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Self consumption predictor

Browse Source 
* Inverter: Self consumption interpolator for better discharge_hour results
 * Small penalty when EV 100% and charge >0
 * Price Forceast (use mean of last 7 days instead of repeat)
 * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
This commit is contained in:
Andreas
2024-12-19 14:45:20 +01:00
committed by Dominique Lasserre
parent 1c75060d8a
commit 410a23e375
15 changed files with 1243 additions and 820 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
tests/test_class_ems.py
View File

@@ -1,3 +1,5 @@
from pathlib import Path
import numpy as np
import pytest
@@ -15,6 +17,9 @@ from akkudoktoreos.devices.battery import (
)
from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
from akkudoktoreos.devices.inverter import Inverter, InverterParameters
from akkudoktoreos.prediction.self_consumption_probability import (
self_consumption_probability_interpolator,
)
start_hour = 1
@@ -35,8 +40,19 @@ def create_ems_instance() -> EnergieManagementSystem:
),
hours=config_eos.prediction_hours,
)
# 1h Load to Sub 1h Load Distribution -> SelfConsumptionRate
sc = self_consumption_probability_interpolator(
Path(__file__).parent.resolve()
/ ".."
/ "src"
/ "akkudoktoreos"
/ "data"
/ "regular_grid_interpolator.pkl"
)
akku.reset()
inverter = Inverter(InverterParameters(max_power_wh=10000), akku)
inverter = Inverter(sc, InverterParameters(max_power_wh=10000), akku)
# Household device (currently not used, set to None)
home_appliance = HomeAppliance(
@@ -306,21 +322,21 @@ def test_simulation(create_ems_instance):
# Verify the total balance
assert (
abs(result["Gesamtbilanz_Euro"] - 1.7880374129090917) < 1e-5
), "Total balance should be 1.7880374129090917."
abs(result["Gesamtbilanz_Euro"] - 1.958185274567674) < 1e-5
), "Total balance should be 1.958185274567674."
# Check total revenue and total costs
assert (
abs(result["Gesamteinnahmen_Euro"] - 1.3169784090909087) < 1e-5
), "Total revenue should be 1.3169784090909087."
abs(result["Gesamteinnahmen_Euro"] - 1.168863124510214) < 1e-5
), "Total revenue should be 1.168863124510214."
assert (
abs(result["Gesamtkosten_Euro"] - 3.1050158220000004) < 1e-5
), "Total costs should be 3.1050158220000004 ."
abs(result["Gesamtkosten_Euro"] - 3.127048399077888) < 1e-5
), "Total costs should be 3.127048399077888 ."
# Check the losses
assert (
abs(result["Gesamt_Verluste"] - 2615.222727272727) < 1e-5
), "Total losses should be 2615.222727272727 ."
abs(result["Gesamt_Verluste"] - 2871.5330639359036) < 1e-5
), "Total losses should be 2871.5330639359036 ."
# Check the values in 'akku_soc_pro_stunde'
assert (

18
tests/test_class_ems_2.py
View File

@@ -1,3 +1,5 @@
from pathlib import Path
import numpy as np
import pytest
@@ -14,6 +16,9 @@ from akkudoktoreos.devices.battery import (
)
from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
from akkudoktoreos.devices.inverter import Inverter, InverterParameters
from akkudoktoreos.prediction.self_consumption_probability import (
self_consumption_probability_interpolator,
)
start_hour = 0
@@ -34,8 +39,19 @@ def create_ems_instance() -> EnergieManagementSystem:
),
hours=config_eos.prediction_hours,
)
# 1h Load to Sub 1h Load Distribution -> SelfConsumptionRate
sc = self_consumption_probability_interpolator(
Path(__file__).parent.resolve()
/ ".."
/ "src"
/ "akkudoktoreos"
/ "data"
/ "regular_grid_interpolator.pkl"
)
akku.reset()
inverter = Inverter(InverterParameters(max_power_wh=10000), akku)
inverter = Inverter(sc, InverterParameters(max_power_wh=10000), akku)
# Household device (currently not used, set to None)
home_appliance = HomeAppliance(

46
tests/test_inverter.py
View File

@@ -1,8 +1,12 @@
from pathlib import Path
from unittest.mock import Mock
import pytest
from akkudoktoreos.devices.inverter import Inverter, InverterParameters
from akkudoktoreos.prediction.self_consumption_probability import (
self_consumption_probability_interpolator,
)
@pytest.fixture
@@ -15,25 +19,38 @@ def mock_battery():
@pytest.fixture
def inverter(mock_battery):
return Inverter(InverterParameters(max_power_wh=500.0), akku=mock_battery)
sc = self_consumption_probability_interpolator(
Path(__file__).parent.resolve()
/ ".."
/ "src"
/ "akkudoktoreos"
/ "data"
/ "regular_grid_interpolator.pkl"
)
return Inverter(sc, InverterParameters(max_power_wh=500.0), akku=mock_battery)
def test_process_energy_excess_generation(inverter, mock_battery):
# Battery charges 100 Wh with 10 Wh loss
mock_battery.charge_energy.return_value = (100.0, 10.0)
generation = 600.0
consumption = 200.0
hour = 12
mock_battery.charge_energy.return_value = (100.0, 10.0)
grid_export, grid_import, losses, self_consumption = inverter.process_energy(
generation, consumption, hour
)
assert grid_export == pytest.approx(290.0, rel=1e-2) # 290 Wh feed-in after battery charges
assert grid_import == 0.0 # No grid draw
assert grid_export == pytest.approx(
286.1737223461208, rel=1e-2
) # 290 Wh feed-in after battery charges
assert grid_import == pytest.approx(3.826277653879151, rel=1e-2) # Little grid draw
assert losses == 10.0 # Battery charging losses
assert self_consumption == 200.0 # All consumption is met
mock_battery.charge_energy.assert_called_once_with(400.0, hour)
mock_battery.charge_energy.assert_called_once_with(
pytest.approx(396.1737223461208, rel=1e-2), hour
)
def test_process_energy_generation_equals_consumption(inverter, mock_battery):
@@ -50,7 +67,8 @@ def test_process_energy_generation_equals_consumption(inverter, mock_battery):
assert losses == 0.0 # No losses
assert self_consumption == 300.0 # All consumption is met with generation
mock_battery.charge_energy.assert_called_once_with(0.0, hour)
mock_battery.charge_energy.assert_not_called()
mock_battery.discharge_energy.assert_not_called()
def test_process_energy_battery_discharges(inverter, mock_battery):
@@ -103,12 +121,14 @@ def test_process_energy_battery_full_at_start(inverter, mock_battery):
)
assert grid_export == pytest.approx(
300.0, rel=1e-2
296.39026480502736, rel=1e-2
) # All excess energy should be fed into the grid
assert grid_import == 0.0 # No grid draw
assert grid_import == pytest.approx(3.609735194972663, rel=1e-2) # Almost no grid draw
assert losses == 0.0 # No losses
assert self_consumption == 200.0 # Only consumption is met
mock_battery.charge_energy.assert_called_once_with(300.0, hour)
mock_battery.charge_energy.assert_called_once_with(
pytest.approx(296.39026480502736, rel=1e-2), hour
)
def test_process_energy_insufficient_generation_no_battery(inverter, mock_battery):
@@ -185,10 +205,14 @@ def test_process_energy_zero_consumption(inverter, mock_battery):
)
assert grid_export == pytest.approx(390.0, rel=1e-2) # Excess energy after battery charges
assert grid_import == 0.0 # No grid draw as no consumption
assert grid_import == pytest.approx(
0.022862368543430378, rel=1e-2
) # Almost no grid draw as no consumption
assert losses == 10.0 # Charging losses
assert self_consumption == 0.0 # Zero consumption
mock_battery.charge_energy.assert_called_once_with(500.0, hour)
mock_battery.charge_energy.assert_called_once_with(
pytest.approx(499.97713763145657, rel=1e-2), hour
)
def test_process_energy_zero_generation_zero_consumption(inverter, mock_battery):

476
tests/testdata/optimize_result_1.json vendored
View File

@@ -1,52 +1,52 @@
{
"ac_charge": [
0.75,
0.75,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.75,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
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0.0,
0.0,
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0.0,
0.0,
0.0,
0.0,
0.0,
0.875,
0.0,
0.875,
0.0,
0.625,
0.75,
0.0,
0.375,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
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0.375,
0.0,
0.0,
0.625,
0.0,
0.0,
0.375,
0.75,
1.0,
0.0,
0.0
],
"dc_charge": [
@@ -102,35 +102,6 @@
"discharge_allowed": [
0,
0,
1,
1,
0,
1,
1,
1,
0,
1,
1,
1,
0,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
0,
1,
1,
0,
0,
1,
0,
@@ -138,8 +109,6 @@
0,
0,
0,
1,
0,
0,
0,
1,
@@ -147,7 +116,38 @@
0,
1,
1,
1
0,
0,
0,
0,
1,
1,
1,
0,
0,
1,
0,
1,
1,
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0,
1,
0,
1,
0,
1,
1,
0,
1,
0,
0,
0,
0,
1,
0
],
"eautocharge_hours_float": null,
"result": {
@@ -179,7 +179,7 @@
992.46,
1155.99,
827.01,
1257.98,
3132.98,
1232.67,
871.26,
860.88,
@@ -237,10 +237,10 @@
0.0,
0.0,
0.0,
0.212927386983471,
0.19275619999999996,
0.1339926,
0.0569765,
0.22334747537370364,
0.19016535126346096,
0.12880892587597292,
0.04260510586128589,
0.0,
0.0,
0.0,
@@ -260,21 +260,21 @@
0.0,
0.0,
0.0,
0.1512260012396691,
0.257789,
0.1516669,
0.09915009999999999,
0.0348376,
0.059176154400016855,
0.25751345302450973,
0.14923279815365575,
0.07926913850394514,
0.024174420063375994,
0.0,
0.0,
0.0,
0.0,
0.0
],
"Gesamt_Verluste": 2071.900103305785,
"Gesamtbilanz_Euro": 0.9349588927768602,
"Gesamteinnahmen_Euro": 1.2913222882231399,
"Gesamtkosten_Euro": 2.226281181,
"Gesamt_Verluste": 2500.030611233429,
"Gesamtbilanz_Euro": 1.4988843060949502,
"Gesamteinnahmen_Euro": 1.1542928225199272,
"Gesamtkosten_Euro": 2.6531771286148773,
"Home_appliance_wh_per_hour": [
null,
null,
@@ -316,84 +316,84 @@
null
],
"Kosten_Euro_pro_Stunde": [
0.0,
0.027996119999999992,
0.0,
0.04475252599999999,
0.0018232052307313393,
0.0,
0.0,
0.0,
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0.0,
0.0,
0.008135265032732555,
0.016809914659344942,
0.061530097476751734,
0.05480703000000003,
0.225316611,
0.291163892,
0.26650619799999997,
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0.19588158,
0.174739608,
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0.199865757,
0.22802125600000003,
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0.162995926,
0.16677339,
0.26411047,
0.0,
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0.028255713342252034,
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0.3629952558325975,
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0.04565364324294593,
0.08231598,
0.174597189,
0.293043269,
0.0,
0.0,
0.0
0.16484566
],
"Netzbezug_Wh_pro_Stunde": [
0.0,
122.78999999999996,
0.0,
213.81999999999994,
9.703061366318996,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
37.01212480770043,
74.05248748610107,
205.30563055305882,
171.54000000000008,
731.31,
980.68,
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0.0,
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516.37,
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127.73830624887898,
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152.3311419517715,
257.64,
566.69,
987.01,
0.0,
0.0,
0.0
592.97
],
"Netzeinspeisung_Wh_pro_Stunde": [
0.0,
@@ -401,10 +401,10 @@
0.0,
0.0,
0.0,
3041.819814049586,
2753.66,
1914.18,
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2716.6478751922996,
1840.127512513899,
608.6443694469413,
0.0,
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@@ -424,11 +424,11 @@
0.0,
0.0,
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2160.371446280987,
3682.7,
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1416.43,
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845.3736342859552,
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2131.8971164807967,
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345.3488580482285,
0.0,
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0.0,
@@ -436,51 +436,74 @@
0.0
],
"Verluste_Pro_Stunde": [
16.744090909090914,
0.0,
2.817272727272737,
0.0,
3.5592000000000112,
582.6179999999995,
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0.0
],
"akku_soc_pro_stunde": [
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79.38253271349862,
79.38253271349862,
79.48139938016529,
95.66523271349861,
80.0,
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79.91107093663912,
79.97759406541806,
96.1614273987495,
100.0,
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100.0,
100.0,
96.85214359504131,
92.63089703856748,
88.7036415289256,
88.7036415289256,
88.7036415289256,
84.96720041322313,
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@@ -489,31 +512,48 @@
100.0,
100.0,
100.0,
100.0,
100.0,
96.26355888429752,
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93.27483643250687,
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95.75150654269973,
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90.0397296831956
96.84060778236915,
96.84060778236915
],
"Electricity_price": [
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]
},
"eauto_obj": {
@@ -626,54 +666,54 @@
"initial_soc_percentage": 54
},
"start_solution": [
18.0,
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],
"washingstart": null
}

672
tests/testdata/optimize_result_2.json vendored
View File

@@ -1,47 +1,47 @@
{
"ac_charge": [
0.5,
0.0,
0.0,
0.0,
0.0,
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@@ -101,17 +101,15 @@
],
"discharge_allowed": [
0,
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1,
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0,
1,
1,
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472
tests/testdata/optimize_result_2_full.json vendored
View File

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