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An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation

Author

Listed:
  • M. Jenabi

    (Amirkabir University of Technology)

  • S. M. T. Fatemi Ghomi

    (Amirkabir University of Technology)

  • S. A. Torabi

    (University of Tehran)

  • Moeen Sammak Jalali

    (Amirkabir University of Technology)

Abstract

This paper proposes a stochastic programming model and a combined solution algorithm to solve integrated resource planning (IRP) problem of electric power systems in which supply and demand side resources are combined to construct a pool of resources to expand the power systems. The problem is formulated as a two-stage recourse model, where random uncertainties in demand, operating costs, equivalent availability of generation units and customer responses to demand side management programs are taken into account. The solution methodology integrates an exterior sampling strategy, the sample average approximation algorithm, with an accelerated Benders decomposition algorithm to compute high quality solutions to the stochastic IRP problem with exponentially large number of scenarios. The proposed integrated algorithm is implemented on the modified 6, 21 and 48 bus IEEE reliability test systems and the confidence intervals of lower and upper bounds of optimal objective function as well as optimality gap are reported.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:4:d:10.1007_s12597-021-00559-9
    DOI: 10.1007/s12597-021-00559-9
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    References listed on IDEAS

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    1. Zahedi Rad, Vahid & Torabi, S. Ali & Shakouri G., Hamed, 2019. "Joint electricity generation and transmission expansion planning under integrated gas and power system," Energy, Elsevier, vol. 167(C), pages 523-537.
    2. Fitiwi, Desta Z. & de Cuadra, F. & Olmos, L. & Rivier, M., 2015. "A new approach of clustering operational states for power network expansion planning problems dealing with RES (renewable energy source) generation operational variability and uncertainty," Energy, Elsevier, vol. 90(P2), pages 1360-1376.
    3. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    4. Seddighi, Amir Hossein & Ahmadi-Javid, Amir, 2015. "Integrated multiperiod power generation and transmission expansion planning with sustainability aspects in a stochastic environment," Energy, Elsevier, vol. 86(C), pages 9-18.
    5. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    6. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
    7. Vahab Vahdat & Mohammad Ali Vahdatzad, 2017. "Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty," Logistics, MDPI, vol. 1(2), pages 1-21, December.
    8. Sansavini, G. & Piccinelli, R. & Golea, L.R. & Zio, E., 2014. "A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation," Renewable Energy, Elsevier, vol. 64(C), pages 71-81.
    9. Poojari, C.A. & Beasley, J.E., 2009. "Improving benders decomposition using a genetic algorithm," European Journal of Operational Research, Elsevier, vol. 199(1), pages 89-97, November.
    10. Julia L. Higle & Suvrajeet Sen, 1991. "Stochastic Decomposition: An Algorithm for Two-Stage Linear Programs with Recourse," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 650-669, August.
    11. Cote, Gilles & Laughton, Michael A., 1984. "Large-scale mixed integer programming: Benders-type heuristics," European Journal of Operational Research, Elsevier, vol. 16(3), pages 327-333, June.
    12. 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.
    13. Khaligh, Vahid & Anvari-Moghaddam, Amjad, 2019. "Stochastic expansion planning of gas and electricity networks: A decentralized-based approach," Energy, Elsevier, vol. 186(C).
    14. T. L. Magnanti & R. T. Wong, 1981. "Accelerating Benders Decomposition: Algorithmic Enhancement and Model Selection Criteria," Operations Research, INFORMS, vol. 29(3), pages 464-484, June.
    15. Matteo Fischetti & Ivana Ljubić & Markus Sinnl, 2017. "Redesigning Benders Decomposition for Large-Scale Facility Location," Management Science, INFORMS, vol. 63(7), pages 2146-2162, July.
    16. Hanif Sherali & Brian Lunday, 2013. "On generating maximal nondominated Benders cuts," Annals of Operations Research, Springer, vol. 210(1), pages 57-72, November.
    17. Nader Azad & Georgios Saharidis & Hamid Davoudpour & Hooman Malekly & Seyed Yektamaram, 2013. "Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach," Annals of Operations Research, Springer, vol. 210(1), pages 125-163, November.
    18. Walter Rei & Jean-François Cordeau & Michel Gendreau & Patrick Soriano, 2009. "Accelerating Benders Decomposition by Local Branching," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 333-345, May.
    19. Dale McDaniel & Mike Devine, 1977. "A Modified Benders' Partitioning Algorithm for Mixed Integer Programming," Management Science, INFORMS, vol. 24(3), pages 312-319, November.
    20. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    21. Hobbs, Benjamin F., 1995. "Optimization methods for electric utility resource planning," European Journal of Operational Research, Elsevier, vol. 83(1), pages 1-20, May.
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