Site-dependent Solutions of Wave Energy Converter Farms with Surrogate Models, Control Co-design, and Layout Optimization

TL;DR

The paper tackles concurrent optimization of wave energy converter (WEC) farms by coupling plant design, layout, and control with site-dependent wave climates. It deploys surrogate models built on many-body expansion (MBE) and neural networks, guided by query-by-committee, to efficiently estimate hydrodynamic coefficients and enable system-level optimization across four sites. Three case studies reveal strong coupling between plant, control, layout, and environment, with site-specific designs and substantial computational speedups over direct multi-scattering calculations. The work demonstrates the value of site-aware, cross-domain optimization for WEC farms and provides an open toolbox while outlining future work to improve control realism and hybrid optimization strategies.

Abstract

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.

Figures (9)

• Figure 1: Joint distribution of $H_s$ and $T_p$ for the selected WEC farm site locations on Alaskan Coast, Pacific Coast, West Coast, and East Coast, based on data from storlazzi2015future.
• Figure 2: Mean squared error for the hydrodynamic coefficients $B$ and $A_{11}$ averaged over all frequencies. (a) $B$ (selected from the single-WEC models), and (b) $A_{11}$ (selected from the two-WEC models).
• Figure 3: Characterization of error from surrogate modeling for $20,000$ randomly selected specifications for a 5-WEC farm.
• Figure 4: Optimized PTO control parameters for the WEC farm at candidate sites.
• Figure 5: Optimized layout for Study i@: plant and layout optimization (drawn in scale).