Advertisement

Abstract

This paper considers the joint repair and restoration of the electrical power system after significant disruptions caused by natural disasters. This problem is computationally challenging because, when the goal is to minimize the size of the blackout, it combines a routing and a power restoration component, both of which are difficult on their own. The joint repair/restoration problem has been successfully approached with a 3-stage decomposition, whose last step is a multiple-vehicle, pickup-and-delivery routing problem with precedence and capacity constraints whose goal is to minimize the sum of the delivery times (PDRPPCCDT). Experimental results have shown that the PDRPPCCDT is a bottleneck and this paper proposes a Randomized Adaptive Vehicle Decomposition (RAVD) to scale to very large power outages. The RAVD approach is shown to produce significant computational benefits and provide high-quality results for infrastructures with more than 24000 components and 1200 damaged items, giving rise to PDRPPCCDT with more than 2500 visits.

Keywords

Power System Delivery Time Precedence Constraint Large Neighborhood Search Merging Oper 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adibi, M.: Power System Restoration (Methodologies & Implementation Strategies) (2000)Google Scholar
  2. 2.
    Adibi, M.M., Fink, L.H.: Power system restoration planning. IEEE Transactions on Power Systems 9(1), 22–28 (1994)CrossRefGoogle Scholar
  3. 3.
    Adibi, M.M., Kafka, L.R.J., Milanicz, D.P.: Expert system requirements for power system restoration. IEEE Transactions on Power Systems 9(3), 1592–1600 (1994)CrossRefGoogle Scholar
  4. 4.
    Ancona, J.J.: A framework for power system restoration following a major power failure. IEEE Transactions on Power Systems 10(3), 1480–1485 (1995)CrossRefGoogle Scholar
  5. 5.
    Bent, R., Van Hentenryck, P.: A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows. Transportation Science 8(4), 515–530 (2004)CrossRefGoogle Scholar
  6. 6.
    Bent, R., Van Hentenryck, P.: A Two-Stage Hybrid Algorithm for Pickup and Delivery Vehicle Routing Problems with Time Windows. Computers and Operations Research (Special Issue on Applications in Combinatorial Optimization), 875–893 (2006)Google Scholar
  7. 7.
    Bent, R., Van Hentenryck, P.: Randomized Adaptive Spatial Decoupling For Large-Scale Vehicle Routing with Time Windows. In: Proceedings of the 22th National Conference on Artificial Intelligence (AAAI 2007). AAAI Press (July 2007)Google Scholar
  8. 8.
    Bent, R., Van Hentenryck, P.: Spatial, Temporal, and Hybrid Decompositions for Large-Scale Vehicle Routing with Time Windows. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 99–113. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Campbell, A.M., Vandenbussche, D., Hermann, W.: Routing for relief efforts. Transportation Science 42(2), 127–145 (2008)CrossRefGoogle Scholar
  10. 10.
    Delgadillo, A., Arroyo, J.M., Alguacil, N.: Analysis of electric grid interdiction with line switching. IEEE Transactions on Power Systems 25(2), 633–641 (2010)CrossRefGoogle Scholar
  11. 11.
    FEMA. Hazus overview (2010), http://www.fema.gov/plan/prevent/hazus/
  12. 12.
    Fisher, E.B., O’Neill, R.P., Ferris, M.C.: Optimal transmission switching. IEEE Transactions on Power Systems 23(3), 1346–1355 (2008)CrossRefGoogle Scholar
  13. 13.
    Duncan Glover, J., Sarma, M.S., Overbye, T.: Power Systems Analysis and Design. CL-Engineering (2007)Google Scholar
  14. 14.
    Huang, J.A., Audette, L., Harrison, S.: A systematic method for power system restoration planning. IEEE Transactions on Power Systems 10(2), 869–875 (1995)CrossRefGoogle Scholar
  15. 15.
    Huang, J.A., Galiana, F.D., Vuong, G.T.: Power system restoration incorporating interactive graphics and optimization, pp. 216–222 (May 1991)Google Scholar
  16. 16.
    Morelato, A.L., Monticelli, A.J.: Heuristic search approach to distribution system restoration. IEEE Transactions on Power Delivery 4(4), 2235–2241 (1989)CrossRefGoogle Scholar
  17. 17.
    Mori, H., Ogita, Y.: A parallel tabu search based approach to optimal network reconfigurations for service restoration in distribution systems, vol. 2, pp. 814–819 (2002)Google Scholar
  18. 18.
    Nagata, T., Sasaki, H., Yokoyama, R.: Power system restoration by joint usage of expert system and mathematical programming approach. IEEE Transactions on Power Systems 10(3), 1473–1479 (1995)CrossRefGoogle Scholar
  19. 19.
    Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: Walsh, T. (ed.) IJCAI, pp. 1997–2002. IJCAI/AAAI (2011)Google Scholar
  20. 20.
    Reed, D.A.: Electric utility distribution analysis for extreme winds. Journal of Wind Engineering and Industrial Aerodynamics 96(1), 123–140 (2008)CrossRefGoogle Scholar
  21. 21.
    Sakaguchi, T., Matsumoto, K.: Development of a knowledge based system for power system restoration. IEEE Transactions on Power Apparatus and Systems 102(2), 320–329 (1983)CrossRefGoogle Scholar
  22. 22.
    Salmeron, J., Wood, K., Baldick, R.: Worst-case interdiction analysis of large-scale electric power grids. IEEE Transactions on Power Systems 24(1), 96–104 (2009)CrossRefGoogle Scholar
  23. 23.
    Van Hentenryck, P., Coffrin, C., Bent, R.: Vehicle routing for the last mile of power system restoration. In: Proceedings of the 17th Power Systems Computation Conference (PSCC 2011), Stockholm, Sweden (August 2011)Google Scholar
  24. 24.
    Yolcu, M.H., Zabar, Z., Birenbaum, L., Granek, S.A.: Adaptation of the simplex algorithm to modeling of cold load pickup of a large secondary network distribution system. IEEE Transactions on Power Apparatus and Systems, PAS 102(7), 2064–2068 (1983)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ben Simon
    • 1
  • Carleton Coffrin
    • 1
  • Pascal Van Hentenryck
    • 2
  1. 1.Brown UniversityProvidenceUSA
  2. 2.Optimization Research GroupNICTA & University of MelbourneAustralia

Personalised recommendations