Systems and methods for selecting grid actions to improve grid outcomes

• US 10,193,384 B2
• Filed: 01/11/2016
• Issued: 01/29/2019
• Est. Priority Date: 01/16/2015
• Status: Active Grant

• Current Assignee: 3M Innovative Properties Company (3M Company)
• Original Assignee: 3M Innovative Properties Company (3M Company)
• Inventors: Brooks, Brian E., Benoit, Gilles J., Lu, Yang
• Primary Examiner(s): Tran, Vincent H
• Application Number:
  US15/541,806
• Publication Number:
  US 20180013320A1
• Time in Patent Office: 1,114 Days
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• 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 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00125 Transmission line or load transient problems, e.g. overvoltage, resonance or self-excitation of inductive loads H02J3/01 takes precedence
• 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 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/40 Display of information, e.g. of data or controls
• CPC Class Codes: Y04S 10/52 Outage or fault management, e.g. fault detection or location
• 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 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

Systems and methods for automatically selecting actions to take on a utility grid to simultaneously reduce uncertainty while selecting actions that improve one or more effectiveness metrics. Grid action effects are represented as confidence intervals, the overlap of which is used as a weight when selecting actions within a constrained search space of grid actions. The response of the utility grid to the grid actions may be measured and parsed by the temporal and spatial reach of the grid action, then used to update the confidence intervals for that particular selected grid action.

Claims

1. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

further comprising determining an opportunity to vary a control on a utility grid by referencing current states of the controls on the utility grid and a multidimensional space representing possible grid control actions.

2. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

wherein the possible grid control actions are defined by a constrained multidimensional space, the points in the constrained space representing the ordinary operational envelope of the utility grid.

3. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

further comprising;

collecting data on utility grid parameters; and

parsing the collected data based at least in part on a spatial reach of the grid control action and a temporal reach of the grid control action.

4. The computer-implemented method of claim 3, further comprising adding data that is within both the temporal reach of the grid control action and the spatial reach of the grid control action to a database configured to store the effectiveness data.

5. The computer-implemented method of claim 3, wherein the spatial reach of the grid control action and the temporal reach of the grid control action are computed using a model of the utility grid.

6. The computer-implemented method of claim 3, wherein the spatial reach of the grid control action and the temporal reach of the grid control action are selected from within confidence intervals derived from the impact of the grid control action on grid response to a second grid control action taken in temporal or spatial proximity to the grid control action.

7. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

wherein the selection of the grid control action is randomized and wherein the randomization is weighted based on the overlap in confidence intervals around the effects.

8. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

wherein the selection of the grid control action is a selection of at least one of the following;

a response to excavation near gas pipelines;

a switch state for a capacitor bank or the position of a load tap hanger; and

a time delay before taking a scheduled action.

9. A computer-implemented method, comprising:

receiving an opportunity to vary a control on a utility grid comprising a plurality of possible grid control actions;

receiving effectiveness data for the possible grid control actions comprising confidence intervals around the effects of each possible grid control action;

selecting a grid control action, using a processor, based on the overlap in confidence intervals of the effectiveness data; and

implementing the selected grid control action on the utility grid,

wherein the effectiveness data comprises a risk of pipeline damage from an excavation.

10. A system for automatically selecting grid control actions, comprising:

utility grid controls,

a search space memory configured to store a search space of utility grid control states;

a knowledge database configured to store effectiveness data comprising confidence intervals around the effects of the utility grid control states; and

a control assignment processor configured to select utility grid control states based on the effectiveness data,

wherein the grid controls are at least one of the following;

dispatching and messaging systems for responding to excavation near pipelines; and

capacitor banks and load tap changers.