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This research proposes a high-fidelity based numerical tank designed to analyze the modified hydrodynamics that develops in waves-current fields, aimed at generating power matrices for wave energy converters (WEC). This tank is developed within the open source DualSPHysics Lagrangian framework using the Smoothed Particle Hydrodynamics (SPH) method, validated with physical data, and applied to simulate a point-absorber WEC. Our proposed numerical facility implements open boundary conditions, employing third-order consistent wave theory for direct generation, with flow field constrained by a Doppler correlation function. Reference data is collected from dedicated physical tests for monochromatic waves; the wave-current numerical basin demonstrates very high accuracy in terms of wave transformation and velocity field. In the second segment of this paper, a current-aware power transfer function is computed for the taut-moored point-absorber Uppsala University WEC (UUWEC). Parametrically defined regular waves with uniform currents are utilized to map an operational sea state featuring currents of different directions and intensities. In terms of power capture capabilities, the modified dynamics observed in presence of currents translates in a dependence of the WEC's power matrix not only on wave parameters, but also on current layouts. The UUWEC's power output has revealed that regardless of current directionality, annual output consistently decreases, with a registered power drop as high as 10% when an expected current field is introduced.

This paper summarises the work conducted within the 1st FOWT (Floating Offshore Wind Turbine) Comparative Study organised by the EPSRC (UK) ‘Extreme loading on FOWTs under complex environmental conditions’ and ‘Collaborative computational project on wave structure interaction (CCP-WSI)’ projects. The hydrodynamic response of a FOWT support structure is simulated with a range of numerical models based on potential theory, Morison equation, Navier-Stokes solvers and hybrid methods coupling different flow solvers. A series of load cases including the static equilibrium tests, free decay tests, operational and extreme focused wave cases are considered for the UMaine VolturnUS-S semi-submersible platform, and the results from 17 contributions are analysed and compared with each other and against the experimental data from a 1:70 scale model test performed in the COAST Laboratory Ocean Basin at the University of Plymouth. It is shown that most numerical models can predict similar results for the heave response, but significant discrepancies exist in the prediction of the surge and pitch responses as well as the mooring line loads. For the extreme focused wave case, while both Navier–Stokes and potential flow base models tend to produce larger errors in terms of the root mean squared error than the operational focused wave case, the Navier-Stokes based models generally perform better. Given the fact that variations in the solutions (sometimes large) also present in the results based the same or similar numerical models, e.g., OpenFOAM, the study highlights uncertainties in setting up a numerical model for complex wave structure interaction simulations such as those involving a FOWT and therefore the importance of proper code validation and verification studies.

In wave energy farms, accurately determining the motion of each wave energy converter is essential for performance evaluation, estimating energy production, and implementing effective control strategies. The primary challenge lies in the real sea environment, where the complex nonlinear hydrodynamic phenomena make it difficult to estimate the motion of each converter precisely. High-fidelity numerical simulations, such as computational fluid dynamics, offer a detailed representation of the wave farm's response to incoming waves. However, they are computationally intensive, making them impractical for real-time implementation and scenario evaluation. Conversely, although widely used in the industry, low-fidelity models based on linear potential flow theory lack accuracy and provide only a general solution trend. Experimental wave tank tests, while offering realistic, high-fidelity system representations, face limitations due to flexibility and costs. A multi-fidelity surrogate modeling approach presents a viable solution for designing and controlling wave energy farms. By leveraging data from various fidelities, low-fidelity numerical simulations, and highfidelity experimental measurements, we develop a model capable of predicting the actual heave motion of each converter within a farm under diverse irregular wave conditions. This model effectively corrects the low- fidelity motion to align with each converter's real heave response. Central to our model is the long-short-term memory machine learning method, which enables the prediction of the devices' temporal response to incoming irregular waves. This model delivers solutions with low computational cost, making it suitable for estimating the actual device response during the design stage of a wave energy farm, facilitating real-time monitoring.

Marine energy technologies face significant challenges in ensuring their survivability under extreme ocean conditions. Quantifying extreme load statistics on marine energy structures is essential for reliable structural design; however, this is a challenging task due to the scarcity of high-quality data and the inherent uncertainties associated with predicting rare events. While computational fluid dynamics (CFD) simulations can accurately capture the nonlinear dynamics and loads in extreme wave–structure interactions, providing high-fidelity data, extracting statistical information through these models is computationally impractical. This study proposes a reduced-order modeling framework for marine energy systems, enabling efficient analysis across diverse scenarios, and facilitating the quantification of extreme load statistics with significantly reduced computational cost. Specifically, a hybrid reduced-order or surrogate model for a wave energy converter is developed to map extreme sea states and design parameters to the resulting loads in the mooring system. The term ”hybrid” refers to the combination of Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) neural networks. The model is developed using two distinct approaches: (1) a baseline approach that relies on existing CFD data for training and validation, and (2) an active learning approach that strategically selects the most informative CFD samples from regions of the input space associated with extreme mooring loads. This procedure iteratively refines the model while minimizing prediction uncertainty, making it particularly effective for real-world applications where obtaining each sample requires substantial time and resources. The developed model demonstrates its exceptional ability to efficiently predict complex load time series, including instantaneous peaks, at speeds significantly faster than traditional modeling methods. Subsequently, the model is utilized to effectively evaluate Monte Carlo samples, providing accurate estimates of the probability of extreme mooring loads. Understanding the expected extreme loads is essential during the design phase of marine energy systems, enabling cost reduction by optimizing strength margins, refining overly conservative safety factors, and enhancing overall system reliability.

The replacement of fossil fuels by intermittent renewable energy sources is transforming energy systems world-wide. A significant part of the future electricity demand will be supplied by offshore renewable energy, especially wind, but emerging technologies such as wave and tidal energy also offer great potential. However, the ability of offshore renewable energy systems – and of power systems and the societies that dependent on them – to cope with hazards such as extreme weather and metocean events is not well known. Resilience has become an increasingly important concept in the study of energy systems, as it addresses not only vulnerability to hazards but also the ability to recover from disturbances. Weather extremes are responsible for a majority of electricity blackouts, and the resilience of power systems to extreme weather hazards has long been an established field of research. However, the topic has not been examined to the same extent for offshore renewable energy systems; for marine energy technologies in particular, resilience is a novel concept. In the present study, we review the research that has been published starting from a discussion on the general resilience concept and its applicability for power systems. By identifying knowledge gaps and outlining directions for future research needed to build resilient and renewable energy systems, the paper contributes to several of the Sustainable Development Goals (SDGs). In particular, the paper supports the goals of affordable and clean energy (SDG 7), climate action (SDG 13), and sustainable cities and communities (SDG 11).

In this study, the impact of data time resolution on long-term voltage stability assessment of a power grid with high penetration of wind-solar hybrid power plants is investigated. Historical and synthetic wind data as well as solar irradiance are used to calculate power output from hypothetical offshore wind-solar hybrid power plants, geographically located off the coast of Massachusetts, USA. The results show that using hourly input data can overestimate the long-term voltage stability, compared with using minute data. However, the relative difference in terms of voltage mean value and standard deviation is marginal whilst the most significant difference is the intensity of the voltage fluctuations. The main drawback of using high-resolution data is the execution time, increasing proportionally with the number of time steps. Thus, it is argued that the choice of da ta time resolution should be based on the aspects of long-term voltage stability and the size of the power grid to be studied.

Reliable access to drinking water and electricity can be a challenge, especially on islands. In this paper, desalination systems powered stand-alone by renewable energy sources are discussed, with a focus on wave power for the Swedish island Gotland. The objective is to evaluate the opportunity of using wave power for a desalination system on Gotland. The method includes assessing the electricity generation from a wave power park to power a desalination plant. The results show that the desalination plant would require 350 MWh annually, whereas the wave power plant could deliver 1891 MWh, supporting that it is technically feasible for a wave power park installed off the coast of Gotland to power a desalination plant.

This article presents the state-of-the-art and challenges related to the optimization and grid integration of farms of wave energy converters (WECs). Various physical and electrical circuit layouts have been proposed to interconnect WECs. The grid impact of wave power farms (WPFs) is associated with energy variability in ocean waves. Although fluctuations in the WPF output power might be reduced due to the farm aggregation effect, it remains highly variable, changing from minimum to maximum within several seconds. Parameters assessing the grid impact of farms of WECs are presented here, and various solutions to reduce the grid impact from WPFs are summarized.

Site-specific environmental conditions in targeted areas for offshore renewable energy deployment often feature underlying currents. The effects of the latter on wave parameters and overall hydrodynamics modifies the behavior of light offshore structures, such as wave energy converters (WEC), with unforeseen alteration of power yield and structural loads. To address such effects on a taut-moored point-absorber — potentially susceptible to wave and current motion — the high-end numerical framework of DualSPHysics is used, as it leverages the flexibility of the Lagrangian Smoothed Particle Hydrodynamics (SPH) method for simulating fluid-structure interaction and external libraries to take on multibody problem posed by power take-off (PTO) and moored connections. A dedicated numerical wave tank capable of reproducing regular waves propagating over steady uniform currents is used to numerically test the Uppsala University WEC (UUWEC), which consists of a buoy connected to a linear PTO. Clear influence of the current traits is visible in the results regarding the point-absorber motion, power output, and anchoring tension. Remarkably, the sole presence of current can hamper the harvesting capabilities of the device, up to different extents depending on its intensity and relative direction with respect to the wave propagation. Tension patterns, instead, are found to be almost linearly sensitive to current intensity and direction, with beneficial effects produced by opposing currents and vice-versa.