Energy management system and methods for the optimization of distributed generation

• US 6,757,591 B2
• Filed: 08/10/2001
• Issued: 06/29/2004
• Est. Priority Date: 08/11/2000
• Status: Expired due to Fees

• Current Assignee: Robert A. Kramer
• Original Assignee: Robert A. Kramer
• Inventors: Kramer, Robert A.
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): FRANK, ELLIOT L
• Application Number:
  US09/928,040
• Publication Number:
  US 20020082747A1
• Time in Patent Office: 1,054 Days
• Field of Search: 700/276, 700/288, 700/291, 700/295-297
• US Class Current: 700/288
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/004 Generation forecast, e.g. methods or systems for forecasting future energy generation
• CPC Class Codes: H02J 3/0075 for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 3/46 Controlling of the sharing of output between the generators, converters, or transformers
• CPC Class Codes: Y02B 10/10 Photovoltaic [PV]
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

An energy management system and method is provided for managing the generation and distribution of energy from an energy source to a building. The building has a desired building environment and a total energy profile including a thermal energy requirement and an electrical energy requirement. The energy management system comprises an energy generator arranged to convert energy from the energy source to thermal energy and electrical energy, a heat recovery unit arranged to recover byproduct heat from the energy generator, a cooling unit arranged to use a first portion of the thermal energy to drive a refrigeration unit, a heating unit arranged to use a second portion of the thermal energy to drive a heating unit, a heat storage unit arranged to store excess heat, and an energy optimizing controller.

Claims

1. A method of selecting an energy source option, the method comprising:

determining a current energy requirement, the current energy requirement including a current electrical energy requirement and a current heat energy requirement;

determining a first energy source option which satisfies the current energy requirement, the first energy source option having a first remote source component and a first local source component, the first remote source component being indicative of a first amount of energy to be purchased from a centralized energy generator, the first local source component being indicative of a first amount of energy to be generated by a distributed energy generator;

determining a second energy source option which satisfies the current energy requirement, the second energy source option having a second remote source component and a second local source component, the second remote source component being indicative of a second amount of energy to be purchased from the centralized energy generator, the second local source component being indicative of a second amount of energy to be generated by the distributed energy generator;

determining a current electrical energy cost and a current heat energy cost;

determining a current electrical energy availability and a current heat energy availability;

determining an energy model, the energy model including a time period, a putative electrical energy requirement associated with the time period, a putative electrical energy cost associated with the time period, a putative heat energy requirement associated with the time period, and a putative heat energy cost associated with the time period; and

selecting the first energy source option instead of the second energy source option based on the current electrical energy cost, the current heat energy cost, the current electrical energy availability, the current heat energy availability, and the energy model.

2. A method as defined in claim 1, wherein the centralized energy generator comprises an electrical grid system.

3. A method as defined in claim 1, wherein the distributed energy generator comprises a micro-turbine system.

4. A method as defined in claim 1, including the step of providing a fast response thermal storage system, and wherein the step of determining a current electrical energy availability and a current heat energy availability comprises the step of determining an amount of heat stored in the fast response thermal storage system.

5. A method as defined in claim 1, wherein at least a portion of the energy model is based on prior usage patterns.

6. A method as defined in claim 1, wherein at least a portion of the energy model is entered into an energy control system by a human operator.

7. A method as defined in claim 1, wherein the step of selecting the first energy source option instead of the second energy source option comprises the step of executing a fuzzy logic algorithm.

8. A method as defined in claim 7, wherein the execution of the fuzzy logic algorithm requires an actual cost of natural gas, an actual cost of grid supplied electrical energy, a current heat requirement, and a current electrical energy requirement as input variables to the fuzzy logic algorithm.

9. A method as defined in claim 8, wherein the execution of the fuzzy logic algorithm further requires an actual cost of electrical solar energy and an actual cost of thermal solar energy as input variables to the fuzzy logic algorithm.

10. A method as defined in claim 8, wherein the execution of the fuzzy logic algorithm further requires a heat storage cost and a heat storage capacity as input variables to the fuzzy logic algorithm.

11. The method as defined in claim 8, wherein the execution of the fuzzy logic algorithm further requires an efficiency metric and reliability metric associated with an energy source as input variables to the fuzzy logic algorithm.

12. A method as defined in claim 8, wherein the execution of the fuzzy logic algorithm produces an index of performance as an output variable from the fuzzy logic algorithm.

13. A method as defined in claim 12, wherein the execution of the fuzzy logic algorithm produces an actual cost of energy as an output variable from the fuzzy logic algorithm.

14. A method as defined in claim 1, wherein the step of selecting the first energy source option instead of the second energy source option comprises the step of executing a neural network algorithm.

15. A method of selecting an energy source option, the method comprising:

continuously monitoring an electrical energy load and a thermal energy load, and determining a current energy requirement, the current energy requirement including a current electrical energy requirement and a current heat energy requirement;

determining a first energy source option which satisfies the current energy requirement, the first energy source option having a first remote source component and a first local source component, the first remote source component being indicative of a first amount of energy to be purchased from a centralized energy generator, the first local source component being indicative of a first amount of energy to be generated by a distributed energy generator;

determining a second energy source option which satisfies the current energy requirement, the second energy source option having a second remote source component and a second local source component, the second remote source component being indicative of a second amount of energy to be purchased from the centralized energy generator, the second local source component being indicative of a second amount of energy to be generated by the distributed energy generator;

determining a current electrical energy cost and a current heat energy cost;

determining a current electrical energy availability and a current heat energy availability;

determining an energy model, the energy model including a time period, a putative electrical energy requirement associated with the time period, a putative electrical energy cost associated with the time period, a putative heat energy requirement associated with the time period, and a putative heat energy cost associated with the time period; and

selecting the first energy source option instead of the second energy source option based on the current electrical energy cost, the current heat energy cost, the current electrical energy availability, the current heat energy availability, and the energy model, and wherein selecting the first energy source option instead of the second energy source option comprises the step of executing a fuzzy logic algorithm.

16. The method as defined in claim 15, wherein the centralized energy generator comprises an electrical grid system.

17. The method as defined in claim 15, wherein the distributed energy generator comprises a micro-turbine system.

18. The method as defined in claim 15, including the step of providing a fast response thermal storage system, and wherein the step of determining a current electrical energy availability and a current heat energy availability comprises the step of determining an amount of heat stored in the fast response thermal storage system.

19. The method as defined in claim 15, wherein at least a portion of the energy model is based on prior usage patterns.

20. The method as defined in claim 15, wherein at least a portion of the energy model is entered into an energy control system by a human operator.

21. The method as defined in claim 15, wherein the execution of the fuzzy logic algorithm requires an actual cost of natural gas, an actual cost of grid supplied electrical energy, a current heat requirement, and a current electrical energy requirement as input variables to the fuzzy logic algorithm.

22. The method as defined in claim 21, wherein the execution of the fuzzy logic algorithm further requires an actual cost of electrical solar energy and an actual cost of thermal solar energy as input variables to the fuzzy logic algorithm.

23. The method as defined in claim 21, wherein the execution of the fuzzy logic algorithm further requires a heat storage cost and a heat storage capacity as input variables to the fuzzy logic algorithm.

24. The method as defined in claim 21, wherein the execution of the fuzzy logic algorithm further requires an efficiency metric and reliability metric associated with an energy source as input variables to the fuzzy logic algorithm.

25. The method as defined in claim 21, wherein the execution of the fuzzy logic algorithm produces an index of performance as an output variable from the fuzzy logic algorithm.

26. The method as defined in claim 21, wherein the execution of the fuzzy logic algorithm produces an actual cost of energy as an output variable from the fuzzy logic algorithm.

27. The method as defined in claim 15, wherein the step of selecting the first energy source option instead of the second energy source option comprises the step of executing a neural network algorithm.