ADAPTIVE STOCHASTIC CONTROLLER FOR DISTRIBUTED ELECTRICAL ENERGY STORAGE MANAGEMENT

• US 20130158725A1
• Filed: 08/20/2012
• Published: 06/20/2013
• Est. Priority Date: 02/24/2010
• Status: Abandoned Application

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Inventors: Anderson, Roger N., Boulanger, Albert, Kressner, Arthur A.
• Application Number:
  US13/589,916
• Publication Number:
  US 20130158725A1
• Time in Patent Office: Days
• US Class Current: 700/286
• CPC Class Codes: B60L 55/00 Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• CPC Class Codes: G05F 5/00 Systems for regulating electric variables by detecting deviations in the electric input to the system and thereby controlling a device within the system to obtain a regulated output
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/322 the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 10/126 the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• CPC Class Codes: Y04S 30/14 Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

A system for managing a battery in communication with an electrical grid that includes (a) a data collector to collect data representative of an electrical grid; (b) an ASC controller operatively coupled to the data collector and adapted to receive collected data therefrom, the ASC controller comprising a financial strategizer to send instructions based on the collected data; and (c) a battery controller operatively coupled to the ASC controller to receive the instructions transmitted by the ASC controller, the battery controller configured to dictate whether the battery receives electricity from, or transmits electricity to the electrical grid.

Claims

1. A system for managing a battery in communication with an electrical grid comprising:

(a) a data collector to collect data representative of an electrical grid;

(b) an ASC controller operatively coupled to the data collector and adapted to receive collected data therefrom, the ASC controller comprising a financial strategizer to send instructions based on the collected data; and

(c) a battery controller operatively coupled to the ASC controller to receive the instructions transmitted by the ASC controller, the battery controller configured to dictate whether the battery receives electricity from, or transmits electricity to the electrical grid.

2. The system of claim 1, wherein the data representative of the electrical grid includes electricity price and load data.

3. The system of claim 2, wherein the data representative of the electrical grid further includes weather and sunlight forecast data.

4. The system of claim 1, wherein the data collector is further adapted to collect data representative of the battery.

5. The system of claim 1, wherein the ASC controller is further adapted to employ adaptive dynamic programming.

6. The system of claim 4, wherein the adaptive dynamic programming comprises reinforcement learning.

7. The system of claim 1, further comprising a data warehouse, operatively coupled to the data collector and ASC controller, to store data from the data collector and actions taken by the ASC controller in response to the data from the data collector.

8. The system of claim 5, wherein the data warehouse is adapted for communication with a machine learning system.

9. The system of claim 1, wherein the financial strategizer is adapted to output real time options that include the option value of arbitrage and the option value of peak shaving.

10. The system of claim 1, wherein the battery is associated with an electrical vehicle.

11. The system of claim 1, wherein the battery is a hybrid flow battery.

12. A method for managing a battery in communication with an electric grid comprising

(a) collecting data representative of an electrical grid;

(b) sending data representative of the electrical grid to an ASC controller, the ASC controller comprising a financial strategizer; and

(c) transmitting instructions from the ASC controller to a battery controller configured to dictate whether the battery receives electricity from, or transmits electricity to the electrical grid.

13. The method of claim 12 wherein the data representative of the electrical grid includes electricity price and load data, and weather and sunlight forecast data.

14. The method of claim 12, wherein the ASC controller employs adaptive dynamic programming.

15. The method of claim 14, wherein the adaptive dynamic programming comprises reinforcement learning.

16. The method of claim 12, further comprising storing the collected data and the response taken by the ASC controller in response to the collected data in a data warehouse.

17. The method of claim 16, wherein the data warehouse is in communication with a machine learning system.

18. The method of claim 12, wherein the financial strategizer outputs real time options that include the option value of arbitrage and the option value of peak shaving.

19. The method of claim 12, wherein the battery is associated with an electrical vehicle.

20. The method of claim 12, wherein the battery is a hybrid flow battery.