def decentralized_main(locator, building_names, gv):
"""
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
:param gv: global variables class
:type locator: class
:type building_names: list
:type gv: class
:return: results of operation of buildings located in locator.get_optimization_disconnected_folder
:rtype: Nonetype
"""
t0 = time.clock()
geothermal_potential = pd.read_csv(locator.get_geothermal_potential(), index_col="Name")
BestData = {}
def calc_new_load(mdot, TsupDH, Tret, gv):
"""
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 * gv.cp * (TsupDH - Tret) * (1 + gv.Qloss_Disc)
if Qload < 0:
Qload = 0
if Qload < -1E-5:
print "Error in discBuildMain, negative heat requirement at hour", hour, building_name
return Qload
for building_name in building_names:
print building_name
loads = pd.read_csv(locator.get_optimization_substations_results_file(building_name),
usecols=["T_supply_DH_result", "T_return_DH_result", "mdot_DH_result"])
Qload = np.vectorize(calc_new_load)(loads["mdot_DH_result"], loads["T_supply_DH_result"],
loads["T_return_DH_result"], gv)
Qannual = Qload.sum()
Qnom = Qload.max()* (1+gv.Qmargin_Disc) # 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"].values
TsupDH = loads["T_supply_DH_result"].values
mdot = loads["mdot_DH_result"].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] += gv.NG_PRICE * Qgas # CHF
result[0][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][6] += gv.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[0][0] += Qload[hour]
if gv.DiscBioGasFlag == 1:
result[0][4] += gv.BG_PRICE * Qgas # CHF
result[0][5] += gv.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][6] += gv.BG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
# Boiler BG
result[1][4] += gv.BG_PRICE * Qgas # CHF
result[1][5] += gv.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[1][6] += gv.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] += gv.NG_PRICE * Qgas - gv.ELEC_PRICE * Qelec # CHF, extra electricity sold to grid
result[2][5] += 0.0874 * Qgas * 3600E-6 + 773 * 0.45 * Qelec * 1E-6 - gv.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 - gv.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(mdot[hour], TsupDH[hour], Tret[hour],
gv.TGround, gv)
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3+i][4] += gv.ELEC_PRICE * wdot_el # CHF
result[3+i][5] += gv.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.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] * gv.cp) + Tret[hour]
(wdot_el, qcolddot, qhotdot_missing, tsup2) = HP.calc_Cop_GHP(mdot[hour], TexitGHP, Tret[hour], gv.TGround, gv)
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3+i][4] += gv.ELEC_PRICE * wdot_el # CHF
result[3+i][5] += gv.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[3+i][0] += QtoBoiler
# Investment Costs / CO2 / Prim
InvCaBoiler = Boiler.calc_Cinv_boiler(Qnom, Qannual, gv)
InvCosts[0][0] = InvCaBoiler
InvCosts[1][0] = InvCaBoiler
InvCosts[2][0] = FC.calc_Cinv_FC(Qnom, gv)
for i in range(10):
result[3+i][0] = i/10
result[3+i][3] = 1-i/10
QnomBoiler = i/10 * Qnom
InvCaBoiler = Boiler.calc_Cinv_boiler(QnomBoiler, QannualB_GHP[i][0], gv)
InvCosts[3+i][0] = InvCaBoiler
InvCaGHP = HP.GHP_InvCost( Wel_GHP[i][0] , gv)
InvCosts[3+i][0] += InvCaGHP * gv.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
# 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/gv.GHP_A) * gv.GHP_HmaxSize #[W_th]
if Qallowed < QGHP:
optsearch[i+3] += 1
Best[i+3][0] = - 1
while not Bestfound and rank<el:
optsearch[CostsS[rank][0]] -= 1
optsearch[CO2S[rank][0]] -= 1
optsearch[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(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()
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_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_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_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 discBuildOp(locator, building_names, gv):
"""
Computes the parameters for the operation of disconnected buildings
output results in csv files
Parameters
----------
locator : string
path to folders
"""
print "Start Disconnected Building Routine \n"
t0 = time.clock()
geothermal_potential = pd.read_csv(locator.get_geothermal_potential, index_col="Name")
BestData = {}
def calc_new_load(mdot, TsupDH, Tret, gv):
Qload = mdot * gv.cp * (TsupDH - Tret) * (1 + gv.Qloss_Disc)
if Qload < 0:
Qload = 0
if Qload < -1E-5:
print "Error in discBuildMain, negative heat requirement at hour", hour, buildName
return Qload
for buildName in building_names:
fName = locator.pathSubsRes + "/" + buildName + "_result.csv"
loads = pd.read_csv(fName, usecols=["T_supply_DH_result", "T_return_DH_result", "mdot_DH_result"])
Qload = np.vectorize(calc_new_load)(loads["mdot_DH_result"], loads["T_supply_DH_result"], loads["T_return_DH_result"], gv)
Qannual = Qload.sum()
Qnom = Qload.max()* (1+gv.Qmargin_Disc) # 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
print "Operation with the Boiler / FC / GHP"
Tret = loads["T_return_DH_result"].values
TsupDH = loads["T_supply_DH_result"].values
mdot = loads["mdot_DH_result"].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] += gv.NG_PRICE * Qgas # CHF
result[0][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][6] += gv.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[0][0] += Qload[hour]
if gv.DiscBioGasFlag == 1:
result[0][4] += gv.BG_PRICE * Qgas # CHF
result[0][5] += gv.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[0][6] += gv.BG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
# Boiler BG
result[1][4] += gv.BG_PRICE * Qgas # CHF
result[1][5] += gv.BG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[1][6] += gv.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] += gv.NG_PRICE * Qgas - gv.ELEC_PRICE * Qelec # CHF, extra electricity sold to grid
result[2][5] += 0.0874 * Qgas * 3600E-6 + 773 * 0.45 * Qelec * 1E-6 - gv.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 - gv.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(mdot[hour], TsupDH[hour], Tret[hour], gv.TGround, gv)
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3+i][4] += gv.ELEC_PRICE * wdot_el # CHF
result[3+i][5] += gv.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.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] * gv.cp) + Tret[hour]
(wdot_el, qcolddot, qhotdot_missing, tsup2) = HP.calc_Cop_GHP(mdot[hour], TexitGHP, Tret[hour], gv.TGround, gv)
if Wel_GHP[i][0] < wdot_el:
Wel_GHP[i][0] = wdot_el
result[3+i][4] += gv.ELEC_PRICE * wdot_el # CHF
result[3+i][5] += gv.SMALL_GHP_TO_CO2_STD * wdot_el * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.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] += gv.NG_PRICE * Qgas # CHF
result[3+i][5] += gv.NG_BACKUPBOILER_TO_CO2_STD * Qgas * 3600E-6 # kgCO2
result[3+i][6] += gv.NG_BACKUPBOILER_TO_OIL_STD * Qgas * 3600E-6 # MJ-oil-eq
resourcesRes[3+i][0] += QtoBoiler
print time.clock() - t0, "seconds process time for the operation \n"
# Investment Costs / CO2 / Prim
InvCaBoiler = Boiler.calc_Cinv_boiler(Qnom, Qannual, gv)
InvCosts[0][0] = InvCaBoiler
InvCosts[1][0] = InvCaBoiler
InvCosts[2][0] = FC.calc_Cinv_FC(Qnom, gv)
for i in range(10):
result[3+i][0] = i/10
result[3+i][3] = 1-i/10
QnomBoiler = i/10 * Qnom
InvCaBoiler = Boiler.calc_Cinv_boiler(QnomBoiler, QannualB_GHP[i][0], gv)
InvCosts[3+i][0] = InvCaBoiler
InvCaGHP = HP.GHP_InvCost( Wel_GHP[i][0] , gv)
InvCosts[3+i][0] += InvCaGHP * gv.EURO_TO_CHF
# Best configuration
print "Find the 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
# Check the GHP area constraint
for i in range(10):
QGHP = (1-i/10) * Qnom
areaAvail = geothermal_potential.ix[buildName,"Area_geo"]
Qallowed = np.ceil(areaAvail/gv.GHP_A) * gv.GHP_HmaxSize #[W_th]
if Qallowed < QGHP:
optsearch[i+3] += 1
Best[i+3][0] = -1
while not Bestfound and rank<el:
optsearch[CostsS[rank][0]] -= 1
optsearch[CO2S[rank][0]] -= 1
optsearch[PrimS[rank][0]] -= 1
if np.count_nonzero(optsearch) != el:
Bestfound = True
indexBest = np.where(optsearch == 0)[0][0]
rank += 1
Best[indexBest][0] = 1
print indexBest, "Best"
Qnom_array = np.ones(len(Best[:,0])) * Qnom
# Save results in csv file
print "Save the results for", buildName, "\n"
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]
os.chdir(locator.pathDiscRes)
fName = buildName + "_result.csv"
results_to_csv = pd.DataFrame(dico)
fName_result = "DiscOp_" + fName[0:(len(fName)-4)] + ".csv"
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[buildName] = BestComb
if 0:
os.chdir(locator.pathDiscRes)
fName = "DiscOpSummary.csv"
results_to_csv = pd.DataFrame(BestData)
results_to_csv.to_csv(fName, sep= ',')
print "Disconnected Building Routine Completed"
print time.clock() - t0, "seconds process time for the Disconnected Building Routine \n"