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FACTS Placement Optimization For Multi-Line Contignecies

FACTS Placement Optimization For Multi-Line Contignecies. Josh Wilkerson November 30, 2005. What’s the Problem?. Line goes down in the power grid Load is redistributed sub-optimally Another line is overloaded due to new distribution Wash, rinse, repeat

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FACTS Placement Optimization For Multi-Line Contignecies

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  1. FACTS Placement Optimization For Multi-Line Contignecies Josh Wilkerson November 30, 2005

  2. What’s the Problem? • Line goes down in the power grid • Load is redistributed sub-optimally • Another line is overloaded due to new distribution • Wash, rinse, repeat • Continues on until islanding occurs or load reaches steady state • Rolling black out occurs (a.k.a. Cascading Failure) • Similar what happened in August, 2003

  3. What’s the Problem? • Why does the grid behave this way? • Not intended for this kind of use • Clash between physics and economics • Cascading failure => great physical and economical damage • Need something to hold us over until the much needed expansion is done

  4. A Means to an Answer • Flexible AC Transmission System (FACTS) • Enhances controllability and power transfer capability • More control is given to the way load is distributed

  5. A Means to an Answer • So why not just put FACTS devices on every line? • A single FACTS device is very expensive • New Problem: How to place a minimum number of FACTS devices while still providing a certain level security to the grid • Solution can be attained by analyzing how the grid behaves after one or more lines go down

  6. What’s Been Done? • A number of the scenarios involving one line going down (single line contingency) • Using brute force • Using evolutionary computation • Next step: scenarios involving multi-line contingencies

  7. What’s Been Done? • Not much work (if any) done involving MLC’s and FACTS placement • It would be more fitting to analyze a placement in the face of MLC’s rather than SLC’s.

  8. My Plan • Brute force? • Way too many scenarios to consider • On the order of 2*1013 scenarios to consider for 2 line MLC’s and 5 FACTS devices on the IEEE 118-Bus • Leaves only evolutionary computation

  9. Why an EA? • Problem space is huge • Problem space is generally unknown • The potential time saved vs. deterministic algorithm • This is the type of problem EA’s were made for

  10. EA Details • Modify the ‘blackbox’ code from assignment 2 to allow for MLC’s • In an attempt to stay par with the SLC version, run 180 MLC scenarios • 6 Parameter sets • 3 base sets which vary on contingency mode used: • SLC mode, 2 Line MLC, 3 Line MLC

  11. EA Details • Representation • Use fixed length arrays to represent the lines which FACTS devices are placed on • Fitness Evaluation • Select random lines to be involved in each MLC scenario using Monte Carlo sampling • Take the average PI Metric across MLC scenarios considered for each placement

  12. EA Details • Population • Size: 75 • Number of Parent Pairs: 20 • Number of Offspring per Parent Pair: 2

  13. EA Details • Selection Method • Boltzmann scheme • Varying selective pressure based off of population diversity (simulated annealing)

  14. EA Details

  15. EA Details • Selection Method • How diversity is gauged • Percentage of solutions within half a standard deviation of the average • Should result in the population bouncing from optima to optima until it gets stuck on global optima

  16. EA Details • Recombination • Uniform recombination • Mutation • Individual Mutation (80%) • Gene Mutation (20% or 40%) • No genetic clones allowed!

  17. Parameter Set 1: SLC 20% gene mutation chance Parameter Set 2: SLC 40% gene mutation chance Parameter Set 3: 2 Line MLC 20% gene mutation chance Parameter Set 4: 2 Line MLC 40% gene mutation chance Parameter Set 5: 3 Line MLC 20% gene mutation chance Parameter Set 6: 3 Line MLC 40% gene mutation chance EA Details

  18. EA Details • The Goal: • Two Objectives: • Initial mapping of problem space • See how highly fit MLC placements perform in SLC scenarios

  19. Results • Some surprising, some not so surprising

  20. Results

  21. Results

  22. Results • Test of highly fit placements from MLC scenarios in SLC scenarios • Were not even competitive with results from SLC EA • Some even performed worse in SLC scenarios than they did in MLC scenarios

  23. Results • Summary: • As the number of lines involved in the contingency increases, so does the PI • Standard deviation also seems to rise as the number of lines in contingency rises • Boltzmann scheme seems to be working as intended • Need better way to pick lines for MLC scenarios, placements getting ‘lucky’ with random lines.

  24. Questions?

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