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Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

Author

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  • Martin N. Hjelmeland

    (Department of Electric Power Engineering, NTNU Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Arild Helseth

    (SINTEF Energy Research, 7034 Trondheim, Norway)

  • Magnus Korpås

    (Department of Electric Power Engineering, NTNU Norwegian University of Science and Technology, 7491 Trondheim, Norway)

Abstract

We propose a model for medium-term hydropower scheduling (MTHS) with variable head and uncertainty in inflow, reserve capacity, and energy price. With an increase of intermittent energy sources in the generation mix, it is expected that a flexible hydropower producer can obtain added profits by participating in markets other than just the energy market. To capture this added potential, the hydropower system should be modeled with a higher level of detail. In this context, we apply an algorithm based on stochastic dual dynamic programming (SDDP) to solve the nonconvex MTHS problem and show that the use of strengthened Benders (SB) cuts to represent the expected future profit (EFP) function provides accurate scheduling results for slightly nonconvex problems. A method to visualize the EFP function in a dynamic programming setting is provided, serving as a useful tool for a priori inspection of the EFP shape and its nonconvexity.

Suggested Citation

  • Martin N. Hjelmeland & Arild Helseth & Magnus Korpås, 2019. "Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty," Energies, MDPI, vol. 12(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:189-:d:195780
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    References listed on IDEAS

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    3. Wolfgang, Ove & Haugstad, Arne & Mo, Birger & Gjelsvik, Anders & Wangensteen, Ivar & Doorman, Gerard, 2009. "Hydro reservoir handling in Norway before and after deregulation," Energy, Elsevier, vol. 34(10), pages 1642-1651.
    4. Arild Helseth & Hallvard Braaten, 2015. "Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling," Energies, MDPI, vol. 8(12), pages 1-11, December.
    5. Cerisola, Santiago & Latorre, Jesus M. & Ramos, Andres, 2012. "Stochastic dual dynamic programming applied to nonconvex hydrothermal models," European Journal of Operational Research, Elsevier, vol. 218(3), pages 687-697.
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    Cited by:

    1. Krešimir Fekete & Srete Nikolovski & Zvonimir Klaić & Ana Androjić, 2019. "Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming," Energies, MDPI, vol. 12(9), pages 1-25, April.
    2. 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.
    3. David Lucas dos Santos Abreu & Erlon Cristian Finardi, 2022. "Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems," Energies, MDPI, vol. 15(5), pages 1-23, February.
    4. Liao, Shengli & Liu, Huan & Liu, Zhanwei & Liu, Benxi & Li, Gang & Li, Shushan, 2021. "Medium-term peak shaving operation of cascade hydropower plants considering water delay time," Renewable Energy, Elsevier, vol. 179(C), pages 406-417.

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