def OptimStockage(n,i=4):
prixBatterie=200 #€/kWh On peut changer le prix pour voir à partir de quel prix le stockage est rentable
P=[] #liste pour stocker le coût du système avec stockage
Pui=[] #liste pour stocker les différentes puissances de stockage
for i in range(n):
PrixTotal={}
Consommation={}
LMultipliers={}
DeltaPrix={}
Deltazz={}
CostFunction={}
TotalCols={}
zz={}
#p_max_out=100.; p_max_in=100.; c_max=10.;
p_max=i*40.; c_max=4.*p_max;
Pui.append(p_max)
areaConsumption["NewConsumption"]=areaConsumption["areaConsumption"]
nbTime=len(areaConsumption["areaConsumption"])
cpt=0
for i in model.areaConsumption: model.areaConsumption[i]=areaConsumption.NewConsumption[i-1]
for cpt in range(2):
print(cpt)
if (cpt==0): zz[cpt]=[0]*nbTime
else : zz[cpt]=areaConsumption["Storage"]
results=opt.solve(model)
Constraints= getConstraintsDual_panda(model)
TotalCols[cpt]=getVariables_panda(model)['energyCosts'].sum()[1]
Prix=Constraints["energyCtr"].assign(Prix=lambda x: x.energyCtr *10**6).Prix.to_numpy()
valueAtZero=Prix*(TotalCols[cpt]/sum(Prix*Prix)-zz[cpt])
tmpCost=GenCostFunctionFromMarketPrices_dict(Prix,r_in=0.95,r_out=0.95,valueAtZero=valueAtZero)
if (cpt==0): CostFunction[cpt]=GenCostFunctionFromMarketPrices(Prix,r_in=0.95,r_out=0.95,valueAtZero=valueAtZero)
else:
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=0.95, r_out=0.95, valueAtZero=valueAtZero)
tmpCost2 = CostFunction[cpt-1]
tmpCost2.Maxf_2Breaks_withO(tmpCost['S1'],tmpCost['S2'],tmpCost['B1'],tmpCost['B2'],tmpCost['f0']) ### etape clé, il faut bien comprendre ici l'utilisation du max de deux fonction de coût
CostFunction[cpt]=tmpCost2
LMultipliers[cpt]=Prix
if cpt>0:
DeltaPrix[cpt]=sum(abs(LMultipliers[cpt]-LMultipliers[cpt-1]))
Deltazz[cpt]=sum(abs(zz[cpt]-zz[cpt-1]))
areaConsumption["Storage"]=CostFunction[cpt].OptimMargInt([-p_max]*nbTime,[p_max]*nbTime,[0]*nbTime,[c_max]*nbTime)
areaConsumption["NewConsumption"]=areaConsumption["areaConsumption"]+areaConsumption["Storage"]
PrixTotal[cpt]=Prix.sum()
Consommation[cpt]=areaConsumption.NewConsumption
for i in model.areaConsumption: model.areaConsumption[i]=areaConsumption.NewConsumption[i-1]
cout=(areaConsumption['NewConsumption']*Prix*0.001).sum()
P.append(cout)
fig=go.Figure()
fig.add_trace(
go.Scatter(x=list(Pui), y=list(P), name="Dépenses"))
fig.update_layout(
title_text="Dépenses en euros",xaxis_title="Puissance du stockage")
fig.update_layout(
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(count=1,
label="en heures",
step="hour",
stepmode="backward")
])
),
rangeslider=dict(
visible=True
),
type="-"
)
)
fig.show()
return()
##### Loop
PrixTotal = {}
Consommation = {}
LMultipliers = {}
DeltaPrix = {}
Deltazz = {}
CostFunction = {}
TotalCols = {}
zz = {}
# p_max_out=100.; p_max_in=100.; c_max=10.;
areaConsumption["NewConsumption"] = areaConsumption["areaConsumption"]
nbTime = len(areaConsumption["areaConsumption"])
cpt = 0
for i in model.areaConsumption: model.areaConsumption[i] = areaConsumption.NewConsumption[i - 1]
DeltaPrix_=tol+1
while ((DeltaPrix_ > tol)&(n<cpt)) :
print(cpt)
if (cpt == 0):
zz[cpt] = [0] * nbTime
else:
zz[cpt] = areaConsumption["Storage"]
results = opt.solve(model)
Constraints = getConstraintsDual_panda(model)
TotalCols[cpt] = getVariables_panda(model)['energyCosts'].sum()[1]
Prix = Constraints["energyCtr"].assign(Prix=lambda x: x.energyCtr * 10 ** 6).Prix.to_numpy()
valueAtZero = Prix * (TotalCols[cpt] / sum(Prix * Prix) - zz[cpt])
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=efficiency_in, r_out=efficiency_out, valueAtZero=valueAtZero)
if (cpt == 0):
CostFunction[cpt] = GenCostFunctionFromMarketPrices(Prix, r_in=efficiency_in, r_out=efficiency_out, valueAtZero=valueAtZero)
else:
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=efficiency_in, r_out=efficiency_out, valueAtZero=valueAtZero)
tmpCost2 = CostFunction[cpt - 1]
if efficiency_in*efficiency_out==1:
tmpCost2.Maxf_1Breaks_withO(tmpCost['S1'], tmpCost['B1'], tmpCost[
'f0'])
else:
tmpCost2.Maxf_2Breaks_withO(tmpCost['S1'], tmpCost['S2'], tmpCost['B1'], tmpCost['B2'], tmpCost[
'f0']) ### etape clé, il faut bien comprendre ici l'utilisation du max de deux fonction de coût
CostFunction[cpt] = tmpCost2
LMultipliers[cpt] = Prix
if cpt > 0:
DeltaPrix[cpt] = sum(abs(LMultipliers[cpt] - LMultipliers[cpt - 1]))/sum(abs(LMultipliers[cpt]))
Deltazz[cpt] = sum(abs(zz[cpt] - zz[cpt - 1]))/sum(abs(zz[cpt]))
DeltaPrix_= DeltaPrix[cpt]
areaConsumption["Storage"] = CostFunction[cpt].OptimMargInt([-p_max] * nbTime, [p_max] * nbTime, [0] * nbTime,
[c_max] * nbTime)
areaConsumption["NewConsumption"] = areaConsumption["areaConsumption"] + areaConsumption["Storage"]
for i in model.areaConsumption: model.areaConsumption[i] = areaConsumption.NewConsumption[i - 1]
cpt=cpt+1
stats = {"DeltaPrix" : DeltaPrix,"Deltazz" : Deltazz}
return {"areaConsumption" : areaConsumption, "model" : model, "stats" : stats};
def GetElectricSystemModel_PlaningMultiNode_with1Storage(areaConsumption,availabilityFactor,
TechParameters,ExchangeParameters,StorageParameters,isAbstract=False,
solver='mosek',n=10,tol=exp(-4)):
"""
This function takes storage caracteristics, system caracteristics and optimise operation Set values
:param areaConsumption: panda table with consumption
:param availabilityFactor: panda table
:param isAbstract: boolean true is the model should be abstract. ConcreteModel otherwise
:param StorageParameters is a panda with p_max (maximal power), c_max (energy capacity in the storage : maximal energy),
:efficiency_in (input efficiency of storage),
:efficiency_out (output efficiency of storage).
:return: a dictionary with model : pyomo model without storage, storage : storage infos
"""
import pandas as pd
import numpy as np
from dynprogstorage.wrappers import GenCostFunctionFromMarketPrices
from dynprogstorage.wrappers import GenCostFunctionFromMarketPrices_dict
#isAbstract=False
AREAS = areaConsumption["AREAS"].unique().tolist()
model = GetElectricSystemModel_PlaningMultiNode(areaConsumption, availabilityFactor, TechParameters,ExchangeParameters,isAbstract=isAbstract)
opt = SolverFactory(solver)
# results = opt.solve(model)
# Variables = getVariables_panda(model) #### obtain optimized variables in panda form
# Constraints = getConstraintsDual_panda(model) #### obtain lagrange multipliers in panda form
##### Loop
Consommation = {};
for AREA in AREAS : Consommation[AREA]={}
LMultipliers = {};
for AREA in AREAS : LMultipliers[AREA]={}
CostFunction = {};
for AREA in AREAS : CostFunction[AREA] = {}
zz = {};
for AREA in AREAS: zz[AREA] = {}
OptimControl=pd.DataFrame(columns=["Step","AREAS","TotalCols","DeltaPrix","Deltazz"])
areaConsumption["NewConsumption"] = areaConsumption["areaConsumption"]
nbTime = len(areaConsumption["TIMESTAMP"].unique())
cpt = 0
areaConsumption=areaConsumption.set_index(["AREAS","TIMESTAMP"])
for i in model.areaConsumption:
model.areaConsumption[i] = areaConsumption.NewConsumption[i]
DeltaPrix_=tol+1
while ( (DeltaPrix_ > tol) & (n>cpt) ) :
print(cpt)
if (cpt == 0):
for AREA in AREAS: zz[AREA][cpt] = [0] * nbTime
areaConsumption["Storage"] = 0
else:
DeltaPrix_ = 0
for AREA in AREAS:
zz[AREA][cpt] = areaConsumption.loc[areaConsumption.index.get_level_values('AREAS') == AREA,"Storage"]
results = opt.solve(model)
Constraints = getConstraintsDual_panda(model)
Variables = getVariables_panda(model)['energyCosts']
for AREA in AREAS:
indexStorage = StorageParameters["AREA"]==AREA
TotalCols = Variables[Variables.AREAS==AREA].energyCosts.sum()
#Constraints["energyCtr"]=Constraints["energyCtr"].set_index(["AREAS","TIMESTAMP"])
Prix = Constraints["energyCtr"][Constraints["energyCtr"].AREAS==AREA].assign(Prix=lambda x: x.energyCtr * 10 ** 6).Prix.to_numpy()
Prix[Prix<=0]=0.0000000001
valueAtZero = Prix * (TotalCols / sum(Prix * Prix) - zz[AREA][cpt])
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=StorageParameters[indexStorage].efficiency_in.tolist()[0],
r_out=StorageParameters[indexStorage].efficiency_out.tolist()[0],
valueAtZero=valueAtZero)
if (cpt == 0):
CostFunction[AREA][cpt] = GenCostFunctionFromMarketPrices(Prix,
r_in=StorageParameters[indexStorage].efficiency_in.tolist()[0],
r_out=StorageParameters[indexStorage].efficiency_out.tolist()[0],
valueAtZero=valueAtZero)
else:
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix,
r_in=StorageParameters[indexStorage].efficiency_in.tolist()[0],
r_out=StorageParameters[indexStorage].efficiency_out.tolist()[0],
valueAtZero=valueAtZero)
tmpCost2 = CostFunction[AREA][cpt - 1]
if StorageParameters[indexStorage].efficiency_in.tolist()[0]*StorageParameters[indexStorage].efficiency_out.tolist()[0]==1:
tmpCost2.Maxf_1Breaks_withO(tmpCost['S1'], tmpCost['B1'], tmpCost['f0'])
else:
tmpCost2.Maxf_2Breaks_withO(tmpCost['S1'], tmpCost['S2'], tmpCost['B1'], tmpCost['B2'], tmpCost['f0']) ### etape clé, il faut bien comprendre ici l'utilisation du max de deux fonction de coût
CostFunction[AREA][cpt] = tmpCost2
LMultipliers[AREA][cpt] = Prix
if cpt > 0:
DeltaPrix = sum(abs(LMultipliers[AREA][cpt] - LMultipliers[AREA][cpt - 1]))/sum(abs(LMultipliers[AREA][cpt]))
if (sum(abs(zz[AREA][cpt]))==0) & (sum(abs(zz[AREA][cpt] - zz[AREA][cpt - 1]))==0) :
Deltazz = 0
else:
Deltazz = sum(abs(zz[AREA][cpt] - zz[AREA][cpt - 1]))/sum(abs(zz[AREA][cpt]))
DeltaPrix_= DeltaPrix_+ DeltaPrix
OptimControl_tmp=pd.DataFrame([cpt, AREA, TotalCols, DeltaPrix, Deltazz]).transpose()
OptimControl_tmp.columns=["Step", "AREAS", "TotalCols", "DeltaPrix", "Deltazz"]
OptimControl=pd.concat([OptimControl,OptimControl_tmp],axis=0)
areaConsumption.loc[areaConsumption.index.get_level_values('AREAS') == AREA,"Storage"] = CostFunction[AREA][cpt].OptimMargInt([-StorageParameters[indexStorage].p_max.tolist()[0]] * nbTime,
[StorageParameters[indexStorage].p_max.tolist()[0]] * nbTime, [0] * nbTime,
[StorageParameters[indexStorage].c_max.tolist()[0]] * nbTime)
areaConsumption.loc[areaConsumption.index.get_level_values('AREAS') == AREA,"NewConsumption"] = areaConsumption.loc[areaConsumption.index.get_level_values('AREAS') == AREA,"areaConsumption"] + areaConsumption.loc[areaConsumption.index.get_level_values('AREAS') == AREA,"Storage"]
for i in model.areaConsumption: model.areaConsumption[i] = areaConsumption.NewConsumption[i]
cpt=cpt+1
stats = {"DeltaPrix" : DeltaPrix,"Deltazz" : Deltazz}
return {"areaConsumption" : areaConsumption, "model" : model, "stats" : stats};
def GetElectricSystemModel_PlaningSingleNode_with1Storage(areaConsumption,availabilityFactor,
TechParameters,StorageParameters,solverpath=-1,isAbstract=False,
solver='mosek',n=10,tol=exp(-4)):
"""
This function takes storage caracteristics, system caracteristics and optimise operation Set values
:param areaConsumption: panda table with consumption
:param availabilityFactor: panda table
:param isAbstract: boolean true is the model should be abstract. ConcreteModel otherwise
:param StorageParameters is a dictionary with p_max (maximal power), c_max (energy capacity in the storage : maximal energy),
:efficiency_in (input efficiency of storage),
:efficiency_out (output efficiency of storage).
:return: a dictionary with model : pyomo model without storage, storage : storage infos
"""
import pandas as pd
import numpy as np
from dynprogstorage.wrappers import GenCostFunctionFromMarketPrices
from dynprogstorage.wrappers import GenCostFunctionFromMarketPrices_dict
#isAbstract=False
model = GetElectricSystemModel_PlaningSingleNode(areaConsumption, availabilityFactor, TechParameters,isAbstract=isAbstract)
if solverpath==-1 : opt = SolverFactory(solver)
else : opt = MySolverFactory(solver, solverpath)
# results = opt.solve(model)
# Variables = getVariables_panda(model) #### obtain optimized variables in panda form
# Constraints = getConstraintsDual_panda(model) #### obtain lagrange multipliers in panda form
##### Loop
PrixTotal = {}
Consommation = {}
LMultipliers = {}
DeltaPrix = {}
Deltazz = {}
CostFunction = {}
TotalCols = {}
zz = {}
# p_max_out=100.; p_max_in=100.; c_max=10.;
areaConsumption["NewConsumption"] = areaConsumption["areaConsumption"]
nbTime = len(areaConsumption["areaConsumption"])
cpt = 0
for i in model.areaConsumption: model.areaConsumption[i] = areaConsumption.NewConsumption[i - 1]
DeltaPrix_=tol+1
while ( (DeltaPrix_ > tol) & (n>cpt) ) :
print(cpt)
if (cpt == 0):
zz[cpt] = [0] * nbTime
else:
zz[cpt] = areaConsumption["Storage"]
#if solver=="mosek" :
# results = opt.solve(model, options= {"dparam.optimizer_max_time": 100.0, "iparam.outlev" : 2, "iparam.optimizer": mosek.optimizertype.primal_simplex},tee=True)
#else :
if (solver=='cplex')| (solver=='cbc'):
results = opt.solve(model,warmstart = True)
else : results = opt.solve(model)
Constraints = getConstraintsDual_panda(model)
#if solver=='cbc':
# Variables = getVariables_panda(model)['energy'].set_index(['TIMESTAMP','TECHNOLOGIES'])
# for i in model.energy: model.energy[i] = Variables.energy[i]
TotalCols[cpt] = getVariables_panda(model)['energyCosts'].sum()[1]
Prix = Constraints["energyCtr"].assign(Prix=lambda x: x.energyCtr * 10 ** 6).Prix.to_numpy()
Prix[Prix<=0]=0.0000000001
valueAtZero = Prix * (TotalCols[cpt] / sum(Prix * Prix) - zz[cpt])
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=StorageParameters['efficiency_in'],
r_out=StorageParameters['efficiency_out'],
valueAtZero=valueAtZero)
if (cpt == 0):
CostFunction[cpt] = GenCostFunctionFromMarketPrices(Prix, r_in=StorageParameters['efficiency_in'],
r_out=StorageParameters['efficiency_out'], valueAtZero=valueAtZero)
else:
tmpCost = GenCostFunctionFromMarketPrices_dict(Prix, r_in=StorageParameters['efficiency_in'],
r_out=StorageParameters['efficiency_out'], valueAtZero=valueAtZero)
tmpCost2 = CostFunction[cpt - 1]
if StorageParameters['efficiency_in']*StorageParameters['efficiency_out']==1:
tmpCost2.Maxf_1Breaks_withO(tmpCost['S1'], tmpCost['B1'], tmpCost[
'f0'])
else:
tmpCost2.Maxf_2Breaks_withO(tmpCost['S1'], tmpCost['S2'], tmpCost['B1'], tmpCost['B2'], tmpCost[
'f0']) ### etape clé, il faut bien comprendre ici l'utilisation du max de deux fonction de coût
CostFunction[cpt] = tmpCost2
LMultipliers[cpt] = Prix
if cpt > 0:
DeltaPrix[cpt] = sum(abs(LMultipliers[cpt] - LMultipliers[cpt - 1]))/sum(abs(LMultipliers[cpt]))
Deltazz[cpt] = sum(abs(zz[cpt] - zz[cpt - 1]))/sum(abs(zz[cpt]))
DeltaPrix_= DeltaPrix[cpt]
areaConsumption["Storage"] = CostFunction[cpt].OptimMargInt([-StorageParameters['p_max']] * nbTime,
[StorageParameters['p_max']] * nbTime, [0] * nbTime,
[StorageParameters['c_max']] * nbTime)
areaConsumption["NewConsumption"] = areaConsumption["areaConsumption"] + areaConsumption["Storage"]
for i in model.areaConsumption: model.areaConsumption[i] = areaConsumption.NewConsumption[i-1]
cpt=cpt+1
stats = {"DeltaPrix" : DeltaPrix,"Deltazz" : Deltazz}
return {"areaConsumption" : areaConsumption, "model" : model, "stats" : stats};