Storage investment

Optimize all technologies

General description

This example shows how to perform a capacity optimization for an energy system with storage. The following energy system is modeled:

               input/output  bgas     bel
                    |          |        |
                    |          |        |
wind(FixedSource)   |------------------>|
                    |          |        |
pv(FixedSource)     |------------------>|
                    |          |        |
gas_resource        |--------->|        |
(Commodity)         |          |        |
                    |          |        |
demand(Sink)        |<------------------|
                    |          |        |
                    |          |        |
pp_gas(Converter)   |<---------|        |
                    |------------------>|
                    |          |        |
storage(Storage)    |<------------------|
                    |------------------>|

The example exists in four variations. The following parameters describe the main setting for the optimization variation 1:

• optimize wind, pv, gas_resource and storage

• set investment cost for wind, pv and storage

• set gas price for kWh

Results show an installation of wind and the use of the gas resource. A renewable energy share of 51% is achieved.

Tip

Have a look at different parameter settings. There are four variations of this example in the same folder.

Code

Download source code: v1_invest_optimize_all_technologies.py

Click to display code

import logging
import os
import pprint as pp
import warnings

import pandas as pd
from oemof.tools import economics
from oemof.tools import logger

from oemof import solph


def main():
    # Read data file
    filename = os.path.join(os.getcwd(), "storage_investment.csv")
    try:
        data = pd.read_csv(filename)
    except FileNotFoundError:
        msg = "Data file not found: {0}. Only one value used!"
        warnings.warn(msg.format(filename), UserWarning)
        data = pd.DataFrame(
            {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]}
        )

    number_timesteps = len(data)

    ##########################################################################
    # Initialize the energy system and read/calculate necessary parameters
    ##########################################################################

    logger.define_logging()
    logging.info("Initialize the energy system")
    date_time_index = solph.create_time_index(2012, number=number_timesteps)
    energysystem = solph.EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

    price_gas = 0.04

    # If the period is one year the equivalent periodical costs (epc) of an
    # investment are equal to the annuity. Use oemof's economic tools.
    epc_wind = economics.annuity(capex=1000, n=20, wacc=0.05)
    epc_pv = economics.annuity(capex=1000, n=20, wacc=0.05)
    epc_storage = economics.annuity(capex=1000, n=20, wacc=0.05)

    ##########################################################################
    # Create oemof objects
    ##########################################################################

    logging.info("Create oemof objects")
    # create natural gas bus
    bgas = solph.Bus(label="natural_gas")

    # create electricity bus
    bel = solph.Bus(label="electricity")

    energysystem.add(bgas, bel)

    # create excess component for the electricity bus to allow overproduction
    excess = solph.components.Sink(
        label="excess_bel", inputs={bel: solph.Flow()}
    )

    # create source object representing the gas commodity (annual limit)
    gas_resource = solph.components.Source(
        label="rgas", outputs={bgas: solph.Flow(variable_costs=price_gas)}
    )

    # create fixed source object representing wind power plants
    wind = solph.components.Source(
        label="wind",
        outputs={
            bel: solph.Flow(
                fix=data["wind"],
                nominal_value=solph.Investment(ep_costs=epc_wind),
            )
        },
    )

    # create fixed source object representing pv power plants
    pv = solph.components.Source(
        label="pv",
        outputs={
            bel: solph.Flow(
                fix=data["pv"], nominal_value=solph.Investment(ep_costs=epc_pv)
            )
        },
    )

    # create simple sink object representing the electrical demand
    demand = solph.components.Sink(
        label="demand",
        inputs={bel: solph.Flow(fix=data["demand_el"], nominal_value=1)},
    )

    # create simple Converter object representing a gas power plant
    pp_gas = solph.components.Converter(
        label="pp_gas",
        inputs={bgas: solph.Flow()},
        outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)},
        conversion_factors={bel: 0.58},
    )

    # create storage object representing a battery
    storage = solph.components.GenericStorage(
        label="storage",
        inputs={bel: solph.Flow(variable_costs=0.0001)},
        outputs={bel: solph.Flow()},
        loss_rate=0.00,
        initial_storage_level=0,
        invest_relation_input_capacity=1 / 6,
        invest_relation_output_capacity=1 / 6,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
        nominal_storage_capacity=solph.Investment(ep_costs=epc_storage),
    )

    energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage)

    ##########################################################################
    # Optimise the energy system
    ##########################################################################

    logging.info("Optimise the energy system")

    # initialise the operational model
    om = solph.Model(energysystem)

    # if tee_switch is true solver messages will be displayed
    logging.info("Solve the optimization problem")
    om.solve(solver="cbc", solve_kwargs={"tee": True})

    ##########################################################################
    # Check and plot the results
    ##########################################################################

    # check if the new result object is working for custom components
    results = solph.processing.results(om)

    electricity_bus = solph.views.node(results, "electricity")

    meta_results = solph.processing.meta_results(om)
    pp.pprint(meta_results)

    my_results = electricity_bus["scalars"]

    # installed capacity of storage in GWh
    my_results["storage_invest_GWh"] = (
        results[(storage, None)]["scalars"]["invest"] / 1e6
    )

    # installed capacity of wind power plant in MW
    my_results["wind_invest_MW"] = (
        results[(wind, bel)]["scalars"]["invest"] / 1e3
    )

    # resulting renewable energy share
    my_results["res_share"] = (
        1
        - results[(pp_gas, bel)]["sequences"].sum()
        / results[(bel, demand)]["sequences"].sum()
    )

    pp.pprint(my_results)


if __name__ == "__main__":
    main()

Data

Download data: storage_investment.csv

Installation requirements

This example requires oemof.solph (v0.5.x), install by:

pip install oemof.solph[examples]

License

MIT license

Optimize only gas and storage

General description

This example shows how to perform a capacity optimization for an energy system with storage. The following energy system is modeled:

               input/output  bgas     bel
                    |          |        |
                    |          |        |
wind(FixedSource)   |------------------>|
                    |          |        |
pv(FixedSource)     |------------------>|
                    |          |        |
gas_resource        |--------->|        |
(Commodity)         |          |        |
                    |          |        |
demand(Sink)        |<------------------|
                    |          |        |
                    |          |        |
pp_gas(Converter)   |<---------|        |
                    |------------------>|
                    |          |        |
storage(Storage)    |<------------------|
                    |------------------>|

The example exists in four variations. The following parameters describe the main setting for the optimization variation 2:

• optimize gas_resource and storage

• set installed capacities for wind and pv

• set investment cost for storage

• set gas price for kWh

Results show a higher renewable energy share than in variation 1 (78% compared to 51%) due to preinstalled renewable capacities. Storage is not installed as the gas resource is cheaper.

Tip

Have a look at different parameter settings. There are four variations of this example in the same folder.

Code

Download source code: v2_invest_optimize_only_gas_and_storage.py

Click to display code

import logging
import os
import pprint as pp
import warnings

import pandas as pd
from oemof.tools import economics
from oemof.tools import logger

from oemof import solph


def main():
    # Read data file
    filename = os.path.join(os.getcwd(), "storage_investment.csv")
    try:
        data = pd.read_csv(filename)
    except FileNotFoundError:
        msg = "Data file not found: {0}. Only one value used!"
        warnings.warn(msg.format(filename), UserWarning)
        data = pd.DataFrame(
            {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]}
        )

    number_timesteps = len(data)

    ##########################################################################
    # Initialize the energy system and read/calculate necessary parameters
    ##########################################################################

    logger.define_logging()
    logging.info("Initialize the energy system")
    date_time_index = solph.create_time_index(2012, number=number_timesteps)

    energysystem = solph.EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

    price_gas = 0.04

    # If the period is one year the equivalent periodical costs (epc) of an
    # investment are equal to the annuity. Use oemof's economic tools.
    epc_storage = economics.annuity(capex=1000, n=20, wacc=0.05)

    ##########################################################################
    # Create oemof objects
    ##########################################################################

    logging.info("Create oemof objects")
    # create natural gas bus
    bgas = solph.Bus(label="natural_gas")

    # create electricity bus
    bel = solph.Bus(label="electricity")

    energysystem.add(bgas, bel)

    # create excess component for the electricity bus to allow overproduction
    excess = solph.components.Sink(
        label="excess_bel", inputs={bel: solph.Flow()}
    )

    # create source object representing the gas commodity (annual limit)
    gas_resource = solph.components.Source(
        label="rgas", outputs={bgas: solph.Flow(variable_costs=price_gas)}
    )

    # create fixed source object representing wind power plants
    wind = solph.components.Source(
        label="wind",
        outputs={bel: solph.Flow(fix=data["wind"], nominal_value=1000000)},
    )

    # create fixed source object representing pv power plants
    pv = solph.components.Source(
        label="pv",
        outputs={bel: solph.Flow(fix=data["pv"], nominal_value=600000)},
    )

    # create simple sink object representing the electrical demand
    demand = solph.components.Sink(
        label="demand",
        inputs={bel: solph.Flow(fix=data["demand_el"], nominal_value=1)},
    )

    # create simple Converter object representing a gas power plant
    pp_gas = solph.components.Converter(
        label="pp_gas",
        inputs={bgas: solph.Flow()},
        outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)},
        conversion_factors={bel: 0.58},
    )

    # create storage object representing a battery
    storage = solph.components.GenericStorage(
        label="storage",
        inputs={bel: solph.Flow(variable_costs=0.0001)},
        outputs={bel: solph.Flow()},
        loss_rate=0.00,
        initial_storage_level=0,
        invest_relation_input_capacity=1 / 6,
        invest_relation_output_capacity=1 / 6,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
        nominal_storage_capacity=solph.Investment(ep_costs=epc_storage),
    )

    energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage)

    ##########################################################################
    # Optimise the energy system
    ##########################################################################

    logging.info("Optimise the energy system")

    # initialise the operational model
    om = solph.Model(energysystem)

    # if tee_switch is true solver messages will be displayed
    logging.info("Solve the optimization problem")
    om.solve(solver="cbc", solve_kwargs={"tee": True})

    ##########################################################################
    # Check and plot the results
    ##########################################################################

    # check if the new result object is working for custom components
    results = solph.processing.results(om)

    electricity_bus = solph.views.node(results, "electricity")

    meta_results = solph.processing.meta_results(om)
    pp.pprint(meta_results)

    my_results = electricity_bus["scalars"]

    # installed capacity of storage in GWh
    my_results["storage_invest_GWh"] = (
        results[(storage, None)]["scalars"]["invest"] / 1e6
    )

    # resulting renewable energy share
    my_results["res_share"] = (
        1
        - results[(pp_gas, bel)]["sequences"].sum()
        / results[(bel, demand)]["sequences"].sum()
    )

    pp.pprint(my_results)


if __name__ == "__main__":
    main()

Data

Download data: storage_investment.csv

Installation requirements

This example requires oemof.solph (v0.5.x), install by:

pip install oemof.solph[examples]

License

MIT license

Optimize only storage with fossil share

General description

This example shows how to perform a capacity optimization for an energy system with storage. The following energy system is modeled:

               input/output  bgas     bel
                    |          |        |
                    |          |        |
wind(FixedSource)   |------------------>|
                    |          |        |
pv(FixedSource)     |------------------>|
                    |          |        |
gas_resource        |--------->|        |
(Commodity)         |          |        |
                    |          |        |
demand(Sink)        |<------------------|
                    |          |        |
                    |          |        |
pp_gas(Converter)   |<---------|        |
                    |------------------>|
                    |          |        |
storage(Storage)    |<------------------|
                    |------------------>|

The example exists in four variations. The following parameters describe the main setting for the optimization variation 3:

• calculate storage

• set installed capacities for wind and pv

• set investment cost for storage

• remove the gas price and set a fossil share

• now it becomes a calculation of storage capacity (no cost optimization)

Results show now the installation of storage because a higher renewable share than achieved in variation 2 is now required (80% compared to 78%).

Tip

Have a look at different parameter settings. There are four variations of this example in the same folder.

Code

Download source code: v3_invest_optimize_only_storage_with_fossil_share.py

Click to display code

import logging
import os
import pprint as pp
import warnings

import pandas as pd
from oemof.tools import economics
from oemof.tools import logger

from oemof import solph


def main():
    # Read data file
    filename = os.path.join(os.getcwd(), "storage_investment.csv")
    try:
        data = pd.read_csv(filename)
    except FileNotFoundError:
        msg = "Data file not found: {0}. Only one value used!"
        warnings.warn(msg.format(filename), UserWarning)
        data = pd.DataFrame(
            {"pv": [0.3, 0.4], "wind": [0.6, 0.5], "demand_el": [500, 400]}
        )

    number_timesteps = len(data)

    ##########################################################################
    # Initialize the energy system and read/calculate necessary parameters
    ##########################################################################

    logger.define_logging()
    logging.info("Initialize the energy system")
    date_time_index = solph.create_time_index(2012, number=number_timesteps)

    energysystem = solph.EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

    fossil_share = 0.2
    consumption_total = data["demand_el"].sum()

    # If the period is one year the equivalent periodical costs (epc) of an
    # investment are equal to the annuity. Use oemof's economic tools.
    epc_storage = economics.annuity(capex=1000, n=20, wacc=0.05)

    ##########################################################################
    # Create oemof objects
    ##########################################################################

    logging.info("Create oemof objects")
    # create natural gas bus
    bgas = solph.Bus(label="natural_gas")

    # create electricity bus
    bel = solph.Bus(label="electricity")

    energysystem.add(bgas, bel)

    # create excess component for the electricity bus to allow overproduction
    excess = solph.components.Sink(
        label="excess_bel", inputs={bel: solph.Flow()}
    )

    # create source object representing the gas commodity (annual limit)
    gas_resource = solph.components.Source(
        label="rgas",
        outputs={
            bgas: solph.Flow(
                nominal_value=fossil_share
                * consumption_total
                / 0.58
                * number_timesteps
                / 8760,
                full_load_time_max=1,
            )
        },
    )

    # create fixed source object representing wind power plants
    wind = solph.components.Source(
        label="wind",
        outputs={bel: solph.Flow(fix=data["wind"], nominal_value=1000000)},
    )

    # create fixed source object representing pv power plants
    pv = solph.components.Source(
        label="pv",
        outputs={bel: solph.Flow(fix=data["pv"], nominal_value=600000)},
    )

    # create simple sink object representing the electrical demand
    demand = solph.components.Sink(
        label="demand",
        inputs={bel: solph.Flow(fix=data["demand_el"], nominal_value=1)},
    )

    # create simple Converter object representing a gas power plant
    pp_gas = solph.components.Converter(
        label="pp_gas",
        inputs={bgas: solph.Flow()},
        outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)},
        conversion_factors={bel: 0.58},
    )

    # create storage object representing a battery
    storage = solph.components.GenericStorage(
        label="storage",
        inputs={bel: solph.Flow(variable_costs=0.0001)},
        outputs={bel: solph.Flow()},
        loss_rate=0.00,
        initial_storage_level=0,
        invest_relation_input_capacity=1 / 6,
        invest_relation_output_capacity=1 / 6,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
        nominal_storage_capacity=solph.Investment(ep_costs=epc_storage),
    )

    energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage)

    ##########################################################################
    # Optimise the energy system
    ##########################################################################

    logging.info("Optimise the energy system")

    # initialise the operational model
    om = solph.Model(energysystem)

    # if tee_switch is true solver messages will be displayed
    logging.info("Solve the optimization problem")
    om.solve(solver="cbc", solve_kwargs={"tee": True})

    ##########################################################################
    # Check and plot the results
    ##########################################################################

    # check if the new result object is working for custom components
    results = solph.processing.results(om)

    electricity_bus = solph.views.node(results, "electricity")

    meta_results = solph.processing.meta_results(om)
    pp.pprint(meta_results)

    my_results = electricity_bus["scalars"]

    # installed capacity of storage in GWh
    my_results["storage_invest_GWh"] = (
        results[(storage, None)]["scalars"]["invest"] / 1e6
    )

    # resulting renewable energy share
    my_results["res_share"] = (
        1
        - results[(pp_gas, bel)]["sequences"].sum()
        / results[(bel, demand)]["sequences"].sum()
    )

    pp.pprint(my_results)


if __name__ == "__main__":
    main()

Data

Download data: storage_investment.csv

Installation requirements

This example requires oemof.solph (v0.5.x), install by:

pip install oemof.solph[examples]

License

MIT license

Optimize all technologies with fossil share

General description

This example shows how to perform a capacity optimization for an energy system with storage. The following energy system is modeled:

               input/output  bgas     bel
                    |          |        |
                    |          |        |
wind(FixedSource)   |------------------>|
                    |          |        |
pv(FixedSource)     |------------------>|
                    |          |        |
gas_resource        |--------->|        |
(Commodity)         |          |        |
                    |          |        |
demand(Sink)        |<------------------|
                    |          |        |
                    |          |        |
pp_gas(Converter)   |<---------|        |
                    |------------------>|
                    |          |        |
storage(Storage)    |<------------------|
                    |------------------>|

The example exists in four variations. The following parameters describe the main setting for the optimization variation 4:

• optimize wind, pv, and storage

• set investment cost for wind, pv and storage

• set a fossil share

Results show now the achievement of 80% renewable energy share by solely installing a little more wind and pv (compared to variation 2). Storage is not installed.

Tip

Have a look at different parameter settings. There are four variations of this example in the same folder.

Code

Download source code: v4_invest_optimize_all_technologies_with_fossil_share.py

Click to display code

import logging
import os
import pprint as pp
import warnings

import pandas as pd
from oemof.tools import economics
from oemof.tools import logger

from oemof import solph


def main():
    # Read data file
    filename = os.path.join(os.getcwd(), "storage_investment.csv")
    try:
        data = pd.read_csv(filename)
    except FileNotFoundError:
        msg = "Data file not found: {0}. Only one value used!"
        warnings.warn(msg.format(filename), UserWarning)
        data = pd.DataFrame(
            {"pv": [0.3, 0.5], "wind": [0.6, 0.5], "demand_el": [500, 400]}
        )

    number_timesteps = len(data)

    ##########################################################################
    # Initialize the energy system and read/calculate necessary parameters
    ##########################################################################

    logger.define_logging()
    logging.info("Initialize the energy system")
    date_time_index = solph.create_time_index(2012, number=number_timesteps)

    energysystem = solph.EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

    fossil_share = 0.2
    consumption_total = data["demand_el"].sum()

    # If the period is one year the equivalent periodical costs (epc) of an
    # investment are equal to the annuity. Use oemof's economic tools.
    epc_wind = economics.annuity(capex=1000, n=20, wacc=0.05)
    epc_pv = economics.annuity(capex=1000, n=20, wacc=0.05)
    epc_storage = economics.annuity(capex=1000, n=20, wacc=0.05)

    ##########################################################################
    # Create oemof objects
    ##########################################################################

    logging.info("Create oemof objects")
    # create natural gas bus
    bgas = solph.Bus(label="natural_gas")

    # create electricity bus
    bel = solph.Bus(label="electricity")

    energysystem.add(bgas, bel)

    # create excess component for the electricity bus to allow overproduction
    excess = solph.components.Sink(
        label="excess_bel", inputs={bel: solph.Flow()}
    )

    # create source object representing the natural gas commodity (annual limit)
    gas_resource = solph.components.Source(
        label="rgas",
        outputs={
            bgas: solph.Flow(
                nominal_value=fossil_share
                * consumption_total
                / 0.58
                * number_timesteps
                / 8760,
                full_load_time_max=1,
            )
        },
    )

    # create fixed source object representing wind power plants
    wind = solph.components.Source(
        label="wind",
        outputs={
            bel: solph.Flow(
                fix=data["wind"],
                nominal_value=solph.Investment(ep_costs=epc_wind),
            )
        },
    )

    # create fixed source object representing pv power plants
    pv = solph.components.Source(
        label="pv",
        outputs={
            bel: solph.Flow(
                fix=data["pv"], nominal_value=solph.Investment(ep_costs=epc_pv)
            )
        },
    )

    # create simple sink object representing the electrical demand
    demand = solph.components.Sink(
        label="demand",
        inputs={bel: solph.Flow(fix=data["demand_el"], nominal_value=1)},
    )

    # create simple Converter object representing a gas power plant
    pp_gas = solph.components.Converter(
        label="pp_gas",
        inputs={bgas: solph.Flow()},
        outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)},
        conversion_factors={bel: 0.58},
    )

    # create storage object representing a battery
    storage = solph.components.GenericStorage(
        label="storage",
        inputs={bel: solph.Flow(variable_costs=0.0001)},
        outputs={bel: solph.Flow()},
        loss_rate=0.00,
        initial_storage_level=0,
        invest_relation_input_capacity=1 / 6,
        invest_relation_output_capacity=1 / 6,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
        nominal_storage_capacity=solph.Investment(ep_costs=epc_storage),
    )

    energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage)

    ##########################################################################
    # Optimise the energy system
    ##########################################################################

    logging.info("Optimise the energy system")

    # initialise the operational model
    om = solph.Model(energysystem)

    # if tee_switch is true solver messages will be displayed
    logging.info("Solve the optimization problem")
    om.solve(solver="cbc", solve_kwargs={"tee": True})

    ##########################################################################
    # Check and plot the results
    ##########################################################################

    # check if the new result object is working for custom components
    results = solph.processing.results(om)

    custom_storage = solph.views.node(results, "storage")
    electricity_bus = solph.views.node(results, "electricity")

    meta_results = solph.processing.meta_results(om)
    pp.pprint(meta_results)

    my_results = electricity_bus["scalars"]

    # installed capacity of storage in GWh
    my_results["storage_invest_GWh"] = (
        results[(storage, None)]["scalars"]["invest"] / 1e6
    )

    # installed capacity of wind power plant in MW
    my_results["wind_invest_MW"] = (
        results[(wind, bel)]["scalars"]["invest"] / 1e3
    )

    # installed capacity of pv power plant in MW
    my_results["pv_invest_MW"] = results[(pv, bel)]["scalars"]["invest"] / 1e3

    # resulting renewable energy share
    my_results["res_share"] = (
        1
        - results[(pp_gas, bel)]["sequences"].sum()
        / results[(bel, demand)]["sequences"].sum()
    )

    pp.pprint(my_results)


if __name__ == "__main__":
    main()

Data

Download data: storage_investment.csv

Installation requirements

This example requires oemof.solph (v0.5.x), install by:

pip install oemof.solph[examples]

License

MIT license