startup_shutdown

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Total Lines	122
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# -*- coding: utf-8 -*-

"""
General description
-------------------
Example that illustrates how to model startup and shutdown costs attributed
to a binary flow.

Installation requirements
-------------------------
This example requires oemof.solph (v0.5.x), install by:

    pip install oemof.solph[examples]

License
-------
`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_

"""
import pandas as pd
from oemof import solph
import matplotlib.pyplot as plt


demand_el = [
    0,
    0,
    0,
    1,
    1,
    1,
    0,
    0,
    1,
    1,
    1,
    0,
    0,
    1,
    1,
    1,
    0,
    0,
    1,
    1,
    1,
    1,
    0,
    0,
]
# create an energy system
idx = solph.create_time_index(2017, number=len(demand_el))
es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)

# power bus and components
bel = solph.Bus(label="bel")

demand_el = solph.components.Sink(
    label="demand_el",
    inputs={bel: solph.Flow(fix=demand_el, nominal_value=10)},
)

# pp1 and pp2 are competing to serve overall 12 units load at lowest cost
# summed costs for pp1 = 12 * 10 * 10.25 = 1230
# summed costs for pp2 = 4*5 + 4*5 + 12 * 10 * 10 = 1240
# => pp1 serves the load despite of higher variable costs since
#    the start and shutdown costs of pp2 change its marginal costs
pp1 = solph.components.Source(
    label="power_plant1",
    outputs={bel: solph.Flow(nominal_value=10, variable_costs=10.25)},
)

# shutdown costs only work in combination with a minimum load
# since otherwise the status variable is "allowed" to be active i.e.
# it permanently has a value of one which does not allow to set the shutdown
# variable which is set to one if the status variable changes from one to zero
pp2 = solph.components.Source(
    label="power_plant2",
    outputs={
        bel: solph.Flow(
            nominal_value=10,
            min=0.5,
            max=1.0,
            variable_costs=10,
            nonconvex=solph.NonConvex(startup_costs=5, shutdown_costs=5),
        )
    },
)
es.add(bel, demand_el, pp1, pp2)
# create an optimization problem and solve it
om = solph.Model(es)

# debugging
# om.write('problem.lp', io_options={'symbolic_solver_labels': True})

# solve model
om.solve(solver="cbc", solve_kwargs={"tee": True})

# create result object
results = solph.processing.results(om)

# plot electrical bus
to_bus = pd.DataFrame(
    {
        k[0].label: v["sequences"]["flow"]
        for k, v in results.items()
        if k[1] == bel
    }
)
from_bus = pd.DataFrame(
    {
        k[1].label: v["sequences"]["flow"] * -1
        for k, v in results.items()
        if k[0] == bel
    }
)
data = pd.concat([from_bus, to_bus], axis=1)
ax = data.plot(kind="line", drawstyle="steps-post", grid=True, rot=0)
ax.set_xlabel("Hour")
ax.set_ylabel("P (MW)")
plt.show()