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Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation

Author

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  • Kenjiro Yagi

    (Tokushu Tokai Paper Co., Ltd.)

  • Ramteen Sioshansi

    (Carnegie Mellon University
    Carnegie Mellon University
    Carnegie Mellon University
    Carnegie Mellon University)

Abstract

Nested Benders’s decomposition is an efficient means to solve large-scale optimization problems with a natural time sequence of decisions. This paper examines the use of the technique to decompose and solve efficiently capacity-expansion problems for electricity systems with hydroelectric and renewable generators. To this end we develop an archetypal planning model that captures key features of hydroelectric and renewable generators and apply it to a case study that is based on the Columbia River system in the northwestern United States of America. We apply standard network and within-year temporal simplifications to reduce the problem’s size. Nevertheless, the remaining problem is large-scale and we demonstrate the use of nested Benders’s decomposition to solve it. We explore refinements of the decomposition method which yield further performance improvements. Overall, we show that nested Benders’s decomposition yields good computational performance with minimal loss of model fidelity.

Suggested Citation

  • Kenjiro Yagi & Ramteen Sioshansi, 2024. "Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation," Computational Management Science, Springer, vol. 21(1), pages 1-31, June.
    • Handle: RePEc:spr:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00469-9
    DOI: 10.1007/s10287-023-00469-9
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    References listed on IDEAS

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    1. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    2. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    3. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    4. Ibanez, Eduardo & Magee, Timothy & Clement, Mitch & Brinkman, Gregory & Milligan, Michael & Zagona, Edith, 2014. "Enhancing hydropower modeling in variable generation integration studies," Energy, Elsevier, vol. 74(C), pages 518-528.
    5. Martin N. Hjelmeland & Arild Helseth & Magnus Korpås, 2019. "Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty," Energies, MDPI, vol. 12(1), pages 1-15, January.
    6. Kenjiro Yagi & Ramteen Sioshansi, 2023. "Simplifying capacity planning for electricity systems with hydroelectric and renewable generation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    7. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    8. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
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