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

• US 10,186,889 B2
• Filed: 08/25/2016
• Issued: 01/22/2019
• Est. Priority Date: 10/08/2015
• Status: Active Grant

• Current Assignee: Con Edison Battery Storage, LLC
• Original Assignee: Taurus Des, LLC
• Inventors: Wenzel, Michael J., Drees, Kirk H., ElBsat, Mohammad N.
• Primary Examiner(s): Tso, Edward
• Application Number:
  US15/247,886
• Publication Number:
  US 20170104346A1
• Time in Patent Office: 880 Days
• Field of Search: 320107, 320109, 320132, 320149, 324427, 702 63, 702 65
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/47 Responding to energy costs
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 11/77 by controlling the speed of ventilators
• CPC Class Codes: F24F 11/83 by controlling the supply of heat-exchange fluids to heat-exchangers
• CPC Class Codes: F24F 11/84 using valves
• CPC Class Codes: F24F 2005/0025 using heat exchange fluid storage tanks
• CPC Class Codes: F24F 2005/0067 with photovoltaic panels
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2130/00 Control inputs relating to environmental factors not covered by group F24F2110/00
• CPC Class Codes: F24F 2130/10 Weather information or forecasts
• CPC Class Codes: F24F 2140/50 Load
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: F24F 5/0017 using cold storage bodies, e.g. ice
• CPC Class Codes: F24F 5/0035 using evaporation
• CPC Class Codes: G05B 13/0205 : not using a model or a simulator of the controlled system
• CPC Class Codes: G05B 15/02 : electric
• CPC Class Codes: G05B 19/406 : characterised by monitoring or safety G05B19/19 takes precedence
• CPC Class Codes: G05B 2219/2642 : Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05B 2219/49068 : Minimum cost adaptive
• CPC Class Codes: G06Q 50/06 : Energy or water supply
• CPC Class Codes: H02J 13/00001 : characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
• CPC Class Codes: H02J 13/00028 : involving the use of Internet protocols
• CPC Class Codes: H02J 15/00 : Systems for storing electric energy mechanical systems therefor F01-F04; in chemical form H01M
• 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 3/28 : Arrangements for balancing of the load in a network by storage of energy
• CPC Class Codes: H02J 3/32 : using batteries with converting means
• CPC Class Codes: H02J 3/38 : Arrangements for parallely feeding a single network by two or more generators, converters or transformers
• 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: H02S 40/32 : comprising DC/AC inverter means associated with the PV module itself, e.g. AC modules
• CPC Class Codes: Y02A 30/272 : Solar heating or cooling
• CPC Class Codes: Y02B 10/10 : Photovoltaic [PV]
• CPC Class Codes: Y02B 10/20 : Solar thermal
• CPC Class Codes: Y02B 30/54 : Free-cooling systems
• CPC Class Codes: Y02B 30/70 : Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
• 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 60/14 : Thermal energy storage
• 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 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 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 40/20 : 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 at each of a plurality of time steps during a frequency response period, and a frequency response controller. The frequency response controller is configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, use the statistics of the regulation signal to generate an optimal frequency response midpoint that achieves a desired change in a state-of-charge (SOC) of the battery while participating in a frequency response program, and use the midpoints 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 regulation signal from an incentive provider,

determine statistics of the regulation signal,

use the statistics of the regulation signal to generate an optimal frequency response midpoint for participating in a frequency response program that simultaneously achieves a predefined change in a state-of-charge (SOC) of the battery, and

use the optimal 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 optimal frequency response 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 statistics of the 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 optimal frequency response midpoint; and

a low level controller 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 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 optimal frequency response midpoint; and

an estimated cost of battery degradation that will result from the optimal frequency response midpoint.

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:

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

or

a second constraint constraining the optimal frequency response 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 generate an objective function comprising:

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

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

8. 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 optimal battery power setpoints, the variables comprising at least one of:

a temperature of the battery;

the state-of-charge of the battery;

a depth of discharge of the battery;

a power ratio of the battery;

or

an effort ratio of the battery.

9. The frequency response optimization 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 setpoints based on both the campus power usage and 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 an optimal frequency response midpoint for participating in a frequency response program that simultaneously achieves a predefined change in a state-of-charge (SOC) of a battery;

using the optimal 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.

11. The method of claim 10, further comprising:

determining the optimal frequency response 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.

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

13. The method of claim 10, further comprising:

generating filter parameters based on the optimal 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 optimal frequency response midpoint; and

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

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

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

or

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

16. The method of claim 10, further comprising generating an objective function comprising:

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

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

17. 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 optimal battery power setpoints, the variables comprising at least one of:

a temperature of the battery;

the state-of-charge of the battery;

a depth of discharge of the battery;

a power ratio of the battery;

or

an effort ratio of the battery.

18. The method of claim 10, further comprising determining the optimal setpoints based on both a campus power usage and the regulation signal, wherein the campus power usage combines with the electric power discharged from the battery at a point of interconnection.

19. A frequency response controller comprising:

a processing circuit configured to;

receive a regulation signal from an incentive provider;

determine statistics of the regulation signal;

use the statistics of the regulation signal to generate an optimal frequency response midpoint for participating in a frequency response program that simultaneously achieves a predefined change in a state-of-charge (SOC) of a battery;

use the optimal 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.

20. The controller of claim 19, wherein the processing circuit comprises:

a high level controller configured to determine the optimal frequency response 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.