Recent studies have demonstrated the merits of spectral-domain (SD) modeling in efficiently addressing nonlinear dynamic behvavior of stand-alone wave energy converters (WECs). However, the potential of the SD modeling approach deserves further exploitation by examining its applicability in simulating the entire wave-to-wire (W2W) process of WEC arrays. This article proposed and verified a SD W2W model of WEC arrays. The WEC arrays are considered as five same-sized heaving cylindrical point absorbers, and they are all equipped with linear Permanent Magnet (PM) generators. The established SD W2W model is verified by being compared with results of a nonlinear time-domain-based W2W model across a variety of operation conditions. The computational efficiency of the two simulation approaches in modeling WEC arrays is also identified and compared. The results suggest that the SD W2W model is associated with a relative error of less than 11 % to the nonlinear time-domain reference, with regard to the estimates of significant statistical performance indicators, such as WEC velocity, absorbed and electrical power of individual power, and total electrical power production of the WEC arrays. At the same time, the SD W2W model presents a high computational efficiency, being around 2000 times faster than the time-domain W2W model of WEC arrays.

Due to the complexity of the ocean environment and wave energy converter (WEC) system, it has been an effort-demanding work to assess either the power performance or fatigue loads of WECs. This work attempts to apply a data-driven approach to increase the efficiency of the collective prediction of the power and fatigue load of a point-absorber type WEC. Nonlinear time-domain modeling is first established to estimate the power and fatigue loads, which is considered the reference data in this work. To demonstrate the performance of the applied data-driven approach, two prevalent power take-off (PTO) mechanisms are implemented to represent different characteristics of WECs. A data-driven approach, active learning Kriging (AK), is adapted to predict power and fatigue loads collectively, and a new learning function is defined to select the enriched wave cases for the active learning process. Results show that the applied active learning approach can accurately and simultaneously predict power and fatigue loads in both PTO mechanisms. Compared to pure numerical simulation, the proposed method only requires 15 simulations of sea state, and the computational effort is reduced by more than 20 times. The maximum prediction error is less than 2%. The data-driven approach could be a powerful tool for WEC system optimization, considering both power performance and fatigue loads.

Marine resources such as wind and wave are expected to play an important role in decarbonising the UK’s energy network, as part of the global transition from fossil fuels to renewable energy. However, potential increases in global weather systems variability due to climate change cast doubt on the long-term sustainability of offshore renewable development and the ambitions of the UK government to rapidly expand current capacity. As such, growing interest in co-located wind–wave systems is being paid as a means of enhancing the climate resilience of the future energy network. This study investigates the impacts of climate change on wind and wave resources in the UK from 2015 to 2100, using available CMIP6 datasets and numerical wave modelling using SWAN. In doing so, an initial assessment of the potential for co-located infrastructure is undertaken to inform future research into its role in strengthening the climate resilience of ocean renewable generation. The results reveal gradual reductions in resource availability under a high emission scenario, with statistically significant annual trends detected across most of the study area. Reductions in average annual wind energy reach −16.0% in coastal areas of Northern Ireland towards the end of the century, while decreases in wave energy of more than 25% are projected in certain regions of the North Sea. These trends are primarily driven by seasonal reductions during summer months, with decreases in average wind power during these months as much as −29.0% and decreases in wave power of over −40%. In addition, increases in resource variability from the mid-century onwards suggest that climate change is likely to negatively affect the availability of the UK’s wind and wave energy resources in the long term.

Wave-to-Wire (W2W) modeling simulates the whole operation process of wave energy converters (WECs), which plays a pivotal role in the systematic design and optimization of WECs. Existing W2W models are predominantly constructed based on time-domain (TD) analysis to coherently incorporate relevant nonlinearities. However, TD models require a high computational cost, which hinders the design iterations of WECs. As a newly emerging alternative approach, spectral-domain (SD) modeling has demonstrated the applicability of describing the W2W process while efficiently covering nonlinear effects through statistical linearization. This study aims to develop an SD W2W modeling approach for WECs coupled with a gearbox and rotary generator. The application of the proposed model is exemplified in two case studies: (1) a point absorber with a rack-pinion system and a rotary generator; (2) a flap-type WEC with a revolving gearbox and a rotary generator. The simulation results obtained by the SD W2W model are compared against a higher-fidelity nonlinear TD W2W model to verify its accuracy across a variety of sea states. A good agreement between the two modeling approaches is observed, in which the maximum relative error is below 7 % with regard to the estimation of important system outputs. Meanwhile, the computational efficiency of the SD W2W model is thousands of times higher than the TD modeling approach.

As the necessity for the decarbonisation of the global electricity market increases, a range of renewable energy technologies will be implemented, one of which is wave energy. A key step in this process is the thorough quantification of both the power resource at locations of interest, and the impacts of these devices on the natural environment. The present work streamlines these 2 processes into one methodology by investigating the long-term impacts of an array of 20 WECs on the nearshore Dutch wave climate, while also calculating the potential power resource at the site considering intra-array wake effects. Simulations of 10 year duration were conducted in the baseline scenario (no farm present) and with 2 array configurations, using the spectral wave model SWAN on an unstructured mesh. It was demonstrated that the power production of the farm during this period, when wake effects are considered, is calculated to be up to 1.8% less than traditional methods. The presence of the farm is shown to reduce significant wave height and wave power in its lee, with the effects being largely attenuated at the coast. It was shown that the magnitude of the change is dependent on both the period and height of the waves at the farm, and notably the magnitude of the reduction does not increase consistently with the wave height, contradicting the sentiment that wave farms are effective protection mechanisms against damaging high-energy conditions. Furthermore, the present work suggests that changes to the nearshore breaker index may impact longshore currents that are essential for nutrient and sediment transport, the effect of which on the ecosystem is not yet well quantified.

In line with the global shift to transition away from fossil fuels to sustainable energy sources, tidal stream energy has emerged as a promising renewable option. This study investigates the tidal stream energy resources along the Dutch coast and focuses on the impact of Mean Sea Level (MSL) rise on the future resource potential. A THETIS high-resolution unstructured model is used. The model is validated against sea surface elevations, and the Dutch tidal stream resource uncertainties are well defined. The validated model is used to evaluate the tidal stream energy potential of the Netherlands, regions in the Wadden Sea and Westerschelde in Zeeland display noteworthy potential, evidenced by maximum average flow velocities of 1.3 m/s and maximum average energy densities of 1600 W/m2 for the Wadden Sea and maximum average flow velocities of 0.75 m/s and maximum average energy densities of 300 W/m2 for the Westerschelde. Forecasting the 2050 tidal stream resource, considering a projected 118 mm MSL rise, results indicate persistent energy characteristics, with minimal fluctuations in average velocities and average energy density when compared to the 2016 model. In the Wadden Sea and Zeeland, respectively, only marginal changes of +25 W/m2, and +8 W/m2 are observed in average energy density for those same locations. Furthermore, the inclusion of long-term constituents has negligible effects on the 2050 results, emphasising the stability of the tidal stream energy source. Tidal stream energy in the Netherlands stands out as a reliable and resilient energy source, demonstrating consistency in the face of projected MSL rise. Such predictability has the potential to contribute significantly to fostering a sustainable and secure energy system in the Netherlands, while aligning with global efforts to combat climate change.

Wave energy as a renewable resource has been shown to have immense potential. Up to date research has predominately focused on sector focused approaches, with devices being explored in small applications, or solely on their own merit. However, when discussing the integration to energy systems with high share of renewable energies, large levels of electrical interconnectivity and grid balancing infrastructure, the role of wave energy is often “lost”. This work further refines the approach of integrating wave energy in the European and UK energy system, highlighting potential future pathways to improve wave energy considerations.

This study assesses the input source of climate data for evaluation of wave energy production and economics. The comparison of ERA5 and a high-fidelity wave dataset across all Europe, the ECHOWAVE database, show a clear benefit for wave energy. When ERA5 is utilised to assess the potential of wave energy, the usefulness and integration of wave energy is not reflected in the system. This works shows that a proper representation of wave energy in terms of climate, power production methodologies and economic considerations.

Harnessing wave energy from the oceans using wave energy converters (WECs) offers a huge opportunity to diversify Europe’s future renewable energy system. Although the energy conversion of this pre-commercial technology is not directly linked to greenhouse gas emissions, environmental sustainability over the full life cycle needs to be ensured for a future-proof large-scale application of WECs. Therefore, we present a cradle-to-grave full life cycle assessment (LCA) study for a generic point absorber WEC based on a fully transparent and adaptable life cycle inventory. Within the study we assess the environmental impacts of a single point absorber device, the influence of different hull materials, hotspots in the impacts of WEC components, and variations induced by different deployment locations. For a WEC deployed in the North Sea, we found a global warming impact of 300-325gCO2eq./kWh with periphery and 52-77gCO2eq./kWh without periphery, depending on the hull material. Using an alternative fibre-reinforced concrete material for the hull can reduce the impact across all categories by between 10% (marine eutrophication) and 78% (human toxicity, carcinogenic). In addition to the WEC itself we found that the electrical cable and vessel operations, particularly for maintenance, are significant contributors. These two elements will also be relevant to other marine renewables such as offshore wind and floating solar. Overall, this study shows potential for improving environmental impacts from WECs and identifies possible levers to achieve such a reduction.

This article investigates the methodology and applicability of the statistical linearization (SL) method to incorporating multi-variate non-differentiable nonlinearities, with a focus on floating renewable energy devices. The SL method serves as a highly competitive approach for analyzing floating renewable energy structures, such as wave energy converters (WECs) and floating wind energy turbines, because it inherently combines adequate accuracy and high computational efficiency. The origin of high accuracy comes from its incorporation of nonlinear effects through statistically linearized representations. Yet, the statistically linearized solutions have only been derived and verified for a limited number of nonlinearities of floating renewable energy devices, mostly simply-formed and differentiable in their mathematical expressions. However, floating renewable energy devices usually exhibit a complex dynamic mechanism, in which the relevant nonlinear effects could appear to be highly complex for linearization process to describe. These nonlinear effects could make a significant impact on the system dynamics, exemplified by external machinery force saturation and nonlinear hydrostatics of floaters with a non-uniform geometry. To push forward the boundary of the SL method, it is crucial to demonstrate how it applies to nonlinearities of different features. In this paper, the existing SL method is extended to address the nonlinear effects expressed as multi-variate non-differentiable functions. Several case studies are carried out to exemplify the application of the extended SL approach to the concerned nonlinearities in floating renewable energy devices. The accuracy and computational efficiency of the extended SL approach are evaluated by verifying against the corresponding nonlinear time-domain (TD) and linear frequency-domain (FD) models. Despite the complexity of the given nonlinearities, the relative errors of the SL approach are no more than 6 % while its computational time is comparable to the FD model, being thousands of times faster than the TD model. Comparatively, the FD model leads to a relative error of over 70% in some cases.

This study examines the potential contribution of marine renewable generators in Greece, in order to achieve a 100% renewable energy system by 2050. Using PyPSA-Eur, a cost-optimization model of the European energy system, possible energy transition pathways are explored, across five-year intervals from 2030 to 2050. For each five-year target, a new cost assumption dataset is used, one that follows estimated cost reduction learning rates. This version of the model is called PyPSA-Eur-MREL, and is modified to include marine power generators, i.e. floating wind, wave, tidal and floating solar, but also high fidelity climate data, in the scale of 5.5 km ² for wind and 4 km ² for wave resources. Three different approaches were employed in this investigation: greenfield, generator constrained, and a high-load scenario inspired by Greece's National Energy and Climate Plan (NECP). The analysis focused on generator capacity and performance, the levels of utilization and availability of each energy carrier and the land-use impact of onshore and offshore generators. While the first two scenarios exhibit similar overall system capacities, they differ in land-use requirements, with the constrained case installing more bottom-fixed wind turbines (1.2 GW), thereby reducing land occupation. The high-load scenario introduces floating wind turbines (4.5 GW), however, the scale of onshore installations remains substantial, covering nearly one-third of Greece's total land area.

HAMS-MREL is a recently developed open-source BIEM solver, which allows for the solution of the diffraction and radiation problem for multiple floating rigid structures, taking into account their interaction. This has shown to highly accurate when compared with semi-analytical solutions/commercial solver WAMIT, within a computationally efficient framework that is parallelized. The solver is currently capable of providing the hydrodynamic coefficients (added mass and radiation damping) and exciting forces for all 6 rigid body modes per body. With this research, the solver has been extended significantly to include the following features for the multiple body interaction problem 1) Removal of irregular frequencies, 2) Global symmetry, 3) Wave fields and 4) Generalized modes. This study contributes further to the open-source domain with the development of highly accurate numerical tools for the accelerated deployment of offshore renewables.

The best wave energy resources are typically found far from land, especially in the far southern latitudes. However, these optimal locations often lie outside any single nation's exclusive economic zone (EEZ) and are located in deep ocean waters, though a few places stand out, namely Kerguelen Islands, the southern coast of Chile and the southern tip of New Zealand. The Kerguelen Islands are situated in a prime wave energy location, with a potential capacity factor exceeding 90% when using best of breed point-absorber wave energy converters (WECs). This could result in a levelised cost of electricity of 31.4 €/MWh by 2050. An earlier study showed that the Kerguelen Islands could support up to 7 GW of installable wave power capacity, utilising only 15% of the EEZ.

To evaluate the feasibility of establishing an energy hub on Kerguelen Islands, a scenario was developed using the EnergyPLAN modelling software. The scenario involves producing e-fuels and e-chemicals, which will be essential for sectors like marine and aviation transportation and chemicals as the world transitions to a defossilised economy. This analysis assumes that the islands could supply fuel for East Asia, particularly Japan, South Korea, and Taiwan. By 2050, the 7 GW wave power system could meet 3% of the demand for e-fuels and e-chemicals in these countries, producing 3.5 TWhth,LHV of e-kerosene, 2.6 TWhth,LHV of e-diesel, 19 TWhth,LHV of e-methanol, 2.5 TWhth,LHV of e-LNG, and 4.9 TWhth,LHV of e-ammonia. The cost projections for 2050 suggest that e-fuels and e-chemicals produced from Kerguelen Islands could be highly competitive, with projected costs of 95.0 €/MWh for e-kerosene and e-diesel, 78.7 €/MWh for e-methanol, 64.5 €/MWh for e-LNG, and 68.4 €/MWh for e-ammonia.

In addition to wave power, the system would incorporate 2 GWhcap of battery storage and 50 GWh of underground rock cavern hydrogen storage, further enhancing the energy hub's capacity and flexibility. These costs, assumed for 2050, are projected to be competitive compared to leading global sites, such as the Atacama Desert, which has excellent solar PV resources. For comparison, the Atacama Desert’s e-fuels production cost is estimated to range from 70-75 €/MWh, e-methanol is between 49-57 €/MWh and e-ammonia at 53 €/MWh.

However, the costs mentioned above do not include shipping costs. Shipping costs for ammonia for 10,000 km to East Asia could add up to 8 €/MWh, while shipping costs for methanol could add up to 4 €/MWh. For the Kerguelen Islands an attractive business model could be established to diversify the global e-fuels and e-chemicals production that may largely shift towards solar energy. This creates a promising opportunity for investment in a gigawatt-scale energy hub for e-fuels and e-chemicals on Kerguelen Islands, including the necessary infrastructure for shipping and workforce to maintain such a system.

A critical challenge to the practical deployment of wave energy converters (WECs) is their vulnerability to extreme wave loads. This study proposes a novel design for an oscillating body-WEC, called adjustable draft WEC (ADWEC), which aims to enhance resilience under extreme wave conditions while maintain extraction efficiency at normal sea states. The present numerical simulations focus on the interaction between regular extreme waves and ADWEC in both fixed and dynamic conditions. It is found that the excitation force is highly sensitive to wave nonlinearities, appearing as an asymmetric excitation force in the horizontal direction and a double-peak phenomenon in the vertical direction. Increasing the draft can significantly reduce vertical loads and heave motion, thereby enhancing survivability by mitigating impact forces and buoyancy fluctuations. A shallower draft allows for greater heave amplitude and higher energy conversion, particularly under short-period conditions. The present findings reveal that the draft of the ADWEC has significant effects on the wave loads, hydrodynamic performance and energy extraction, which provides guidance to the practical design of WECs for survivability.

To date the use of the JONSWAP parametric spectrum is still widely accepted in many engineering applications, including the wave energy sector. Nevertheless, in the last 15 years many studies have progressively shown the necessity to implement more detailed and realistic spectral information in order to reduce the errors (or differences) introduced when the JONSWAP spectrum fails to describe more complex sea states. In the present study, the changes in produced power estimations, related to different spectral representations, are analysed. All power production estimates are obtained through 30 years simulations of point-absorber Wave Energy Converter (WEC) arrays, using the HAMS-MREL Boundary Element Method (BEM) solver. To assess the effects of the wave energy distribution on power production, 3 different spectral forcing are considered. Two based on the JONSWAP spectrum, and 1 using spectra time series from the ECHOWAVE hindcast specially developed for wave energy applications. For comparison purposes, the hindcast spectra is used as reference, since it can accurately represent the sea states evolution in time including the occurrence of multimodal conditions, which are not considered by the JONSWAP formulation. Additionally, 3 locations with different wave climates are analysed within European coastal waters. Recent results, focused on the response of a single (point-absorber) WEC, show that the differences in the mean yearly production can be > 12% when compared to reference hindcast data. The generalised analysis presented here, including the hydrodynamic interactions between multiple WECs within an array, is an important step forward in the understanding and quantification of the uncertainties present in power production assessments.

Wave energy converter (WEC) arrays should be designed to ensure consistent and optimal power production over long operational periods. This requires an understanding of stochastic wave variability, interactive effects among devices and their mutual dependence. In this work, a computationally efficient surrogate modelling framework was developed using data-driven polynomial chaos expansion (PCE) to analyze the performance of WEC arrays under realistic sea state conditions spanning 30 years. For this purpose, using Latin hypercube sampling scheme on a joint probability distribution derived from the ECHOWAVE hindcast dataset, resulting $10^6$ combinations of significant wave height (Hs), wave period (Tp), and WEC radius (R) for two array configurations—interacting and non-interacting cases were evaluated. The surrogate model was set up to evaluate the performance of WEC arrays by means of global sensitivity analysis using Sobol indices. The results conclude that the interactive effects significantly alter the contribution of design parameters (like geometry and spatial configurations) to power output, emphasizing the inadequacy of single-device analysis for array optimization. The findings highlight the importance of tailored WEC design within arrays and offer a robust approach for long-term performance prediction and optimization of wave energy farms.

In order to reduce the Levelized Cost Of Energy (LCOE) of Wave Energy Converters (WECs) and make them competitive with conventional energy sources, they would need to be deployed in large numbers as farms similar to Offshore Wind. Given their significant capacity, Offshore wind turbines are often placed at large distances apart, to reduce destructive wake effects, while maintaining a high energy density per unit area. However, WECs within a farm, are much smaller with much lower capacities and stronger inter device interactions due to the presence of a highly dense fluid. Therefore, larger number of WECs can be deployed in closer proximity to produce comparable energy density per unit area. As we move towards hybrid systems with floating solar, wind and wave energy amongst others, efficiency in deployment within an area becomes key. Conventional wave farm concepts that have been extensively studied such as 1) wave farms of different types of WECs (Point Absorber, Attenuator, Flap etc) also referred to as homogeneous arrays (same device in multiple numbers) and 2) wave farms with different sizes and drafts of one type of WEC also referred to as heterogeneous arrays. To date, studies have focused on multiple devices with similar geometries interacting through the same degrees of freedom. With this research, the authors explore mixed wave energy farms, which are wave farms utilizing different types of wave energy converters in the same farm. With the focus on the hydrodynamics and power produced by wave energy converters, this research provides for the first time insights into the interaction of devices, with varying geometries and degrees of freedom, thus entering an entirely new domain of wave farm research.

This study presents a first long term (30 years) assessment to quantify the effects of both, the wave spectrum representation, and occurrences of multi-modal sea states, on power production estimations from a point-absorber Wave Energy Converter (WEC). Analysis in 3 different offshore locations (Portugal, Ireland and The Netherlands) is included to ensure robustness of results. In general, traditional methods based on the use of the JONSWAP spectrum, with an adequate gamma shape value, can lead to mean overestimation in yearly power production >12% when compared to reference hindcast spectral data. This can be partially reduced when capping is applied to power production, but still can be close to 10%. An alternative method is proposed to modulate the JONSWAP spectrum at each time step which helps to reduce differences, but leads to slight yearly underestimations (−2.5 to −5% in average). Although in all analyzed sites the occurrences of multi-modal spectra is >30%, contribution to errors due to misrepresentation of these sea states are estimated to be of about 2.5%. These findings provide valuable insights on the uncertainties introduced in power production estimations, related to wave conditions characterization, that can have important economic impact when planning for large scale deployments.

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

The Energy Transition requires meticulous planning, taking into consideration economic, technical, social, and resource constraints. In Europe ambitious targets have been set for system electrification, however, integrating the potential of marine renewables have not been thoroughly investigated. This study extends the framework of PyPSA-Eur into PyPSA-Eur-MREL that for the first time incorporates all marine renewables, using high resolution datasets, that uncover the potential of marine renewables. Marine renewables are modelled in terms of power estimations, deployment strategies and revised packing density, and expected benefits for 2030, and 2050 across all European Countries are quantified. Higher spatio-temporal data have an immediate impact in estimates, and reduction of energy storage by 73%. Wind energy has a reduced installation capacity by 50%, but the higher fidelity of resource matches production to demand and reduces curtailments up to 60%. System costs with high resolution data are 40% reduced to 160 billion € for a 2030 100% renewable reliant system. The benefits of having more marine renewables are not limited to cost and more efficient demand matching, reduced energy storage, but it also with the area required to decarbonise the system. The results are encouraging and outline the importance and further need for marine renewable energies.

Wave energy holds substantial promise as a renewable resource, but its commercial deployment remains limited. Research primarily focuses on individual wave energy converter (WEC) devices, while the interactions within WEC arrays have received less attention. Optimizing these interactions is essential for maximizing energy capture and minimizing operational costs. However, due to the variability of wave conditions, it is unlikely that a single WEC configuration will be effective across all scenarios. Therefore, to optimize performance, a large number of simulations are required, which is computationally expensive with traditional high-fidelity numerical methods. This paper addresses this challenge by utilizing a surrogate model based on polynomial chaos expansion (PCE), which efficiently captures the behavior of a WEC array over a 30-year probabilistic based on a high-fidelity wave dataset. The surrogate model is compared to a frequency domain model, demonstrating a high efficiency. The surrogate model is used to simulate the performance of an array of five point absorber WECs under varying wave conditions. The study highlights the following requirements for optimal array performance: the spatial configuration of WECs must consistently produce optimal power throughout the operational period and must adapt to the high variability of wave parameters. The results reveal that the fixed array configuration under study, produces power that is inconsistent over varying sea conditions, showing suboptimal energy production under most wave conditions, and higher power output only under less probable wave scenarios. These findings provide insights into the physical interactions influencing WEC array performance and can inform future design methodologies for wave energy farms. The proposed surrogate modeling framework offers a highly efficient tool for conducting large-scale probabilistic analyses of WEC arrays, significantly reducing computational effort while enabling more accurate performance predictions.