SYSTEM AND METHOD FOR ESTIMATING DEMAND RESPONSE IN ELECTRIC POWER SYSTEMS

• US 20120136496A1
• Filed: 11/30/2010
• Published: 05/31/2012
• Est. Priority Date: 11/30/2010
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Black, Jason Wayne, Tyagi, Rajesh, Massey, Jerry Steven, Williams, James Dickson
• Application Number:
  US12/957,299
• Publication Number:
  US 20120136496A1
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: H02J 2310/60 Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
• CPC Class Codes: H02J 3/144 Demand-response operation of the power transmission or distribution network
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A system and method for use in a network of utility customers to estimate demand response for various utility DR programs. The system first aggregates the premises into clusters based upon customer information for each of the premises. A response estimation function is developed for each of the clusters. The demand response event request is then processed according to the clusters and the response estimation functions.

Claims

1. A method, comprising:

aggregating premises in a network of utility premises into clusters based upon premise information for each of the premises;

developing a response estimation function for each of the clusters; and

processing a demand response event request using the clusters based upon the response estimation function for each of the clusters.

2. The method of claim 1, wherein the response estimation function is developed from at least one of an empirical model, regression techniques, Bayesian techniques, and artificial intelligence.

3. The method of claim 1, further comprising:

updating the response estimation function.

4. The method of claim 1, further comprising:

updating the clusters periodically.

5. The method of claim 1, wherein the customer information comprises at least one of demand response program parameters, event response information, load and demand characteristics of the premises, rebound parameters of the premises, and premise attributes including geographic location, power phase, types of appliances, and load shed windows.

6. The method of claim 1, wherein processing the demand response event request comprises:

estimating a response of each of the clusters to the demand response event request using the response estimation function; and

assigning the premises from one or more of the clusters into dispatch groups based upon the response of each of the clusters; and

dispatching the dispatch groups to respond to the demand response event request.

7. The method of claim 6, further comprising:

determining an event response of the premises in each of the clusters used to respond to the demand response event request; and

updating the response estimation function for each of the clusters used to process the demand response event request based on the event response.

8. The method of claim 6, wherein the dispatch groups comprise premises from one or more of the clusters.

9. The method of claim 6, wherein estimating the response comprises:

sampling a subset of the premises in each of the clusters to determine substantially real-time usage information for each of the premises in the subset; and

assigning the substantially real-time usage information of the subset in each of the clusters to the entire corresponding cluster.

10. The method of claim 7, wherein determining the event response of the premises in each of the clusters used to respond to the demand response event request comprises:

comparing the event response of each of the premises in each of the clusters that responded to the demand response event request with premises in the cluster not participating in the demand response program; and

determining the event response of the premises in each of the clusters based upon a comparison result.

11. The method of claim 1, wherein aggregating premises into clusters comprises:

aggregating the premises into response clusters based on event response information for each of the premises;

wherein the premise information for each of the premises comprises the event response information.

12. The method of claim 1, wherein aggregating premises into clusters comprises:

aggregating the premises into usage clusters based upon the premise information for each of the premises.

13. The method of claim 12, further comprises:

dividing the premises in each of the usage clusters into response clusters based on event response information for each of the premises;

developing a response estimation function for each of the response clusters; and

wherein the premise information for each of the premises comprises the event response information.

14. The method of claim 13, wherein processing the demand response event request comprises:

estimating a response of each of the response clusters to the demand response event request using the response estimation function; and

assigning the premises from one or more of the response clusters for one or more of the usage clusters into dispatch groups based upon the response of each of the response clusters; and

dispatching the dispatch groups to respond to the demand response event request.

15. The method of claim 14, further comprising:

determining an event response of the premises in each of the response clusters associated with each of the usage clusters used to respond to the demand response event request; and

updating the response estimation function for each of the response clusters used to process the demand response event request based on the event response.

16. The method of claim 13, wherein the response estimation function is developed from at least one of an empirical model, regression techniques, Bayesian techniques, and artificial intelligence.

17. The method of claim 14, wherein estimating the response comprises:

sampling a subset of the premises in each of the response clusters to determine substantially real-time usage information for each of the premises in the subset; and

assigning the substantially real-time usage information of the subset in each of the response clusters to the entire corresponding response cluster.

18. The method of claim 15, wherein determining the event response of the premises in each of the response clusters associated with each of the usage clusters used to respond to the demand response event request comprises:

comparing the event response of each of the premises in each of the response clusters that responded to the demand response event request with premises in the usage cluster not participating in the demand response program; and

determining the event response of the premises in each of the response clusters based upon a comparison result.

19. The method of claim 18, further comprising:

updating the response clusters in each of the associated usage clusters.

20. The method of claim 19, further comprising:

updating the usage clusters periodically.

21. The method of claim 14, wherein the dispatch groups comprise premises from one or more of the usage clusters.

22. The method of claim 13, wherein dividing the premises in each of the usage clusters into the response clusters comprises:

dividing the premises in each of the usage clusters into response cluster sets for each demand response program.

23. The method of claim 17, wherein each of the usage clusters comprises a response cluster comprising premises that are not participating in the demand response program;

wherein the sampling comprises sampling a subset of premises in the response cluster comprising premises that are not participating in the demand response program; and

assigning the substantially real-time usage information of the subset of premises not participating in the demand response program in each of the response clusters to the entire corresponding response cluster.

24. A non-transitory computer-readable medium comprising computer-readable instructions of a computer program that, when executed by a processor, cause the processor to perform a method, comprising:

aggregating premises in a network of utility premises into clusters based upon premise information for each of the premises;

developing a response estimation function for each of the clusters; and

processing a demand response event request using the clusters based upon the response estimation function for each of the clusters.

25. The non-transitory computer-readable medium of claim 24, further comprising:

updating the response estimation function; and

updating the clusters periodically.

26. The non-transitory computer-readable medium of claim 24, wherein processing the demand response event request comprises:

estimating a response of each of the clusters to the demand response event request using the response estimation function; and

assigning the premises from one or more of the clusters into dispatch groups based upon the response of each of the clusters; and

dispatching the dispatch groups to respond to the demand response event request.

27. The non-transitory computer-readable medium of claim 26, further comprising:

determining an event response of the premises in each of the clusters used to respond to the demand response event request; and

updating the response estimation function for each of the clusters used to process the demand response event request based on the event response.

28. The non-transitory computer-readable medium of claim 26, wherein the dispatch groups comprise premises from one or more of the clusters.

29. The non-transitory computer-readable medium of claim 26, wherein estimating the response comprises:

sampling a subset of the premises in each of the clusters to determine substantially real-time usage information for each of the premises in the subset; and

assigning the substantially real-time usage information of the subset in each of the clusters to the entire corresponding cluster.

30. The non-transitory computer-readable medium of claim 27, wherein determining the event response of the premises in each of the clusters used to respond to the demand response event request comprises:

comparing the event response of each of the premises in each of the clusters that responded to the demand response event request with premises in the cluster not participating in the demand response program; and

determining the event response of the premises in each of the clusters based upon a comparison result.

31. The non-transitory computer-readable medium of claim 24, wherein aggregating premises into clusters comprises:

aggregating the premises into response clusters based on event response information for each of the premises;

wherein the premise information for each of the premises comprises the event response information.

32. The non-transitory computer-readable medium of claim 24, wherein aggregating premises into clusters comprises:

aggregating the premises into usage clusters based upon the premise information for each of the premises.

33. The non-transitory computer-readable medium of claim 32, further comprises:

dividing the premises in each of the usage clusters into response clusters based on event response information for each of the premises;

developing a response estimation function for each of the response clusters; and

wherein the premise information for each of the premises comprises the event response information.

34. A system for processing demand response events in a utility network, comprising:

premises connected to the utility network, wherein each of the premises comprises one or more utility consuming devices; and

a demand response module communicatively coupled to the premises and one or more utilities, wherein the demand response module;

aggregates premises in a network of utility premises into clusters based upon premise information for each of the premises;

develops a response estimation function for each of the clusters; and

processes a demand response event request using the clusters based upon the response estimation function for each of the clusters.

35. The system of claim 34, further comprising:

a database coupled to the demand response module for storing demand response event information.

36. The system of claim 34, wherein the demand response module is further configured to:

estimate a response of each of the clusters to the demand response event request for the demand response program using a response estimation function;

assign the premises from one or more of the clusters into dispatch groups based upon the response of each of the clusters; and

dispatch the dispatch groups to respond to the demand response event request.

37. The system of claim 36, wherein the demand response module is further configured to:

determine an event response of the premises in each of the clusters used to respond to the demand response event request; and

update the response estimation function for each of the clusters used to process the demand response event request based on the event response.

38. The system of claim 34, wherein the demand response module is further configured to:

aggregate the premises into response clusters based on event response information for each of the premises;

wherein the premise information for each of the premises comprises the event response information.

39. The system of claim 34, wherein the demand response module is further configured to:

aggregate the premises into usage clusters based upon the premise information for each of the premises.

40. The system of claim 39, wherein the demand response module is further configured to:

divide the premises in each of the usage clusters into response clusters based on event response information for each of the premises;

develop a response estimation function for each of the response clusters; and

wherein the premise information for each of the premises comprises the event response information.