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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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641
src/akkudoktoreos/core/ems.py
View File

@@ -1,123 +1,50 @@
import traceback
from typing import Any, ClassVar, Optional
from asyncio import Lock, get_running_loop
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from typing import ClassVar, Optional
import numpy as np
from loguru import logger
from numpydantic import NDArray, Shape
from pendulum import DateTime
from pydantic import ConfigDict, Field, computed_field, field_validator, model_validator
from typing_extensions import Self
from pydantic import computed_field
from akkudoktoreos.core.cache import CacheUntilUpdateStore
from akkudoktoreos.core.cache import CacheEnergyManagementStore
from akkudoktoreos.core.coreabc import ConfigMixin, PredictionMixin, SingletonMixin
from akkudoktoreos.core.pydantic import ParametersBaseModel, PydanticBaseModel
from akkudoktoreos.devices.battery import Battery
from akkudoktoreos.devices.generic import HomeAppliance
from akkudoktoreos.devices.inverter import Inverter
from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime
from akkudoktoreos.utils.utils import NumpyEncoder
from akkudoktoreos.core.emplan import EnergyManagementPlan
from akkudoktoreos.core.emsettings import EnergyManagementMode
from akkudoktoreos.core.pydantic import PydanticBaseModel
from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
from akkudoktoreos.optimization.genetic.geneticparams import (
GeneticOptimizationParameters,
)
from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
from akkudoktoreos.optimization.optimization import OptimizationSolution
from akkudoktoreos.utils.datetimeutil import DateTime, compare_datetimes, to_datetime
class EnergyManagementParameters(ParametersBaseModel):
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: 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:
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 SimulationResult(ParametersBaseModel):
"""This object contains the results of the simulation and provides insights into various parameters over the entire forecast period."""
Last_Wh_pro_Stunde: list[Optional[float]] = Field(description="TBD")
EAuto_SoC_pro_Stunde: list[Optional[float]] = Field(
description="The state of charge of the EV for each hour."
)
Einnahmen_Euro_pro_Stunde: list[Optional[float]] = Field(
description="The revenue from grid feed-in or other sources in euros per hour."
)
Gesamt_Verluste: float = Field(
description="The total losses in watt-hours over the entire period."
)
Gesamtbilanz_Euro: float = Field(
description="The total balance of revenues minus costs in euros."
)
Gesamteinnahmen_Euro: float = Field(description="The total revenues in euros.")
Gesamtkosten_Euro: float = Field(description="The total costs in euros.")
Home_appliance_wh_per_hour: list[Optional[float]] = Field(
description="The energy consumption of a household appliance in watt-hours per hour."
)
Kosten_Euro_pro_Stunde: list[Optional[float]] = Field(
description="The costs in euros per hour."
)
Netzbezug_Wh_pro_Stunde: list[Optional[float]] = Field(
description="The grid energy drawn in watt-hours per hour."
)
Netzeinspeisung_Wh_pro_Stunde: list[Optional[float]] = Field(
description="The energy fed into the grid in watt-hours per hour."
)
Verluste_Pro_Stunde: list[Optional[float]] = Field(
description="The losses in watt-hours per hour."
)
akku_soc_pro_stunde: list[Optional[float]] = Field(
description="The state of charge of the battery (not the EV) in percentage per hour."
)
Electricity_price: list[Optional[float]] = Field(
description="Used Electricity Price, including predictions"
)
@field_validator(
"Last_Wh_pro_Stunde",
"Netzeinspeisung_Wh_pro_Stunde",
"akku_soc_pro_stunde",
"Netzbezug_Wh_pro_Stunde",
"Kosten_Euro_pro_Stunde",
"Einnahmen_Euro_pro_Stunde",
"EAuto_SoC_pro_Stunde",
"Verluste_Pro_Stunde",
"Home_appliance_wh_per_hour",
"Electricity_price",
mode="before",
)
def convert_numpy(cls, field: Any) -> Any:
return NumpyEncoder.convert_numpy(field)[0]
# The executor to execute the CPU heavy energy management run
executor = ThreadPoolExecutor(max_workers=1)
class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBaseModel):
# Disable validation on assignment to speed up simulation runs.
model_config = ConfigDict(
validate_assignment=False,
)
"""Energy management."""
# Start datetime.
_start_datetime: ClassVar[Optional[DateTime]] = None
# last run datetime. Used by energy management task
_last_datetime: ClassVar[Optional[DateTime]] = None
_last_run_datetime: ClassVar[Optional[DateTime]] = None
# energy management plan of latest energy management run with optimization
_plan: ClassVar[Optional[EnergyManagementPlan]] = None
# opimization solution of the latest energy management run
_optimization_solution: ClassVar[Optional[OptimizationSolution]] = None
# Solution of the genetic algorithm of latest energy management run with optimization
# For classic API
_genetic_solution: ClassVar[Optional[GeneticSolution]] = None
# energy management lock (for energy management run)
_run_lock: ClassVar[Lock] = Lock()
@computed_field # type: ignore[prop-decorator]
@property
@@ -127,9 +54,15 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
EnergyManagement.set_start_datetime()
return EnergyManagement._start_datetime
@computed_field # type: ignore[prop-decorator]
@property
def last_run_datetime(self) -> Optional[DateTime]:
"""The datetime the last energy management was run."""
return EnergyManagement._last_run_datetime
@classmethod
def set_start_datetime(cls, start_datetime: Optional[DateTime] = None) -> DateTime:
"""Set the start datetime for the next energy management cycle.
"""Set the start datetime for the next energy management run.
If no datetime is provided, the current datetime is used.
@@ -148,142 +81,208 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
cls._start_datetime = start_datetime.set(minute=0, second=0, microsecond=0)
return cls._start_datetime
# -------------------------
# TODO: Take from prediction
# -------------------------
@classmethod
def plan(cls) -> Optional[EnergyManagementPlan]:
"""Get the latest energy management plan.
load_energy_array: Optional[NDArray[Shape["*"], float]] = Field(
default=None,
description="An array of floats representing the total load (consumption) in watts for different time intervals.",
)
pv_prediction_wh: Optional[NDArray[Shape["*"], float]] = Field(
default=None,
description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals.",
)
elect_price_hourly: Optional[NDArray[Shape["*"], float]] = Field(
default=None,
description="An array of floats representing the electricity price in euros per watt-hour for different time intervals.",
)
elect_revenue_per_hour_arr: Optional[NDArray[Shape["*"], float]] = Field(
default=None,
description="An array of floats representing the feed-in compensation in euros per watt-hour.",
)
Returns:
Optional[EnergyManagementPlan]: The latest energy management plan or None.
"""
return cls._plan
# -------------------------
# TODO: Move to devices
# -------------------------
@classmethod
def optimization_solution(cls) -> Optional[OptimizationSolution]:
"""Get the latest optimization solution.
battery: Optional[Battery] = Field(default=None, description="TBD.")
ev: Optional[Battery] = Field(default=None, description="TBD.")
home_appliance: Optional[HomeAppliance] = Field(default=None, description="TBD.")
inverter: Optional[Inverter] = Field(default=None, description="TBD.")
Returns:
Optional[OptimizationSolution]: The latest optimization solution.
"""
return cls._optimization_solution
# -------------------------
# TODO: Move to devices
# -------------------------
@classmethod
def genetic_solution(cls) -> Optional[GeneticSolution]:
"""Get the latest solution of the genetic algorithm.
ac_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
dc_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
Returns:
Optional[GeneticSolution]: The latest solution of the genetic algorithm.
"""
return cls._genetic_solution
def __init__(self, *args: Any, **kwargs: Any) -> None:
if hasattr(self, "_initialized"):
return
super().__init__(*args, **kwargs)
def set_parameters(
self,
parameters: EnergyManagementParameters,
ev: Optional[Battery] = None,
home_appliance: Optional[HomeAppliance] = None,
inverter: Optional[Inverter] = None,
) -> None:
self.load_energy_array = np.array(parameters.gesamtlast, float)
self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float)
self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float)
self.elect_revenue_per_hour_arr = (
parameters.einspeiseverguetung_euro_pro_wh
if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list)
else np.full(
len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float
)
)
if inverter:
self.battery = inverter.battery
else:
self.battery = None
self.ev = ev
self.home_appliance = home_appliance
self.inverter = inverter
self.ac_charge_hours = np.full(self.config.prediction.hours, 0.0)
self.dc_charge_hours = np.full(self.config.prediction.hours, 1.0)
self.ev_charge_hours = np.full(self.config.prediction.hours, 0.0)
def set_akku_discharge_hours(self, ds: np.ndarray) -> None:
if self.battery:
self.battery.set_discharge_per_hour(ds)
def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None:
self.ac_charge_hours = ds
def set_akku_dc_charge_hours(self, ds: np.ndarray) -> None:
self.dc_charge_hours = ds
def set_ev_charge_hours(self, ds: np.ndarray) -> None:
self.ev_charge_hours = ds
def set_home_appliance_start(self, ds: int, global_start_hour: int = 0) -> None:
if self.home_appliance:
self.home_appliance.set_starting_time(ds, global_start_hour=global_start_hour)
def reset(self) -> None:
if self.ev:
self.ev.reset()
if self.battery:
self.battery.reset()
def run(
self,
start_hour: Optional[int] = None,
@classmethod
def _run(
cls,
start_datetime: Optional[DateTime] = None,
mode: Optional[EnergyManagementMode] = None,
genetic_parameters: Optional[GeneticOptimizationParameters] = None,
genetic_individuals: Optional[int] = None,
genetic_seed: Optional[int] = None,
force_enable: Optional[bool] = False,
force_update: Optional[bool] = False,
) -> None:
"""Run energy management.
"""Run the energy management.
Sets `start_datetime` to current hour, updates the configuration and the prediction, and
starts simulation at current hour.
This method initializes the energy management run by setting its
start datetime, updating predictions, and optionally starting
optimization depending on the selected mode or configuration.
Args:
start_hour (int, optional): Hour to take as start time for the energy management. Defaults
to now.
force_enable (bool, optional): If True, forces to update even if disabled. This
is mostly relevant to prediction providers.
force_update (bool, optional): If True, forces to update the data even if still cached.
start_datetime (DateTime, optional): The starting timestamp
of the energy management run. Defaults to the current datetime
if not provided.
mode (EnergyManagementMode, optional): The management mode to use. Must be one of:
- "OPTIMIZATION": Runs the optimization process.
- "PREDICTION": Updates the forecast without optimization.
Defaults to the mode defined in the current configuration.
genetic_parameters (GeneticOptimizationParameters, optional): The
parameter set for the genetic algorithm. If not provided, it will
be constructed based on the current configuration and predictions.
genetic_individuals (int, optional): The number of individuals for the
genetic algorithm. Defaults to the algorithm's internal default (400)
if not specified.
genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
to the algorithm's internal random seed if not specified.
force_enable (bool, optional): If True, bypasses any disabled state
to force the update process. This is mostly applicable to
prediction providers.
force_update (bool, optional): If True, forces data to be refreshed
even if a cached version is still valid.
Returns:
None
"""
# Throw away any cached results of the last run.
CacheUntilUpdateStore().clear()
self.set_start_hour(start_hour=start_hour)
# Ensure there is only one optimization/ energy management run at a time
if mode not in (None, "PREDICTION", "OPTIMIZATION"):
raise ValueError(f"Unknown energy management mode {mode}.")
# Check for run definitions
if self.start_datetime is None:
error_msg = "Start datetime unknown."
logger.error(error_msg)
raise ValueError(error_msg)
if self.config.prediction.hours is None:
error_msg = "Prediction hours unknown."
logger.error(error_msg)
raise ValueError(error_msg)
if self.config.optimization.hours is None:
error_msg = "Optimization hours unknown."
logger.error(error_msg)
raise ValueError(error_msg)
logger.info("Starting energy management run.")
self.prediction.update_data(force_enable=force_enable, force_update=force_update)
# TODO: Create optimisation problem that calls into devices.update_data() for simulations.
# Remember/ set the start datetime of this energy management run.
# None leads
cls.set_start_datetime(start_datetime)
logger.info("Energy management run (crippled version - prediction update only)")
# Throw away any memory cached results of the last energy management run.
CacheEnergyManagementStore().clear()
def manage_energy(self) -> None:
if mode is None:
mode = cls.config.ems.mode
if mode is None or mode == "PREDICTION":
# Update the predictions
cls.prediction.update_data(force_enable=force_enable, force_update=force_update)
logger.info("Energy management run done (predictions updated)")
return
# Prepare optimization parameters
# This also creates default configurations for missing values and updates the predictions
logger.info(
"Starting energy management prediction update and optimzation parameter preparation."
)
if genetic_parameters is None:
genetic_parameters = GeneticOptimizationParameters.prepare()
if not genetic_parameters:
logger.error(
"Energy management run canceled. Could not prepare optimisation parameters."
)
return
# Take values from config if not given
if genetic_individuals is None:
genetic_individuals = cls.config.optimization.genetic.individuals
if genetic_seed is None:
genetic_seed = cls.config.optimization.genetic.seed
if cls._start_datetime is None: # Make mypy happy - already set by us
raise RuntimeError("Start datetime not set.")
logger.info("Starting energy management optimization.")
try:
optimization = GeneticOptimization(
verbose=bool(cls.config.server.verbose),
fixed_seed=genetic_seed,
)
solution = optimization.optimierung_ems(
start_hour=cls._start_datetime.hour,
parameters=genetic_parameters,
ngen=genetic_individuals,
)
except:
logger.exception("Energy management optimization failed.")
return
# Make genetic solution public
cls._genetic_solution = solution
# Make optimization solution public
cls._optimization_solution = solution.optimization_solution()
# Make plan public
cls._plan = solution.energy_management_plan()
logger.debug("Energy management genetic solution:\n{}", cls._genetic_solution)
logger.debug("Energy management optimization solution:\n{}", cls._optimization_solution)
logger.debug("Energy management plan:\n{}", cls._plan)
logger.info("Energy management run done (optimization updated)")
async def run(
self,
start_datetime: Optional[DateTime] = None,
mode: Optional[EnergyManagementMode] = None,
genetic_parameters: Optional[GeneticOptimizationParameters] = None,
genetic_individuals: Optional[int] = None,
genetic_seed: Optional[int] = None,
force_enable: Optional[bool] = False,
force_update: Optional[bool] = False,
) -> None:
"""Run the energy management.
This method initializes the energy management run by setting its
start datetime, updating predictions, and optionally starting
optimization depending on the selected mode or configuration.
Args:
start_datetime (DateTime, optional): The starting timestamp
of the energy management run. Defaults to the current datetime
if not provided.
mode (EnergyManagementMode, optional): The management mode to use. Must be one of:
- "OPTIMIZATION": Runs the optimization process.
- "PREDICTION": Updates the forecast without optimization.
Defaults to the mode defined in the current configuration.
genetic_parameters (GeneticOptimizationParameters, optional): The
parameter set for the genetic algorithm. If not provided, it will
be constructed based on the current configuration and predictions.
genetic_individuals (int, optional): The number of individuals for the
genetic algorithm. Defaults to the algorithm's internal default (400)
if not specified.
genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
to the algorithm's internal random seed if not specified.
force_enable (bool, optional): If True, bypasses any disabled state
to force the update process. This is mostly applicable to
prediction providers.
force_update (bool, optional): If True, forces data to be refreshed
even if a cached version is still valid.
Returns:
None
"""
async with self._run_lock:
loop = get_running_loop()
# Create a partial function with parameters "baked in"
func = partial(
EnergyManagement._run,
start_datetime=start_datetime,
mode=mode,
genetic_parameters=genetic_parameters,
genetic_individuals=genetic_individuals,
genetic_seed=genetic_seed,
force_enable=force_enable,
force_update=force_update,
)
# Run optimization in background thread to avoid blocking event loop
await loop.run_in_executor(executor, func)
async def manage_energy(self) -> None:
"""Repeating task for managing energy.
This task should be executed by the server regularly (e.g., every 10 seconds)
@@ -304,13 +303,13 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
current_datetime = to_datetime()
interval = self.config.ems.interval # interval maybe changed in between
if EnergyManagement._last_datetime is None:
if EnergyManagement._last_run_datetime is None:
# Never run before
try:
# Remember energy run datetime.
EnergyManagement._last_datetime = current_datetime
EnergyManagement._last_run_datetime = current_datetime
# Try to run a first energy management. May fail due to config incomplete.
self.run()
await self.run()
except Exception as e:
trace = "".join(traceback.TracebackException.from_exception(e).format())
message = f"EOS init: {e}\n{trace}"
@@ -322,14 +321,14 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
return
if (
compare_datetimes(current_datetime, EnergyManagement._last_datetime).time_diff
compare_datetimes(current_datetime, EnergyManagement._last_run_datetime).time_diff
< interval
):
# Wait for next run
return
try:
self.run()
await self.run()
except Exception as e:
trace = "".join(traceback.TracebackException.from_exception(e).format())
message = f"EOS run: {e}\n{trace}"
@@ -337,187 +336,13 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
# Remember the energy management run - keep on interval even if we missed some intervals
while (
compare_datetimes(current_datetime, EnergyManagement._last_datetime).time_diff
compare_datetimes(current_datetime, EnergyManagement._last_run_datetime).time_diff
>= interval
):
EnergyManagement._last_datetime = EnergyManagement._last_datetime.add(seconds=interval)
def set_start_hour(self, start_hour: Optional[int] = None) -> None:
"""Sets start datetime to given hour.
Args:
start_hour (int, optional): Hour to take as start time for the energy management. Defaults
to now.
"""
if start_hour is None:
self.set_start_datetime()
else:
start_datetime = to_datetime().set(hour=start_hour, minute=0, second=0, microsecond=0)
self.set_start_datetime(start_datetime)
def simulate_start_now(self) -> dict[str, Any]:
start_hour = to_datetime().now().hour
return self.simulate(start_hour)
def simulate(self, start_hour: int) -> dict[str, Any]:
"""Simulate energy usage and costs for the given start hour.
akku_soc_pro_stunde begin of the hour, initial hour state!
last_wh_pro_stunde integral of last hour (end state)
"""
# Check for simulation integrity
required_attrs = [
"load_energy_array",
"pv_prediction_wh",
"elect_price_hourly",
"ev_charge_hours",
"ac_charge_hours",
"dc_charge_hours",
"elect_revenue_per_hour_arr",
]
missing_data = [
attr.replace("_", " ").title() for attr in required_attrs if getattr(self, attr) is None
]
if missing_data:
logger.error("Mandatory data missing - %s", ", ".join(missing_data))
raise ValueError(f"Mandatory data missing: {', '.join(missing_data)}")
# Pre-fetch data
load_energy_array = np.array(self.load_energy_array)
pv_prediction_wh = np.array(self.pv_prediction_wh)
elect_price_hourly = np.array(self.elect_price_hourly)
ev_charge_hours = np.array(self.ev_charge_hours)
ac_charge_hours = np.array(self.ac_charge_hours)
dc_charge_hours = np.array(self.dc_charge_hours)
elect_revenue_per_hour_arr = np.array(self.elect_revenue_per_hour_arr)
# Fetch objects
battery = self.battery
if battery is None:
raise ValueError(f"battery not set: {battery}")
ev = self.ev
home_appliance = self.home_appliance
inverter = self.inverter
if not (len(load_energy_array) == len(pv_prediction_wh) == len(elect_price_hourly)):
error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(pv_prediction_wh)}, Electricity Price = {len(elect_price_hourly)}"
logger.error(error_msg)
raise ValueError(error_msg)
end_hour = len(load_energy_array)
total_hours = end_hour - start_hour
# Pre-allocate arrays for the results, optimized for speed
loads_energy_per_hour = np.full((total_hours), np.nan)
feedin_energy_per_hour = np.full((total_hours), np.nan)
consumption_energy_per_hour = np.full((total_hours), np.nan)
costs_per_hour = np.full((total_hours), np.nan)
revenue_per_hour = np.full((total_hours), np.nan)
soc_per_hour = np.full((total_hours), np.nan)
soc_ev_per_hour = np.full((total_hours), np.nan)
losses_wh_per_hour = np.full((total_hours), np.nan)
home_appliance_wh_per_hour = np.full((total_hours), np.nan)
electricity_price_per_hour = np.full((total_hours), np.nan)
# Set initial state
soc_per_hour[0] = battery.current_soc_percentage()
if ev:
soc_ev_per_hour[0] = ev.current_soc_percentage()
for hour in range(start_hour, end_hour):
hour_idx = hour - start_hour
# save begin states
soc_per_hour[hour_idx] = battery.current_soc_percentage()
if ev:
soc_ev_per_hour[hour_idx] = ev.current_soc_percentage()
# Accumulate loads and PV generation
consumption = load_energy_array[hour]
losses_wh_per_hour[hour_idx] = 0.0
# Home appliances
if home_appliance:
ha_load = home_appliance.get_load_for_hour(hour)
consumption += ha_load
home_appliance_wh_per_hour[hour_idx] = ha_load
# E-Auto handling
if ev and ev_charge_hours[hour] > 0:
loaded_energy_ev, verluste_eauto = ev.charge_energy(
None, hour, relative_power=ev_charge_hours[hour]
)
consumption += loaded_energy_ev
losses_wh_per_hour[hour_idx] += verluste_eauto
# Process inverter logic
energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = (
0.0
EnergyManagement._last_run_datetime = EnergyManagement._last_run_datetime.add(
seconds=interval
)
hour_ac_charge = ac_charge_hours[hour]
hour_dc_charge = dc_charge_hours[hour]
hourly_electricity_price = elect_price_hourly[hour]
hourly_energy_revenue = elect_revenue_per_hour_arr[hour]
battery.set_charge_allowed_for_hour(hour_dc_charge, hour)
if inverter:
energy_produced = pv_prediction_wh[hour]
(
energy_feedin_grid_actual,
energy_consumption_grid_actual,
losses,
eigenverbrauch,
) = inverter.process_energy(energy_produced, consumption, hour)
# AC PV Battery Charge
if hour_ac_charge > 0.0:
battery.set_charge_allowed_for_hour(1, hour)
battery_charged_energy_actual, battery_losses_actual = battery.charge_energy(
None, hour, relative_power=hour_ac_charge
)
total_battery_energy = battery_charged_energy_actual + battery_losses_actual
consumption += total_battery_energy
energy_consumption_grid_actual += total_battery_energy
losses_wh_per_hour[hour_idx] += battery_losses_actual
# Update hourly arrays
feedin_energy_per_hour[hour_idx] = energy_feedin_grid_actual
consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual
losses_wh_per_hour[hour_idx] += losses
loads_energy_per_hour[hour_idx] = consumption
electricity_price_per_hour[hour_idx] = hourly_electricity_price
# Financial calculations
costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_energy_revenue
total_cost = np.nansum(costs_per_hour)
total_losses = np.nansum(losses_wh_per_hour)
total_revenue = np.nansum(revenue_per_hour)
# Prepare output dictionary
return {
"Last_Wh_pro_Stunde": loads_energy_per_hour,
"Netzeinspeisung_Wh_pro_Stunde": feedin_energy_per_hour,
"Netzbezug_Wh_pro_Stunde": consumption_energy_per_hour,
"Kosten_Euro_pro_Stunde": costs_per_hour,
"akku_soc_pro_stunde": soc_per_hour,
"Einnahmen_Euro_pro_Stunde": revenue_per_hour,
"Gesamtbilanz_Euro": total_cost - total_revenue,
"EAuto_SoC_pro_Stunde": soc_ev_per_hour,
"Gesamteinnahmen_Euro": total_revenue,
"Gesamtkosten_Euro": total_cost,
"Verluste_Pro_Stunde": losses_wh_per_hour,
"Gesamt_Verluste": total_losses,
"Home_appliance_wh_per_hour": home_appliance_wh_per_hour,
"Electricity_price": electricity_price_per_hour,
}
# Initialize the Energy Management System, it is a singleton.
ems = EnergyManagement()