Dynamic economic load dispatch

• US 20080154810A1
• Filed: 12/12/2006
• Published: 06/26/2008
• Est. Priority Date: 09/29/2006
• Status: Active Grant

• Current Assignee: Honeywell Technology Solutions Incorporated (KBR Incorporated)
• Original Assignee: Honeywell International Inc.
• Inventors: He, JinWei, Ye, Chen Zhou, Zhang, Dan Qing, Xiang, Xunwei
• Granted Patent: US 7,752,150 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• 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

Abstract

In an embodiment, a system and method provide a constraint on a genetic algorithm, and further provide dynamic programming capability to the genetic algorithm. The system and method then allocate load demand over a finite number of time intervals among a number of power generating units as a function of the constraint on the genetic algorithm and the dynamic programming capability of the genetic algorithm.

Claims

1. A process comprising:

providing a constraint on a genetic algorithm;

providing dynamic programming capability to the genetic algorithm; and

allocating load demand over a finite number of time intervals among a number of power generating units as a function of the constraint on the genetic algorithm and the dynamic programming capability of the genetic algorithm.

2. The process of claim 1, wherein the constraint of the genetic algorithm comprises one or more of:

the load demand is equal to a sum of all outputs of the power generating units;

each power generating unit includes a lower output limit and an upper output limit;

a limit on ramp up and ramp down rates of the power generating units; and

one or more prohibited operating zones of the power generating units.

3. The process of claim 1, wherein a fuel cost function of one or more of the power generating units comprises one or more of a non-smooth characteristic and a non-convex characteristic.

4. The process of claim 1, wherein the provision of a constraint and the provision of a dynamic programming capability on the genetic algorithm comprises modifying one or more initialization, crossover, and mutation operators, thereby maintaining load dispatch solutions evolving freely near a feasible region.

5. The process of claim 4, wherein the modifying of the initialization operator comprises providing a biased and heuristic initialization to ensure a diversified initial population.

6. The process of claim 5, wherein the biased and heuristic initialization comprises:

assigning a highest priority to one or more power generating units operating in a prohibited operating zone;

assigning a normal priority to one or more power generating units selected randomly before the initialization;

assigning a lowest priority to any remaining power generating units not assigned the highest priority or the normal priority;

iteratively selecting a power generating unit according to the prioritized power generating units; and

allocating unfulfilled load demand to that power generating unit without violating the constraints on the genetic algorithm.

7. The process of claim 4, wherein the modifying of the crossover operator comprises selecting feasible positions for crossover to accommodate the constraints on the genetic algorithm.

8. The process of claim 4, wherein the modifying of the mutation operator comprises heuristically maintaining locations of possible mutation points, and providing a positive mutation operator to reduce fuel cost and a negative mutation operator to achieve load equality;

and further wherein a positive mutation operation affects a single time interval at a time, and a negative mutation operation affects multiple time intervals at a time.

9. The process of claim 8, further comprising providing a discretized search strategy to one or more of the positive mutation operator and the negative mutation operator.

10. A machine readable medium comprising instructions for executing a process comprising:

providing a constraint on a genetic algorithm;

providing dynamic programming capability to the genetic algorithm; and

allocating load demand over a finite number of time intervals among a number of power generating units as a function of the constraint on the genetic algorithm and the dynamic programming capability of the genetic algorithm.

11. The machine readable medium of claim 10, wherein the constraint of the genetic algorithm comprises one or more of:

the load demand is equal to a sum of all outputs of the power generating units;

each power generating unit includes a lower output limit and an upper output limit;

a limit on ramp up and ramp down rates of the power generating units; and

one or more prohibited operating zones of the power generating units.

12. The machine readable medium of claim 10, wherein a fuel cost function of one or more of the power generating units comprises one or more of a non-smooth characteristic and a non-convex characteristic.

13. The machine readable medium of claim 10, wherein the provision of a constraint and the provision of a dynamic programming capability on the genetic algorithm comprises modifying one or more initialization, crossover, and mutation operators, thereby maintaining load dispatch solutions evolving freely near a feasible region.

14. The machine readable medium of claim 13, wherein the modifying of the initialization operator comprises providing a biased and heuristic initialization to ensure a diversified initial population.

15. The machine readable medium of claim 14, wherein the biased and heuristic initialization comprises:

assigning a highest priority to one or more power generating units operating in a prohibited operating zone;

assigning a normal priority to one or more power generating units selected randomly before the initialization;

assigning a lowest priority to any remaining power generating units not assigned the highest priority or the normal priority;

iteratively selecting a power generating unit according to the prioritized power generating units; and

allocating unfulfilled load demand to that power generating unit without violating the constraints on the genetic algorithm.

16. The machine readable medium of claim 13, wherein the modifying of the crossover operator comprises selecting feasible positions for crossover to accommodate the constraints on the genetic algorithm.

17. The machine readable medium claim 13, wherein the modifying of the mutation operator comprises heuristically maintaining locations of possible mutation points, and providing a positive mutation operator to reduce fuel cost and a negative mutation operator to achieve load equality;

and further wherein a positive mutation operation affects a single time interval at a time, and a negative mutation operation affects multiple time intervals at a time.

18. The machine readable medium of claim 17, further comprising providing a discretized search strategy to one or more of the positive mutation operator and the negative mutation operator.

19. A system comprising:

a module that provides a constraint on a genetic algorithm;

a module that provides dynamic programming capability to the genetic algorithm; and

a module that allocates load demand over a finite number of time intervals among a number of power generating units as a function of the constraint on the genetic algorithm and the dynamic programming capability of the genetic algorithm.

20. The system of claim 19, wherein the constraint of the genetic algorithm comprises one or more of:

the load demand is equal to a sum of all outputs of the power generating units;

each power generating unit includes a lower output limit and an upper output limit;

a limit on ramp up and ramp down rates of the power generating units; and

one or more prohibited operating zones of the power generating units.