Hybrid robust predictive optimization method of power system dispatch

• US 20090062969A1
• Filed: 08/28/2007
• Published: 03/05/2009
• Est. Priority Date: 08/28/2007
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Aerospace)
• Original Assignee: General Electric Company
• Inventors: Bose, Sumit, Liu, Yan, de Bedout, Juan Manuel, Chandra, Ramu Sharat
• Granted Patent: US 7,991,512 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02P 80/14 District level solutions, i.e. local energy networks
• CPC Class Codes: Y02P 80/15 On-site combined power, heat or cool generation or distribution, e.g. combined heat and power [CHP] supply
• 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 method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Claims

1. A system for controlling a microgrid, the system comprising:

a plurality of microgrid assets, at least one of the microgrid assets comprising a different type of asset than another one of the microgrid assets; and

a dispatch controller coupled to the plurality of microgrid assets and configured for providing control signals to adjust operation of respective microgrid assets in response to a predictive optimization control algorithm, such that heat and power generated together or independently by the plurality of microgrid assets interconnected on electric and/or thermal grids of any specified topology, are scheduled to optimize the solution of an objective function associated with microgrid operation over a predetermined time horizon based on predicted future microgrid asset conditions.

2. The system of claim 1 wherein the microgrid assets are selected from electric and heat generators, loads, electric and thermal storage components, thermal components, stochastic renewable energy sources, water pumps, water storage components, and network components.

3. The system of claim 1 wherein the predictive optimization control algorithm is selected from Merit Order Loading, Range Elimination, Binary Section, Secant Section, Graphical/Table Look-Up, Convex Simplex, Dantzig-Wolf Decomposition, Separable Convex Linear Programming, Reduced Gradient with Linear Constraints, Steepest Descent Gradient, First Order Gradient, Merit Order Reduced Gradient, Second Order Cone Programming, and Nonlinear Programming algorithms.

4. The system of claim 1, wherein the dispatch controller is responsive to the predictive optimization control algorithm such that the heat and power generated together or independently, are based on future predicted values of loads, renewable energy sources, and fuel and electricity prices.

5. The system of claim 1, wherein the predictive optimization control algorithm is configured to control the dispatch controller based on future predicted values of microgrid constrained variables.

6. The system of claim 1, wherein the dispatch controller is configured to be responsive to the predictive optimization control algorithm based on predicted fuel costs over a desired prediction horizon.

7. The system of claim 1, wherein the dispatch controller is configured to be responsive to the predictive optimization control algorithm based on fuel consumption curves associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

8. The system of claim 1, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on costs of importing and exporting power from the microgrid.

9. The system of claim 1, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on load shedding costs that are proportional to the amount of shortfall between the demanded and supplied load.

10. The system of claim 1, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on start-up costs associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

11. The system of claim 1, wherein the objective function comprises the total predicted cost of microgrid operation over the predetermined time horizon based on the predicted future microgrid asset conditions.

12. A method of controlling a microgrid, the method comprising:

monitoring a plurality of microgrid assets; and

dispatching respective microgrid assets in response to a predictive optimization control algorithm, such that heat and power generated together or independently by the plurality of microgrid assets interconnected on electric and/or thermal grids of any specified topology, are scheduled to optimize the solution of an objective function associated with microgrid operation over a predetermined time horizon based on predicted future microgrid asset conditions.

13. The method of claim 12, wherein monitoring a plurality of microgrid assets comprises monitoring microgrid assets selected from electric and heat generators, electric and heat loads, electric and thermal storage components, thermal components, stochastic energy sources such as wind and solar PV, water pumps, water storage components, and network components.

14. The method of claim 12, wherein dispatching respective microgrid assets in response:

to a predictive optimization control algorithm comprises dispatching respective microgrid assets in response to a predictive optimization control process based on any one or more of Merit Order Loading, Range Elimination, Binary Section, Secant Section, Graphical/Table Look-Up, Convex Simplex, Dantzig-Wolf Decomposition, Separable Convex Linear Programming, Reduced Gradient with Linear Constraints, Steepest Descent Gradient, First Order Gradient, and Merit Order Reduced Gradient algorithms.

15. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching heat and power generated together or independently, based on future predicted values of loads, stochastic energy sources, and fuel and electricity prices.

16. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on future predicted values of microgrid constrained variables.

17. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on predicted fuel costs over a desired prediction horizon.

18. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on fuel consumption curves associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

19. The method of claim 12, wherein dispatching respective microgrid assets in response, to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on costs of importing and exporting power from the microgrid.

20. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on load shedding costs that are proportional to the amount of shortfall between the demanded and supplied load.

21. The method of claim 12, wherein dispatching respective microgrid assets in response to a predictive optimization control algorithm comprises dispatching respective microgrid assets based on start-up costs associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

22. The method of claim 12, wherein the objective function comprises the total predicted cost of microgrid operation over the predetermined time horizon based on the predicted future microgrid asset conditions.

23. A system for controlling a microgrid, the system comprising:

a plurality of microgrid assets, at least one of the microgrid assets comprising a different type of asset than another one of the microgrid assets; and

means for adjusting operation of respective microgrid assets such that heat and power generated together or independently by the plurality of microgrid assets interconnected on electric and/or thermal grids of any specified topology, are scheduled to optimize the solution of an objective function associated with microgrid operation over a predetermined time horizon based on predicted future microgrid asset conditions.

24. The system of claim 23 wherein the microgrid assets are selected from electric generators, heat generators, combined electric and heat generators, loads, electric and thermal storage components, thermal components, stochastic renewable energy sources, water pumps, water storage components, and network components.

25. The system of claim 23, wherein the means for adjusting comprises a dispatch controller coupled to the plurality of microgrid assets and configured for providing control signals to adjust operation of respective microgrid assets in response to a predictive optimization control algorithm.

26. The system of claim 25 wherein the predictive optimization control algorithm is selected from Merit Order Loading, Range Elimination, Binary Section, Secant Section, Graphical/Table Look-Up, Convex Simplex, Dantzig-Wolf Decomposition, Separable Convex Linear Programming, Reduced Gradient with Linear Constraints, Steepest Descent Gradient, First Order Gradient, Merit Order Reduced Gradient, Second Order Cone Programming, and Nonlinear Programming algorithms.

27. The system of claim 25, wherein the dispatch controller is responsive to the predictive optimization control algorithm such that the heat and power generated together or independently, are based on future predicted values of loads, renewable energy sources, and fuel and electricity prices.

28. The system of claim 25, wherein the predictive optimization control algorithm is configured to control the dispatch controller based on future predicted values of microgrid constrained variables.

29. The system of claim 25, wherein the dispatch controller is configured to be responsive to the predictive optimization control algorithm based on predicted fuel costs over a desired prediction horizon.

30. The system of claim 25, wherein the dispatch controller is configured to be responsive to the predictive optimization control algorithm based on fuel consumption curves associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

31. The system of claim 25, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on costs of importing and exporting power from the microgrid.

32. The system of claim 25, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on load shedding costs that are proportional to the amount of shortfall between the demanded and supplied load.

33. The system of claim 25, wherein the dispatch controller is configured to be responsive to the prediction optimization control algorithm based on start-up costs associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.

34. The system of claim 23, wherein the objective function comprises the total predicted cost of microgrid operation over the predetermined time horizon based on the predicted future microgrid asset conditions.