Demand response automated load characterization systems and methods

• US 9,530,169 B2
• Filed: 07/18/2013
• Issued: 12/27/2016
• Est. Priority Date: 07/18/2013
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Strelec, Martin, Fisera, Radek
• Primary Examiner(s): KASENGE, CHARLES R
• Application Number:
  US13/945,506
• Publication Number:
  US 20150025698A1
• Time in Patent Office: 1,258 Days
• Field of Search: 700/286, 700/291, 700/295, 705/412
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Demand response automated load characterization systems and methods are described herein. One method includes identifying a plurality of load models that include a variable that influences an energy demand, normalizing the plurality of load models, aggregating the normalized plurality of load models to generate an aggregated model for the variable.

Claims

1. A computer implemented method for demand response automated load characterization, comprising:

identifying a plurality of load models that includes a variable that influences an energy demand of a demand response electrical device, wherein the variable includes a selected state setting for an area and wherein the demand response electrical device is a demand response resource to alter electrical demand within an electrical grid;

altering the variable of the plurality of load models to a demand response input for the demand response electrical device, wherein the demand response input includes a setting of the demand response electrical device;

normalizing the identified plurality of load models that includes the demand response input;

aggregating the normalized plurality of load models to generate an aggregated model for the demand response input; and

altering the demand response input for the demand response electrical device to cure a predicted power imbalance of the electrical grid based on the aggregated plurality of load models.

2. The method of claim 1, comprising transforming the plurality of load models from a first constraint to an input constraint.

3. The method of claim 2, wherein the first constraint is a state constraint determined by a user.

4. The method of claim 1, comprising categorizing the plurality of load models based on model properties of the plurality of load models.

5. The method of claim 4, wherein categorizing the plurality of load models includes utilizing a number of load dynamic features of the plurality of load models.

6. The method of claim 1, wherein normalizing the plurality of load models includes utilizing a standardized method for normalizing the plurality of load models.

7. The method of claim 1, comprising implementing the aggregated model with a control strategy of a utility company.

8. A non-transitory computer readable medium, comprising computer readable instructions stored thereon that are executable by a processor to:

identify a plurality of load models that each includes a variable that influences an energy demand of a demand response electrical device, wherein the variable includes a selected state setting for an area and wherein the demand response electrical device is a demand response resource to alter electrical demand within an electrical grid;

alter the variable of the plurality of load models to a demand response input for the demand response electrical device, wherein the demand response input includes a setting of the demand response electrical device;

normalize each of the plurality of load models that includes the demand response input;

categorize each of the plurality of normalized load models based on the demand response inputs;

aggregate each of the normalized plurality of load models to generate an aggregated model for each category; and

alter the demand response input for the demand response electrical device to cure a predicted power imbalance of the electrical grid based on the aggregated plurality of load models.

9. The medium of claim 8, comprising instructions to transform state constraints selected by a number of users to input constraints.

10. The medium of claim 8, wherein the aggregated model is utilized to represent load dynamics of the plurality of load models.

11. The medium of claim 8, wherein the variable includes a particular load dynamic.

12. The medium of claim 8, wherein the plurality of load models are based in part on historical data.

13. The medium of claim 8, wherein the plurality of load models are normalized utilizing a unified mathematical apparatus.

14. The medium of claim 8, wherein the plurality of load models are categorized based on load reduction potential and load dynamics.

15. A computing device for demand response automated load characterization, comprising:

a memory; and

a processor configured to execute executable instructions stored in the memory to;

identify a plurality of load models that includes a number of state variables that influence an energy demand of a demand response electrical device, wherein the number of state variables include a selected state setting for an area and wherein the demand response electrical device is a demand response resource to alter electrical demand within an electrical grid;

alter the number of state variables to demand response inputs for the demand response electrical device, wherein the demand respond inputs include settings of the demand response electrical device;

normalize each of the plurality of load models that includes the demand response inputs;

categorize each of the normalized plurality of load models based on the demand response inputs;

aggregate each of the normalized plurality of load models to generate an aggregated model for each category; and

alter the demand response inputs for the demand response electrical device to cure a predicted power imbalance of the electrical grid based on the aggregated model for each category.

16. The system of claim 15, wherein instructions to normalize each of the plurality of load models include instructions to normalize each of the plurality of load models within the same range of values.

17. The system of claim 15, wherein the aggregated model includes representations of load behavior from each of the plurality of load models.

18. The system of claim 15, wherein the aggregated model comprises a predetermined level of detail to represent a category of the plurality of load models.

19. The system of claim 15, wherein the aggregated model is utilized to predict an expected behavior of demand resources.