Workflow for the EnergyLand energy system¶

In this application of the ETHOS.FINE framework, a single node energy system is modeled and optimized. Compared to the previous examples, this example includes a lot more technologies considered in the system.

The workflow is structures as follows:

1. Required packages are imported and the input data path is set
2. An energy system model instance is created
3. Commodity sources are added to the energy system model
4. Commodity conversion components are added to the energy system model
5. Commodity storages are added to the energy system model
6. Commodity sinks are added to the energy system model
7. The energy system model is optimized
8. Selected optimization results are presented

In [1]:

from IPython.display import Image
from pathlib import Path

Image(Path("images/strukturExample.png"))

from IPython.display import Image from pathlib import Path Image(Path("images/strukturExample.png"))

Out[1]:

1. Import packages¶

The FINE framework is imported which provides the required classes and functions for modeling the energy system. The working directory and the underlying excelfile which provides some of the input data is imported.

In [ ]:

import fine as fn
from getData import getData
import pandas as pd
from pathlib import Path


cwd = Path.cwd()
data = getData()

import fine as fn from getData import getData import pandas as pd from pathlib import Path cwd = Path.cwd() data = getData()

2. Set up energy system model instance¶

The structure of the energy system model is given by the considered locations, in this case we consider only one location (EnergyLand), commodities, the number of time steps as well as the hours per time step.

The commodities are specified by a unit, which can be given as an energy or mass unit per hour.

In [3]:

locations = {"EnergyLand"}
commodityUnitDict = {
    "electricity": r"GW$_{el}$",
    "hydrogen": r"GW$_{H_{2},LHV}$",
    "nGas": r"GW$_{CH4}$",
    "coal": r"GW$_{coal}$",
    "PHeat": r"GW$_{Pheat}$",
    "LTHeat": r"GW$_{LTHeat}$",
    "CO2": r"kt$_{CO_{2}}$/h",
    "pTransport": "Mio pkm/h",
    "fTransport": "Mio tkm/h",
    "crudeOil": r"GW$_{Oil}$",
    "wood": r"GW$_{wood}$",
    "biowaste": r"GW$_{biowaste}$",
    "bioslurry": r"GW$_{bioslurry}$",
    "diesel": r"GW$_{diesel}$",
    "biogas": r"GW$_{CH4}$",
    "nGasImp": r"GW$_{CH4}$",
}
commodities = {
    "electricity",
    "hydrogen",
    "nGas",
    "coal",
    "PHeat",
    "LTHeat",
    "CO2",
    "pTransport",
    "fTransport",
    "crudeOil",
    "wood",
    "biowaste",
    "bioslurry",
    "diesel",
    "nGasImp",
    "biogas",
}
numberOfTimeSteps = 8760
hoursPerTimeStep = 1

balanceLimit = pd.DataFrame(
    index=["CO2_cap"], columns=["EnergyLand", "lowerBound"], data=[[-210000, True]]
)

locations = {"EnergyLand"} commodityUnitDict = { "electricity": r"GW$_{el}$", "hydrogen": r"GW$_{H_{2},LHV}$", "nGas": r"GW$_{CH4}$", "coal": r"GW$_{coal}$", "PHeat": r"GW$_{Pheat}$", "LTHeat": r"GW$_{LTHeat}$", "CO2": r"kt$_{CO_{2}}$/h", "pTransport": "Mio pkm/h", "fTransport": "Mio tkm/h", "crudeOil": r"GW$_{Oil}$", "wood": r"GW$_{wood}$", "biowaste": r"GW$_{biowaste}$", "bioslurry": r"GW$_{bioslurry}$", "diesel": r"GW$_{diesel}$", "biogas": r"GW$_{CH4}$", "nGasImp": r"GW$_{CH4}$", } commodities = { "electricity", "hydrogen", "nGas", "coal", "PHeat", "LTHeat", "CO2", "pTransport", "fTransport", "crudeOil", "wood", "biowaste", "bioslurry", "diesel", "nGasImp", "biogas", } numberOfTimeSteps = 8760 hoursPerTimeStep = 1 balanceLimit = pd.DataFrame( index=["CO2_cap"], columns=["EnergyLand", "lowerBound"], data=[[-210000, True]] )

In [4]:

esM = fn.EnergySystemModel(
    locations=locations,
    commodities=commodities,
    numberOfTimeSteps=8760,
    commodityUnitsDict=commodityUnitDict,
    hoursPerTimeStep=1,
    costUnit="1e6 Euro",
    lengthUnit="km",
    verboseLogLevel=0,
    balanceLimit=balanceLimit,
)

esM = fn.EnergySystemModel( locations=locations, commodities=commodities, numberOfTimeSteps=8760, commodityUnitsDict=commodityUnitDict, hoursPerTimeStep=1, costUnit="1e6 Euro", lengthUnit="km", verboseLogLevel=0, balanceLimit=balanceLimit, )

3. Sources¶

Source components transfer a commodity from outside the system boundary of EnergyLand into the system.

3.1 Electricity sources¶

Wind turbines¶

Onshore Wind Turbines¶

In [5]:

esM.add(
    fn.Source(
        esM=esM,
        name="Wind_Onshore",
        commodity="electricity",
        hasCapacityVariable=True,
        operationRateMax=data["Wind_onshore, operationRateMax"],
        capacityMax=data["Wind_onshore, capacityMax"],
        investPerCapacity=1250,
        opexPerCapacity=1250 * 0.02,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Source( esM=esM, name="Wind_Onshore", commodity="electricity", hasCapacityVariable=True, operationRateMax=data["Wind_onshore, operationRateMax"], capacityMax=data["Wind_onshore, capacityMax"], investPerCapacity=1250, opexPerCapacity=1250 * 0.02, interestRate=0.08, economicLifetime=20, ) )

Offshore Wind Turbines¶

In [6]:

esM.add(
    fn.Source(
        esM=esM,
        name="Wind_Offshore",
        commodity="electricity",
        hasCapacityVariable=True,
        operationRateMax=data["Wind_offshore, operationRateMax"],
        capacityMax=data["Wind_offshore, capacityMax"],
        investPerCapacity=2530,
        opexPerCapacity=2530 * 0.045,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Source( esM=esM, name="Wind_Offshore", commodity="electricity", hasCapacityVariable=True, operationRateMax=data["Wind_offshore, operationRateMax"], capacityMax=data["Wind_offshore, capacityMax"], investPerCapacity=2530, opexPerCapacity=2530 * 0.045, interestRate=0.08, economicLifetime=20, ) )

Photovoltaic¶

In [7]:

esM.add(
    fn.Source(
        esM=esM,
        name="PV",
        commodity="electricity",
        hasCapacityVariable=True,
        operationRateMax=data["PV, operationRateMax"],
        capacityMax=data["PV, capacityMax"],
        investPerCapacity=800,
        opexPerCapacity=800 * 0.019,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Source( esM=esM, name="PV", commodity="electricity", hasCapacityVariable=True, operationRateMax=data["PV, operationRateMax"], capacityMax=data["PV, capacityMax"], investPerCapacity=800, opexPerCapacity=800 * 0.019, interestRate=0.08, economicLifetime=20, ) )

Electricity import¶

In [8]:

esM.add(
    fn.Source(
        esM=esM,
        name="el_Import",
        commodity="electricity",
        hasCapacityVariable=False,
        operationRateMax=data["el_Import, operationRateMax"],
    )
)

esM.add( fn.Source( esM=esM, name="el_Import", commodity="electricity", hasCapacityVariable=False, operationRateMax=data["el_Import, operationRateMax"], ) )

3.2 Hydrogen source¶

In [9]:

esM.add(
    fn.Source(
        esM=esM,
        name="H2_Import",
        commodity="hydrogen",
        hasCapacityVariable=False,
        operationRateMax=data["H2_Import, operationRateMax"],
        commodityCost=0.132,
    )
)

esM.add( fn.Source( esM=esM, name="H2_Import", commodity="hydrogen", hasCapacityVariable=False, operationRateMax=data["H2_Import, operationRateMax"], commodityCost=0.132, ) )

3.3 Coal source¶

In [10]:

esM.add(
    fn.Source(
        esM=esM,
        name="CoalSource",
        commodity="coal",
        hasCapacityVariable=False,
        commodityCost=0.021,
    )
)

esM.add( fn.Source( esM=esM, name="CoalSource", commodity="coal", hasCapacityVariable=False, commodityCost=0.021, ) )

3.4 Crude Oil source¶

In [11]:

esM.add(
    fn.Source(
        esM=esM,
        name="CrudeOilSource",
        commodity="crudeOil",
        hasCapacityVariable=False,
        commodityCost=0.036,
    )
)

esM.add( fn.Source( esM=esM, name="CrudeOilSource", commodity="crudeOil", hasCapacityVariable=False, commodityCost=0.036, ) )

3.5 Natural gas source¶

In [12]:

esM.add(
    fn.Source(
        esM=esM,
        name="nGasSource",
        commodity="nGasImp",
        hasCapacityVariable=False,
        commodityCost=0.0256,
    )
)

esM.add( fn.Source( esM=esM, name="nGasSource", commodity="nGasImp", hasCapacityVariable=False, commodityCost=0.0256, ) )

3.6 Biomass sources¶

Wood Source¶

In [13]:

esM.add(
    fn.Source(
        esM=esM,
        name="WoodSource",
        commodity="wood",
        hasCapacityVariable=True,
        capacityMax=data["wood_source, capacityMax"],
        commodityCost=0.028,
    )
)

esM.add( fn.Source( esM=esM, name="WoodSource", commodity="wood", hasCapacityVariable=True, capacityMax=data["wood_source, capacityMax"], commodityCost=0.028, ) )

Biowaste Source¶

In [14]:

esM.add(
    fn.Source(
        esM=esM,
        name="BiowasteSource",
        commodity="biowaste",
        hasCapacityVariable=True,
        capacityMax=data["biowaste_source, capacityMax"],
        commodityCost=0.07,
    )
)

esM.add( fn.Source( esM=esM, name="BiowasteSource", commodity="biowaste", hasCapacityVariable=True, capacityMax=data["biowaste_source, capacityMax"], commodityCost=0.07, ) )

Bioslurry Source¶

In [15]:

esM.add(
    fn.Source(
        esM=esM,
        name="BioslurrySource",
        commodity="bioslurry",
        hasCapacityVariable=True,
        capacityMax=data["bioslurry_source, capacityMax"],
        commodityCost=0.07,
    )
)

esM.add( fn.Source( esM=esM, name="BioslurrySource", commodity="bioslurry", hasCapacityVariable=True, capacityMax=data["bioslurry_source, capacityMax"], commodityCost=0.07, ) )

4. Conversion components¶

These are the components which can transfer one commodity into another one.

4.1 Biomas to biogas¶

Bioslurry to Biogas¶

In [16]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="bioslurry-biogas",
        physicalUnit=r"GW$_{CH4}$",
        commodityConversionFactors={"bioslurry": -1, "biogas": 1},
        hasCapacityVariable=False,
    )
)

esM.add( fn.Conversion( esM=esM, name="bioslurry-biogas", physicalUnit=r"GW$_{CH4}$", commodityConversionFactors={"bioslurry": -1, "biogas": 1}, hasCapacityVariable=False, ) )

Biowaste to Biogas¶

In [17]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="biowaste-biogas",
        physicalUnit=r"GW$_{CH4}$",
        commodityConversionFactors={"biowaste": -1, "biogas": 1},
        hasCapacityVariable=False,
    )
)

esM.add( fn.Conversion( esM=esM, name="biowaste-biogas", physicalUnit=r"GW$_{CH4}$", commodityConversionFactors={"biowaste": -1, "biogas": 1}, hasCapacityVariable=False, ) )

4.2 Methane Slip (Virtual conversion)¶

In [18]:

methaneSlip = 0.1
esM.add(
    fn.Conversion(
        esM=esM,
        name="CH4Slip",
        physicalUnit=r"GW$_{CH4}$",
        commodityConversionFactors={
            "nGasImp": -1,
            "nGas": 1,
            "CO2": methaneSlip * 2.014,
        },
        hasCapacityVariable=False,
    )
)

methaneSlip = 0.1 esM.add( fn.Conversion( esM=esM, name="CH4Slip", physicalUnit=r"GW$_{CH4}$", commodityConversionFactors={ "nGasImp": -1, "nGas": 1, "CO2": methaneSlip * 2.014, }, hasCapacityVariable=False, ) )

4.3 Biogas to Methane (Virtual conversion)¶

In [19]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="biogas-nGas",
        physicalUnit=r"GW$_{CH4}$",
        commodityConversionFactors={"biogas": -1, "nGas": 1, "CO2": -0.2014},
        hasCapacityVariable=True,
        opexPerOperation=0.0003,
        investPerCapacity=343,
        opexPerCapacity=343 * 0.025,
        interestRate=0.08,
        economicLifetime=15,
    )
)

esM.add( fn.Conversion( esM=esM, name="biogas-nGas", physicalUnit=r"GW$_{CH4}$", commodityConversionFactors={"biogas": -1, "nGas": 1, "CO2": -0.2014}, hasCapacityVariable=True, opexPerOperation=0.0003, investPerCapacity=343, opexPerCapacity=343 * 0.025, interestRate=0.08, economicLifetime=15, ) )

4.4 Transport¶

Batterie Electric Vehicle¶

BEV Car¶

In [20]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="BEV_PCar",
        physicalUnit=r"Mio pkm/h",
        commodityConversionFactors={"electricity": -1 / 7.676226, "pTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=15694,
        opexPerCapacity=15694 * 0.009,
        interestRate=0.08,
        economicLifetime=12,
    )
)

esM.add( fn.Conversion( esM=esM, name="BEV_PCar", physicalUnit=r"Mio pkm/h", commodityConversionFactors={"electricity": -1 / 7.676226, "pTransport": 1}, hasCapacityVariable=True, investPerCapacity=15694, opexPerCapacity=15694 * 0.009, interestRate=0.08, economicLifetime=12, ) )

BEV Truck¶

In [21]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="BEV_Truck",
        physicalUnit=r"Mio tkm/h",
        commodityConversionFactors={"electricity": -1 / 11.401, "fTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=4304,
        opexPerCapacity=4304 * 0.009,
        interestRate=0.08,
        economicLifetime=15,
    )
)

esM.add( fn.Conversion( esM=esM, name="BEV_Truck", physicalUnit=r"Mio tkm/h", commodityConversionFactors={"electricity": -1 / 11.401, "fTransport": 1}, hasCapacityVariable=True, investPerCapacity=4304, opexPerCapacity=4304 * 0.009, interestRate=0.08, economicLifetime=15, ) )

Fuel Cell Electric Vehicle¶

FCEV Car¶

In [22]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="FCEV_PCar",
        physicalUnit=r"Mio pkm/h",
        commodityConversionFactors={"hydrogen": -1 / 4.7472, "pTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=15694,
        opexPerCapacity=15694 * 0.009,
        interestRate=0.08,
        economicLifetime=12,
    )
)

esM.add( fn.Conversion( esM=esM, name="FCEV_PCar", physicalUnit=r"Mio pkm/h", commodityConversionFactors={"hydrogen": -1 / 4.7472, "pTransport": 1}, hasCapacityVariable=True, investPerCapacity=15694, opexPerCapacity=15694 * 0.009, interestRate=0.08, economicLifetime=12, ) )

FCEV Truck¶

In [23]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="FCEV_Truck",
        physicalUnit=r"Mio tkm/h",
        commodityConversionFactors={"hydrogen": -1 / 8.251, "fTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=4283,
        opexPerCapacity=4283 * 0.009,
        interestRate=0.08,
        economicLifetime=15,
    )
)

esM.add( fn.Conversion( esM=esM, name="FCEV_Truck", physicalUnit=r"Mio tkm/h", commodityConversionFactors={"hydrogen": -1 / 8.251, "fTransport": 1}, hasCapacityVariable=True, investPerCapacity=4283, opexPerCapacity=4283 * 0.009, interestRate=0.08, economicLifetime=15, ) )

Fossil Vehicles¶

Fossil Car¶

In [24]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="FossilCar",
        physicalUnit=r"Mio pkm/h",
        commodityConversionFactors={"diesel": -1 / 3.1308, "pTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=15694,
        opexPerCapacity=15694 * 0.016,
        interestRate=0.08,
        economicLifetime=12,
    )
)

esM.add( fn.Conversion( esM=esM, name="FossilCar", physicalUnit=r"Mio pkm/h", commodityConversionFactors={"diesel": -1 / 3.1308, "pTransport": 1}, hasCapacityVariable=True, investPerCapacity=15694, opexPerCapacity=15694 * 0.016, interestRate=0.08, economicLifetime=12, ) )

Fossil Truck¶

In [25]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="FossilTruck",
        physicalUnit=r"Mio tkm/h",
        commodityConversionFactors={"diesel": -1 / 7.938, "fTransport": 1},
        hasCapacityVariable=True,
        investPerCapacity=3342,
        opexPerCapacity=3342 * 0.016,
        interestRate=0.08,
        economicLifetime=15,
    )
)

esM.add( fn.Conversion( esM=esM, name="FossilTruck", physicalUnit=r"Mio tkm/h", commodityConversionFactors={"diesel": -1 / 7.938, "fTransport": 1}, hasCapacityVariable=True, investPerCapacity=3342, opexPerCapacity=3342 * 0.016, interestRate=0.08, economicLifetime=15, ) )

4.5 Diesel Refinery¶

In [26]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="DieselRef",
        physicalUnit=r"GW$_{diesel}$",
        commodityConversionFactors={"crudeOil": -1 / 0.364, "diesel": 1, "CO2": 0.725},
        hasCapacityVariable=True,
        investPerCapacity=1 / 0.364,
        opexPerCapacity=(1 / 0.364) * 0.001,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="DieselRef", physicalUnit=r"GW$_{diesel}$", commodityConversionFactors={"crudeOil": -1 / 0.364, "diesel": 1, "CO2": 0.725}, hasCapacityVariable=True, investPerCapacity=1 / 0.364, opexPerCapacity=(1 / 0.364) * 0.001, interestRate=0.08, economicLifetime=20, ) )

4.6 Power Plants¶

Combined Cycle Gas Turbine¶

Natural Gas CCGT¶

In [27]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="CCGT plants (NGas)",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={"electricity": 1, "nGas": -1 / 0.65, "CO2": 0.31},
        hasCapacityVariable=True,
        investPerCapacity=850,
        opexPerCapacity=850 * 0.03,
        opexPerOperation=0.002,
        interestRate=0.08,
        economicLifetime=30,
    )
)

esM.add( fn.Conversion( esM=esM, name="CCGT plants (NGas)", physicalUnit=r"GW$_{el}$", commodityConversionFactors={"electricity": 1, "nGas": -1 / 0.65, "CO2": 0.31}, hasCapacityVariable=True, investPerCapacity=850, opexPerCapacity=850 * 0.03, opexPerOperation=0.002, interestRate=0.08, economicLifetime=30, ) )

H2 CCGT¶

In [28]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="CCGT plants (hydrogen)",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={"electricity": 1, "hydrogen": -1 / 0.6},
        hasCapacityVariable=True,
        investPerCapacity=760,
        opexPerCapacity=760 * 0.014,
        opexPerOperation=0.002,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="CCGT plants (hydrogen)", physicalUnit=r"GW$_{el}$", commodityConversionFactors={"electricity": 1, "hydrogen": -1 / 0.6}, hasCapacityVariable=True, investPerCapacity=760, opexPerCapacity=760 * 0.014, opexPerOperation=0.002, interestRate=0.08, economicLifetime=20, ) )

Fuel cell¶

In [29]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="LS-SOFC",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={
            "electricity": 1,
            "hydrogen": -1 / 0.7,
            "LTHeat": 0.25 / 0.7,
        },
        hasCapacityVariable=True,
        investPerCapacity=1210,
        opexPerCapacity=1210 * 0.008,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="LS-SOFC", physicalUnit=r"GW$_{el}$", commodityConversionFactors={ "electricity": 1, "hydrogen": -1 / 0.7, "LTHeat": 0.25 / 0.7, }, hasCapacityVariable=True, investPerCapacity=1210, opexPerCapacity=1210 * 0.008, interestRate=0.08, economicLifetime=20, ) )

Coal power plant¶

In [30]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="CoalPP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={"electricity": 1, "coal": -1 / 0.5, "CO2": 0.674},
        hasCapacityVariable=True,
        opexPerOperation=0.0015,
        investPerCapacity=1450,
        opexPerCapacity=1450 * 0.026,
        interestRate=0.08,
        economicLifetime=40,
    )
)

esM.add( fn.Conversion( esM=esM, name="CoalPP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={"electricity": 1, "coal": -1 / 0.5, "CO2": 0.674}, hasCapacityVariable=True, opexPerOperation=0.0015, investPerCapacity=1450, opexPerCapacity=1450 * 0.026, interestRate=0.08, economicLifetime=40, ) )

Combined Heat and Power Plants¶

Coal CHP¶

In [31]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="CoalCHP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={
            "electricity": 1,
            "LTHeat": 0.51 / 0.38,
            "coal": -1 / 0.38,
            "CO2": 0.886,
        },
        hasCapacityVariable=True,
        opexPerOperation=0.0051,
        investPerCapacity=1847,
        opexPerCapacity=1847 * 0.027,
        interestRate=0.08,
        economicLifetime=35,
    )
)

esM.add( fn.Conversion( esM=esM, name="CoalCHP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={ "electricity": 1, "LTHeat": 0.51 / 0.38, "coal": -1 / 0.38, "CO2": 0.886, }, hasCapacityVariable=True, opexPerOperation=0.0051, investPerCapacity=1847, opexPerCapacity=1847 * 0.027, interestRate=0.08, economicLifetime=35, ) )

Wood CHP¶

In [32]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="WoodCHP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={
            "electricity": 1,
            "LTHeat": 0.826 / 0.291,
            "wood": -1 / 0.291,
        },
        hasCapacityVariable=True,
        opexPerOperation=0.0038,
        investPerCapacity=3000,
        opexPerCapacity=3000 * 0.029,
        interestRate=0.08,
        economicLifetime=25,
    )
)

esM.add( fn.Conversion( esM=esM, name="WoodCHP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={ "electricity": 1, "LTHeat": 0.826 / 0.291, "wood": -1 / 0.291, }, hasCapacityVariable=True, opexPerOperation=0.0038, investPerCapacity=3000, opexPerCapacity=3000 * 0.029, interestRate=0.08, economicLifetime=25, ) )

Natural Gas CHP¶

In [33]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="nGasCHP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={
            "electricity": 1,
            "LTHeat": 0.5 / 0.35,
            "nGas": -1 / 0.35,
            "CO2": 0.575,
        },
        hasCapacityVariable=True,
        opexPerOperation=0.0015,
        investPerCapacity=666,
        opexPerCapacity=666 * 0.041,
        interestRate=0.08,
        economicLifetime=30,
    )
)

esM.add( fn.Conversion( esM=esM, name="nGasCHP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={ "electricity": 1, "LTHeat": 0.5 / 0.35, "nGas": -1 / 0.35, "CO2": 0.575, }, hasCapacityVariable=True, opexPerOperation=0.0015, investPerCapacity=666, opexPerCapacity=666 * 0.041, interestRate=0.08, economicLifetime=30, ) )

Biogas CHP¶

In [34]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="BioGasCHP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={"electricity": 1, "LTHeat": 1, "biogas": -1 / 0.47},
        hasCapacityVariable=True,
        opexPerOperation=0.008,
        investPerCapacity=850,
        opexPerCapacity=850 * 0.01,
        interestRate=0.08,
        economicLifetime=25,
    )
)

esM.add( fn.Conversion( esM=esM, name="BioGasCHP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={"electricity": 1, "LTHeat": 1, "biogas": -1 / 0.47}, hasCapacityVariable=True, opexPerOperation=0.008, investPerCapacity=850, opexPerCapacity=850 * 0.01, interestRate=0.08, economicLifetime=25, ) )

H2 CHP¶

In [35]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="H2CHP",
        physicalUnit=r"GW$_{el}$",
        commodityConversionFactors={
            "electricity": 1,
            "LTHeat": 0.41 / 0.49,
            "hydrogen": -1 / 0.49,
        },
        hasCapacityVariable=True,
        opexPerOperation=0.0006,
        investPerCapacity=715,
        opexPerCapacity=715 * 0.001,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="H2CHP", physicalUnit=r"GW$_{el}$", commodityConversionFactors={ "electricity": 1, "LTHeat": 0.41 / 0.49, "hydrogen": -1 / 0.49, }, hasCapacityVariable=True, opexPerOperation=0.0006, investPerCapacity=715, opexPerCapacity=715 * 0.001, interestRate=0.08, economicLifetime=20, ) )

4.7 Thermal power plants¶

Oil Boiler¶

In [36]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="oilBoiler",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"crudeOil": -1 / 0.96, "LTHeat": 1, "CO2": 0.275},
        hasCapacityVariable=True,
        investPerCapacity=330,
        opexPerCapacity=330 * 0.041,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="oilBoiler", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"crudeOil": -1 / 0.96, "LTHeat": 1, "CO2": 0.275}, hasCapacityVariable=True, investPerCapacity=330, opexPerCapacity=330 * 0.041, interestRate=0.08, economicLifetime=20, ) )

Gas Boiler¶

In [37]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="gasBoiler",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"nGas": -1 / 0.96, "LTHeat": 1, "CO2": 0.21},
        hasCapacityVariable=True,
        investPerCapacity=330,
        opexPerCapacity=330 * 0.012,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="gasBoiler", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"nGas": -1 / 0.96, "LTHeat": 1, "CO2": 0.21}, hasCapacityVariable=True, investPerCapacity=330, opexPerCapacity=330 * 0.012, interestRate=0.08, economicLifetime=20, ) )

H2 Boiler¶

In [38]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="H2Boiler",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"hydrogen": -1 / 0.98, "LTHeat": 1},
        hasCapacityVariable=True,
        investPerCapacity=655,
        opexPerCapacity=655 * 0.01,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="H2Boiler", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"hydrogen": -1 / 0.98, "LTHeat": 1}, hasCapacityVariable=True, investPerCapacity=655, opexPerCapacity=655 * 0.01, interestRate=0.08, economicLifetime=20, ) )

Heat pump¶

In [39]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="Heatpump",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"electricity": -1 / 0.45, "LTHeat": 1},
        hasCapacityVariable=True,
        investPerCapacity=725,
        opexPerCapacity=725 * 0.02,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="Heatpump", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"electricity": -1 / 0.45, "LTHeat": 1}, hasCapacityVariable=True, investPerCapacity=725, opexPerCapacity=725 * 0.02, interestRate=0.08, economicLifetime=20, ) )

Heating rod¶

In [40]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="Heating rod",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"electricity": -1 / 0.99, "LTHeat": 1},
        hasCapacityVariable=True,
        investPerCapacity=800,
        opexPerCapacity=800 * 0.0125,
        interestRate=0.08,
        economicLifetime=30,
    )
)

esM.add( fn.Conversion( esM=esM, name="Heating rod", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"electricity": -1 / 0.99, "LTHeat": 1}, hasCapacityVariable=True, investPerCapacity=800, opexPerCapacity=800 * 0.0125, interestRate=0.08, economicLifetime=30, ) )

Electrode boiler¶

In [41]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="electrode boiler",
        physicalUnit=r"GW$_{Pheat}$",
        commodityConversionFactors={"electricity": -1 / 0.99, "PHeat": 1},
        hasCapacityVariable=True,
        investPerCapacity=140,
        opexPerCapacity=140 * 0.02,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="electrode boiler", physicalUnit=r"GW$_{Pheat}$", commodityConversionFactors={"electricity": -1 / 0.99, "PHeat": 1}, hasCapacityVariable=True, investPerCapacity=140, opexPerCapacity=140 * 0.02, interestRate=0.08, economicLifetime=20, ) )

Stove¶

In [42]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="woood Stove",
        physicalUnit=r"GW$_{LTHeat}$",
        commodityConversionFactors={"wood": -1 / 0.75, "LTHeat": 1},
        hasCapacityVariable=True,
        investPerCapacity=775,
        opexPerCapacity=775 * 0.06,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Conversion( esM=esM, name="woood Stove", physicalUnit=r"GW$_{LTHeat}$", commodityConversionFactors={"wood": -1 / 0.75, "LTHeat": 1}, hasCapacityVariable=True, investPerCapacity=775, opexPerCapacity=775 * 0.06, interestRate=0.08, economicLifetime=20, ) )

4.8 Electrolyzer¶

In [43]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="electroylzer",
        physicalUnit=r"GW$_{H_{2},LHV}$",
        commodityConversionFactors={"electricity": -1 / 0.7, "hydrogen": 1},
        hasCapacityVariable=True,
        investPerCapacity=500,
        opexPerCapacity=500 * 0.03,
        interestRate=0.08,
        economicLifetime=10,
    )
)

esM.add( fn.Conversion( esM=esM, name="electroylzer", physicalUnit=r"GW$_{H_{2},LHV}$", commodityConversionFactors={"electricity": -1 / 0.7, "hydrogen": 1}, hasCapacityVariable=True, investPerCapacity=500, opexPerCapacity=500 * 0.03, interestRate=0.08, economicLifetime=10, ) )

5. Storages¶

The storages which can be used by the EnergyLand model are constructed.

Lithium ion batteries¶

In [44]:

esM.add(
    fn.Storage(
        esM=esM,
        name="Li-ion batteries",
        commodity="electricity",
        hasCapacityVariable=True,
        chargeEfficiency=0.99,
        dischargeEfficiency=0.99,
        selfDischarge=0.004,
        doPreciseTsaModeling=False,
        investPerCapacity=120,
        opexPerCapacity=120 * 0.014,
        opexPerChargeOperation=0.0001,
        interestRate=0.08,
        economicLifetime=10,
    )
)

esM.add( fn.Storage( esM=esM, name="Li-ion batteries", commodity="electricity", hasCapacityVariable=True, chargeEfficiency=0.99, dischargeEfficiency=0.99, selfDischarge=0.004, doPreciseTsaModeling=False, investPerCapacity=120, opexPerCapacity=120 * 0.014, opexPerChargeOperation=0.0001, interestRate=0.08, economicLifetime=10, ) )

Hydrogen filled salt caverns¶

In [45]:

esM.add(
    fn.Storage(
        esM=esM,
        name="H2Storage",
        commodity="hydrogen",
        hasCapacityVariable=True,
        chargeEfficiency=0.98,
        dischargeEfficiency=0.998,
        doPreciseTsaModeling=False,
        investPerCapacity=362,
        opexPerCapacity=362 * 0.02,
        opexPerChargeOperation=0.0001,
        interestRate=0.08,
        economicLifetime=40,
    )
)

esM.add( fn.Storage( esM=esM, name="H2Storage", commodity="hydrogen", hasCapacityVariable=True, chargeEfficiency=0.98, dischargeEfficiency=0.998, doPreciseTsaModeling=False, investPerCapacity=362, opexPerCapacity=362 * 0.02, opexPerChargeOperation=0.0001, interestRate=0.08, economicLifetime=40, ) )

Heat storage¶

In [46]:

esM.add(
    fn.Storage(
        esM=esM,
        name="LTHeatstorage",
        commodity="LTHeat",
        hasCapacityVariable=True,
        chargeEfficiency=0.95,
        dischargeEfficiency=0.95,
        selfDischarge=0.0003,
        chargeRate=1,
        dischargeRate=1,
        doPreciseTsaModeling=False,
        investPerCapacity=147,
        opexPerCapacity=147 * 0.01,
        opexPerChargeOperation=0.0001,
        interestRate=0.08,
        economicLifetime=20,
    )
)

esM.add( fn.Storage( esM=esM, name="LTHeatstorage", commodity="LTHeat", hasCapacityVariable=True, chargeEfficiency=0.95, dischargeEfficiency=0.95, selfDischarge=0.0003, chargeRate=1, dischargeRate=1, doPreciseTsaModeling=False, investPerCapacity=147, opexPerCapacity=147 * 0.01, opexPerChargeOperation=0.0001, interestRate=0.08, economicLifetime=20, ) )

6. Sinks¶

Electricity, heat and transport demand are set in the following components.

Electricity demand¶

In [47]:

eDemand = 516
esM.add(
    fn.Sink(
        esM=esM,
        name="Electricity demand",
        commodity="electricity",
        hasCapacityVariable=False,
        operationRateFix=data["Electricity demand, operationRateFix"] * eDemand,
    )
)

eDemand = 516 esM.add( fn.Sink( esM=esM, name="Electricity demand", commodity="electricity", hasCapacityVariable=False, operationRateFix=data["Electricity demand, operationRateFix"] * eDemand, ) )

Passenger Transportation demand¶

In [48]:

pTdemand = 867
esM.add(
    fn.Sink(
        esM=esM,
        name="pT_demand",
        commodity="pTransport",
        hasCapacityVariable=False,
        operationRateFix=data["T_demand, operationRateFix"] * pTdemand,
    )
)

pTdemand = 867 esM.add( fn.Sink( esM=esM, name="pT_demand", commodity="pTransport", hasCapacityVariable=False, operationRateFix=data["T_demand, operationRateFix"] * pTdemand, ) )

Freight Transportation demand¶

In [49]:

fTdemand = 945.5
esM.add(
    fn.Sink(
        esM=esM,
        name="fT_demand",
        commodity="fTransport",
        hasCapacityVariable=False,
        operationRateFix=data["T_demand, operationRateFix"] * fTdemand,
    )
)

fTdemand = 945.5 esM.add( fn.Sink( esM=esM, name="fT_demand", commodity="fTransport", hasCapacityVariable=False, operationRateFix=data["T_demand, operationRateFix"] * fTdemand, ) )

Heat demand¶

Process heat demand¶

In [50]:

pHeatDemand = 423.75
esM.add(
    fn.Sink(
        esM=esM,
        name="PHeat_demand",
        commodity="PHeat",
        hasCapacityVariable=False,
        operationRateFix=data["pHeat_demand, operationRateFix"] * pHeatDemand,
    )
)

pHeatDemand = 423.75 esM.add( fn.Sink( esM=esM, name="PHeat_demand", commodity="PHeat", hasCapacityVariable=False, operationRateFix=data["pHeat_demand, operationRateFix"] * pHeatDemand, ) )

Low temperature residential heat demand¶

In [51]:

LTHeatDemand = 560.8
esM.add(
    fn.Sink(
        esM=esM,
        name="LTHeat_demand",
        commodity="LTHeat",
        hasCapacityVariable=False,
        operationRateFix=data["LtHeat_demand, operationRateFix"] * LTHeatDemand,
    )
)

LTHeatDemand = 560.8 esM.add( fn.Sink( esM=esM, name="LTHeat_demand", commodity="LTHeat", hasCapacityVariable=False, operationRateFix=data["LtHeat_demand, operationRateFix"] * LTHeatDemand, ) )

Environment¶

The CO2 limit is set in this component.

In [52]:

esM.add(
    fn.Sink(
        esM=esM,
        name="environment",
        commodity="CO2",
        hasCapacityVariable=False,
        balanceLimitID="CO2_cap",
    )
)

esM.add( fn.Sink( esM=esM, name="environment", commodity="CO2", hasCapacityVariable=False, balanceLimitID="CO2_cap", ) )

7. Optimization of EnergyLand¶

In [53]:

esM.aggregateTemporally(numberOfTypicalPeriods=48)

esM.aggregateTemporally(numberOfTypicalPeriods=48)

Clustering time series data with 48 typical periods and 24 time steps per period 
further clustered to 12 segments per period...
		(13.4556 sec)

In [54]:

nbval-skip

esM.optimize(
    timeSeriesAggregation=True, solver=fn.utils.ImplementedSolvers.GUROBI.value
)

esM.optimize( timeSeriesAggregation=True, solver=fn.utils.ImplementedSolvers.GUROBI.value )

Time series aggregation specifications:
Number of typical periods:48, number of time steps per period:24, number of segments per period:12

Declaring sets, variables and constraints for SourceSinkModel
	declaring sets... 
	declaring variables... 
	declaring constraints... 
		(1.5911 sec)

Declaring sets, variables and constraints for ConversionModel
	declaring sets... 
	declaring variables... 
	declaring constraints... 
		(1.6577 sec)

Declaring sets, variables and constraints for StorageModel
	declaring sets... 
	declaring variables... 
	declaring constraints... 
		(0.7759 sec)

Declaring shared potential constraint...
		(0.0012 sec)

Declaring linked component quantity constraint...
		(0.0001 sec)

Declaring commodity balances...
		(0.5096 sec)

		(0.0018 sec)

Declaring objective function...
		(2.0107 sec)

Set parameter ServerPassword
Set parameter TSPort to value 41955
Set parameter GURO_PAR_SPECIAL
Set parameter TokenServer to value "iek3028"
Read LP format model from file /tmp/tmpk6hnc76c.pyomo.lp
Reading time = 0.09 seconds
x1: 40198 rows, 32770 columns, 122151 nonzeros
Set parameter QCPDual to value 1
Set parameter Threads to value 3
Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Rocky Linux 8.8 (Green Obsidian)")

CPU model: Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
Thread count: 36 physical cores, 72 logical processors, using up to 3 threads

Optimize a model with 40198 rows, 32770 columns and 122151 nonzeros
Model fingerprint: 0x4d1dc8f1
Coefficient statistics:
  Matrix range     [6e-07, 2e+01]
  Objective range  [2e-04, 2e+03]
  Bounds range     [4e-01, 8e+02]
  RHS range        [2e+05, 2e+05]
Presolve removed 10257 rows and 10174 columns
Presolve time: 0.08s
Presolved: 29941 rows, 22596 columns, 110225 nonzeros

Concurrent LP optimizer: dual simplex and barrier
Showing barrier log only...

Ordering time: 0.27s

Barrier statistics:
 Dense cols : 30
 Free vars  : 417
 AA' NZ     : 2.545e+05
 Factor NZ  : 1.265e+06 (roughly 30 MB of memory)
 Factor Ops : 2.323e+08 (less than 1 second per iteration)
 Threads    : 2

                  Objective                Residual
Iter       Primal          Dual         Primal    Dual     Compl     Time
   0   8.35818215e+07 -4.87993160e+07  1.85e+06 2.85e+00  3.10e+06     0s
   1   1.09895183e+08 -1.30708071e+08  1.35e+06 1.90e+02  1.91e+06     1s
   2   1.20639012e+08 -1.97368783e+08  5.65e+05 8.14e+01  8.42e+05     1s
   3   1.29294112e+08 -2.33537306e+08  1.91e+05 2.08e+01  2.59e+05     1s
   4   1.21035819e+08 -2.21427134e+08  1.06e+05 7.39e+00  1.23e+05     1s
   5   1.02912484e+08 -2.03329339e+08  6.56e+04 2.65e+00  6.58e+04     1s
   6   8.14755220e+07 -1.74373549e+08  4.38e+04 8.11e-01  3.79e+04     1s
   7   5.59631580e+07 -1.47857012e+08  2.59e+04 3.60e-01  2.19e+04     1s
   8   3.98508356e+07 -1.25447276e+08  1.63e+04 2.03e-01  1.37e+04     1s
   9   2.72131877e+07 -1.06836912e+08  9.57e+03 1.16e-01  8.27e+03     1s
  10   1.82561837e+07 -6.93606860e+07  5.14e+03 3.29e-02  4.07e+03     1s
  11   7.84682616e+06 -3.31575996e+07  1.32e+03 6.90e-03  1.18e+03     1s
  12   4.27458212e+06 -1.77370151e+07  5.43e+02 2.29e-03  5.23e+02     1s
  13   2.90680016e+06 -1.08287742e+07  3.00e+02 1.21e-03  2.98e+02     1s
  14   2.28866336e+06 -5.59718435e+06  2.11e+02 5.45e-04  1.65e+02     1s
  15   1.67360251e+06 -3.39804294e+06  1.32e+02 7.58e-04  1.01e+02     1s
  16   1.38575643e+06 -1.92978380e+06  9.75e+01 1.99e-03  6.45e+01     1s
  17   1.11427392e+06 -9.76592967e+05  6.49e+01 1.15e-03  3.96e+01     1s
  18   8.61983965e+05 -2.87625923e+05  3.81e+01 5.51e-04  2.13e+01     1s
  19   7.07442480e+05  9.80126568e+04  2.24e+01 3.36e-04  1.11e+01     1s
  20   6.26213770e+05  2.18192851e+05  1.47e+01 2.44e-04  7.36e+00     1s
  21   5.99302336e+05  2.82010310e+05  1.24e+01 1.54e-04  5.70e+00     1s
  22   5.26033171e+05  3.16760085e+05  6.93e+00 1.74e-04  3.74e+00     1s
  23   4.95796503e+05  3.59430923e+05  4.76e+00 1.15e-04  2.43e+00     1s
  24   4.75033316e+05  3.73647184e+05  3.39e+00 8.42e-05  1.81e+00     1s
  25   4.64891696e+05  3.85738325e+05  2.74e+00 1.19e-04  1.41e+00     2s
  26   4.47490606e+05  3.93542348e+05  1.66e+00 1.42e-04  9.60e-01     2s
  27   4.40130764e+05  4.04009954e+05  1.24e+00 1.01e-04  6.42e-01     2s
  28   4.33843580e+05  4.07206598e+05  8.88e-01 1.10e-04  4.74e-01     2s
  29   4.29579145e+05  4.10213588e+05  6.57e-01 8.77e-05  3.44e-01     2s
  30   4.26811055e+05  4.12557943e+05  5.14e-01 1.19e-04  2.53e-01     2s
  31   4.24464787e+05  4.13472897e+05  3.93e-01 1.02e-04  1.95e-01     2s
  32   4.20817051e+05  4.14737702e+05  2.04e-01 6.71e-05  1.08e-01     2s
  33   4.19402334e+05  4.15660220e+05  1.34e-01 5.72e-05  6.65e-02     2s
  34   4.18343170e+05  4.15905819e+05  8.28e-02 4.65e-05  4.33e-02     2s
  35   4.17693909e+05  4.16157734e+05  5.10e-02 3.19e-05  2.73e-02     2s
  36   4.17124803e+05  4.16349715e+05  2.45e-02 2.36e-05  1.38e-02     2s
  37   4.16885544e+05  4.16450215e+05  1.37e-02 2.61e-05  7.73e-03     2s
  38   4.16762379e+05  4.16489345e+05  8.61e-03 2.80e-05  4.85e-03     2s
  39   4.16630104e+05  4.16520570e+05  3.14e-03 2.38e-05  1.94e-03     2s
  40   4.16575945e+05  4.16535209e+05  1.09e-03 1.87e-05  7.23e-04     2s
  41   4.16558926e+05  4.16539124e+05  5.35e-04 1.35e-05  3.52e-04     2s
  42   4.16554643e+05  4.16540069e+05  3.90e-04 9.83e-06  2.59e-04     2s
  43   4.16550599e+05  4.16541122e+05  2.53e-04 6.65e-06  1.68e-04     2s
  44   4.16546282e+05  4.16541945e+05  1.16e-04 7.35e-06  7.71e-05     2s
  45   4.16544717e+05  4.16542322e+05  6.61e-05 8.76e-06  4.26e-05     2s
  46   4.16544493e+05  4.16542352e+05  5.90e-05 8.55e-06  3.81e-05     2s
  47   4.16543914e+05  4.16542355e+05  4.24e-05 8.28e-06  2.78e-05     2s
  48   4.16543369e+05  4.16542410e+05  2.58e-05 4.83e-06  1.71e-05     2s
  49   4.16542872e+05  4.16542408e+05  1.16e-05 1.01e-05  8.21e-06     3s
  50   4.16542451e+05  4.16542415e+05  3.75e-08 4.22e-06  6.53e-07     3s
  51   4.16542441e+05  4.16542424e+05  8.24e-07 4.22e-06  3.07e-07     3s
  52   4.16542428e+05  4.16542427e+05  2.44e-07 4.23e-06  2.68e-08     3s
  53   4.16542428e+05  4.16542427e+05  1.38e-06 4.23e-06  2.18e-08     3s

Barrier solved model in 53 iterations and 2.77 seconds (1.89 work units)
Optimal objective 4.16542428e+05

Crossover log...

   14539 DPushes remaining with DInf 0.0000000e+00                 3s
       0 DPushes remaining with DInf 0.0000000e+00                 3s

    3299 PPushes remaining with PInf 1.1634352e-02                 3s
       0 PPushes remaining with PInf 0.0000000e+00                 4s

  Push phase complete: Pinf 0.0000000e+00, Dinf 3.1037636e-02      4s

Iteration    Objective       Primal Inf.    Dual Inf.      Time
   11638    4.1654243e+05   0.000000e+00   3.103764e-02      4s

Solved with barrier
Extra simplex iterations after uncrush: 1
   11652    4.1654243e+05   0.000000e+00   0.000000e+00      4s

Solved in 11652 iterations and 3.70 seconds (2.93 work units)
Optimal objective  4.165424262e+05

Status: ok
Return code: 0
Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.
Termination condition: optimal
Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.
Wall time: 3.703886032104492
Error rc: 0
Time: 4.190406560897827


Name: x1
Lower bound: 416542.42615464196
Upper bound: 416542.42615464196
Number of objectives: 1
Number of constraints: 40198
Number of variables: 32770
Number of binary variables: 0
Number of integer variables: 0
Number of continuous variables: 32770
Number of nonzeros: 122151
Sense: minimize

Solve time: 5.00289249420166 sec.

Processing optimization output...
for SourceSinkModel ...(1.4575sec)
for ConversionModel ...(0.8742sec)

/fast/home/k-okosun/iek3-models/fine/fine/storage.py:1984: UserWarning: Charge and discharge at the same time for component LTHeatstorage
  warnings.warn(

for StorageModel ...  (1.1298sec)
		(3.5214 sec)

8. Results¶

In [56]:

nbval-skip

esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)

esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)

Out[56]:

			EnergyLand
Component	Property	Unit
BioslurrySource	NPVcontribution	[1e6 Euro]	1388.317174
	TAC	[1e6 Euro/a]	1388.317174
	capacity	[GW$_{bioslurry}$]	2.9
	commissioning	[GW$_{bioslurry}$]	2.9
	commodCosts	[1e6 Euro/a]	1388.317174
...	...	...	...
nGasSource	commodCosts	[1e6 Euro/a]	4488.148228
	operation	[GW$_{CH4}$*h/a]	175318.290169
		[GW$_{CH4}$*h]	175318.290169
pT_demand	operation	[Mio pkm/h*h/a]	867000.0
		[Mio pkm/h*h]	867000.0

82 rows × 1 columns

In [56]:

nbval-skip

esM.getOptimizationSummary("ConversionModel", outputLevel=2)

esM.getOptimizationSummary("ConversionModel", outputLevel=2)

Out[56]:

			EnergyLand
Component	Property	Unit
BEV_PCar	NPVcontribution	[1e6 Euro]	220091.47299
	TAC	[1e6 Euro/a]	220091.47299
	capacity	[Mio pkm/h]	98.972603
	capexCap	[1e6 Euro/a]	206111.988744
	commissioning	[Mio pkm/h]	98.972603
...	...	...	...
oilBoiler	commissioning	[GW$_{LTHeat}$]	21.758642
	invest	[1e6 Euro]	7180.351733
	operation	[GW$_{LTHeat}$*h/a]	81395.70118
		[GW$_{LTHeat}$*h]	81395.70118
	opexCap	[1e6 Euro/a]	294.394421

112 rows × 1 columns

In [57]:

nbval-skip

esM.getOptimizationSummary("StorageModel", outputLevel=2)

esM.getOptimizationSummary("StorageModel", outputLevel=2)

Out[57]:

			EnergyLand
Component	Property	Unit
LTHeatstorage	NPVcontribution	[1e6 Euro]	4079.380451
	TAC	[1e6 Euro/a]	4079.380451
	capacity	[GW$_{LTHeat}$*h]	247.387544
	capexCap	[1e6 Euro/a]	3703.954264
	commissioning	[GW$_{LTHeat}$*h]	247.387544
	invest	[1e6 Euro]	36365.968952
	operationCharge	[GW$_{LTHeat}$*h/a]	117664.977687
		[GW$_{LTHeat}$*h]	117664.977687
	operationDischarge	[GW$_{LTHeat}$*h/a]	105911.368227
		[GW$_{LTHeat}$*h]	105911.368227
	opexCap	[1e6 Euro/a]	363.65969
	opexCharge	[1e6 Euro/a]	11.766498
Li-ion batteries	NPVcontribution	[1e6 Euro]	4013.435069
	TAC	[1e6 Euro/a]	4013.435069
	capacity	[GW$_{el}$*h]	204.845137
	capexCap	[1e6 Euro/a]	3663.355922
	commissioning	[GW$_{el}$*h]	204.845137
	invest	[1e6 Euro]	24581.416432
	operationCharge	[GW$_{el}$*h/a]	59393.168636
		[GW$_{el}$*h]	59393.168636
	operationDischarge	[GW$_{el}$*h/a]	55281.19631
		[GW$_{el}$*h]	55281.19631
	opexCap	[1e6 Euro/a]	344.13983
	opexCharge	[1e6 Euro/a]	5.939317