basic_example.main()

Complexity

Conditions

4

Size

Total Lines	212
Code Lines	97

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

# -*- coding: utf-8 -*-
def main(dump_and_restore=False):
    # For models that need a long time to optimise, saving and loading the
    # EnergySystem might be advised. By default, we do not do this here. Feel
    # free to experiment with this once you understood the rest of the code.
    dump_results = restore_results = dump_and_restore

    # *************************************************************************
    # ********** PART 1 - Define and optimise the energy system ***************
    # *************************************************************************

    # Read data file
    file_name = "basic_example.csv"
    data = get_data_from_file_path(file_name)

    solver = "cbc"  # 'glpk', 'gurobi',....
    debug = False  # Set number_of_timesteps to 3 to get a readable lp-file.
    number_of_time_steps = len(data)
    solver_verbose = False  # show/hide solver output

    # initiate the logger (see the API docs for more information)
    logger.define_logging(
        logfile="oemof_example.log",
        screen_level=logging.INFO,
        file_level=logging.INFO,
    )

    logging.info("Initialize the energy system")
    date_time_index = create_time_index(2012, number=number_of_time_steps)

    # create the energysystem and assign the time index
    energysystem = EnergySystem(
        timeindex=date_time_index, infer_last_interval=False
    )

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

    logging.info("Create oemof objects")

    # The bus objects were assigned to variables which makes it easier to
    # connect components to these buses (see below).

    # create natural gas bus
    bus_gas = buses.Bus(label="natural_gas")

    # create electricity bus
    bus_electricity = buses.Bus(label="electricity")

    # adding the buses to the energy system
    energysystem.add(bus_gas, bus_electricity)

    # create excess component for the electricity bus to allow overproduction
    energysystem.add(
        components.Sink(
            label="excess_bus_electricity",
            inputs={bus_electricity: flows.Flow()},
        )
    )

    energysystem.add(
        DSO(
            label="DSO",
            el_bus=bus_electricity,
            energy_price=0.1,
            feedin_tariff=0.04,
        )
    )

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

    # create fixed source object representing pv power plants
    energysystem.add(
        components.Source(
            label="pv",
            outputs={
                bus_electricity: flows.Flow(
                    fix=data["pv"], nominal_value=582000
                )
            },
        )
    )

    # create simple sink object representing the electrical demand
    # nominal_value is set to 1 because demand_el is not a normalised series
    energysystem.add(
        components.Sink(
            label="demand",
            inputs={
                bus_electricity: flows.Flow(
                    fix=data["demand_el"], nominal_value=1
                )
            },
        )
    )

    # create storage object representing a battery
    nominal_capacity = 10077997
    nominal_value = nominal_capacity / 6

    battery_storage = components.GenericStorage(
        nominal_storage_capacity=nominal_capacity,
        label=STORAGE_LABEL,
        inputs={bus_electricity: flows.Flow(nominal_value=nominal_value)},
        outputs={
            bus_electricity: flows.Flow(
                nominal_value=nominal_value, variable_costs=0.001
            )
        },
        loss_rate=0.00,
        initial_storage_level=None,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
    )

    energysystem.add(battery_storage)

    ##########################################################################
    # Optimise the energy system and plot the results
    ##########################################################################

    logging.info("Optimise the energy system")

    # initialise the operational model
    energysystem_model = Model(energysystem)

    # This is for debugging only. It is not(!) necessary to solve the problem
    # and should be set to False to save time and disc space in normal use. For
    # debugging the timesteps should be set to 3, to increase the readability
    # of the lp-file.
    if debug:
        file_path = os.path.join(
            helpers.extend_basic_path("lp_files"), "basic_example.lp"
        )
        logging.info(f"Store lp-file in {file_path}.")
        io_option = {"symbolic_solver_labels": True}
        energysystem_model.write(file_path, io_options=io_option)

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

    logging.info("Store the energy system with the results.")

    # The processing module of the outputlib can be used to extract the results
    # from the model transfer them into a homogeneous structured dictionary.

    # add results to the energy system to make it possible to store them.
    energysystem.results["main"] = processing.results(energysystem_model)
    energysystem.results["meta"] = processing.meta_results(energysystem_model)

    # The default path is the '.oemof' folder in your $HOME directory.
    # The default filename is 'es_dump.oemof'.
    # You can omit the attributes (as None is the default value) for testing
    # cases. You should use unique names/folders for valuable results to avoid
    # overwriting.
    if dump_results:
        energysystem.dump(dpath=None, filename=None)

    # *************************************************************************
    # ********** PART 2 - Processing the results ******************************
    # *************************************************************************

    # Saved data can be restored in a second script. So you can work on the
    # data analysis without re-running the optimisation every time. If you do
    # so, make sure that you really load the results you want. For example,
    # if dumping fails, you might exidentially load outdated results.
    if restore_results:
        logging.info("**** The script can be divided into two parts here.")
        logging.info("Restore the energy system and the results.")

        energysystem = EnergySystem()
        energysystem.restore(dpath=None, filename=None)

    # define an alias for shorter calls below (optional)
    results = energysystem.results["main"]
    storage = energysystem.groups[STORAGE_LABEL]

    # print a time slice of the state of charge
    start_time = datetime(2012, 2, 25, 8, 0, 0)
    end_time = datetime(2012, 2, 25, 17, 0, 0)

    print("\n********* State of Charge (slice) *********")
    print(f"{results[(storage, None)]['sequences'][start_time : end_time]}\n")

    # get all variables of a specific component/bus
    custom_storage = views.node(results, STORAGE_LABEL)
    electricity_bus = views.node(results, "electricity")

    # plot the time series (sequences) of a specific component/bus
    plot_figures_for(custom_storage)
    plot_figures_for(electricity_bus)

    # print the solver results
    print("********* Meta results *********")
    pp.pprint(f"{energysystem.results['meta']}\n")

    # print the sums of the flows around the electricity bus
    print("********* Main results *********")
    print(electricity_bus["sequences"].sum(axis=0))