simple_dispatch.main()

Complexity

Conditions

4

Size

Total Lines	224
Code Lines	134

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

# -*- coding: utf-8 -*-
def main():
    # Read data file
    filename = os.path.join(os.getcwd(), "input_data.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, 0.7],
                "wind": [0.6, 0.5],
                "demand_el": [500, 400],
                "demand_th": [400, 300],
            }
        )

    solver = "cbc"

    # Create an energy system and optimize the dispatch at least costs.
    # ####################### initialize and provide data #####################
    datetimeindex = create_time_index(2016, number=len(data))
    energysystem = EnergySystem(
        timeindex=datetimeindex, infer_last_interval=False
    )

    # ######################### 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="bel")
    bth = Bus(label="bth")

    energysystem.add(bcoal, bgas, boil, blig, bel, bth)

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

    # sources
    energysystem.add(
        Source(
            label="wind",
            outputs={bel: Flow(fix=data["wind"], nominal_value=66.3)},
        )
    )

    energysystem.add(
        Source(
            label="pv", outputs={bel: Flow(fix=data["pv"], nominal_value=65.3)}
        )
    )

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

    energysystem.add(
        Sink(
            label="demand_th",
            inputs={bth: Flow(nominal_value=40, fix=data["demand_th"])},
        )
    )

    # power plants
    energysystem.add(
        Transformer(
            label="pp_coal",
            inputs={bcoal: Flow()},
            outputs={bel: Flow(nominal_value=20.2, variable_costs=25)},
            conversion_factors={bel: 0.39},
        )
    )

    energysystem.add(
        Transformer(
            label="pp_lig",
            inputs={blig: Flow()},
            outputs={bel: Flow(nominal_value=11.8, variable_costs=19)},
            conversion_factors={bel: 0.41},
        )
    )

    energysystem.add(
        Transformer(
            label="pp_gas",
            inputs={bgas: Flow()},
            outputs={bel: Flow(nominal_value=41, variable_costs=40)},
            conversion_factors={bel: 0.50},
        )
    )

    energysystem.add(
        Transformer(
            label="pp_oil",
            inputs={boil: Flow()},
            outputs={bel: Flow(nominal_value=5, variable_costs=50)},
            conversion_factors={bel: 0.28},
        )
    )

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

    # heat pump with a coefficient of performance (COP) of 3
    b_heat_source = Bus(label="b_heat_source")
    energysystem.add(b_heat_source)

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

    cop = 3
    energysystem.add(
        Transformer(
            label="heat_pump",
            inputs={bel: Flow(), b_heat_source: Flow()},
            outputs={bth: Flow(nominal_value=10)},
            conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
        )
    )

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

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

    # solve problem
    optimization_model.solve(
        solver=solver, solve_kwargs={"tee": True, "keepfiles": False}
    )

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

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

    # 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(optimization_model.results(), "bel")
    data["sequences"].info()
    print("Optimization successful. Showing some results:")

    # see: https://pandas.pydata.org/pandas-docs/stable/visualization.html
    node_results_bel = views.node(optimization_model.results(), "bel")
    node_results_flows = node_results_bel["sequences"]

    fig, ax = plt.subplots(figsize=(10, 5))
    node_results_flows.plot(
        ax=ax, kind="bar", stacked=True, linewidth=0, width=1
    )
    ax.set_title("Sums for optimization period")
    ax.legend(loc="upper right", bbox_to_anchor=(1, 1))
    ax.set_xlabel("Energy (MWh)")
    ax.set_ylabel("Flow")
    plt.legend(loc="center left", prop={"size": 8}, bbox_to_anchor=(1, 0.5))
    fig.subplots_adjust(right=0.8)

    dates = node_results_flows.index
    tick_distance = int(len(dates) / 7) - 1
    ax.set_xticks(range(0, len(dates), tick_distance), minor=False)
    if tick_distance > 0:
        ax.set_xticklabels(
            [
                item.strftime("%d-%m-%Y")
                for item in dates.tolist()[0::tick_distance]
            ],
            rotation=90,
            minor=False,
        )

    plt.show()

    node_results_bth = views.node(optimization_model.results(), "bth")
    node_results_flows = node_results_bth["sequences"]

    fig, ax = plt.subplots(figsize=(10, 5))
    node_results_flows.plot(
        ax=ax, kind="bar", stacked=True, linewidth=0, width=1
    )
    ax.set_title("Sums for optimization period")
    ax.legend(loc="upper right", bbox_to_anchor=(1, 1))
    ax.set_xlabel("Energy (MWh)")
    ax.set_ylabel("Flow")
    plt.legend(loc="center left", prop={"size": 8}, bbox_to_anchor=(1, 0.5))
    fig.subplots_adjust(right=0.8)

    dates = node_results_flows.index
    tick_distance = int(len(dates) / 7) - 1
    ax.set_xticks(range(0, len(dates), tick_distance), minor=False)
    if tick_distance > 0:
        ax.set_xticklabels(
            [
                item.strftime("%d-%m-%Y")
                for item in dates.tolist()[0::tick_distance]
            ],
            rotation=90,
            minor=False,
        )

    plt.show()