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The impact of model predictive control structures and constraints on a wave energy converter with hydraulic power take off system

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  • Hall, Carrie
  • Sheng, Wanan
  • Wu, Yueqi
  • Aggidis, George

Abstract

Ocean waves present a promising renewable energy source, but are challenging to harness given their irregular nature. In order to maximize energy capture on wave energy converters (WECs), power take off (PTO) systems are typically used to effectively adjust the device’s resonant frequency. Optimal control techniques can oversee the PTO operation to maximize overall power output, but optimization in real-time poses difficulties given the wave variability and underlying constraints of the system. This study compares two different model predictive control approaches. One method uses only a model of the hydrodynamics of the WEC while the second has a state space model that includes the WEC hydrodynamics as the dynamics of a hydraulic PTO system. The impact of the PTO constraints, control structure and control prediction horizon on the wave energy converter control performance was explored and quantified for irregular wave conditions. Results show that utilizing a model that includes both the hydrodynamics and PTO dynamics can increase power output by 23% compared to an approach that uses the hydrodynamics only.

Suggested Citation

  • Hall, Carrie & Sheng, Wanan & Wu, Yueqi & Aggidis, George, 2024. "The impact of model predictive control structures and constraints on a wave energy converter with hydraulic power take off system," Renewable Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124002374
    DOI: 10.1016/j.renene.2024.120172
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    References listed on IDEAS

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    1. Tunde Aderinto & Hua Li, 2018. "Ocean Wave Energy Converters: Status and Challenges," Energies, MDPI, vol. 11(5), pages 1-26, May.
    2. Li, L. & Gao, Y. & Ning, D.Z. & Yuan, Z.M., 2021. "Development of a constraint non-causal wave energy control algorithm based on artificial intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Aleix Maria-Arenas & Aitor J. Garrido & Eugen Rusu & Izaskun Garrido, 2019. "Control Strategies Applied to Wave Energy Converters: State of the Art," Energies, MDPI, vol. 12(16), pages 1-19, August.
    4. Li, Guang & Belmont, Michael R., 2014. "Model predictive control of sea wave energy converters – Part I: A convex approach for the case of a single device," Renewable Energy, Elsevier, vol. 69(C), pages 453-463.
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    Cited by:

    1. Tawfiq Chekifi & Amine Benmoussa & Moustafa Boukraa, 2024. "Desalination Powered by Renewables: A Challenge and an AI Opportunity," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(14), pages 5419-5461, November.

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