Storage level constraint¶
Constrain inflows and outflows to storage level¶
General description¶
Example that shows the storage_level_constraint.
Code¶
Download source code: storage_level_constraint.py
Click to display code
import pandas as pd
from oemof.solph import Bus
from oemof.solph import EnergySystem
from oemof.solph import Flow
from oemof.solph import Model
from oemof.solph.components import GenericStorage
from oemof.solph.components import Sink
from oemof.solph.components import Source
from oemof.solph.constraints import storage_level_constraint
from oemof.solph.processing import results
def main(optimize=True):
es = EnergySystem(
timeindex=pd.date_range("2022-01-01", freq="1H", periods=24),
infer_last_interval=True,
)
multiplexer = Bus(
label="multiplexer",
)
storage = GenericStorage(
label="storage",
nominal_capacity=3,
initial_storage_level=1,
balanced=True,
loss_rate=0.05,
inputs={multiplexer: Flow()},
outputs={multiplexer: Flow()},
)
es.add(multiplexer, storage)
in_0 = Source(
label="in_0",
outputs={multiplexer: Flow(nominal_capacity=0.5, variable_costs=0.15)},
)
es.add(in_0)
in_1 = Source(
label="in_1", outputs={multiplexer: Flow(nominal_capacity=0.1)}
)
es.add(in_1)
out_0 = Sink(
label="out_0",
inputs={multiplexer: Flow(nominal_capacity=0.25, variable_costs=-0.1)},
)
es.add(out_0)
out_1 = Sink(
label="out_1",
inputs={multiplexer: Flow(nominal_capacity=0.15, variable_costs=-0.1)},
)
es.add(out_1)
if optimize is False:
return es
model = Model(es)
storage_level_constraint(
model=model,
name="multiplexer",
storage_component=storage,
multiplexer_bus=multiplexer,
input_levels={in_1: 1 / 3}, # in_0 is always active
output_levels={out_0: 1 / 6, out_1: 1 / 2},
)
model.solve()
my_results = results(model)
df = pd.DataFrame(my_results[(storage, None)]["sequences"])
df["in1_status"] = my_results[(in_1, None)]["sequences"]
df["out1_status"] = my_results[(out_1, None)]["sequences"]
df["out0_status"] = my_results[(out_0, None)]["sequences"]
df["in1"] = my_results[(in_1, multiplexer)]["sequences"]
df["in0"] = my_results[(in_0, multiplexer)]["sequences"]
df["out0"] = my_results[(multiplexer, out_0)]["sequences"]
df["out1"] = my_results[(multiplexer, out_1)]["sequences"]
plt.step(df.index, df["in0"], where="post", label="inflow (<= 1)")
plt.step(df.index, df["in1"], where="post", label="inflow (< 1/3)")
plt.step(df.index, df["out0"], where="post", label="outflow (> 1/6)")
plt.step(df.index, df["out1"], where="post", label="outflow (> 1/2)")
plt.grid()
plt.legend()
plt.ylabel("Flow Power (arb. units)")
plt.ylim(0, 0.5)
plt.twinx()
plt.plot(df.index, df["storage_content"], "k--", label="storage content")
plt.ylim(0, 3)
plt.legend(loc="center right")
plt.ylabel("Stored Energy (arb. units)")
print(df)
plt.show()
if __name__ == "__main__":
main()
Installation requirements¶
This example requires oemof.solph (at least v0.5.0) and matplotlib, install by:
pip install oemof.solph>=0.5 matplotlib