mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-02-24 01:46:21 +00:00
6498c7dc32
The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
525 lines
15 KiB
Python
Executable File
525 lines
15 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import asyncio
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import cProfile
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import json
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import pstats
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import sys
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import time
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from typing import Any
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import numpy as np
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from loguru import logger
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from akkudoktoreos.core.coreabc import get_config, get_ems, get_prediction
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from akkudoktoreos.core.emsettings import EnergyManagementMode
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticOptimizationParameters,
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)
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from akkudoktoreos.utils.datetimeutil import to_datetime
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config_eos = get_config()
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prediction_eos = get_prediction()
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ems_eos = get_ems()
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def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
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"""Prepare and return optimization parameters with real world data.
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Returns:
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GeneticOptimizationParameters: Configured optimization parameters
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"""
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# Make a config
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"optimization": {
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"horizon_hours": 24,
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"interval": 3600,
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"genetic": {
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"individuals": 300,
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"generations": 400,
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"seed": None,
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"penalties": {
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"ev_soc_miss": 10,
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},
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},
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},
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# PV Forecast
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"pvforecast": {
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"provider": "PVForecastAkkudoktor",
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"planes": [
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{
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"peakpower": 5.0,
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"surface_azimuth": -10,
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"surface_tilt": 7,
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"userhorizon": [20, 27, 22, 20],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 4.8,
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"surface_azimuth": -90,
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"surface_tilt": 7,
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"userhorizon": [30, 30, 30, 50],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 1.4,
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"surface_azimuth": -40,
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"surface_tilt": 60,
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"userhorizon": [60, 30, 0, 30],
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"inverter_paco": 2000,
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},
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{
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"peakpower": 1.6,
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"surface_azimuth": 5,
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"surface_tilt": 45,
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"userhorizon": [45, 25, 30, 60],
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"inverter_paco": 1400,
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},
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],
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},
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# Weather Forecast
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"weather": {
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"provider": "ClearOutside",
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},
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# Electricity Price Forecast
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"elecprice": {
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"provider": "ElecPriceAkkudoktor",
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},
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# Load Forecast
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"load": {
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"provider": "LoadAkkudoktor",
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"provider_settings": {
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"LoadAkkudoktor": {
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"loadakkudoktor_year_energy_kwh": 5000, # Energy consumption per year in kWh
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},
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},
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},
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# -- Simulations --
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# Assure we have charge rates for the EV
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"devices": {
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"max_electric_vehicles": 1,
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"electric_vehicles": [
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{
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"charge_rates": [
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0.0,
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6.0 / 16.0,
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8.0 / 16.0,
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10.0 / 16.0,
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12.0 / 16.0,
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14.0 / 16.0,
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1.0,
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],
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},
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],
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},
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}
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# Update/ set configuration
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config_eos.merge_settings_from_dict(settings)
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# Get current prediction data for optimization run
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ems_eos.set_start_datetime()
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print(
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f"Real data prediction from {prediction_eos.ems_start_datetime} to {prediction_eos.end_datetime}"
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)
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prediction_eos.update_data()
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# PV Forecast (in W)
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pv_forecast = prediction_eos.key_to_array(
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key="pvforecast_ac_power",
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start_datetime=prediction_eos.ems_start_datetime,
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end_datetime=prediction_eos.end_datetime,
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)
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print(f"pv_forecast: {pv_forecast}")
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# Temperature Forecast (in degree C)
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temperature_forecast = prediction_eos.key_to_array(
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key="weather_temp_air",
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start_datetime=prediction_eos.ems_start_datetime,
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end_datetime=prediction_eos.end_datetime,
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)
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print(f"temperature_forecast: {temperature_forecast}")
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# Electricity Price (in Euro per Wh)
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strompreis_euro_pro_wh = prediction_eos.key_to_array(
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key="elecprice_marketprice_wh",
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start_datetime=prediction_eos.ems_start_datetime,
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end_datetime=prediction_eos.end_datetime,
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)
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print(f"strompreis_euro_pro_wh: {strompreis_euro_pro_wh}")
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# Overall System Load (in W)
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gesamtlast = prediction_eos.key_to_array(
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key="load_mean",
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start_datetime=prediction_eos.ems_start_datetime,
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end_datetime=prediction_eos.end_datetime,
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)
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print(f"gesamtlast: {gesamtlast}")
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# Start Solution (binary)
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start_solution = None
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print(f"start_solution: {start_solution}")
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# Define parameters for the optimization problem
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return GeneticOptimizationParameters(
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**{
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"ems": {
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"preis_euro_pro_wh_akku": 0e-05,
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"einspeiseverguetung_euro_pro_wh": 7e-05,
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"gesamtlast": gesamtlast,
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"pv_prognose_wh": pv_forecast,
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"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
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},
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"pv_akku": {
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"device_id": "battery 1",
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"capacity_wh": 26400,
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"initial_soc_percentage": 15,
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"min_soc_percentage": 15,
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},
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"inverter": {
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"device_id": "inverter 1",
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"max_power_wh": 10000,
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"battery_id": "battery 1",
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},
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"eauto": {
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"device_id": "electric vehicle 1",
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"min_soc_percentage": 50,
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"capacity_wh": 60000,
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"charging_efficiency": 0.95,
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"max_charge_power_w": 11040,
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"initial_soc_percentage": 5,
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},
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"temperature_forecast": temperature_forecast,
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"start_solution": start_solution,
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}
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)
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def prepare_optimization_parameters() -> GeneticOptimizationParameters:
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"""Prepare and return optimization parameters with predefined data.
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Returns:
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GeneticOptimizationParameters: Configured optimization parameters
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"""
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# Initialize the optimization problem using the default configuration
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {
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"horizon_hours": 48,
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"interval": 3600,
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"genetic": {
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"individuals": 300,
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"generations": 400,
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"seed": None,
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"penalties": {
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"ev_soc_miss": 10,
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},
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},
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},
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# Assure we have charge rates for the EV
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"devices": {
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"max_electric_vehicles": 1,
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"electric_vehicles": [
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{
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"device_id": "Default EV",
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"charge_rates": [
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0.0,
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6.0 / 16.0,
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8.0 / 16.0,
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10.0 / 16.0,
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12.0 / 16.0,
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14.0 / 16.0,
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1.0,
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],
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},
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],
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},
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}
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)
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# PV Forecast (in W)
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pv_forecast = np.zeros(48)
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pv_forecast[12] = 5000
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# Temperature Forecast (in degree C)
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temperature_forecast = [
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18.3,
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17.8,
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16.9,
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16.2,
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15.6,
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15.1,
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14.6,
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14.2,
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14.3,
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14.8,
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15.7,
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16.7,
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17.4,
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18.0,
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18.6,
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19.2,
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19.1,
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18.7,
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18.5,
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17.7,
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16.2,
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14.6,
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13.6,
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13.0,
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12.6,
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12.2,
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11.7,
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11.6,
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11.3,
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11.0,
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10.7,
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10.2,
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11.4,
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14.4,
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16.4,
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18.3,
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19.5,
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20.7,
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21.9,
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22.7,
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23.1,
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23.1,
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22.8,
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21.8,
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20.2,
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19.1,
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18.0,
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17.4,
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]
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# Electricity Price (in Euro per Wh)
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strompreis_euro_pro_wh = np.full(48, 0.001)
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strompreis_euro_pro_wh[0:10] = 0.00001
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strompreis_euro_pro_wh[11:15] = 0.00005
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strompreis_euro_pro_wh[20] = 0.00001
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# Overall System Load (in W)
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gesamtlast = [
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676.71,
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876.19,
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527.13,
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468.88,
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531.38,
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517.95,
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483.15,
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472.28,
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1011.68,
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995.00,
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1053.07,
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1063.91,
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1320.56,
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1132.03,
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1163.67,
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1176.82,
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1216.22,
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1103.78,
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1129.12,
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1178.71,
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1050.98,
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988.56,
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912.38,
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704.61,
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516.37,
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868.05,
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694.34,
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608.79,
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556.31,
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488.89,
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506.91,
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804.89,
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1141.98,
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1056.97,
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992.46,
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1155.99,
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827.01,
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1257.98,
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1232.67,
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871.26,
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860.88,
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1158.03,
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1222.72,
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1221.04,
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949.99,
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987.01,
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733.99,
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592.97,
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]
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# Start Solution (binary)
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start_solution = None
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# Define parameters for the optimization problem
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return GeneticOptimizationParameters(
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**{
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"ems": {
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"preis_euro_pro_wh_akku": 0e-05,
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"einspeiseverguetung_euro_pro_wh": 7e-05,
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"gesamtlast": gesamtlast,
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"pv_prognose_wh": pv_forecast,
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"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
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},
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"pv_akku": {
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"device_id": "battery 1",
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"capacity_wh": 26400,
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"initial_soc_percentage": 15,
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"min_soc_percentage": 15,
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},
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"inverter": {
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"device_id": "inverter 1",
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"max_power_wh": 10000,
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"battery_id": "battery 1",
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},
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"eauto": {
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"device_id": "electric vehicle 1",
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"min_soc_percentage": 50,
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"capacity_wh": 60000,
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"charging_efficiency": 0.95,
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"max_charge_power_w": 11040,
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"initial_soc_percentage": 5,
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},
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"temperature_forecast": temperature_forecast,
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"start_solution": start_solution,
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}
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)
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def run_optimization(
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real_world: bool, start_hour: int, verbose: bool, seed: int, parameters_file: str, ngen: int
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) -> Any:
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"""Run the optimization problem.
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Args:
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start_hour (int, optional): Starting hour for optimization. Defaults to 0.
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verbose (bool, optional): Whether to print verbose output. Defaults to False.
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Returns:
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dict: Optimization result as a dictionary
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"""
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# Prepare parameters
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if parameters_file:
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with open(parameters_file, "r") as f:
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parameters = GeneticOptimizationParameters(**json.load(f))
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elif real_world:
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parameters = prepare_optimization_real_parameters()
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else:
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parameters = prepare_optimization_parameters()
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logger.info("Optimization Parameters:")
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logger.info(parameters.model_dump_json(indent=4))
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if start_hour is None:
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start_datetime = None
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else:
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start_datetime = to_datetime().set(hour=start_hour)
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asyncio.run(
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ems_eos.run(
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start_datetime=start_datetime,
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mode=EnergyManagementMode.OPTIMIZATION,
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genetic_parameters=parameters,
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genetic_individuals=ngen,
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genetic_seed=seed,
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)
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)
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solution = ems_eos.genetic_solution()
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if solution is None:
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return None
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return solution.model_dump_json()
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def main():
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"""Main function to run the optimization script with optional profiling."""
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parser = argparse.ArgumentParser(description="Run Energy Optimization Simulation")
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parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
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parser.add_argument(
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"--verbose", action="store_true", help="Enable verbose output during optimization"
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)
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parser.add_argument(
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"--real-world", action="store_true", help="Use real world data for predictions"
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)
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parser.add_argument(
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"--start-hour", type=int, default=0, help="Starting hour for optimization (default: 0)"
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)
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parser.add_argument(
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"--parameters-file",
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type=str,
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default="",
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help="Load optimization parameters from json file (default: unset)",
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)
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parser.add_argument("--seed", type=int, default=42, help="Use fixed random seed (default: 42)")
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parser.add_argument(
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"--ngen",
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type=int,
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default=400,
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help="Number of generations during optimization process (default: 400)",
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)
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args = parser.parse_args()
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if args.profile:
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# Run with profiling
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profiler = cProfile.Profile()
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try:
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result = profiler.runcall(
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run_optimization,
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real_world=args.real_world,
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start_hour=args.start_hour,
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verbose=args.verbose,
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seed=args.seed,
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parameters_file=args.parameters_file,
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ngen=args.ngen,
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)
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# Print profiling statistics
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stats = pstats.Stats(profiler)
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stats.strip_dirs().sort_stats("cumulative").print_stats(200)
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# Print result
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if args.verbose:
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print("\nOptimization Result:")
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print(result)
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except Exception as e:
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print(f"Error during optimization: {e}", file=sys.stderr)
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sys.exit(1)
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else:
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# Run without profiling
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try:
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start_time = time.time()
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result = run_optimization(
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real_world=args.real_world,
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start_hour=args.start_hour,
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verbose=args.verbose,
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seed=args.seed,
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parameters_file=args.parameters_file,
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ngen=args.ngen,
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)
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end_time = time.time()
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elapsed_time = end_time - start_time
|
|
if args.verbose:
|
|
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
|
|
print("\nOptimization Result:")
|
|
print(result)
|
|
|
|
except Exception as e:
|
|
print(f"Error during optimization: {e}", file=sys.stderr)
|
|
sys.exit(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|