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Solution sensitivity-based scenario reduction for stochastic unit commitment

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  • Yonghan Feng
  • Sarah Ryan

Abstract

A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the US, FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial. Copyright Springer-Verlag Berlin Heidelberg 2016

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  • Yonghan Feng & Sarah Ryan, 2016. "Solution sensitivity-based scenario reduction for stochastic unit commitment," Computational Management Science, Springer, vol. 13(1), pages 29-62, January.
    • Handle: RePEc:spr:comgts:v:13:y:2016:i:1:p:29-62
    DOI: 10.1007/s10287-014-0220-z
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    Cited by:

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    3. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
    4. Yilin Xie & Ying Xu, 2022. "Transmission Expansion Planning Considering Wind Power and Load Uncertainties," Energies, MDPI, vol. 15(19), pages 1-18, September.
    5. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    6. Jiménez, Diego & Angulo, Alejandro & Street, Alexandre & Mancilla-David, Fernando, 2023. "A closed-loop data-driven optimization framework for the unit commitment problem: A Q-learning approach under real-time operation," Applied Energy, Elsevier, vol. 330(PB).
    7. Didem Sarı Ay & Sarah M. Ryan, 2019. "Observational data-based quality assessment of scenario generation for stochastic programs," Computational Management Science, Springer, vol. 16(3), pages 521-540, July.

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