METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT TO OPTIMIZE POWER PLANT OUTPUT AND OPERATION

• US 20120049516A1
• Filed: 08/25/2010
• Published: 03/01/2012
• Est. Priority Date: 08/25/2010
• Status: Abandoned Application

• Current Assignee: Vestas Wind Systems A/S
• Original Assignee: Vestas Wind Systems A/S
• Inventors: Viassolo, Daniel
• Application Number:
  US12/868,459
• Publication Number:
  US 20120049516A1
• Time in Patent Office: Days
• US Class Current: 290/44
• CPC Class Codes: F03D 80/88 of mechanical components
• CPC Class Codes: F03D 9/10 Combinations of wind motors with apparatus storing energy
• CPC Class Codes: F03D 9/11 storing electrical energy
• CPC Class Codes: F03D 9/255 connected to electrical distribution networks; Arrangements therefor
• CPC Class Codes: F03D 9/257 the wind motor being part of a wind farm
• CPC Class Codes: H02P 2101/15 for wind-driven turbines
• CPC Class Codes: H02P 9/04 Control effected upon non-electric prime mover and dependent upon electric output value of the generator
• CPC Class Codes: Y02E 10/72 Wind turbines with rotation axis in wind direction
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin

Abstract

Method, power plant, and computer program product for use in optimizing power plant power and operation. The power plant includes a wind farm, an energy storage system, and a supervisory controller implementing a control algorithm that receives information on the wind farm and on the energy storage and, based on that information, computes a power reference for the wind farm and a power reference for the energy storage. These power references optimize a given power plant objective subject to a given set of constraints on the power plant.

Claims

1. A power plant for outputting power to a point of common connection with a power grid, the power plant comprising:

a wind farm including a plurality of wind turbines configured to generate and output a first portion of the power to the point of common connection;

an energy storage system including an energy storage device configured to be charged by the wind turbines, the energy storage device configured to output a second portion of the power to the point of common connection; and

a supervisory controller coupled in communication with the energy storage system and in communication with the wind farm, the supervisory controller configured to implement a control algorithm to dynamically compute a first power reference for the first portion of the power output by the wind farm and a second power reference for the second portion of the power output by the energy storage system.

2. The power plant of claim 1 wherein the energy storage device includes a rechargeable battery.

3. The power plant of claim 1 wherein the energy storage device includes a rechargeable battery, a flywheel, a capacitor bank, or any combination thereof.

4. The power plant of claim 1 wherein the control algorithm is a model predictive control algorithm that uses a numerical algorithm representing a dynamic model of the power plant.

5. The power plant of claim 1 further comprising:

at least one first sensor configured to provide sensor readings to the supervisory controller representing state information of the wind farm; and

at least one second sensor configured to provide sensor readings to the supervisory controller representing state information of the energy storage system;

wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references, the state information of the wind farm as a first input to the control algorithm, and the state information of the energy storage device as a second input to the control algorithm.

6. The system of claim 5 wherein the supervisory controller includes a soft sensor configured to compute the state information based upon the sensor readings from the at least one first sensor or configured to compute the state information based upon the sensor readings from the at least one second sensor.

7. The power plant of claim 1 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references, and an input to the control algorithm is an application for the energy storage system at the power plant.

8. The power plant of claim 1 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references, and an input to the control algorithm is at least one of a lifetime of the wind farm, an operating expense of the wind farm, a lifetime of the energy storage device, or an operating expense for the energy storage system.

9. The power plant of claim 1 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references, and an input to the control algorithm is at least one of a restriction on one or more controls for the wind turbines in the wind farm or a restriction on the energy storage device.

10. The power plant of claim 1 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references, and an input to the control algorithm is revenue from the power output by the power plant over a time period.

11. The power plant of claim 1 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references in real time, and to communicate the optimal path to the energy storage system and to the wind farm in real time.

12. A computer-implemented method for controlling power output by a power plant to point of common connection with a power grid, the method comprising:

using a control algorithm to dynamically compute a first power reference for a first portion of the power output from a wind farm of the power plant and a second power reference for a second portion of the power output by an energy storage system of the power plant;

controlling the energy storage system to output the first portion of the power to the point of common connection based upon the first power reference; and

controlling the wind farm to output the second portion of the power to the point of common connection based upon the second power reference.

13. The computer-implemented method of claim 12 wherein the energy storage device includes a rechargeable battery.

14. The computer-implemented method of claim 12 wherein the energy storage device includes a rechargeable battery, a flywheel, a capacitor bank, or any combination thereof.

15. The computer-implemented method of claim 12 wherein the control algorithm is a model predictive control algorithm that uses a numerical algorithm representing a dynamic model of the power plant.

16. The computer-implemented method of claim 12 further comprising:

providing state information of the wind farm as a first input to the control algorithm;

providing state information of the energy storage system as a second input to the control algorithm; and

generating an optimal path for the first and second power references using the control algorithm.

17. The computer-implemented method of claim 12 further comprising:

providing an application for the energy storage system at the power plant as an input to the control algorithm; and

generating an optimal path for the first and second power references using the control algorithm.

18. The computer-implemented method of claim 12 further comprising:

providing at least one of a lifetime of the wind farm, an operating expense of the wind farm, a lifetime of the energy storage device, or an operating expense for the energy storage system to the control algorithm; and

generating an optimal path for the first and second power references using the control algorithm.

19. The computer-implemented method of claim 12 further comprising:

providing at least one of a restriction on controls for the wind turbines in the wind farm or a restrictions on the energy storage device to the control algorithm; and

generating an optimal path for the first and second power references using the control algorithm.

20. The computer-implemented method of claim 12 further comprising:

providing revenue of the power plant over a time period to the control algorithm; and

generating an optimal path for the first and second power references using the control algorithm.

21. The computer-implemented method of claim 12 wherein the supervisory controller causes the control algorithm to generate an optimal path for the first and second power references in real time, and to communicate the optimal path to the energy storage system and to the wind farm in real time.

22. A computer program product comprising:

a computer readable storage medium; and

program instructions for performing the method of claim 12,

wherein the program instructions are stored on the computer readable storage medium.