Managing peak power consumption for distributed assets using battery charging schedules

• US 10,396,581 B2
• Filed: 04/06/2017
• Issued: 08/27/2019
• Est. Priority Date: 04/06/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chandan, Vikas, Dayama, Pankaj, Narayanam, Krishnasuri
• Primary Examiner(s): Lindsay, Bernard G
• Application Number:
  US15/480,878
• Publication Number:
  US 20180294665A1
• Time in Patent Office: 873 Days
• Field of Search: None
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 2219/25419 Scheduling
• CPC Class Codes: H02J 3/28 Arrangements for balancing of the load in a network by storage of energy
• CPC Class Codes: H02J 7/0071 with a programmable schedule

Abstract

Methods, systems, and computer program products for managing peak power consumption for distributed assets are provided herein. A computer-implemented method includes segmenting the multiple distributed assets into groups based on energy demand behavior attributed to each of the assets and user-level constraints, wherein each of the assets is associated with one or more users, and wherein each of the users is associated with one or more organizations of the hierarchy, determining group-level constraints for each of the groups based on correlating the energy demand behavior across the users of the groups, generating a distinct battery charging schedule for each of the groups based on the determined group-level constraints, user-level objectives and organization-level objectives, and outputting the generated battery charging schedule to each of the multiple distributed assets within the hierarchy.

Claims

1. A computer-implemented method for generating a battery charging schedule for multiple distributed assets within a hierarchy, the method comprising steps of:

segmenting the multiple distributed assets into two or more groups based on (i) energy demand behavior attributed to each of the assets and (ii) one or more user-level constraints, wherein each of the assets is associated with one or more users at a user-level of the hierarchy, and wherein each of the users is associated with one or more organizations at an organization-level of the hierarchy;

determining one or more group-level constraints for each of the two or more groups based on correlating the energy demand behavior across the users of the two or more groups, wherein said correlating comprises performing a multi-variate time series analysis of the energy demand behavior across the users of the two or more groups to identify one or more cross correlations across the user energy demands of the two or more groups;

generating a distinct battery charging schedule for each of the two or more groups based on (i) the one or more determined group-level constraints, (ii) one or more user-level objectives and (iii) one or more organization-level objectives;

outputting the generated battery charging schedule to (i) each of the multiple distributed assets within the hierarchy and (ii) a grid services component associated with a grid;

managing the multiple distributed assets within the hierarchy in accordance with the generated battery charging schedule; and

providing the grid services component access to the battery of each of the multiple distributed assets within the hierarchy, in accordance with the generated battery charging schedule of each of the multiple distributed assets, to manage load balance across the grid;

wherein the steps are carried out by at least one computing device.

2. The computer-implemented method of claim 1, comprising:

generating an updated battery charging schedule for each of the two or more groups based on any deviation between (i) observed real-time demand data and (ii) the energy demand behavior attributed to each of the assets; and

outputting the updated battery charging schedule to each of the multiple distributed assets within the hierarchy.

3. The computer-implemented method of claim 1, comprising:

generating one or more incentives for one or more of the users based on (i) one or more user preferences and (ii) usage of one or more prioritized services attributed to the one or more users.

4. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprises information pertaining to the organization with which each user is associated.

5. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprise one or more user requirements pertaining to a minimum battery state of charge for one or more of the assets.

6. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprises information pertaining to battery lifetime for one or more of the assets.

7. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprises availability information pertaining to one or more of the users.

8. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprises information pertaining to each user'"'"'s role within one or more of the organizations.

9. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprises one or more movement patterns attributed to one or more of the users.

10. The computer-implemented method of claim 1, wherein the one or more user-level constraints comprise one or more user priority constraints.

11. The computer-implemented method of claim 1, wherein the one or more user-level objectives comprises optimizing battery lifetime for one or more of the assets.

12. The computer-implemented method of claim 1, wherein the one or more user-level objectives comprises optimizing the minimum battery state of charge for one or more of the assets.

13. The computer-implemented method of claim 1, wherein the one or more organization-level objectives comprises reducing peak load.

14. The computer-implemented method of claim 1, wherein the one or more organization-level objectives comprises optimizing battery lifetime for one or more of the assets.

15. The computer-implemented method of claim 1, wherein the one or more organization-level objectives comprises reducing energy costs.

16. The computer-implemented method of claim 1, wherein the one or more organization-level objectives comprises optimizing the minimum battery state of charge for one or more of the assets.

17. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:

segment multiple distributed assets into two or more groups based on (i) energy demand behavior attributed to each of the assets and (ii) one or more user-level constraints, wherein each of the assets is associated with one or more users at a user-level of the hierarchy, and wherein each of the users is associated with one or more organizations at an organization-level of the hierarchy;

determine one or more group-level constraints for each of the two or more groups based on correlating the energy demand behavior across the users of the two or more groups, wherein said correlating comprises performing a multi-variate time series analysis of the energy demand behavior across the users of the two or more groups to identify one or more cross correlations across the user energy demands of the two or more groups;

generate a distinct battery charging schedule for each of the two or more groups based on (i) the one or more determined group-level constraints, (ii) one or more user-level objectives and (iii) one or more organization-level objectives;

output the generated battery charging schedule to (i) each of the multiple distributed assets within the hierarchy and (ii) a grid services component associated with a grid;

manage the multiple distributed assets within the hierarchy in accordance with the generated battery charging schedule; and

provide the grid services component access to the battery of each of the multiple distributed assets within the hierarchy, in accordance with the generated battery charging schedule of each of the multiple distributed assets, to manage load balance across the grid.

18. A system comprising:

a memory; and

at least one processor operably coupled to the memory and configured for;

segmenting multiple distributed assets into two or more groups based on (i) energy demand behavior attributed to each of the assets and (ii) one or more user-level constraints, wherein each of the assets is associated with one or more users at a user-level of the hierarchy, and wherein each of the users is associated with one or more organizations at an organization-level of the hierarchy;

determining one or more group-level constraints for each of the two or more groups based on correlating the energy demand behavior across the users of the two or more groups, wherein said correlating comprises performing a multi-variate time series analysis of the energy demand behavior across the users of the two or more groups to identify one or more cross correlations across the user energy demands of the two or more groups;

generating a distinct battery charging schedule for each of the two or more groups based on (i) the one or more determined group-level constraints, (ii) one or more user-level objectives and (iii) one or more organization-level objectives;

outputting the generated battery charging schedule to (i) each of the multiple distributed assets within the hierarchy and (ii) a grid services component associated with a grid;

managing the multiple distributed assets within the hierarchy in accordance with the generated battery charging schedule; and

providing the grid services component access to the battery of each of the multiple distributed assets within the hierarchy, in accordance with the generated battery charging schedule of each of the multiple distributed assets, to manage load balance across the grid.