“Multistage Model Predictive Control with Simplified Scenario Ensembles for Robust Control of Hydropower Station”

Authors: Changhun Jeong, Beathe Furenes and Roshan Sharma,
Affiliation: University of South-Eastern Norway and Skagerak Kraft AS
Reference: 2023, Vol 44, No 2, pp. 43-54.

Keywords: Multistage model predictive control, Uncertainty, Simplified method, Renewable energy

Abstract: This paper proposes simplification of the scenario ensembles that describe the uncertainty present in a hydropower plant. The simplified scenario tree is further used with a multistage model predictive control for optimal operation of the hydropower station. The proposed method reduces the number of considered scenario ensembles of water inflow forecast into the reservoir in the Dalsfoss hydropower plant, which leads to less computational demand of the multistage MPC. The method takes two steps: the creation of three synthesis scenario ensembles and the estimation of the probability of occurrence of the three synthesis scenario ensembles. The simulation results of multistage MPC with 4 different types of scenario ensembles demonstrate that the proposed simplified method reduces the computation demand of the multistage MPC by 15 times approximately, without degrading its performance.

PDF PDF (1099 Kb)        DOI: 10.4173/mic.2023.2.1

DOI forward links to this article:
[1] Changhun Jeong, Beathe Furenes and Roshan Sharma (2023), doi:10.1016/j.compchemeng.2023.108409
[2] Changhun Jeong and Roshan Sharma (2023), doi:10.1109/CCTA54093.2023.10252685
[3] Changhun Jeong, Ole Magnus Brastein, Nils-Olav Skeie and Roshan Sharma (2023), doi:10.1109/ACCESS.2023.3324691
[1] Andersson, J. A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (2019). CasADi -- A software framework for nonlinear optimization and optimal control, Mathematical Programming Computation. 11(1):1--36. doi:10.1007/s12532-018-0139-4
[2] Birge, J.R. (1997). State-of-the-art-survey--stochastic programming: Computation and applications, INFORMS Journal on Computing. 9(2):111--133. doi:10.1287/ijoc.9.2.111
[3] Campo, P.J. and Morari, M. (1987). Robust model predictive control, In 1987 American Control Conference. pages 1021--1026. doi:10.23919/ACC.1987.4789462
[4] IEA. (2021). Hydropower special market report—analysis and forecast to 2030, https://www.iea.org/reports/hydropower-special-market-report, License: CC BY 4.0.
[5] Jeong, C., Furenes, B., and Sharma, R. (2021). MPC operation with improved optimal control problem at dalsfoss power plant, Proceedings of SIMS EUROSIM conference 2021. 11(1):226--233. doi:10.3384/ecp21185226
[6] Jeong, C. and Sharma, R. (2022). Stochastic mpc for optimal operation of hydropower station under uncertainty, IFAC PapersOnLine, 2022. 55(7):155--160. doi:10.1016/j.ifacol.2022.07.437
[7] Jeong, C. and Sharma, R. (2022). Tuning model predictive control for rigorous operation of the dalsfoss hydropower plant, Energies (Basel), 2022. 15(22):8678. doi:10.3390/en15228678
[8] Klintberg, E., Dahl, J., Fredriksson, J., and Gros, S. (2016). An improved dual newton strategy for scenario-tree mpc, 2016 IEEE 55th Conference on Decision and Control (CDC). pages 3675--3681. doi:10.1109/CDC.2016.7798822
[9] Lucia, S., Finkler, T., and Engell, S. (2013). Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty, Journal of Process Control. 23(9):1306--1319. doi:10.1016/j.jprocont.2013.08.008
[10] Maiworm, M., Bäthge, T., and Findeisen, R. (2015). Scenario-based model predictive control: Recursive feasibility and stability, In IFAC-PapersOnLine, volume48. pages 50--56. doi:10.1016/j.ifacol.2015.08.156
[11] Mayne, D., Rawlings, J., Rao, C., and Scokaert, P. (2000). Constrained model predictive control: Stability and optimality, Automatica (Oxford). 36(6):789--814. doi:10.1016/S0005-1098(99)00214-9
[12] Menchacatorre, I., Sharma, R., Furenes, B., and Lie, B. (2019). Flood management of lake toke: Mpc operation under uncertainty, 2019. doi:10.3384/ecp20179
[13] Mesbah, A. (2016). Stochastic model predictive control: An overview and perspectives for future research, IEEE control systems. 36(6):30--44. doi:10.1109/MCS.2016.2602087
[14] Morari, M. and Lee, J.H. (1999). Model predictive control : past, present and future, Computers & chemical engineering. 23(4-5):667--682. doi:10.1016/S0098-1354(98)00301-9
[15] NVE. (2021). Supervision of dams, (accessed: 24, 05.2021). https://www.nve.no/supervision-of-dams/?ref=mainmenu.
[16] Scokaert, P. and Mayne, D. (1998). Min-max feedback model predictive control for constrained linear systems, IEEE transactions on automatic control. 43(8):1136--1142. doi:10.1109/9.704989
[17] Shapiro, A., Dentcheva, D., and Ruszczyński, A. (2009). Lectures on stochastic programming: modeling and theory, MOS-SIAM Series on Optimization. Society for Industrial and Applied Mathematics.
[18] SkagerakKraft. (2021). Dalsfos, (accessed: 24, 05.2021). https://www.skagerakkraft.no/dalsfos/category1277.html, 2021.
[19] SkagerakKraft. (2021). Kragerø watercourse system, (accessed: 24, 05.2021). https://www.skagerakkraft.no/kragero-watercourse/category2391.html, 2021.
[20] Torabi Haghighi, A., Ashraf, F.B., Riml, J., Koskela, J., Kløve, B., and Marttila, H. (2019). A power market-based operation support model for sub-daily hydropower regulation practices, Applied Energy. 255:113905. doi:https://doi.org/10.1016/j.apenergy.2019.113905

  title={{Multistage Model Predictive Control with Simplified Scenario Ensembles for Robust Control of Hydropower Station}},
  author={Jeong, Changhun and Furenes, Beathe and Sharma, Roshan},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}