Energy resource allocation including renewable energy sources

• US 8,620,634 B2
• Filed: 07/22/2010
• Issued: 12/31/2013
• Est. Priority Date: 07/24/2009
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Foslien Graber, Wendy, Schindler, Zdenek, Stluka, Petr, Parthasarathy, Girija
• Primary Examiner(s): PHAN, THAI Q
• Application Number:
  US12/841,345
• Publication Number:
  US 20110040550A1
• Time in Patent Office: 1,258 Days
• Field of Search: 703/5, 703/19, 290/44, 415/208.1
• US Class Current: 703/5
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: Y02A 30/00 Adapting or protecting infrastructure or their operation
• CPC Class Codes: Y02P 90/80 Management or planning
• 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

Abstract

A computer implemented method includes providing information related to wind speed and direction prediction to a wind turbine energy prediction module, providing wind energy production prediction information to a resource allocation engine, and combining the wind energy production prediction information with information about non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

Claims

1. A computer implemented method comprising:

providing information related to wind speed and direction prediction to a wind turbine energy prediction module running on the computer;

providing wind energy production prediction information from the wind turbine energy prediction module to a resource allocation engine running on the computer; and

combining the wind energy production prediction information with power level information for non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

2. The method of claim 1 wherein the information related to wind speed prediction and direction comprises a feed from a weather service correlated with local topography to form a mesoscale forecast.

3. The method of claim 2 wherein the information related to wind speed prediction comprises historical wind speed and direction information correlated to specific wind turbines.

4. The method of claim 1 wherein the information related to wind speed and direction prediction comprises wind speed and direction simulation data.

5. The method of claim 1 wherein wind energy production prediction information is a function of predicted wind speed, wind direction, and wind power curves for specific wind turbines.

6. The method of claim 1 wherein wind energy production prediction information includes:

retrieving a sample of historical local wind speed and direction data in past situations with mesoscale predictions similar to a current mesoscale predicted wind speed and direction; and

computing a wind turbine location specific prediction of wind speed and direction by statistical interpolation or extrapolation from the retrieved sample.

7. The method of claim 1 wherein wind energy production prediction information includes:

receiving a forecast in the form of most likely values at specified times and/or peak values over specified periods of time;

using the received forecast to generate daily curves of forecasted variables by combining the forecast and climatic data for a given locality;

calibrating the daily forecast curves to compensate for deviations of on-site sensor readings from the meteorological measurements; and

correcting the calibrated daily forecast curves to compensate for forecast errors.

8. The method of claim 1 wherein power level information for non-renewable energy resources includes pricing data, and is used to provide for optimal utilization of energy producing resources for load servicing.

9. The method of claim 1 wherein wind energy production prediction information includes:

retrieving a sample of historical wind generation data in situations similar to a mesoscale predicted wind speed and direction; and

computing a wind turbine location specific prediction of wind generation by statistical interpolation or extrapolation from the retrieved sample.

10. A computer readable device having instructions for causing a computer to implement a method, the method comprising:

providing information related to wind speed and direction prediction to a wind turbine energy prediction module running on the computer;

providing wind energy production prediction information from the wind turbine energy prediction module to a resource allocation engine running on the computer; and

combining the wind energy production prediction information with power level information for non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

11. The computer readable device of claim 10 wherein the information related to wind speed prediction and direction comprises a feed from a weather service and includes historical wind speed and direction information correlated to specific wind turbines.

12. The computer readable device of claim 10 wherein the information related to wind speed and direction prediction comprises wind speed and direction simulation data and wherein wind energy production prediction information is a function of predicted wind speed, wind direction, and wind power curves for specific wind turbines.

13. The computer readable device of claim 10 wherein wind energy production prediction information includes:

retrieving a sample of historical local wind speed and direction data in past situations with mesoscale predictions similar to a current mesoscale predicted wind speed and direction; and

computing a wind turbine location specific prediction of wind speed and direction by statistical interpolation or extrapolation from the retrieved sample.

14. The computer readable device of claim 10 wherein wind energy production prediction information includes:

a forecast in the form of most likely values at specified times and/or peak values over specified periods of time;

wherein the received forecast is used by the executing instructions to generate daily curves of forecasted variables by combining the forecast and climatic data for a given locality;

wherein the daily forecast curves are used by the executing instructions to compensate for deviations of on-site sensor readings from the meteorological measurements; and

the calibrated daily forecast curves are corrected by the executing instructions to compensate for forecast errors.

15. The computer readable device of claim 10 wherein power level information for non-renewable energy resources includes pricing data, and is used to provide for optimal utilization of energy producing resources for load servicing.

16. The computer readable device of claim 10 wherein wind energy production prediction information includes:

retrieving a sample of historical wind generation data in situations similar to a meso scale predicted wind speed and direction; and

computing a wind turbine location specific prediction of wind generation by statistical interpolation or extrapolation from the retrieved sample.

17. A system comprising:

a wind turbine energy prediction module running on the computer to receive information related to wind speed and direction prediction;

a resource allocation engine running on the computer to receive wind energy production prediction information from the wind turbine energy prediction module to; and

a processing module running on the computer to combine the wind energy production prediction information with power level information for non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

18. The system of claim 17 wherein the information related to wind speed prediction and direction comprises a feed from a weather service and the information related to wind speed prediction comprises historical wind speed and direction information correlated to specific wind turbines.

19. The system of claim 17 wherein the information related to wind speed and direction prediction comprises wind speed and direction simulation data, and wherein the wind turbine energy prediction module receives wind energy production prediction information that is a function of predicted wind speed, wind direction, and wind power curves for specific wind turbines.

20. The system of claim 17 wherein wind turbine energy prediction module receives wind energy production prediction information that includes:

a sample of data similar to a meso scale predicted wind speed and direction, and the wind energy turbine energy prediction module computes a wind turbine location specific prediction of wind speed and direction by statistical interpolation or extrapolation from the found sample of data.

21. The system of claim 17 wherein wind energy production prediction module further:

receives a forecast in the form of most likely values at specified times and/or peak values over specified periods of time;

uses the received forecast to generate daily curves of forecasted variables by combining the forecast and climatic data for a given locality;

calibrates the daily forecast curves to compensate for deviations of on-site sensor readings from the meteorological measurements; and

corrects the calibrated daily forecast curves to compensate for forecast errors.

22. The system of claim 17 wherein power level information for non-renewable energy resources includes pricing data, and is used to provide for optimal utilization of energy producing resources for load servicing.