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Advances and applications of chance-constrained approaches to systems optimisation under uncertainty

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  • Abebe Geletu
  • Michael Klöppel
  • Hui Zhang
  • Pu Li

Abstract

A chance-constrained optimisation (CCOPT) model has a dual goal: guaranteeing performance as well as system reliability under uncertainty. The beginning of CCOPT methods dates back in the 1950s. Recently, CCOPT approaches are gaining momentum as modern engineering and finance applications are forced to consider reliability and risk metrics at the design and planning stages. Although theoretical development and practical applications have been made, many open problems remain to be addressed in this area. This article attempts to provide a brief survey of major application areas, structure properties, challenges and solution approaches to CCOPT. In particular, we present our research results achieved in the past few years.

Suggested Citation

  • Abebe Geletu & Michael Klöppel & Hui Zhang & Pu Li, 2013. "Advances and applications of chance-constrained approaches to systems optimisation under uncertainty," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1209-1232.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:7:p:1209-1232
    DOI: 10.1080/00207721.2012.670310
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    Cited by:

    1. Maximilien Germain & Huyên Pham & Xavier Warin, 2022. "A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection," Post-Print hal-03498263, HAL.
    2. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    3. Miguel A. Lejeune & François Margot, 2016. "Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities," Operations Research, INFORMS, vol. 64(4), pages 939-957, August.
    4. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    5. Maximilien Germain & Huy^en Pham & Xavier Warin, 2021. "A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection," Papers 2112.11059, arXiv.org, revised Nov 2022.
    6. Maximilien Germain & Huyên Pham & Xavier Warin, 2021. "A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection," Working Papers hal-03498263, HAL.
    7. Patrizia Beraldi & Antonio Violi & Maria Elena Bruni & Gianluca Carrozzino, 2017. "A Probabilistically Constrained Approach for the Energy Procurement Problem," Energies, MDPI, vol. 10(12), pages 1-17, December.
    8. Liu, Benxi & Cheng, Chuntian & Wang, Sen & Liao, Shengli & Chau, Kwok-Wing & Wu, Xinyu & Li, Weidong, 2018. "Parallel chance-constrained dynamic programming for cascade hydropower system operation," Energy, Elsevier, vol. 165(PA), pages 752-767.
    9. Balata, Alessandro & Ludkovski, Michael & Maheshwari, Aditya & Palczewski, Jan, 2021. "Statistical learning for probability-constrained stochastic optimal control," European Journal of Operational Research, Elsevier, vol. 290(2), pages 640-656.
    10. Fontem, Belleh & Smith, Jeremiah, 2019. "Analysis of a chance-constrained new product risk model with multiple customer classes," European Journal of Operational Research, Elsevier, vol. 272(3), pages 999-1016.
    11. Alessandro Balata & Michael Ludkovski & Aditya Maheshwari & Jan Palczewski, 2019. "Statistical Learning for Probability-Constrained Stochastic Optimal Control," Papers 1905.00107, arXiv.org, revised Aug 2020.
    12. Peng, Shen & Maggioni, Francesca & Lisser, Abdel, 2022. "Bounds for probabilistic programming with application to a blend planning problem," European Journal of Operational Research, Elsevier, vol. 297(3), pages 964-976.
    13. Roland Braune & Walter J. Gutjahr & Petra Vogl, 2022. "Stochastic radiotherapy appointment scheduling," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1239-1277, December.

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