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This thesis contains interrelated studies aimed at increasing annual energy produc-tion and enhancing the hydrodynamic interaction within hybrid wind–wave energy converter systems. The first stage investigates how the mass distribution and posi-tion of a wave absorber can be adjusted to enable passive motion control, thereby improving wave energy capture. Following this, a geometric optimization framework is developed for a semi-submersible platform, employing genetic algorithm to identify design parameters that maximize power generation by optimizing the relative motion between the platform and integrated wave absorbers. The research further emphasizes the importance of reliable wave absorber models, demonstrating how robust forecast-ing, using machine learning, methods can be applied to adapt the system for varied oceanic conditions. The study extends the optimization framework to a multi-source offshore renewable energy park that includes wind turbines, floating photovoltaics, and wave converters. A permutation-based aggregator logic, inspired by a 3–8 line decoder and optimized using a genetic algorithm, allows for partial or full curtailment of individual energy sources in discrete steps. This strategy minimizes energy losses at the point of common coupling and balances the capacity factor. Finally, the study examines the impact of the wind turbine’s aerodynamic forces on the performance of the wave absorbers, revealing that steady wind conditions enhance wave energy capture, while turbulent wind introduces variability in absorber motion, slightly re-ducing efficiency. Collectively, the findings show an integrated approach, combining analytical models, numerical simulations, and advanced optimization techniques, that can substantially improve wave energy extraction, system stability, and overall annual energy yield.

This study uses a genetic algorithm(GA) to investigate the practicality of optimizing the geometry and dimensions of a floating platform, which houses pitching wave energy converters (WEC). Using frequency- domain analysis, sensitivity tests for the search start point, choice of optimized variable, number of iterations, simulation time, and contents of the search space are made. Results show that the required number of iterations to convergence increases with an increased number of optimized variables. Furthermore, for the studied platform geometry, no single global optimum exists. Instead, various combinations of characteristic features can lead to comparable performances of the integrated wave absorber. Finally, it is observed that when the solution space is controlled and made to contain a subset of potential solutions known to improve the system performance, computation time, absorption efficiency and range are observed to improve. Additionally, the GA optimum tends towards platform geometries for which the wave absorber's resonance response corresponds to the dominating wave climate frequencies. A key contribution of this study is the controlled manipulation of the solution space to contain a subset of potential solutions that enhance system performance. This controlled approach leads to improvements in computation time, absorption efficiency, and range of the system.

This paper proposes and analyzes a genetic algorithm based permutation control logic applied to the aggregator of an offshore multi-source park. The energy losses at the common coupling point are accounted for in the feedback. This paper focuses on offshore distributed energy resources, such as floating photovoltaic (PV), wind, and wave power. The main contributions of this research are the development of a control system that is capable of tracking the set-point imposed by the demand curve for each source individually, the introduction of a capacity factor for combined offshore floating PV/wind/wave power farms, and the unveiling of pure offshore renewable sources as potential storage-less flexibility service providers. The results of a case study for a site near San Francisco showed that energy losses and capacity factors are positively influenced by implementing the proposed approach.

The present paper considers pitching wave energy converters (WECs) integrated in a floating platform, e.g., floating foundation for a wind turbine. Each WEC consists of a partially submerged wave absorber that rotates about the hinge located above the still water level under the influence of waves. Each wave absorber contains separated ballast tanks that are used to ensure the desired initial tilting angle of the absorber with respect to the floating foundation (called the rest angle). The same rest angle can be achieved by filling different ballast compartments that impacts the inertia moment about the hinge, response amplitude operator (RAO), resonance frequency of the absorber, and the power absorption performance. The exhaustive search for a suitable ballast configuration can quickly become a computationally expensive task depending on the number of ballast tanks. In this paper, the ballast optimization algorithm based on an analytical model is developed. The algorithm is applied to investigate the impact of the ballasts on the rest angle, RAO and resonance frequency of the wave absorber. It provides a base for ballast design and location for improved power absorption performance. The proposed algorithm can be adapted to the ballast optimization of other pitching WECs.

Accurately predicting wave energy absorption is essential for advancing wave energy converter technologies. This study investigates the application of machine learning (ML) to model and predict the coupled and complex dynamics of the platform of a floating wind turbine and a co-located pitching wave energy absorber. Two approaches are compared in this study: the multi-step and the hybrid machine learning models. The multi-step model is designed to predict time-series energy absorption through sequential forecasting. In contrast, the hybrid model integrates physics-based knowledge and multi-fidelity data to enhance prediction accuracy. Using high- and low-fidelity numerical wave models, we analyze the models’ performance in capturing complex nonlinear dynamics of wave-structure and structure-structure interactions. The results highlight the strengths and limitations of each approach, offering insights into their predictive capabilities, computational efficiency, and adaptability for real-world wave energy systems. This comparative analysis aims to inform the development of robust prediction frameworks for optimizing energy absorption in wave energy converters. The findings demonstrate that ML can accurately predict the impact of environmental conditions on hydrodynamic characteristics. The proposed ML-based frameworks offer a computationally efficient alternative to conventional simulation methods, facilitating rapid iteration during system design and optimization. This knowledge can inform the development of adaptive control strategies, improving the performance, survivability, and effectiveness of integrated wave energy systems.

Offshore renewable energy systems, particularly floating offshore wind turbines, are gaining traction as viable solutions for deep-water energy generation. The integration of wave energy converters with floating wind platforms offers potential benefits in infrastructure sharing, cost reduction, and energy efficiency. This study presents a fully coupled time-domain model to investigate the aero-hydro-elastic interactions in a hybrid wind-wave energy system. Utilizing the Fast2Aqwa framework, the study captures the complex coupling between rotor aerodynamics, hydrodynamics, and wave energy conversion. Key results indicate that steady wind conditions can enhance wave energy capture, while turbulent wind introduces variability in absorber motion. Response amplitude operators and power spectral density analyses reveal the frequency-dependent behavior of the platform and wave energy converters under different wind conditions. The findings contribute to optimizing hybrid offshore wind-wave systems by improving control co-design strategies and system performance assessments.