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Tuning Model Predictive Control for Rigorous Operation of the Dalsfoss Hydropower Plant

Author

Listed:
  • Changhun Jeong

    (Department of Electrical Engineering, Information Technology and Cybernetics, University of South-Eastern Norway, N-3918 Porsgrunn, Norway)

  • Roshan Sharma

    (Department of Electrical Engineering, Information Technology and Cybernetics, University of South-Eastern Norway, N-3918 Porsgrunn, Norway)

Abstract

Model predictive control is considered an attractive control strategy for the operation of hydropower station systems. It is due to the operational constraints or requirements of the hydropower system for safe and eco-friendly operation. However, it is mandatory to tune the model predictive control to achieve its best and most efficient performance. This paper determines the appropriate tunning on the weight parameters and the length of the prediction horizon for implementing model predictive control on the Dalsfoss hydropower system. For that, several test sets of the weight parameter for the optimal control problem and different lengths of the prediction horizon are simulated and compared.

Suggested Citation

  • Changhun Jeong & Roshan Sharma, 2022. "Tuning Model Predictive Control for Rigorous Operation of the Dalsfoss Hydropower Plant," Energies, MDPI, vol. 15(22), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8678-:d:977435
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    References listed on IDEAS

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    1. Torabi Haghighi, Ali & Ashraf, Faisal Bin & Riml, Joakim & Koskela, Jarkko & Kløve, Bjørn & Marttila, Hannu, 2019. "A power market-based operation support model for sub-daily hydropower regulation practices," Applied Energy, Elsevier, vol. 255(C).
    2. Ahmed, T. & Muttaqi, K.M. & Agalgaonkar, A.P., 2012. "Climate change impacts on electricity demand in the State of New South Wales, Australia," Applied Energy, Elsevier, vol. 98(C), pages 376-383.
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