Electrical energy storage system with battery power setpoint optimization based on battery degradation costs and expected frequency response revenue

• US 10,222,427 B2
• Filed: 08/25/2016
• Issued: 03/05/2019
• 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., Lenhardt, Brett M., Drees, Kirk H.
• Primary Examiner(s): Kaplan, Hal
• Application Number:
  US15/247,784
• Publication Number:
  US 20170102434A1
• Time in Patent Office: 922 Days
• Field of Search: 307 72
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• 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 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 7/0068 Battery or charger load switching, e.g. concurrent charging and load supply H02J7/0013 takes precedence
• 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
• CPC Class Codes: Y04S 50/14 Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

An electrical energy storage system includes a battery configured to store and discharge electric power to an energy grid, 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 controller. The controller is configured to generate optimal values for the battery power setpoints as a function of both an estimated amount of battery degradation and an estimated amount of frequency response revenue that will result from the battery power setpoints.

Claims

1. An electrical energy storage system comprising:

a battery configured to store and discharge electric power to an energy grid;

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 controller configured to generate optimal values for the battery power setpoints as a function of both an estimated amount of battery degradation and an estimated amount of frequency response revenue that will result from the battery power setpoints.

2. The system of claim 1, wherein the controller is configured to estimate the amount of battery degradation that will result from the battery power setpoints using a battery life model.

3. The system of claim 2, wherein the battery life model is a parametric model comprising a regression coefficient for each of a plurality of variables in the battery life model;

wherein the controller is configured to perform a curve fitting process to determine values for the regression coefficients.

4. The system of claim 3, wherein the curve fitting process comprises:

providing the power inverter with known battery power setpoints;

determining values for each of the plurality of variables in the battery life model based on the known battery power setpoints;

measuring an amount of battery degradation that results from the known battery power setpoints; and

using the values for each of the plurality of variables in the battery life model and the measured amount of battery degradation to determine the values for the regression coefficients.

5. The system of claim 1, wherein the controller is configured to:

generate frequency regulation power setpoints based on a frequency of the energy grid;

generate ramp rate control power setpoints based on a power output of a photovoltaic field; and

combine the frequency regulation power setpoints and the ramp rate control power setpoints to generate the battery power setpoints.

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

estimate a monetary cost of the battery degradation that will result from the battery power setpoints; and

generate the optimal values for the battery power setpoints by optimizing an objective function comprising the estimated amount of frequency response revenue and the monetary cost of the battery degradation that will result from the battery power setpoints.

7. The system of claim 6, wherein the controller is configured to estimate the monetary cost of the battery degradation by:

determining a total loss in the frequency response revenue that will result from the battery power setpoints; and

calculating a present value of the total loss in the frequency response revenue.

8. The system of claim 1, wherein the estimated amount of battery degradation comprises an estimated loss in battery capacity that will result from the battery power setpoints.

9. The system of claim 1, wherein the controller is configured to generate the optimal values for the battery power setpoints by:

identifying constraints on a state-of-charge (SOC) of the battery;

determining a relationship between the SOC of the battery and the battery power setpoints; and

generating the optimal values of the battery power setpoints such that a predicted SOC of the battery during an optimization period does not violate the constraints on the SOC of the battery.

10. The system of claim 9, wherein the controller is configured to generate the predicted SOC of the battery using a random walk model;

wherein the constraints on the SOC of the battery ensure that the battery will not become fully charged or fully depleted during the optimization period.

11. A method for operating an electrical energy storage system, the method comprising:

using a battery life model to identify a relationship between battery power setpoints and an estimated amount of battery degradation that will result from the battery power setpoints, the battery life model comprising a plurality of variables that depend on the battery power setpoints;

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

generating optimal values for the battery power setpoints as a function of both the estimated amount of battery degradation and the estimated amount of frequency response revenue that will result from the battery power setpoints; and

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

12. The method of claim 11, wherein the battery life model is a parametric model comprising a regression coefficient for each of the plurality of variables in the battery life model.

13. The method of claim 12, further comprising performing a curve fitting process to determine values for the regression coefficients.

14. The method of claim 13, wherein the curve fitting process comprises:

providing a power inverter with known battery power setpoints;

determining values for each of the variables in the battery life model based on the known battery power setpoints;

measuring an amount of battery degradation that results from the known battery power setpoints; and

using the values for each of the variables in the battery life model and the measured amount of battery degradation to determine the values for the regression coefficients.

15. The method of claim 11, further comprising:

generating frequency regulation power setpoints based on a frequency of an energy grid;

generating ramp rate control power setpoints based on a power output of a photovoltaic field; and

combining the frequency regulation power setpoints and the ramp rate control power setpoints to generate the battery power setpoints.

16. The method of claim 11, further comprising estimating a monetary cost of the battery degradation that will result from the battery power setpoints;

wherein generating the optimal values for the battery power setpoints comprises optimizing an objective function comprising the estimated amount of frequency response revenue and the monetary cost of the battery degradation that will result from the battery power setpoints.

17. The method of claim 16, wherein estimating the monetary cost of the battery degradation comprises:

determining a total loss in the frequency response revenue that will result from the battery power setpoints; and

calculating a present value of the total loss in the frequency response revenue.

18. The method of claim 11, wherein the estimated amount of battery degradation comprises an estimated loss in battery capacity that will result from the battery power setpoints.

19. The method of claim 11, wherein generating optimal values for the battery power setpoints comprises:

identifying constraints on a state-of-charge (SOC) of the battery;

determining a relationship between the SOC of the battery and the battery power setpoints; and

generating the optimal values of the battery power setpoints such that a predicted SOC of the battery during an optimization period does not violate the constraints on the SOC of the battery.

20. The method of claim 19, further comprising generating the predicted SOC of the battery using a random walk model;

wherein the constraints on the SOC of the battery ensure that the battery will not become fully charged or fully depleted during the optimization period.