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(a) Difference in model and altimeter H s against altimeter H s. Black crosses: individual values; red circles: bin average. (b) Standard deviation of difference in H s against altimeter H s and fitted linear relationship.
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The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit hi...
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Context 1
... hindcast, giving a maximum separation of 90 min. This gives a total of 2129 collocated data points, over the period September 1992- December 2005. Fig. 2 shows a scatter plot of the collocated altimeter and model H s . The level of scatter is low and there are few outliers. There is good agreement even at in very large seas, up to nearly 14 m. Fig. 3(a) shows the average difference between the model and altimeter H s , binned by altimeter H s . There is a small bias at low H s , which may be a result of problems with altimeter measurements at low H s . At higher H s the bias is low compared to the level of scatter and the model does not show the underestimation of high H s which ...
Context 2
... estimate of T e we will use the hindcast without calibration. Biases in the model data are not so important in this section since we are interested in the variability in the resource rather than a precise estimate of the mean value. The effect of uncertainty in the historic data on predictions of the future resource is discussed in Section 4.2. Fig. 3(b) shows the standard deviation of the differences between model and altimeter H s against altimeter H s . As for the hindcasts examined in [1], a linear increase in standard deviation with H s is observed. The standard deviation is slightly higher than for the WW3 hindcast at EMEC (see [1]). This could be a result of the temporal ...
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... To date, within the realm of wave energy conversion, the literature on robust optimization has predominantly focused on the development of robust control frameworks [10][11][12][13][14][15][16][17]. Furthermore, concerning the study of wave energy and WEC uncertainties, the existing literature primarily covers environmental [18][19][20][21][22] and full/high-scale prototyping or experimental uncertainties and subsystems' influence on performance [23][24][25][26][27][28][29]. The neighboring Reliability-Based Design Optimization (RBDO) field of research directs its attention to WEC structural and maintenance cost uncertainties [30] (in which the so-called Wavestar device is considered as a case study) and the power take-off (PTO) reliability relationship with hull geometry [31]. ...
Among the challenges generated by the global climate crisis, a significant concern is the constant increase in energy demand. This leads to the need to ensure that any novel energy systems are not only renewable but also reliable in their performance. A viable solution to increase the available renewable energy mix involves tapping into the potential available in ocean waves and harvesting it via so-called wave energy converters (WECs). In this context, a relevant engineering problem relates to finding WEC design solutions that are not only optimal in terms of energy extraction but also exhibit robust behavior in spite of the harsh marine environment. Indeed, the vast majority of design optimization studies available in the state-of-the-art consider only perfect knowledge of nominal (idealized) conditions, neglecting the impact of uncertainties. This study aims to investigate the information that different robustness metrics can provide to designers regarding optimal WEC design solutions under uncertainty. The applied methodology is based on stochastic uncertainty propagation via a Monte Carlo simulation, exploiting a meta-model to reduce the computational burden. The analysis is conducted over a dataset obtained with a genetic algorithm-based optimization process for nominal WEC design. The results reveal a significant deviation in terms of robustness between the nominal Pareto set and those generated by setting different thresholds for robustness metrics, as well as between devices belonging to the same nominal Pareto frontier. This study elucidates the intrinsic need for incorporating robust optimization processes in WEC design.
... This is the energy created by waves, which is quickly gaining traction as a viable alternative to reducing the environmental impact of fossil fuel use (Abanades et al., 2014). Wave energy has a number of advantages, including, but not limited to, large energy capacity One of the most concentrated, reliable, and longlasting energy sources, wave energy is available in many nations but is underutilized (Mackay et al., 2010). ...
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... Historical hindcast data are often used to quantify the wave energy resource. Nevertheless, while offering longer time coverage with respect to observations, they are often affected by biases in the estimation of climatological means, and fail to capture the high natural inter-annual variability that characterizes wave climate, as well as climatechange induced variations, due to both insufficient resolution and to the inadequate representation of relevant processes (Mackay et al., 2010a;Mackay et al., 2010b). However, for the Mediterranean Sea, sufficiently long reanalysis hourly time series of wave parameters are now available at a spatial resolution of 1/24° (Korres et al., 2021), which would further allow to characterize wave statistics, so as to better evaluate the projected omnidirectional wave power (P w ) and the expected productivity of a farm, via the performance metrics generally used to compare and rank the prospective operative devices (e.g., average power output, P E , Capture Width Ratio, CWR, and capacity factor, C f ), ultimately allowing the assessment of their economic performance via the Levelised Cost of Energy (LCoE) (Astariz and Iglesias, 2015). ...
Ocean Energy is now emerging as a viable long-term form of renewable energy, which might contribute around 10% of EU power demand by 2050, if sufficient support is guaranteed along its road to full commercialization, allowing to further demonstrate the reliability, robustness and overall economic competitiveness of technologies. Although wave energy is still less developed than other marine renewables, its high density, great potential and minimal environmental impact have renewed the interest of developers, investors and governments globally, also in view of the increasing awareness of climate change and of the necessity to reduce carbon emissions. In parallel with technological development, the reliable characterization of wave climate and of the associated energy resource is crucial to the design of efficient Wave Energy Converters and to an effective site-technology matching, especially in low-energy seas. The preliminary scrutiny of suitable technologies and the identification of promising sites for their deployment often rely on wave climatological atlases, yet a more detailed characterization of the local resource is needed to account for high-frequency spatial and temporal variability that significantly impact power generation and the economic viability of WEC farms. We present a high-resolution assessment of the wave energy resource at specific locations in the Mediterranean Sea, based on a 7-years dataset derived from the operative wave forecast system that has been developed at ENEA and has been running since 2013. The selected areas correspond to the target regions of the Blue Deal project, where energy resource estimates were combined with technical and environmental considerations, so as to identify optimal sites for Blue Energy exploitation, from a Maritime Spatial Planning perspective. The available resource at selected sites is analysed together with site theoretical productivity for three state-of-the art WECs, showing interesting potential for future deployment.
... In more sophisticated approaches, the variability of sea states has been addressed from a predictive standpoint to assess the propagation of variability from the original data sets to extrapolations for upcoming times or from off-shore wave data to near-shore predictions [32]. One common procedure is the implementation of Monte Carlo algorithms [33][34][35], whose procedure has been briefly described as follows. ...
This study presents a statistical approach that allows estimating the sea wave power (Pmax) by using the joint probability distribution of the wave’s significant height (Hm) and period (T), pHm,T. Unlike other works in the literature, the goal of the present paper is not to find the best locations for wave farming or to extrapolate the data in space or time, but to achieve a more suitable design of wave electric generators in terms of a reduced delivery uncertainty. When the raw values of Hm and T are used, the estimations of Pmax end up spanning over several orders of magnitude owing to the nonlinear amplification of the fluctuations through the calculations. This typically results in large overestimations of Pmax that preclude their use for a modest design. Another associated issue is the low temporal resolution of the data available, which forbids using measurements of Hm and T in numerical simulations directly. These issues have been overcome with the proposed methodology, whose core feature consists of generating a new series of random Hm,T pairs, based on pHm,T, which allow estimating the wave power potential realistically while reducing the chances of over/underrating. In this new series, large fluctuations are suppressed, but the essential statistics of the measurements are preserved. Additionally, since the new series of Hm,T pairs is time-independent, it can be fed directly into numerical simulations. To illustrate our proposal, we used one-year long datasets of hourly measurements of Hm and T recorded at a maritime buoy, for the design and simulation of a linear electric generator with a single degree of freedom.
... TWh/year globally [9], with greater density, predictability and consistency [10][11][12]. Even so, the development of wave energy converters (WECs) has not yet reached its full potential due to the lack of technological consensus [10,11,13,14], uncompetitive energy generation costs [15][16][17], the irregular nature of waves at sea [18,19] and the harsh ocean environment, both in terms of biochemical degradation and survivability to extreme sea-states [13,20]. ...
Ocean related activities are often supported by offshore equipment with particular power demands. These are usually deployed at remote locations and have limited space, thus small energy harvesting technologies, such as photovoltaic panels or wind turbines, are used to power their instruments. However, the inherent energy sources are intermittent and have lower density and predictability than an alternative source: wave energy. Here, we propose and critically assess triboelectric nanogenerators (TENGs) as a promising technology for integration into wave buoys. Three TENGs based on rolling-spheres were developed and their performance compared in both a “dry” bench testing system under rotating motions, and in a large-scale wave basin under realistic sea-states installed within a scaled navigation buoy. Both experiments show that the electrical outputs of these TENGs increase with decreasing wave periods and increasing wave amplitudes. However, the wave basin tests clearly demonstrated a significant dependency of the electrical outputs on the pitch degree of freedom and the need to take into account the full dynamics of the buoy, and not only that of TENGs, when subjected to the excitations of waves. This work opens new horizons and strategies to apply TENGs in marine applications, considering realistic hydrodynamic behaviors of floating bodies.
... Finally, if suitably extensive data sets are available, it is important to ensure that the reference data period used for all of the analysis is representative of the long-term wave climate, and that the data set was not extracted during a period of low/high wave activity due to a natural or anthropogenic climatic variability. For example, Mackay et al. [48] show that the North Atlantic Oscillation can greatly affect the results of a wave resource assessment if only 5 years of hindcast data are used, and they illustrate that use of 20 years of hindcast data greatly reduces these effects. The IEC Technical Specification currently recommends a minimum of 10 years [44]. ...
Our communal global ocean energy resources are substantial and, perhaps even, one of the last untapped significant renewable resources which has the ability to assist in global decarbonization efforts. As we globally move towards a renewable energy future, ocean energy (e.g. waves, tides, currents and offshore winds) need to be considered in the suite of energy supply options to create a resilient, affordable and reliable future energy system. Estimates suggest global wave resources could theoretically generate 32,000 terawatt-hours (TWh) annually. As such, the opportunity associated with the responsible development of the wave energy sector can not be overstated. For context, the total United States of America electrical demand was approximately 3.8 TWh in 2019. This chapter provides an overview of the latest efforts of characterize and parameterize our global wave energy resources, a review of the fundamental operating principles of wave energy converter (WEC) technologies, and best practices to predict the ultimate performance and power production from the wave energy sector.
... Researchers and developers suggested several wave energy converters' concepts for electricity generation; however, progress from pre-commercial to full-scale prototype testing for commercialization of WEC has been relatively slow (Morim et al., 2019a). It is because of financial risks associated with the variable nature of the wave energy resource and subsequent potential power production performance throughout the expected lifetime of the projects (Morim et al., 2019a;Mackay et al., 2010). ...
The present study investigates the feasibility of wave energy exploitation along the Indian coast using 19-year (2000-2018) hindcasted wave data generated by WAVEWATCH-III with a fine spatial resolution (0.1 • × 0.1 •) and temporal resolution (6 h). The wave model is validated against 12-month measured wave data collected at multiple buoy sites and compared with 19-year ERA5 data across the study area. The results show good agreement between model data and the other two datasets. The spatio-temporal variability analysis of wave energy flux reveals that southern coast of India has the mean wave energy flux ranging from 6 to 10 kW/m with a minimum monthly (<2.0), seasonal (<1.0) and annual (<0.2) variability which could be a promising area for future wave energy exploitation. A detailed analysis of wave power resource has been discussed for nine hotspot locations which were selected based on the optimum hotspot index, geographical restrictions on bathymetry and distance to the shore. Further, each hotspot has been analysed through combined scatter and energy diagrams. Finally, electrical power output is estimated at each hotspot using five wave energy converters and observed that the annual energy production at most of the locations is above 800 MWh with more than 70% availability.
... Two recent studies, which this chapter is based on, presented practical applications of uncertainty analysis in OWC wave energy converter experiments [40,41] (Appendices D and E). More broadly, uncertainty-related studies have been carried out in other areas of the wave energy field, including the uncertainty in wave energy resource assessment [100,105,149,150,107,151] , ocean test results [152], and Mean ...
Ocean wave energy has significant technical potential but limited full-scale deployments and technology convergence. Consequently, guidelines for developing wave energy converters (WECs) are themselves developing, especially around uncertainty in experimental testing. To better understand this uncertainty and develop methods to mitigate it, we carried out several experimental investigations into key uncertainties: measurement uncertainty, scale effects, and laboratory effects. Experiments consisted of hydrodynamic model tests of an Oscillating Water Column (OWC) WEC based on Wave Swell Energy’s Uniwave technology (bottom-fixed, unidirectional airflow PTO), and assessed power performance and hydrodynamic loads in regular and irregular waves. The experiments were: (1) a 1:30 scale model experiment in the Australian Maritime College (AMC) Model Test Basin (MTB), conducted to understand measurement uncertainty and develop a WEC-specific uncertainty analysis (UA) methodology; (2) model tests at three scales (1:40, 1:30, 1:20) in the AMC MTB, conducted to understand and evaluate scale effects; and (3) reproducing this model test at 1:30 scale in the Queen’s University Belfast Coastal Wave Basin to understand and evaluate laboratory effects. The comprehensive UA methodology developed in (1) was applied in (2) and (3) to determine that, despite the experiments being carried out according to international guidelines, some results differed across model scales and between laboratories by an amount larger than the measurement uncertainty, which was itself considerable. These findings suggest measurement uncertainty, scale, and the laboratory can significantly influence experimental results. In conclusion, when conducting WEC model test experiments these uncertainties should be carefully considered and mitigated; our UA methodology could facilitate the considerations and mitigation. This research will help refine international technical guidelines on WEC model test experiments.
... To accurately predict the long-term wave energy resource and WEC output in a certain area, it is necessary to take into account the temporal variation of wave features due to natural variability and climate change [48]. Although a number of studies analyzed the long-term evolution of the wave energy resource [26,29,41,[49][50][51][52][53][54][55], only a few have addressed the impacts of climate change on it [48,[56][57][58][59][60]. ...
The increasing demand for energy and the impacts generated by CO2 emissions make it necessary to harness all possible renewable sources of energy, like wave power. Nevertheless, climate change may generate significant variations in the amount of wave energy available in a certain area. The aim of this paper is to study potential changes in the wave energy resource in the Mediterranean coast of Morocco due to climate change. To do this, wave datasets obtained by four institutes during the Coordinated Regional Climate Downscaling Experiment in the Mediterranean Region (Med-CORDEX) project are used. The future conditions correspond to the RCP4.5 and RCP8.5 scenarios from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The results show that projected future wave power is very similar to that of the present considering the whole area, although at some specific points there are slight changes that are more evident for the RCP8.5 scenario. Another remarkable result of this study is the significant increase of the temporal variability of wave power in future scenarios, in particular for RCP8.5. This will be detrimental for the deployment of wave energy converters in this area since their energy output will be more unevenly distributed over time, thus decreasing their efficiency.
... Several renewable energy-generating sources such as wave power, tides, and current which are associated with marine have always been misunderstood though it has strong predictability and other physical properties [2]. Wave energy presents a number of advantages with respect to other CO 2 -free energy sources-high-power density, a relatively high utilization factor, and last, but not the least, low environmental and visual impact [3]. Wave energy resource assessments fall into two categories. ...
The conversion efficiency of wave energy converters is not only unsatisfactory but also expensive, which is why the popularity of wave energy as an alternative to conventional energy sources is subjacent. This means that besides wave height and period, there are many other factors which influence the amount of "utilizable" wave energy potential. The present study attempts to identify these important factors and predict power potential as a function of these factors. Accordingly, a polynomial neural network was utilized, and fuzzy logic was applied to identify the most important factors. According to the results, wave height was found to have the maximum importance followed by wave period, water depth, and salinity. In total, 12 different neural network models were developed to predict the same output, among which the model with all of the 4 inputs was found to have optimal performance.
