Offshore Wind and Wave Energy Complementarity in the Greek Seas Based on ERA5 Data

Abstract

In this work, 20 years (2000–2019) of ERA5 wave and wind data are analyzed and evaluated for the Greek Seas by means of in-situ measurements derived from the POSEIDON marine monitoring system. Four different statistical measures were used at six locations, where in-situ wind and wave measurements are available from oceanographic buoys. Furthermore, the ERA5 wind and wave datasets were utilized for the estimation of the available wind and wave energy potential for the Greek Seas, as well as for the assessment of complementarity and synergy between the two resources. In this respect, an event-based approach was adopted. The spatial distribution of the available wind and wave energy potential resembles qualitatively and quantitatively the distributions derived from other reanalysis datasets. Locations with high synergy and complementarity indices were identified taking into account water depth. Finally, taking into consideration a particular offshore wind turbine power curve and the power matrix of the PELAMIS wave energy converter, the estimation of the combined energy potential on a mean annual basis is performed.

Offshore wind resource assessment using reanalysis data

“…The accuracy of reanalysis data is commonly evaluated by comparison with measured data using statistical analysis (Kardakaris et al, 2021; Majidi Nezhad et al, 2021a). The evaluation of the reanalysis data is initial step in wind resource assessment.…”

“…ERA5 data provides wind data having temporal resolution of 1-hour, spatial resolution of 30 km (0.28125° × 0. 28125°), elevation levels of 10 and 100 m and 137 pressure levels from 1979 to date (Kardakaris et al, 2021; Olauson, 2018). The use of satellite and reanalysis data has been reported for wind resource assessment in previous studies (Gil Ruiz et al, 2021; Kistler et al, 2001; Kokkos et al, 2021; Majidi Nezhad et al, 2021a; Nezhad et al, 2021b, 2021c).…”

“…The evaluation of reanalysis data against in-situ measured data is performed using statistical parameters, and reanalysis data with known accuracy is used for wind resource assessment as reported in recent literature (Gil Ruiz et al, 2021; Kistler et al, 2001; Kokkos et al, 2021; Nezhad et al, 2021c). The assessment based on wind speed is usually reported in previous literature (de Assis Tavares et al, 2020; Kardakaris et al, 2021; Nezhad et al, 2021b; Taoufik and Fekri, 2021) but use of wind direction is not common and reported in few studies (Gil Ruiz et al, 2021; Kokkos et al, 2021). The wind resource assessment using reanalysis data by European Center for Medium-Range Weather Forecasts (ECMWF) is commonly reported in previous literature (Gil Ruiz et al, 2021; Kardakaris et al, 2021; Nezhad et al, 2021b, 2021c).…”

“…Nezhad et al (2021b) evaluated ERA-interim reanalysis data against measured data at 90 m height for Samothraki island in the Mediterranean using statistical analysis, the offshore wind farm site assessment was reported in terms of contour plots and time-series of annual and monthly mean wind speed. Kardakaris et al (2021) evaluated ERA5 reanalysis data against buoys measured data at six different locations at 3 m height using statistical analysis, the wind resource was assessed at 100 m using reanalysis data in terms of contour plots for annual mean WPD. Gil Ruiz et al (2021) evaluated ERA5 reanalysis data with measured data from 13 sites at 10 m height in the Caribbean region of Colombia, wind resource assessment was performed in terms of Weibull probability distributions, wind rose diagrams and contour plots of monthly mean wind speed and WPD.…”

Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data

“…Worldwide, ecofriendly energy development is being actively carried out to protect the environment and reduce carbon dioxide emissions [7]. Wind power is one of the fastest-growing ecofriendly energy sources [8].…”