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Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform

Author

Listed:
  • Carlotta Tubeuf

    (Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria)

  • Felix Birkelbach

    (Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria)

  • Anton Maly

    (Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria)

  • René Hofmann

    (Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria)

Abstract

The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.

Suggested Citation

  • Carlotta Tubeuf & Felix Birkelbach & Anton Maly & René Hofmann, 2023. "Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform," Energies, MDPI, vol. 16(4), pages 1-10, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1796-:d:1065335
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    References listed on IDEAS

    as
    1. Wei Xu & Xiaoli Zhang & Anbang Peng & Yue Liang, 2020. "Deep Reinforcement Learning for Cascaded Hydropower Reservoirs Considering Inflow Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3003-3018, July.
    2. Kougias, Ioannis & Aggidis, George & Avellan, François & Deniz, Sabri & Lundin, Urban & Moro, Alberto & Muntean, Sebastian & Novara, Daniele & Pérez-Díaz, Juan Ignacio & Quaranta, Emanuele & Schild, P, 2019. "Analysis of emerging technologies in the hydropower sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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