Electrical energy storage system with battery power setpoint optimization using predicted values of a frequency regulation signal

Patent metadata

Field	Value
Publication number	US-10197632-B2
Application number	US-201615247777-A
Country	US
Kind code	B2
Filing date	Aug 25, 2016
Priority date	Oct 8, 2015
Publication date	Feb 5, 2019
Grant date	Feb 5, 2019

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.

First claim

What is claimed is:

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 in a frequency response (FR) program, wherein the regulation signal indicates a proportion of a regulation award power amount to add to or remove from an electric grid relative to a frequency response midpoint,
predict future values of the regulation signal for each time within an optimization time period,
use the predicted values of the regulation signal to generate the battery power setpoints for the power inverter for each time within the optimization time period; and
operate the power inverter using the generated battery power setpoints to cause the battery to store or discharge the electric power.

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 generate the 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 generate the battery power setpoints based on both the campus power usage and the predicted future values of the regulation 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 in a frequency response (FR) program, wherein the frequency regulation signal indicates a proportion of a regulation award power amount to add to or remove from an electric grid relative to a frequency response midpoint;
predicting future values of the frequency regulation signal for each time within an optimization time period;
using the predicted values of the frequency regulation signal to generate battery power setpoints for each time within the optimization time period;
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 the 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 frequency 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 frequency regulation signal; and
using a low level controller to filter the predicted future values of the frequency regulation signal based on the filter parameters and determine the optimal battery power setpoints using the filtered predicted future values of the frequency 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 powe

Assignees

• Johnson Controls Tech Co
• Taurus Des Llc

Inventors

• Wenzel Michael J
• Drees Kirk H

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• G05B13/048

  using a predictor · CPC title

• H02M7/44

  by static converters · CPC title

• G01R31/3651 Primary

  Physics · mapped topic

• Y02E40/76

  Cross-Sectional Technologies · mapped topic