Real-time predictive systems for intelligent energy monitoring and management of electrical power networks

• US 9,031,824 B2
• Filed: 11/04/2014
• Issued: 05/12/2015
• Est. Priority Date: 07/19/2006
• Status: Expired due to Fees

• Current Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Original Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Inventors: Nasle, Adib
• Primary Examiner(s): Chad, Aniss
• Assistant Examiner(s): Louis, Andre Pierre
• Application Number:
  US14/532,559
• Publication Number:
  US 20150057821A1
• Time in Patent Office: 189 Days
• Field of Search: 703/13, 703/18, 700/244, 700/286, 700/291, 700/292, 700/295
• US Class Current: 703/13
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• 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 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: Y02P 80/14 District level solutions, i.e. local energy networks
• 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 50/14 Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores acid processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Claims

1. A system for intelligent monitoring and management of an electrical microgrid system, comprising:

a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system;

a server communicatively connected to the data acquisition component comprising,

a real-time energy pricing engine connected to a utility power pricing data table and configured to generate real-time utility power pricing data,

a virtual system modeling engine configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system and the real-time utility power pricing data,

an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, the analytics engine further configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold based on frequency, voltage, and/or stability,

a machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output, the machine learning engine further configured to forecast an aspect of the electrical system,

an energy management system engine configured to process the real-time data output,

the predicted data output and forecasted aspect to generate a user interface that conveys an operational state of the electrical system; and

a client terminal communicatively connected to the power analytics server and configured to display the user interface.

2. The system of claim 1, wherein calibration and synchronization of real-time model and virtual model function as an energy management system for frequency, voltage stability, reliability, and power flow.

3. The system of claim 1, wherein the calibration and synchronization functions as security constrained economic dispatch.

4. The system of claim 1, wherein the calibration and synchronization functions as state estimation of the electrical microgrid system.

5. The system of claim 1, wherein the calibration and synchronization is used for the stability of the electrical microgrid system.

6. The system of claim 1, wherein the sensors that provide the information for the virtual model and real-time model provide information necessary to effect frequency control, voltage stability, reliability, and power flow.

7. The system of claim 1, wherein, the machine learning engine includes, an associative memory layer, a sensory layer, and a neocortical model.

8. The system of claim 1, wherein the operational state is the real-time operational performance of the electrical system.

9. The system of claim 1, wherein the operational state is the predicted operational performance of the electrical system.

10. The system of claim 8, wherein the real-time operational performance of the electrical system includes the real-time cost of energy utilized by the electrical system.

11. The system of claim 1, further including a historical data trending database connected to the energy management system engine and configured to store the real-time data output, the predicted data output and the forecasted aspects output from the power analytics server.

12. The system of claim 1, wherein the forecasted aspect is a predicted ability of the electrical system to resist system output deviations from defined tolerance limits of the electrical system including frequency and/or voltage.

13. The system of claim 1, wherein the forecasted aspect is a predicted reliability and availability of the electrical system.

14. The system of claim 1, wherein the forecasted aspect is a predicted total power capacity of the electrical system.

15. The system of claim 1, wherein the forecasted aspect is a predicted ability of the electrical system to maintain availability of total power capacity.

16. The system of claim 1, wherein the forecasted aspect is a predicted utilization of the total power capacity of the electrical system.

17. The system of claim 1, wherein the virtual system model is stored on a virtual system model database communicatively connected with the power analytics server.

18. The system of claim 1, wherein the forecasted aspect is a predicted ability of the electrical system to withstand a contingency event that results in stress to the electrical system including load shedding and/or load adding.

19. A system for intelligent monitoring and management of a datacenter electrical system, comprising:

a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system;

a server communicatively connected to the data acquisition component comprising,

a real-time energy pricing engine configured to generate real-time power pricing data,

a virtual system modeling engine configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system and the real-time power pricing data,

an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, the analytics engine further configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold based on frequency, voltage, and/or stability,

a machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output, the machine learning engine further configured to forecast an aspect of the electrical system,

an energy management system engine configured to process the real-time data output,

the predicted data output and forecasted aspect to generate a user interface that conveys an operational state of the electrical system; and

a client terminal communicatively connected to the power analytics server and configured to display the user interface.

20. The system of claim 19, wherein calibration and synchronization of real-time model and virtual model function as an energy management system for frequency, voltage stability, reliability, and power

21. The system of claim 20, wherein the calibration and synchronization functions as security constrained economic dispatch.

22. The system of claim 20, wherein the calibration and synchronization functions as state estimation of the electrical system.

23. The system of claim 20, wherein the calibration and synchronization is used for the stability of the data center.

24. The system of claim 20, wherein the calibration and synchronization is used for the stability of a microgrid associated with the data center.

25. The system of claim 20, wherein the sensors that provide the information for the virtual model and real-time model provide information necessary to effect frequency control, voltage stability, reliability, and power flow.