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Nested config, devices registry

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* All config now nested.
    - Use default config from model field default values. If providers
      should be enabled by default, non-empty default config file could
      be provided again.
    - Environment variable support with EOS_ prefix and __ between levels,
      e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case
      insensitive.
      For more information see:
      https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
    - Use devices as registry for configured devices. DeviceBase as base
      class with for now just initializion support (in the future expand
      to operations during optimization).
    - Strip down ConfigEOS to the only configuration instance. Reload
      from file or reset to defaults is possible.

 * Fix multi-initialization of derived SingletonMixin classes.
This commit is contained in:
Dominique Lasserre
2025-01-12 05:19:37 +01:00
parent f09658578a
commit be26457563
72 changed files with 1297 additions and 1712 deletions
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84
single_test_optimization.py
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@@ -30,42 +30,52 @@ def prepare_optimization_real_parameters() -> OptimizationParameters:
"""
# Make a config
settings = {
# -- General --
"prediction_hours": 48,
"prediction_historic_hours": 24,
"latitude": 52.52,
"longitude": 13.405,
# -- Predictions --
"prediction": {
"prediction_hours": 48,
"prediction_historic_hours": 24,
"latitude": 52.52,
"longitude": 13.405,
},
# PV Forecast
"pvforecast_provider": "PVForecastAkkudoktor",
"pvforecast0_peakpower": 5.0,
"pvforecast0_surface_azimuth": -10,
"pvforecast0_surface_tilt": 7,
"pvforecast0_userhorizon": [20, 27, 22, 20],
"pvforecast0_inverter_paco": 10000,
"pvforecast1_peakpower": 4.8,
"pvforecast1_surface_azimuth": -90,
"pvforecast1_surface_tilt": 7,
"pvforecast1_userhorizon": [30, 30, 30, 50],
"pvforecast1_inverter_paco": 10000,
"pvforecast2_peakpower": 1.4,
"pvforecast2_surface_azimuth": -40,
"pvforecast2_surface_tilt": 60,
"pvforecast2_userhorizon": [60, 30, 0, 30],
"pvforecast2_inverter_paco": 2000,
"pvforecast3_peakpower": 1.6,
"pvforecast3_surface_azimuth": 5,
"pvforecast3_surface_tilt": 45,
"pvforecast3_userhorizon": [45, 25, 30, 60],
"pvforecast3_inverter_paco": 1400,
"pvforecast4_peakpower": None,
"pvforecast": {
"pvforecast_provider": "PVForecastAkkudoktor",
"pvforecast0_peakpower": 5.0,
"pvforecast0_surface_azimuth": -10,
"pvforecast0_surface_tilt": 7,
"pvforecast0_userhorizon": [20, 27, 22, 20],
"pvforecast0_inverter_paco": 10000,
"pvforecast1_peakpower": 4.8,
"pvforecast1_surface_azimuth": -90,
"pvforecast1_surface_tilt": 7,
"pvforecast1_userhorizon": [30, 30, 30, 50],
"pvforecast1_inverter_paco": 10000,
"pvforecast2_peakpower": 1.4,
"pvforecast2_surface_azimuth": -40,
"pvforecast2_surface_tilt": 60,
"pvforecast2_userhorizon": [60, 30, 0, 30],
"pvforecast2_inverter_paco": 2000,
"pvforecast3_peakpower": 1.6,
"pvforecast3_surface_azimuth": 5,
"pvforecast3_surface_tilt": 45,
"pvforecast3_userhorizon": [45, 25, 30, 60],
"pvforecast3_inverter_paco": 1400,
"pvforecast4_peakpower": None,
},
# Weather Forecast
"weather_provider": "ClearOutside",
"weather": {
"weather_provider": "ClearOutside",
},
# Electricity Price Forecast
"elecprice_provider": "ElecPriceAkkudoktor",
"elecprice": {
"elecprice_provider": "ElecPriceAkkudoktor",
},
# Load Forecast
"load_provider": "LoadAkkudoktor",
"loadakkudoktor_year_energy": 5000, # Energy consumption per year in kWh
"load": {
"load_provider": "LoadAkkudoktor",
"provider_settings": {
"loadakkudoktor_year_energy": 5000, # Energy consumption per year in kWh
},
},
# -- Simulations --
}
config_eos = get_config()
@@ -129,11 +139,14 @@ def prepare_optimization_real_parameters() -> OptimizationParameters:
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
},
"pv_akku": {
"device_id": "battery1",
"capacity_wh": 26400,
"initial_soc_percentage": 15,
"min_soc_percentage": 15,
},
"inverter": {"device_id": "iv1", "max_power_wh": 10000, "battery": "battery1"},
"eauto": {
"device_id": "ev1",
"min_soc_percentage": 50,
"capacity_wh": 60000,
"charging_efficiency": 0.95,
@@ -283,11 +296,14 @@ def prepare_optimization_parameters() -> OptimizationParameters:
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
},
"pv_akku": {
"device_id": "battery1",
"capacity_wh": 26400,
"initial_soc_percentage": 15,
"min_soc_percentage": 15,
},
"inverter": {"device_id": "iv1", "max_power_wh": 10000, "battery": "battery1"},
"eauto": {
"device_id": "ev1",
"min_soc_percentage": 50,
"capacity_wh": 60000,
"charging_efficiency": 0.95,
@@ -330,7 +346,9 @@ def run_optimization(
# Initialize the optimization problem using the default configuration
config_eos = get_config()
config_eos.merge_settings_from_dict({"prediction_hours": 48, "optimization_hours": 48})
config_eos.merge_settings_from_dict(
{"prediction": {"prediction_hours": 48}, "optimization": {"optimization_hours": 48}}
)
opt_class = optimization_problem(verbose=verbose, fixed_seed=seed)
# Perform the optimisation based on the provided parameters and start hour