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A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

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Abstract

This study develops a multiobjective stochastic programming model for informing hedging decisions for hydropower operations under an electricity market environment considering the benefit from selling energy production and the cost of penalizing energy shortfall. Aiming to determine the optimal strategy that hedges the risk of energy shortfall while keeping a high level of direct revenue from energy production under uncertain streamflows and inexact penalizing price conditions, competing objectives of minimizing energy shortfall percentage and maximizing direct revenue from energy production are analyzed. The conflict is resolved by determining the optimal level of energy shortfall percentage such that the net benefit of the hydropower system is maximized. The first-order optimality condition of maximized system net revenue is derived, which states that the marginal benefit of hedging equals the marginal cost of hedging at optimality. The tradeoff ratio between the competing objectives serves as the marginal cost of hedging and the penalizing price of energy shortfall represents the marginal benefit of hedging. Using the optimality condition, sensitivity tests are conducted for investigating the influence of different ranges of penalizing prices and reservoir initial storages on hedging decisions. The proposed method is evaluated on the operations of the Three Gorges cascade hydropower system during the drawdown season. Results show that: (1) minimizing the energy shortfall percentage adversely affects the maximization in system direct revenue from energy production, and the conflicting results are related to the depletion strategies of reservoir storage; (2) to reduce the energy shortfall percentage to the lowest level could result in significant reduction in total energy production and the direct revenue, especially when reservoir initial storages are low; and (3) the optimal level of energy shortfall percentage would decrease as penalizing price increases, when the influence of penalizing cost from energy shortfall gradually dominates the influence of energy production on the net revenue. The model framework and the implications could be applied to rationalize hedging decisions for hydropower operations under inexact information upon streamflow and penalizing prices.

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Acknowledgements

We would like to thank two anonymous reviewers for their in-depth reviews and constructive suggestions. The remarks and summary of reviewer comments provided by the Editor and Associate Editor are also greatly appreciated. These have facilitated major improvements in this paper. The authors are grateful to Dr. Scott E. Boyce, Dr. Xiaoliang Deng and Dr. Qingwen Lu for the help on revising the manuscript.

This study is supported by the National Key Technologies R&D Program of China (Grant No. 2016YFC0400909), the Fundamental Research Funds for the Central Universities (Grant No. 2015B28414), the National Natural Science Foundation of China (Grant No. 51609062 and Grant No. 51579068), and the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China (Grant No. 201501007).

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Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, NO.1, Xikang Road, Nanjing, 210098, China

    Bin Xu, Ping-an Zhong, Yenan Wu, Fangming Fu & Yuting Chen

  2. Nanjing Hydraulic Research Institute, No.223, Guangzhou Road, Nanjing, 210029, China

    Bin Xu

  3. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, NO.1, Xikang Road, Nanjing, 210098, China

    Ping-an Zhong

  4. China Yangtze power Co., Ltd., Beijing, 100032, China

    Yunfa Zhao

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  1. Bin Xu
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  2. Ping-an Zhong
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  4. Fangming Fu
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  5. Yuting Chen
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Correspondence to Bin Xu.

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Xu, B., Zhong, Pa., Wu, Y. et al. A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information. Water Resour Manage 31, 4649–4667 (2017). https://doi.org/10.1007/s11269-017-1771-x

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  • DOI: https://doi.org/10.1007/s11269-017-1771-x

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