How to use the DR simulator Tool
How to use this package
Use this package to simulate any incentive-based demand response events for a custom distribution or distribution learned from historic demand response events.
Essentially you need to set two types of parameters
Program Parameters
Simulation Parameters
3. Program Parameters
These are the set of parameters that describes the DR program and its rules.
Parameter Name |
Definition |
|---|---|
Minimum number of event days |
The minimum number of days the program event is called for a customer per month |
Maximum number of event days |
The maximum number of days the program event is called for a customer per month |
Minimum duration of event |
The event should last for more than the minimum duration specified by the program |
Maximum duration of event |
The event should not last longer than the maximum duration specified by the program |
Program start time |
The program can be either 24 hours or last for a specified period of time and the start time is generally provided if it is not a 24-hour period |
Program end time |
The program can be either 24 hours or last for a specified period of time and the end time is generally provided if it is not a 24-hour period |
Events per day |
The maximum event that a customer can provide on a single day. |
Maximum consecutive event days |
The maximum consecutive days the customer can be called in a particular month |
Notification type |
The event is generally notified the day before or the day of and is captures by this parameter |
Notification time |
If the event is notified the day before or the day of the program generally specifies the time. Note: This can also be historic event related |
Number of similar weekdays |
The number of previous weekdays used to calculate the baseline |
1. Simulation Parameters
These parameters describes the likelihood of the DR event’s number, duration and dates
Parameter Name |
Definition |
Distribution Type (default) |
Distribution Parameters |
|---|---|---|---|
Number of days |
The number of event days given the time period |
Poisson |
λ_days - mean number of days |
Event duration |
The event duration for the DR events |
Poisson |
λ_dur - mean duration of events |
Start time |
Start time of the event |
Uniform |
Ts - Program start time, Te - Program end time |
Event days |
The probability of each day is selected |
Uniform |
d ∈ D, where D = Weekdays |
Output
After you populate both the parameter’s values, you can simulate DR events for any given month & year. The output from a sample of a DR event would look like
Event Date |
Duration |
Start Time |
End Time |
Notification Time |
Similar Weekdays |
|---|---|---|---|---|---|
2020-08-10 |
1 |
19:00 |
20:00 |
2020-08-09 17:00 |
2020-08-07, 2020-08-06,… |
2020-08-12 |
3 |
17:00 |
20:00 |
2020-08-11 17:00 |
2020-08-11, 2020-08-07,… |
2020-08-28 |
1 |
19:00 |
20:00 |
2020-08-27 17:00 |
2020-08-27, 2020-08-26,… |
2020-08-31 |
3 |
17:00 |
20:00 |
2020-08-30 17:00 |
2020-08-27, 2020-08-26,… |
(the above sample output is simulated from INSERT LINK program and simulation parameters)
You can also generate Monte-Carlo samples by calling DemandResponseEvents.create_dr_events_mtcs function.