• Energy Research

Abstract Wind climate analysis and modelling is of most importance during site selection for offshore wind farm development. In this regard, reliable long-term wind data are required. Buoy measurements are considered as a reference source in relevant applications including evaluation and calibration of wind data obtained from less reliable sources, combined assessment, blending and homogenization of multi-source wind data, etc. Most of these applications are based on regression techniques elaborated by using the principle of ordinary least squares (OLS). However, wind data usually contain several outliers, which may question the validity of the regression analysis, if not properly considered. This study is focused on the implementation of the most important robust regression methods, which can identify and reveal outliers, and retain at the same time their efficiency. Long-term reference wind data series obtained from buoys at six locations in the Mediterranean Sea are used to calibrate hindcast (model) wind data by applying robust methods and OLS. The obtained results are compared according to several statistical measures. The effects of the calibration methods are also assessed with respect to the available wind power potential. The results clearly suggest that least trimmed squares and L1-estimator perform in all respects better than OLS.

Commercial electricity production from marine renewable sources is becoming a necessity at a global scale. Offshore wind and solar resources can be combined to reduce construction and maintenance costs. In this respect, the aim of this study is two-fold: i) analyse offshore wind and solar resource and their variability in the Mediterranean Sea at the annual and seasonal scales based on the recently published ERA5 reanalysis dataset, and; ii) perform a preliminary assessment of some important features of complementarity, synergy, and availability of the examined resources using an event-based probabilistic approach. A robust coefficient of variation is introduced to examine the variability of each resource and a joint coefficient of variation is implemented for the first time to evaluate the joint variability of offshore wind and solar potential. The association between the resources is examined by introducing a robust measure of correlation, along with the Pearson's r and Kendall's tau correlation coefficient and the corresponding results are compared. Several metrics are used to examine the degree of complementarity affected by variability and intermittency issues. Areas with high potential and low variability for both resources include the Aegean and Alboran seas, while significant synergy (over 52%) is identified in the gulfs of Lion, Gabes and Sidra, Aegean Sea and northern Cyprus Isl. The advantage of combining these two resources is highlighted at selected locations in terms of the monthly energy production. 63 pages, 19 figures

One preliminary key step for developing an offshore wind farm is identifying favorable sites. The process of sitting involves multiple requirements and constraints, and therefore, its feasible implementation requires either approximating assumptions or an optimization method that is capable of handling non-linear relationships and heterogeneous factors. A new optimization method is proposed to address this problem that efficiently and accurately combines essential technical criteria, such as wind speed, water depth, and distance from shore, to identify favorable areas for offshore wind farm development through a user-friendly data-driven tool. Appropriate ranks and weighting factors are carefully selected to obtain realistic results. The proposed methodology is applied in the central Aegean Sea, which has a high offshore wind energy potential. The application of the proposed optimization method reveals large areas suitable for developing floating wind energy structures. The algorithm matches the accuracy of the exhaustive search method. It, therefore, produces the optimum outcome, however, at a lower computational expense demonstrating the proposed method’s potential for larger spatial-scale analysis and use as a decision support tool.

In this study long-term wind data obtained from high-resolution hindcast simulations is used to analytically assess offshore wind power potential in the Aegean and Ionian Seas and provide wind climate and wind power potential characteristics at selected locations, where offshore wind farms are at the concept/planning phase. After ensuring the good model performance through detailed validation against buoy measurements, offshore wind speed and wind direction at 10 m above sea level are statistically analyzed on the annual and seasonal time scale. The spatial distribution of the mean wind speed and wind direction are provided in the appropriate time scales, along with the mean annual and the inter-annual variability; these statistical quantities are useful in the offshore wind energy sector as regards the preliminary identification of favorable sites for exploitation of offshore wind energy. Moreover, the offshore wind power potential and its variability are also estimated at 80 m height above sea level. The obtained results reveal that there are specific areas in the central and the eastern Aegean Sea that combine intense annual winds with low variability; the annual offshore wind power potential in these areas reach values close to 900 W/m2, suggesting that a detailed assessment of offshore wind energy would be worth noticing and could lead in attractive investments. Furthermore, as a rough estimate of the availability factor, the equiprobable contours of the event [4 m/s ≤ wind speed ≤ 25 m/s] are also estimated and presented. The selected lower and upper bounds of wind speed correspond to typical cut-in and cut-out wind speed thresholds, respectively, for commercial offshore wind turbines. Finally, for seven offshore wind farms that are at the concept/planning phase the main wind climate and wind power density characteristics are also provided.

For offshore wind energy assessment it is necessary to appropriately model and describe wind climate. In this connection, the Rayleigh and Weibull distributions are widely suggested for offshore wind speed modelling. Although the use of these distributions is theoretically consistent, in practice, they are often proved to be inadequate. In two recently published papers some, less known, multi-parameter distributions (Johnson SB, Kappa and Wakeby) were introduced and proved to describe more accurately the stochastic behaviour of wind speed measurements obtained from buoys located in entirely different sea areas of the world. In order to evaluate their fitting performance with reference to coastal wind speed data, in this paper we assessed long-term time series obtained from ten meteorological land-based stations across the Italian coasts. The obtained results confirmed that the Johnson SB, Kappa and Wakeby distributions are of general validity for any wind data set analysed, since they performed fairly well for the modelling of coastal wind speeds as well. These distributions adapted better than the Weibull and are suggested as reliable and prominent candidates for the modelling of offshore and coastal wind speed in any sea area.

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.

In this work, an extended overview of the marine renewable energy in the Mediterranean Sea is provided as regards current status, potential problems, challenges, and perspectives of development. An integrated and holistic approach is necessary for the economic viability and sustainability of marine renewable energy projects; this approach comprises three different frameworks, not always aligned, i.e., geotechnical/engineering, socio-economic, and environmental/ecological frameworks. In this context, the geomorphological, climatological, socio-economic, and environmental/ecological particularities of the Mediterranean basin are discussed, as they constitute key issues of the spatial context in which marine renewable energy projects are to be implemented. General guidelines for the sustainable development of marine renewable energy in the Mediterranean are also provided.

Abstract. The development of offshore wind farms (OWFs) and the establishment of marine protected areas (MPAs) comprise two main elements for the production of clean energy, and the simultaneous maintenance and protection of biodiversity in the Mediterranean and Black seas. Successful, efficient, and sustainable coupling of these two aspects presumes that the criteria for selecting suitable locations for the deployment of OWFs should not only include technical-engineering terms (e.g. high wind energy efficiency, bottom suitability, inland infrastructures) but also ecological–environmental considerations (e.g. the least possible impact on biodiversity, ecosystem functioning) and socio-economic aspects (e.g. effects on coastal and marine activities, development of marine spatial planning). In the context of the FP7 CoCoNet project, the integration between OWFs and MPAs is based on four main steps: (i) the identification of existing (networks of) MPAs focusing on the biodiversity distribution patterns and current legislation, (ii) the coupling of offshore wind potential within networks of MPAs, (iii) the evaluation of the knowledge gained up to date and the theoretical approaches at the two pilot sites of the Mediterranean and Black sea basins, and (iv) the development of the "Smart Wind Chart", a convenient and rational tool addressed to scientists and policy makers for the evaluation of maritime policy management schemes. The latter step comprises the core of this work.