test_simple_invest.test_dispatch_example()

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2

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Total Lines	181
Code Lines	111

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0

# -*- coding: utf-8 -*-
def test_dispatch_example(solver="cbc", periods=24 * 5):
    """Create an energy system and optimize the dispatch at least costs."""

    filename = os.path.join(os.path.dirname(__file__), "input_data.csv")
    data = pd.read_csv(filename, sep=",")

    # ######################### create energysystem components ################

    # resource buses
    bcoal = Bus(label="coal", balanced=False)
    bgas = Bus(label="gas", balanced=False)
    boil = Bus(label="oil", balanced=False)
    blig = Bus(label="lignite", balanced=False)

    # electricity and heat
    bel = Bus(label="b_el")
    bth = Bus(label="b_th")

    # an excess and a shortage variable can help to avoid infeasible problems
    excess_el = Sink(label="excess_el", inputs={bel: Flow()})
    # shortage_el = Source(label='shortage_el',
    #                      outputs={bel: Flow(variable_costs=200)})

    # sources
    ep_wind = economics.annuity(capex=1000, n=20, wacc=0.05)
    wind = Source(
        label="wind",
        outputs={
            bel: Flow(
                fix=data["wind"],
                nominal_capacity=Investment(ep_costs=ep_wind, existing=100),
            )
        },
    )

    ep_pv = economics.annuity(capex=1500, n=20, wacc=0.05)
    pv = Source(
        label="pv",
        outputs={
            bel: Flow(
                fix=data["pv"],
                nominal_capacity=Investment(ep_costs=ep_pv, existing=80),
            )
        },
    )

    # demands (electricity/heat)
    demand_el = Sink(
        label="demand_elec",
        inputs={bel: Flow(nominal_capacity=85, fix=data["demand_el"])},
    )

    demand_th = Sink(
        label="demand_therm",
        inputs={bth: Flow(nominal_capacity=40, fix=data["demand_th"])},
    )

    # power plants
    pp_coal = Converter(
        label="pp_coal",
        inputs={bcoal: Flow()},
        outputs={bel: Flow(nominal_capacity=20.2, variable_costs=25)},
        conversion_factors={bel: 0.39},
    )

    pp_lig = Converter(
        label="pp_lig",
        inputs={blig: Flow()},
        outputs={bel: Flow(nominal_capacity=11.8, variable_costs=19)},
        conversion_factors={bel: 0.41},
    )

    pp_gas = Converter(
        label="pp_gas",
        inputs={bgas: Flow()},
        outputs={bel: Flow(nominal_capacity=41, variable_costs=40)},
        conversion_factors={bel: 0.50},
    )

    pp_oil = Converter(
        label="pp_oil",
        inputs={boil: Flow()},
        outputs={bel: Flow(nominal_capacity=5, variable_costs=50)},
        conversion_factors={bel: 0.28},
    )

    # combined heat and power plant (chp)
    pp_chp = Converter(
        label="pp_chp",
        inputs={bgas: Flow()},
        outputs={
            bel: Flow(nominal_capacity=30, variable_costs=42),
            bth: Flow(nominal_capacity=40),
        },
        conversion_factors={bel: 0.3, bth: 0.4},
    )

    # heatpump with a coefficient of performance (COP) of 3
    b_heat_source = Bus(label="b_heat_source")

    heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})

    cop = 3
    heat_pump = Converter(
        label="el_heat_pump",
        inputs={bel: Flow(), b_heat_source: Flow()},
        outputs={bth: Flow(nominal_capacity=10)},
        conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
    )

    datetimeindex = pd.date_range("1/1/2012", periods=periods, freq="h")
    energysystem = EnergySystem(
        timeindex=datetimeindex, infer_last_interval=True
    )
    energysystem.add(
        bcoal,
        bgas,
        boil,
        bel,
        bth,
        blig,
        excess_el,
        wind,
        pv,
        demand_el,
        demand_th,
        pp_coal,
        pp_lig,
        pp_oil,
        pp_gas,
        pp_chp,
        b_heat_source,
        heat_source,
        heat_pump,
    )

    # ################################ optimization ###########################

    # create optimization model based on energy_system
    optimization_model = Model(energysystem=energysystem)

    # solve problem
    optimization_model.solve(solver=solver)

    # write back results from optimization object to energysystem
    optimization_model.results()

    # ################################ results ################################

    # generic result object
    results = processing.results(model=optimization_model)

    # subset of results that includes all flows into and from electrical bus
    # sequences are stored within a pandas.DataFrames and scalars e.g.
    # investment values within a pandas.Series object.
    # in this case the entry data['scalars'] does not exist since no investment
    # variables are used
    data = views.node(results, "b_el")

    # generate results to be evaluated in tests
    comp_results = data["sequences"].sum(axis=0).to_dict()
    comp_results["pv_capacity"] = results[(pv, bel)]["scalars"].invest
    comp_results["wind_capacity"] = results[(wind, bel)]["scalars"].invest

    test_results = {
        (("wind", "b_el"), "flow"): 9239,
        (("pv", "b_el"), "flow"): 1147,
        (("b_el", "demand_elec"), "flow"): 7440,
        (("b_el", "excess_el"), "flow"): 6261,
        (("pp_chp", "b_el"), "flow"): 477,
        (("pp_lig", "b_el"), "flow"): 850,
        (("pp_gas", "b_el"), "flow"): 934,
        (("pp_coal", "b_el"), "flow"): 1256,
        (("pp_oil", "b_el"), "flow"): 0,
        (("b_el", "el_heat_pump"), "flow"): 202,
        "pv_capacity": 44,
        "wind_capacity": 246,
    }

    for key in test_results.keys():
        assert comp_results[key] == pytest.approx(test_results[key], abs=0.5)