ELECTRICAL ENERGY STORAGE SYSTEM WITH CASCADED FREQUENCY RESPONSE OPTIMIZATION

• US 20170104345A1
• 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., ElBsat, Mohammad N.
• Granted Patent: US 10,418,833 B2
• Time in Patent Office: Days
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: H02J 13/00017 using optical fiber
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2310/10 The network having a local or delimited stationary reach
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 3/241 The oscillation concerning frequency
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 3/40 Synchronising a generator for connection to a network or to another generator
• CPC Class Codes: H02J 7/35 with light sensitive cells
• CPC Class Codes: Y02B 10/10 Photovoltaic [PV]
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• 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: Y02E 70/30 : Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y04S 10/123 : the energy generation units being or involving renewable energy sources
• CPC Class Codes: Y04S 20/12 : Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
• CPC Class Codes: Y04S 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/124 : using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 50/10 : Energy trading, including energy flowing from end-user application to grid

Abstract

A frequency response controller includes a high level controller configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate a frequency response midpoint. The controller further includes a low level controller configured to 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 a battery during a frequency response period.

Claims

1. A frequency response controller comprising:

a high level controller configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate a frequency response midpoint; and

a low level controller configured to 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 a battery during a frequency response period.

2. The frequency response controller of claim 1, wherein the high level controller is configured to determine the frequency response midpoint that maintains the battery at a same state-of-charge at a beginning and an end of the frequency response period while participating in a frequency response program.

3. The frequency response controller of claim 1, wherein the high level controller is configured to determine an optimal frequency response midpoint that achieves a desired change in a state-of-charge of the battery while participating in a frequency response program.

4. The frequency response controller of claim 1, wherein:

the high level controller is configured to generate filter parameters based on the frequency response midpoint; and

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

5. The frequency response controller 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 controller 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 a power inverter for the battery.

7. The frequency response controller 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 controller of claim 1, wherein the high level controller is configured to provide a bid price to the incentive provider and receive the regulation signal specifying a regulation award from the incentive provider.

9. The frequency response controller of claim 1, further comprising a battery power loss estimator configured to estimate an amount of power lost in the battery based on the statistics of the regulation signal.

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

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;

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 a battery during a frequency response period.

11. The method of claim 10, wherein generating the frequency response midpoint comprises determining the frequency response midpoint that maintains the battery at a same state-of-charge at a beginning and an end of the frequency response period while participating in a frequency response program.

12. The method of claim 10, wherein generating the frequency response midpoint comprises determining an optimal frequency response midpoint that achieves a desired change in a state-of-charge of the battery while participating in a frequency response program.

13. The method of claim 10, further comprising:

generating filter parameters based on the frequency response midpoint;

using the filter parameters to filter the regulation signal; and

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

14. The method of claim 10, 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 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 for the battery.

16. The method of claim 10, 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.

17. The method of claim 10, further comprising:

providing a bid price to the incentive provider; and

receiving the regulation signal specifying a regulation award from the incentive provider.

18. The method of claim 10, further comprising estimating an amount of power lost in the battery based on the statistics of the regulation signal.

19. A frequency response optimization system comprising:

a battery configured to store and discharge electric power;

a battery 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;

a high level controller configured to generate a frequency response midpoint based on a regulation signal; and

a low level controller configured to use the frequency response midpoint to determine optimal setpoints for the battery power inverter.

20. The frequency response optimization system of claim 19, wherein:

the high level controller is configured to generate filter parameters based on the frequency response midpoint; and

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