Method and apparatus for delivering power using external data

• US 8,880,233 B2
• Filed: 04/30/2014
• Issued: 11/04/2014
• Est. Priority Date: 08/14/2012
• Status: Active Grant

• Current Assignee: Stem, Inc.
• Original Assignee: Stem, Inc.
• Inventors: Kearns, Ben, Burlinson, Jon, Stevens, Drew
• Primary Examiner(s): Masinick, Michael D
• Application Number:
  US14/265,614
• Publication Number:
  US 20140236369A1
• Time in Patent Office: 188 Days
• Field of Search: 700/295, 700/297
• US Class Current: 700/295
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 2219/39361 Minimize time-energy cost
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00 Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• 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 3/32 using batteries with converting means
• CPC Class Codes: H02J 4/00 Circuit arrangements for mains or distribution networks not specified as ac or dc
• 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: Y02P 80/10 Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y04S 10/14 Energy storage units
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• 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

Abstract

A control system for an energy storage system located behind a utility meter uses a unique, feedback-based, communication and control method to reliably and efficiently maximize economic return of the energy storage system. Operating parameters for the energy storage system are calculated at an external, centralized data center, and are selected to prevent electrical power demand of an electric load location from exceeding a specified set-point by discharging energy storage devices, such as DC batteries, through a bidirectional energy converter during peak demand events. The control system can operate autonomously in the case of a communications failure.

Claims

1. A method for generating operating instructions for a distributed energy storage system, the method comprising:

iteratively executing a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric bad location associated with the distributed energy storage system, and

transmitting the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric bad location based on the one or more battery state-of-charge profiles.

2. The method of claim 1, further comprising:

generating a bad forecast for the electric bad location,

wherein the bad forecast includes one or more forward-looking forecasted bad profiles for the electric bad location, and

wherein iteratively executing the simulation includes iteratively executing the simulation based on the load forecast.

3. The method of claim 2, wherein the load forecast is generated based on historical load data that describes previous load amounts drawn by the electric load location.

4. The method of claim 3, wherein generating the load forecast comprises generating a set of coefficients, wherein each coefficient comprises a weight to be applied to the historical load data and is associated with at least one of weather data, business hours data, maintenance event data, and special events data.

5. The method of claim 3, wherein the simulation comprises simulated operations of the distributed energy storage system based on at least one of a peak billing time, tariff information, current operation conditions associated with the electric bad location, and the load forecast for the electric load location.

6. The method of claim 1, wherein iteratively executing the simulation comprises verifying that the electric load location can maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles.

7. The method of claim 1, wherein iteratively executing the simulation comprises determining that the electric bad location cannot maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles, and modifying one of the one or more demand set-point curves and one or more battery state-of-charge profiles in response to generate one or more of an adjusted demand set-point curve and an adjusted battery state-of-charge profile.

8. The method of claim 7, wherein iteratively executing the simulation further comprises executing simulated operations of the distributed energy storage system based on one of the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile.

9. The method of claim 8, further comprising terminating iteratively executing the simulation upon determining that the distributed energy storage system can maintain operation with the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile.

10. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the steps of:

iteratively executing a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric bad location associated with the distributed energy storage system, and

transmitting the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric bad location based on the one or more battery state-of-charge profiles.

11. The non-transitory computer-readable medium of claim 10, further storing instructions that cause the processor to execute the step of:

generating a bad forecast for the electric bad location,

wherein the bad forecast includes one or more forward-looking forecasted bad profiles for the electric bad location, and

wherein iteratively executing the simulation includes iteratively executing the simulation based on the bad forecast.

12. The non-transitory computer-readable medium of claim 11, wherein the bad forecast is generated based on historical load data that describes previous load amounts drawn by the electric load location.

13. The non-transitory computer-readable medium of claim 12, wherein generating the load forecast comprises generating a set of coefficients, wherein each coefficient comprises a weight to be applied to the historical bad data and is associated with at least one of weather data, business hours data, maintenance event data, and special events data.

14. The non-transitory computer-readable medium of claim 12, wherein the simulation comprises simulated operations of the distributed energy storage system based on at least one of a peak buffing time, tariff information, current operation conditions associated with the electric bad location, and the bad forecast for the electric bad location.

15. The non-transitory computer-readable medium of claim 10, wherein iteratively executing the simulation comprises verifying that the electric load location can maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles.

16. The non-transitory computer-readable medium of claim 10, wherein iteratively executing the simulation comprises determining that the electric load location cannot maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles, and modifying one of the one or more demand set-point curves and one or more battery state-of-charge profiles in response to generate one or more of an adjusted demand set-point curve and an adjusted battery state-of-charge profile.

17. The non-transitory computer-readable medium of claim 16, wherein iteratively executing the simulation further comprises executing simulated operations of the distributed energy storage system based on one of the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile.

18. The non-transitory computer-readable medium of claim 17, further storing instructions that cause the processor to execute the step of terminating iteratively executing the simulation upon determining that the distributed energy storage system can maintain operation with the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile.

19. A system for generating operating instructions for a distributed energy storage system, the system comprising:

a simulation engine configured to;

iteratively execute a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric load location associated with the distributed energy storage system, and

transmit the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric load location based on the one or more battery state-of-charge profiles.

20. The system of claim 19, further comprising:

a forecast engine configured to;

generate a load forecast for the electric load location, and

transmit the load forecast to the simulation engine for iteratively executing the simulation,

wherein the load forecast includes one or more forward-looking forecasted load profiles for the electric load location, and

wherein iteratively executing the simulation includes iteratively executing the simulation based on the load forecast.