save results in csv file
performance_results = {
"DX to AHU_ARU_SCU Share": operation_results[:, 0],
"VCC to AHU_ARU_SCU Share": operation_results[:, 1],
"single effect ACH to AHU_ARU_SCU Share (FP)": operation_results[:, 2],
"single effect ACH to AHU_ARU_SCU Share (ET)": operation_results[:, 3],
"VCC to AHU_ARU Share": operation_results[:, 4],
"VCC to SCU Share": operation_results[:, 5],
"single effect ACH to SCU Share (FP)": operation_results[:, 6],
"Capex_a_USD": Capex_a_USD[:, 0],
"Capex_total_USD": Capex_total_USD[:, 0],
"Opex_a_USD": Opex_a_USD[:, 1],
"Opex_a_fixed_USD": Opex_a_fixed_USD[:, 0],
"Opex_a_var_USD": operation_results[:, 7],
"GHG_tonCO2": operation_results[:, 8],
"PEN_MJoil": operation_results[:, 9],
"TAC_USD": TAC_USD[:, 1],
"Best configuration": Best[:, 0]
}
performance_results_df = pd.DataFrame(performance_results)
performance_results_df.to_csv(
locator.get_optimization_decentralized_folder_building_result_cooling(building_name))
# save activation for the best supply system configuration
best_activation_df = pd.DataFrame.from_dict(cooling_dispatch[indexBest]) #
best_activation_df.to_csv(
locator.get_optimization_decentralized_folder_building_cooling_activation(building_name))
print time.clock() - t0, "seconds process time for the decentralized Building Routine \n"
def addCosts(DHN_barcode, DCN_barcode, buildList, locator,
master_to_slave_vars, Q_uncovered_design_W, Q_uncovered_annual_W,
solarFeat, ntwFeat, gv, config, prices, lca):
"""
Computes additional costs / GHG emisions / primary energy needs
for the individual
addCosts = additional costs
addCO2 = GHG emissions
addPrm = primary energy needs
:param DHN_barcode: parameter indicating if the building is connected or not
:param buildList: list of buildings in the district
:param locator: input locator set to scenario
:param master_to_slave_vars: class containing the features of a specific individual
:param Q_uncovered_design_W: hourly max of the heating uncovered demand
:param Q_uncovered_annual_W: total heating uncovered
:param solarFeat: solar features
:param ntwFeat: network features
:param gv: global variables
:type indCombi: string
:type buildList: list
:type locator: string
:type master_to_slave_vars: class
:type Q_uncovered_design_W: float
:type Q_uncovered_annual_W: float
:type solarFeat: class
:type ntwFeat: class
:type gv: class
:return: returns the objectives addCosts, addCO2, addPrim
:rtype: tuple
"""
addcosts_Capex_a = 0
addcosts_Opex_fixed = 0
addCO2 = 0
addPrim = 0
nBuildinNtw = 0
# Add the features from the disconnected buildings
CostDiscBuild = 0
CO2DiscBuild = 0
PrimDiscBuild = 0
Capex_Disconnected = 0
Opex_Disconnected = 0
Capex_a_furnace = 0
Capex_a_CHP = 0
Capex_a_Boiler = 0
Capex_a_Boiler_peak = 0
Capex_a_Lake = 0
Capex_a_Sewage = 0
Capex_a_GHP = 0
Capex_a_PV = 0
Capex_a_SC = 0
Capex_a_PVT = 0
Capex_a_Boiler_backup = 0
Capex_a_HEX = 0
Capex_a_storage_HP = 0
Capex_a_HP_storage = 0
Opex_fixed_SC = 0
Opex_fixed_PVT = 0
Opex_fixed_HP_PVT = 0
Opex_fixed_furnace = 0
Opex_fixed_CHP = 0
Opex_fixed_Boiler = 0
Opex_fixed_Boiler_peak = 0
Opex_fixed_Boiler_backup = 0
Opex_fixed_Lake = 0
Opex_fixed_wasteserver_HEX = 0
Opex_fixed_wasteserver_HP = 0
Opex_fixed_PV = 0
Opex_fixed_GHP = 0
Opex_fixed_storage = 0
Opex_fixed_Sewage = 0
Opex_fixed_HP_storage = 0
StorageInvC = 0
NetworkCost = 0
SubstHEXCost_capex = 0
SubstHEXCost_opex = 0
PVTHEXCost_Capex = 0
PVTHEXCost_Opex = 0
SCHEXCost_Capex = 0
SCHEXCost_Opex = 0
pumpCosts = 0
GasConnectionInvCost = 0
cost_PV_disconnected = 0
CO2_PV_disconnected = 0
Eprim_PV_disconnected = 0
if config.optimization.isheating:
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "0":
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_heating(
building_name))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
else:
nBuildinNtw += 1
if config.optimization.iscooling:
PV_barcode = ''
for (index, building_name) in zip(DCN_barcode, buildList):
if index == "0": # choose the best decentralized configuration
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, configuration='AHU_ARU_SCU'))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (FP)"].iloc[0] == 1:
to_PV = 0
else: # adding costs for buildings in which the centralized plant provides a part of the load requirements
DCN_unit_configuration = master_to_slave_vars.DCN_supplyunits
if DCN_unit_configuration == 1: # corresponds to AHU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU_SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 2: # corresponds to ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 3: # corresponds to SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU_ARU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU_ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU_ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 4: # corresponds to AHU + ARU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'SCU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to SCU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to SCU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 5: # corresponds to AHU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'ARU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to ARU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to ARU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 6: # corresponds to ARU + SCU in the central plant, so remaining load need to be provided by decentralized plant
decentralized_configuration = 'AHU'
df = pd.read_csv(
locator.
get_optimization_disconnected_folder_building_result_cooling(
building_name, decentralized_configuration))
dfBest = df[df["Best configuration"] == 1]
CostDiscBuild += dfBest["Total Costs [CHF]"].iloc[
0] # [CHF]
CO2DiscBuild += dfBest["CO2 Emissions [kgCO2-eq]"].iloc[
0] # [kg CO2]
PrimDiscBuild += dfBest[
"Primary Energy Needs [MJoil-eq]"].iloc[
0] # [MJ-oil-eq]
Capex_Disconnected += dfBest[
"Annualized Investment Costs [CHF]"].iloc[0]
Opex_Disconnected += dfBest["Operation Costs [CHF]"].iloc[
0]
to_PV = 1
if dfBest["single effect ACH to AHU Share (FP)"].iloc[
0] == 1:
to_PV = 0
if dfBest["single effect ACH to AHU Share (ET)"].iloc[
0] == 1:
to_PV = 0
if DCN_unit_configuration == 7: # corresponds to AHU + ARU + SCU from central plant
to_PV = 1
nBuildinNtw += 1
PV_barcode = PV_barcode + str(to_PV)
addcosts_Capex_a += CostDiscBuild
addCO2 += CO2DiscBuild
addPrim += PrimDiscBuild
if not config.optimization.isheating:
if PV_barcode.count("1") > 0:
df1 = pd.DataFrame({'A': []})
for (i, index) in enumerate(PV_barcode):
if index == str(1):
if df1.empty:
data = pd.read_csv(locator.PV_results(buildList[i]))
df1 = data
else:
data = pd.read_csv(locator.PV_results(buildList[i]))
df1 = df1 + data
if not df1.empty:
df1.to_csv(locator.PV_network(PV_barcode),
index=True,
float_format='%.2f')
solar_data = pd.read_csv(locator.PV_network(PV_barcode),
usecols=['E_PV_gen_kWh', 'Area_PV_m2'],
nrows=8760)
E_PV_sum_kW = np.sum(solar_data['E_PV_gen_kWh'])
E_PV_W = solar_data['E_PV_gen_kWh'] * 1000
Area_AvailablePV_m2 = np.max(solar_data['Area_PV_m2'])
Q_PowerPeakAvailablePV_kW = Area_AvailablePV_m2 * ETA_AREA_TO_PEAK
KEV_RpPerkWhPV = calc_Crem_pv(Q_PowerPeakAvailablePV_kW * 1000.0)
KEV_total = KEV_RpPerkWhPV / 100 * np.sum(E_PV_sum_kW)
addcosts_Capex_a = addcosts_Capex_a - KEV_total
addCO2 = addCO2 - (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_CO2 - lca.EL_TO_CO2_GREEN) *
WH_TO_J / 1.0E6)
addPrim = addPrim - (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_OIL_EQ - lca.EL_TO_OIL_EQ_GREEN)
* WH_TO_J / 1.0E6)
cost_PV_disconnected = KEV_total
CO2_PV_disconnected = (E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_CO2 - lca.EL_TO_CO2_GREEN) *
WH_TO_J / 1.0E6)
Eprim_PV_disconnected = (
E_PV_sum_kW * 1000 *
(lca.EL_PV_TO_OIL_EQ - lca.EL_TO_OIL_EQ_GREEN) * WH_TO_J /
1.0E6)
network_data = pd.read_csv(
locator.get_optimization_network_data_folder(
master_to_slave_vars.network_data_file_cooling))
E_total_req_W = np.array(network_data['Electr_netw_total_W'])
cooling_data = pd.read_csv(
locator.get_optimization_slave_cooling_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number))
E_from_CHP_W = np.array(cooling_data[
'E_gen_CCGT_associated_with_absorption_chillers_W'])
E_CHP_to_directload_W = np.zeros(8760)
E_CHP_to_grid_W = np.zeros(8760)
E_PV_to_directload_W = np.zeros(8760)
E_PV_to_grid_W = np.zeros(8760)
E_from_grid_W = np.zeros(8760)
for hour in range(8760):
E_hour_W = E_total_req_W[hour]
if E_hour_W > 0:
if E_PV_W[hour] > E_hour_W:
E_PV_to_directload_W[hour] = E_hour_W
E_PV_to_grid_W[
hour] = E_PV_W[hour] - E_total_req_W[hour]
E_hour_W = 0
else:
E_hour_W = E_hour_W - E_PV_W[hour]
E_PV_to_directload_W[hour] = E_PV_W[hour]
if E_from_CHP_W[hour] > E_hour_W:
E_CHP_to_directload_W[hour] = E_hour_W
E_CHP_to_grid_W[hour] = E_from_CHP_W[hour] - E_hour_W
E_hour_W = 0
else:
E_hour_W = E_hour_W - E_from_CHP_W[hour]
E_CHP_to_directload_W[hour] = E_from_CHP_W[hour]
E_from_grid_W[hour] = E_hour_W
date = network_data.DATE.values
results = pd.DataFrame({
"DATE":
date,
"E_total_req_W":
E_total_req_W,
"E_PV_W":
solar_data['E_PV_gen_kWh'] * 1000,
"Area_PV_m2":
solar_data['Area_PV_m2'],
"KEV":
KEV_RpPerkWhPV / 100 * solar_data['E_PV_gen_kWh'],
"E_from_grid_W":
E_from_grid_W,
"E_PV_to_directload_W":
E_PV_to_directload_W,
"E_CHP_to_directload_W":
E_CHP_to_directload_W,
"E_CHP_to_grid_W":
E_CHP_to_grid_W,
"E_PV_to_grid_W":
E_PV_to_grid_W
})
results.to_csv(
locator.
get_optimization_slave_electricity_activation_pattern_cooling(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
index=False)
# Add the features for the distribution
if DHN_barcode.count("1") > 0 and config.optimization.isheating:
os.chdir(
locator.get_optimization_slave_results_folder(
master_to_slave_vars.generation_number))
# Add the investment costs of the energy systems
# Furnace
if master_to_slave_vars.Furnace_on == 1:
P_design_W = master_to_slave_vars.Furnace_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern_heating(
master_to_slave_vars.configKey,
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfFurnace = pd.read_csv(fNameSlavePP, usecols=["Q_Furnace_W"])
arrayFurnace_W = np.array(dfFurnace)
Q_annual_W = 0
for i in range(int(np.shape(arrayFurnace_W)[0])):
Q_annual_W += arrayFurnace_W[i][0]
Capex_a_furnace, Opex_fixed_furnace = furnace.calc_Cinv_furnace(
P_design_W, Q_annual_W, config, locator, 'FU1')
addcosts_Capex_a += Capex_a_furnace
addcosts_Opex_fixed += Opex_fixed_furnace
# CC
if master_to_slave_vars.CC_on == 1:
CC_size_W = master_to_slave_vars.CC_GT_SIZE
Capex_a_CHP, Opex_fixed_CHP = chp.calc_Cinv_CCGT(
CC_size_W, locator, config)
addcosts_Capex_a += Capex_a_CHP
addcosts_Opex_fixed += Opex_fixed_CHP
# Boiler Base
if master_to_slave_vars.Boiler_on == 1:
Q_design_W = master_to_slave_vars.Boiler_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerBase = pd.read_csv(fNameSlavePP,
usecols=["Q_BaseBoiler_W"])
arrayBoilerBase_W = np.array(dfBoilerBase)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerBase_W)[0])):
Q_annual_W += arrayBoilerBase_W[i][0]
Capex_a_Boiler, Opex_fixed_Boiler = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler
addcosts_Opex_fixed += Opex_fixed_Boiler
# Boiler Peak
if master_to_slave_vars.BoilerPeak_on == 1:
Q_design_W = master_to_slave_vars.BoilerPeak_Q_max
fNameSlavePP = locator.get_optimization_slave_heating_activation_pattern(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfBoilerPeak = pd.read_csv(fNameSlavePP,
usecols=["Q_PeakBoiler_W"])
arrayBoilerPeak_W = np.array(dfBoilerPeak)
Q_annual_W = 0
for i in range(int(np.shape(arrayBoilerPeak_W)[0])):
Q_annual_W += arrayBoilerPeak_W[i][0]
Capex_a_Boiler_peak, Opex_fixed_Boiler_peak = boiler.calc_Cinv_boiler(
Q_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler_peak
addcosts_Opex_fixed += Opex_fixed_Boiler_peak
# HP Lake
if master_to_slave_vars.HP_Lake_on == 1:
HP_Size_W = master_to_slave_vars.HPLake_maxSize
Capex_a_Lake, Opex_fixed_Lake = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a += Capex_a_Lake
addcosts_Opex_fixed += Opex_fixed_Lake
# HP Sewage
if master_to_slave_vars.HP_Sew_on == 1:
HP_Size_W = master_to_slave_vars.HPSew_maxSize
Capex_a_Sewage, Opex_fixed_Sewage = hp.calc_Cinv_HP(
HP_Size_W, locator, config, 'HP2')
addcosts_Capex_a += Capex_a_Sewage
addcosts_Opex_fixed += Opex_fixed_Sewage
# GHP
if master_to_slave_vars.GHP_on == 1:
fNameSlavePP = locator.get_optimization_slave_electricity_activation_pattern_heating(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number)
dfGHP = pd.read_csv(fNameSlavePP, usecols=["E_GHP_req_W"])
arrayGHP_W = np.array(dfGHP)
GHP_Enom_W = np.amax(arrayGHP_W)
Capex_a_GHP, Opex_fixed_GHP = hp.calc_Cinv_GHP(
GHP_Enom_W, locator, config)
addcosts_Capex_a += Capex_a_GHP * prices.EURO_TO_CHF
addcosts_Opex_fixed += Opex_fixed_GHP * prices.EURO_TO_CHF
# Solar technologies
PV_installed_area_m2 = master_to_slave_vars.SOLAR_PART_PV * solarFeat.A_PV_m2 #kW
Capex_a_PV, Opex_fixed_PV = pv.calc_Cinv_pv(PV_installed_area_m2,
locator, config)
addcosts_Capex_a += Capex_a_PV
addcosts_Opex_fixed += Opex_fixed_PV
SC_ET_area_m2 = master_to_slave_vars.SOLAR_PART_SC_ET * solarFeat.A_SC_ET_m2
Capex_a_SC_ET, Opex_fixed_SC_ET = stc.calc_Cinv_SC(
SC_ET_area_m2, locator, config, 'ET')
addcosts_Capex_a += Capex_a_SC_ET
addcosts_Opex_fixed += Opex_fixed_SC_ET
SC_FP_area_m2 = master_to_slave_vars.SOLAR_PART_SC_FP * solarFeat.A_SC_FP_m2
Capex_a_SC_FP, Opex_fixed_SC_FP = stc.calc_Cinv_SC(
SC_FP_area_m2, locator, config, 'FP')
addcosts_Capex_a += Capex_a_SC_FP
addcosts_Opex_fixed += Opex_fixed_SC_FP
PVT_peak_kW = master_to_slave_vars.SOLAR_PART_PVT * solarFeat.A_PVT_m2 * N_PVT #kW
Capex_a_PVT, Opex_fixed_PVT = pvt.calc_Cinv_PVT(
PVT_peak_kW, locator, config)
addcosts_Capex_a += Capex_a_PVT
addcosts_Opex_fixed += Opex_fixed_PVT
# Back-up boiler
Capex_a_Boiler_backup, Opex_fixed_Boiler_backup = boiler.calc_Cinv_boiler(
Q_uncovered_design_W, locator, config, 'BO1')
addcosts_Capex_a += Capex_a_Boiler_backup
addcosts_Opex_fixed += Opex_fixed_Boiler_backup
# Hex and HP for Heat recovery
if master_to_slave_vars.WasteServersHeatRecovery == 1:
df = pd.read_csv(os.path.join(
locator.get_optimization_network_results_folder(),
master_to_slave_vars.network_data_file_heating),
usecols=["Qcdata_netw_total_kWh"])
array = np.array(df)
Q_HEX_max_kWh = np.amax(array)
Capex_a_wasteserver_HEX, Opex_fixed_wasteserver_HEX = hex.calc_Cinv_HEX(
Q_HEX_max_kWh, locator, config, 'HEX1')
addcosts_Capex_a += (Capex_a_wasteserver_HEX)
addcosts_Opex_fixed += Opex_fixed_wasteserver_HEX
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPServerHeatDesignArray_kWh"])
array = np.array(df)
Q_HP_max_kWh = np.amax(array)
Capex_a_wasteserver_HP, Opex_fixed_wasteserver_HP = hp.calc_Cinv_HP(
Q_HP_max_kWh, locator, config, 'HP2')
addcosts_Capex_a += (Capex_a_wasteserver_HP)
addcosts_Opex_fixed += Opex_fixed_wasteserver_HP
# if master_to_slave_vars.WasteCompressorHeatRecovery == 1:
# df = pd.read_csv(
# os.path.join(locator.get_optimization_network_results_folder(), master_to_slave_vars.network_data_file_heating),
# usecols=["Ecaf_netw_total_kWh"])
# array = np.array(df)
# Q_HEX_max_kWh = np.amax(array)
#
# Capex_a_wastecompressor_HEX, Opex_fixed_wastecompressor_HEX = hex.calc_Cinv_HEX(Q_HEX_max_kWh, locator,
# config, 'HEX1')
# addcosts_Capex_a += (Capex_a_wastecompressor_HEX)
# addcosts_Opex_fixed += Opex_fixed_wastecompressor_HEX
# df = pd.read_csv(
# locator.get_optimization_slave_storage_operation_data(master_to_slave_vars.individual_number,
# master_to_slave_vars.generation_number),
# usecols=["HPCompAirDesignArray_kWh"])
# array = np.array(df)
# Q_HP_max_kWh = np.amax(array)
# Capex_a_wastecompressor_HP, Opex_fixed_wastecompressor_HP = hp.calc_Cinv_HP(Q_HP_max_kWh, locator, config, 'HP2')
# addcosts_Capex_a += (Capex_a_wastecompressor_HP)
# addcosts_Opex_fixed += Opex_fixed_wastecompressor_HP
# Heat pump from solar to DH
df = pd.read_csv(
locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["HPScDesignArray_Wh", "HPpvt_designArray_Wh"])
array = np.array(df)
Q_HP_max_PVT_wh = np.amax(array[:, 1])
Q_HP_max_SC_Wh = np.amax(array[:, 0])
Capex_a_HP_PVT, Opex_fixed_HP_PVT = hp.calc_Cinv_HP(
Q_HP_max_PVT_wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_PVT)
addcosts_Opex_fixed += Opex_fixed_HP_PVT
Capex_a_HP_SC, Opex_fixed_HP_SC = hp.calc_Cinv_HP(
Q_HP_max_SC_Wh, locator, config, 'HP2')
Capex_a_storage_HP += (Capex_a_HP_SC)
addcosts_Opex_fixed += Opex_fixed_HP_SC
# HP for storage operation for charging from solar and discharging to DH
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=[
"E_aux_ch_W", "E_aux_dech_W",
"Q_from_storage_used_W", "Q_to_storage_W"
])
array = np.array(df)
Q_HP_max_storage_W = 0
for i in range(DAYS_IN_YEAR * HOURS_IN_DAY):
if array[i][0] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][3] + array[i][0])
elif array[i][1] > 0:
Q_HP_max_storage_W = max(Q_HP_max_storage_W,
array[i][2] + array[i][1])
Capex_a_HP_storage, Opex_fixed_HP_storage = hp.calc_Cinv_HP(
Q_HP_max_storage_W, locator, config, 'HP2')
addcosts_Capex_a += (Capex_a_HP_storage)
addcosts_Opex_fixed += Opex_fixed_HP_storage
# Storage
df = pd.read_csv(locator.get_optimization_slave_storage_operation_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["Storage_Size_m3"],
nrows=1)
StorageVol_m3 = np.array(df)[0][0]
Capex_a_storage, Opex_fixed_storage = storage.calc_Cinv_storage(
StorageVol_m3, locator, config, 'TES2')
addcosts_Capex_a += Capex_a_storage
addcosts_Opex_fixed += Opex_fixed_storage
# Costs from distribution configuration
if gv.ZernezFlag == 1:
NetworkCost += network.calc_Cinv_network_linear(
gv.NetworkLengthZernez, gv) * nBuildinNtw / len(buildList)
else:
NetworkCost += ntwFeat.pipesCosts_DHN * nBuildinNtw / len(
buildList)
addcosts_Capex_a += NetworkCost
# HEX (1 per building in ntw)
for (index, building_name) in zip(DHN_barcode, buildList):
if index == "1":
df = pd.read_csv(
locator.get_optimization_substations_results_file(
building_name),
usecols=["Q_dhw_W", "Q_heating_W"])
subsArray = np.array(df)
Q_max_W = np.amax(subsArray[:, 0] + subsArray[:, 1])
Capex_a_HEX_building, Opex_fixed_HEX_building = hex.calc_Cinv_HEX(
Q_max_W, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_building
addcosts_Opex_fixed += Opex_fixed_HEX_building
# HEX for solar
roof_area_m2 = np.array(
pd.read_csv(locator.get_total_demand(), usecols=["Aroof_m2"]))
areaAvail = 0
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
areaAvail += roof_area_m2[i][0]
for i in range(len(DHN_barcode)):
index = DHN_barcode[i]
if index == "1":
share = roof_area_m2[i][0] / areaAvail
#print share, "solar area share", buildList[i]
Q_max_SC_ET_Wh = solarFeat.Q_nom_SC_ET_Wh * master_to_slave_vars.SOLAR_PART_SC_ET * share
Capex_a_HEX_SC_ET, Opex_fixed_HEX_SC_ET = hex.calc_Cinv_HEX(
Q_max_SC_ET_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_SC_ET
addcosts_Opex_fixed += Opex_fixed_HEX_SC_ET
Q_max_SC_FP_Wh = solarFeat.Q_nom_SC_FP_Wh * master_to_slave_vars.SOLAR_PART_SC_FP * share
Capex_a_HEX_SC_FP, Opex_fixed_HEX_SC_FP = hex.calc_Cinv_HEX(
Q_max_SC_FP_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_SC_FP
addcosts_Opex_fixed += Opex_fixed_HEX_SC_FP
Q_max_PVT_Wh = solarFeat.Q_nom_PVT_Wh * master_to_slave_vars.SOLAR_PART_PVT * share
Capex_a_HEX_PVT, Opex_fixed_HEX_PVT = hex.calc_Cinv_HEX(
Q_max_PVT_Wh, locator, config, 'HEX1')
addcosts_Capex_a += Capex_a_HEX_PVT
addcosts_Opex_fixed += Opex_fixed_HEX_PVT
# Pump operation costs
Capex_a_pump, Opex_fixed_pump, Opex_var_pump = pumps.calc_Ctot_pump(
master_to_slave_vars, ntwFeat, gv, locator, lca, config)
addcosts_Capex_a += Capex_a_pump
addcosts_Opex_fixed += Opex_fixed_pump
# import gas consumption data from:
if DHN_barcode.count("1") > 0 and config.optimization.isheating:
# import gas consumption data from:
EgasPrimaryDataframe_W = pd.read_csv(
locator.get_optimization_slave_cost_prime_primary_energy_data(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
usecols=["E_gas_PrimaryPeakPower_W"])
E_gas_primary_peak_power_W = float(np.array(EgasPrimaryDataframe_W))
GasConnectionInvCost = ngas.calc_Cinv_gas(E_gas_primary_peak_power_W,
gv)
else:
GasConnectionInvCost = 0.0
addcosts_Capex_a += GasConnectionInvCost
# Save data
results = pd.DataFrame({
"Capex_a_SC": [Capex_a_SC],
"Opex_fixed_SC": [Opex_fixed_SC],
"Capex_a_PVT": [Capex_a_PVT],
"Opex_fixed_PVT": [Opex_fixed_PVT],
"Capex_a_Boiler_backup": [Capex_a_Boiler_backup],
"Opex_fixed_Boiler_backup": [Opex_fixed_Boiler_backup],
"Capex_a_storage_HEX": [Capex_a_HP_storage],
"Opex_fixed_storage_HEX": [Opex_fixed_HP_storage],
"Capex_a_storage_HP": [Capex_a_storage_HP],
"Capex_a_CHP": [Capex_a_CHP],
"Opex_fixed_CHP": [Opex_fixed_CHP],
"StorageInvC": [StorageInvC],
"StorageCostSum": [StorageInvC + Capex_a_storage_HP + Capex_a_HEX],
"NetworkCost": [NetworkCost],
"SubstHEXCost": [SubstHEXCost_capex],
"DHNInvestCost": [addcosts_Capex_a - CostDiscBuild],
"PVTHEXCost_Capex": [PVTHEXCost_Capex],
"CostDiscBuild": [CostDiscBuild],
"CO2DiscBuild": [CO2DiscBuild],
"PrimDiscBuild": [PrimDiscBuild],
"Capex_a_furnace": [Capex_a_furnace],
"Opex_fixed_furnace": [Opex_fixed_furnace],
"Capex_a_Boiler": [Capex_a_Boiler],
"Opex_fixed_Boiler": [Opex_fixed_Boiler],
"Capex_a_Boiler_peak": [Capex_a_Boiler_peak],
"Opex_fixed_Boiler_peak": [Opex_fixed_Boiler_peak],
"Capex_Disconnected": [Capex_Disconnected],
"Opex_Disconnected": [Opex_Disconnected],
"Capex_a_Lake": [Capex_a_Lake],
"Opex_fixed_Lake": [Opex_fixed_Lake],
"Capex_a_Sewage": [Capex_a_Sewage],
"Opex_fixed_Sewage": [Opex_fixed_Sewage],
"SCHEXCost_Capex": [SCHEXCost_Capex],
"Capex_a_pump": [Capex_a_pump],
"Opex_fixed_pump": [Opex_fixed_pump],
"Opex_var_pump": [Opex_var_pump],
"Sum_CAPEX": [addcosts_Capex_a],
"Sum_OPEX_fixed": [addcosts_Opex_fixed],
"GasConnectionInvCa": [GasConnectionInvCost],
"CO2_PV_disconnected": [CO2_PV_disconnected],
"cost_PV_disconnected": [cost_PV_disconnected],
"Eprim_PV_disconnected": [Eprim_PV_disconnected]
})
results.to_csv(locator.get_optimization_slave_investment_cost_detailed(
master_to_slave_vars.individual_number,
master_to_slave_vars.generation_number),
sep=',')
return (addcosts_Capex_a + addcosts_Opex_fixed, addCO2, addPrim)