dual_variable_example.main()

Complexity

Conditions

1

Size

Total Lines	259
Code Lines	181

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

# -*- coding: utf-8 -*-
def main():
    # *************************************************************************
    # ********** PART 1 - Define and optimise the energy system ***************
    # *************************************************************************

    ###########################################################################
    # imports
    ###########################################################################

    import pandas as pd
    from matplotlib import pyplot as plt
    from oemof.tools import logger

    from oemof.solph import EnergySystem
    from oemof.solph import Model
    from oemof.solph import buses
    from oemof.solph import components as cmp
    from oemof.solph import flows
    from oemof.solph import processing

    solver = "cbc"  # 'glpk', 'gurobi',....
    number_of_time_steps = 48
    solver_verbose = False  # show/hide solver output

    # initiate the logger (see the API docs for more information)
    logger.define_logging()

    date_time_index = pd.date_range(
        "1/1/2012", periods=number_of_time_steps, freq="H"
    )

    energysystem = EnergySystem(
        timeindex=date_time_index, infer_last_interval=True
    )

    demand = [
        209,
        207,
        200,
        191,
        185,
        180,
        172,
        170,
        171,
        179,
        189,
        201,
        208,
        207,
        205,
        206,
        217,
        232,
        237,
        232,
        224,
        219,
        223,
        213,
        201,
        192,
        187,
        184,
        184,
        182,
        180,
        191,
        207,
        222,
        231,
        238,
        241,
        237,
        234,
        235,
        242,
        264,
        265,
        260,
        245,
        238,
        241,
        231,
    ]
    pv = [
        0.18,
        0.11,
        0.05,
        0.05,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.05,
        0.07,
        0.11,
        0.13,
        0.15,
        0.22,
        0.28,
        0.33,
        0.25,
        0.17,
        0.09,
        0.09,
        0.07,
        0.05,
        0.05,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.09,
        0.21,
        0.33,
        0.44,
        0.54,
        0.61,
        0.65,
        0.67,
        0.64,
        0.59,
        0.52,
    ]

    ##########################################################################
    # Create oemof object
    ##########################################################################

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

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

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

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

    # create source object representing the gas commodity (annual limit)
    energysystem.add(
        cmp.Source(
            label="rgas",
            outputs={bus_gas: flows.Flow(variable_costs=38)},
        )
    )

    # create fixed source object representing pv power plants
    energysystem.add(
        cmp.Source(
            label="pv",
            outputs={bus_elec: flows.Flow(fix=pv, nominal_value=700)},
        )
    )

    # create simple sink object representing the electrical demand
    energysystem.add(
        cmp.Sink(
            label="demand",
            inputs={bus_elec: flows.Flow(fix=demand, nominal_value=1)},
        )
    )

    # create simple transformer object representing a gas power plant
    energysystem.add(
        cmp.Transformer(
            label="pp_gas",
            inputs={bus_gas: flows.Flow()},
            outputs={bus_elec: flows.Flow(nominal_value=400)},
            conversion_factors={bus_elec: 0.5},
        )
    )

    # create storage object representing a battery
    cap = 400
    storage = cmp.GenericStorage(
        nominal_storage_capacity=cap,
        label="storage",
        inputs={bus_elec: flows.Flow(nominal_value=cap / 6)},
        outputs={
            bus_elec: flows.Flow(nominal_value=cap / 6, variable_costs=0.001)
        },
        loss_rate=0.00,
        initial_storage_level=0,
        inflow_conversion_factor=1,
        outflow_conversion_factor=0.8,
    )

    energysystem.add(storage)

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

    # initialise the operational model
    model = Model(energysystem)

    model.receive_duals()

    # if tee_switch is true solver messages will be displayed
    model.solve(solver=solver, solve_kwargs={"tee": solver_verbose})

    # add results to the energy system to make it possible to store them.
    results = processing.results(model)

    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] == bus_elec
        }
    )
    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] == bus_elec
        }
    )

    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
        }
    )

    duals = pd.DataFrame(
        {
            str(k[0].label): v["sequences"]["duals"]
            for k, v in results.items()
            if k[1] is None and isinstance(k[0], buses.Bus)
        }
    )

    my_flows = pd.concat(
        [flows_to_bus, flows_from_bus, storage, duals],
        keys=["to_bus", "from_bus", "content", "duals"],
        axis=1,
    )

    my_flows.plot()
    plt.show()