MICROGRID MODEL BASED AUTOMATED REAL TIME SIMULATION FOR MARKET BASED ELECTRIC POWER SYSTEM OPTIMIZATION

• US 20110082597A1
• Filed: 09/30/2010
• Published: 04/07/2011
• Est. Priority Date: 10/01/2009
• Status: Abandoned Application

• Current Assignee: WaveTech Global Inc.
• Original Assignee: EDSA Micro Corporation (PAC Global, Inc.)
• Inventors: Meagher, Kevin
• Application Number:
  US12/895,597
• Publication Number:
  US 20110082597A1
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/04 for connecting networks of the same frequency but supplied from different sources
• CPC Class Codes: H02J 3/38 Arrangements for parallely feeding a single network by two or more generators, converters or transformers
• 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: 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 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

Systems and methods for optimizing energy consumption in multi-energy sources sites are provided. These techniques include developing a real-time model and a virtual model of the electrical system of a multi-energy source site, such as a microgrid. The real-time model represents a current state of the electrical system can be developed by collecting data from sensors interfaced with the various components of the electrical system. The virtual model of the electrical system mirrors the real-time model of the electrical system and can be used to generate predictions regarding the performance, availability, and reliability of cost and reliability of various distributed energy sources and to predict the price of acquiring energy from these sources. The virtual model can be used to test “what if” scenarios, such as routine maintenance, system changes, and unplanned events that impact the utilization and capacity of the microgrid.

Claims

1. A system for real-time modeling of electrical system performance of a microgrid electrical system, comprising:

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

an analytics server communicatively connected to the data acquisition component, comprising,

a virtual system modeling engine configured to generate predicted data output for the electrical system utilizing a first virtual system model of the electrical system,

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 first virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold, and

a network optimization simulation engine configured to use the virtual system model updated based on the real-time data to forecast the cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system.

2. The system for making real-time predictions to optimize the operation of the microgrid electrical system, as recited in claim 1, wherein the network optimization simulation engine is further configured to receive modified operational parameters for the first virtual system model to create a second virtual system model and to forecast the cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system operating under the modified parameters of the second virtual system model.

3. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 2, wherein the modified parameters include changing a mix of distributed energy sources being used to generate power for the microgrid.

4. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 2, wherein the modified parameters include changing an electricity output of a distributed energy source being used to generate power for the microgrid.

5. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 2, wherein the modified parameters include changing a mix of energy obtained from distributed energy sources of the microgrid and energy from energy sources outside of the microgrid.

6. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 2, further comprising a client terminal communicatively connected to the power system simulation engine, the client terminal configured to allow a system administrator to modify the parameters of the first virtual system model when the power system simulation engine is operating in the scenario builder mode and display a report of the forecasted aspects.

7. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 6, wherein the forecasted cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system is communicated by way of graphics on a display interfaced with the client terminal.

9. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 6, wherein the forecasted cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system is communicated by way of synthesized speech generated by the client terminal.

20. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 6, wherein the forecasted cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system is communicated by way of synthesized speech generated by the client terminal.

8. The system for making real-time predictions to optimize the operation of a microgrid electrical system, as recited in claim 8, wherein the forecasted cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system is communicated by way of text on a display interfaced with the client terminal.

10. A computer implemented method for real-time modeling of the performance of a microgrid electrical system, wherein one or more processors are programmed to perform steps, comprising:

creating a first virtual system model of the microgrid electrical system;

acquiring real-time data from sensors interfaced with components of the electrical system;

calculating predicated data forecasting the cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system, the predicted data being calculated using the first virtual system model of the microgrid electrical system;

initiating a calibration and synchronization calibration and synchronization operation to update the first virtual system model when a difference between the real-time data and the predicted data exceeds a threshold; and

recalculating the predicated data forecasting the cost of operating the microgrid electrical system and the reliability and availability of the microgrid electrical system using the calibrated first virtual system model of the microgrid electrical system.

11. The method of claim 10, further comprising:

receiving one or more modified operational parameters to be tested;

updating the first virtual model to use the modified operational parameters; and

generating predicted data using the updated first virtual model.

12. The method of claim 11, further comprising:

creating a second virtual model based on the first virtual model that includes the modified operational variables instead of updating the first virtual model to use the modified operational variables;

generating a first set of predicted data using the first virtual model; and

generating a second set of predicted data using the second virtual model.

13. The method of claim 12, further comprising:

generating a comparison of the first predicted data and the second predicted data to the real-time data to identify an optimal operating configuration for the electrical network.

14. The method of claim 13, further comprising:

displaying the comparison on the display of a client terminal.

15. The method of claim 13 wherein displaying the comparison on the display of a client terminal further comprises displaying the comparison as a set of graphics on a display interface of the client terminal.

16. The method of claim 13 wherein displaying the comparison on the display of a client terminal further comprises displaying the comparison as text on a display interface of the client terminal.

17. The method of claim 11 wherein the modified operational parameters include changing a mix of distributed energy sources being used to generate power for the microgrid.

18. The method of claim 11 wherein the modified operational parameters include changing an electricity output of a distributed energy source being used to generate power for the microgrid.

19. The method of claim 11 wherein the modified operational parameters include changing a mix of energy obtained from distributed energy sources of the microgrid and energy from energy sources outside of the microgrid.