ELECTRICAL ENERGY STORAGE SYSTEM WITH BATTERY POWER SETPOINT OPTIMIZATION USING PREDICTED VALUES OF A FREQUENCY REGULATION SIGNAL

• US 20170102433A1
• Filed: 08/25/2016
• Published: 04/13/2017
• Est. Priority Date: 10/08/2015
• Status: Active Grant

• Current Assignee: Con Edison Battery Storage, LLC
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Wenzel, Michael J., Drees, Kirk H.
• Granted Patent: US 10,197,632 B2
• Time in Patent Office: Days
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02M 7/44 by static converters
• CPC Class Codes: Y02B 10/10 Photovoltaic [PV]
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• 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
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A frequency response optimization includes a battery that stores and discharges electric power, a power inverter that uses battery power setpoints to control an amount of the electric power stored or discharged from the battery, and a frequency response controller. The frequency response controller receives a regulation signal from an incentive provider, predicts future values of the regulation signal, and uses the predicted values of the regulation signal to generate the battery power setpoints for the power inverter.

Claims

1. A frequency response optimization system comprising:

a battery configured to store and discharge electric power;

a power inverter configured to use battery power setpoints to control an amount of the electric power stored or discharged from the battery; and

a frequency response controller configured to;

receive a regulation signal from an incentive provider,

predict future values of the regulation signal, and

use the predicted values of the regulation signal to generate the battery power setpoints for the power inverter.

2. The system of claim 1, wherein the frequency response controller is configured to use an autoregressive model to predict the future values of the regulation signal based on a history of past values of the regulation signal.

3. The system of claim 1, wherein the frequency response controller comprises a low pass filter configured to filter the predicted values of the regulation signal;

wherein the frequency response controller is configured to use the filtered values of the regulation signal to generate the battery power setpoints.

4. The system of claim 1, wherein the frequency response controller is configured to generate an objective function comprising:

an estimated amount of frequency response revenue that will result from the battery power setpoints; and

an estimated cost of battery degradation that will result from the battery power setpoints.

5. The system of claim 4, wherein the frequency response controller is configured to:

use dynamic programming to select scaling coefficients for the regulation signal; and

adjust the scaling coefficients to achieve an optimal value for the objective function.

6. The system of claim 1, wherein the frequency response controller is configured to:

calculate a frequency response performance score that will result from the battery power setpoints; and

use the frequency response performance score to estimate an amount of frequency response revenue that will result from the battery power setpoints.

7. The system of claim 1, wherein the frequency response controller is configured to:

use a battery life model to estimate an amount of battery degradation that will result from the battery power setpoints; and

use the estimated amount of battery degradation to determine a cost of the battery degradation.

8. The system of claim 7, wherein the battery life model comprises a plurality of variables that depend on the battery power setpoints, the variables comprising at least one of:

a temperature of the battery;

a state of charge of the battery;

a depth of discharge of the battery;

a power ratio of the battery; and

an effort ratio of the battery.

9. The system of claim 1, wherein the frequency response controller comprises:

a high level controller configured to generate filter parameters based on the predicted values of the regulation signal; and

a low level controller configured to use the filter parameters to filter the predicted regulation signal and determine the optimal battery power setpoints using the filtered regulation signal.

10. The system of claim 1, further comprising a campus having a campus power usage and a point of intersection at which the campus power usage combines with the electric power discharged from the battery;

wherein the frequency response controller is configured to determine the optimal battery power setpoints based on both the campus power usage and the frequency response signal.

11. A method for generating battery power setpoints in a frequency response optimization system, the method comprising:

receiving a frequency regulation signal from an incentive provider;

predicting future values of the frequency regulation signal;

using the predicted values of the frequency regulation signal to generate battery power setpoints;

providing the battery power setpoints to a power inverter; and

using the battery power setpoints to control an amount of electric power stored or discharged by the battery in response to the frequency response signal.

12. The method of claim 11, wherein predicting future values of the frequency regulation signal comprises using an autoregressive model to predict the future values of the frequency regulation signal based on a history of past values of the frequency regulation signal.

13. The method of claim 11, further comprising:

filtering the predicted values of the regulation signal using a low pass filter; and

using the filtered values of the regulation signal to generate the battery power setpoints.

14. The method of claim 11, further comprising generating an objective function comprising:

an estimated amount of frequency response revenue that will result from the battery power setpoints; and

an estimated cost of battery degradation that will result from the battery power setpoints.

15. The method of claim 14, further comprising using dynamic programming to select scaling coefficients for the regulation signal and adjust the scaling coefficients to achieve an optimal value for the objective function.

16. The method of claim 11, further comprising:

calculating a frequency response performance score that will result from the battery power setpoints; and

using the frequency response performance score to estimate an amount of frequency response revenue that will result from the battery power setpoints.

17. The method of claim 11, further comprising:

using a battery life model to estimate an amount of battery degradation that will result from the battery power setpoints; and

using the estimated amount of battery degradation to determine a cost of the battery degradation.

18. The method of claim 17, wherein the battery life model comprises a plurality of variables that depend on the battery power setpoints, the variables comprising at least one of:

a temperature of the battery;

a state of charge of the battery;

a depth of discharge of the battery;

a power ratio of the battery; and

an effort ratio of the battery.

19. The method of claim 11, further comprising:

using a high level controller to generate filter parameters based on the predicted values of the regulation signal; and

using a low level controller to filter the predicted regulation signal based on the filter parameters and determine the optimal battery power setpoints using the filtered regulation signal.

20. The method of claim 11, wherein the frequency regulation system comprises a campus having a campus power usage and a point of intersection at which the campus power usage combines with the electric power discharged from the battery;

the method further comprising determining the optimal battery power setpoints based on both the campus power usage and the frequency response signal.