Granted Patent B2

US 9,846,886 · App. 14/074,571 · Granted Dec 19, 2017

Abstract

Claims (182)

1. A computer-executable method for utilizing an energy asset to provide one or more modes of operation services to an energy grid, comprising:

identifying, by a computer, at least one opportunity associated with a mode of operation for the energy asset within the energy grid based on market conditions;

performing, by the computer, a prognostic analysis of an operation of the energy asset in the same mode, based on an existing energy asset unit model of the energy asset, wherein the model comprises a set of parameters which indicate at least one of: time, a temperature of the asset, and a discharge rate of the asset;

receiving, by the computer from a sensor, information characterizing a state of health (SOH) of the energy asset during a recent usage in the same mode of operation within the energy grid;

determining a mismatch between the SOH during the usage and a predicted SOH from the prognostic analysis;

updating the set of parameters of the model for the same asset based on the SOH during the usage, resulting in an updated model;

refining the prognostic analysis based on the updated model;

determining, by the computer, an increased confidence level in the refined prognostic analysis that exceeds a predetermined threshold; and

controlling, by an operation controller, the same energy asset to operate in the same mode of operation within the energy grid, based on the updated model of the energy asset and an expected profit.

2. The method of claim 1 , wherein controlling the energy asset based on the updated model and the expected profit further comprises:

performing an economic transaction which involves placing a bid for the mode of operation opportunity and receiving a notice that the bid is accepted; and

in response to the economic transaction, placing the energy asset in the operation mode in the grid associated with the opportunity, wherein the operation mode includes one or more of:

night-to-day arbitrage;

contingency reserves; and

voltage support.

3. The method of claim 1 , further comprising:

in response to detecting the mismatch or the determined confidence level below the predetermined threshold, identifying one or more focused tests;

performing an identified focused test for the energy asset based on the mode of operation opportunity; and

improving the model of the energy asset based on outcomes of the focused test, thereby enabling better future prognostics for the energy asset.

4. The method of claim 3 , further comprising:

performing a second prognostic analysis using the improved model; and

performing an economic optimization based on outcomes of the second prognostic analysis.

5. The method of claim 3 , wherein identifying the one or more focused test involves performing one or more of following economic analyses:

a sensitivity analysis;

an entropy measure analysis; and

a decision theoretic analysis.

6. The method of claim 3 , wherein the focused test includes one or more of:

an online test;

an offline test; and

a laboratory test.

7. The method of claim 3 , further comprising placing the improved model in a model library.

8. The method of claim 1 , further comprising:

in response to the determined confidence level being below the predetermined threshold, placing a bid larger than an expected lifecycle cost of the energy asset based on an uncertainty about battery wear to reduce a likelihood of an unprofitable operation; and

in response to the large bid not being accepted:

performing a focused test to improve confidence in a value of a respective parameter for the energy asset, wherein the focused test comprises measuring a characteristic of the asset during an online usage of the asset within the grid; and

improving the energy asset unit model based on outcomes of the focused test, thereby enabling better future prognostics for the energy asset.

9. The method of claim 1 , wherein the energy asset includes a grid-tied energy storage device, and wherein the grid-tied energy storage device includes a heterogeneous mixture of energy storage devices that includes one or more of:

a flywheel;

a super capacitor;

a plugged-in electrical vehicle;

a Li-ion battery;

a lead-acid battery;

a nickel metal hydride (NiMH) battery;

a flow battery;

a new battery; and

a used battery.

10. The method of claim 1 , wherein the prognostic analysis is performed based on a current health condition of the grid-tied energy storage device.

11. The method of claim 1 , wherein the energy asset unit model includes one or more of:

a battery cell-level model;

a battery pack-level model; and

a circuit-based archetype battery model.

12. The method of claim 1 , wherein the operation mode associated with the opportunity includes one or more of:

night-to-day energy arbitrage;

voltage support;

contingency reserves;

frequency control; and

regulation.

13. The method of claim 1 , wherein the energy asset includes one or more of:

a battery;

a backup energy generator;

a fuel cell; and

a demand-response load management system.

14. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for utilizing an energy asset to provide one or more modes of operation services to an energy grid, the method comprising:

identifying, by the computer, at least one opportunity associated with a mode of operation for the energy asset within the energy grid based on market conditions;

performing, by the computer, a prognostic analysis of an operation of the energy asset in the same mode, based on an existing energy asset unit model of the energy asset, wherein the model comprises a set of parameters which indicate at least one of: time, a temperature of the asset, and a discharge rate of the asset;

receiving, by the computer from a sensor, information characterizing a state of health (SOH) of the energy asset during a recent usage in the same mode of operation within the energy grid;

in response to determining a mismatch between the SOH during the usage and a predicted SOH from the prognostic analysis, refining the set of parameters of the model for the same asset based on the SOH during the usage, resulting in an updated model, and refining the prognostic analysis based on the updated model; and

in response to determining, by the computer, an increased confidence level in the refined prognostic analysis that exceeds a predetermined threshold:

sending instructions to an operation controller, to control the same energy asset to operate in the same mode of operation within the energy grid, based on the updated model of the energy asset and an expected profit.

15. The non-transitory computer-readable storage medium of claim 14 , wherein sending instructions to the operation controller to deploy the energy asset based on the updated model and the expected profit further comprises:

performing an economic transaction which involves placing a bid for the mode of operation opportunity and receiving a notice that the bid is accepted; and

in response to the economic transaction, placing the energy asset in the operation mode in the grid associated with the opportunity, wherein the operation mode includes one or more of:

night-to-day arbitrage;

contingency reserves; and

voltage support.

16. The non-transitory computer-readable storage medium of claim 14 , wherein the method further comprises:

in response to detecting the mismatch or the determined confidence level below the predetermined threshold, identifying one or more focused tests;

performing an identified focused test for the energy asset based on the mode of operation opportunity; and

improving the model of the energy asset based on outcomes of the focused test, thereby enabling better future prognostics for the energy asset.

17. The non-transitory computer-readable storage medium of claim 16 , wherein the method further comprises:

performing a second prognostic analysis using the improved model;

performing an economic optimization based on outcomes of the second prognostic analysis.

18. The non-transitory computer-readable storage medium of claim 16 , wherein identifying the one or more focused test involves performing one or more of following economic analyses:

a sensitivity analysis;

an entropy measure; and

a decision theoretic analysis.

19. The non-transitory computer-readable storage medium of claim 16 , wherein the focused test includes one or more of:

an online test;

an offline test; and

a laboratory test.

20. The non-transitory computer-readable storage medium of claim 16 , wherein the method further comprises placing the improved model in a model library.

21. The non-transitory computer-readable storage medium of claim 14 , wherein the method further comprises:

in response to the determined confidence level being below the predetermined threshold, placing a bid larger than an expected lifecycle cost of the energy asset based on an uncertainty about battery wear to reduce a likelihood of an unprofitable operation; and

in response to the large bid not being accepted:

performing a focused test to improve confidence in a value of a respective parameter for the energy asset,

wherein the focused test comprises measuring a characteristic of the asset during an online usage of the asset within the grid; and

improving the energy asset unit model based on outcomes of the focused

test, thereby enabling better future prognostics for the energy asset.

22. The non-transitory computer-readable storage medium of claim 14 , wherein the energy asset includes a grid-tied energy storage device, and wherein the grid-tied energy storage device includes a heterogeneous mixture of batteries that includes one or more of:

a flywheel;

a super capacitor;

a plugged-in electrical vehicle;

a Li-ion battery;

a lead-acid battery;

a nickel metal hydride (NiMH) battery;

a flow battery;

a new battery; and

a used battery.

23. The non-transitory computer-readable storage medium of claim 22 , wherein the prognostic analysis is performed based on a current health condition of the grid-tied energy storage device.

24. The non-transitory computer-readable storage medium of claim 22 , wherein the energy asset unit model includes one or more of:

a battery cell-level model;

a battery pack-level model; and

a circuit-based archetype battery model.

25. The non-transitory computer-readable storage medium of claim 14 , wherein the operation mode associated with the opportunity includes one or more of:

night-to-day energy arbitrage;

voltage support;

contingency reserves;

frequency control; and

regulation.

26. The non-transitory computer-readable storage medium of claim 14 , wherein the energy asset includes one or more of:

a battery;

a backup energy generator;

a fuel cell; and

a demand-response load management system.

27. An energy-asset control system for utilizing an energy asset to provide one or more modes of operation services to an energy grid, comprising:

an economic optimizer configured to identify at least one opportunity associated with a mode of operation for the energy asset within the energy grid based on market conditions;

a prognostics module configured to:

perform a prognostic analysis of an operation of the energy asset in the same mode, based on an existing energy asset unit model of the energy asset, wherein the model comprises a set of parameters which indicate at least one of: time, a temperature of the asset, and a discharge rate of the asset;

receive, from a sensor, information characterizing a state of health (SOH) of the energy asset during a recent usage within the energy grid;

in response to determining a mismatch between the SOH during the usage and a predicted SOH from the prognostic analysis, updating the set of parameters of the model for the same asset based on the SOH during the usage, resulting in an updated model, and refining the prognostic analysis based on the updated model; and

an operation controller that can control the same energy asset to operate in the same mode of operation, within the energy grid;

wherein the economic optimizer is further configured to:

in response to determining an increased confidence level in the refined prognostic analysis that exceeds a predetermined threshold:

send instructions to the operation controller to deploy the same energy asset to operate in the same mode of operation within the energy grid, based on the updated model of the energy asset and an expected profit.

28. The energy-asset control system of claim 27 , wherein while sending instructions to the operation controller to deploy the energy asset based on the updated model and the expected profit, the economic optimizer is configured to perform an economic transaction which involves placing a bid for the mode of operation opportunity and receiving a notice that the bid is accepted; and

wherein the operation controller is configured to, in response to the economic transaction, place the energy asset in the operation mode in the grid associated with the opportunity, wherein the operation mode includes one or more of:

night-to-day arbitrage;

contingency reserves; and

voltage support.

29. The energy-asset control system of claim 27 , wherein the economic optimizer is further configured to, in response to detecting the mismatch or the prognostics module determining the confidence level below the predetermined threshold, identify one or more focused tests, and wherein the energy-asset control system further comprises a focused testing module configured to:

perform an identified focused test for the energy asset based on the mode of operation opportunity; and

improve the model of the energy asset based on outcomes of the focused test, thereby enabling better future prognostics for the energy asset.

30. The energy-asset control system of claim 29 ,

wherein the prognostics module is configured to perform a second prognostic analysis using the improved model; and

wherein the economic optimizer is further configured to:

perform an economic optimization based on outcomes of the second prognostic analysis.

31. The energy-asset control system of claim 29 , wherein while identifying the one or more focused tests, the economic optimizer is configured to perform one or more of the following economic analyses:

a sensitivity analysis;

an entropy measure analysis; and

a decision theoretic analysis.

32. The energy-asset control system of claim 29 , wherein the focused test includes one or more of:

an online test;

an offline test; and

a laboratory test.

33. The energy-asset control system of claim 29 , further comprising a model library configured to store the improved model.

34. The energy-asset control system of claim 27 , wherein the economic optimizer is configured to, in response to the determined confidence level being below the predetermined threshold, place a bid larger than an expected lifecycle cost of the energy asset based on an uncertainty about battery wear to reduce a likelihood of an unprofitable operation.

35. The energy-asset control system of claim 27 , wherein the energy asset includes a grid-tied energy storage device, and wherein the grid-tied energy storage device includes a heterogeneous mixture of batteries that includes one or more of:

a flywheel;

a super capacitor;

a plugged-in electrical vehicle;

a Li-ion battery;

a lead-acid battery;

a nickel metal hydride (NiMH) battery;

a flow battery;

a new battery; and

a used battery.

36. The energy-asset control system of claim 35 , wherein the prognostic analysis is performed based on a current health condition of the grid-tied energy storage device.

37. The energy-asset control system of claim 35 , wherein the energy asset unit model includes one or more of:

a battery cell-level model;

a battery pack-level model; and

a circuit-based archetype battery model.

38. The energy-asset control system of claim 27 , wherein the operation mode associated with the opportunity includes one or more of:

night-to-day energy arbitrage;

voltage support;

contingency reserves;

frequency control; and

regulation.

39. The energy-asset control system of claim 27 , wherein the energy asset includes one or more of:

a battery;

a backup energy generator;

a fuel cell; and

a demand-response load management system.

Assignments (10)