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chore: eosdash improve plan display (#739)
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* chore: improve plan solution display

Add genetic optimization results to general solution provided by EOSdash plan display.

Add total results.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

* fix: genetic battery and home appliance device simulation

Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or
home appliance start time reflect what the simulation was doing. Sometimes
the simulation decided to charge less or to start the appliance at another
time and this was not brought back to e.g. ac_charge.

Make home appliance simulation activate time window for the next day if it can not be
run today.

Improve simulation speed.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

---------

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2025-11-08 15:42:18 +01:00
committed by GitHub
parent c50cdd95cb
commit 3599088dce
18 changed files with 1769 additions and 1345 deletions
Download Patch File Download Diff File Expand all files Collapse all files

113
src/akkudoktoreos/optimization/genetic/geneticsolution.py
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@@ -6,7 +6,9 @@ import pandas as pd
from loguru import logger
from pydantic import Field, field_validator
from akkudoktoreos.config.config import get_config
from akkudoktoreos.core.coreabc import (
ConfigMixin,
)
from akkudoktoreos.core.emplan import (
DDBCInstruction,
EnergyManagementPlan,
@@ -109,7 +111,7 @@ class GeneticSimulationResult(GeneticParametersBaseModel):
return NumpyEncoder.convert_numpy(field)[0]
class GeneticSolution(GeneticParametersBaseModel):
class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
"""**Note**: The first value of "Last_Wh_per_hour", "Netzeinspeisung_Wh_per_hour", and "Netzbezug_Wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
ac_charge: list[float] = Field(
@@ -228,18 +230,20 @@ class GeneticSolution(GeneticParametersBaseModel):
"""
from akkudoktoreos.core.ems import get_ems
config = get_config()
start_datetime = get_ems().start_datetime
start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
interval_hours = 1
power_to_energy_per_interval_factor = 1.0
# --- Create index based on list length and interval ---
n_points = len(self.result.Kosten_Euro_pro_Stunde)
# Ensure we only use the minimum of results and commands if differing
periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_hour)
time_index = pd.date_range(
start=start_datetime,
periods=n_points,
periods=periods,
freq=f"{interval_hours}h",
)
n_points = len(time_index)
end_datetime = start_datetime.add(hours=n_points)
# Fill solution into dataframe with correct column names
@@ -256,26 +260,42 @@ class GeneticSolution(GeneticParametersBaseModel):
solution = pd.DataFrame(
{
"date_time": time_index,
"load_energy_wh": self.result.Last_Wh_pro_Stunde,
"grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde,
"grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde,
"elec_price_prediction_amt_kwh": [v * 1000 for v in self.result.Electricity_price],
"costs_amt": self.result.Kosten_Euro_pro_Stunde,
"revenue_amt": self.result.Einnahmen_Euro_pro_Stunde,
"losses_energy_wh": self.result.Verluste_Pro_Stunde,
# result starts at start_day_hour
"load_energy_wh": self.result.Last_Wh_pro_Stunde[:n_points],
"grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde[:n_points],
"grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde[:n_points],
"costs_amt": self.result.Kosten_Euro_pro_Stunde[:n_points],
"revenue_amt": self.result.Einnahmen_Euro_pro_Stunde[:n_points],
"losses_energy_wh": self.result.Verluste_Pro_Stunde[:n_points],
},
index=time_index,
)
# Add battery data
solution["battery1_soc_factor"] = [v / 100 for v in self.result.akku_soc_pro_stunde]
operation: dict[str, list[float]] = {}
for hour, rate in enumerate(self.ac_charge):
if hour >= n_points:
solution["battery1_soc_factor"] = [
v / 100
for v in self.result.akku_soc_pro_stunde[:n_points] # result starts at start_day_hour
]
operation: dict[str, list[float]] = {
"genetic_ac_charge_factor": [],
"genetic_dc_charge_factor": [],
"genetic_discharge_allowed_factor": [],
}
# ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
for hour_idx, rate in enumerate(self.ac_charge):
if hour_idx < start_day_hour:
continue
if hour_idx >= start_day_hour + n_points:
break
ac_charge_hour = self.ac_charge[hour_idx]
dc_charge_hour = self.dc_charge[hour_idx]
discharge_allowed_hour = bool(self.discharge_allowed[hour_idx])
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour])
ac_charge_hour, dc_charge_hour, discharge_allowed_hour
)
operation["genetic_ac_charge_factor"].append(ac_charge_hour)
operation["genetic_dc_charge_factor"].append(dc_charge_hour)
operation["genetic_discharge_allowed_factor"].append(discharge_allowed_hour)
for mode in BatteryOperationMode:
mode_key = f"battery1_{mode.lower()}_op_mode"
factor_key = f"battery1_{mode.lower()}_op_factor"
@@ -289,15 +309,22 @@ class GeneticSolution(GeneticParametersBaseModel):
operation[mode_key].append(0.0)
operation[factor_key].append(0.0)
for key in operation.keys():
if len(operation[key]) != n_points:
error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
logger.error(error_msg)
raise ValueError(error_msg)
solution[key] = operation[key]
# Add EV battery solution
# eautocharge_hours_float start at hour 0 of start day
# result.EAuto_SoC_pro_Stunde start at start_datetime.hour
if self.eauto_obj:
if self.eautocharge_hours_float is None:
# Electric vehicle is full enough. No load times.
solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
self.eauto_obj.initial_soc_percentage / 100.0
] * n_points
solution["genetic_ev_charge_factor"] = [0.0] * n_points
# operation modes
operation_mode = BatteryOperationMode.IDLE
for mode in BatteryOperationMode:
@@ -311,12 +338,17 @@ class GeneticSolution(GeneticParametersBaseModel):
solution[factor_key] = [0.0] * n_points
else:
solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
v / 100 for v in self.result.EAuto_SoC_pro_Stunde
v / 100 for v in self.result.EAuto_SoC_pro_Stunde[:n_points]
]
operation = {}
for hour, rate in enumerate(self.eautocharge_hours_float):
if hour >= n_points:
operation = {
"genetic_ev_charge_factor": [],
}
for hour_idx, rate in enumerate(self.eautocharge_hours_float):
if hour_idx < start_day_hour:
continue
if hour_idx >= start_day_hour + n_points:
break
operation["genetic_ev_charge_factor"].append(rate)
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
rate, 0.0, False
)
@@ -333,11 +365,16 @@ class GeneticSolution(GeneticParametersBaseModel):
operation[mode_key].append(0.0)
operation[factor_key].append(0.0)
for key in operation.keys():
if len(operation[key]) != n_points:
error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
logger.error(error_msg)
raise ValueError(error_msg)
solution[key] = operation[key]
# Add home appliance data
if self.washingstart:
solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour
# result starts at start_day_hour
solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour[:n_points]
# Fill prediction into dataframe with correct column names
# - pvforecast_ac_energy_wh_energy_wh: PV energy prediction (positive) in wh
@@ -445,10 +482,13 @@ class GeneticSolution(GeneticParametersBaseModel):
generated_at=to_datetime(),
comment="Optimization solution derived from GeneticSolution.",
valid_from=start_datetime,
valid_until=start_datetime.add(hours=config.optimization.horizon_hours),
valid_until=start_datetime.add(hours=self.config.optimization.horizon_hours),
total_losses_energy_wh=self.result.Gesamt_Verluste,
total_revenues_amt=self.result.Gesamteinnahmen_Euro,
total_costs_amt=self.result.Gesamtkosten_Euro,
fitness_score={
self.result.Gesamtkosten_Euro,
},
prediction=PydanticDateTimeDataFrame.from_dataframe(prediction),
solution=PydanticDateTimeDataFrame.from_dataframe(solution),
)
@@ -460,6 +500,7 @@ class GeneticSolution(GeneticParametersBaseModel):
from akkudoktoreos.core.ems import get_ems
start_datetime = get_ems().start_datetime
start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
plan = EnergyManagementPlan(
id=f"plan-genetic@{to_datetime(as_string=True)}",
generated_at=to_datetime(),
@@ -471,10 +512,15 @@ class GeneticSolution(GeneticParametersBaseModel):
last_operation_mode: Optional[str] = None
last_operation_mode_factor: Optional[float] = None
resource_id = "battery1"
logger.debug("BAT: {} - {}", resource_id, self.ac_charge)
for hour, rate in enumerate(self.ac_charge):
# ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_hour:])
for hour_idx, rate in enumerate(self.ac_charge):
if hour_idx < start_day_hour:
continue
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour])
self.ac_charge[hour_idx],
self.dc_charge[hour_idx],
bool(self.discharge_allowed[hour_idx]),
)
if (
operation_mode == last_operation_mode
@@ -484,7 +530,7 @@ class GeneticSolution(GeneticParametersBaseModel):
continue
last_operation_mode = operation_mode
last_operation_mode_factor = operation_mode_factor
execution_time = start_datetime.add(hours=hour)
execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
plan.add_instruction(
FRBCInstruction(
resource_id=resource_id,
@@ -496,6 +542,7 @@ class GeneticSolution(GeneticParametersBaseModel):
)
# Add EV battery instructions (fill rate based control)
# eautocharge_hours_float start at hour 0 of start day
if self.eauto_obj:
resource_id = self.eauto_obj.device_id
if self.eautocharge_hours_float is None:
@@ -513,8 +560,12 @@ class GeneticSolution(GeneticParametersBaseModel):
else:
last_operation_mode = None
last_operation_mode_factor = None
logger.debug("EV: {} - {}", resource_id, self.eauto_obj.charge_array)
for hour, rate in enumerate(self.eautocharge_hours_float):
logger.debug(
"EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:]
)
for hour_idx, rate in enumerate(self.eautocharge_hours_float):
if hour_idx < start_day_hour:
continue
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
rate, 0.0, False
)
@@ -526,7 +577,7 @@ class GeneticSolution(GeneticParametersBaseModel):
continue
last_operation_mode = operation_mode
last_operation_mode_factor = operation_mode_factor
execution_time = start_datetime.add(hours=hour)
execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
plan.add_instruction(
FRBCInstruction(
resource_id=resource_id,
@@ -542,7 +593,7 @@ class GeneticSolution(GeneticParametersBaseModel):
resource_id = "homeappliance1"
operation_mode = ApplianceOperationMode.RUN # type: ignore[assignment]
operation_mode_factor = 1.0
execution_time = start_datetime.add(hours=self.washingstart)
execution_time = start_datetime.add(hours=self.washingstart - start_day_hour)
plan.add_instruction(
DDBCInstruction(
resource_id=resource_id,