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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.OptimizeGasStorage as ogs
import dp.DynamicProgrammingByRegression as dyn
import StOptReg as reg
import StOptGrids
import unittest
accuracyClose = 1.5
class ZeroFunction :
def __init__(self) :
return None
def set(self, a, b, c) :
return 0.
# valorization of a given gas storage on a grid
# p_grid the grid
# p_maxLevelStorage maximum level
def gasStorage(p_grid, p_maxLevelStorage) :
# storage
injectionRateStorage = 60000
withdrawalRateStorage = 45000
injectionCostStorage = 0.35
withdrawalCostStorage = 0.35
maturity = 1.
nstep = 100
# define a a time grid
timeGrid = rsg.OneDimRegularSpaceGrid(np.zeros(1), maturity / nstep, nstep)
# future values
futValues = np.zeros(nstep + 1)
# periodicity factor
iPeriod = 52
for i in list(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
# no actualization
rate=0
# a backward simulator
bForward = False
backSimulator = mrsim.MeanRevertingSimulator(futureGrid, sigma, mr, rate,maturity, nstep, nbsimulOpt, bForward)
# optimizer
storage = ogs.OptimizeGasStorage(injectionRateStorage, withdrawalRateStorage, injectionCostStorage, withdrawalCostStorage)
# regressor
nMesh = 6
nbMesh = np.zeros(1, dtype = np.int32) + nMesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = ZeroFunction()
# initial values
initialStock = np.zeros(1) + p_maxLevelStorage
initialRegime = 0 # only one regime
# Optimize
fileToDump = "CondExpGasStorage"
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptim = dyn.DynamicProgrammingByRegression(p_grid, storage, regressor, vFunction, initialStock, initialRegime, fileToDump)
print("valueOptim", valueOptim)
return valueOptim
class testGasStorageTest(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)
gasStorage(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)
gasStorage(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)
gasStorage(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)
gasStorage(grid, maxLevelStorage)
if __name__ == '__main__':
unittest.main()
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