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Rename settings variables (remove prefixes)

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This commit is contained in:
Dominique Lasserre
2025-01-18 14:26:34 +01:00
parent 1e1bac9fdb
commit 3257dac92b
58 changed files with 867 additions and 918 deletions
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57
src/akkudoktoreos/optimization/genetic.py
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@@ -110,12 +110,8 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
):
"""Initialize the optimization problem with the required parameters."""
self.opti_param: dict[str, Any] = {}
self.fixed_eauto_hours = (
self.config.prediction.prediction_hours - self.config.optimization.optimization_hours
)
self.possible_charge_values = (
self.config.optimization.optimization_ev_available_charge_rates_percent
)
self.fixed_eauto_hours = self.config.prediction.hours - self.config.optimization.hours
self.possible_charge_values = self.config.optimization.ev_available_charge_rates_percent
self.verbose = verbose
self.fix_seed = fixed_seed
self.optimize_ev = True
@@ -182,27 +178,25 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
total_states = 3 * len_ac
# 1. Mutating the charge_discharge part
charge_discharge_part = individual[: self.config.prediction.prediction_hours]
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.prediction_hours] = charge_discharge_mutated
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.prediction_hours : self.config.prediction.prediction_hours
* 2
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.prediction_hours - self.fixed_eauto_hours :
] = [0] * self.fixed_eauto_hours
individual[
self.config.prediction.prediction_hours : self.config.prediction.prediction_hours
* 2
] = ev_charge_part_mutated
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:
@@ -216,15 +210,13 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
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.prediction_hours)
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.prediction_hours)
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
@@ -257,7 +249,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
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.prediction_hours)
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:
@@ -279,17 +271,12 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
3. Dishwasher start time (integer if applicable).
"""
# Discharge hours as a NumPy array of ints
discharge_hours_bin = np.array(
individual[: self.config.prediction.prediction_hours], dtype=int
)
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 = (
np.array(
individual[
self.config.prediction.prediction_hours : self.config.prediction.prediction_hours
* 2
],
individual[self.config.prediction.hours : self.config.prediction.hours * 2],
dtype=int,
)
if self.optimize_ev
@@ -401,7 +388,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
)
self.ems.set_ev_charge_hours(eautocharge_hours_float)
else:
self.ems.set_ev_charge_hours(np.full(self.config.prediction.prediction_hours, 0))
self.ems.set_ev_charge_hours(np.full(self.config.prediction.hours, 0))
return self.ems.simulate(self.ems.start_datetime.hour)
@@ -463,7 +450,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
# 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.optimization_ev_available_charge_rates_percent)
# 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
@@ -512,7 +499,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
if parameters.eauto and self.ems.ev
else 0
)
* self.config.optimization.optimization_penalty,
* self.config.optimization.penalty,
)
return (gesamtbilanz,)
@@ -580,7 +567,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
start_hour = self.ems.start_datetime.hour
einspeiseverguetung_euro_pro_wh = np.full(
self.config.prediction.prediction_hours, parameters.ems.einspeiseverguetung_euro_pro_wh
self.config.prediction.hours, parameters.ems.einspeiseverguetung_euro_pro_wh
)
# TODO: Refactor device setup phase out
@@ -591,7 +578,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
if parameters.pv_akku:
akku = Battery(parameters.pv_akku)
self.devices.add_device(akku)
akku.set_charge_per_hour(np.full(self.config.prediction.prediction_hours, 1))
akku.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
eauto: Optional[Battery] = None
if parameters.eauto:
@@ -599,7 +586,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
parameters.eauto,
)
self.devices.add_device(eauto)
eauto.set_charge_per_hour(np.full(self.config.prediction.prediction_hours, 1))
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
)