1. Modern Heuristic Optimization Techniques and Potential Applications to Power System Control Mohamed A El-Sharkawi The CIA lab Department of Electrical Engineering University of Washington Seattle, WA 98195-2500 elsharkawi@ee.washington.eduhttp://cialab.ee.washington.edu

2. Heuristic Optimization Techniques Genetic AlgorithmsGenetic Algorithms Evolutionary ProgrammingEvolutionary Programming Swarm IntelligenceSwarm Intelligence Particle SwarmParticle Swarm DNA ComputingDNA Computing Artificial LifeArtificial Life Intelligent AgentsIntelligent Agents

3. Biocomputation The use of biological processes or behavior as metaphor, inspiration, or enabler in developing new computing technologies The field is highly multidisciplinary, Engineers, computer scientists, molecular biologists, geneticists, mathematicians, physicists, and others.

4. Nature is a Powerful Paradigm Brain  neural networks Evolution theory  genetic algorithms Flock of birds  particle swarm optimization Insects  swarm intelligence ……

5. Classical Control: Design System inputs Control Inputs Constraints

6. Classical Control: Operation System inputs Control Inputs Constraints

7. PSO Control System inputs Control Inputs Constraints

8. PSO/NN Control System inputs Control Inputs Constraints

9. Gradient Search vs MAS Gradient Search MAS

10. Evolutionary Algorithms

11. Population Pool 100111111100000011110000... Byte 1Byte 2Byte n 11 00111111100000011110000... 1001111111000000111100 00 1001 1 111110000001 1 110000 0 individual #1 #2 #3 #K 22 nn

12. Fitness Evaluation #1 #2 #3 Individuals 1001110 001110 1001110 1001110 0 #n Fitness Computations f(.) Normalize Ranked Individuals #q #p 001110 1001110 #q 0 0011100 1001110 #1 1001110 1001110 #3 #n 1001110 #2 0011100

13. Two-point Crossover Two crossover points are obtained by a random number generator #p #q 0011100 1001110 Crossover 1 00 11 1 0 10 00 01 1 #p #q Crossover points 1 2 1 2

14. Mutation 0101001 0100001 mutation #p

15. Particle Swarm Optimization

17. Personal Best at previous step Current motion Component in the direction of personal best Component in the direction of previous motion Component in the direction of global best New Motion Global best

18. Border (Edge) Identification

19. The Art of Fitness Function To find points anywhere on the boundary Metric: |f(x)-boundary value|

20. Results - Case 1

21. The Art of Fitness Function Distribute points uniformly on the boundary Metric: |f(x)-boundary value| - Distance to closest neighbor (to penalize proximity to neighbors)

22. Results - Case 2

23. The Art of Fitness Function Distribute points uniformly on the boundary close to current state Metric: |f(x)-boundary value| -Distance to closest neighbor + Distance to current state (penalize proximity to neighbors, penalize distance from current state)

24. Results - Case 3

25. Test System WSCC 179 Bus System Cascading event Base Case 61,411 MW 12,330 MVAR

26. First Event – Initial Contingency Three Phase fault on the line between John Day (#76) and Grizzly (#82) Second Event Trip the line between John Day (#76) and Hanford (#78) Third Event Trip the line between John Day (#78) and North 500 (#80)

27. Swarm Intelligence

28. Swarm Intelligence Coordination without Swarm Intelligence = Coordination without Direct Communication

29. Swarm Intelligence Appears in biological swarms of certain insect species Interactions is indirect (stigmergy) The end result is accomplishment of very complex forms of social behavior and fulfillment of a number of tasks

30. Pheromone Trails

31. A B C D G E F AB 0.23 BC 0.11 AB 0.23 CD 0.14 BC 0.11 AB 0.23 DE 0.15 CD 0.14 BC 0.11 AB 0.23

32. A B C D G E F BC 0.11 AB 0.23 CD 0.14 BC 0.11 AB 0.23 DE 0.15 CD 0.14 BC 0.11 AB 0.23

33. Finis