FINE

Following sections give detailed instructions on how to create ready-to-use energy systems using FINE Energy System Model

Concept 

FINE aims to be a free software toolbox simulating and optimizing energy supply systems with multiple regions, commodities and time steps. Using the time series aggregation module TSAM , the time series under investigation can be divided into periods to reduce the memory requirement and computation time. This is particularly useful for larger amounts of data under investigation.

It is developed by the Forschungszentrum Jülich with the Helmholtz Association as partner in the context of Energy System 2050 - Central platform of the Research Field Energy.

Purpose 

Within the context of tessif, FINE serves another pyomo solver interface. The goal of optimization it’s is to minimize the costs incurred by the energy system. Thereby it is possible to formulate emission restrictions, that will be respected.

Using FINE through tessif is somewhat of a challenge The clear technical parameterization allows an efficient transformation of the energy system from Tessif to FINE. In addition, the components are designed in order to allow flexible use. This enables components to be implemented that are not actually part of the predefined FINE structure .

Examples 

Minimum Working Example 

Note

The exact same energy system can be accessed using tessif.examples.data.fine.py_hard.create_mwe().

Import the needed packages:

# standard library
import os
import pathlib

# third party
import FINE as fn

# local
import tessif.frused.namedtuples as nts
from tessif.frused.paths import write_dir
import tessif.write.tools as write_tools

Create an energy system model instance

esM = fine.energySystemModel.EnergySystemModel(
        locations={"Germany"},
        commodities={"Electricity", "gas"},
        numberOfTimeSteps=2,
        commodityUnitsDict={"Electricity": "GW_el", "gas": "GW_CH4"},
        hoursPerTimeStep=1,
        costUnit="€",)

Add an Uid container to the energy system model for utilizing Tessif’s Labeling Concept:
```
setattr(esM, 'uid_dict', dict())
```
Do the modular arrangement of all components:

Add a Transmission as ideal power line with predefined UID:

# create uid
transmission_uid = nts.Uid(
            name='Power Line', latitude=53, longitude=10,
            region='Germany', sector='Power', carrier='Electricity',
            component='Bus', node_type='AC_Bus')

# create component
esM.add(
fn.Transmission(
    esM=esM,
    hasCapacityVariable=True,
    name=str(transmission_uid),
    commodity=transmission_uid.carrier,)
    )

# add the UID to the container:
esM.uid_dict.update({str(transmission_uid): transmission_uid})

Add a Transmission as ideal chemical bound energy transmission with predefined UID:

# create uid
gas_transmission_uid = nts.Uid(
    name='CBET', latitude=53, longitude=10,
    region='Germany', sector='Power', carrier='gas',
    component='Bus', node_type='gas-Bus')

# create component
esM.add(
fn.Transmission(
    esM=esM,
    hasCapacityVariable=True,
    name=str(gas_transmission_uid),
    commodity=gas_transmission_uid.carrier,)
    )

# add the UID to the container:
esM.uid_dict.update({str(gas_transmission_uid): gas_transmission_uid})

Add a Demand as sink needing 10 energy units per timestep:

# create uid
demand_uid = nts.Uid(
    name='Demand', latitude=53, longitude=10,
    region='Germany', sector='Power', carrier='Electricity',
    component='Sink', node_type='Sink')

# create component
esM.add(
    fn.Sink(
        esM=esM,
        hasCapacityVariable=False,
        name=str(demand_uid),
        commodity=demand_uid.carrier,
        operationRateFix=pd.DataFrame({"Germany": [10, 10]}),
    )
)

# add the UID to the container:
esM.uid_dict.update({str(demand_uid): demand_uid})

Add a renewable Source producing 8 and 2 energy units with a cost of 9:

# create uid
renewable_uid = nts.Uid(
        name='Renewable', latitude=53, longitude=10,
        region='Germany', sector='Power', carrier='Electricity',
        component='Source', node_type='AC-Source')

# create component
esM.add(
    fn.Source(
        esM=esM,
        name=str(renewable_uid),
        commodity=renewable_uid.carrier,
        hasCapacityVariable=True,
        operationRateMax=pd.DataFrame({'Germany': [0.8, 0.2]}),
        capacityMax=10,
        opexPerOperation=9,
    )
)

# add the UID to the container:
esM.uid_dict.update({str(conversion_uid): conversion_uid})

Add a chemical bound energy source Source:

# create uid
CBE_uid = nts.Uid(
    name='Gas Station', latitude=53, longitude=10,
    region='Germany', sector='Power', carrier='gas',
    component='Source', node_type='gas import')

# create component
esM.add(
    fn.Source(
        esM=esM,
        name=str(CBE_uid),
        commodity=CBE_uid.carrier,
        hasCapacityVariable=True,
    )
)

# add the UID to the container:
esM.uid_dict.update({str(conversion_uid): conversion_uid})

Add a conventional engergy Conversion producing up to 10 energy units for a cost of 10:

    # create uid
    conversion_uid = nts.Uid(
        name='Transformer', latitude=53, longitude=10,
        region='Germany', sector='Power', carrier='gas',
        component='Transformer', node_type='gas-powerplant'
    )

    # create component
    # Transformer parameters are related to inflow -> devide outflow related params with efficiency
    esM.add(
        fn.Conversion(
            esM=esM,
            name=str(conversion_uid),
            physicalUnit="GW_el",
            commodityConversionFactors={"gas": -1, "Electricity": 0.42},
            opexPerOperation=10/0.42,
        )
    )

# add the UID to the container:
esM.uid_dict.update({str(conversion_uid): conversion_uid})

Optimize the energy system model instance:
```
esM.optimize()
```

Store the energy system into tessif/src/tessf/write/fine/mwe:

import os
import pathlib

from tessif.frused.paths import write_dir

# Use fine to save the data into an excel spreadsheet
fn.writeOptimizationOutputToExcel(esM=esM, outputFileName=f, optSumOutputLevel=0)

# Moving the file to the correct destination in the tessif write dir
src = os.getcwd()  # source folder
f = f + ".xlsx"  # exact file name with ending
shutil.move(os.path.join(src, f), os.path.join(d, f))

Using this newly generated energy system in a different python context by importing it:

Note

The code of steps 1 to 5 is wrapped in a create_mwe() function for convenience meaning it is copy pastable.

Import the wrapper functionality:

>>> from tessif.examples.data.fine.py_hard import create_mwe
>>> esys = create_mwe()

Confirm the expected output:

>>> for model in esys.componentModelingDict:
...    for node in esys.componentModelingDict[model].componentsDict:
...        print(node)
PowerLine
CBET
Demand
Renewable
Gas Station
Transformer

For examples on how to extract result information out ouf the optimized energy system using tessif, see tessif.transform.es2mapping.fine

FINE

Concept

Purpose

Examples

Minimum Working Example

Concept 

Purpose 

Examples 

Minimum Working Example 