Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2025-10-30 22:36:21 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

Time problems in EMS / Optimization

Browse Source
This commit is contained in:
Andreas
2024-10-22 08:58:07 +02:00
committed by Andreas
parent ad550c874b
commit 24c7593056
2 changed files with 8 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
src/akkudoktoreos/class_ems.py
View File

@@ -82,10 +82,10 @@ class EnergieManagementSystem:
akku_soc_pro_stunde[0] = self.akku.ladezustand_in_prozent()
if self.eauto:
eauto_soc_pro_stunde[0] = self.eauto.ladezustand_in_prozent()
for stunde in range(start_stunde + 1, ende):
for stunde in range(start_stunde , ende):
stunde_since_now = stunde - start_stunde
# Accumulate loads and PV generation
verbrauch = self.gesamtlast[stunde]
verluste_wh_pro_stunde[stunde_since_now] = 0.0

10
src/akkudoktoreos/class_optimize.py
View File

@@ -219,13 +219,13 @@ class optimization_problem:
# Register separate mutation functions for each type of value:
# - Discharge state mutation (-5, 0, 1)
if self.optimize_dc_charge:
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.1)
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.2)
else:
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=6, indpb=0.1)
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=6, indpb=0.2)
# - Float mutation for EV charging values
self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(possible_ev_charge_currents) - 1, indpb=0.1)
self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(possible_ev_charge_currents) - 1, indpb=0.2)
# - Start hour mutation for household devices
self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.1)
self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.2)
# Register custom mutation function
self.toolbox.register("mutate", self.mutate)
@@ -426,7 +426,7 @@ class optimization_problem:
"evaluate",
lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case),
)
start_solution, extra_data = self.optimize(parameter["start_solution"], ngen=ngen)
start_solution, extra_data = self.optimize(parameter["start_solution"], ngen=ngen) #
# Perform final evaluation on the best solution
o = self.evaluate_inner(start_solution, ems, start_hour)