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Model predictive control of sea wave energy converters – Part II: The case of an array of devices

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  • Li, Guang
  • Belmont, Mike R.

Abstract

This paper addresses model predictive control (MPC) of highly-coupled clusters of sea wave energy converters (WECs). Since each WEC is not only a wave absorber but also a wave generator, the motion of each WEC can be affected by the waves generated by its adjacent WECs when they are close to each other. A distributed MPC strategy is developed to maximize the energy output of the whole array and guarantee the safe operation of all the WECs with a reasonable computational load. The system for an array is partitioned into subsystems and each subsystem is controlled by a local MPC controller. The local MPC controllers run cooperatively by transmitting information to each other. Within one sampling period, each MPC controller performs optimizations iteratively so that a global optimization for the whole array can be approximated. The computational burden for the whole array is also distributed to the local controllers. A numerical simulation demonstrates the efficacy of the proposed control strategy. For the WECs operating under constraints explored, it is found that the optimized power output is an increasing function of degree of WEC–WEC coupling. Increases in power of up to 20% were achieved using realistic ranges of parameters with respect to the uncoupled case.

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  • Li, Guang & Belmont, Mike R., 2014. "Model predictive control of sea wave energy converters – Part II: The case of an array of devices," Renewable Energy, Elsevier, vol. 68(C), pages 540-549.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:540-549
    DOI: 10.1016/j.renene.2014.02.028
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    References listed on IDEAS

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    1. Li, Guang & Weiss, George & Mueller, Markus & Townley, Stuart & Belmont, Mike R., 2012. "Wave energy converter control by wave prediction and dynamic programming," Renewable Energy, Elsevier, vol. 48(C), pages 392-403.
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    Cited by:

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    2. Ni, Wenchi & Zhang, Xu & Zhang, Wei & Liang, Shuangling, 2021. "Numerical investigation of adaptive damping control for raft-type wave energy converters," Renewable Energy, Elsevier, vol. 175(C), pages 520-531.
    3. Shahroozi, Zahra & Göteman, Malin & Engström, Jens, 2024. "Neural network survivability approach of a wave energy converter considering uncertainties in the prediction of future knowledge," Renewable Energy, Elsevier, vol. 228(C).
    4. Zou, Shangyan & Abdelkhalik, Ossama, 2020. "Collective control in arrays of wave energy converters," Renewable Energy, Elsevier, vol. 156(C), pages 361-369.
    5. Zou, Shangyan & Abdelkhalik, Ossama, 2020. "Time-varying linear quadratic Gaussian optimal control for three-degree-of-freedom wave energy converters," Renewable Energy, Elsevier, vol. 149(C), pages 217-225.
    6. Li, Liang & Gao, Yan, 2023. "Development of a real-time wave energy control with consideration of control latency," Energy, Elsevier, vol. 277(C).
    7. Faÿ, François-Xavier & Henriques, João C. & Kelly, James & Mueller, Markus & Abusara, Moahammad & Sheng, Wanan & Marcos, Marga, 2020. "Comparative assessment of control strategies for the biradial turbine in the Mutriku OWC plant," Renewable Energy, Elsevier, vol. 146(C), pages 2766-2784.
    8. Ozkop, Emre & Altas, Ismail H., 2017. "Control, power and electrical components in wave energy conversion systems: A review of the technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 106-115.
    9. Del Pozo Gonzalez, Hector & Bianchi, Fernando D. & Dominguez-Garcia, Jose Luis & Gomis-Bellmunt, Oriol, 2023. "Co-located wind-wave farms: Optimal control and grid integration," Energy, Elsevier, vol. 272(C).
    10. Vakis, Antonis I. & Anagnostopoulos, John S., 2016. "Mechanical design and modeling of a single-piston pump for the novel power take-off system of a wave energy converter," Renewable Energy, Elsevier, vol. 96(PA), pages 531-547.
    11. Son, Daewoong & Yeung, Ronald W., 2017. "Optimizing ocean-wave energy extraction of a dual coaxial-cylinder WEC using nonlinear model predictive control," Applied Energy, Elsevier, vol. 187(C), pages 746-757.
    12. 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).
    13. Guo, Bingyong & Patton, Ron J. & Jin, Siya & Lan, Jianglin, 2018. "Numerical and experimental studies of excitation force approximation for wave energy conversion," Renewable Energy, Elsevier, vol. 125(C), pages 877-889.

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