Electrical energy storage system with constant state-of charge frequency response optimization

• US 10,418,832 B2
• Filed: 08/25/2016
• Issued: 09/17/2019
• Est. Priority Date: 10/08/2015
• Status: Active Grant

• Current Assignee: Con Edison Battery Storage, LLC
• Original Assignee: Con Edison Battery Storage, LLC
• Inventors: Wenzel, Michael J., Drees, Kirk H., ElBsat, Mohammad N.
• Primary Examiner(s): Isla, Richard
• Assistant Examiner(s): Bui, Dung V
• Application Number:
  US15/247,883
• Publication Number:
  US 20170104344A1
• Time in Patent Office: 1,118 Days
• Field of Search: 320134
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• 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: 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 system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery, and a frequency response controller. The frequency response controller includes receiving a regulation signal from an incentive provider, determining statistics of the regulation signal, using the statistics of the regulation signal to generate a frequency response midpoint, and using the frequency response midpoint to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.

Claims

1. A frequency response optimization system comprising:

a battery configured to store and discharge electric power;

a power inverter configured to control an amount of the electric power stored or discharged from the battery at each of a plurality of time steps during a frequency response period; and

a frequency response controller configured to;

receive a power regulation signal from an incentive provider,

predict statistics of the power regulation signal from the incentive provider during the frequency response period,

use the predicted statistics of the power regulation signal to generate a frequency response midpoint that maintains the battery at a constant state-of-charge (SOC) at a beginning and an end of the frequency response period while participating in a frequency response program, wherein the frequency response midpoint is a baseline power consumption value relative to which the frequency response controller causes the electric power to be added to an energy grid or removed from the energy grid in response to the power regulation signal, and

use the frequency response midpoint to determine optimal battery power setpoints for the power inverter;

wherein the power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.

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

a high level controller configured to determine the midpoint at the beginning of the frequency response period; and

a low level controller configured to determine the optimal battery power setpoints at each of a plurality of time steps during the frequency response period.

3. The frequency response optimization system of claim 1, wherein the frequency response controller comprises a battery power loss estimator configured to estimate an amount of power lost in the battery based on the predicted statistics of the power regulation signal.

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

a high level controller configured to generate filter parameters based on the midpoint; and

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

5. The frequency response optimization 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.

6. The frequency response optimization system of claim 5, wherein the frequency response controller is configured to implement constraints on the objective function, the constraints comprising at least one of:

constraining a state-of-charge of the battery between a minimum state-of-charge and a maximum state-of-charge; and

constraining the midpoint such that a sum of the midpoint and a campus power usage does not exceed a maximum power rating of the power inverter.

7. The frequency response optimization system of claim 1, wherein the frequency response controller is configured to use a battery life model to determine an estimated cost of battery degradation, the battery life model comprising 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.

8. The frequency response optimization system of claim 1, further comprising a campus having a campus power usage and a point of interconnection 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 setpoints based on both the campus power usage and a frequency response signal.

9. A method for optimizing frequency response, the method comprising:

receiving a power regulation signal from an incentive provider;

predicting statistics of the power regulation signal from the incentive provider during a frequency response period;

using the predicted statistics of the power regulation signal to generate a frequency response midpoint that maintains a battery at a constant state-of-charge (SOC) at a beginning and an end of the frequency response period while participating in a frequency response program, wherein the frequency response midpoint is a baseline power consumption value relative to which electric power is added to an energy grid or removed from the energy grid in response to the power regulation signal;

using the frequency response midpoint to determine optimal battery power setpoints; and

using the optimal battery power setpoints to control an amount of electric power stored or discharged from the battery.

10. The method of claim 9, further comprising:

determining the midpoint at the beginning of the frequency response period; and

determining the optimal battery power setpoints at each of a plurality of time steps during the frequency response period.

11. The method of claim 9, further comprising estimating an amount of power lost in the battery based on the predicted statistics of the power regulation signal.

12. The method of claim 9, further comprising:

generating filter parameters based on the midpoint;

using the filter parameters to filter the power regulation signal; and

determining the optimal battery power setpoints using the filtered power regulation signal.

13. The method of claim 9, 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.

14. The method of claim 13, further comprising implementing constraints on the objective function, the constraints comprising at least one of:

constraining a state-of-charge of the battery between a minimum state-of-charge and a maximum state-of-charge; and

constraining the midpoint such that a sum of the midpoint and a campus power usage does not exceed a maximum power rating of a power inverter.

15. The method of claim 9, further comprising using a battery life model to determine an estimated cost of battery degradation, the battery life model comprising 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.

16. The method of claim 9, further comprising determining the optimal setpoints based on both a campus power usage and the power regulation signal, wherein the campus power usage combines with the electric power discharged from the battery at a point of interconnection.

17. A frequency response controller comprising:

a processing circuit configured to;

receive a power regulation signal from an incentive provider;

predict statistics of the power regulation signal from the incentive provider during a frequency response period;

use the predicted statistics of the power regulation signal to generate a frequency response midpoint that maintains a battery at a constant state-of-charge (SOC) at a beginning and an end of the frequency response period while participating in a frequency response program, wherein the frequency response midpoint is a baseline power consumption value relative to which the processing circuit causes the electric power to be added to an energy grid or removed from the energy grid in response to the power regulation signal;

use the frequency response midpoint to determine optimal battery power setpoints; and

use the optimal battery power setpoints to control an amount of electric power stored or discharged from the battery.

18. The controller of claim 17, wherein the processing circuit is configured to:

determine the midpoint at the beginning of the frequency response period; and

determine the optimal battery power setpoints at each of a plurality of time steps during the frequency response period.

19. The controller of claim 17, wherein the processing circuit is configured to:

generate filter parameters based on the midpoint;

use the filter parameters to filter the power regulation signal; and

determine the optimal battery power setpoints using the filtered power regulation signal.

20. The controller of claim 17, wherein the processing circuit 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.