Site-dependent Solutions of Wave Energy Converter Farms with Surrogate Models, Control Co-design, and Layout Optimization
<|reference_start|>Site-dependent Solutions of Wave Energy Converter Farms with Surrogate Models, Control Co-design, and Layout Optimization: Design of wave energy converter farms entails multiple domains that are coupled, and thus, their concurrent representation and consideration in early-stage design optimization has the potential to offer new insights and promising solutions with improved performance. Concurrent optimization of physical attributes (e.g., plant) and the control system design is often known as control co-design or CCD. To further improve performance, the layout of the farm must be carefully optimized in order to ensure that constructive effects from hydrodynamic interactions are leveraged, while destructive effects are avoided. The variations in the joint probability distribution of waves, stemming from distinct site locations, affect the farm's performance and can potentially influence decisions regarding optimal plant selection, control strategies, and layout configurations. Therefore, this paper undertakes a concurrent exploration of control co-design and layout optimization for a farm comprising five devices, modeled as heaving cylinders in the frequency domain, situated across four distinct site locations: Alaskan Coasts, East Coast, Pacific Islands, and West Coast. The challenge of efficiently and accurately estimating hydrodynamic coefficients within the optimization loop was mitigated through the application of surrogate modeling and many-body expansion principles. Results indicate the optimized solutions exhibit variations in plant, control, and layout for each candidate site, signifying the importance of system-level design with environmental considerations from the early stages of the design process.<|reference_end|>
@article{azad2024site-dependent,
    title={Site-dependent Solutions of Wave Energy Converter Farms with Surrogate
  Models, Control Co-design, and Layout Optimization},
    author={Saeed Azad and Daniel R. Herber and Suraj Khanal and Gaofeng Jia},
    journal={arXiv preprint arXiv:2405.06794},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06794},
    primaryClass={eess.SY cs.SY}
}