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In this study, an extreme value analysis of wind and wave parameters is presented for three specific locations in the Greek seas that are known to be advantageous in terms of joint power production (both offshore wind and wave) and bathymetric conditions. The analysis is conducted via the Peak-Over-Threshold method, examining wind speed, significant wave height and peak wave period data from the ERA5 reanalysis dataset. Moreover, a multi-purpose floating platform suitable for offshore energy production is presented, which combines wind and wave energy resources exploitation and can be adequately utilized at the selected locations. The analysis is built to incorporate the solutions of the diffraction, motion-dependent and pressure-dependent radiation problems around the floating structure, along with the mooring line and wind turbine (WT) characteristics. Subsequently, a coupled hydro-aero-elastic analysis was performed in the frequency domain, while a dynamic analysis was conducted in order to evaluate the mooring characteristics. Lastly, offshore wind output and absorbed wave energy values were estimated, and different types of mooring systems were compared in terms of efficiency. It has been concluded that the wind energy capacity factor is higher than 50% in all the examined locations, and by the mooring system comparison, the tension-leg platform (TLP) represents the best-case scenario for wave energy absorption. [ABSTRACT FROM AUTHOR]

The theory of time series and especially of the autoregressive moving average (ARMA) models is used for modelling the sea surface elevation. Sea surface elevation is usually recorded by wave measuring devices, but rarely has it been analysed or analytically modelled. Except other traditional applications, forecasting of the wave characteristics in the short-term time scale is important for the optimization of the efficiency of the wave energy converters. The main objective of this work is, by using the Box-Jenkins methodology, the analytical description and forecasting of the sea-surface elevation process. A sample of 50825 records of sea surface elevation was collected by four different deep water locations at the Aegean and Ionian seas in Greece from 2000 to 2011. Analytical ARMA(p,q) models were fitted to a variety of potential parameter combinations, i.e. p, q = {0,1,2,3,4,5} for the examined time series in order to identify the most suitable ARMA model for this purpose. The diagnostic and suitability check, as well as the selection of the best model is carried out according to the Akaike and the Bayesian information criteria. The results obtained suggest that the ARMA(2,5) model is the optimum choice for the representation of the sea surface elevation. A further attempt has been made in order to highlight a possible relation between the best ARMA models and various spectral characteristics such as the significant height, the spectral peak period, the mean wave direction and the spectral bandwidth parameters of the corresponding sea-state. Finally, based on the best-fit ARMA model, a forecast of free surface elevation is carried out, confirming the model sufficiency by estimating the forecast errors. [ABSTRACT FROM AUTHOR]

The analytic wind and wave climate modeling is important for a variety of ocean engineering applications. Usually, the probabilistic description of the wind and wave regime is confined to the linear characteristics of sea states and wind conditions, i.e., the significant wave height, wave period and wind speed. Rapidly emerging applications, such as offshore wind and wave energy utilization, are largely dependent on the accurate description of the directional wind and wave characteristics of the areas under study. In this work, two analytic bivariate probability models for the joint distributions of: (a) significant wave height–wave direction, (b) wind speed–wind direction and (c) wave direction–wind direction are presented, discussed and applied in detail. The proposed models, which take into consideration the dependence of the corresponding variables and are defined by closed-form relations, are theoretically sound and tractable. Numerical results are provided for three locations along the US coasts, which are characterized by different characteristics of wind and wave climate. The proposed joint distributions elucidate the probabilistic structure of the wind and wave climate patterns and, furthermore, they allow the assessment of the special and important cases of aligned and opposing wind and sea states. [ABSTRACT FROM AUTHOR]

It is common practice to represent wave climate and sea severity through significant wave height. The development of accurate probabilistic models of wave height is paramount in ocean-related activities (e.g., offshore structures, weather routing, wave energy assessment studies). In this work, several models for the probabilistic description of the significant wave height are examined and evaluated along with a new distribution that is introduced for the first time in wave height modelling, namely the Extended Generalized Inverse Gaussian model. The corresponding density and the cumulative distribution function are expressed through closed forms. All the examined models were applied on long-term measured wave data at four different locations of the Greek Seas and wave height data obtained from four buoys of the US NOAA National Data Buoy Centre. The coefficient of determination, the Bayesian information criterion and the average deviation criteria were used for the evaluation of the examined models' performance. The final selection is based on the multiplicative criterion that combines the above criteria into a single expression. The proposed distribution outperforms the examined parametric models and describes in an optimal way the probabilistic structure of significant wave height. • The extended generalized inverse Gaussian (EGIG) distribution is introduced. • Data from areas of very different wave climate characteristics have been used. • EGIG performs better for the majority of the examined areas. • The Johnson S B distribution is a promising candidate for wave height modelling. • The right-tail and extreme value behavior of EGIG should be further elaborated. [ABSTRACT FROM AUTHOR]

The present paper deals with the development of a multi-purpose floating tension leg platform (TLP) concept suitable for the combined offshore wind and wave energy resources exploitation, taking into account the prevailing environmental conditions at selected locations along the European coastline. The examined Renewable Energy Multi-Purpose Floating Offshore System (REFOS) platform encompasses an array of hydrodynamically interacting oscillating water column (OWC) devices, moored through tensioned tethers as a TLP platform supporting a 10 MW wind turbine (WT). The system consists of a triangular platform supported by cylindrical floaters, with the WT mounted at the deck's center and the cylindrical OWC devices at its corners. Details of the modelling of the system are discussed and hydro-aero-elastic coupling between the floater; the mooring system; and the WT is presented. The analysis incorporates the solutions of the diffraction; the motion- and the pressure-dependent radiation problems around the moored structure, along with the aerodynamics of the WT into an integrated design approach validated through extensive experimental hydrodynamic scaled-down model tests. The verified theoretical results attest to the importance of the WT loading and the OWC characteristics on the dynamics of the system. [ABSTRACT FROM AUTHOR]

In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained. [ABSTRACT FROM AUTHOR]