System and Method for Optimizing A Demand Response Event

• US 20140278687A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Trove Predictive Data Science LLC
• Original Assignee: Gridglo, LLC
• Inventors: McConky, Katie, Viens, Richard
• Application Number:
  US13/833,815
• Publication Number:
  US 20140278687A1
• Time in Patent Office: Days
• US Class Current: 705/7.22
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling

Abstract

A system and method is disclosed for optimizing a demand response event. Strategizes for demand response events developed according to the present disclosure consider a customer'"'"'s individual satisfaction ranking in creating a customer-specific demand response participation schedule so that customer dissatisfaction is reduced and a more uniform customer response across the entire demand response event is achieved. Customers participating in a demand response event need not participate in the entire event and can limit their participation to coincide with their individual satisfaction ranking.

Claims

1. A method for optimizing a demand response event of an entity, the method comprising the steps of:

(a) determining, using a first processor, a customer event forecast for each customer of a plurality of customers;

(b) determining, using the first processor, a customer satisfaction ranking for each customer of the plurality of customers;

(c) selecting, using the first processor, a group of customers from the plurality of customers, wherein the selecting is based at least in part on the determined customer event forecast and the determined customer satisfaction ranking;

(d) determining, using the first processor, for each customer in the group of customers a participation schedule for the demand response event;

(e) calculating, using the first processor, a first predicted performance metric for the demand response event for one of the customers in the group of customers, wherein the first predicted performance metric is based at least in part on the participation schedule for the one customer in the group of customers; and

(f) transmitting to the entity the participation schedule for each customer in the group of customers.

2. The method of claim 1 further comprising the steps of:

(g) receiving from the entity data from the demand response event;

(h) calculating, using a second processor, a first actual performance metric from the data for the one customer in the group of customers; and

(i) comparing, using the second processor, the first actual performance metric to the first predicted performance metric.

3. The method of claim 2 wherein said first processor and said second processor are the same.

4. The method of claim 1 further comprising the step of:

(g) calculating, using the first processor, a second predicted performance metric for the demand response event, wherein the second predicted performance metric is based at least in part on an aggregation of predicted performance metrics for each one of a second plurality of customers in the group of customers.

5. The method of claim 4 further comprising the steps of:

(g) receiving from the entity data from the demand response event;

(h) calculating, using a second processor, a second actual performance metric for the data, wherein the second actual performance metric is based at least in part on an aggregation of actual performance metrics for said each one of a plurality of customers in the group of customers; and

(i) comparing, using the second processor, the second actual performance metric to the second predicted performance metric.

6. The method of claim 5 wherein said first processor and said second processor are the same.

7. The method of claim 1 further comprising the steps of:

(g) determining, using the first processor, an aggregate customer satisfaction curve for the customers in the group of customers; and

(h) determining, using the first processor, a target demand response request based at least in part on the aggregate customer satisfaction curve.

8. The method of claim 1 wherein the customer event forecast is determined based on an attribute selected from the group consisting of:

customer defined participation levels, customer historical consumption data, customer historical demand response performance, customer demand response contract information, demand response event parameters, and combinations thereof.

9. The method of claim 1 wherein the customer satisfaction ranking is determined based on an attribute selected from the group consisting of:

customer defined participation levels, external customer attributes, customer historical consumption data, customer historical demand response performance, demand response event parameters, and combinations thereof.

10. The method of claim 1 wherein the participation schedule comprises a plurality of participation windows, and wherein said each customer in the group of customers is scheduled to participate in the demand response event for at least one of said plurality of participation windows.

11. The method of claim 10 wherein a first number of participation windows scheduled for a first customer in the group of customers is different than a second number of participation windows scheduled for a second customer in the group of customers.

12. The method of claim 1 wherein the customer event forecast includes a first plurality of customer-defined participation levels for a first customer and a second plurality of customer-defined participation levels for a second customer.

13. The method of claim 12 wherein a customer-defined participation level from the first customer is different than a corresponding customer-defined participation level from the second customer.

14. The method of claim 12 wherein the first plurality of customer-defined participation levels for the first customer includes a first satisfaction level selected by the first customer.

15. The method of claim 14 wherein the second plurality of customer-defined participation levels for the second customer includes a second satisfaction level selected by the second customer which is different than the first satisfaction level selected by the first customer.

16. The method of claim 1 wherein the customer satisfaction ranking includes a first plurality of customer-defined participation levels for a first customer and a second plurality of customer-defined participation levels for a second customer.

17. The method of claim 16 wherein a customer-defined participation level from the first customer is different than a corresponding customer-defined participation level from the second customer.

18. The method of claim 16 wherein the first plurality of customer-defined participation levels for the first customer includes a first satisfaction level selected by the first customer.

19. The method of claim 18 wherein the second plurality of customer-defined participation levels for the second customer includes a second satisfaction level selected by the second customer which is different than the first satisfaction level selected by the first customer.

20. A system for optimizing a demand response event of an entity, the system comprising:

a memory device for storing customer information and for storing parameters for the demand response event;

a first processor for determining a customer event forecast for each of a plurality of customers;

said first processor for determining a customer satisfaction ranking for each of the plurality of customers;

said first processor for selecting a group of customers from the plurality of customers, wherein the selecting is based at least in part on the determined customer event forecast and the determined customer satisfaction ranking;

said first processor for determining for each customer in the group of customers a participation schedule for the demand response event;

said first processor for calculating a first predicted performance metric for the demand response event for one of the customers in the group of customers, wherein the first predicted performance metric is based at least in part on the participation schedule for the one customer in the group of customers; and

a transmitter for transmitting to the entity the participation schedule for each customer in the group of customers.

21. The system of claim 20 further comprising:

a receiver for receiving from the entity data from the demand response event;

a second processor for calculating a first actual performance metric from the data for the one customer in the group of customers; and

said second processor for comparing the first actual performance metric to the first predicted performance metric.

22. The system of claim 21 wherein said transmitter and said receiver are incorporated into a single transceiver device, and wherein said first processor and said second processor are the same.

23. A machine-readable medium having stored thereon a plurality of executable instructions to be executed by a processor, the plurality of executable instructions comprising instructions to:

(a) determine a customer event forecast for each of a plurality of customers;

(b) determine a customer satisfaction ranking for each of the plurality of customers;

(c) select a group of customers from the plurality of customers, wherein the selecting is based at least in part on the determined customer event forecast and the determined customer satisfaction ranking;

(d) determine for each customer in the group of customers a participation schedule for the demand response event;

(e) calculate a first predicted performance metric for the demand response event for one of the customers in the group of customers, wherein the first predicted performance metric is based at least in part on the participation schedule for the one customer in the group of customers; and

(f) transmit to the entity the participation schedule for each customer in the group of customers.