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Long-Range Generation Planning Using Generalized Benders' Decomposition: Implementation and Experience

Author

Listed:
  • Jeremy A. Bloom

    (General Public Utilities Service Corporation, Parsippany, New Jersey)

  • Michael Caramanis

    (Boston University, Boston, Massachusetts)

  • Leonid Charny

    (Stone and Webster Engineering Corporation, Boston, Massachusetts)

Abstract

This paper describes experience in implementing and using a generalized Benders' decomposition model for planning electricity generating capacity expansion. The model divides the problem into a master linear program, which generates trial expansion plans, and a set of nonlinear subproblems, which compute production cost and system reliability for the trial plan. Modifications of the original model described in the paper include a more efficient method for the subproblem computations based on the Gram-Charlier representation of probability distributions, representation of multiple unit plants, computation of upper and lower bounds on the optimal cost, and inclusion of nonthermal generating technologies. The paper also describes computational experience with the model and comparison with a dynamic programming model of the same problem. The Appendix discusses the convexity properties of the model with the modifications introduced in the paper.

Suggested Citation

  • Jeremy A. Bloom & Michael Caramanis & Leonid Charny, 1984. "Long-Range Generation Planning Using Generalized Benders' Decomposition: Implementation and Experience," Operations Research, INFORMS, vol. 32(2), pages 290-313, April.
    • Handle: RePEc:inm:oropre:v:32:y:1984:i:2:p:290-313
    DOI: 10.1287/opre.32.2.290
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    Citations

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    Cited by:

    1. Sajad Aliakbari Sani & Olivier Bahn & Erick Delage & Rinel Foguen Tchuendom, 2022. "Robust Integration of Electric Vehicles Charging Load in Smart Grid’s Capacity Expansion Planning," Dynamic Games and Applications, Springer, vol. 12(3), pages 1010-1041, September.
    2. Timo Lohmann & Michael R. Bussieck & Lutz Westermann & Steffen Rebennack, 2021. "High-Performance Prototyping of Decomposition Methods in GAMS," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 34-50, January.
    3. Wei Jing-Yuan & Yves Smeers, 1999. "Spatial Oligopolistic Electricity Models with Cournot Generators and Regulated Transmission Prices," Operations Research, INFORMS, vol. 47(1), pages 102-112, February.
    4. Jikai Zou & Shabbir Ahmed & Xu Andy Sun, 2018. "Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 388-401, May.
    5. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2016. "Optimization modeling to support renewables integration in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 316-325.
    6. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    7. Sergio Montoya-Bueno & Jose Ignacio Muñoz-Hernandez & Javier Contreras & Luis Baringo, 2020. "A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems," Energies, MDPI, vol. 13(5), pages 1-19, March.
    8. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
    9. Francisco Munoz & Jean-Paul Watson, 2015. "A scalable solution framework for stochastic transmission and generation planning problems," Computational Management Science, Springer, vol. 12(4), pages 491-518, October.
    10. Timo Lohmann & Steffen Rebennack, 2017. "Tailored Benders Decomposition for a Long-Term Power Expansion Model with Short-Term Demand Response," Management Science, INFORMS, vol. 63(6), pages 2027-2048, June.

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