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EOS/tests/test_class_optimize.py
Normann dd3363b714 test for class_optimize in PR #88 
Test meant for class_optimize.
Will only work with PR #88 since the old class has no way to use a fixed random seed.
2024-10-05 23:54:05 +02:00

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26 KiB
Python
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import numpy as np
import pytest
from modules.class_optimize import optimization_problem
# Sample known result (replace with the actual expected output)
EXPECTED_RESULT = {
"discharge_hours_bin": [
1,
1,
1,
0,
1,
1,
0,
1,
1,
1,
0,
1,
1,
1,
1,
1,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
],
"eautocharge_hours_float": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.0,
0.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.0,
0.0,
],
"result": {
"Last_Wh_pro_Stunde": [
None,
1063.91,
1320.56,
1132.03,
1163.67,
1176.82,
1216.22,
1103.78,
1129.12,
1178.71,
1050.98,
988.56,
912.38,
704.61,
516.37,
868.05,
694.34,
608.79,
556.31,
488.89,
506.91,
804.89,
1141.98,
1056.97,
992.46,
1155.99,
827.01,
1257.98,
1232.67,
871.26,
860.88,
1158.03,
1222.72,
1221.04,
949.99,
987.01,
733.99,
592.97,
],
"Netzeinspeisung_Wh_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
2924.2707438016537,
2753.66,
1914.18,
813.95,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1311.3858057851144,
497.68000000000006,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Netzbezug_Wh_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Kosten_Euro_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"akku_soc_pro_stunde": [
None,
79.91107093663912,
78.99070247933885,
79.08956914600552,
95.27340247933884,
100.0,
100.0,
100.0,
100.0,
99.26162190082644,
96.11376549586775,
91.89251893939392,
87.96526342975206,
84.93233471074379,
82.70966769972449,
78.97322658402202,
75.98450413223138,
73.36402376033054,
70.96943870523413,
68.86505681818178,
66.68310950413219,
63.24022899449031,
59.76919765840215,
58.25555268595038,
58.684419352617034,
60.18041935261703,
64.6149860192837,
65.19921935261704,
80.15195268595036,
92.42761935261704,
99.64985268595038,
100.0,
100.0,
98.89101239669421,
96.45174758953168,
92.20325413223141,
89.04386191460057,
86.4914772727273,
],
"Einnahmen_Euro_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.20469895206611574,
0.19275619999999996,
0.1339926,
0.0569765,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.091797006404958,
0.0348376,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Gesamtbilanz_Euro": np.float64(-0.7150588584710738),
"E-Auto_SoC_pro_Stunde": [
None,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
],
"Gesamteinnahmen_Euro": np.float64(0.7150588584710738),
"Gesamtkosten_Euro": np.float64(0.0),
"Verluste_Pro_Stunde": [
None,
2.817272727272737,
29.157272727272726,
3.5592000000000112,
582.6179999999995,
170.1575107438016,
0.0,
0.0,
0.0,
23.391818181818195,
99.72409090909093,
133.72909090909081,
124.41545454545451,
96.08318181818186,
70.41409090909087,
118.37045454545455,
94.68272727272722,
83.01681818181817,
75.86045454545456,
66.66681818181814,
69.12409090909085,
109.0704545454546,
109.96227272727276,
47.952272727272714,
15.439199999999985,
53.855999999999995,
159.6443999999999,
21.032399999999996,
538.2984000000001,
441.924,
260.0003999999999,
12.605303305786279,
0.0,
35.132727272727266,
77.27590909090907,
134.59227272727276,
100.08954545454549,
80.85954545454547,
],
"Gesamt_Verluste": np.float64(4041.523450413223),
"Haushaltsgeraet_wh_pro_stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
},
"eauto_obj": {
"kapazitaet_wh": 60000,
"start_soc_prozent": 54,
"soc_wh": 32400.000000000004,
"hours": 48,
"discharge_array": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
],
"charge_array": [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"lade_effizienz": 0.95,
"entlade_effizienz": 1.0,
"max_ladeleistung_w": 11040,
},
"start_solution": [
1,
1,
1,
0,
1,
1,
0,
1,
1,
1,
0,
1,
1,
1,
1,
1,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.0,
0.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.0,
0.0,
],
"spuelstart": None,
"simulation_data": {
"Last_Wh_pro_Stunde": [
None,
1063.91,
1320.56,
1132.03,
1163.67,
1176.82,
1216.22,
1103.78,
1129.12,
1178.71,
1050.98,
988.56,
912.38,
704.61,
516.37,
868.05,
694.34,
608.79,
556.31,
488.89,
506.91,
804.89,
1141.98,
1056.97,
992.46,
1155.99,
827.01,
1257.98,
1232.67,
871.26,
860.88,
1158.03,
1222.72,
1221.04,
949.99,
987.01,
733.99,
592.97,
],
"Netzeinspeisung_Wh_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
2924.2707438016537,
2753.66,
1914.18,
813.95,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1311.3858057851144,
497.68000000000006,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Netzbezug_Wh_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Kosten_Euro_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"akku_soc_pro_stunde": [
None,
79.91107093663912,
78.99070247933885,
79.08956914600552,
95.27340247933884,
100.0,
100.0,
100.0,
100.0,
99.26162190082644,
96.11376549586775,
91.89251893939392,
87.96526342975206,
84.93233471074379,
82.70966769972449,
78.97322658402202,
75.98450413223138,
73.36402376033054,
70.96943870523413,
68.86505681818178,
66.68310950413219,
63.24022899449031,
59.76919765840215,
58.25555268595038,
58.684419352617034,
60.18041935261703,
64.6149860192837,
65.19921935261704,
80.15195268595036,
92.42761935261704,
99.64985268595038,
100.0,
100.0,
98.89101239669421,
96.45174758953168,
92.20325413223141,
89.04386191460057,
86.4914772727273,
],
"Einnahmen_Euro_pro_Stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.20469895206611574,
0.19275619999999996,
0.1339926,
0.0569765,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.091797006404958,
0.0348376,
0.0,
0.0,
0.0,
0.0,
0.0,
],
"Gesamtbilanz_Euro": np.float64(-0.7150588584710738),
"E-Auto_SoC_pro_Stunde": [
None,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
54.0,
],
"Gesamteinnahmen_Euro": np.float64(0.7150588584710738),
"Gesamtkosten_Euro": np.float64(0.0),
"Verluste_Pro_Stunde": [
None,
2.817272727272737,
29.157272727272726,
3.5592000000000112,
582.6179999999995,
170.1575107438016,
0.0,
0.0,
0.0,
23.391818181818195,
99.72409090909093,
133.72909090909081,
124.41545454545451,
96.08318181818186,
70.41409090909087,
118.37045454545455,
94.68272727272722,
83.01681818181817,
75.86045454545456,
66.66681818181814,
69.12409090909085,
109.0704545454546,
109.96227272727276,
47.952272727272714,
15.439199999999985,
53.855999999999995,
159.6443999999999,
21.032399999999996,
538.2984000000001,
441.924,
260.0003999999999,
12.605303305786279,
0.0,
35.132727272727266,
77.27590909090907,
134.59227272727276,
100.08954545454549,
80.85954545454547,
],
"Gesamt_Verluste": np.float64(4041.523450413223),
"Haushaltsgeraet_wh_pro_stunde": [
None,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
},
}
@pytest.fixture
def setup_opt_class():
# Initialize the optimization_problem class with parameters
start_hour = 10
# PV Forecast (in W)
pv_forecast = [
0,
0,
0,
0,
0,
0,
0,
8.05,
352.91,
728.51,
930.28,
1043.25,
1106.74,
1161.69,
6018.82,
5519.07,
3969.88,
3017.96,
1943.07,
1007.17,
319.67,
7.88,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5.04,
335.59,
705.32,
1121.12,
1604.79,
2157.38,
1433.25,
5718.49,
4553.96,
3027.55,
2574.46,
1720.4,
963.4,
383.3,
0,
0,
0,
]
# Temperature Forecast (in degree C)
temperature_forecast = [
18.3,
17.8,
16.9,
16.2,
15.6,
15.1,
14.6,
14.2,
14.3,
14.8,
15.7,
16.7,
17.4,
18.0,
18.6,
19.2,
19.1,
18.7,
18.5,
17.7,
16.2,
14.6,
13.6,
13.0,
12.6,
12.2,
11.7,
11.6,
11.3,
11.0,
10.7,
10.2,
11.4,
14.4,
16.4,
18.3,
19.5,
20.7,
21.9,
22.7,
23.1,
23.1,
22.8,
21.8,
20.2,
19.1,
18.0,
17.4,
]
# Electricity Price (in Euro per Wh)
strompreis_euro_pro_wh = [
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
]
# Overall System Load (in W)
gesamtlast = [
676.71,
876.19,
527.13,
468.88,
531.38,
517.95,
483.15,
472.28,
1011.68,
995.00,
1053.07,
1063.91,
1320.56,
1132.03,
1163.67,
1176.82,
1216.22,
1103.78,
1129.12,
1178.71,
1050.98,
988.56,
912.38,
704.61,
516.37,
868.05,
694.34,
608.79,
556.31,
488.89,
506.91,
804.89,
1141.98,
1056.97,
992.46,
1155.99,
827.01,
1257.98,
1232.67,
871.26,
860.88,
1158.03,
1222.72,
1221.04,
949.99,
987.01,
733.99,
592.97,
]
# Start Solution (binary)
start_solution = [
1,
1,
1,
1,
0,
1,
0,
0,
1,
1,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
]
# Define parameters for the optimization problem
parameter = {
"preis_euro_pro_wh_akku": 10e-05, # Cost of storing energy in battery (per Wh)
"pv_soc": 80, # Initial state of charge (SOC) of PV battery (%)
"pv_akku_cap": 26400, # Battery capacity (in Wh)
"year_energy": 4100000, # Yearly energy consumption (in Wh)
"einspeiseverguetung_euro_pro_wh": 7e-05, # Feed-in tariff for exporting electricity (per Wh)
"max_heizleistung": 1000, # Maximum heating power (in W)
"gesamtlast": gesamtlast, # Overall load on the system
"pv_forecast": pv_forecast, # PV generation forecast (48 hours)
"temperature_forecast": temperature_forecast, # Temperature forecast (48 hours)
"strompreis_euro_pro_wh": strompreis_euro_pro_wh, # Electricity price forecast (48 hours)
"eauto_min_soc": 0, # Minimum SOC for electric car
"eauto_cap": 60000, # Electric car battery capacity (Wh)
"eauto_charge_efficiency": 0.95, # Charging efficiency of the electric car
"eauto_charge_power": 11040, # Charging power of the electric car (W)
"eauto_soc": 54, # Current SOC of the electric car (%)
"pvpowernow": 211.137503624, # Current PV power generation (W)
"start_solution": start_solution, # Initial solution for the optimization
"haushaltsgeraet_wh": 937, # Household appliance consumption (Wh)
"haushaltsgeraet_dauer": 0, # Duration of appliance usage (hours)
}
# Create an instance of the optimization problem class
opt_class = optimization_problem(
prediction_hours=48, strafe=10, optimization_hours=24, fixed_seed=42
)
yield (
opt_class,
parameter,
start_hour,
) # Yield the class and parameters for use in tests
def test_optimierung_ems(setup_opt_class):
opt_class, parameter, start_hour = setup_opt_class
# Call the optimization function
ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
# Compare the result with the known expected result
assert (
ergebnis == EXPECTED_RESULT
) # Use appropriate comparison based on the structure of ergebnis
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