Modern Heuristic Optimization Techniques and Potential Applications to Power System Control

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.edu http://cialab.ee.washington.edu

2. Heuristic Optimization Techniques • Genetic Algorithms • Evolutionary Programming • Swarm Intelligence • Particle Swarm • DNA Computing • Artificial Life • Intelligent 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. Constraints System inputs Objectives Control Inputs Simplified System FEEDBACK Classical Control: Design

6. Constraints System inputs Objectives Control Inputs Actual System FEEDBACK Classical Control: Operation

7. Constraints System inputs Objectives Control Inputs Detailed System PSO PSO Control

8. PSO PSO/NN Control Constraints System inputs NN Model Objectives Control Inputs

9. Gradient Search vs MAS MAS Gradient Search

10. Evolutionary Algorithms

11. Population Pool Byte 1 Byte 2 Byte n individual 1 n 2 ... #1 1 0 0 1 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0 ... #2 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 0 0 ... #3 1 0 1 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 1 ... #K 1 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1

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

13. Two-point Crossover • Two crossover points are obtained by a random number generator Crossover points 2 1 2 1 #p Crossover #p 0 1 0 1 0 1 0 0 1 1 1 0 1 0 #q #q 1 0 1 1 0 0 1 1 0 0 1 0 0 1

14. #p 0 1 0 1 0 0 1 mutation #p 0 1 0 0 0 0 1 Mutation

15. Particle Swarm Optimization

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

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. Cascading event Test System WSCC 179 Bus System 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 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 socialbehavior and fulfillment of a number of tasks

30. Pheromone Trails

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

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

33. Finis