Learning power grid characteristics to anticipate load

Patent metadata

Field	Value
Publication number	US-11416786-B2
Application number	US-202016843221-A
Country	US
Kind code	B2
Filing date	Apr 8, 2020
Priority date	Apr 9, 2018
Publication date	Aug 16, 2022
Grant date	Aug 16, 2022

Abstract

Improving the operations of a computer system that is located within a power grid and that is associated with its own power sources. Past operational characteristics of the power grid are analyzed to derive learned characteristics for the power grid. Future operational characteristics of the power grid are also monitored. A prediction regarding a future load event associated with the power grid is then generated using the learned characteristics and the monitored characteristics. In response to this prediction, one or more operations are performed to balance the computer system with the power grid during the future load event, and to ensure a determined availability of services associated with the computer system during the future load event.

First claim

What is claimed is:

1. A computer system comprising:
one or more processors; and
one or more computer-readable hardware storage devices having stored thereon computer-executable instructions that are structured to be executable by the one or more processors to thereby cause the computer system to:
analyze one or more past operational characteristics for a power grid within which the computer system operates to derive one or more learned characteristics for the power grid, the computer system being associated with one or more power sources that are structured to provide power to the computer system independently of the power grid;
identify one or more possible future operational characteristics for the power grid;
use the one or more learned characteristics and the one or more identified future operational characteristics to generate a prediction regarding a future load event associated with the power grid, the prediction including a buffer selected to compensate for at least some unpredictability associated with the future load of the power grid; and
in response to the prediction, perform one or more operations using the one or more power sources at the computer system to reduce fluctuating supply voltage peaks that occur on the power grid during the future load event.

2. The computer system of claim 1 , wherein the future load event comprises at least one of a future social event, a future political event, a future weather event, a future climate event, a future hardware failure event, or a future hardware replacement event.

3. The computer system of claim 1 , wherein the one or more operations includes at least one of offering electricity stored by the one or more power sources to the power grid, or offering electricity generated by the one or more power sources to the power grid.

4. The computer system of claim 1 , further comprising, in response to the prediction, causing the computer system to consume more power, and wherein determining how much more power to consume is based, at least partially, on a determined mean time to failure associated with one or more hardware devices of the computer system.

5. The computer system of claim 1 , wherein the one or more operations includes causing the one or more power sources to store power.

6. The computer system of claim 1 , further comprising, in response to the prediction, migrating one or more services from the computer system to a different computer system, and wherein the different computer system uses a different power grid.

7. The computer system of claim 1 , wherein performing the one or more operations is based, at least partially, on a service level agreement.

8. The computer system of claim 1 , wherein deriving the one or more learned characteristics for the power grid includes analyzing past environmental conditions that have occurred in a geographic area associated with the power grid.

9. The computer system of claim 1 , wherein the one or more learned characteristics include information corresponding to a load of the power grid, and wherein the information includes one or more of timing metrics for the power grid, cost metrics for the power grid, location data for the power grid, past political events that have impacted the power grid, or past social events that have impacted the power grid.

10. The computer system of claim 1 , wherein generating the prediction regarding the future load event on the power grid includes generating a level of confidence associated with the prediction, the level of confidence indicating an estimated likelihood that the future load event will actually occur.

11. The computer system of claim 1 , wherein the one or more operations are based, at least partially, on a power generation ability of the one or more power sources, a power storage ability of the one or more power sources, or a power consumption ability of the computer system.

12. The computer system of claim 1 , wherein analyzing the one or more past operational characteristics for the power grid includes monitoring fluctuations in a load of the power grid during different times of a day or during different times of a year.

13. The computer system of claim 1 , wherein analyzing the one or more past operational characteristics for the power grid includes analyzing at least one of weather data or climate data for a geographic region serviced by the power grid.

14. The computer system of claim 1 , wherein deriving the one or more learned characteristics is performed using a neural network, and wherein analyzing the one or more past operational characteristics is performed by the neural network.

15. The computer system of claim 1 , wherein the one or more operations are performed after generating a decision tree that takes as input (1) the prediction and (2) a latency between the computer system and at least one other computer system.

16. A method, implemented at a computer system that includes one or more processors, the method comprising:
analyzing one or more past operational characteristics for a power grid within which the computer system operates to derive one or more learned characteristics for the power grid, the computer system being associated with one or more power sources that are structured to provide power to the computer system independently of the power grid;
identifying one or more possible future operational characteristics for the power grid;
using the one or more learned characteristics and the one or more identified future operational characteristics to generate a prediction regarding a future load event associated with the power grid, the prediction including a buffer selected to compensate for at least some unpredictability associated with the future load of the power grid; and
in response to the prediction, performing one or more operations using the one or more power sources at the computer system to reduce fluctuating supply voltage peaks that occur on the power grid during the future load event.

17. The method of claim 16 , wherein the future load event comprises at least one of a future social event, a future political event, a future weather event, a future climate event, a future hardware failure event, or a future hardware replacement event.

18. The method of claim 16 , wherein the one or more operations includes at least one of causing the one or more power sources to store power, offering electricity stored by the one or more power sources to the power grid, or offering electricity generated by the one or more power sources to the power grid.

19. A computer program product comprising one or more hardware storage devices having stored thereon computer-executable instructions that are structured to be executable by one or more processors to thereby cause a computer system to:
analyze one or more past operational characteristics for a power grid within which the computer system operates to derive one or more learned characteristics for the power grid, the computer system being associated with one or more power sources that are structured to provide power to the computer system independently of the power grid;
identify one or more possible future operational characteristics for the power grid;
use the one or more learned characteristics and the one or more identified future operational characteristics to generate a prediction regarding a future load event associated with the power grid, the prediction including a buffer selected to compensate for at least some unpredictability associated with the future load of the power grid; and
in response to the prediction, per

Assignees

• Microsoft Technology Licensing Llc
• Microsoft Tech Licesning Llc

Inventors

• Helsel Michael Rees
• Blas Vincent Joseph
• Keehn Nicholas Andrew
• James Sean Michael
• Middaugh Brandon

Classifications

• G06Q10/04 Primary

  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 title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title

• Y04S10/50

  Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications · CPC title

• G06Q10/06312

  Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling · CPC title

• G06Q50/06

  Energy or water supply · CPC title