IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v32y1984i2p290-313.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.32.2.290
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.32.2.290?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:32:y:1984:i:2:p:290-313. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.