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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 StOptReg as reg
import StOptGrids
import StOptGlobal
import Utils
import Simulators as sim
import Optimizers as opt
import dp.DynamicProgrammingByRegressionHighLevel as dyn
import dp.SimulateRegressionControlHighLevel as srt
import unittest
import importlib
accuracyClose = 1.5
# valorization of a given gas storage on a grid
# p_grid the grid
# p_maxLevelStorage maximum level
def gasStorageHighLevel(p_grid, p_maxLevelStorage) :
# test MPI
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
# storage
injectionRateStorage = 60000.
withdrawalRateStorage = 45000.
injectionCostStorage = 0.35
withdrawalCostStorage = 0.35
maturity = 1.
nstep = 100
# define a a time grid
timeGrid = StOptGrids.OneDimRegularSpaceGrid(0., maturity / nstep, nstep)
# future values
futValues = []
# periodicity factor
iPeriod = 52
for i in list(range(nstep + 1)) :
futValues.append(50. + 20. * math.sin((math.pi * i * iPeriod) / nstep))
# define the future curve
futureGrid = Utils.FutureCurve(timeGrid, futValues)
# one dimensional factors
nDim = 1
sigma = np.zeros(nDim) + 0.94
mr = np.zeros(nDim) + 0.29
# number of simulations
nbsimulOpt = 20000
# no actualization
rate= 0.
# a backward simulator
bForward = False
backSimulator = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate,maturity, nstep, nbsimulOpt, bForward)
# optimizer
storage = opt.OptimizeGasStorageMeanReverting(injectionRateStorage, withdrawalRateStorage, injectionCostStorage, withdrawalCostStorage)
# regressor
nMesh = 6
nbMesh = np.zeros(1, dtype = np.int32) + nMesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = Utils.ZeroPayOff()
# initial values
initialStock = np.zeros(1) + p_maxLevelStorage
initialRegime = 0 # only one regime
# Optimize
fileToDump = "CondExpGasStorageHL"
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptim = dyn.DynamicProgrammingByRegressionHighLevel(p_grid, storage, regressor, vFunction, initialStock, initialRegime, fileToDump)
print("valOptim", valueOptim)
nbsimulSim = 40000
bForward = True
forSimulator2 = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate,maturity, nstep, nbsimulSim, bForward)
storage.setSimulator(forSimulator2)
valSimu2 = srt.SimulateRegressionControl(p_grid, storage, vFunction, initialStock, initialRegime, fileToDump)
print("valSimu2", valSimu2, "valOptim", valueOptim)
class testGasStorageHighLevelTest(unittest.TestCase):
def test_simpleStorage(self):
# storage
maxLevelStorage = 90000
# grid
nGrid = 10
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
grid = StOptGrids.RegularSpaceGrid(lowValues, step, nbStep)
gasStorageHighLevel(grid, maxLevelStorage)
def test_simpleStorageLegendreLinear(self):
# storage
maxLevelStorage = 90000
# grid
nGrid = 10
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 1
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
gasStorageHighLevel(grid, maxLevelStorage)
def test_simpleStorageLegendreQuadratic(self):
# storage
maxLevelStorage = 90000
# grid
nGrid = 5
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 2
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
gasStorageHighLevel(grid, maxLevelStorage)
def test_simpleStorageLegendreCubic(self):
# storage
maxLevelStorage = 90000
# grid
nGrid = 5
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 3
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
gasStorageHighLevel(grid, maxLevelStorage)
if __name__ == '__main__':
unittest.main()
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