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

Author

Listed:
  • Bin Xu

    (Hohai University
    Nanjing Hydraulic Research Institute)

  • Ping-an Zhong

    (Hohai University
    Hohai University)

  • Yenan Wu

    (Hohai University)

  • Fangming Fu

    (Hohai University)

  • Yuting Chen

    (Hohai University)

  • Yunfa Zhao

    (China Yangtze power Co., Ltd.)

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.

Suggested Citation

  • Bin Xu & Ping-an Zhong & Yenan Wu & Fangming Fu & Yuting Chen & Yunfa Zhao, 2017. "A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4649-4667, November.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:14:d:10.1007_s11269-017-1771-x
    DOI: 10.1007/s11269-017-1771-x
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    References listed on IDEAS

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    1. Charles T. Howard & Louis J. D'Antonio, 1994. "The cost of hedging and the optimal hedge ratio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 14(2), pages 237-258, April.
    2. L. Shao & X. Qin & Y. Xu, 2011. "A Conditional Value-at-Risk Based Inexact Water Allocation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2125-2145, July.
    3. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    4. Rangel, Luiz Fernando, 2008. "Competition policy and regulation in hydro-dominated electricity markets," Energy Policy, Elsevier, vol. 36(4), pages 1292-1302, April.
    5. Rui Hui & Jay Lund & Jianshi Zhao & Tongtiegang Zhao, 2016. "Optimal Pre-storm Flood Hedging Releases for a Single Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5113-5129, November.
    6. Abdelaziz, Fouad Ben, 2012. "Solution approaches for the multiobjective stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 1-16.
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    Cited by:

    1. Changjun Wang & Shutong Chen, 2019. "Planning of Cascade Hydropower Stations with the Consideration of Long-Term Operations under Uncertainties," Complexity, Hindawi, vol. 2019, pages 1-23, November.

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