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CVaR-based factorial stochastic optimization of water resources systems with correlated uncertainties

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Abstract

In this paper, a conditional value-at-risk based factorial stochastic programming approach is proposed to address random uncertainties and their interactions in a systematic manner. Random variables can be addressed through a risk-averse method within the two-stage stochastic programming framework. Interactions between random variables are examined through conducting a multi-level factorial analysis. The proposed approach is applied to a case study of water resources management to demonstrate its validity and applicability. A number of decision alternatives are obtained under different risk coefficients, which are useful for decision-makers to make sound water management plan and to perform an in-depth analysis of trade-offs between economic objectives and associated risks. Results obtained from the factorial experiment uncover the multi-level interactions between uncertain parameters and their contributions to the variability of net benefits. The performance of the proposed approach is compared with a factorial two-stage stochastic programming method.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (51225904, 51190095 and 51109077), the 111 Project (B14008), and the Fundamental Research Funds for the Central Universities (2014XS69). The authors would like to express thanks to the editor and the anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. MOE Key Laboratory of Regional Energy Systems Optimization, S&C Resources and Environmental Research Academy, North China Electric Power University, Beijing, 102206, China

    Y. Y. Wang

  2. Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada

    G. H. Huang & S. Wang

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  1. Y. Y. Wang
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  2. G. H. Huang
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  3. S. Wang
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Correspondence to G. H. Huang.

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Wang, Y.Y., Huang, G.H. & Wang, S. CVaR-based factorial stochastic optimization of water resources systems with correlated uncertainties. Stoch Environ Res Risk Assess 31, 1543–1553 (2017). https://doi.org/10.1007/s00477-016-1276-9

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