Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2026-08-31 12:46:38 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

fix(genetic): limit EV charging to unmet min SoC

Browse Source
This commit is contained in:
Andreas
2026-07-05 12:56:08 +02:00
parent 631d428e89
commit cd5cdf8b47
2 changed files with 90 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/akkudoktoreos/optimization/genetic/genetic.py
+23 -13
View File
@@ -983,10 +983,7 @@ class GeneticOptimization(OptimizationBase):
"Penalty function parameter `ev_soc_miss` not configured, using {}.", penalty
)
ev_soc_percentage = self.simulation.ev.current_soc_percentage()
if (
ev_soc_percentage < parameters.eauto.min_soc_percentage
or ev_soc_percentage > parameters.eauto.max_soc_percentage
):
if ev_soc_percentage < parameters.eauto.min_soc_percentage:
gesamtbilanz += (
abs(parameters.eauto.min_soc_percentage - ev_soc_percentage) * penalty
)
@@ -1020,10 +1017,25 @@ class GeneticOptimization(OptimizationBase):
logger.debug("Start optimize: {}", start_solution)
# Insert the start solution into the population if provided
# Insert the start solution into the population if provided and compatible with the
# currently active genome layout. EV optimization adds one gene per prediction hour,
# so a cached solution from a previous run without EV optimization must not be reused.
if start_solution is not None:
for _ in range(10):
population.insert(0, creator.Individual(start_solution))
expected_length = self.config.prediction.hours
if self.optimize_ev:
expected_length += self.config.prediction.hours
if self.opti_param.get("home_appliance", 0) > 0:
expected_length += 1
if len(start_solution) == expected_length:
for _ in range(10):
population.insert(0, creator.Individual(start_solution))
else:
logger.warning(
"Ignoring start_solution with incompatible length {} (expected {}).",
len(start_solution),
expected_length,
)
# Run the evolutionary algorithm
pop, log = algorithms.eaMuPlusLambda(
@@ -1105,7 +1117,7 @@ class GeneticOptimization(OptimizationBase):
)
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
parameters.eauto.min_soc_percentage > parameters.eauto.initial_soc_percentage
)
# electrical vehicle charge rates
if parameters.eauto.charge_rates is not None:
@@ -1208,11 +1220,9 @@ class GeneticOptimization(OptimizationBase):
if self.simulation.home_appliance:
washingstart_int = self.simulation.home_appliance_start_hour
eautocharge_hours_float = (
[self.ev_possible_charge_values[i] for i in eautocharge_hours_index]
if eautocharge_hours_index is not None
else None
)
eautocharge_hours_float = None
if eautocharge_hours_index is not None and self.simulation.ev is not None:
eautocharge_hours_float = self.simulation.ev.charge_array.tolist()
# Simulation may have changed something, use simulation values
ac_charge_hours = self.simulation.ac_charge_hours