IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1796-d1065335.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1796/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1796/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ama Ranawaka & Damminda Alahakoon & Yuan Sun & Kushan Hewapathirana, 2024. "Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review," Energies, MDPI, vol. 17(21), pages 1-52, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    2. Nathan Companez & Aldeida Aleti, 2016. "Can Monte-Carlo Tree Search learn to sacrifice?," Journal of Heuristics, Springer, vol. 22(6), pages 783-813, December.
    3. Thomas Pirard & Vasileios Kitsikoudis & Sebastien Erpicum & Michel Pirotton & Pierre Archambeau & Benjamin Dewals, 2022. "Discharge Redistribution as a Key Process for Heuristic Optimization of Energy Production with Pumps as Turbines in a Water Distribution Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1237-1250, March.
    4. Prasasti, E.B. & Joseph, M. & Miao, X. & Zangeneh, M. & Terheiden, K., 2024. "Design of shaft- and rim-driven contra-rotating reversible pump-turbine to optimize novel low-head pumped hydro energy storages," Energy, Elsevier, vol. 306(C).
    5. Zhewei Zhang & Youngjin Yoo & Kalle Lyytinen & Aron Lindberg, 2021. "The Unknowability of Autonomous Tools and the Liminal Experience of Their Use," Information Systems Research, INFORMS, vol. 32(4), pages 1192-1213, December.
    6. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    7. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    8. Li Xia, 2020. "Risk‐Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2808-2827, December.
    9. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    10. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    11. Eva Bílková & Jiří Souček & Martin Kantor & Roman Kubíček & Petr Nowak, 2023. "Variable-Speed Propeller Turbine for Small Hydropower Applications," Energies, MDPI, vol. 16(9), pages 1-14, April.
    12. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    13. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    14. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    15. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    16. Raul-Alexandru Szakal & Alexandru Doman & Sebastian Muntean, 2021. "Influence of the Reshaped Elbow on the Unsteady Pressure Field in a Simplified Geometry of the Draft Tube," Energies, MDPI, vol. 14(5), pages 1-21, March.
    17. Zhimian Chen & Yizeng Wang & Hao Hu & Zhipeng Zhang & Chengwei Zhang & Shukun Zhou, 2024. "Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones," Mathematics, MDPI, vol. 12(23), pages 1-22, December.
    18. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    19. Paweł Tomczyk & Mirosław Wiatkowski, 2021. "The Effects of Hydropower Plants on the Physicochemical Parameters of the Bystrzyca River in Poland," Energies, MDPI, vol. 14(8), pages 1-29, April.
    20. Lang, Xiao & Nilsson, Håkan & Mao, Wengang, 2024. "Analysis of hydropower plant guide bearing vibrations by machine learning based identification of steady operations," Renewable Energy, Elsevier, vol. 236(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1796-:d:1065335. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.