mirror of
https://github.com/Akkudoktor-EOS/EOS.git
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Andreas
a2f4ef6f54
The optimizer treated the end of `optimization.horizon_hours` as the end of the world: energy left in the battery there was worth a single configured price per kWh, so it either dumped the battery into the last hours or hoarded it, depending on that one number. The horizon is now two spans. `horizon_hours` still receives every control command. The new `optimization.tail_horizon_hours` (default 48 h) is a pure lookahead that never produces a command. In AUTO terminal-value mode a deterministic dynamic program solves that tail backwards on a 101-point SoC grid using the production battery and inverter models - SoC bounds, power caps, conversion losses, configured charge and export rates, direct-marketing permission and LCOS on delivered DC energy - and the existing AUTO proxy supplies the continuation value at the tail end. Genetic fitness reads the resulting curve. `tail_horizon_hours: 0` restores the plain proxy at the control end, FIXED is unchanged. The forecast budget is reported, never enforced by refusal: a tail that does not fit is shortened to what the forecast covers and reported as `effective_tail_hours`, and a control horizon that does not fit is warned about at configuration time and rejected by the optimizer at run time, which knows which series ran out. `prediction.hours` defaults to 72 so the new defaults fit out of the box; existing shorter configurations keep starting. Control arrays and warm-start genomes now begin at the run timestamp rather than midnight, flagged by `controls_start_at_now` so the adapters still read older solutions. `forecast_interval_seconds` declares the resolution of shortened native quarter-hour inputs. Required forecasts are no longer silently replaced by demo providers. A missing PV, price, load, feed-in or weather forecast used to rewrite the configured provider and retry, so a run could quietly optimize against invented data. Missing values now stay missing, and provider values are held only within their own source interval instead of being extended indefinitely. Also fixes a config update that could leave EOS half-updated: the merged candidate is validated before the singleton is reinitialized. Four provider tests that hard-coded the old 48 h prediction default are rewritten to derive their expectations from the configured horizon.
287 lines
9.2 KiB
Python
287 lines
9.2 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 = 0
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# Example initialization of necessary components
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@pytest.fixture
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def genetic_simulation_2(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": {"tail_horizon_hours": 0, "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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# Flexible consumer (fixed start at slot 2 for this deterministic test)
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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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home_appliance.build_load_curve([2])
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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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# Parameters based on previous example data
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pv_prognose_wh = [0.0] * config_eos.prediction.hours
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pv_prognose_wh[10] = 5000.0
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pv_prognose_wh[11] = 5000.0
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strompreis_euro_pro_wh = [0.001] * config_eos.prediction.hours
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strompreis_euro_pro_wh[0:10] = [0.00001] * 10
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strompreis_euro_pro_wh[11:15] = [0.00005] * 4
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strompreis_euro_pro_wh[20] = 0.00001
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einspeiseverguetung_euro_pro_wh = [0.00007] * len(strompreis_euro_pro_wh)
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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_appliances=[home_appliance],
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)
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ac = np.full(config_eos.prediction.hours, 0.0)
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ac[20] = 1
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simulation.ac_charge_hours = ac
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dc = np.full(config_eos.prediction.hours, 0.0)
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dc[11] = 1
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simulation.dc_charge_hours = dc
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return simulation
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def test_simulation(genetic_simulation_2):
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"""Test the EnergyManagement simulation method."""
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simulation = genetic_simulation_2
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# Simulate starting from hour 0 (this value can be adjusted)
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result = simulation.simulate(start_hour=start_hour)
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# --- Pls do not remove! ---
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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 GeneticSimulationResult(**result) is not None
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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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# Verify that the expected keys are present in the result
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expected_keys = [
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"Last_Wh_pro_Stunde",
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"Netzeinspeisung_Wh_pro_Stunde",
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"Netzbezug_Wh_pro_Stunde",
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"Kosten_Euro_pro_Stunde",
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"akku_soc_pro_stunde",
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"Einnahmen_Euro_pro_Stunde",
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"Gesamtbilanz_Euro",
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"EAuto_SoC_pro_Stunde",
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"Gesamteinnahmen_Euro",
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"Gesamtkosten_Euro",
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"Verluste_Pro_Stunde",
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"Gesamt_Verluste",
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"Home_appliance_wh_per_hour",
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]
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for key in expected_keys:
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assert key in result, f"The key '{key}' should be present in the result."
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# Check the length of the main arrays
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assert len(result["Last_Wh_pro_Stunde"]) == 48, (
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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"]) == 48, (
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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"]) == 48, (
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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"]) == 48, (
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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"]) == 48, (
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"The length of 'akku_soc_pro_stunde' should be 48."
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)
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# Verfify DC and AC Charge Bins
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assert abs(result["akku_soc_pro_stunde"][2] - 80.0) < 1e-5, (
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"'akku_soc_pro_stunde[2]' should be 80.0."
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)
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assert abs(result["akku_soc_pro_stunde"][10] - 80.0) < 1e-5, (
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"'akku_soc_pro_stunde[10]' should be 80."
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)
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assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][10] - 3946.93) < 1e-3, (
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"'Netzeinspeisung_Wh_pro_Stunde[11]' should be 3946.93."
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)
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assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 2799.7263636361786) < 1e-3, (
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"'Netzeinspeisung_Wh_pro_Stunde[11]' should be 2799.7263636361786."
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)
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assert abs(result["akku_soc_pro_stunde"][20] - 100) < 1e-5, (
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"'akku_soc_pro_stunde[20]' should be 100."
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)
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assert abs(result["Last_Wh_pro_Stunde"][20] - 1050.98) < 1e-3, (
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"'Last_Wh_pro_Stunde[20]' should be 1050.98."
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)
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print("All tests passed successfully.")
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def test_set_parameters(genetic_simulation_2):
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"""Test the set_parameters method of EnergyManagement."""
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simulation = genetic_simulation_2
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# Check if parameters are set correctly
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assert simulation.load_energy_array is not None, "load_energy_array should not be None"
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assert simulation.pv_prediction_wh is not None, "pv_prediction_wh should not be None"
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assert simulation.elect_price_hourly is not None, "elect_price_hourly should not be None"
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assert simulation.elect_revenue_per_hour_arr is not None, (
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"elect_revenue_per_hour_arr should not be None"
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)
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def test_reset(genetic_simulation_2):
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"""Test the reset method of EnergyManagement."""
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simulation = genetic_simulation_2
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simulation.reset()
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assert simulation.ev.current_soc_percentage() == simulation.ev.parameters.initial_soc_percentage, "EV SOC should be reset to initial value"
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assert simulation.battery.current_soc_percentage() == simulation.battery.parameters.initial_soc_percentage, (
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"Battery SOC should be reset to initial value"
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)
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