EOS/tests/test_genetic_complete_solution.py

# ruff: noqa: S101

import numpy as np

from akkudoktoreos.devices.devicesabc import BatteryOperationMode
from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution


def test_battery_discharge_allowed_remains_local_load_mode(config_eos):
    config_eos.merge_settings_from_dict({"feedintariff": {"direct_marketing_enabled": True}})
    solution = GeneticSolution

    operation_mode, operation_mode_factor = solution._battery_operation_from_solution(
        ac_charge=0.0,
        dc_charge=0.0,
        discharge_allowed=True,
    )

    assert operation_mode == BatteryOperationMode.PEAK_SHAVING
    assert operation_mode_factor == 1.0


def test_battery_grid_export_signal_maps_to_grid_support_export(config_eos):
    config_eos.merge_settings_from_dict({"feedintariff": {"direct_marketing_enabled": True}})
    solution = GeneticSolution

    operation_mode, operation_mode_factor = solution._battery_operation_from_solution(
        ac_charge=0.0,
        dc_charge=0.0,
        discharge_allowed=False,
        battery_grid_export_allowed=True,
    )

    assert operation_mode == BatteryOperationMode.GRID_SUPPORT_EXPORT
    assert operation_mode_factor == 1.0


def test_decode_charge_discharge_has_separate_battery_grid_export_state():
    optimization = GeneticOptimization()
    optimization.bat_possible_charge_values = [1.0]
    optimization.optimize_dc_charge = True
    optimization.optimize_battery_grid_export = True

    ac_charge, dc_charge, discharge, battery_grid_export = optimization.decode_charge_discharge(
        np.array([5])
    )

    assert ac_charge.tolist() == [0.0]
    assert dc_charge.tolist() == [0]
    assert discharge.tolist() == [0]
    assert battery_grid_export.tolist() == [1]


def test_decode_charge_discharge_has_self_consumption_state_after_legacy_export():
    optimization = GeneticOptimization()
    optimization.bat_possible_charge_values = [1.0]
    optimization.optimize_dc_charge = True
    optimization.optimize_battery_grid_export = True

    layout = optimization._battery_state_layout()
    ac_charge, dc_charge, discharge, battery_grid_export = optimization.decode_charge_discharge(
        np.array([6])
    )

    assert layout.total_states == 7
    assert layout.grid_export_state == 5
    assert layout.self_consumption_state == 6
    assert ac_charge.tolist() == [0.0]
    assert dc_charge.tolist() == [1]
    assert discharge.tolist() == [1]
    assert battery_grid_export.tolist() == [0]


def test_graded_grid_export_states_decode_to_rates():
    """Each configured export rate gets its own state; state 5 stays full power."""
    optimization = GeneticOptimization()
    optimization.bat_possible_charge_values = [1.0]
    optimization.bat_possible_grid_export_values = [1.0, 0.5, 0.25]
    optimization.optimize_dc_charge = True
    optimization.optimize_battery_grid_export = True

    layout = optimization._battery_state_layout()

    assert layout.grid_export_states == (5, 6, 7)
    # The full-power state keeps its index, so existing seeds stay valid.
    assert layout.grid_export_state == 5
    assert layout.self_consumption_state == 8
    assert layout.total_states == 9

    _, _, _, battery_grid_export = optimization.decode_charge_discharge(np.array([0, 5, 6, 7]))
    assert battery_grid_export.tolist() == [0.0, 1.0, 0.5, 0.25]


def test_single_export_rate_keeps_all_or_nothing_layout():
    """Without configured rates the state space is the one from before grading."""
    optimization = GeneticOptimization()
    optimization.bat_possible_charge_values = [1.0]
    optimization.optimize_dc_charge = True
    optimization.optimize_battery_grid_export = True

    layout = optimization._battery_state_layout()

    assert layout.grid_export_states == (5,)
    assert layout.total_states == 7


def test_battery_grid_export_factor_becomes_operation_factor(config_eos):
    """A partial export level is reported as the GRID_SUPPORT_EXPORT factor."""
    config_eos.merge_settings_from_dict({"feedintariff": {"direct_marketing_enabled": True}})
    solution = GeneticSolution

    operation_mode, operation_mode_factor = solution._battery_operation_from_solution(
        ac_charge=0.0,
        dc_charge=0.0,
        discharge_allowed=False,
        battery_grid_export_allowed=True,
        battery_grid_export_factor=0.25,
    )

    assert operation_mode == BatteryOperationMode.GRID_SUPPORT_EXPORT
    assert operation_mode_factor == 0.25


def test_disjoint_cycle_masks_keep_feasible_non_deadline_order(config_eos):
    from akkudoktoreos.core.coreabc import get_ems
    from akkudoktoreos.optimization.genetic.geneticparams import (
        GeneticOptimizationParameters,
    )
    from akkudoktoreos.utils.datetimeutil import to_datetime

    config_eos.merge_settings_from_dict(
        {
            "optimization": {
                "genetic": {
                    "interval_sec": 900,
                    "horizon_hours": 2,
                    "tail_horizon_hours": 0,
                    "terminal_value_mode": "FIXED",
                }
            }
        }
    )
    get_ems(init=True).set_start_datetime(
        to_datetime("2026-09-16T00:00:00+02:00", in_timezone="Europe/Berlin")
    )
    params = GeneticOptimizationParameters.model_validate(
        {
            "ems": {
                "pv_forecast_wh": [0.0] * 8,
                "total_load": [0.0] * 8,
                "electricity_price_per_wh": [0.0003] * 8,
                "feed_in_tariff_per_wh": 0.0,
                "price_per_wh_battery": 0.0,
            },
            "forecast_interval_seconds": 900,
            "pv_battery": None,
            "ev": None,
            "inverter": {"device_id": "inv", "max_power_wh": 1000},
            "home_appliances": [
                {
                    "device_id": "washer",
                    "num_cycles": 2,
                    "load_profile_power_w": [1000.0, 1000.0],
                    "load_profile_interval_seconds": 900,
                    "time_windows": {
                        "windows": [
                            {"start_time": "01:00", "duration": "30 minutes", "value": 0},
                            {"start_time": "00:00", "duration": "30 minutes", "value": 1},
                            {"start_time": "01:15", "duration": "30 minutes", "value": 1},
                        ]
                    },
                }
            ],
        }
    )
    optimizer = GeneticOptimization(fixed_seed=42)
    solution = optimizer.optimize_ems(params, ngen=1, individuals=6)
    assert [(item.hour, item.minute) for item in solution.appliance_starts["washer"]] == [
        (0, 0),
        (1, 0),
    ]
    assert sum(solution.result.home_appliance_energy_wh["washer"]) == 1000.0
    # Even a candidate choosing the second disconnected window repairs to the
    # feasible order, without dropping a configured run or crossing its mask.
    genes = [0, 1]
    assert optimizer._decode_appliance_starts(genes) == {0: [0, 4]}
    assert genes == [0, 0]