import json from io import BytesIO from pathlib import Path from typing import Any from unittest.mock import patch import numpy as np import pytest from pydantic import ValidationError from pypdf import PdfReader from akkudoktoreos.config.config import ConfigEOS from akkudoktoreos.core.cache import CacheEnergyManagementStore from akkudoktoreos.core.coreabc import get_ems from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization from akkudoktoreos.optimization.genetic.geneticparams import ( GeneticOptimizationParameters, ) from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution from akkudoktoreos.optimization.genetic.geneticvisualize import ( genetic_prepare_visualize, ) from akkudoktoreos.utils.datetimeutil import to_datetime ems_eos = get_ems(init=True) # init once DIR_TESTDATA = Path(__file__).parent / "testdata" / "genetic" def compare_dict(actual: dict[str, Any], expected: dict[str, Any]): assert set(actual) == set(expected) for key, value in expected.items(): if isinstance(value, dict): assert isinstance(actual[key], dict) compare_dict(actual[key], value) elif isinstance(value, list): assert isinstance(actual[key], list) assert actual[key] == pytest.approx(value) else: assert actual[key] == pytest.approx(value) @pytest.mark.asyncio @pytest.mark.parametrize( "fn_in, fn_out, ngen, break_even", [ ("optimize_input_1.json", "optimize_result_1.json", 3, 0), ("optimize_input_2.json", "optimize_result_2.json", 3, 0), ("optimize_input_2.json", "optimize_result_2_full.json", 400, 0), ("optimize_input_1.json", "optimize_result_1_be.json", 3, 1), ("optimize_input_2.json", "optimize_result_2_be.json", 3, 1), ], ) async def test_optimize( fn_in: str, fn_out: str, ngen: int, break_even: int, config_eos: ConfigEOS, is_finalize: bool, ): """Test optimize_ems.""" # Test parameters fixed_start_hour = 10 fixed_seed = 42 # Assure configuration holds the correct values config_eos.merge_settings_from_dict( { "prediction": { "hours": 48 }, "optimization": { "algorithm": "GENETIC", "genetic": { "horizon_hours": 48, "individuals": 300, "generations": 10, "penalties": { "ev_soc_miss": 10, "ac_charge_break_even": break_even, } } }, "devices": { "max_electric_vehicles": 1, "electric_vehicles": { "ev1": { "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], } }, } } ) # Load input and output data parameter_file = DIR_TESTDATA / fn_in with parameter_file.open("r") as f_in: input_data = GeneticOptimizationParameters(**json.load(f_in)) # Fake energy management run start datetime ems_eos.set_start_datetime(to_datetime().set(hour=fixed_start_hour)) # Throw away any cached results of the last energy management run. CacheEnergyManagementStore().clear() genetic_optimization = GeneticOptimization(fixed_seed=fixed_seed) # Activate with pytest --finalize if ngen > 10 and not is_finalize: pytest.skip() # Call the optimization function genetic_solution = genetic_optimization.optimize_ems( parameters=input_data, start_hour=fixed_start_hour, ngen=ngen ) # Write test output to file, so we can take it as new data on intended change TESTDATA_FILE = DIR_TESTDATA / f"new_{fn_out}" with TESTDATA_FILE.open("w", encoding="utf-8", newline="\n") as f_out: f_out.write(genetic_solution.model_dump_json(indent=4, exclude_unset=True)) solution_file = DIR_TESTDATA / fn_out # In case a new test case is added, we don't want to fail here, so the new output is written # to disk before try: with solution_file.open("r") as f_out: expected_data = json.load(f_out) expected_result = GeneticSolution(**expected_data) except ValidationError: # Expected genetic solution data does not fit to GeneticSolution data schema # Possibly the GeneticSolution class changed. pytest.fail( f"ValidationError: Can not load expected solution from {solution_file}\n" f"cp {TESTDATA_FILE} {solution_file}\n" ) except FileNotFoundError: # Should not happen pytest.fail( f"FileNotFoundError: Can not load expected solution from {solution_file}\n" f"cp {TESTDATA_FILE} {solution_file}\n" ) # Keep the output contract, but do not demand an identical stochastic # schedule or monetary golden from the previous direct-consumption model. assert set(genetic_solution.model_dump()) == set(expected_result.model_dump()) result = genetic_solution.result expected_slots = len(input_data.ems.pv_forecast_wh) - fixed_start_hour assert len(result.grid_consumption_wh_per_hour) == expected_slots assert len(result.grid_feed_in_wh_per_hour) == expected_slots prices = np.asarray(genetic_solution.parameters.ems.electricity_price_per_wh)[fixed_start_hour:] tariffs = np.asarray(genetic_solution.parameters.ems.feed_in_tariff_per_wh) if tariffs.ndim > 0: tariffs = tariffs[fixed_start_hour:] expected_costs = np.asarray(result.grid_consumption_wh_per_hour) * prices expected_revenues = np.asarray(result.grid_feed_in_wh_per_hour) * tariffs np.testing.assert_allclose(result.costs_per_hour, expected_costs) np.testing.assert_allclose(result.revenue_per_hour, expected_revenues) assert result.total_costs == pytest.approx(sum(expected_costs)) assert result.total_revenue == pytest.approx(sum(expected_revenues)) assert result.total_balance == pytest.approx(sum(expected_costs) - sum(expected_revenues)) assert result.total_losses == pytest.approx(sum(result.losses_per_hour)) assert all(value >= 0 for value in result.grid_consumption_wh_per_hour) assert all(value >= 0 for value in result.grid_feed_in_wh_per_hour) assert all(0 <= value <= 100 for value in result.battery_soc_per_hour) assert all(0 <= value <= 100 for value in result.ev_soc_per_hour) # Check the correct generic optimization solution is created optimization_solution = await genetic_solution.optimization_solution() # @TODO # Check the correct generic energy management plan is created plan = genetic_solution.energy_management_plan() # @TODO # Check visualization works pdf = genetic_prepare_visualize( solution=genetic_solution, ) assert pdf.startswith(b"%PDF-") reader = PdfReader(BytesIO(pdf)) assert len(reader.pages) == 6 # Everything passed, remove generated files TESTDATA_FILE.unlink()