Appliances with multiple cycles¶
An example of an appliance with multiple cycle is a fridge. Fridges usually have different duty cycles, which can be estimated based on seasonal temperature trends and/or frequency of user interaction (e.g., how often the door gets opened).
In this example a fridge with 3 different duty cycles is modelled. The time windows are defined for 3 different cycles across 3 different season types:
season type |
Standard cycle |
Intermediate cycle |
Intensive cycle |
|---|---|---|---|
Hot |
00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 |
05:00:00 - 07:59:00 |
08:00:00 - 20:00:00 |
Warm |
00:00:00 - 04:59:00 & 18:01:00 - 23:59:00 |
05:00:00 - 09:39:00 |
09:40:00 - 18:00:00 |
Cold |
00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 |
05:00:00 - 20:00:00 |
Creating the user and appliance¶
# importing functions
from ramp import User, UseCase, get_day_type
import pandas as pd
# creating user
household = User()
# creating the appliance
fridge = household.Appliance(
name="Fridge",
number=1,
power=200,
num_windows=1,
func_time=1400,
time_fraction_random_variability=0,
func_cycle=30,
fixed="yes",
fixed_cycle=3, # number of cycles
)
# setting the functioning windows
fridge.windows([0, 1440]) # always on during the whole year
Assigining the specific cycles¶
# assiging the specific cycles
# first cycle: standard cycle
fridge.specific_cycle_1(
p_11=200,
t_11=20,
p_12=5,
t_12=10,
)
# second cycle: intermediate cycle
fridge.specific_cycle_2(
p_21=200,
t_21=15,
p_22=5,
t_22=15,
)
# third cycle: intensive cycle
fridge.specific_cycle_3(
p_31=200,
t_31=10,
p_32=5,
t_32=20,
)
After defining the cycle power and duration parameters, the time windows of year at which the cycles happens should be specifid by:
# defining cycle behaviour
fridge.cycle_behaviour(
cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]
)
Buidling the profiles¶
use_case = UseCase(users=[household])
peak_time_range = use_case.calc_peak_time_range()
# days to build the profiles
days = [
"2020-05-16",
"2020-08-16",
"2020-12-16",
]
profiles = pd.DataFrame(index=range(0, 1440), columns=days)
for day_idx, day in enumerate(days):
profile = household.generate_single_load_profile(
prof_i=day_idx, # the day to generate the profile
peak_time_range=peak_time_range,
day_type=get_day_type(day),
)
profiles[day] = profile
print(profiles)
2020-05-16 2020-08-16 2020-12-16
0 0.001 5.000 0.001
1 0.001 5.000 0.001
2 0.001 5.000 0.001
3 5.000 5.000 0.001
4 5.000 5.000 0.001
... ... ... ...
1435 0.001 0.001 0.001
1436 0.001 0.001 0.001
1437 0.001 0.001 0.001
1438 0.001 0.001 0.001
1439 0.001 0.001 0.001
[1440 rows x 3 columns]
# plotting a part of the days
profiles.iloc[400:500].plot()
<AxesSubplot:>
