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A Benders decomposition algorithm for a multi-area, multi-stage integrated resource planning in power systems

Author

Listed:
  • M Jenabi

    (Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran)

  • S M T Fatemi Ghomi

    (Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran)

  • S A Torabi

    (Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)

  • S H Hosseinian

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran)

Abstract

The aim of this paper is to propose an integrated model for resource planning in power systems by taking into account both supply and demand sides options simultaneously. At supply-side, investment in generation capacity and transmission lines is considered. Demand side management (DSM) technologies are also incorporated to correct the shape of the load duration curve in terms of peak clipping and load shifting programmes. A mixed integer non-linear programming model is developed to find the optimal location and timing of electricity generation/transmission as well as DSM options. To solve the resulting complex model, nonlinearity caused by transmission loss terms are first eliminated using the piecewise linearization technique. Then, a Benders decomposition (BD) algorithm is developed to solve the linearized model. The performance of the proposed BD algorithm is validated via applying it to the 6-bus Garver test system and a modified 21-bus IEEE reliability test system.

Suggested Citation

  • M Jenabi & S M T Fatemi Ghomi & S A Torabi & S H Hosseinian, 2013. "A Benders decomposition algorithm for a multi-area, multi-stage integrated resource planning in power systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(8), pages 1118-1136, August.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:8:p:1118-1136
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    Citations

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    Cited by:

    1. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    2. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    3. M. Jenabi & S. Fatemi Ghomi & S. Torabi & S. Hosseinian, 2015. "Acceleration strategies of Benders decomposition for the security constraints power system expansion planning," Annals of Operations Research, Springer, vol. 235(1), pages 337-369, December.
    4. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.

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