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fix: automatic optimization (#596)

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This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.

This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.

The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.

This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.

* fix: automatic optimization

  Allow optimization to automatically run on configured intervals gathering all
  optimization parameters from configuration and predictions. The automatic run
  can be configured to only run prediction updates skipping the optimization.
  Extend documentaion to also cover automatic optimization. Lock automatic runs
  against runs initiated by the /optimize or other endpoints. Provide new
  endpoints to retrieve the energy management plan and the genetic solution
  of the latest automatic optimization run. Offload energy management to thread
  pool executor to keep the app more responsive during the CPU heavy optimization
  run.

* fix: EOS servers recognize environment variables on startup

  Force initialisation of EOS configuration on server startup to assure
  all sources of EOS configuration are properly set up and read. Adapt
  server tests and configuration tests to also test for environment
  variable configuration.

* fix: Remove 0.0.0.0 to localhost translation under Windows

  EOS imposed a 0.0.0.0 to localhost translation under Windows for
  convenience. This caused some trouble in user configurations. Now, as the
  default IP address configuration is 127.0.0.1, the user is responsible
  for to set up the correct Windows compliant IP address.

* fix: allow names for hosts additional to IP addresses

* fix: access pydantic model fields by class

  Access by instance is deprecated.

* fix: down sampling key_to_array

* fix: make cache clear endpoint clear all cache files

  Make /v1/admin/cache/clear clear all cache files. Before it only cleared
  expired cache files by default. Add new endpoint /v1/admin/clear-expired
  to only clear expired cache files.

* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin

  timezonefinder 8.10 got more inaccurate for timezones in europe as there is
  a common timezone. Use new package tzfpy instead which is still returning
  Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
  timezonefinder.

* fix: provider settings configuration

  Provider configuration used to be a union holding the settings for several
  providers. Pydantic union handling does not always find the correct type
  for a provider setting. This led to exceptions in specific configurations.
  Now provider settings are explicit comfiguration items for each possible
  provider. This is a breaking change as the configuration structure was
  changed.

* fix: ClearOutside weather prediction irradiance calculation

  Pvlib needs a pandas time index. Convert time index.

* fix: test config file priority

  Do not use config_eos fixture as this fixture already creates a config file.

* fix: optimization sample request documentation

  Provide all data in documentation of optimization sample request.

* fix: gitlint blocking pip dependency resolution

  Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
  Gitlint dependencies blocked pip from dependency resolution.

* fix: sync pre-commit config to actual dependency requirements

  .pre-commit-config.yaml was out of sync, also requirements-dev.txt.

* fix: missing babel in requirements.txt

  Add babel to requirements.txt

* feat: setup default device configuration for automatic optimization

  In case the parameters for automatic optimization are not fully defined a
  default configuration is setup to allow the automatic energy management
  run. The default configuration may help the user to correctly define
  the device configuration.

* feat: allow configuration of genetic algorithm parameters

  The genetic algorithm parameters for number of individuals, number of
  generations, the seed and penalty function parameters are now avaliable
  as configuration options.

* feat: allow configuration of home appliance time windows

  The time windows a home appliance is allowed to run are now configurable
  by the configuration (for /v1 API) and also by the home appliance parameters
  (for the classic /optimize API). If there is no such configuration the
  time window defaults to optimization hours, which was the standard before
  the change. Documentation on how to configure time windows is added.

* feat: standardize mesaurement keys for battery/ ev SoC measurements

  The standardized measurement keys to report battery SoC to the device
  simulations can now be retrieved from the device configuration as a
  read-only config option.

* feat: feed in tariff prediction

  Add feed in tarif predictions needed for automatic optimization. The feed in
  tariff can be retrieved as fixed feed in tarif or can be imported. Also add
  tests for the different feed in tariff providers. Extend documentation to
  cover the feed in tariff providers.

* feat: add energy management plan based on S2 standard instructions

  EOS can generate an energy management plan as a list of simple instructions.
  May be retrieved by the /v1/energy-management/plan endpoint. The instructions
  loosely follow the S2 energy management standard.

* feat: make measurement keys configurable by EOS configuration.

  The fixed measurement keys are replaced by configurable measurement keys.

* feat: make pendulum DateTime, Date, Duration types usable for pydantic models

  Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
  added to the datetimeutil utility. Remove custom made pendulum adaptations
  from EOS pydantic module. Make EOS modules use the pydantic pendulum types
  managed by the datetimeutil module instead of the core pendulum types.

* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.

  The time windows are are added to support home appliance time window
  configuration. All time classes are also pydantic models. Time is the base
  class for time definition derived from pendulum.Time.

* feat: Extend DataRecord by configurable field like data.

  Configurable field like data was added to support the configuration of
  measurement records.

* feat: Add additional information to health information

  Version information is added to the health endpoints of eos and eosDash.
  The start time of the last optimization and the latest run time of the energy
  management is added to the EOS health information.

* feat: add pydantic merge model tests

* feat: add plan tab to EOSdash

  The plan tab displays the current energy management instructions.

* feat: add predictions tab to EOSdash

  The predictions tab displays the current predictions.

* feat: add cache management to EOSdash admin tab

  The admin tab is extended by a section for cache management. It allows to
  clear the cache.

* feat: add about tab to EOSdash

  The about tab resembles the former hello tab and provides extra information.

* feat: Adapt changelog and prepare for release management

  Release management using commitizen is added. The changelog file is adapted and
  teh changelog and a description for release management is added in the
  documentation.

* feat(doc): Improve install and devlopment documentation

  Provide a more concise installation description in Readme.md and add extra
  installation page and development page to documentation.

* chore: Use memory cache for interpolation instead of dict in inverter

  Decorate calculate_self_consumption() with @cachemethod_until_update to cache
  results in memory during an energy management/ optimization run. Replacement
  of dict type caching in inverter is now possible because all optimization
  runs are properly locked and the memory cache CacheUntilUpdateStore is properly
  cleared at the start of any energy management/ optimization operation.

* chore: refactor genetic

  Refactor the genetic algorithm modules for enhanced module structure and better
  readability. Removed unnecessary and overcomplex devices singleton. Also
  split devices configuration from genetic algorithm parameters to allow further
  development independently from genetic algorithm parameter format. Move
  charge rates configuration for electric vehicles from optimization to devices
  configuration to allow to have different charge rates for different cars in
  the future.

* chore: Rename memory cache to CacheEnergyManagementStore

  The name better resembles the task of the cache to chache function and method
  results for an energy management run. Also the decorator functions are renamed
  accordingly: cachemethod_energy_management, cache_energy_management

* chore: use class properties for config/ems/prediction mixin classes

* chore: skip debug logs from mathplotlib

  Mathplotlib is very noisy in debug mode.

* chore: automatically sync bokeh js to bokeh python package

  bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.

* chore: rename hello.py to about.py

  Make hello.py the adapted EOSdash about page.

* chore: remove demo page from EOSdash

  As no the plan and prediction pages are working without configuration, the demo
  page is no longer necessary

* chore: split test_server.py for system test

  Split test_server.py to create explicit test_system.py for system tests.

* chore: move doc utils to generate_config_md.py

  The doc utils are only used in scripts/generate_config_md.py. Move it there to
  attribute for strong cohesion.

* chore: improve pydantic merge model documentation

* chore: remove pendulum warning from readme

* chore: remove GitHub discussions from contributing documentation

  Github discussions is to be replaced by Akkudoktor.net.

* chore(release): bump version to 0.1.0+dev for development

* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1

  bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.

* build(deps): bump uvicorn from 0.36.0 to 0.37.0

BREAKING CHANGE: EOS configuration changed. V1 API changed.

  - The available_charge_rates_percent configuration is removed from optimization.
    Use the new charge_rate configuration for the electric vehicle
  - Optimization configuration parameter hours renamed to horizon_hours
  - Device configuration now has to provide the number of devices and device
    properties per device.
  - Specific prediction provider configuration to be provided by explicit
    configuration item (no union for all providers).
  - Measurement keys to be provided as a list.
  - New feed in tariff providers have to be configured.
  - /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
  - /v1/admin/cache/clear now clears all cache files. Use
    /v1/admin/cache/clear-expired to only clear all expired cache files.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2025-10-28 02:50:31 +01:00
committed by GitHub
parent 20a9eb78d8
commit b397b5d43e
146 changed files with 22024 additions and 5339 deletions
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src/akkudoktoreos/optimization/genetic/geneticparams.py Normal file
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"""GENETIC algorithm paramters.
This module defines the Pydantic-based configuration and input parameter models
used in the energy optimization routines, including photovoltaic forecasts,
electricity pricing, and system component parameters.
It also provides a method to assemble these parameters from predictions,
forecasts, and fallback defaults, preparing them for optimization runs.
"""
from typing import Optional, Union
from loguru import logger
from pydantic import Field, field_validator, model_validator
from typing_extensions import Self
from akkudoktoreos.core.coreabc import (
ConfigMixin,
MeasurementMixin,
PredictionMixin,
)
from akkudoktoreos.optimization.genetic.geneticabc import GeneticParametersBaseModel
from akkudoktoreos.optimization.genetic.geneticdevices import (
ElectricVehicleParameters,
HomeApplianceParameters,
InverterParameters,
SolarPanelBatteryParameters,
)
from akkudoktoreos.utils.datetimeutil import to_duration
# Do not import directly from akkudoktoreos.core.coreabc
# EnergyManagementSystemMixin - Creates circular dependency with ems.py
# StartMixin - Creates circular dependency with ems.py
class GeneticEnergyManagementParameters(GeneticParametersBaseModel):
"""Encapsulates energy-related forecasts and costs used in GENETIC optimization."""
pv_prognose_wh: list[float] = Field(
description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals."
)
strompreis_euro_pro_wh: list[float] = Field(
description="An array of floats representing the electricity price in euros per watt-hour for different time intervals."
)
einspeiseverguetung_euro_pro_wh: Union[list[float], float] = Field(
description="A float or array of floats representing the feed-in compensation in euros per watt-hour."
)
preis_euro_pro_wh_akku: float = Field(
description="A float representing the cost of battery energy per watt-hour."
)
gesamtlast: list[float] = Field(
description="An array of floats representing the total load (consumption) in watts for different time intervals."
)
@model_validator(mode="after")
def validate_list_length(self) -> Self:
"""Validate that all input lists are of the same length.
Raises:
ValueError: If input list lengths differ.
"""
pv_prognose_length = len(self.pv_prognose_wh)
if (
pv_prognose_length != len(self.strompreis_euro_pro_wh)
or pv_prognose_length != len(self.gesamtlast)
or (
isinstance(self.einspeiseverguetung_euro_pro_wh, list)
and pv_prognose_length != len(self.einspeiseverguetung_euro_pro_wh)
)
):
raise ValueError("Input lists have different lengths")
return self
class GeneticOptimizationParameters(
ConfigMixin,
MeasurementMixin,
PredictionMixin,
# EnergyManagementSystemMixin, # Creates circular dependency with ems.py
# StartMixin, # Creates circular dependency with ems.py
GeneticParametersBaseModel,
):
"""Main parameter class for running the genetic energy optimization.
Collects all model and configuration parameters necessary to run the
optimization process, such as forecasts, pricing, battery and appliance models.
"""
ems: GeneticEnergyManagementParameters
pv_akku: Optional[SolarPanelBatteryParameters]
inverter: Optional[InverterParameters]
eauto: Optional[ElectricVehicleParameters]
dishwasher: Optional[HomeApplianceParameters] = None
temperature_forecast: Optional[list[Optional[float]]] = Field(
default=None,
description="An array of floats representing the temperature forecast in degrees Celsius for different time intervals.",
)
start_solution: Optional[list[float]] = Field(
default=None, description="Can be `null` or contain a previous solution (if available)."
)
@model_validator(mode="after")
def validate_list_length(self) -> Self:
"""Ensure that temperature forecast list matches the PV forecast length.
Raises:
ValueError: If list lengths mismatch.
"""
arr_length = len(self.ems.pv_prognose_wh)
if self.temperature_forecast is not None and arr_length != len(self.temperature_forecast):
raise ValueError("Input lists have different lengths")
return self
@field_validator("start_solution")
def validate_start_solution(
cls, start_solution: Optional[list[float]]
) -> Optional[list[float]]:
"""Validate that the starting solution has at least two elements.
Args:
start_solution (list[float]): Optional list of solution values.
Returns:
list[float]: Validated list.
Raises:
ValueError: If the solution is too short.
"""
if start_solution is not None and len(start_solution) < 2:
raise ValueError("Requires at least two values.")
return start_solution
@classmethod
def prepare(cls) -> "Optional[GeneticOptimizationParameters]":
"""Prepare optimization parameters from config, forecast and measurement data.
Fills in values needed for optimization from available configuration, predictions and
measurements. If some data is missing, default or demo values are used.
Parameters start by definition of the genetic algorithm at hour 0 of the actual date
(not at start datetime of energy management run)
Returns:
GeneticOptimizationParameters: The fully prepared optimization parameters.
Raises:
ValueError: If required configuration values like start time are missing.
"""
# Avoid circular dependency
from akkudoktoreos.core.ems import get_ems
ems = get_ems()
# The optimization paramters
oparams: "Optional[GeneticOptimizationParameters]" = None
# Check for run definitions
if ems.start_datetime is None:
error_msg = "Start datetime unknown."
logger.error(error_msg)
raise ValueError(error_msg)
# Check for general predictions conditions
if cls.config.general.latitude is None:
default_latitude = 52.52
logger.error(f"Latitude unknown - defaulting to {default_latitude}.")
cls.config.general.latitude = default_latitude
if cls.config.general.longitude is None:
default_longitude = 13.405
logger.error(f"Longitude unknown - defaulting to {default_longitude}.")
cls.config.general.longitude = default_longitude
if cls.config.prediction.hours is None:
logger.error("Prediction hours unknown - defaulting to 48 hours.")
cls.config.prediction.hours = 48
if cls.config.prediction.historic_hours is None:
logger.error("Prediction historic hours unknown - defaulting to 24 hours.")
cls.config.prediction.historic_hours = 24
# Check optimization definitions
if cls.config.optimization.horizon_hours is None:
logger.error("Optimization horizon unknown - defaulting to 24 hours.")
cls.config.optimization.horizon_hours = 24
if cls.config.optimization.interval is None:
logger.error("Optimization interval unknown - defaulting to 3600 seconds.")
cls.config.optimization.interval = 3600
if cls.config.optimization.interval != 3600:
logger.error(
"Optimization interval '{}' seconds not supported - forced to 3600 seconds."
)
cls.config.optimization.interval = 3600
# Check genetic algorithm definitions
if cls.config.optimization.genetic is None:
logger.error(
"Genetic optimization configuration not configured - defaulting to demo config."
)
cls.config.optimization.genetic = {
"individuals": 300,
"generations": 400,
"seed": None,
"penalties": {
"ev_soc_miss": 10,
},
}
if cls.config.optimization.genetic.individuals is None:
logger.error("Genetic individuals unknown - defaulting to 300.")
cls.config.optimization.genetic.individuals = 300
if cls.config.optimization.genetic.generations is None:
logger.error("Genetic generations unknown - defaulting to 400.")
cls.config.optimization.genetic.generations = 400
if cls.config.optimization.genetic.penalties is None:
logger.error("Genetic penalties unknown - defaulting to demo config.")
cls.config.optimization.genetic.penalties = {"ev_soc_miss": 10}
if "ev_soc_miss" not in cls.config.optimization.genetic.penalties:
logger.error("ev_soc_miss penalty function parameter unknown - defaulting to 100.")
cls.config.optimization.genetic.penalties["ev_soc_miss"] = 10
# Add forecast and device data
interval = to_duration(cls.config.optimization.interval)
power_to_energy_per_interval_factor = cls.config.optimization.interval / 3600
parameter_start_datetime = ems.start_datetime.set(hour=0, second=0, microsecond=0)
parameter_end_datetime = parameter_start_datetime.add(hours=cls.config.prediction.hours)
max_retries = 10
for attempt in range(1, max_retries + 1):
# Collect all the data for optimisation, but do not exceed max retries
if attempt > max_retries:
error_msg = f"Maximum retries {max_retries} for parameter collection exceeded. Parameter preparation attempt {attempt}."
logger.error(error_msg)
raise ValueError(error_msg)
# Assure predictions are uptodate
cls.prediction.update_data()
try:
pvforecast_ac_power = (
cls.prediction.key_to_array(
key="pvforecast_ac_power",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
fill_method="linear",
)
* power_to_energy_per_interval_factor
).tolist()
except:
logger.exception(
"No PV forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.merge_settings_from_dict(
{
"pvforecast": {
"provider": "PVForecastAkkudoktor",
"planes": [
{
"peakpower": 5.0,
"surface_azimuth": 170,
"surface_tilt": 7,
"userhorizon": [20, 27, 22, 20],
"inverter_paco": 10000,
},
{
"peakpower": 4.8,
"surface_azimuth": 90,
"surface_tilt": 7,
"userhorizon": [30, 30, 30, 50],
"inverter_paco": 10000,
},
{
"peakpower": 1.4,
"surface_azimuth": 140,
"surface_tilt": 60,
"userhorizon": [60, 30, 0, 30],
"inverter_paco": 2000,
},
{
"peakpower": 1.6,
"surface_azimuth": 185,
"surface_tilt": 45,
"userhorizon": [45, 25, 30, 60],
"inverter_paco": 1400,
},
],
},
}
)
# Retry
continue
try:
elecprice_marketprice_wh = cls.prediction.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
fill_method="ffill",
).tolist()
except:
logger.exception(
"No Electricity Marketprice forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.elecprice.provider = "ElecPriceAkkudoktor"
# Retry
continue
try:
load_mean_adjusted = cls.prediction.key_to_array(
key="load_mean_adjusted",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
fill_method="ffill",
).tolist()
except:
logger.exception(
"No Load forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.merge_settings_from_dict(
{
"load": {
"provider": "LoadAkkudoktor",
"provider_settings": {
"LoadAkkudoktor": {
"loadakkudoktor_year_energy": "1000",
},
},
},
}
)
# Retry
continue
try:
feed_in_tariff_wh = cls.prediction.key_to_array(
key="feed_in_tariff_wh",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
fill_method="ffill",
).tolist()
except:
logger.exception(
"No feed in tariff forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.merge_settings_from_dict(
{
"feedintariff": {
"provider": "FeedInTariffFixed",
"provider_settings": {
"FeedInTariffFixed": {
"feed_in_tariff_kwh": 0.078,
},
},
},
}
)
# Retry
continue
try:
weather_temp_air = cls.prediction.key_to_array(
key="weather_temp_air",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
fill_method="ffill",
).tolist()
except:
logger.exception(
"No weather forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.weather.provider = "BrightSky"
# Retry
continue
# Add device data
# Batteries
# ---------
if cls.config.devices.max_batteries is None:
logger.error("Number of battery devices not configured - defaulting to 1.")
cls.config.devices.max_batteries = 1
if cls.config.devices.max_batteries == 0:
battery_params = None
battery_lcos_kwh = 0
else:
if cls.config.devices.batteries is None:
logger.error("No battery device data available - defaulting to demo data.")
cls.config.devices.batteries = [{"device_id": "battery1", "capacity_wh": 8000}]
try:
battery_config = cls.config.devices.batteries[0]
battery_params = SolarPanelBatteryParameters(
device_id=battery_config.device_id,
capacity_wh=battery_config.capacity_wh,
charging_efficiency=battery_config.charging_efficiency,
discharging_efficiency=battery_config.discharging_efficiency,
max_charge_power_w=battery_config.max_charge_power_w,
min_soc_percentage=battery_config.min_soc_percentage,
max_soc_percentage=battery_config.max_soc_percentage,
)
except:
logger.exception(
"No battery device data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.devices.batteries = [{"device_id": "battery1", "capacity_wh": 8000}]
# Retry
continue
# Levelized cost of ownership
if battery_config.levelized_cost_of_storage_kwh is None:
logger.error(
"No battery device LCOS data available - defaulting to 0 €/kWh. Parameter preparation attempt {}.",
attempt,
)
battery_config.levelized_cost_of_storage_kwh = 0
battery_lcos_kwh = battery_config.levelized_cost_of_storage_kwh
# Initial SOC
try:
initial_soc_factor = cls.measurement.key_to_value(
key=battery_config.measurement_key_soc_factor,
target_datetime=ems.start_datetime,
)
if initial_soc_factor > 1.0 or initial_soc_factor < 0.0:
logger.error(
f"Invalid battery initial SoC factor {initial_soc_factor} - defaulting to 0.0."
)
initial_soc_factor = 0.0
# genetic parameter is 0..100 as int
initial_soc_percentage = int(initial_soc_factor * 100)
except:
initial_soc_percentage = None
if initial_soc_percentage is None:
logger.error(
f"No battery device SoC data (measurement key = '{battery_config.measurement_key_soc_factor}') available - defaulting to 0."
)
initial_soc_percentage = 0
battery_params.initial_soc_percentage = initial_soc_percentage
# Electric Vehicles
# -----------------
if cls.config.devices.max_electric_vehicles is None:
logger.error("Number of electric_vehicle devices not configured - defaulting to 1.")
cls.config.devices.max_electric_vehicles = 1
if cls.config.devices.max_electric_vehicles == 0:
electric_vehicle_params = None
else:
if cls.config.devices.electric_vehicles is None:
logger.error(
"No electric vehicle device data available - defaulting to demo data."
)
cls.config.devices.max_electric_vehicles = 1
cls.config.devices.electric_vehicles = [
{
"device_id": "ev11",
"capacity_wh": 50000,
"charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
"min_soc_percentage": 70,
}
]
try:
electric_vehicle_config = cls.config.devices.electric_vehicles[0]
electric_vehicle_params = ElectricVehicleParameters(
device_id=electric_vehicle_config.device_id,
capacity_wh=electric_vehicle_config.capacity_wh,
charging_efficiency=electric_vehicle_config.charging_efficiency,
discharging_efficiency=electric_vehicle_config.discharging_efficiency,
max_charge_power_w=electric_vehicle_config.max_charge_power_w,
min_soc_percentage=electric_vehicle_config.min_soc_percentage,
max_soc_percentage=electric_vehicle_config.max_soc_percentage,
)
except:
logger.exception(
"No electric_vehicle device data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.devices.max_electric_vehicles = 1
cls.config.devices.electric_vehicles = [
{
"device_id": "ev12",
"capacity_wh": 50000,
"charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
"min_soc_percentage": 70,
}
]
# Retry
continue
# Initial SOC
try:
initial_soc_factor = cls.measurement.key_to_value(
key=electric_vehicle_config.measurement_key_soc_factor,
target_datetime=ems.start_datetime,
)
if initial_soc_factor > 1.0 or initial_soc_factor < 0.0:
logger.error(
f"Invalid electric vehicle initial SoC factor {initial_soc_factor} - defaulting to 0.0."
)
initial_soc_factor = 0.0
# genetic parameter is 0..100 as int
initial_soc_percentage = int(initial_soc_factor * 100)
except:
initial_soc_percentage = None
if initial_soc_percentage is None:
logger.error(
f"No electric vehicle device SoC data (measurement key = '{electric_vehicle_config.measurement_key_soc_factor}') available - defaulting to 0."
)
initial_soc_percentage = 0
electric_vehicle_params.initial_soc_percentage = initial_soc_percentage
# Inverters
# ---------
if cls.config.devices.max_inverters is None:
logger.error("Number of inverter devices not configured - defaulting to 1.")
cls.config.devices.max_inverters = 1
if cls.config.devices.max_inverters == 0:
inverter_params = None
else:
if cls.config.devices.inverters is None:
logger.error("No inverter device data available - defaulting to demo data.")
cls.config.devices.inverters = [
{
"device_id": "inverter1",
"max_power_w": 10000,
"battery_id": battery_config.device_id,
}
]
try:
inverter_config = cls.config.devices.inverters[0]
inverter_params = InverterParameters(
device_id=inverter_config.device_id,
max_power_wh=inverter_config.max_power_w,
battery_id=inverter_config.battery_id,
)
except:
logger.exception(
"No inverter device data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.devices.inverters = [
{
"device_id": "inverter1",
"max_power_w": 10000,
"battery_id": battery_config.device_id,
}
]
# Retry
continue
# Home Appliances
# ---------------
if cls.config.devices.max_home_appliances is None:
logger.error("Number of home appliance devices not configured - defaulting to 1.")
cls.config.devices.max_home_appliances = 1
if cls.config.devices.max_home_appliances == 0:
home_appliance_params = None
else:
home_appliance_params = None
if cls.config.devices.home_appliances is None:
logger.error(
"No home appliance device data available - defaulting to demo data."
)
cls.config.devices.home_appliances = [
{
"device_id": "dishwasher1",
"consumption_wh": 2000,
"duration_h": 3.0,
"time_windows": {
"windows": [
{
"start_time": "08:00",
"duration": "5 hours",
},
{
"start_time": "15:00",
"duration": "3 hours",
},
],
},
}
]
try:
home_appliance_config = cls.config.devices.home_appliances[0]
home_appliance_params = HomeApplianceParameters(
device_id=home_appliance_config.device_id,
consumption_wh=home_appliance_config.consumption_wh,
duration_h=home_appliance_config.duration_h,
time_windows=home_appliance_config.time_windows,
)
except:
logger.exception(
"No home appliance device data available - defaulting to demo data. Parameter preparation attempt {}.",
attempt,
)
cls.config.devices.home_appliances = [
{
"device_id": "dishwasher1",
"consumption_wh": 2000,
"duration_h": 3.0,
"time_windows": None,
}
]
# Retry
continue
# We got all parameter data
try:
oparams = GeneticOptimizationParameters(
ems=GeneticEnergyManagementParameters(
pv_prognose_wh=pvforecast_ac_power,
strompreis_euro_pro_wh=elecprice_marketprice_wh,
einspeiseverguetung_euro_pro_wh=feed_in_tariff_wh,
gesamtlast=load_mean_adjusted,
preis_euro_pro_wh_akku=battery_lcos_kwh / 1000,
),
temperature_forecast=weather_temp_air,
pv_akku=battery_params,
eauto=electric_vehicle_params,
inverter=inverter_params,
dishwasher=home_appliance_params,
)
except:
logger.exception(
"Can not prepare optimization parameters - will retry. Parameter preparation attempt {}.",
attempt,
)
oparams = None
# Retry
continue
# Parameters prepared
break
return oparams