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class_ems: AC / DC Charging

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class_optimize: Timing Bugs fixed
class_numpy_encoder: JSON Encoder with Numpy support
visualize: AC / DC / Discharge
test_class_ems_2: New Test for AC / DC charging decision
This commit is contained in:
Andreas
2024-10-16 15:40:04 +02:00
committed by Andreas
parent cafed7eaca
commit 87ec02a90e
8 changed files with 585 additions and 240 deletions
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248
single_test_optimization.py
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@@ -5,60 +5,63 @@ import time
# Import necessary modules from the project
from akkudoktoreos.class_optimize import optimization_problem
start_hour = 10
from akkudoktoreos.visualize import *
from akkudoktoreos.class_numpy_encoder import *
start_hour = 0
# PV Forecast (in W)
pv_forecast = [
0,
0,
0,
0,
0,
0,
0,
8.05,
352.91,
728.51,
930.28,
1043.25,
1106.74,
1161.69,
6018.82,
5519.07,
3969.88,
3017.96,
1943.07,
1007.17,
319.67,
7.88,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5.04,
335.59,
705.32,
1121.12,
1604.79,
2157.38,
1433.25,
5718.49,
4553.96,
3027.55,
2574.46,
1720.4,
963.4,
383.3,
0,
0,
0,
]
pv_forecast = np.zeros(48)
pv_forecast[12] = 5000
# [
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 8.05,
# 352.91,
# 728.51,
# 930.28,
# 1043.25,
# 1106.74,
# 1161.69,
# 1018.82,
# 1519.07,
# 1969.88,
# 1017.96,
# 1043.07,
# 1007.17,
# 319.67,
# 7.88,
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 0,
# 5.04,
# 335.59,
# 705.32,
# 1121.12,
# 1604.79,
# 2157.38,
# 1433.25,
# 5718.49,
# 4553.96,
# 3027.55,
# 2574.46,
# 1720.4,
# 963.4,
# 383.3,
# 0,
# 0,
# 0,
# ]
# Temperature Forecast (in degree C)
temperature_forecast = [
@@ -113,56 +116,60 @@ temperature_forecast = [
]
# Electricity Price (in Euro per Wh)
strompreis_euro_pro_wh = [
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
0.0003384,
0.0003318,
0.0003284,
0.0003283,
0.0003289,
0.0003334,
0.0003290,
0.0003302,
0.0003042,
0.0002430,
0.0002280,
0.0002212,
0.0002093,
0.0001879,
0.0001838,
0.0002004,
0.0002198,
0.0002270,
0.0002997,
0.0003195,
0.0003081,
0.0002969,
0.0002921,
0.0002780,
]
strompreis_euro_pro_wh = np.full(48, 0.001)
strompreis_euro_pro_wh [0:10] = 0.00001
strompreis_euro_pro_wh [11:15] = 0.00005
strompreis_euro_pro_wh [20] = 0.00001
# [
# 0.0000384,
# 0.0000318,
# 0.0000284,
# 0.0008283,
# 0.0008289,
# 0.0008334,
# 0.0008290,
# 0.0003302,
# 0.0003042,
# 0.0002430,
# 0.0002280,
# 0.0002212,
# 0.0002093,
# 0.0001879,
# 0.0001838,
# 0.0002004,
# 0.0002198,
# 0.0002270,
# 0.0002997,
# 0.0003195,
# 0.0003081,
# 0.0002969,
# 0.0002921,
# 0.0002780,
# 0.0003384,
# 0.0003318,
# 0.0003284,
# 0.0003283,
# 0.0003289,
# 0.0003334,
# 0.0003290,
# 0.0003302,
# 0.0003042,
# 0.0002430,
# 0.0002280,
# 0.0002212,
# 0.0002093,
# 0.0001879,
# 0.0001838,
# 0.0002004,
# 0.0002198,
# 0.0002270,
# 0.0002997,
# 0.0003195,
# 0.0003081,
# 0.0002969,
# 0.0002921,
# 0.0002780,
# ]
# Overall System Load (in W)
gesamtlast = [
@@ -221,10 +228,10 @@ start_solution = None
# Define parameters for the optimization problem
parameter = {
# Cost of storing energy in battery (per Wh)
"preis_euro_pro_wh_akku": 10e-05,
# Value of energy in battery (per Wh)
"preis_euro_pro_wh_akku": 0e-05,
# Initial state of charge (SOC) of PV battery (%)
"pv_soc": 80,
"pv_soc": 15,
# Battery capacity (in Wh)
"pv_akku_cap": 26400,
# Yearly energy consumption (in Wh)
@@ -242,7 +249,7 @@ parameter = {
# Electricity price forecast (48 hours)
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
# Minimum SOC for electric car
"eauto_min_soc": 20,
"eauto_min_soc": 50,
# Electric car battery capacity (Wh)
"eauto_cap": 60000,
# Charging efficiency of the electric car
@@ -250,7 +257,7 @@ parameter = {
# Charging power of the electric car (W)
"eauto_charge_power": 11040,
# Current SOC of the electric car (%)
"eauto_soc": 5,
"eauto_soc": 15,
# Current PV power generation (W)
"pvpowernow": 211.137503624,
# Initial solution for the optimization
@@ -258,7 +265,7 @@ parameter = {
# Household appliance consumption (Wh)
"haushaltsgeraet_wh": 5000,
# Duration of appliance usage (hours)
"haushaltsgeraet_dauer": 2,
"haushaltsgeraet_dauer": 0,
# Minimum Soc PV Battery
"min_soc_prozent": 15,
}
@@ -282,8 +289,25 @@ elapsed_time = end_time - start_time
print(f"Elapsed time: {elapsed_time:.4f} seconds")
# Print or visualize the result
# pprint(ergebnis)
#json_data = json.dumps(ergebnis)
#print(json_data)
ac_charge, dc_charge, discharge = (ergebnis["ac_charge"],ergebnis["dc_charge"],ergebnis["discharge_allowed"])
visualisiere_ergebnisse(
gesamtlast,
pv_forecast,
strompreis_euro_pro_wh,
ergebnis["result"],
ac_charge,
dc_charge,
discharge,
temperature_forecast,
start_hour,
48,
np.full(48, parameter["einspeiseverguetung_euro_pro_wh"]),
filename="visualization_results.pdf",
extra_data=None,
)
json_data = NumpyEncoder.dumps(ergebnis)
print(json_data)