min_max_runtimes

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# -*- coding: utf-8 -*-

"""
General description
-------------------
Example that illustrates how to model min and max runtimes.

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>`_

"""
from matplotlib import pyplot as plt
from oemof.network.network import Node

from oemof import solph

# Create demand data
demand_el = [0] * 24
for n in [10, 15, 19]:
    demand_el[n] = 1

# create an energy system
idx = solph.create_time_index(2017, number=24)
es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
Node.registry = es

# 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)},
)

dummy_el = solph.components.Sink(
    label="dummy_el", inputs={bel: solph.Flow(variable_costs=10)}
)

pp1 = solph.components.Source(
    label="plant_min_down_constraints",
    outputs={
        bel: solph.Flow(
            nominal_value=10,
            min=0.5,
            max=1.0,
            variable_costs=10,
            nonconvex=solph.NonConvex(minimum_downtime=4, initial_status=0),
        )
    },
)

pp2 = solph.components.Source(
    label="plant_min_up_constraints",
    outputs={
        bel: solph.Flow(
            nominal_value=10,
            min=0.5,
            max=1.0,
            variable_costs=10,
            nonconvex=solph.NonConvex(minimum_uptime=2, initial_status=1),
        )
    },
)

# 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 data
data = solph.views.node(results, "bel")["sequences"]
data[[(("bel", "demand_el"), "flow"), (("bel", "dummy_el"), "flow")]] *= -1
exclude = ["dummy_el", "status"]
columns = [
    c for c in data.columns if not any(s in c[0] or s in c[1] for s in exclude)
]
data = data[columns]
fig, ax = plt.subplots(figsize=(10, 5))
data.plot(ax=ax, kind="line", drawstyle="steps-post", grid=True, rot=0)
ax.set_xlabel("Hour")
ax.set_ylabel("P [MW]")
plt.legend(loc="upper center", bbox_to_anchor=(0.5, 1.3), ncol=1)
fig.subplots_adjust(top=0.8)
plt.show()