test_generic_caes.test_gen_caes() - Code Metrics - Inspection of "Merge pull request #1208 from oemof/revision/clean..." - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — dev ( 68ddc7...49e927 )

by Patrik

created 2025-08-20 14:53 UTC

test_generic_caes.test_gen_caes() B

↳ Parent: test_generic_caes

Complexity

Conditions

Size

Total Lines	113
Code Lines	79

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	79
dl	0
loc	113
rs	7.6872
c	0
b	0
f	0
cc	2
nop	0

How to fix Long Method

# -*- coding: utf-8 -*-
"""
Example that illustrates how to use component `GenericCHP` can be used.

In this case it is used to model a combined cycle extraction turbine.

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_generic_caes/test_generic_caes.py

SPDX-License-Identifier: MIT
"""

import os

import pandas as pd
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 Sink
from oemof.solph.components import Source
from oemof.solph.components.experimental import GenericCAES
from oemof.solph.flows import Flow


def test_gen_caes():
    # read sequence data
    full_filename = os.path.join(os.path.dirname(__file__), "generic_caes.csv")
    data = pd.read_csv(full_filename)

    # select periods
    periods = len(data)

    # create an energy system
    idx = pd.date_range("1/1/2017", periods=periods, freq="h")
    es = EnergySystem(timeindex=idx, infer_last_interval=True)

    # resources
    bgas = Bus(label="bgas")
    es.add(bgas)

    es.add(Source(label="rgas", outputs={bgas: Flow(variable_costs=20)}))

    # power
    bel_source = Bus(label="bel_source")
    es.add(bel_source)
    es.add(
        Source(
            label="source_el",
            outputs={bel_source: Flow(variable_costs=data["price_el_source"])},
        )
    )

    bel_sink = Bus(label="bel_sink")
    es.add(bel_sink)
    es.add(
        Sink(
            label="sink_el",
            inputs={bel_sink: Flow(variable_costs=data["price_el_sink"])},
        )
    )

    # dictionary with parameters for a specific CAES plant
    # based on thermal modelling and linearization techniques
    concept = {
        "cav_e_in_b": 0,
        "cav_e_in_m": 0.6457267578,
        "cav_e_out_b": 0,
        "cav_e_out_m": 0.3739636077,
        "cav_eta_temp": 1.0,
        "cav_level_max": 211.11,
        "cmp_p_max_b": 86.0918959849,
        "cmp_p_max_m": 0.0679999932,
        "cmp_p_min": 1,
        "cmp_q_out_b": -19.3996965679,
        "cmp_q_out_m": 1.1066036114,
        "cmp_q_tes_share": 0,
        "exp_p_max_b": 46.1294016678,
        "exp_p_max_m": 0.2528340303,
        "exp_p_min": 1,
        "exp_q_in_b": -2.2073411014,
        "exp_q_in_m": 1.129249765,
        "exp_q_tes_share": 0,
        "tes_eta_temp": 1.0,
        "tes_level_max": 0.0,
    }

    # generic compressed air energy storage (caes) plant
    es.add(
        GenericCAES(
            label="caes",
            electrical_input={bel_source: Flow()},
            fuel_input={bgas: Flow()},
            electrical_output={bel_sink: Flow()},
            params=concept,
        )
    )

    # create an optimization problem and solve it
    om = Model(es)

    # solve model
    om.solve(solver="cbc")

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

    data = (
        views.node(results, "caes", keep_none_type=True)["sequences"]
        .sum(axis=0)
        .to_dict()
    )

    test_dict = {
        (("caes", None), "cav_level"): 25658.82964382,
        (("caes", None), "exp_p"): 5020.801997000007,
        (("caes", None), "exp_q_fuel_in"): 5170.880360999999,
        (("caes", None), "tes_e_out"): 0.0,
        (("caes", None), "exp_st"): 226.0,
        (("bgas", "caes"), "flow"): 5170.880360999999,
        (("caes", None), "cav_e_out"): 1877.5972265299995,
        (("caes", None), "exp_p_max"): 17512.352336,
        (("caes", None), "cmp_q_waste"): 2499.9125993000007,
        (("caes", None), "cmp_p"): 2907.7271520000004,
        (("caes", None), "exp_q_add_in"): 0.0,
        (("caes", None), "cmp_st"): 37.0,
        (("caes", None), "cmp_q_out_sum"): 2499.9125993000007,
        (("caes", None), "tes_level"): 0.0,
        (("caes", None), "tes_e_in"): 0.0,
        (("caes", None), "exp_q_in_sum"): 5170.880360999999,
        (("caes", None), "cmp_p_max"): 22320.76334300001,
        (("caes", "bel_sink"), "flow"): 5020.801997000007,
        (("bel_source", "caes"), "flow"): 2907.7271520000004,
        (("caes", None), "cav_e_in"): 1877.597226,
    }

    for key in test_dict.keys():
        assert data[key] == pytest.approx(test_dict[key])


1			# -- coding: utf-8 --
2			"""
3			Example that illustrates how to use component `GenericCHP` can be used.
4
5			In this case it is used to model a combined cycle extraction turbine.
6
7			This file is part of project oemof (github.com/oemof/oemof). It's copyrighted
8			by the contributors recorded in the version control history of the file,
9			available from its original location
10			oemof/tests/test_scripts/test_solph/test_generic_caes/test_generic_caes.py
11
12			SPDX-License-Identifier: MIT
13			"""
14
15			import os
16
17			import pandas as pd
18			import pytest
19
20			from oemof.solph import EnergySystem
21			from oemof.solph import Model
22			from oemof.solph import processing
23			from oemof.solph import views
24			from oemof.solph.buses import Bus
25			from oemof.solph.components import Sink
26			from oemof.solph.components import Source
27			from oemof.solph.components.experimental import GenericCAES
28			from oemof.solph.flows import Flow
29
30
31			def test_gen_caes():
32			# read sequence data
33			full_filename = os.path.join(os.path.dirname(__file__), "generic_caes.csv")
34			data = pd.read_csv(full_filename)
35
36			# select periods
37			periods = len(data)
38
39			# create an energy system
40			idx = pd.date_range("1/1/2017", periods=periods, freq="h")
41			es = EnergySystem(timeindex=idx, infer_last_interval=True)
42
43			# resources
44			bgas = Bus(label="bgas")
45			es.add(bgas)
46
47			es.add(Source(label="rgas", outputs={bgas: Flow(variable_costs=20)}))
48
49			# power
50			bel_source = Bus(label="bel_source")
51			es.add(bel_source)
52			es.add(
53			Source(
54			label="source_el",
55			outputs={bel_source: Flow(variable_costs=data["price_el_source"])},
56			)
57			)
58
59			bel_sink = Bus(label="bel_sink")
60			es.add(bel_sink)
61			es.add(
62			Sink(
63			label="sink_el",
64			inputs={bel_sink: Flow(variable_costs=data["price_el_sink"])},
65			)
66			)
67
68			# dictionary with parameters for a specific CAES plant
69			# based on thermal modelling and linearization techniques
70			concept = {
71			"cav_e_in_b": 0,
72			"cav_e_in_m": 0.6457267578,
73			"cav_e_out_b": 0,
74			"cav_e_out_m": 0.3739636077,
75			"cav_eta_temp": 1.0,
76			"cav_level_max": 211.11,
77			"cmp_p_max_b": 86.0918959849,
78			"cmp_p_max_m": 0.0679999932,
79			"cmp_p_min": 1,
80			"cmp_q_out_b": -19.3996965679,
81			"cmp_q_out_m": 1.1066036114,
82			"cmp_q_tes_share": 0,
83			"exp_p_max_b": 46.1294016678,
84			"exp_p_max_m": 0.2528340303,
85			"exp_p_min": 1,
86			"exp_q_in_b": -2.2073411014,
87			"exp_q_in_m": 1.129249765,
88			"exp_q_tes_share": 0,
89			"tes_eta_temp": 1.0,
90			"tes_level_max": 0.0,
91			}
92
93			# generic compressed air energy storage (caes) plant
94			es.add(
95			GenericCAES(
96			label="caes",
97			electrical_input={bel_source: Flow()},
98			fuel_input={bgas: Flow()},
99			electrical_output={bel_sink: Flow()},
100			params=concept,
101			)
102			)
103
104			# create an optimization problem and solve it
105			om = Model(es)
106
107			# solve model
108			om.solve(solver="cbc")
109
110			# create result object
111			results = processing.results(om)
112
113			data = (
114			views.node(results, "caes", keep_none_type=True)["sequences"]
115			.sum(axis=0)
116			.to_dict()
117			)
118
119			test_dict = {
120			(("caes", None), "cav_level"): 25658.82964382,
121			(("caes", None), "exp_p"): 5020.801997000007,
122			(("caes", None), "exp_q_fuel_in"): 5170.880360999999,
123			(("caes", None), "tes_e_out"): 0.0,
124			(("caes", None), "exp_st"): 226.0,
125			(("bgas", "caes"), "flow"): 5170.880360999999,
126			(("caes", None), "cav_e_out"): 1877.5972265299995,
127			(("caes", None), "exp_p_max"): 17512.352336,
128			(("caes", None), "cmp_q_waste"): 2499.9125993000007,
129			(("caes", None), "cmp_p"): 2907.7271520000004,
130			(("caes", None), "exp_q_add_in"): 0.0,
131			(("caes", None), "cmp_st"): 37.0,
132			(("caes", None), "cmp_q_out_sum"): 2499.9125993000007,
133			(("caes", None), "tes_level"): 0.0,
134			(("caes", None), "tes_e_in"): 0.0,
135			(("caes", None), "exp_q_in_sum"): 5170.880360999999,
136			(("caes", None), "cmp_p_max"): 22320.76334300001,
137			(("caes", "bel_sink"), "flow"): 5020.801997000007,
138			(("bel_source", "caes"), "flow"): 2907.7271520000004,
139			(("caes", None), "cav_e_in"): 1877.597226,
140			}
141
142			for key in test_dict.keys():
143			assert data[key] == pytest.approx(test_dict[key])
144

oemof / oemof-solph

Push — dev ( 68ddc7...49e927 )

test_generic_caes.test_gen_caes() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

Duplication Side-by-Side

Filter issues like