def METHOD_NAME():
    # The gradient for the output of the natural gas power plant.
    # Change the gradient between 0.1 and 0.0001 and check the results. The
    # more flexible the power plant can be run the less the storage will be
    # used.
    gradient = 0.01
    date_time_index = pd.date_range("1/1/2012", periods=48, freq="H")
    print(date_time_index)
    energysystem = EnergySystem(timeindex=date_time_index, timemode="explicit")
    demand = [
        209643,
        207497,
        200108,
        191892,
        185717,
        180672,
        172683,
        170048,
        171132,
        179532,
        189155,
        201026,
        208466,
        207718,
        205443,
        206255,
        217240,
        232798,
        237321,
        232387,
        224306,
        219280,
        223701,
        213926,
        201834,
        192215,
        187152,
        184355,
        184438,
        182786,
        180105,
        191509,
        207104,
        222501,
        231127,
        238410,
        241184,
        237413,
        234469,
        235193,
        242730,
        264196,
        265950,
        260283,
        245578,
        238849,
        241553,
        231372,
    ]
    # create natural gas bus
    bgas = buses.Bus(label="natural_gas")
    # create electricity bus
    bel = buses.Bus(label="electricity")
    # adding the buses to the energy system
    energysystem.add(bgas, bel)
    # create excess component for the electricity bus to allow overproduction
    energysystem.add(cmp.Sink(label="excess_bel", inputs={bel: flows.Flow()}))
    # create source object representing the gas commodity (annual limit)
    energysystem.add(
        cmp.Source(
            label="rgas",
            outputs={bgas: flows.Flow(variable_costs=5)},
        )
    )
    # create simple sink object representing the electrical demand
    energysystem.add(
        cmp.Sink(
            label="demand",
            inputs={bel: flows.Flow(fix=demand, nominal_value=1)},
        )
    )
    # create simple Converter object representing a gas power plant
    energysystem.add(
        cmp.Converter(
            label="pp_gas",
            inputs={bgas: flows.Flow()},
            outputs={
                bel: flows.Flow(
                    nominal_value=10e5,
                    negative_gradient_limit=gradient,
                    positive_gradient_limit=gradient,
                )
            },
            conversion_factors={bel: 0.58},
        )
    )
    # create storage object representing a battery
    storage = cmp.GenericStorage(
        nominal_storage_capacity=999999999,
        label="storage",
        inputs={bel: flows.Flow()},
        outputs={bel: flows.Flow()},
        loss_rate=0.0,
        initial_storage_level=None,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
    )
    energysystem.add(storage)
    # initialise the operational model
    model = Model(energysystem)
    # solve
    model.solve(solver="cbc")
    # processing the results
    results = processing.results(model)
    # *** Create a table with all sequences and store it into a file (csv/xlsx)
    flows_to_bus = pd.DataFrame(
        {
            str(k[0].label): v["sequences"]["flow"]
            for k, v in results.items()
            if k[1] is not None and k[1] == bel
        }
    )
    flows_from_bus = pd.DataFrame(
        {
            str(k[1].label): v["sequences"]["flow"]
            for k, v in results.items()
            if k[1] is not None and k[0] == bel
        }
    )
    storage = pd.DataFrame(
        {
            str(k[0].label): v["sequences"]["storage_content"]
            for k, v in results.items()
            if k[1] is None and k[0] == storage
        }
    )
    my_flows = pd.concat(
        [flows_to_bus, flows_from_bus, storage],
        keys=["to_bus", "from_bus", "content", "duals"],
        axis=1,
    )
    print(my_flows)
    my_flows.plot()
    plt.show()