Hybrid robust predictive optimization method of power system dispatch
View Patent ↗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.
1. A system for controlling a microgrid, the system comprising:
a plurality of microgrid assets comprising electric generators, heat generators, electric loads, heat loads, thermal storage components, water pumps and water storage components; 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, and further such that water dispatch, are together 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, electric generator up-time and down-time constraints, thermal generator up-time and down-time constraints, and combined heat and power generator up-time and down-time constraints, wherein the dispatch controller is further 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.
2. The system of claim 1 wherein the microgrid assets further comprise one or more of thermal components, stochastic renewable energy sources, 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 start-up costs associated with at least one generator type selected from pure electrical generators, pure heat generators, and combined electrical and heat generators.
10. 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.
11. A method of controlling a microgrid, the method comprising:
monitoring a plurality of microgrid assets comprising heat generators, electric generators, heat loads, electric loads, thermal storage components, water pumps, and water storage components; 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, and further such that water dispatch, are together 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, electric generator up-time and down-time constraints, thermal generator up-time and down-time constraints, and combined heat and power generator up-time and down-time constraints, and further such that dispatching respective microgrid assets is based upon load shedding costs that are proportional to the amount of shortfall between the demanded and supplied load.
12. The method of claim 11 , wherein monitoring a plurality of microgrid assets further comprises monitoring one or more microgrid assets selected from thermal components, stochastic energy sources such as wind and solar PV, and network components.
13. The method of claim 11 , 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.
14. The method of claim 11 , 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.
15. The method of claim 11 , 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.
16. The method of claim 11 , 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.
17. The method of claim 11 , 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.
18. The method of claim 11 , 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.
19. The method of claim 11 , 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.
20. The method of claim 11 , 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.