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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/genetic.py Normal file
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"""Genetic algorithm."""
import random
import time
from typing import Any, Optional
import numpy as np
from deap import algorithms, base, creator, tools
from loguru import logger
from numpydantic import NDArray, Shape
from pydantic import ConfigDict, Field
from akkudoktoreos.core.pydantic import PydanticBaseModel
from akkudoktoreos.devices.genetic.battery import Battery
from akkudoktoreos.devices.genetic.homeappliance import HomeAppliance
from akkudoktoreos.devices.genetic.inverter import Inverter
from akkudoktoreos.optimization.genetic.geneticparams import (
GeneticEnergyManagementParameters,
GeneticOptimizationParameters,
)
from akkudoktoreos.optimization.genetic.geneticsolution import (
GeneticSimulationResult,
GeneticSolution,
)
from akkudoktoreos.optimization.optimizationabc import OptimizationBase
class GeneticSimulation(PydanticBaseModel):
"""Device simulation for GENETIC optimization algorithm."""
# Disable validation on assignment to speed up simulation runs.
model_config = ConfigDict(
validate_assignment=False,
)
start_hour: int = Field(
default=0, ge=0, le=23, description="Starting hour on day for optimizations."
)
optimization_hours: Optional[int] = Field(
default=24, ge=0, description="Number of hours into the future for optimizations."
)
prediction_hours: Optional[int] = Field(
default=48, ge=0, description="Number of hours into the future for predictions"
)
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.",
)
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.")
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")
def prepare(
self,
parameters: GeneticEnergyManagementParameters,
optimization_hours: int,
prediction_hours: int,
ev: Optional[Battery] = None,
home_appliance: Optional[HomeAppliance] = None,
inverter: Optional[Inverter] = None,
) -> None:
self.optimization_hours = optimization_hours
self.prediction_hours = prediction_hours
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.prediction_hours, 0.0)
self.dc_charge_hours = np.full(self.prediction_hours, 1.0)
self.ev_charge_hours = np.full(self.prediction_hours, 0.0)
"""Prepare simulation runs."""
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
)
)
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 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)
"""
# Remember start hour
self.start_hour = start_hour
# 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
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
if battery:
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
if battery:
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
)
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]
if battery:
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 battery and 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,
}
class GeneticOptimization(OptimizationBase):
"""GENETIC algorithm to solve energy optimization."""
def __init__(
self,
verbose: bool = False,
fixed_seed: Optional[int] = None,
):
"""Initialize the optimization problem with the required parameters."""
self.opti_param: dict[str, Any] = {}
self.fixed_eauto_hours = (
self.config.prediction.hours - self.config.optimization.horizon_hours
)
self.ev_possible_charge_values: list[float] = [1.0]
self.verbose = verbose
self.fix_seed = fixed_seed
self.optimize_ev = True
self.optimize_dc_charge = False
self.fitness_history: dict[str, Any] = {}
# Set a fixed seed for random operations if provided or in debug mode
if self.fix_seed is not None:
random.seed(self.fix_seed)
elif logger.level == "DEBUG":
self.fix_seed = random.randint(1, 100000000000) # noqa: S311
random.seed(self.fix_seed)
# Create Simulation
self.simulation = GeneticSimulation()
def decode_charge_discharge(
self, discharge_hours_bin: np.ndarray
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Decode the input array into ac_charge, dc_charge, and discharge arrays."""
discharge_hours_bin_np = np.array(discharge_hours_bin)
len_ac = len(self.ev_possible_charge_values)
# Categorization:
# Idle: 0 .. len_ac-1
# Discharge: len_ac .. 2*len_ac - 1
# AC Charge: 2*len_ac .. 3*len_ac - 1
# DC optional: 3*len_ac (not allowed), 3*len_ac + 1 (allowed)
# Idle has no charge, Discharge has binary 1, AC Charge has corresponding values
# Idle states
idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_ac)
# Discharge states
discharge_mask = (discharge_hours_bin_np >= len_ac) & (discharge_hours_bin_np < 2 * len_ac)
# AC states
ac_mask = (discharge_hours_bin_np >= 2 * len_ac) & (discharge_hours_bin_np < 3 * len_ac)
ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_ac).astype(int)
# DC states (if enabled)
if self.optimize_dc_charge:
dc_not_allowed_state = 3 * len_ac
dc_allowed_state = 3 * len_ac + 1
dc_charge = np.where(discharge_hours_bin_np == dc_allowed_state, 1, 0)
else:
dc_charge = np.ones_like(discharge_hours_bin_np, dtype=float)
# Generate the result arrays
discharge = np.zeros_like(discharge_hours_bin_np, dtype=int)
discharge[discharge_mask] = 1 # Set Discharge states to 1
ac_charge = np.zeros_like(discharge_hours_bin_np, dtype=float)
ac_charge[ac_mask] = [self.ev_possible_charge_values[i] for i in ac_indices]
# Idle is just 0, already default.
return ac_charge, dc_charge, discharge
def mutate(self, individual: list[int]) -> tuple[list[int]]:
"""Custom mutation function for the individual."""
# Calculate the number of states
len_ac = len(self.ev_possible_charge_values)
if self.optimize_dc_charge:
total_states = 3 * len_ac + 2
else:
total_states = 3 * len_ac
# 1. Mutating the charge_discharge part
charge_discharge_part = individual[: self.config.prediction.hours]
(charge_discharge_mutated,) = self.toolbox.mutate_charge_discharge(charge_discharge_part)
# Instead of a fixed clamping to 0..8 or 0..6 dynamically:
charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, total_states - 1)
individual[: self.config.prediction.hours] = charge_discharge_mutated
# 2. Mutating the EV charge part, if active
if self.optimize_ev:
ev_charge_part = individual[
self.config.prediction.hours : self.config.prediction.hours * 2
]
(ev_charge_part_mutated,) = self.toolbox.mutate_ev_charge_index(ev_charge_part)
ev_charge_part_mutated[self.config.prediction.hours - self.fixed_eauto_hours :] = [
0
] * self.fixed_eauto_hours
individual[self.config.prediction.hours : self.config.prediction.hours * 2] = (
ev_charge_part_mutated
)
# 3. Mutating the appliance start time, if applicable
if self.opti_param["home_appliance"] > 0:
appliance_part = [individual[-1]]
(appliance_part_mutated,) = self.toolbox.mutate_hour(appliance_part)
individual[-1] = appliance_part_mutated[0]
return (individual,)
# Method to create an individual based on the conditions
def create_individual(self) -> list[int]:
# Start with discharge states for the individual
individual_components = [
self.toolbox.attr_discharge_state() for _ in range(self.config.prediction.hours)
]
# Add EV charge index values if optimize_ev is True
if self.optimize_ev:
individual_components += [
self.toolbox.attr_ev_charge_index() for _ in range(self.config.prediction.hours)
]
# Add the start time of the household appliance if it's being optimized
if self.opti_param["home_appliance"] > 0:
individual_components += [self.toolbox.attr_int()]
return creator.Individual(individual_components)
def merge_individual(
self,
discharge_hours_bin: np.ndarray,
eautocharge_hours_index: Optional[np.ndarray],
washingstart_int: Optional[int],
) -> list[int]:
"""Merge the individual components back into a single solution list.
Parameters:
discharge_hours_bin (np.ndarray): Binary discharge hours.
eautocharge_hours_index (Optional[np.ndarray]): EV charge hours as integers, or None.
washingstart_int (Optional[int]): Dishwasher start time as integer, or None.
Returns:
list[int]: The merged individual solution as a list of integers.
"""
# Start with the discharge hours
individual = discharge_hours_bin.tolist()
# Add EV charge hours if applicable
if self.optimize_ev and eautocharge_hours_index is not None:
individual.extend(eautocharge_hours_index.tolist())
elif self.optimize_ev:
# Falls optimize_ev aktiv ist, aber keine EV-Daten vorhanden sind, fügen wir Nullen hinzu
individual.extend([0] * self.config.prediction.hours)
# Add dishwasher start time if applicable
if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int is not None:
individual.append(washingstart_int)
elif self.opti_param.get("home_appliance", 0) > 0:
# Falls ein Haushaltsgerät optimiert wird, aber kein Startzeitpunkt vorhanden ist
individual.append(0)
return individual
def split_individual(
self, individual: list[int]
) -> tuple[np.ndarray, Optional[np.ndarray], Optional[int]]:
"""Split the individual solution into its components.
Components:
1. Discharge hours (binary as int NumPy array),
2. Electric vehicle charge hours (float as int NumPy array, if applicable),
3. Dishwasher start time (integer if applicable).
"""
# Discharge hours as a NumPy array of ints
discharge_hours_bin = np.array(individual[: self.config.prediction.hours], dtype=int)
# EV charge hours as a NumPy array of ints (if optimize_ev is True)
eautocharge_hours_index = (
# append ev charging states to individual
np.array(
individual[self.config.prediction.hours : self.config.prediction.hours * 2],
dtype=int,
)
if self.optimize_ev
else None
)
# Washing machine start time as an integer (if applicable)
washingstart_int = (
int(individual[-1])
if self.opti_param and self.opti_param.get("home_appliance", 0) > 0
else None
)
return discharge_hours_bin, eautocharge_hours_index, washingstart_int
def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None:
"""Set up the DEAP environment with fitness and individual creation rules."""
self.opti_param = opti_param
# Remove existing definitions if any
for attr in ["FitnessMin", "Individual"]:
if attr in creator.__dict__:
del creator.__dict__[attr]
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
self.toolbox = base.Toolbox()
len_ac = len(self.ev_possible_charge_values)
# Total number of states without DC:
# Idle: len_ac states
# Discharge: len_ac states
# AC-Charge: len_ac states
# Total without DC: 3 * len_ac
# With DC: + 2 states
if self.optimize_dc_charge:
total_states = 3 * len_ac + 2
else:
total_states = 3 * len_ac
# State space: 0 .. (total_states - 1)
self.toolbox.register("attr_discharge_state", random.randint, 0, total_states - 1)
# EV attributes
if self.optimize_ev:
self.toolbox.register(
"attr_ev_charge_index",
random.randint,
0,
len_ac - 1,
)
# Household appliance start time
self.toolbox.register("attr_int", random.randint, start_hour, 23)
self.toolbox.register("individual", self.create_individual)
self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
self.toolbox.register("mate", tools.cxTwoPoint)
# Mutation operator for charge/discharge states
self.toolbox.register(
"mutate_charge_discharge", tools.mutUniformInt, low=0, up=total_states - 1, indpb=0.2
)
# Mutation operator for EV states
self.toolbox.register(
"mutate_ev_charge_index",
tools.mutUniformInt,
low=0,
up=len_ac - 1,
indpb=0.2,
)
# Mutation for household appliance
self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.2)
# Custom mutate function remains unchanged
self.toolbox.register("mutate", self.mutate)
self.toolbox.register("select", tools.selTournament, tournsize=3)
def evaluate_inner(self, individual: list[int]) -> dict[str, Any]:
"""Simulates the energy management system (EMS) using the provided individual solution.
This is an internal function.
"""
self.simulation.reset()
discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
individual
)
if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int:
self.simulation.set_home_appliance_start(
washingstart_int, global_start_hour=self.ems.start_datetime.hour
)
ac, dc, discharge = self.decode_charge_discharge(discharge_hours_bin)
self.simulation.set_akku_discharge_hours(discharge)
# Set DC charge hours only if DC optimization is enabled
if self.optimize_dc_charge:
self.simulation.set_akku_dc_charge_hours(dc)
self.simulation.set_akku_ac_charge_hours(ac)
if eautocharge_hours_index is not None:
eautocharge_hours_float = np.array(
[self.ev_possible_charge_values[i] for i in eautocharge_hours_index],
float,
)
self.simulation.set_ev_charge_hours(eautocharge_hours_float)
else:
self.simulation.set_ev_charge_hours(np.full(self.config.prediction.hours, 0))
# Do the simulation and return result.
return self.simulation.simulate(self.ems.start_datetime.hour)
def evaluate(
self,
individual: list[int],
parameters: GeneticOptimizationParameters,
start_hour: int,
worst_case: bool,
) -> tuple[float]:
"""Evaluate the fitness of an individual solution based on the simulation results."""
try:
o = self.evaluate_inner(individual)
except Exception as e:
return (100000.0,) # Return a high penalty in case of an exception
gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
individual
)
# EV 100% & charge not allowed
if self.optimize_ev:
eauto_soc_per_hour = np.array(o.get("EAuto_SoC_pro_Stunde", [])) # Beispielkey
if eauto_soc_per_hour is None or eautocharge_hours_index is None:
raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None")
min_length = min(eauto_soc_per_hour.size, eautocharge_hours_index.size)
eauto_soc_per_hour_tail = eauto_soc_per_hour[-min_length:]
eautocharge_hours_index_tail = eautocharge_hours_index[-min_length:]
# Mask
invalid_charge_mask = (eauto_soc_per_hour_tail == 100) & (
eautocharge_hours_index_tail > 0
)
if np.any(invalid_charge_mask):
invalid_indices = np.where(invalid_charge_mask)[0]
if len(invalid_indices) > 1:
eautocharge_hours_index_tail[invalid_indices[1:]] = 0
eautocharge_hours_index[-min_length:] = eautocharge_hours_index_tail.tolist()
adjusted_individual = self.merge_individual(
discharge_hours_bin, eautocharge_hours_index, washingstart_int
)
individual[:] = adjusted_individual
# New check: Activate discharge when battery SoC is 0
# battery_soc_per_hour = np.array(
# o.get("akku_soc_pro_stunde", [])
# ) # Example key for battery SoC
# if battery_soc_per_hour is not None:
# if battery_soc_per_hour is None or discharge_hours_bin is None:
# raise ValueError("battery_soc_per_hour or discharge_hours_bin is None")
# min_length = min(battery_soc_per_hour.size, discharge_hours_bin.size)
# battery_soc_per_hour_tail = battery_soc_per_hour[-min_length:]
# discharge_hours_bin_tail = discharge_hours_bin[-min_length:]
# len_ac = len(self.config.optimization.ev_available_charge_rates_percent)
# # # Find hours where battery SoC is 0
# # zero_soc_mask = battery_soc_per_hour_tail == 0
# # discharge_hours_bin_tail[zero_soc_mask] = (
# # len_ac + 2
# # ) # Activate discharge for these hours
# # When Battery SoC then set the Discharge randomly to 0 or 1. otherwise it's very
# # unlikely to get a state where a battery can store energy for a longer time
# # Find hours where battery SoC is 0
# zero_soc_mask = battery_soc_per_hour_tail == 0
# # discharge_hours_bin_tail[zero_soc_mask] = (
# # len_ac + 2
# # ) # Activate discharge for these hours
# set_to_len_ac_plus_2 = np.random.rand() < 0.5 # True mit 50% Wahrscheinlichkeit
# # Werte setzen basierend auf der zufälligen Entscheidung
# value_to_set = len_ac + 2 if set_to_len_ac_plus_2 else 0
# discharge_hours_bin_tail[zero_soc_mask] = value_to_set
# # Merge the updated discharge_hours_bin back into the individual
# adjusted_individual = self.merge_individual(
# discharge_hours_bin, eautocharge_hours_index, washingstart_int
# )
# individual[:] = adjusted_individual
# More metrics
individual.extra_data = ( # type: ignore[attr-defined]
o["Gesamtbilanz_Euro"],
o["Gesamt_Verluste"],
parameters.eauto.min_soc_percentage - self.simulation.ev.current_soc_percentage()
if parameters.eauto and self.simulation.ev
else 0,
)
# Adjust total balance with battery value and penalties for unmet SOC
if self.simulation.battery:
restwert_akku = (
self.simulation.battery.current_energy_content()
* parameters.ems.preis_euro_pro_wh_akku
)
gesamtbilanz += -restwert_akku
if self.optimize_ev:
try:
penalty = self.config.optimization.genetic.penalties["ev_soc_miss"]
except:
# Use default
penalty = 10
logger.error(
"Penalty function parameter `ev_soc_miss` not configured, using {}.", penalty
)
gesamtbilanz += max(
0,
(
parameters.eauto.min_soc_percentage
- self.simulation.ev.current_soc_percentage()
if parameters.eauto and self.simulation.ev
else 0
)
* penalty,
)
return (gesamtbilanz,)
def optimize(
self,
start_solution: Optional[list[float]] = None,
ngen: int = 200,
) -> tuple[Any, dict[str, list[Any]]]:
"""Run the optimization process using a genetic algorithm.
@TODO: optimize() ngen default (200) is different from optimierung_ems() ngen default (400).
"""
# Set the number of inviduals in a generation
try:
individuals = self.config.optimization.genetic.individuals
if individuals is None:
raise
except:
individuals = 300
logger.error("Individuals not configured. Using {}.", individuals)
population = self.toolbox.population(n=individuals)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", np.min)
stats.register("avg", np.mean)
stats.register("max", np.max)
logger.debug("Start optimize: {}", start_solution)
# Insert the start solution into the population if provided
if start_solution is not None:
for _ in range(10):
population.insert(0, creator.Individual(start_solution))
# Run the evolutionary algorithm
pop, log = algorithms.eaMuPlusLambda(
population,
self.toolbox,
mu=100,
lambda_=150,
cxpb=0.6,
mutpb=0.4,
ngen=ngen,
stats=stats,
halloffame=hof,
verbose=self.verbose,
)
# Store fitness history
self.fitness_history = {
"gen": log.select("gen"), # Generation numbers (X-axis)
"avg": log.select("avg"), # Average fitness for each generation (Y-axis)
"max": log.select("max"), # Maximum fitness for each generation (Y-axis)
"min": log.select("min"), # Minimum fitness for each generation (Y-axis)
}
member: dict[str, list[float]] = {"bilanz": [], "verluste": [], "nebenbedingung": []}
for ind in population:
if hasattr(ind, "extra_data"):
extra_value1, extra_value2, extra_value3 = ind.extra_data
member["bilanz"].append(extra_value1)
member["verluste"].append(extra_value2)
member["nebenbedingung"].append(extra_value3)
return hof[0], member
def optimierung_ems(
self,
parameters: GeneticOptimizationParameters,
start_hour: Optional[int] = None,
worst_case: bool = False,
ngen: Optional[int] = None,
) -> GeneticSolution:
"""Perform EMS (Energy Management System) optimization and visualize results."""
if start_hour is None:
start_hour = self.ems.start_datetime.hour
# Start hour has to be in sync with energy management
if start_hour != self.ems.start_datetime.hour:
raise ValueError(
f"Start hour not synced. EMS {self.ems.start_datetime.hour} vs. GENETIC {start_hour}."
)
# Set the number of generations
generations = ngen
if generations is None:
try:
generations = self.config.optimization.genetic.generations
except:
generations = 400
logger.error("Generations not configured. Using {}.", generations)
einspeiseverguetung_euro_pro_wh = np.full(
self.config.prediction.hours, parameters.ems.einspeiseverguetung_euro_pro_wh
)
self.simulation.reset()
# Initialize PV and EV batteries
akku: Optional[Battery] = None
if parameters.pv_akku:
akku = Battery(
parameters.pv_akku,
prediction_hours=self.config.prediction.hours,
)
akku.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
eauto: Optional[Battery] = None
if parameters.eauto:
eauto = Battery(
parameters.eauto,
prediction_hours=self.config.prediction.hours,
)
eauto.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
self.optimize_ev = (
parameters.eauto.min_soc_percentage - parameters.eauto.initial_soc_percentage >= 0
)
try:
charge_rates = self.config.devices.electric_vehicles[0].charge_rates
if charge_rates is None:
raise
except:
error_msg = "No charge rates provided for electric vehicle."
logger.exception(error_msg)
raise ValueError(error_msg)
self.ev_possible_charge_values = charge_rates
else:
self.optimize_ev = False
# Initialize household appliance if applicable
dishwasher = (
HomeAppliance(
parameters=parameters.dishwasher,
optimization_hours=self.config.optimization.horizon_hours,
prediction_hours=self.config.prediction.hours,
)
if parameters.dishwasher is not None
else None
)
# Initialize the inverter and energy management system
inverter: Optional[Inverter] = None
if parameters.inverter:
inverter = Inverter(
parameters.inverter,
battery=akku,
)
# Prepare device simulation
self.simulation.prepare(
parameters=parameters.ems,
optimization_hours=self.config.optimization.horizon_hours,
prediction_hours=self.config.prediction.hours,
inverter=inverter, # battery is part of inverter
ev=eauto,
home_appliance=dishwasher,
)
# Setup the DEAP environment and optimization process
self.setup_deap_environment({"home_appliance": 1 if dishwasher else 0}, start_hour)
self.toolbox.register(
"evaluate",
lambda ind: self.evaluate(ind, parameters, start_hour, worst_case),
)
start_time = time.time()
start_solution, extra_data = self.optimize(parameters.start_solution, ngen=generations)
elapsed_time = time.time() - start_time
logger.debug(f"Time evaluate inner: {elapsed_time:.4f} sec.")
# Perform final evaluation on the best solution
simulation_result = self.evaluate_inner(start_solution)
# Prepare results
discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
start_solution
)
# home appliance may have choosen a different appliance start hour
if self.simulation.home_appliance:
washingstart_int = self.simulation.home_appliance.get_appliance_start()
eautocharge_hours_float = (
[self.ev_possible_charge_values[i] for i in eautocharge_hours_index]
if eautocharge_hours_index is not None
else None
)
ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin)
# Visualize the results
visualize = {
"ac_charge": ac_charge.tolist(),
"dc_charge": dc_charge.tolist(),
"discharge_allowed": discharge.tolist(),
"eautocharge_hours_float": eautocharge_hours_float,
"result": simulation_result,
"eauto_obj": self.simulation.ev.to_dict() if self.simulation.ev else None,
"start_solution": start_solution,
"spuelstart": washingstart_int,
"extra_data": extra_data,
"fitness_history": self.fitness_history,
"fixed_seed": self.fix_seed,
}
from akkudoktoreos.utils.visualize import prepare_visualize
prepare_visualize(parameters, visualize, start_hour=start_hour)
return GeneticSolution(
**{
"ac_charge": ac_charge,
"dc_charge": dc_charge,
"discharge_allowed": discharge,
"eautocharge_hours_float": eautocharge_hours_float,
"result": GeneticSimulationResult(**simulation_result),
"eauto_obj": self.simulation.ev,
"start_solution": start_solution,
"washingstart": washingstart_int,
}
)