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Observational data-based quality assessment of scenario generation for stochastic programs

Author

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  • Didem Sarı Ay

    (Alanya Alaaddin Keykubat University)

  • Sarah M. Ryan

    (Iowa State University)

Abstract

In minimization problems with uncertain parameters, cost savings can be achieved by solving stochastic programming (SP) formulations instead of using expected parameter values in a deterministic formulation. To obtain such savings, it is crucial to employ scenarios of high quality. An appealing way to assess the quality of scenarios produced by a given method is to conduct a re-enactment of historical instances in which the scenarios produced are used when solving the SP problem and the costs are assessed under the observed values of the uncertain parameters. Such studies are computationally very demanding. We propose two approaches for assessment of scenario generation methods using past instances that do not require solving SP instances. Instead of comparing scenarios to observations directly, these approaches consider the impact of each scenario in the SP problem. The methods are tested in simulation studies of server location and unit commitment, and then demonstrated in a case study of unit commitment with uncertain variable renewable energy generation.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:3:d:10.1007_s10287-019-00349-1
    DOI: 10.1007/s10287-019-00349-1
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    References listed on IDEAS

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    1. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
    2. PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Ignacio Rios & Roger Wets & David Woodruff, 2015. "Multi-period forecasting and scenario generation with limited data," Computational Management Science, Springer, vol. 12(2), pages 267-295, April.
    4. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    5. 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.
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    Cited by:

    1. Rachunok, Benjamin & Staid, Andrea & Watson, Jean-Paul & Woodruff, David L., 2020. "Assessment of wind power scenario creation methods for stochastic power systems operations," Applied Energy, Elsevier, vol. 268(C).

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