System and method for optimal aggregation of small-scale energy storage

• US 10,608,436 B2
• Filed: 12/07/2017
• Issued: 03/31/2020
• Est. Priority Date: 12/07/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: de Hoog, Julian, Vishwanath, Arun, Lynar, Timothy M., Eberbach, Adam
• Primary Examiner(s): Lee, Thomas C
• Assistant Examiner(s): Sharmin, Anzuman
• Application Number:
  US15/834,412
• Publication Number:
  US 20190181641A1
• Time in Patent Office: 845 Days
• Field of Search: 700295
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: H02J 13/00017 using optical fiber
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/14 Energy storage units
• 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
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 40/124 using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A system for optimal aggregation of small-scale energy storage capacity includes a processor operatively coupled to memory. The processor is configured to implement the steps of: generating predicted energy consumption data based on a model of expected energy usage within a given electrical network; generating predicted energy generation data based on a model of expected energy generation for the given electrical network; generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network; determining a set of energy storage devices that need to be dispatched for the given electrical network; comparing the predicted energy consumption data with the predicted energy generation data; and dispatching the set of energy storage devices for the given electrical network.

Claims

1. A method for aggregation of small-scale energy storage capacity comprising:

generating predicted energy consumption data based on a model of expected energy usage within a given electrical network;

generating predicted energy generation data based on a model of expected energy generation for the given electrical network;

generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network;

determining a set of energy storage devices that need to be dispatched for the given electrical network;

in response to a user of the energy storage device accepting or rejecting a request to be dispatched, comparing the predicted energy consumption data with the predicted energy generation data; and

dispatching the accepted set of energy storage devices for the given electrical network,

wherein the steps of the method are performed in accordance with a processor and a memory.

2. The method of claim 1, further comprising creating the model for expected energy usage based on one or more sets of data.

3. The method of claim 2, wherein the model for expected energy usage is created as a function of one or more variables.

4. The method of claim 2, wherein the one or more sets of data comprise one or more of a set of historical electricity usage data, a set of demographic data, a set of traffic data and a set of weather data.

5. The method of claim 1, further comprising creating the model for expected energy generation based on one or more sets of data.

6. The method of claim 5, wherein the model for expected energy generation is created as a function of one or more variables.

7. The method of claim 5, wherein the one or more sets of data comprise one or more of a set of historical electricity generation data, a set of demographic data, a set of historical traffic data, a set of satellite or radar data and a set of historical weather data.

8. The method of claim 1, wherein the step of comparing comprises calculating a difference between the predicted energy consumption data and the predicted energy generation data.

9. The method of claim 8, wherein when the predicted energy generation data is greater than the predicted energy consumption data, the step of dispatching the set of energy storage devices comprises discharging the set of energy storage devices.

10. The method of claim 8, wherein when the predicted energy generation data is less than the predicted energy consumption data, the step of dispatching the set of energy storage devices comprises charging the set of energy storage devices.

11. The method of claim 8, wherein when the predicted energy generation data is greater than the predicted energy consumption data, the method further comprises rewarding a user of the energy storage device.

12. The method of claim 1, wherein the step of generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network comprises generating a score for each energy storing device.

13. The method of claim 12, wherein the step of determining a set of energy storage devices that need to be dispatched for the given electrical network comprises ranking each energy storing device based on the score.

14. A system comprising:

a memory and a processor operatively coupled to the memory and configured to implement the steps of;

generating predicted energy consumption data based on a model of expected energy usage within a given electrical network;

generating predicted energy generation data based on a model of expected energy generation for the given electrical network;

generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network;

determining a set of energy storage devices that need to be dispatched for the given electrical network;

in response to a user of the energy storage device accepting or rejecting a request to be dispatched, comparing the predicted energy consumption data with the predicted energy generation data; and

dispatching the accepted set of energy storage devices for the given electrical network.

15. The system of claim 14, further comprising creating the model for expected energy usage based on one or more sets of data comprising one or more of a set of historical electricity usage data, a set of demographic data, a set of traffic data and a set of weather data.

16. The system of claim 14, further comprising creating the model for expected energy generation based on one or more sets of data comprising one or more of a set of historical electricity generation data, a set of demographic data, a set of historical traffic data, a set of satellite or radar data and a set of historical weather data.

17. The system of claim 14, wherein the step of generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network comprises generating a score for each energy storing device.

18. The system of claim 17, wherein the step of determining a set of energy storage devices that need to be dispatched for the given electrical network comprises ranking each energy storing device based on the score.

19. The system of claim 17, wherein the step of comparing comprises calculating a difference between the predicted energy consumption data and the predicted energy generation data.

20. A computer program product comprising a non-transitory computer readable storage medium for storing computer readable program code which, when executed, causes a computer to:

generate predicted energy consumption data based on a model of expected energy usage within a given electrical network;

generate predicted energy generation data based on a model of expected energy generation for the given electrical network;

generate dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network;

determine a set of energy storage devices that need to be dispatched for the given electrical network;

in response to a user of the energy storage device accepting or rejecting a request to be dispatched, compare the predicted energy consumption data with the predicted energy generation data; and

dispatch the set of energy storage devices for the given electrical network.