ENERGY STORAGE MODELING AND CONTROL

• US 20140039710A1
• Filed: 03/15/2013
• Published: 02/06/2014
• Est. Priority Date: 04/04/2012
• Status: Active Grant

• Current Assignee: IHI Terrasun Solutions Inc.
• Original Assignee: 24M Technologies, Inc.
• Inventors: Carter, W. Craig, Ferrara, Marco, Goroff, Michael, Mann, Maxwell
• Granted Patent: US 9,509,176 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 19/0428 Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05F 1/67 to the maximum power available from a generator, e.g. from solar cell
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• 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

Systems and methods for optimal planning and real-time control of energy storage systems for multiple simultaneous applications are provided. Energy storage applications can be analyzed for relevant metrics such as profitability and impact on the functionality of the electric grid, subject to system-wide and energy storage hardware constraints. The optimal amount of storage capacity and the optimal operating strategy can then be derived for each application and be prioritized according to a dispatch stack, which can be statically or dynamically updated according to data forecasts. Embodiments can consist of both planning tools and real-time control algorithms.

Claims

1. A computerized method for controlling a charge and a discharge of an energy storage asset in an energy storage system using a computer, the method comprising:

receiving at the computer a first physical model for an energy storage application;

retrieving from a first database, using the computer, historical and forecast data related to an operation of the energy storage system;

receiving at the computer a configuration for the energy storage system;

receiving at the computer a cost model for the energy storage system;

receiving at the computer financial incentive information of serving the energy storage application;

receiving at the computer an operating strategy of the energy storage system;

receiving at the computer a second physical model for the energy storage asset;

calculating, using the computer, an energy storage asset charge and discharge strategy that maximizes the financial worth of the energy storage application, accounting for an energy storage system depreciation, based on at least one of the historical and forecast data, the first and second physical models, the financial incentive information, energy storage system constraints, the configuration, and the cost model; and

charging or discharging, using a controller, the energy storage asset according to the calculated charge and discharge strategy.

2. The computerized method of claim 1, wherein the energy storage asset comprises at least one of an electrochemical battery, an electrical capacitor, a superconducting magnetic ring, a mechanical flywheel, a compressed air energy storage system, and a hydrostatic system.

3. The computerized method of claim 1, wherein the energy storage application comprises at least one of a wind power optimization application, a renewable generation balancing application, a power arbitrage optimization application, a peak load shaving application, a transformer overload protection application, and an energy cost minimizing application.

4. The computerized method of claim 1, wherein the calculating step comprises calculating the energy storage asset charge or discharge strategy based on at least one of the historical and forecast data, the first physical model, the second physical model, the cost model, the financial incentive information, the energy storage system constraints, the configuration, and charge and discharge strategy.

5. The computerized method of claim 4, wherein the energy storage system configuration comprises at least one of capacity information, power rating, charge rate information, discharge rate information, efficiency loss information, energy storage asset fatigue and aging information, and useful life information.

6. The computerized method of claim 1, wherein the calculating step comprises at least one of a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

7. The computerized method of claim 1, further comprising calculating an optimal energy storage system capacity and an optimal operating strategy that maximize the financial worth of the energy storage application.

8. The computerized method of claim 1, wherein a plurality of energy storage applications are simultaneously served by the energy storage system.

9. A system for controlling a charge and a discharge of an energy storage asset in an energy storage system, the system comprising:

a computer configured to;

receive a first physical model for an energy storage application;

retrieve from a first database historical and forecast data related to an operation of the energy storage system;

receive a configuration for the energy storage system;

receive a cost model for the energy storage system;

receive financial incentive information of serving the energy storage application;

receiving an operating strategy of the energy storage system;

receive a second physical model for the energy storage asset;

calculate an energy storage charge and discharge strategy that maximize the financial worth of the energy storage application, accounting for an energy storage system depreciation, based on at least one of the historical and forecast data, the first and second physical models, the financial incentive information, energy storage system constraints, the configuration, and the cost model; and

a controller configured to charge or discharge the energy storage asset according to the calculated charge and discharge strategy.

10. The system of claim 9, wherein the energy storage asset comprises at least one of an electrochemical battery, an electrical capacitor, a superconducting magnetic ring, a mechanical flywheel, a compressed air energy storage system, and a hydrostatic system.

11. The system of claim 9, wherein the energy storage application comprises at least one of a wind power optimization application, a renewable generation balancing application, a power arbitrage optimization application, a peak load shaving application, a transformer overload protection application, and an energy cost minimizing application.

12. The system of claim 9, wherein the computer is further configured to calculate the the energy storage charge and discharge strategy based on at least one of the historical and forecast data, the first physical model, the second physical model, the cost model, the financial incentive information, the energy storage system constraints, the configuration, and charge and discharge strategy.

13. The system of claim 12, wherein the energy storage system configuration comprises at least one of capacity information, power rating, charge rate information, discharge rate information, efficiency loss information, energy storage asset fatigue and aging information, and useful life information.

14. The system of claim 9, wherein the computer is further configured to calculate the energy storage charge and discharge strategy based on at least one of a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

15. The system of claim 9, wherein the computer is further configured to calculate an optimal energy storage system capacity and an optimal operating strategy that maximize the financial worth of the energy storage application.

16. The system of claim 9, wherein a plurality of energy storage applications are simultaneously served by the energy storage system.

17. A non-transitory computer readable medium having stored thereon executable code which causes a computer to perform a method for controlling a charge and a discharge of an energy storage asset in an energy storage system, the method comprising:

receiving at the computer a first physical model for an energy storage application;

retrieving from a first database, using the computer, historical and forecast data related to an operation of the energy storage system;

receiving at the computer a configuration for the energy storage system;

receiving at the computer a cost model for the energy storage system;

receiving at the computer financial incentive information of serving the energy storage application;

receiving at the computer an operating strategy of the energy storage system;

receiving at the computer a second physical model for the energy storage asset;

calculating, using the computer, an energy storage charge and discharge strategy that maximizes the financial worth of an energy storage application, accounting for an energy storage system depreciation, based on at least one of the historical and forecast data, the first and second physical models, the financial incentive information, energy storage system constraints, the configuration, and the cost model; and

charging or discharging, using a controller, the energy storage asset according to the calculated charge and discharge strategy.

18. The computer readable medium of claim 17, wherein the energy storage asset comprises at least one of an electrochemical battery, an electrical capacitor, a superconducting magnetic ring, a mechanical flywheel, a compressed air energy storage system, and a hydrostatic system.

19. The computer readable medium of claim 17, wherein the energy storage application comprises at least one of a wind power optimization application, a renewable generation balancing application, a power arbitrage optimization application, a peak load shaving application, a transformer overload protection application, and an energy cost minimizing application.

20. The computer readable medium of claim 17, wherein the calculating step comprises calculating the energy storage asset charge or discharge strategy based on at least one of the historical and forecast data, the first physical model, the second physical model, the cost model, the financial incentive information, the energy storage system constraints, the configuration, and charge and discharge strategy.

21. The computer readable medium of claim 20 wherein the energy storage system configuration comprises at least one of capacity information, power rating, charge rate information, discharge rate information, efficiency loss information, energy storage asset fatigue and aging information, and useful life information.

22. The computer readable medium of claim 17, wherein the calculating step comprises at least one of a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

23. The computer readable medium of claim 17, further comprising calculating an optimal energy storage system capacity and an optimal operating strategy that maximize the financial worth of the energy storage application.

24. The computer readable medium of claim 17, wherein a plurality of energy storage applications are simultaneously served by the energy storage system.