Demand response load reduction estimation

• US 8,532,836 B2
• Filed: 11/08/2010
• Issued: 09/10/2013
• Est. Priority Date: 11/08/2010
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Schmid, James Joseph, Kulkarni, Ninad Vijaykumar, Massey, Jerry Steven, Knodle, Chad Eric, Wilson, Bobby Antione, Meyerhofer, Mark Joseph
• Primary Examiner(s): Padmanabhan, Kavita
• Assistant Examiner(s): CUMMINS, PATRICK D
• Application Number:
  US12/941,462
• Publication Number:
  US 20120116600A1
• Time in Patent Office: 1,037 Days
• Field of Search: 700/291, 700/286, 700295-297
• US Class Current: 700/291
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• 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
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

Certain embodiments of the invention may include systems, methods, and apparatus for estimating demand response load change. According to an example embodiment of the invention, a method is provided for estimating energy usage change associated with a demand event. The method may include determining energy potential change during a demand event for one or more devices associated with a location; estimating energy usage patterns for the location over predefined time periods before and after the demand event; determining pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; determining an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

Claims

1. A method for estimating energy usage change associated with a demand event,

comprising:

receiving, with a processor, a plurality of demand events associated with a location;

determining, with the processor, energy potential change during a respective demand event, from the plurality of demand events, for one or more devices associated with the location;

determining, with the processor, a number of remaining demand events, of the plurality of demand events, associated with the location;

predicting energy usage patterns, with the processor, over predefined time periods before the respective demand event and resulting from the respective demand event, for the location, wherein the predefined time periods are determined based at least in part on the number of remaining demand events associated with the location;

determining, with the processor, pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; and

determining, with the processor, an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

2. The method of claim 1, wherein determining energy potential change comprises estimating energy consumption of a device over a predefined time period based at least in part on one or more of historical usage data, temperature, or artificial intelligence based at least in part on the historical usage data.

3. The method of claim 1, wherein predicting energy usage patterns is based at least in part on historical usage data or artificial intelligence based at least in part on the historical usage data.

4. The method of claim 1, further comprising determining energy potential change for a group comprising one or more locations based at least in part on the determined energy change profile over a predefined available time period and a temperature for the one or more premises.

5. The method of claim 4, wherein the predefined available time period comprises a product of a maximum demand event time and the number of remaining demand events associated with the location.

6. The method of claim 4, further comprising determining a percentage of remaining energy change available for the group based at least in part on the determined energy potential change for the group and a total energy change for the group, wherein the total energy change comprises a sum of all determined energy change profiles in the group over predefined total time periods and temperatures, wherein the predefined total time periods comprising products of maximum demand event times and a number of total demand events for the one or more locations.

7. The method of claim 6, further comprising outputting the determined energy change profile or determined percentage of remaining energy change available for the group for graphical or numeric display.

8. A system for estimating energy usage change associated with a demand event,

comprising:

one or more advanced metering infrastructure (AMI) communications networks;

at least one memory for storing data and computer-executable instructions;

one or more databases and

at least one processor configured to access the at least one memory, the one or more databases, and the one or more AMI communication networks, and further configured to execute the computer-executable instructions for;

receiving a plurality of demand events associated with a location;

determining energy potential change during a respective demand event, from the plurality of demand events, for one or more devices associated with the location;

determining a number of remaining demand events, of the plurality of demand events, associated with the location;

predicting energy usage patterns, over predefined time periods before the respective demand event and resulting from the respective demand event, for the location;

wherein the predefined time periods are determined based at least in part on the number of remaining demand events associated with the location;

determining pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; and

determining an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

9. The system of claim 8, wherein determining energy potential change comprises estimating energy consumption of a device over a predefined time period based at least in part on one or more of historical usage data, temperature, or artificial intelligence based at least in part on the historical usage data.

10. The system of claim 8, wherein predicting energy usage patterns is based at least in part on historical usage data or artificial intelligence based at least in part on the historical usage data.

11. The system of claim 8, wherein the at least one processor is further configured for determining energy potential change for a group comprising one or more locations based at least in part on the determined energy change profile over a predefined available time period and a temperature for the one or more premises.

12. The system of claim 11, wherein the predefined available time period comprises a product of a maximum demand event time and the number of remaining demand events associated with the location.

13. The system of claim 11, wherein the at least one processor is further configured for determining a percentage of remaining energy change available for the group based at least in part on the determined energy potential change for the group and a total energy change for the group, wherein the total energy change comprises a sum of all determined energy change profiles in the group over predefined total time periods and temperatures, wherein the predefined total time periods comprising products of maximum demand event times and a number of total demand events for the one or more locations.

14. The system of claim 13, wherein the at least one processor is further configured for outputting the determined energy change profile or determined percentage of remaining energy change available for the group for graphical or numeric display.

15. An apparatus for estimating energy usage change associated with a demand event, comprising:

at least one memory for storing data and computer-executable instructions;

at least one processor configured to access the at least one memory, and further configured to execute the computer-executable instructions for;

receiving a plurality of demand events associated with a location;

determining energy potential change during a respective demand event, from the plurality of demand events, for one or more devices associated with the location;

determining a number of remaining demand events s, of the plurality of demand events, associated with the location;

predicting energy usage patterns, over predefined time periods before the respective demand event and resulting from the respective demand event, for the location, wherein the predefined time periods are determined based at least in part on the number of remaining demand events associated with the location;

determining pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; and

determining an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

16. The apparatus of claim 15, wherein determining energy potential change comprises estimating energy consumption of a device over a predefined time period based at least in part on one or more of historical usage data, temperature, or artificial intelligence based at least in part on the historical usage data.

17. The apparatus of claim 15, wherein predicting energy usage patterns is based at least in part on historical usage data or artificial intelligence based at least in part on the historical usage data.

18. The apparatus of claim 15, wherein the at least one processor is further configured for determining energy potential change for a group comprising one or more locations based at least in part on the determined energy change profile over a predefined available time period and a temperature for the one or more locations, wherein the predefined available time period comprises a product of a maximum demand event time and a number of remaining demand events for a location.

19. The apparatus of claim 18, wherein the at least one processor is further configured for determining a percentage of remaining energy change available for the group based at least in part on the determined energy potential change for the group and a total energy change for the group, wherein the total energy change comprises a sum of all determined energy change profiles in the group over predefined total time periods and temperatures, wherein the predefined total time periods comprising products of maximum demand event times and a number of total demand events for the one or more locations.

20. The apparatus of claim 19, wherein the at least one processor is further configured for outputting the determined energy change profile or determined percentage of remaining energy change available for the group for graphical or numeric display.