def test_rampUpMax():
# read in original results
results = [8.0, 10.0, 0.0, 0.0, 4.0, 5.0, 5.0, 0.0, 0.0, 4.0, 8.0, 5.0, 4.0, 0.0, 4.0 , 5.0, 5.0, 0.0, 0.0, 4.0]
# 2. Create an energy system model instance
locations = {'example_region1', 'example_region2'}
commodityUnitDict = {'electricity': r'GW$_{el}$', 'methane': r'GW$_{CH_{4},LHV}$'}
commodities = {'electricity', 'methane'}
esM = fn.EnergySystemModel(locations=locations, commodities=commodities, numberOfTimeSteps=20,
commodityUnitsDict=commodityUnitDict,
hoursPerTimeStep=1, costUnit='1e9 Euro', lengthUnit='km', verboseLogLevel=0)
data_cost = {'example_region1': [10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10], 'example_region2': [10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10]}
data_cost_df = pd.DataFrame(data=data_cost)
esM.add(fn.Source(esM=esM, name='Natural gas purchase', commodity='methane',
hasCapacityVariable=False, commodityCostTimeSeries= data_cost_df))
# 4. Add conversion components to the energy system model
### Combined cycle gas turbine plants
data_cap =pd.Series(index=['example_region1','example_region2' ], data=[10,10])
esM.add(fn.ConversionDynamic(esM=esM, name='unrestricted', physicalUnit=r'GW$_{el}$',
commodityConversionFactors={'electricity':1, 'methane':-1/0.625},
capacityFix = data_cap, partLoadMin = 0.1, bigM =100,
investPerCapacity=0.65, opexPerCapacity=0.021, opexPerOperation =10, interestRate=0.08,
economicLifetime=33))
esM.add(fn.ConversionDynamic(esM=esM, name='restricted', physicalUnit=r'GW$_{el}$',
commodityConversionFactors={'electricity':1, 'methane':-1/0.625},
capacityFix = data_cap, partLoadMin = 0.3, bigM= 100, rampUpMax=0.4,
investPerCapacity=0.5, opexPerCapacity=0.021, opexPerOperation =1, interestRate=0.08,
economicLifetime=33))
data_demand = {'example_region1': [10,10,2,2,4,5,5,2,2,10,10,5,4,2,4,5,5,2,2,4], 'example_region2': [10,10,2,2,4,5,5,2,2,10,10,5,4,2,4,5,5,2,2,4]}
data_demand_df = pd.DataFrame(data= data_demand)
esM.add(fn.Sink(esM=esM, name='Electricity demand', commodity='electricity',
hasCapacityVariable=False, operationRateFix=data_demand_df))
esM.optimize(timeSeriesAggregation=False, solver = 'glpk')
print('restricted dispatch:\n')
print(esM.componentModelingDict['ConversionDynamicModel'].operationVariablesOptimum.xs('restricted'))
print('unrestricted dispatch:\n')
print(esM.componentModelingDict['ConversionDynamicModel'].operationVariablesOptimum.xs('unrestricted'))
# print(esM.componentModelingDict['ConversionDynamicModel'].operationVariablesOptimum.xs('unrestricted'))
# test if here solved fits with original results
# test if here solved fits with original results
testresults = esM.componentModelingDict["ConversionDynamicModel"].operationVariablesOptimum.xs('restricted')
np.testing.assert_array_almost_equal(testresults.values[0], results,decimal=2)
np.testing.assert_array_almost_equal(testresults.values[1], results,decimal=2)
def test_ConversionDynamicNeedsCapacity():
esM = fn.EnergySystemModel(
locations={
"example_region1",
},
commodities={"electricity", "methane"},
commodityUnitsDict={
"electricity": r"GW$_{el}$",
"methane": r"GW$_{th}$"
},
verboseLogLevel=2,
)
with pytest.raises(ValueError, match=r".*hasCapacityVariable.*"):
fn.ConversionDynamic(
esM=esM,
name="restricted",
physicalUnit=r"GW$_{el}$",
commodityConversionFactors={
"electricity": 1,
"methane": -1 / 0.625
},
partLoadMin=0.3,
bigM=100,
rampDownMax=0.5,
investPerCapacity=0.5,
opexPerCapacity=0.021,
opexPerOperation=1,
interestRate=0.08,
economicLifetime=33,
hasCapacityVariable=False,
)
def test_ConversionDynamicNeedsHigherOperationRate():
numberOfTimeSteps = 4
locations = {"ElectrolyzerLocation", "IndustryLocation"}
esM = fn.EnergySystemModel(
locations=locations,
commodities={"electricity", "methane"},
commodityUnitsDict={
"electricity": r"GW$_{el}$",
"methane": r"GW$_{th}$"
},
numberOfTimeSteps=numberOfTimeSteps,
verboseLogLevel=2,
)
operationRateMax = pd.DataFrame(
[
[
0.2,
0.4,
1.0,
1.0,
],
[
0.0,
0.0,
0.0,
0.0,
],
],
index=locations,
columns=range(0, numberOfTimeSteps),
).T
with pytest.raises(ValueError, match=r".*operationRateMax.*"):
fn.ConversionDynamic(
esM=esM,
name="restricted",
physicalUnit=r"GW$_{el}$",
commodityConversionFactors={
"electricity": 1,
"methane": -1 / 0.625
},
partLoadMin=0.3,
bigM=100,
rampDownMax=0.5,
operationRateMax=operationRateMax,
investPerCapacity=0.5,
opexPerCapacity=0.021,
opexPerOperation=1,
interestRate=0.08,
economicLifetime=33,
)
def test_ConversionDynamicHasHigherOperationRate():
numberOfTimeSteps = 4
locations = {'ElectrolyzerLocation', 'IndustryLocation'}
esM = fn.EnergySystemModel(locations=locations,
commodities={'electricity','methane'},
commodityUnitsDict={'electricity': r'GW$_{el}$','methane': r'GW$_{th}$'},
numberOfTimeSteps = numberOfTimeSteps, verboseLogLevel=2)
operationRateMax = pd.DataFrame([[0., 0.4, 1., 1.,],[0., 0., 0., 0.,]],
index = locations, columns = range(0,numberOfTimeSteps)).T
fn.ConversionDynamic(esM=esM, name='restricted', physicalUnit=r'GW$_{el}$',
commodityConversionFactors={'electricity':1, 'methane':-1/0.625},
partLoadMin = 0.3, bigM= 100, rampDownMax=0.5, operationRateMax= operationRateMax,
investPerCapacity=0.5, opexPerCapacity=0.021, opexPerOperation =1, interestRate=0.08,
economicLifetime=33,)
def test_minimumDownTime():
# read in original results
results = [
5.0,
5.0,
0.0,
0.0,
0.0,
5.0,
5.0,
0.0,
0.0,
0.0,
5.0,
5.0,
0.0,
0.0,
0.0,
5.0,
0.0,
0.0,
0.0,
4.0,
]
# 2. Create an energy system model instance
locations = {"example_region1", "example_region2"}
commodityUnitDict = {"electricity": r"GW$_{el}$", "methane": r"GW$_{CH_{4},LHV}$"}
commodities = {"electricity", "methane"}
esM = fn.EnergySystemModel(
locations=locations,
commodities=commodities,
numberOfTimeSteps=20,
commodityUnitsDict=commodityUnitDict,
hoursPerTimeStep=1,
costUnit="1e9 Euro",
lengthUnit="km",
verboseLogLevel=0,
)
data_cost = {
"example_region1": [
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
],
"example_region2": [
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
10,
],
}
data_cost_df = pd.DataFrame(data=data_cost)
esM.add(
fn.Source(
esM=esM,
name="Natural gas purchase",
commodity="methane",
hasCapacityVariable=False,
commodityCostTimeSeries=data_cost_df,
)
)
# 4. Add conversion components to the energy system model
### Combined cycle gas turbine plants
data_cap = pd.Series(index=["example_region1", "example_region2"], data=[10, 10])
esM.add(
fn.ConversionDynamic(
esM=esM,
name="unrestricted",
physicalUnit=r"GW$_{el}$",
commodityConversionFactors={"electricity": 1, "methane": -1 / 0.625},
capacityFix=data_cap,
partLoadMin=0.1,
bigM=100,
investPerCapacity=0.65,
opexPerCapacity=0.021,
opexPerOperation=10,
interestRate=0.08,
economicLifetime=33,
)
)
esM.add(
fn.ConversionDynamic(
esM=esM,
name="restricted",
physicalUnit=r"GW$_{el}$",
commodityConversionFactors={"electricity": 1, "methane": -1 / 0.625},
capacityFix=data_cap,
partLoadMin=0.4,
bigM=100,
downTimeMin=3,
investPerCapacity=0.5,
opexPerCapacity=0.021,
opexPerOperation=1,
interestRate=0.08,
economicLifetime=33,
)
)
data_demand = {
"example_region1": [5, 5, 2, 2, 4, 5, 5, 2, 2, 4, 5, 5, 2, 2, 4, 5, 5, 2, 2, 4],
"example_region2": [5, 5, 2, 2, 4, 5, 5, 2, 2, 4, 5, 5, 2, 2, 4, 5, 5, 2, 2, 4],
}
data_demand_df = pd.DataFrame(data=data_demand)
esM.add(
fn.Sink(
esM=esM,
name="Electricity demand",
commodity="electricity",
hasCapacityVariable=False,
operationRateFix=data_demand_df,
)
)
esM.optimize(timeSeriesAggregation=False, solver="glpk")
print("restricted dispatch:\n")
print(
esM.componentModelingDict[
"ConversionDynamicModel"
].operationVariablesOptimum.xs("restricted")
)
print("unrestricted dispatch:\n")
print(
esM.componentModelingDict[
"ConversionDynamicModel"
].operationVariablesOptimum.xs("unrestricted")
)
# print(esM.componentModelingDict['ConversionDynamicModel'].operationVariablesOptimum.xs('unrestricted'))
# test if here solved fits with original results
# test if here solved fits with original results
testresults = esM.componentModelingDict[
"ConversionDynamicModel"
].operationVariablesOptimum.xs("restricted")
np.testing.assert_array_almost_equal(testresults.values[0], results, decimal=2)