Dynamic contingency avoidance and mitigation system

Patent metadata

Field	Value
Publication number	US-10229376-B2
Application number	US-201615208373-A
Country	US
Kind code	B2
Filing date	Jul 12, 2016
Priority date	Feb 20, 2009
Publication date	Mar 12, 2019
Grant date	Mar 12, 2019

Abstract

The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks. A disclosed system includes one or more processors, each having respective communication interfaces to receive data from the infrastructure, the data comprising infrastructure network data, one or more software applications, operatively coupled to and at least partially controlling the one or more processors, to process and characterize the infrastructure network data; and a display, coupled to the one or more processors, for visually presenting a depiction of at least a portion of the infrastructure including any changes in condition thereof, and one or more controllers in communication with the one or more processors, to manage processing of the resource, wherein the resource is obtained and/or distributed based on the characterization of the real time infrastructure data.

First claim

What is claimed is:

1. A system, comprising:
at least one dynamic contingency avoidance and mitigation system (DCAMS) control console,
wherein the at least one DCAMS console is configured to display a predictive load resource management output indicative of a simulation modeling of at least one integrated Demand Response (iDR) action which is related to implementing at least one resource-related activity over at least one distribution grid for at least one resource,
wherein the at least one resource-related activity is at least one of: at least one resource curtailment activity and at least one resource supply activity;
at least one communication interface which is specifically designed to physically interface with at least one infrastructure network comprising the at least one distribution grid and a plurality of resource-consuming assets, resource-producing assets, or both;
at least one DCAMS processor which is configured to perform at least the following:
dynamically scheduling and initiating, over a particular time period, the at least one iDR action;
electronically receiving, via the at least one communication interface, iDR action feedback data representative of an implementation of the at least one iDR action;
dynamically verifying the iDR action feedback data to determine at least one verified iDR action effect resulting from the at least one iDR action, wherein the at least one verified iDR action effect is at least representative of at least one change in:
i) resource load,
ii) resource supply,
iii) resource pricing,
iv) resource cost, and
v) any combination thereof;
dynamically performing, utilizing a machine learning engine and based, at least in part on the at least one verified iDR action effect, at least one simulation modelling to obtain the predictive load resource management output which is indicative of a prediction on how to manage, by at least one utility, resource loads during at least one future iDR action for at least one utility-operated region;
wherein the predictive load resource management output comprise at least one recommended future resource-related activity to be performed by the at least one utility during a resource deficiency concern period in the at least one utility-operated region to at least mitigate a potential infrastructure equipment usage which is above a designed capacity at a specific location; and
outputting, via the at least one DCAMS console, the predictive load resource management output.

2. The system of claim 1 , wherein the at least one recommended future resource-related activity is an electronic recommendation alert to be transmitted to at least one customer of the at least one utility, wherein the electronic recommendation alert recommends the at least one customer curtailment of a resource load for at least one time period.

3. The system of claim 1 , wherein the at least one DCAMS processor is further configured to perform a sequential Monte Carlo analysis using a topological model of the at least one distribution grid located in the at least one utility-operated region to determine a mean time between failure (MTBF) and a mean time to repair (MTTR) to result in the predictive load resource management output which provides reliability statistics for the at least one distribution grid located in the at least one utility-operated region in real time and during predicted future resource crisis time periods.

4. The system of claim 1 , wherein the at least one distribution grid is an electrical grid and the at least one resource is electricity.

5. The system of claim 4 , wherein the electricity is obtained, at least in part, from one or more sources selected from wind power, solar power, battery power, geothermal power, tidal power and nuclear power.

6. The system of claim 4 , wherein the at least one resource curtailment activity is curtailing a load associated with at least one of electrical vehicle charging, building load, energy storage, distributed generation, and any combination thereof.

7. The system of claim 1 , wherein the dynamically performing the at least one simulation modelling is further based on at least one weather pattern, at least one customer use pattern, or both.

8. The system of claim 7 , wherein the at least one recommended future resource-related activity is based on at least one predicted weather pattern, at least one predicted customer use pattern, or both.

9. The system of claim 8 , wherein the at least one predicted customer use pattern is based, at least in part, on a customer response to a hypothetical change to a real-time cost of the at least one resource attributed to at least one customer.

10. The system of claim 1 , wherein the machine learning engine includes one of a martingale boosting algorithm, a SVM algorithm, Monte Carlo risk assessment, and a combination thereof.

11. A method, comprising:
causing, by at least one programmed dynamic contingency avoidance and mitigation system (DCAMS) processor, to display a predictive load resource management output indicative of a simulation modeling of at least one integrated Demand Response (iDR) action on at least one DCAMS control console, wherein the iDR action is related to implementing at least one resource-related activity over at least one distribution grid for at least one resource,
wherein the at least one resource-related activity is at least one of: at least one resource curtailment activity and at least one resource supply activity;
dynamically scheduling and initiating, by the at least one DCAMS processor, over a particular time period, the at least one iDR action;
electronically receiving, via at least one communication interface, by the at least one DCAMS processor, iDR action feedback data representative of an implementation of the at least one iDR action over at least one infrastructure network comprising a plurality of the at least one distribution grid and a plurality of resource-consuming assets, resource-producing assets, or both;
dynamically verifying, by the at least one DCAMS processor, the iDR action feedback data to determine at least one verified iDR action effect resulting from the at least one iDR action, wherein the at least one verified iDR action effect is at least representative of at least one change in:
i) resource load,
ii) resource supply,
iii) resource pricing,
iv) resource cost, and
v) any combination thereof;
dynamically performing, by the at least one DCAMS processor, utilizing a machine learning engine and based, at least in part on the at least one verified iDR action effect, at least one simulation modelling to obtain the predictive load resource management output which is indicative of a prediction on how to manage, by at least one utility, resource loads during at least one future iDR action for at least one utility-operated region;
wherein the predictive load resource management output comprise at least one recommended future resource-related activity to be performed by the at least one utility during a resource deficiency concern period in the at least one utility-operated region to at least mitigate a potential infrastructure equipment usage which is above a designed capacity at a specific location; and
outputting, by the at least one DCAMS processor, via the at least one DCAMS console, the predictive load resource management output.

12. The method of claim 11 , wherein the at least one recommended future resource-related activity is an electronic recommendation alert to be transmitted to at least one customer of the at least one utility, wherein the electronic recommendation alert recommends the at least one customer curtailment of a resource load for at least one time per

Assignees

• Calm Energy Inc
• Univ Columbia

Inventors

• Anderson Roger N
• Boulanger Albert
• Johnson John A

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• G05B15/02

  electric · CPC title

• G06Q10/06315 Primary

  Needs-based resource requirements planning or analysis · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title