System and method for hybrid power generation

• US 10,530,290 B2
• Filed: 02/21/2017
• Issued: 01/07/2020
• Est. Priority Date: 12/02/2014
• Status: Active Grant

• Current Assignee: Electronic Power Design, Inc
• Original Assignee: Electronic Power Design, Inc
• Inventors: Janik, John Bradford
• Primary Examiner(s): Dager, Jonathan M
• Application Number:
  US15/438,697
• Publication Number:
  US 20170302218A1
• Time in Patent Office: 1,050 Days
• Field of Search: None
• CPC Class Codes: B63B 2021/505 Methods for installation or mooring of floating offshore platforms on site
• CPC Class Codes: B63B 21/50 Anchoring arrangements or methods for special vessels, e.g. for floating drilling platforms or dredgers
• CPC Class Codes: B63J 3/00 Driving of auxiliaries transmission elements per se F16
• CPC Class Codes: F02B 63/04 for electric generators
• CPC Class Codes: F02D 41/04 Introducing corrections for particular operating conditions F02D41/14 takes precedence
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 7/35 with light sensitive cells
• CPC Class Codes: H02P 7/18 by master control with auxiliary power
• CPC Class Codes: H02S 10/10 including a supplementary source of electric power, e.g. hybrid diesel-PV energy systems combinations with gas-turbine plants F02C6/00
• CPC Class Codes: H02S 40/38 Energy storage means, e.g. batteries, structurally associated with PV modules
• CPC Class Codes: Y02E 10/50 Photovoltaic [PV] energy
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y02T 70/50 Measures to reduce greenhouse gas emissions related to the propulsion system

Abstract

A hybrid power generation system is disclosed including but not limited to a processor in data communication with a non-transitory computer readable medium for controlling a plurality of power sources to substantially optimize energy generation by the plurality of power sources, wherein the plurality of power sources comprise a solar panel and a battery that generate energy; a computer program comprising instructions stored in the non-transitory computer readable medium that are executed by the processor, the computer program comprising, instructions to determine a current operating state for each of the plurality of power sources; instructions to determine a new operating state for each of the plurality of power sources to substantially optimize power generated for each of the power sources; and instructions to replace the current operating state for each of the plurality of power sources to the new operating state for each of the plurality of power sources.

Claims

1. A hybrid power generation system comprising:

a processor in data communication with a non-transitory computer readable medium for controlling a plurality of power sources to substantially optimize energy generation by the plurality of power sources, wherein the plurality of power sources comprises a solar panel and a battery that generate energy;

a computer program comprising instructions stored in the non-transitory computer readable medium that are executed by the processor, the computer program comprising, instructions to determine a current operating state for each of the plurality of power sources;

instructions to determine a new operating state for each of the plurality of power sources to substantially optimize power generated for each of the power sources;

instructions to replace the current operating state for each of the plurality of power sources to the new operating state for each of the plurality of power sources; and

instructions to determine a current system load serviced by the plurality of power sources, wherein the operating states further comprises a load on a diesel engine, speed of the diesel engine and air fuel mixture supplied to the diesel engine, wherein the operating state further comprises torque of the diesel engine.

2. The system of claim 1, wherein the operating state comprises a load on a natural gas engine, speed of the natural gas engine and air fuel mixture supplied to the natural gas engine, wherein the operating state further comprises torque of the natural gas engine.

3. The system of claim 1, wherein the power sources further comprise a plurality of different types of batteries and the operating states further comprises a load on the batteries.

4. The system of claim 1, wherein the computer program is a linear program.

5. The system of claim 1, wherein the computer program is an expert system.

6. The system of claim 1, wherein the computer program is a neural network.

7. A computerized method for producing by from a plurality of power sources, the method comprising:

determining on an energy management processor, a current operating state for each of the plurality of power sources;

determining on the energy management processor a new operating state for each of the plurality of power sources to substantially optimize power generated for each of the power sources;

replacing on the energy management processor the current operating state for each of the plurality of power sources to the new operating state for each of the plurality of power sources; and

determining a current system load serviced by the plurality of power sources, wherein the operating states further comprises a load on the diesel engine, speed of a diesel engine and air fuel mixture supplied to the diesel engine, wherein the operating state further comprises torque of the diesel engine.

8. The method of claim 7, wherein the operating state comprises a load on a natural gas engine, speed of the natural gas engine and air fuel mixture supplied to the natural gas engine, wherein the operating state further comprises torque of the natural gas engine.

9. The method of claim 7, wherein the power sources further comprise a plurality of different types of batteries and the operating states further comprises a load on the batteries.

10. The method of claim 7, wherein the energy management processor is a linear program.

11. The method of claim 7, wherein the energy management processor is an expert system.

12. The method of claim 7, wherein the energy management processor is a neural network.

13. A computer readable medium contain instructions that are executed

a processor in data communication with a non-transitory computer readable medium to substantially optimize energy generated by a plurality of power sources;

a processor in data communication with a non-transitory computer readable medium for controlling a plurality of power sources to substantially optimize energy generation by the plurality of power sources, wherein the plurality of power sources comprises a solar panel and a battery that generate energy;

a computer program comprising instructions stored in the non-transitory computer readable medium that are executed by the processor, the computer program comprising, instructions to determine a current operating state for each of the plurality of power sources;

instructions to determine a new operating state for each of the plurality of power sources to substantially optimize power generated for each of the power sources;

instructions to replace the current operating state for each of the plurality of power sources to the new operating state for each of the plurality of power sources; and

instructions to determine a current system load serviced by the plurality of power sources, wherein the operating states further comprises a load on a diesel engine, speed of the diesel engine and air fuel mixture supplied to the diesel engine, wherein the operating state further comprises torque of the diesel engine.

14. The medium of claim 13, wherein the operating state comprises a load on a natural gas engine, speed of the natural gas engine and air fuel mixture supplied to the natural gas engine, wherein the operating state further comprises torque of the natural gas engine.

15. The medium of claim 13, wherein the power sources further comprise a plurality of different types of batteries and the operating states further comprises a load on the batteries.

16. The medium of claim 13, wherein the computer program is a linear program.

17. The medium of claim 13, wherein the computer program is a neural network.