Electric grid optimization

• US 8,812,165 B1
• Filed: 01/13/2012
• Issued: 08/19/2014
• Est. Priority Date: 02/02/2011
• Status: Active Grant

• Current Assignee: Duke Energy Corporation
• Original Assignee: Duke Energy Corporation
• Inventors: Smith, Raiford, Miller, Melanie, Mohseni, Pedram, Masters, David
• Primary Examiner(s): Ali, Mohammad
• Assistant Examiner(s): Norton, Jennifer L
• Application Number:
  US13/350,443
• Time in Patent Office: 949 Days
• Field of Search: 700/28, 700/32, 700/33, 700/286, 700/291, 700/295, 702/60, 702/61, 717/116, 717/153, 717/165
• US Class Current: 700/291
• CPC Class Codes: G01R 19/2513 Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging
• CPC Class Codes: G01R 22/06 by electronic methods
• CPC Class Codes: G05B 11/32 with inputs from more than one sensing element; with outputs to more than one correcting element
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G06F 8/24 Object-oriented
• CPC Class Codes: G06F 9/449 Object-oriented method invocation or resolution
• 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 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00016 using a wired telecommunication network or a data transmission bus
• 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 13/00034 the elements or equipment being or involving an electric power substation
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• 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: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 50/10 : Energy trading, including energy flowing from end-user application to grid

Abstract

Various embodiments are directed to systems and methods for optimizing energy use with one or more optimization engine objects. An optimization engine object may have a plurality of dependent objects including at least a dependent optimization engine object, or at least one of a supply-side or demand-side object. The optimization engine object may receive dependent object attribute data from each of the at least one dependent objects; determine an optimal configuration for the plurality of dependent objects over the first time period subject to at least one optimization constraint; and conditioned upon the optimization engine depending from a superior optimization engine, transmit a net energy usage associated with the optimal configuration to the superior optimization engine.

Claims

1. A computer-implemented system for optimizing energy usage, the system comprising:

a computer system comprising at least one processor and operatively associated memory, the computer system programmed to execute an optimization engine object, wherein the optimization engine object is in communication with a plurality of dependent objects comprising at least one of;

a dependent optimization engine object; and

at least one of a dependent supply-side object to generate energy and a dependent demand-side object to use energy;

wherein the optimization object is programmed to;

receive dependent object attribute data from each of the at least one dependent objects, wherein the dependent object attribute data for each dependent object of the optimization engine comprises, for a first time period;

at least one attribute selected from the group consisting of;

a schedule of net resources indicating supply-side capacity of the dependent object; and

a schedule of net demand indicating demand-side usage of the dependent object; and

preference data indicating usage preferences associated with the dependent object;

determine an optimal configuration for the plurality of dependent objects over the first time period subject to at least one optimization constraint, wherein determining the optimal configuration comprises determining a net energy usage of the plurality of dependent objects;

conditioned upon the optimization engine depending from a superior optimization engine, transmit the net energy usage to the superior optimization engine.

2. The system of claim 1, wherein each optimization engine is further programmed to issue a control command to a first object selected from the plurality of dependent objects.

3. The system of claim 2, wherein the first object is a supply-side object, and wherein the control command instructs the first object to modify a property of energy generated by the supply-object.

4. The system of claim 2, wherein, when the first object is a demand-side object, and wherein the control command instructs the first object to reduce its energy consumption.

5. The system of claim 2, wherein the first object is a dependent optimization engine object and the control command instructs the dependent optimization engine to determine a new optimal configuration for at least one dependent object of the dependent optimization engine subject to at least one new optimization constraint.

6. The system of claim 1, wherein the optimization engine is also programmed to select the plurality of dependent objects from a plurality of potential dependent objects based on at least one factor selected from the group consisting of:

a physical proximity between each of the plurality of potential dependent objects and the computer system;

a proximity between each of the plurality of potential dependent objects and the computer system on an energy grid; and

a relative proximity of the computer system and the at least one dependent object to at least one of a supply-side object and a demand-side object.

7. The system of claim 1, wherein the at least one optimization constraint comprises a minimum difference between a price charged for energy provided to the demand-side objects and a cost of generating energy by the supply-side objects.

8. The system of claim 1, wherein the plurality of dependent objects comprises a first supply-side object.

9. The system of claim 8, wherein the schedule of net resources received from the first supply-side object comprises an indication of energy, power and capacitance to be generated by the first supply-side object during the first time period.

10. The system of claim 8, wherein the preference data received from the first supply-side object comprises at least one of:

a conditional preference during anomalous grid events;

a preference for avoiding negative grid conditions; and

a preference for maintaining a predetermined balance between generation phases.

11. The system of claim 1, wherein the plurality of dependent objects comprise at least one generation supply-side object and at least one transmission supply-side object.

12. The system of claim 1, wherein the plurality of dependent objects comprises a first demand-side object.

13. The system of claim 12, wherein a schedule of net demand received from the first demand-side object comprises an indication of energy, power and capacitance to be used by the demand-side object during the first time period.

14. The system of claim 12, wherein the preference data received from the first demand-side object comprises usage preferences from a customer associated with the demand-side object.

15. The system of claim 14, wherein the usage preferences comprise explicit preferences received directly from the customer and implicit preferences received through observation of the demand-side object.

16. The system of claim 14, wherein the usage preferences comprise customer billing preferences.

17. The system of claim 12, wherein each optimization engine is further programmed to cause the computer system to:

receive an indication of a location of a customer associated with the first demand-side object; and

modify at least one of a preference associated with the first demand-side object and a schedule of net demand received from the first demand-side object considering the location.

18. The system of claim 12, wherein each optimization engine is further programmed to cause the computer system to:

receive an indication of an activity of a customer associated with the first demand-side object; and

modify at least one of a preference associated with the first demand-side object and a schedule of net demand received from the first demand-side object based on the activity.

19. The system of claim 1, wherein the net energy usage is non-zero.

20. The system of claim 1, wherein the computer system comprises a communications node of an electric grid system comprising at least a portion of the supply-side objects and demand-side objects.

21. A computer-implemented method for optimizing energy usage, the method comprising:

receiving, by a computer system, dependent object attribute data associated with associated with a plurality of dependent objects of an optimization engine object executed by the computer system, the plurality of dependent objects comprising;

a dependent optimization engine object; and

at least one of a dependent supply-side object to generate energy and a dependent demand-side object to use energy, wherein the computer system comprises at least one processor and operatively associated memory, and wherein the dependent object attribute data for each dependent object comprises, for a first time period;

at least one attribute selected from the group consisting of;

a schedule of net resources indicating supply-side capacity of the dependent object; and

a schedule of net demand indicating demand-side usage of the dependent object; and

preference data indicating usage preferences associated with the dependent object;

determining, by the computer system, an optimal configuration for the plurality of dependent objects over the first time period subject to at least one optimization constraint, wherein determining the optimal configuration comprises determining a net energy usage of the plurality of dependent objects;

conditioned upon the optimization engine depending from a superior optimization engine, transmitting, by the computer system, the net energy usage to the superior optimization engine.