test_simple_dispatch_one.test_dispatch_one_time_step() - Code Metrics - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

test_dispatch_one_time_step() B
last analyzed 2025-09-17 12:43 UTC

↳ Parent: test_simple_dispatch_one

Complexity

Conditions

Size

Total Lines	95
Code Lines	52

Duplication

Lines	95
Ratio	100 %

Importance

Changes

Metric	Value
eloc	52
dl	95
loc	95
rs	8.5709
c	0
b	0
f	0
cc	2
nop	1

How to fix Long Method

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

"""This example shows how to create an energysystem with oemof objects and
solve it with the solph module.

This file is part of project oemof (github.com/oemof/oemof). It's copyrighted
by the contributors recorded in the version control history of the file,
available from its original location
oemof/tests/test_scripts/test_solph/test_simple_dispatch/test_simple_dispatch.py

SPDX-License-Identifier: MIT
"""

import pytest

from oemof.solph import EnergySystem
from oemof.solph import Model
from oemof.solph import processing
from oemof.solph import views
from oemof.solph.buses import Bus
from oemof.solph.components import Converter
from oemof.solph.components import Sink
from oemof.solph.components import Source
from oemof.solph.flows import Flow


def test_dispatch_one_time_step(solver="cbc"):

    """Create an energy system and optimize the dispatch at least costs."""

    # ######################### create energysystem components ################
    # resource buses
    bgas = Bus(label="gas", 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()})

    # sources
    wind = Source(
        label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
    )

    # demands (electricity/heat)
    demand_el = Sink(
        label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
    )

    demand_th = Sink(
        label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
    )

    # 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="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},
    )

    energysystem = EnergySystem(timeincrement=[1])
    energysystem.add(
        bgas,
        bel,
        bth,
        excess_el,
        wind,
        demand_el,
        demand_th,
        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 ################################
    data = views.node(processing.results(model=optimization_model), "b_el")

    # generate results to be evaluated in tests
    results = data["sequences"].sum(axis=0).to_dict()

    print("DateTimeIndex:", data["sequences"].index)

    test_results = {
        (("wind", "b_el"), "flow"): 33,
        (("b_el", "demand_elec"), "flow"): 26,
        (("b_el", "excess_el"), "flow"): 5,
        (("b_el", "heat_pump"), "flow"): 3,
    }

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


1		# -- coding: utf-8 --
2
3		"""This example shows how to create an energysystem with oemof objects and
4		solve it with the solph module.
5
6		This file is part of project oemof (github.com/oemof/oemof). It's copyrighted
7		by the contributors recorded in the version control history of the file,
8		available from its original location
9		oemof/tests/test_scripts/test_solph/test_simple_dispatch/test_simple_dispatch.py
10
11		SPDX-License-Identifier: MIT
12		"""
13
14		import pytest
15
16		from oemof.solph import EnergySystem
17		from oemof.solph import Model
18		from oemof.solph import processing
19		from oemof.solph import views
20		from oemof.solph.buses import Bus
21		from oemof.solph.components import Converter
22		from oemof.solph.components import Sink
23		from oemof.solph.components import Source
24		from oemof.solph.flows import Flow
25
26
27	View Code Duplication	def test_dispatch_one_time_step(solver="cbc"):
		0 ignored issues – show Duplication introduced 2022-02-08 13:51 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
28		"""Create an energy system and optimize the dispatch at least costs."""
29
30		# ######################### create energysystem components ################
31		# resource buses
32		bgas = Bus(label="gas", balanced=False)
33
34		# electricity and heat
35		bel = Bus(label="b_el")
36		bth = Bus(label="b_th")
37
38		# an excess and a shortage variable can help to avoid infeasible problems
39		excess_el = Sink(label="excess_el", inputs={bel: Flow()})
40
41		# sources
42		wind = Source(
43		label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
44		)
45
46		# demands (electricity/heat)
47		demand_el = Sink(
48		label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
49		)
50
51		demand_th = Sink(
52		label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
53		)
54
55		# combined heat and power plant (chp)
56		pp_chp = Converter(
57		label="pp_chp",
58		inputs={bgas: Flow()},
59		outputs={
60		bel: Flow(nominal_capacity=30, variable_costs=42),
61		bth: Flow(nominal_capacity=40),
62		},
63		conversion_factors={bel: 0.3, bth: 0.4},
64		)
65
66		# heatpump with a coefficient of performance (COP) of 3
67		b_heat_source = Bus(label="b_heat_source")
68
69		heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})
70
71		cop = 3
72		heat_pump = Converter(
73		label="heat_pump",
74		inputs={bel: Flow(), b_heat_source: Flow()},
75		outputs={bth: Flow(nominal_capacity=10)},
76		conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
77		)
78
79		energysystem = EnergySystem(timeincrement=[1])
80		energysystem.add(
81		bgas,
82		bel,
83		bth,
84		excess_el,
85		wind,
86		demand_el,
87		demand_th,
88		pp_chp,
89		b_heat_source,
90		heat_source,
91		heat_pump,
92		)
93
94		# ################################ optimization ###########################
95
96		# create optimization model based on energy_system
97		optimization_model = Model(energysystem=energysystem)
98
99		# solve problem
100		optimization_model.solve(solver=solver)
101
102		# write back results from optimization object to energysystem
103		optimization_model.results()
104
105		# ################################ results ################################
106		data = views.node(processing.results(model=optimization_model), "b_el")
107
108		# generate results to be evaluated in tests
109		results = data["sequences"].sum(axis=0).to_dict()
110
111		print("DateTimeIndex:", data["sequences"].index)
112
113		test_results = {
114		(("wind", "b_el"), "flow"): 33,
115		(("b_el", "demand_elec"), "flow"): 26,
116		(("b_el", "excess_el"), "flow"): 5,
117		(("b_el", "heat_pump"), "flow"): 3,
118		}
119
120		for key in test_results.keys():
121		assert results[key] == pytest.approx(test_results[key], abs=0.5)
122

oemof / oemof-solph

test_dispatch_one_time_step() B last analyzed 2025-09-17 12:43 UTC

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test_dispatch_one_time_step() B
last analyzed 2025-09-17 12:43 UTC