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Fix config and prediction revamp. (#259)

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Extend single_test_optimization.py to be able to use real world data from new prediction classes.
- .venv/bin/python single_test_optimization.py --real_world --verbose
Can also be run with profiling "--profile".

Add single_test_prediction.py to fetch predictions from remote prediction providers
- .venv/bin/python single_test_prediction.py --verbose --provider-id PVForecastAkkudoktor | more
- .venv/bin/python single_test_prediction.py --verbose --provider-id LoadAkkudoktor | more
- .venv/bin/python single_test_prediction.py --verbose --provider-id ElecPriceAkkudoktor | more
- .venv/bin/python single_test_prediction.py --verbose --provider-id BrightSky | more
- .venv/bin/python single_test_prediction.py --verbose --provider-id ClearOutside | more
Can also be run with profiling "--profile".

single_test_optimization.py is an example on how to retrieve prediction data for optimization and
use it to set up the optimization parameters.

Changes:
- load: Only one load provider at a time (vs. 5 before)

Bug fixes:
- prediction: only use providers that are enabled to retrieve or set data.
- prediction: fix pre pendulum format strings
- dataabc: Prevent error when resampling data with no datasets.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2024-12-16 20:26:08 +01:00
committed by GitHub
parent 810cc17c0b
commit 31bd2de18b
21 changed files with 415 additions and 713 deletions
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154
single_test_optimization.py
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@@ -9,10 +9,137 @@ import time
import numpy as np
from akkudoktoreos.config.config import get_config
from akkudoktoreos.core.ems import get_ems
from akkudoktoreos.optimization.genetic import (
OptimizationParameters,
optimization_problem,
)
from akkudoktoreos.prediction.prediction import get_prediction
def prepare_optimization_real_parameters() -> OptimizationParameters:
"""Prepare and return optimization parameters with real world data.
Returns:
OptimizationParameters: Configured optimization parameters
"""
# Make a config
settings = {
# -- General --
"prediction_hours": 48,
"prediction_historic_hours": 24,
"latitude": 52.52,
"longitude": 13.405,
# -- Predictions --
# PV Forecast
"pvforecast_provider": "PVForecastAkkudoktor",
"pvforecast0_peakpower": 5.0,
"pvforecast0_surface_azimuth": -10,
"pvforecast0_surface_tilt": 7,
"pvforecast0_userhorizon": [20, 27, 22, 20],
"pvforecast0_inverter_paco": 10000,
"pvforecast1_peakpower": 4.8,
"pvforecast1_surface_azimuth": -90,
"pvforecast1_surface_tilt": 7,
"pvforecast1_userhorizon": [30, 30, 30, 50],
"pvforecast1_inverter_paco": 10000,
"pvforecast2_peakpower": 1.4,
"pvforecast2_surface_azimuth": -40,
"pvforecast2_surface_tilt": 60,
"pvforecast2_userhorizon": [60, 30, 0, 30],
"pvforecast2_inverter_paco": 2000,
"pvforecast3_peakpower": 1.6,
"pvforecast3_surface_azimuth": 5,
"pvforecast3_surface_tilt": 45,
"pvforecast3_userhorizon": [45, 25, 30, 60],
"pvforecast3_inverter_paco": 1400,
"pvforecast4_peakpower": None,
# Weather Forecast
"weather_provider": "ClearOutside",
# Electricity Price Forecast
"elecprice_provider": "ElecPriceAkkudoktor",
# Load Forecast
"load_provider": "LoadAkkudoktor",
"loadakkudoktor_year_energy": 5000, # Energy consumption per year in kWh
# -- Simulations --
}
config_eos = get_config()
prediction_eos = get_prediction()
ems_eos = get_ems()
# Update/ set configuration
config_eos.merge_settings_from_dict(settings)
# Get current prediction data for optimization run
ems_eos.set_start_datetime()
print(
f"Real data prediction from {prediction_eos.start_datetime} to {prediction_eos.end_datetime}"
)
prediction_eos.update_data()
# PV Forecast (in W)
pv_forecast = prediction_eos.key_to_array(
key="pvforecast_ac_power",
start_datetime=prediction_eos.start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"pv_forecast: {pv_forecast}")
# Temperature Forecast (in degree C)
temperature_forecast = prediction_eos.key_to_array(
key="weather_temp_air",
start_datetime=prediction_eos.start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"temperature_forecast: {temperature_forecast}")
# Electricity Price (in Euro per Wh)
electricity_market_price_euros_per_kwh = prediction_eos.key_to_array(
key="elecprice_marketprice",
start_datetime=prediction_eos.start_datetime,
end_datetime=prediction_eos.end_datetime,
)
strompreis_euro_pro_wh = electricity_market_price_euros_per_kwh * 0.001
print(f"strompreis_euro_pro_wh: {strompreis_euro_pro_wh}")
# Overall System Load (in W)
gesamtlast = prediction_eos.key_to_array(
key="load_mean",
start_datetime=prediction_eos.start_datetime,
end_datetime=prediction_eos.end_datetime,
)
print(f"gesamtlast: {gesamtlast}")
# Start Solution (binary)
start_solution = None
print(f"start_solution: {start_solution}")
# Define parameters for the optimization problem
return OptimizationParameters(
**{
"ems": {
"preis_euro_pro_wh_akku": 0e-05,
"einspeiseverguetung_euro_pro_wh": 7e-05,
"gesamtlast": gesamtlast,
"pv_prognose_wh": pv_forecast,
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
},
"pv_akku": {
"kapazitaet_wh": 26400,
"start_soc_prozent": 15,
"min_soc_prozent": 15,
},
"eauto": {
"min_soc_prozent": 50,
"kapazitaet_wh": 60000,
"lade_effizienz": 0.95,
"max_ladeleistung_w": 11040,
"start_soc_prozent": 5,
},
"temperature_forecast": temperature_forecast,
"start_solution": start_solution,
}
)
def prepare_optimization_parameters() -> OptimizationParameters:
@@ -166,7 +293,7 @@ def prepare_optimization_parameters() -> OptimizationParameters:
)
def run_optimization(start_hour: int = 0, verbose: bool = False) -> dict:
def run_optimization(real_world: bool = False, start_hour: int = 0, verbose: bool = False) -> dict:
"""Run the optimization problem.
Args:
@@ -176,14 +303,21 @@ def run_optimization(start_hour: int = 0, verbose: bool = False) -> dict:
Returns:
dict: Optimization result as a dictionary
"""
# Prepare parameters
if real_world:
parameters = prepare_optimization_real_parameters()
else:
parameters = prepare_optimization_parameters()
if verbose:
print("\nOptimization Parameters:")
print(parameters.model_dump_json(indent=4))
# Initialize the optimization problem using the default configuration
config_eos = get_config()
config_eos.merge_settings_from_dict({"prediction_hours": 48, "optimization_hours": 24})
opt_class = optimization_problem(verbose=verbose, fixed_seed=42)
# Prepare parameters
parameters = prepare_optimization_parameters()
# Perform the optimisation based on the provided parameters and start hour
result = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour)
@@ -197,6 +331,9 @@ def main():
parser.add_argument(
"--verbose", action="store_true", help="Enable verbose output during optimization"
)
parser.add_argument(
"--real-world", action="store_true", help="Use real world data for predictions"
)
parser.add_argument(
"--start-hour", type=int, default=0, help="Starting hour for optimization (default: 0)"
)
@@ -208,7 +345,10 @@ def main():
profiler = cProfile.Profile()
try:
result = profiler.runcall(
run_optimization, start_hour=args.start_hour, verbose=args.verbose
run_optimization,
real_world=args.real_world,
start_hour=args.start_hour,
verbose=args.verbose,
)
# Print profiling statistics
stats = pstats.Stats(profiler)
@@ -224,7 +364,9 @@ def main():
# Run without profiling
try:
start_time = time.time()
result = run_optimization(start_hour=args.start_hour, verbose=args.verbose)
result = run_optimization(
real_world=args.real_world, start_hour=args.start_hour, verbose=args.verbose
)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")