DYNAMIC LOAD PROFILING

• US 20120185105A1
• Filed: 01/18/2011
• Published: 07/19/2012
• Est. Priority Date: 01/18/2011
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: McMullin, Dale Robert
• Granted Patent: US 8,712,595 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A method for dynamic load profiling in a power network can include receiving static load data in the power network, generating a load forecast from the static load data, generating dynamic load data from data related to distributed assets in the power network and modifying the load forecast based on the dynamic load data for profiling the dynamic load data.

Claims

1. A method for dynamic load profiling in a power network, the method comprising:

receiving static load data in the power network;

generating a load forecast from the static load data;

predicting and generating dynamic load data from data related to distributed assets in the power network; and

modifying the load forecast based on the dynamic load data for profiling the dynamic load data.

2. The method as claimed in claim 1 further comprising receiving distributed load data from the distributed assets to generate current and predicted dynamic load data.

3. The method as claimed in claim 2 wherein generating dynamic load data comprises:

receiving distributed asset registration data;

predicting locations of the distributed assets from the distributed asset registration data; and

predicting load requirements of the distributed assets from the asset registration data.

4. The method as claimed in claim 3 wherein the distributed registration data includes predetermined location data for the distributed assets.

5. The method as claimed in claim 3 wherein the distributed asset registration data includes times of day of predetermined locations and time predictions of connections to the power network.

6. The method as claimed in claim 1 further comprising generating a power dispatch plan.

7. The method as claimed in claim 6 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine distribution of power.

8. The method as claimed in claim 6 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine modifying an amount of power in the power network.

9. A computer program product for dynamic load profiling in a power network, the computer program product including a computer readable medium having instructions for causing a computer to implement a method, the method comprising:

receiving static load data in the power network;

generating a load forecast from the static load data;

predicting and generating dynamic load data from data related to distributed assets in the power network; and

modifying the load forecast based on the dynamic load data for profiling the dynamic load data.

10. The computer program product as claimed in claim 9 further comprising receiving distributed load data from the distributed assets to generate current and predicted dynamic load data.

11. The computer program product as claimed in claim 10 wherein generating dynamic load data comprises:

receiving distributed asset registration data;

predicting locations of the distributed assets from the distributed asset registration data; and

predicting load requirements of the distributed assets from the asset registration data.

12. The computer program product as claimed in claim 11 wherein the distributed registration data includes predetermined location data for the distributed assets.

13. The computer program product as claimed in claim 11 wherein the distributed asset registration data includes times of day of predetermined locations and time predictions of connections to the power network.

14. The computer program product as claimed in claim 9 further comprising generating a power dispatch plan.

15. The computer program product as claimed in claim 14 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine distribution of power.

16. The computer program product as claimed in claim 14 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine modifying an amount of power in the power network.

17. A system for dynamic load profiling, the system comprising:

a processor configured to;

receive static load data in a power network;

generate a load forecast from the static load data;

predict and generate dynamic load data from data related to distributed assets in the power network; and

modify the load forecast based on the dynamic load data for profiling the dynamic load.

18. The system as claimed in claim 17 wherein the processor is further configured to receive distributed load data from the distributed assets to generate current and predicted dynamic load data.

19. The system as claimed in claim 18 wherein the processor is further configured to:

receive distributed asset registration data;

predict locations of the distributed assets from the distributed asset registration data; and

predict load requirements of the distributed assets from the asset registration data.

20. The system as claimed in claim 19 wherein the distributed registration data includes at least one of predetermined location data for the distributed assets, times of day of predetermined locations and time predictions of connections to the power network.

21. A method for dynamic load profiling of plug-in electric vehicles (PEVs) in a power network, the method comprising:

receiving static load data in the power network;

generating a load forecast from the static load data;

predicting and generating dynamic load data from data related to the PEVs in the power network; and

modifying the load forecast based on the dynamic load data for profiling the PEVs.

22. The method as claimed in claim 21 further comprising receiving distributed load data from the PEVs to generate current and predicted dynamic load data.

23. The method as claimed in claim 22 wherein generating dynamic load data comprises:

receiving PEV registration data for the PEVs;

predicting locations of the PEVs in the power network from the PEV registration data; and

predicting load requirements of the PEVs from the asset registration data.

24. The method as claimed in claim 23 wherein the PEV registration data includes predetermined location data for each of the PEVs.

25. The method as claimed in claim 23 wherein the PEV registration data includes times of day of predetermined locations and time predictions of PEV connections to the power network.

26. The method as claimed in claim 21 further comprising generating a power dispatch plan.

27. The method as claimed in claim 26 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine distribution of power.

28. The method as claimed in claim 26 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine modifying an amount of power in the power network.

29. A computer program product for dynamic load profiling of plug-in electric vehicles (PEVs) in a power network, the computer program product including a computer readable medium having instructions for causing a computer to implement a method, the method comprising:

receiving static load data in the power network;

generating a load forecast from the static load data;

predicting and generating dynamic load data from data related to the PEVs in the power network; and

modifying the load forecast based on the dynamic load data for profiling the PEVs.

30. The computer program product as claimed in claim 29 further comprising receiving distributed load data from the PEVs to generate current and predicted dynamic load data.

31. The computer program product as claimed in claim 30 wherein generating dynamic load data comprises:

receiving PEV registration data for the PEVs;

predicting locations of the PEVs from the PEV registration data; and

predicting load requirements of the PEVs from the PEV registration data.

32. The computer program product as claimed in claim 31 wherein the PEV registration data includes predetermined location data for the PEVs.

33. The computer program product as claimed in claim 31 wherein the PEV registration data includes times of day of predetermined locations and time predictions of connections to the power network.

34. The computer program product as claimed in claim 29 further comprising generating a power dispatch plan.

35. The computer program product as claimed in claim 34 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine distribution of power.

36. The computer program product as claimed in claim 34 wherein the power dispatch plan includes a state of the power network and load demand locations in the power network in order to determine modifying an amount of power in the power network.

37. A system for dynamic load profiling of plug-in electric vehicles (PEVs), the system comprising:

a processor configured to;

receive static load data in a power network;

generate a load forecast from the static load data;

predict and generate dynamic load data from data related to PEVs in the power network; and

modify the load forecast based on the dynamic load data for profiling the PEVs.

38. The system as claimed in claim 37 wherein the processor is further configured to receive distributed load data from the PEVs to generate the dynamic load data.

39. The system as claimed in claim 38 wherein the processor is further configured to:

receive PEV registration data;

predict locations of the PEVs from the PEV registration data; and

predict load requirements of the PEVs from the PEV registration data.

40. The system as claimed in claim 39 wherein the PEV registration data includes at least one of predetermined location data for the PEVs, times of day of predetermined locations and time predictions of connections to the power network.