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#!/usr/bin/python3
# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import math
import utils.OneDimRegularSpaceGrid as rsg
import utils.OneDimData as data
import simulators.MeanRevertingSimulator as mrsim
import dp.OptimizeGasStorageSwitchingCost as ogssc
import StOptReg as reg
import StOptGrids
import dp.DynamicProgrammingByRegression as dyn
import unittest
import importlib
accuracyClose = 0.5
class ZeroFunction:
def __init__(self):
return None
def set(self, a, b, c):
return 0.
def testGasStorage() :
# test MPI
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
#########
maxLevelStorage = 360000
injectionRateStorage = 60000
withdrawalRateStorage = 45000
injectionCostStorage = 0.35
withdrawalCostStorage = 0.35
switchingCostStorage = 4.
maturity = 1.
nstep = 10
# define a a time grid
timeGrid = rsg.OneDimRegularSpaceGrid(np.zeros(1), maturity / nstep, nstep)
futValues = np.zeros(nstep + 1)
# periodicity factor
iPeriod = 52
for i in range(nstep + 1):
futValues[i] = 50. + 20 * math.sin((math.pi * i * iPeriod) / nstep)
# define the future curve
futureGrid = data.OneDimData(timeGrid, futValues)
# one dimensional factors
nDim = 1
sigma = np.zeros(nDim) + 0.94
mr = np.zeros(nDim) + 0.29
# number of simulations
nbsimulOpt = 20000
# grid
#####
nGrid = 40
lowValues = np.zeros(1, dtype = float)
step = np.zeros(1, dtype = float) + maxLevelStorage / nGrid
nbStep = np.zeros(1, dtype = np.int32) + nGrid
grid = StOptGrids.RegularSpaceGrid(lowValues, step, nbStep)
# no actualization
rate=0.
# a backward simulator
######################
bForward = False
backSimulator = mrsim.MeanRevertingSimulator(futureGrid, sigma, mr, rate,maturity, nstep, nbsimulOpt, bForward)
# optimizer
############
tval = np.zeros(2)
tval[0] = 0.
tval[1] = 1.e30
timeGrid = StOptGrids.OneDimSpaceGrid(tval)
values = np.zeros(2, dtype = np.int32)
values[0] = 3
values[1] = 3
regime = data.OneDimData(timeGrid, values)
storage = ogssc.OptimizeGasStorageSwitchingCost(injectionRateStorage, withdrawalRateStorage, injectionCostStorage, withdrawalCostStorage, switchingCostStorage, regime)
# regressor
##########
nMesh = 4
nbMesh = np.zeros(1, dtype = np.int32) + nMesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = ZeroFunction()
# initial values
initialStock = np.zeros(1) + maxLevelStorage
initialRegime = 0 # here do nothing (no injection, no withdrawal)
# Optimize
###########
fileToDump = "CondExpGasSwiCost"
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptim = dyn.DynamicProgrammingByRegression(grid, storage, regressor, vFunction, initialStock, initialRegime, fileToDump)
print("valOP", valueOptim)
class testGasStorageSwitchCostTest(unittest.TestCase):
def test_gasStorageSwitchCost(self):
testGasStorage()
if __name__ == '__main__':
unittest.main()
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