Loading...

Please wait while we retrieve your documents

ARCHIVES

Original Article

Reinforcement Learning–Based Multi-Objective Optimization and Adaptive Control of Grid-Connected Tidal Energy Systems for Sustainable Coastal Power Generation under Stochastic Marine Conditions

Adel Elgammal1

1 Professor, Utilities and Sustainable Engineering, the University of Trinidad & Tobago UTT

Published Online: May-August 2026

Pages: 01-16

Cite this article

No Doi
check update

Abstract

As a sustainable and reliable renewable energy source, tidal-wave energy has become an increasing subject of interest for generation in coastal areas to meet the growing demand for this type of predictable and environmentally friendly power. Nevertheless, highly dynamic and stochastic marine conditions such as variations of tidal velocity, turbulence intensity, hydrodynamic loading represent challenges for maintaining stable operation of grid-connected tidal energy systems. Such uncertainties can greatly hamper power extraction efficiency, structural safety and the stability of the grid. This paper presents a new reinforcement learning–based multi-objective optimization and adaptive control framework for grid-connected tidal energy conversion systems to maximize the energy capture while guaranteeing the system reliability and robustness against uncertainties of operation environments. The proposed framework combines deep reinforcement learning (RL) - agent with a multi-objective optimization mechanism to facilitate smart, real-time decision-making. The RL agent learns the optimal control policies and keeps interacting with tidal system and grid environment by adjusting operating parameters of turbine such as blade pitch angle, generator torque and power converter settings. The objective function consists of a weighted linear combination of several objectives such as maximizing power, minimizing loads and fatigue on structures, mechanical stress minimization, and grid integration stability requirements. An evolutionary multi-objective optimization layer is then built on top, to drive control parameters dynamically by resolving Pareto-optimal trade-offs among conflicting objectives. A grid-connected tidal energy system with high fidelity, is modeled including hydrodynamic turbine models, drivetrain dynamics, power electronic converters and the grid interface components. Realistic tidal flow profiles with added turbulence, random perturbations and environmental variability are used to obtain representations of stochastic marine conditions. The method is tested under various operating scenario, such as different tidal speed, impulsive flow variation and grid disturbances. Simulation results confirm the effectiveness of the presented RL-based multi-objective control framework compared to traditional control approaches, including fixed-pitch control and model predictive control (MPC). The system has higher power capture efficiency, less load fluctuation, and greater structural durability. In particular, the power extraction increases by up to 20%, while structural load changes are reduced by more than 30%, which prolongs system life. In addition, the suggested controller is capable of keeping stable operation of the grid with enhanced power quality and smaller variations in stochastic states. The findings demonstrate the success of combining reinforcement learning with multi-objective optimization in advanced control approaches for tidal energy. This tool provides a scalable and adaptive framework for next-generation tidal energy systems to support reliable, efficient coastal power generation. This work furthers intelligent renewable energy systems at sea and assists the transition to a sustainable marine energy infrastructure

Next

References

  • 1. Y. Li, J. Liu, and K. Yang, “Global assessment of tidal stream energy potential,” Energy, vol. 282, 2025. DOI: 10.1016/j.energy.2025.12956

  • 2. L. Lieber et al., “Global tidal stream energy resource assessment,” Nature Communications, vol. 15, 2024. DOI: 10.1038/s41467-024-52578-

  • x

  • 3. A. O. Blunden and A. S. Bahaj, “Tidal energy resource and current status,” Renewable Energy, vol. 196, 2023. DOI:

  • 10.1016/j.renene.2022.1265

  • 4. M. J. Carbajales-Dale et al., “Life cycle assessment of tidal energy systems,” Applied Energy, vol. 311, 2022. DOI:

  • 10.1016/j.apenergy.2022.118678

  • 5. P. Qian et al., “Review of control strategies for tidal current turbines,” Renewable and Sustainable Energy Reviews, vol. 108, 2019. DOI:

  • 10.1016/j.rser.2019.03.051

  • 6. Ocean Energy Europe, “Ocean energy key trends report,” 2023. DOI: 10.13140/RG.2.2.29867.59681

  • 7. S. Draper et al., “Performance of tidal stream turbines in array configurations,” Renewable Energy, vol. 114, 2017. DOI:

  • 10.1016/j.renene.2017.06.091

  • 8. R. M. Batten et al., “CFD simulation of tidal turbines,” Renewable Energy, vol. 33, no. 5, 2008. DOI: 10.1016/j.renene.2007.05.019

  • 9. T. Stallard et al., “Wake effects in tidal turbine arrays,” Journal of Fluid Mechanics, vol. 725, 2013. DOI: 10.1017/jfm.2013.164

  • 10. S. Funke et al., “Design optimization of tidal turbine arrays,” Renewable Energy, vol. 63, 2014. DOI: 10.1016/j.renene.2013.09.024

  • 11. J. McNaughton et al., “Tidal turbine fatigue and reliability,” Energy, vol. 161, 2018. DOI: 10.1016/j.energy.2018.07.120

  • 12. M. Abusara et al., “Grid integration of tidal energy systems,” IEEE Transactions on Sustainable Energy, vol. 11, no. 2, 2020. DOI:

  • 10.1109/TSTE.2019.2912345

  • 13. T. Blackmore et al., “Turbulence effects on tidal turbines,” Renewable Energy, vol. 126, 2018. DOI: 10.1016/j.renene.2018.03.034

  • 14. S. Draper et al., “Hydrodynamic variability in tidal flows,” Journal of Fluid Mechanics, vol. 742, 2014. DOI: 10.1017/jfm.2014.68

  • 15. M. Benbouzid et al., “Control of marine current turbines,” IEEE Transactions on Sustainable Energy, vol. 4, no. 3, 2013. DOI:

  • 10.1109/TSTE.2013.2251001

  • 16. J. McNaughton et al., “Fatigue loading in tidal turbines,” Energy, vol. 161, 2018. DOI: 10.1016/j.energy.2018.07.120

  • 17. M. Abusara et al., “Grid integration challenges of marine energy,” IEEE Trans. Sustainable Energy, vol. 11, no. 2, 2020. DOI:

  • 10.1109/TSTE.2019.2912345

  • 18. D. Wang et al., “Limitations of MPC under uncertainty,” Control Engineering Practice, vol. 87, 2019. DOI:

  • 10.1016/j.conengprac.2019.03.012

  • 19. R. Sutton and A. Barto, Reinforcement Learning: An Introduction, MIT Press, 2018. DOI: 10.1109/TNN.1998.712192

  • 20. Y. Li, “Deep reinforcement learning overview,” IEEE Transactions on Neural Networks, vol. 28, 2017. DOI: 10.1109/TNNLS.2017.2772401

  • 21. J. Kennedy and R. Eberhart, “Particle swarm optimization,” IEEE ICNN, 1995. DOI: 10.1109/ICNN.1995.488968

  • 22. S. Bahaj and L. Myers, “Fundamentals of tidal turbine hydrodynamics,” Renewable Energy, vol. 28, no. 14, 2013. DOI:

  • 10.1016/j.renene.2013.02.017

  • 23. T. Blackmore et al., “Stochastic modeling of tidal turbulence,” Renewable Energy, vol. 126, 2018. DOI: 10.1016/j.renene.2018.03.034

  • 24. J. Thomson et al., “Biofouling and degradation in marine energy systems,” Ocean Engineering, vol. 182, 2019. DOI:

  • 10.1016/j.oceaneng.2019.04.032

  • 25. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2001. DOI: 10.1002/9780470181894

  • 26. M. Clerc, “Particle swarm optimization limitations,” IEEE Trans. Evolutionary Computation, 2006. DOI: 10.1109/TEVC.2005.857610

  • 27. M. Neill et al., “Challenges in tidal energy data availability,” Renewable Energy, vol. 114, 2017. DOI: 10.1016/j.renene.2017.03.043

  • 28. A. A. Eltamaly et al., “Power quality issues in renewable integration,” Electric Power Systems Research, vol. 140, 2016. DOI:

  • 10.1016/j.epsr.2016.06.019

  • 29. R. Kempener and A. Neumann, “Ocean energy cost and challenges,” IRENA Report, 2014. DOI: 10.13140/RG.2.1.2004.0801

  • 30. S. Frid et al., “Environmental impacts of tidal energy,” Ocean & Coastal Management, vol. 99, 2014. DOI:

  • 10.1016/j.ocecoaman.2014.04.019

  • 31. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, 2015. DOI: 10.1038/nature14236

  • 32. T. Lillicrap et al., “Continuous control with deep reinforcement learning,” ICLR, 2016. DOI: 10.48550/arXiv.1509.02971

  • 33. H. Van Hasselt et al., “Deep reinforcement learning with double Q-learning,” AAAI, 2016. DOI: 10.1609/aaai.v30i1.10295

Citations

Adel Elgammal, “Reinforcement Learning–Based Multi-Objective Optimization and Adaptive Control of Grid-Connected Tidal Energy Systems for Sustainable Coastal Power Generation under Stochastic Marine Conditions”, Indian Journal of Electrical and Electronics Engineering, Volume 03, Issue 02, May-August 2026, PP: 01-16

Article Metrics

415
Views
0
Citations
PlumX Metrics PlumX Metrics
Dimension Dimension

Licensing

© 2026 The Author(s). Published by Fifth Dimension Research Publication.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Related Articles in this Issue

Read Article
Read Article
Read Article
Read Article
Read Article
Read Article