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feat: add filename linter and AI coding guidelines (#1477)
Add a configurable filename linter to enforce naming conventions and an AGENTS.md to guide AI coding agents in following project conventions. The guidelines steer code generation; the linter independently verifies filename compliance. Together, they improve consistency and reduce unnecessary files and review effort. Conflicting file names were renamed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
@@ -0,0 +1,528 @@
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from unittest.mock import Mock
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import numpy as np
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import pytest
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from akkudoktoreos.devices.genetic.battery import (
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Battery,
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ElectricVehicleParameters,
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SolarPanelBatteryParameters,
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)
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from akkudoktoreos.devices.genetic.homeappliance import (
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HomeAppliance,
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HomeApplianceParameters,
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)
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from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
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from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticEnergyManagementParameters,
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)
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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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{
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"prediction": {"hours": 48},
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"optimization": {"hours": 24, "genetic": {"tail_horizon_hours": 0}},
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}
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)
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assert config_eos.prediction.hours == 48
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assert config_eos.optimization.genetic.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(
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device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id
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),
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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.genetic.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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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.model_validate(
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dict(
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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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),
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optimization_hours=config_eos.optimization.genetic.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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# 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.bat_grid_export_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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return simulation
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def test_ev_charging_uses_raw_input_energy_for_load_and_grid(config_eos):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 1},
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"optimization": {"genetic": {"tail_horizon_hours": 0, "horizon_hours": 1}},
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}
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)
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ev = Battery(
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ElectricVehicleParameters(
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device_id="ev1",
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capacity_wh=1000,
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charging_efficiency=0.8,
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max_charge_power_w=100,
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initial_soc_percentage=0,
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min_soc_percentage=0,
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),
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prediction_hours=1,
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)
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inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters.model_validate(
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dict(
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pv_prognose_wh=[0.0],
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strompreis_euro_pro_wh=[0.001],
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einspeiseverguetung_euro_pro_wh=[0.0],
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preis_euro_pro_wh_akku=0.0,
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gesamtlast=[0.0],
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)
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),
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optimization_hours=1,
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prediction_hours=1,
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inverter=inverter,
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ev=ev,
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)
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simulation.ev_charge_hours = np.array([1.0])
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result = simulation.simulate(start_hour=0)
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assert result["Last_Wh_pro_Stunde"][0] == pytest.approx(100.0)
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assert result["Netzbezug_Wh_pro_Stunde"][0] == pytest.approx(100.0)
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assert result["Kosten_Euro_pro_Stunde"][0] == pytest.approx(0.1)
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assert result["Verluste_Pro_Stunde"][0] == pytest.approx(20.0)
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assert ev.current_soc_percentage() == pytest.approx(8.0)
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def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 2},
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"optimization": {"genetic": {"tail_horizon_hours": 0, "horizon_hours": 2}},
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}
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)
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inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
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monkeypatch.setattr(
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inverter.self_consumption_predictor,
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"calculate_expected_direct_consumption",
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Mock(side_effect=min),
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)
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters.model_validate(
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dict(
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pv_prognose_wh=[500.0, 500.0],
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strompreis_euro_pro_wh=[-0.0001, -0.0001],
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einspeiseverguetung_euro_pro_wh=[-0.0001, -0.0001],
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preis_euro_pro_wh_akku=0.0,
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gesamtlast=[0.0, 0.0],
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)
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),
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optimization_hours=config_eos.optimization.genetic.horizon_hours,
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prediction_hours=config_eos.prediction.hours,
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inverter=inverter,
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direct_marketing_enabled=True,
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)
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result = simulation.simulate(start_hour=0)
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assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == 0.0
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assert result["Einnahmen_Euro_pro_Stunde"][0] == 0.0
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assert result["Verluste_Pro_Stunde"][0] == pytest.approx(500.0)
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def _direct_marketing_battery_export_simulation(
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config_eos,
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levelized_cost_of_storage_kwh: float = 0.0,
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dc_to_ac_efficiency: float = 1.0,
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) -> GeneticSimulation:
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 2},
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"optimization": {"genetic": {"tail_horizon_hours": 0, "horizon_hours": 2}},
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}
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)
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battery = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=1000,
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initial_soc_percentage=100,
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min_soc_percentage=0,
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charging_efficiency=1.0,
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discharging_efficiency=1.0,
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levelized_cost_of_storage_kwh=levelized_cost_of_storage_kwh,
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max_charge_power_w=500,
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),
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prediction_hours=config_eos.prediction.hours,
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)
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inverter = Inverter(
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InverterParameters(
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device_id="inverter1",
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max_power_wh=500.0,
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battery_id=battery.parameters.device_id,
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dc_to_ac_efficiency=dc_to_ac_efficiency,
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),
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battery=battery,
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)
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simulation = GeneticSimulation()
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simulation.prepare(
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GeneticEnergyManagementParameters.model_validate(
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dict(
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pv_prognose_wh=[0.0, 0.0],
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strompreis_euro_pro_wh=[0.0, 0.0],
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einspeiseverguetung_euro_pro_wh=[0.0002, 0.0002],
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preis_euro_pro_wh_akku=0.0,
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gesamtlast=[0.0, 0.0],
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)
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),
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optimization_hours=config_eos.optimization.genetic.horizon_hours,
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prediction_hours=config_eos.prediction.hours,
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inverter=inverter,
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direct_marketing_enabled=True,
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)
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return simulation
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def test_direct_marketing_discharge_allowed_does_not_export_battery(config_eos):
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simulation = _direct_marketing_battery_export_simulation(config_eos)
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assert simulation.bat_discharge_hours is not None
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simulation.bat_discharge_hours[0] = 1
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result = simulation.simulate(start_hour=0)
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assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == 0.0
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assert simulation.battery is not None
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assert simulation.battery.current_soc_percentage() == 100.0
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def test_direct_marketing_battery_grid_export_uses_separate_signal(config_eos):
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simulation = _direct_marketing_battery_export_simulation(config_eos)
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assert simulation.bat_grid_export_hours is not None
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||||
simulation.bat_grid_export_hours[0] = 1
|
||||
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||||
result = simulation.simulate(start_hour=0)
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||||
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||||
assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == pytest.approx(500.0)
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||||
assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.1)
|
||||
assert simulation.battery is not None
|
||||
assert simulation.battery.current_soc_percentage() == 50.0
|
||||
|
||||
|
||||
def test_direct_marketing_grid_export_rate_limits_exported_energy(config_eos):
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"""A partial export level exports that share of the rated discharge power."""
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simulation = _direct_marketing_battery_export_simulation(config_eos)
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||||
assert simulation.bat_grid_export_hours is not None
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||||
# 500 W rated discharge power over a one hour slot -> 500 Wh at rate 1.0.
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simulation.bat_grid_export_hours[0] = 0.5
|
||||
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||||
result = simulation.simulate(start_hour=0)
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||||
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||||
assert result["Netzeinspeisung_Wh_pro_Stunde"][0] == pytest.approx(250.0)
|
||||
assert simulation.battery is not None
|
||||
assert simulation.battery.current_soc_percentage() == 75.0
|
||||
|
||||
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||||
def test_battery_lcos_is_charged_once_on_delivered_energy(config_eos):
|
||||
simulation = _direct_marketing_battery_export_simulation(
|
||||
config_eos,
|
||||
levelized_cost_of_storage_kwh=0.12,
|
||||
dc_to_ac_efficiency=0.8,
|
||||
)
|
||||
assert simulation.bat_grid_export_hours is not None
|
||||
simulation.bat_grid_export_hours[0] = 1
|
||||
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||||
result = simulation.simulate(start_hour=0)
|
||||
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||||
# The battery delivers 500 Wh DC, so LCOS is 0.5 kWh * 0.12 EUR/kWh
|
||||
# = 0.06 EUR exactly once. After the 80% inverter, 400 Wh AC reaches
|
||||
# the grid and earns 400 Wh * 0.0002 EUR/Wh = 0.08 EUR.
|
||||
assert result["Kosten_Euro_pro_Stunde"][0] == pytest.approx(0.06)
|
||||
assert result["Gesamtkosten_Euro"] == pytest.approx(0.06)
|
||||
assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.08)
|
||||
assert result["Gesamtbilanz_Euro"] == pytest.approx(-0.02)
|
||||
|
||||
|
||||
def test_disabled_ac_charging_clears_the_reported_plan(config_eos):
|
||||
"""With AC charging off the reported plan must not keep charge commands.
|
||||
|
||||
The simulation ignores the AC charge genes when the inverter forbids grid
|
||||
charging. The solution is read back from the same array, so a controller
|
||||
acting on it would grid-charge the battery although no such charge was ever
|
||||
simulated or paid for.
|
||||
"""
|
||||
config_eos.merge_settings_from_dict(
|
||||
{
|
||||
"prediction": {"hours": 2},
|
||||
"optimization": {"genetic": {"tail_horizon_hours": 0, "horizon_hours": 2}},
|
||||
}
|
||||
)
|
||||
|
||||
battery = Battery(
|
||||
SolarPanelBatteryParameters(
|
||||
device_id="battery1",
|
||||
capacity_wh=10000,
|
||||
initial_soc_percentage=50,
|
||||
min_soc_percentage=0,
|
||||
charging_efficiency=1.0,
|
||||
discharging_efficiency=1.0,
|
||||
max_charge_power_w=5000,
|
||||
),
|
||||
prediction_hours=config_eos.prediction.hours,
|
||||
)
|
||||
inverter = Inverter(
|
||||
InverterParameters(
|
||||
device_id="inverter1",
|
||||
max_power_wh=5000.0,
|
||||
battery_id=battery.parameters.device_id,
|
||||
max_ac_charge_power_w=0, # Netzladen deaktiviert
|
||||
),
|
||||
battery=battery,
|
||||
)
|
||||
|
||||
simulation = GeneticSimulation()
|
||||
simulation.prepare(
|
||||
GeneticEnergyManagementParameters.model_validate(
|
||||
dict(
|
||||
pv_prognose_wh=[0.0, 0.0],
|
||||
strompreis_euro_pro_wh=[0.0003, 0.0003],
|
||||
einspeiseverguetung_euro_pro_wh=[0.0001, 0.0001],
|
||||
preis_euro_pro_wh_akku=0.0,
|
||||
gesamtlast=[0.0, 0.0],
|
||||
)
|
||||
),
|
||||
optimization_hours=config_eos.optimization.genetic.horizon_hours,
|
||||
prediction_hours=config_eos.prediction.hours,
|
||||
inverter=inverter,
|
||||
)
|
||||
simulation.ac_charge_hours = np.array([0.8, 0.0])
|
||||
|
||||
soc_before = battery.current_soc_percentage()
|
||||
simulation.simulate(start_hour=0)
|
||||
|
||||
# Nothing was charged ...
|
||||
assert battery.current_soc_percentage() == pytest.approx(soc_before)
|
||||
# ... and the plan says so.
|
||||
assert simulation.ac_charge_hours is not None
|
||||
assert list(simulation.ac_charge_hours) == [0.0, 0.0]
|
||||
Reference in New Issue
Block a user