Experimental Analysis of Algorithms for Bilateral-Contract Clearing Mechanisms Arising in Deregulated Power Industry

  • Chris Barrett
  • Doug Cook
  • Gregory Hicks
  • Vance Faber
  • Achla Marathe
  • Madhav Marathe
  • Aravind Srinivasan
  • Yoram J. Sussmann
  • Heidi Thornquist
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2141)


We consider the bilateral contract satisfaction problem arising from electrical power networks due to the proposed deregulation of the electric utility industry in the USA. Given a network and a (multi)set of pairs of vertices (contracts) with associated demands, the goal is to find the maximum number of simultaneously satisfiable contracts. We study how four different algorithms perform in fairly realistic settings; we use an approximate electrical power network from Colorado. Our experiments show that three heuristics outperform a theoretically better algorithm. We also test the algorithms on four types of scenarios that are likely to occur in a deregulated marketplace. Our results show that the networks that are adequate in a regulated marketplace might be inadequate for satisfying all the bilateral contracts in a deregulated industry.


Sink Node Alamos National Laboratory Electric Power Research Institute Energy Information Administration Federal Energy Regulatory Commission 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahuja, R. K., Magnanti, T. L., Orlin, J. B.: Network flows: theory, algorithms, and applications. Prentice Hall, Englewood Cliffs, New Jersey. (1993)Google Scholar
  2. 2.
  3. 3.
    Barrett, C., Hunt, H. B., Marathe, M., Ravi, S. S., Rosenkrantz, D., Stearns, R.: Dichotomy Results for Sequential Dynamical Systems. To appear in Proc. MFCS (2001)Google Scholar
  4. 4.
    Cardell, J. B., Hitt, C. C., Hogan, W. W.: Market Power and Strategic Interaction in Electricity Networks. Resource and Energy Economics Vol. 19 (1997) 109–137.CrossRefGoogle Scholar
  5. 5.
    California Public Utilities Commission. Order Instituting Rulemaking on the Commission’s Proposed Policies Governing Restructuring California’s Electric Services Industry and Reforming Regulation, Decision 95-12-063 (1995)Google Scholar
  6. 6.
    Cook, D., Faber, V., Marathe, M., Srinivasan, A., Sussmann, Y. J.: Low Bandwidth Routing and Electrical Power Networks. Proc. 25th International Colloquium on Automata, Languages and Programming (ICALP) Aalborg, Denmark, LNCS 1443, Springer Verlag, (1998) 604–615Google Scholar
  7. 7.
    Dowell, L. J., Drozda, M., Henderson, D. B., Loose, V., Marathe, M., Roberts, D.: ELISIMS: Comprehensive Detailed Simulation of the Electric Power Industry. Technical Report LA-UR-98-1739, Los Alamos National Laboratory (1998)Google Scholar
  8. 8.
    The Changing Structure of the Electric Power Industry: Selected Issues. DOE 0562(98), Energy Information Administration, US Department of Energy, Washington, D. C. (1998)Google Scholar
  9. 9.
    Fryer, H. C.: Concepts and Methods of Experimental Statistics. Allyn and Bacon Inc. (1968)Google Scholar
  10. 10.
    Glass, G. V., Hopkins, K. D.: 1996. Statistical Methods in Education and Psychology. third edition (1996)Google Scholar
  11. 11.
    EPRI-Workshop: Underlying Technical Issues in Electricity Deregulation. Technical report forthcoming, Electric Power Research Institute (EPRI), April 25–27 (1997)Google Scholar
  12. 12.
    The U. S. Federal Energy Regulatory Commission. Notice of Proposed Rule-making (“NOPRA”). Dockets No. RM95-8-000, RM94-7-001, RM95-9-000, RM96-11-000, April 24 (1996)Google Scholar
  13. 13.
    Garey, M. R., Johnson, D. S.: Computers and Intractability. A Guide to the Theory of NP-Completeness. Freeman, San Francisco CA (1979)Google Scholar
  14. 14.
    Hogan, W.: Contract networks for electric power transmission. J. Regulatory Economics. (1992) 211–242Google Scholar
  15. 15.
    Kolliopoulos, S. G., Stein, C.: Improved approximation algorithms for unsplittable flow problems. In Proc. IEEE Symposium on Foundations of Computer Science. (1997) 426–435Google Scholar
  16. 16.
    S-Plus5, “Guide to Statistics”, MathSoft Inc. September (1988)Google Scholar
  17. 17.
    Srinivasan, A.: Approximation Algorithms via Randomized Rounding: A Survey. Lectures on Approximation and Randomized Algorithms (M. Karoński and H. J. Prömel, eds.), Series in Advanced Topics in Mathematics. Polish Scientific Publishers PWN, Warsaw.(1999) 9–71Google Scholar
  18. 18.
    Wildberger, A. M.: Issues associated with real time pricing. Unpublished Technical report, Electric Power Research Institute (EPRI) (1997)Google Scholar
  19. 19.
    Websites: and (also see references therein). (Energy Information Administration)
  20. 20.
    Wolak, F.: An Empirical Analysis of the Impact of Hedge Contracts on Bidding Behavior in a Competitive Electricity Market, (2000)
  21. 21.
    The Wall Street Journal, August 04 2000, A2, August 14 2000 A6 and August 21 2000, November 27 2000, December 13, 14 2000.Google Scholar
  22. 22.
    Wood, A. J., Wollenberg, B. F.: Power Generation, Operation and Control. John Wiley and Sons. (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Chris Barrett
    • 2
  • Doug Cook
    • 1
  • Gregory Hicks
    • 5
  • Vance Faber
    • 4
  • Achla Marathe
    • Madhav Marathe
      • Aravind Srinivasan
        • 3
      • Yoram J. Sussmann
      • Heidi Thornquist
        • 6
      1. 1.Department of EngineeringColorado School of MinesGolden
      2. 2.Los Alamos National LaboratoryLos Alamos
      3. 3.Lucent TechnologiesMurray Hill
      4. 4.Lizardtech Inc.USA
      5. 5.Department of MathematicsNorth Carolina State UniversityRaleigh
      6. 6.Department of Computational ScienceRice UniversityHouston

      Personalised recommendations