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Add a fixed electricity prediction that supports prices per time window.
The time windows may flexible be defined by day or date.
The prediction documentation is updated to also cover the ElecPriceFixed
provider.
The feature includes several changes that are not directly related to the
electricity price prediction implementation but are necessary to keep
EOS running properly and to test and document the changes.
* feat: add value time windows
Add time windows with an associated float value.
* feat: harden eos measurements endpoints error detection and reporting
Cover more errors that may be raised during endpoint access. Report the
errors including trace information to ease debugging.
* feat: extend server configuration to cover all arguments
Make the argument controlled options also available in server configuration.
* fix: eos config configuration by cli arguments
Move the command line argument handling to config eos so that it is
excuted whenever eos config is rebuild or reset.
* chore: extend measurement endpoint system test
* chore: refactor time windows
Move time windows to configabc as they are only used in configurations.
Also move all tests to test_configabc.
* chore: provide config update errors in eosdash with summarized error text
If there is an update error provide the error text as a summary. On click
provide the full error text.
* chore: force eosdash ip address and port in makefile dev run
Ensure eosdash ip address and port are correctly set for development runs.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
377 lines
10 KiB
Python
377 lines
10 KiB
Python
import numpy as np
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import pytest
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from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
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from akkudoktoreos.devices.genetic.battery import Battery
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from akkudoktoreos.devices.genetic.homeappliance import HomeAppliance
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from akkudoktoreos.devices.genetic.inverter import Inverter
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from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
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from akkudoktoreos.optimization.genetic.geneticdevices import (
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ElectricVehicleParameters,
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HomeApplianceParameters,
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InverterParameters,
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SolarPanelBatteryParameters,
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)
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticEnergyManagementParameters,
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GeneticOptimizationParameters,
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)
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from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSimulationResult
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from akkudoktoreos.utils.datetimeutil import to_duration, to_time
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start_hour = 1
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# Example initialization of necessary components
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@pytest.fixture
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def genetic_simulation(config_eos) -> GeneticSimulation:
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"""Fixture to create an EnergyManagement instance with given test parameters."""
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# Assure configuration holds the correct values
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config_eos.merge_settings_from_dict(
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{"prediction": {"hours": 48}, "optimization": {"hours": 24}}
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)
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assert config_eos.prediction.hours == 48
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assert config_eos.optimization.horizon_hours == 24
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# Initialize the battery and the inverter
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akku = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=5000,
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initial_soc_percentage=80,
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min_soc_percentage=10,
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),
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prediction_hours = config_eos.prediction.hours,
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)
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akku.reset()
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inverter = Inverter(
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InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
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battery = akku,
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)
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# Household device (currently not used, set to None)
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home_appliance = HomeAppliance(
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HomeApplianceParameters(
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device_id="dishwasher1",
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consumption_wh=2000,
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duration_h=2,
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time_windows=None,
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),
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optimization_hours = config_eos.optimization.horizon_hours,
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prediction_hours = config_eos.prediction.hours,
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)
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# Example initialization of electric car battery
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eauto = Battery(
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ElectricVehicleParameters(
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device_id="ev1", capacity_wh=26400, initial_soc_percentage=10, min_soc_percentage=10
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),
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prediction_hours = config_eos.prediction.hours,
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)
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eauto.set_charge_per_hour(np.full(config_eos.prediction.hours, 1))
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# Parameters based on previous example data
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pv_prognose_wh = [
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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8.05,
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352.91,
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728.51,
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930.28,
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1043.25,
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1106.74,
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1161.69,
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6018.82,
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5519.07,
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3969.88,
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3017.96,
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1943.07,
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1007.17,
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319.67,
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7.88,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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5.04,
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335.59,
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705.32,
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1121.12,
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1604.79,
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2157.38,
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1433.25,
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5718.49,
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4553.96,
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3027.55,
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2574.46,
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1720.4,
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963.4,
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383.3,
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0,
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0,
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0,
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]
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strompreis_euro_pro_wh = [
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0.0003384,
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0.0003318,
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0.0003284,
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0.0003283,
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0.0003289,
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0.0003334,
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0.0003290,
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0.0003302,
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0.0003042,
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0.0002430,
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0.0002280,
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0.0002212,
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0.0002093,
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0.0001879,
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0.0001838,
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0.0002004,
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0.0002198,
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0.0002270,
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0.0002997,
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0.0003195,
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0.0003081,
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0.0002969,
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0.0002921,
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0.0002780,
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0.0003384,
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0.0003318,
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0.0003284,
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0.0003283,
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0.0003289,
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0.0003334,
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0.0003290,
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0.0003302,
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0.0003042,
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0.0002430,
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0.0002280,
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0.0002212,
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0.0002093,
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0.0001879,
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0.0001838,
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0.0002004,
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0.0002198,
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0.0002270,
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0.0002997,
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0.0003195,
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0.0003081,
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0.0002969,
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0.0002921,
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0.0002780,
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]
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einspeiseverguetung_euro_pro_wh = 0.00007
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preis_euro_pro_wh_akku = 0.0001
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gesamtlast = [
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676.71,
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876.19,
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527.13,
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468.88,
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531.38,
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517.95,
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483.15,
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472.28,
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1011.68,
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995.00,
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1053.07,
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1063.91,
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1320.56,
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1132.03,
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1163.67,
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1176.82,
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1216.22,
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1103.78,
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1129.12,
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1178.71,
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1050.98,
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988.56,
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912.38,
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704.61,
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516.37,
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868.05,
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694.34,
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608.79,
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556.31,
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488.89,
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506.91,
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804.89,
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1141.98,
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1056.97,
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992.46,
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1155.99,
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827.01,
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1257.98,
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1232.67,
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871.26,
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860.88,
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1158.03,
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1222.72,
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1221.04,
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949.99,
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987.01,
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733.99,
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592.97,
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]
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# Initialize the energy management system with the respective parameters
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters(
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pv_prognose_wh=pv_prognose_wh,
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strompreis_euro_pro_wh=strompreis_euro_pro_wh,
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einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh,
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preis_euro_pro_wh_akku=preis_euro_pro_wh_akku,
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gesamtlast=gesamtlast,
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),
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optimization_hours = config_eos.optimization.horizon_hours,
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prediction_hours = config_eos.prediction.hours,
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inverter=inverter,
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ev=eauto,
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home_appliance=home_appliance,
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)
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# Init for test
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assert simulation.ac_charge_hours is not None
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assert simulation.dc_charge_hours is not None
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assert simulation.bat_discharge_hours is not None
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assert simulation.ev_charge_hours is not None
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simulation.ac_charge_hours[start_hour] = 1.0
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simulation.dc_charge_hours[start_hour] = 1.0
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simulation.bat_discharge_hours[start_hour] = 1.0
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simulation.ev_charge_hours[start_hour] = 1.0
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simulation.home_appliance_start_hour = 2
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return simulation
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def test_simulation(genetic_simulation):
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"""Test the EnergyManagement simulation method."""
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simulation = genetic_simulation
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# Simulate starting from hour 1 (this value can be adjusted)
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result = simulation.simulate(start_hour=start_hour)
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# visualisiere_ergebnisse(
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# simulation.gesamtlast,
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# simulation.pv_prognose_wh,
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# simulation.strompreis_euro_pro_wh,
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# result,
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# simulation.akku.discharge_array+simulation.akku.charge_array,
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# None,
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# simulation.pv_prognose_wh,
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# start_hour,
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# 48,
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# np.full(48, 0.0),
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# filename="visualization_results.pdf",
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# extra_data=None,
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# )
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# Assertions to validate results
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assert result is not None, "Result should not be None"
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assert isinstance(result, dict), "Result should be a dictionary"
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assert "Last_Wh_pro_Stunde" in result, "Result should contain 'Last_Wh_pro_Stunde'"
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"""
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Check the result of the simulation based on expected values.
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"""
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# Example result returned from the simulation (used for assertions)
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assert result is not None, "Result should not be None."
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# Check that the result is a dictionary
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assert isinstance(result, dict), "Result should be a dictionary."
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assert GeneticSimulationResult(**result) is not None
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# Check the length of the main arrays
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assert len(result["Last_Wh_pro_Stunde"]) == 47, (
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"The length of 'Last_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Netzeinspeisung_Wh_pro_Stunde"]) == 47, (
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"The length of 'Netzeinspeisung_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Netzbezug_Wh_pro_Stunde"]) == 47, (
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"The length of 'Netzbezug_Wh_pro_Stunde' should be 48."
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)
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assert len(result["Kosten_Euro_pro_Stunde"]) == 47, (
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"The length of 'Kosten_Euro_pro_Stunde' should be 48."
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)
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assert len(result["akku_soc_pro_stunde"]) == 47, (
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"The length of 'akku_soc_pro_stunde' should be 48."
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)
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# Verify specific values in the 'Last_Wh_pro_Stunde' array
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assert result["Last_Wh_pro_Stunde"][1] == 1527.13, (
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"The value at index 1 of 'Last_Wh_pro_Stunde' should be 1527.13."
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)
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assert result["Last_Wh_pro_Stunde"][2] == 1468.88, (
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"The value at index 2 of 'Last_Wh_pro_Stunde' should be 1468.88."
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)
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assert result["Last_Wh_pro_Stunde"][12] == 1132.03, (
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"The value at index 12 of 'Last_Wh_pro_Stunde' should be 1132.03."
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)
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# Verify that the value at index 0 is 'None'
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# Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent
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assert result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13, (
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"The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
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)
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# Verify the total balance
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assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, (
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"Total balance should be 6.612835813556755."
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)
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# Check total revenue and total costs
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assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
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"Total revenue should be 1.964301131937134."
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)
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assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, (
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"Total costs should be 8.577136945493889 ."
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)
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# Check the losses
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assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
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"Total losses should be 1620.0 ."
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)
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# Check the values in 'akku_soc_pro_stunde'
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assert result["akku_soc_pro_stunde"][-1] == 98.0, (
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"The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
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)
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assert result["akku_soc_pro_stunde"][1] == 98.0, (
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"The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
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)
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# Check home appliances
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assert sum(simulation.home_appliance.get_load_curve()) == 2000, (
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"The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
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)
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assert (
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np.nansum(
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np.where(
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result["Home_appliance_wh_per_hour"] is None,
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np.nan,
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np.array(result["Home_appliance_wh_per_hour"]),
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)
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)
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== 2000
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), "The sum of 'Home_appliance_wh_per_hour' should be 2000."
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print("All tests passed successfully.")
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