A Preliminary Assessment of Offshore Winds at the Potential Organized Development Areas of the Greek Seas Using CERRA Dataset

Abstract

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity.

1. Introduction

The Greek Seas is among the top-ranked locations in the Mediterranean basinregarding offshore wind energy development. Previous studies [1,2,3] indicated that the Aegean Sea is considered one of the windiest areas in the Mediterranean, mainly due to the Etesian winds that occur during the summer season [4,5], contributing to a steadier energy production throughout the year. The development of offshore wind farms (OWF) is an important national strategy and is expected to significantly enhance the energy transition plan and contribute to energy security by providing clean and affordable energy to the energy mix of the country.

Despite the significant offshore wind potential, several factors have hindered the development of OWF in Greece. The main technical obstacle is the deep coastal waters that entail the development of floating OWF projects. Floating offshore wind technology has been applied globally in pre-commercial projects so far, while its expansion towards technology industrialization and commercial viability has been prioritized by many countries, including Greece. Other key challenges are the transmission system and the necessary infrastructure to support and build capacity for optimization of the technology at various stages of development.

The Hellenic Parliament passed on 30 July 2022 a new legislation (L. 4964/2022, GGI A’50) concerning the development of offshore wind energy and the associated licensing procedures. According to Article 66 of this law, the Greek State has the exclusive responsibility for the research, exploration, and identification of the potential OWFODA, as well as the authority to grant the corresponding rights to an institution. On behalf of the Greek State, the Hellenic Hydrocarbons & Energy Resources Management Company SA (HEREMA) has been nominated as the entity for the management of the above-mentioned rights. In the following, when the term “OWFODA” is used, it should be understood as “potential OWFODA”.

In the absence of a Maritime Spatial Plan, the elaboration and development of the NDP—OWF is a critical milestone for the realization of the offshore wind projects. The draft NDP—OWF, along with the Strategic Environmental Impact Assessment (SEIA), was published on 31 October 2023 [6], and it provides the most important aspects for the development of OWF while also defining the medium (2030–2032) and long-term (from 2030–2032 onwards) targets regarding the available estimated capacity. It includes, among others, the preliminary delimitation of the 23 OWFODA that consist in 30 potential sites (polygons), and a preliminary estimation of their potentially available capacities, along with their prioritization in the medium and long term. The main spatial restrictions that were adopted for the Aegean and Ionian Seas refer to the minimum (1 nm) and the maximum distance from the baseline (6 nm for the Aegean and 12 nm for the Ionian Sea). Twenty (20) exclusion criteria were directly applied, which were related to specific technical restrictions, environmental conditions, cultural heritage sites, infrastructure networks, and rules and proposals from competent authorities, while sixteen (16) additional exclusion criteria were indirectly taken into account as the applied minimum distance from the baseline (e.g., 1 nm) overcame the required minimum distances from the SSF-RES. The lower wind speed thresholds that were defined for the development of fixed-bottom and floating OWF were set at 6.5 m/s and 8 m/s (at 100 m asl), respectively, while areas with water depths greater than 1000 m have also been excluded from the analysis due to techno-economic reasons.

In this work, the preliminary estimation of the technically available offshore wind energy in Greece is performed. Firstly, the main wind speed characteristics of the Greek Seas are estimated at the annual and seasonal time scales, including the annual and interannual variability as well as the estimation of the statistically significant long-term trends. In addition, $n-$years ($n=10,20,\dots ,100$) return levels (design values) of wind speed are estimated based on the block maxima approach. The accuracy of the recently released CERRA reanalysis wind dataset with around 5.5 km × 5.5 km horizontal resolution is also examined regarding its suitability for offshore wind analysis and wind energy applications in the Greek Seas. CERRA reveals important local wind features that were shadowed from hindcast models of lower spatial resolution; e.g., [1,3,7]. Soukissian et al. [7] assessed the wind speed and direction over the Greek Seas using long-term (1995–2009) hindcast wind data of 0.1° × 0.1° horizontal resolution (roughly corresponding to 11 km × 11 km), while Kardakaris et al. [3] performed the wind power density analysis over the same area using the ERA5 reanalysis dataset of 0.25° × 0.25° spatial resolution (roughly corresponding to 28 km × 28 km). The same dataset has also been used by Soukissian and Sotiriou [1] for assessing the wind speed variability over the entire Mediterranean basin. At the OWFODA spatial scale, the analysis refers firstly to the wind speed and wind power density assessment at the hourly and annual time scale, along with the estimation of the local extreme wind speed characteristics and the identification of the long-term trends. The wind energy production analysis is performed at the annual, monthly, daily, and hourly time scales, while spatial correlation and cross-correlation analysis reveal important synergy characteristics between the OWFODA. The aim of this work is of great interest to the global offshore wind energy industry that plans to invest in Greece since it provides baseline information regarding the wind climate of the Greek Seas and the energy characteristics of the OWFODA.

For the preliminary estimation of the technically available offshore wind energy in Greece, two additional parameters are assumed, i.e., the wind turbine hub height and the turbine nominal capacity. Another assumption is that the same wind turbines operate at the same hub heights at all examined OWFODA. According to Wind Europe [8], the mean power rating of offshore wind turbines ordered in 2023 was 14.9 MW (12.2 MW in 2022). Taking this number as a conservative estimate of the offshore wind turbine capacity for OWF development in Greece from 2030 onwards, we assume a wind turbine nominal capacity of 15 MW and a hub height of 150 m asl, which is a realistic assumption (See https://www.vestas.com/en/energy-solutions/offshore-wind-turbines/V236-15MW, accessed on 29 January 2025; https://www.gevernova.com/wind-power/offshore-wind/haliade-x-offshore-turbine, accessed on 29 January 2025). Let it be noted that CERRA provides wind speed directly at several heights, including 150 m asl; thus, there is no need to extrapolate model wind speeds. In this context and since the (draft) delimitation of the OWFODA is already available, the preliminary estimation of the technically available offshore wind energy in the Greek seas is feasible and is performed here for the first time.

The original contributions of this work, which, as far as we are aware, are provided for the first time here, refer to the following:

• The evaluation of CERRA wind speed and direction data for the Greek Seas is based on in situ wind measurements.
• The detailed characterization of the wind climate and the assessment of wind power density at the OWFODA, including long-term trends and extreme value analysis of wind speed. Long-term trends should be taken into account in the feasibility study of an OWF, while extreme wind speed is a critical design parameter for offshore wind turbines.
• The preliminary evaluation of the offshore wind energy production at the OWFODA at the annual, monthly, and daily time scales. Monthly scale reveals the seasonality characteristics of the produced energy, while the daily and hourly time scales will greatly facilitate the efficient design and management of the energy transmission network.
• Finally, the identification of synergies and complementarities between the OWFODA is of utmost importance as it regards the coordination of the electric power transmission and distribution system.

The structure of this work is as follows: The wind data sources that are used are described in Section 2. The theoretical background and the methodology are provided in Section 3 along with the statistical procedures and the evaluation metrics that are implemented. In Section 4, the available wind data sources are statistically compared, and the evaluation of the CERRA wind dataset is presented. Section 5 presents the results regarding the analysis of offshore wind speed. Section 6 and Section 7 include the detailed, yet preliminary, results regarding wind speed characteristics and the technically exploitable offshore wind energy of the OWFODA, respectively. Section 8 refers to various correlation, synergy, and complementarity aspects between the energy production of the OWFODA, and in Section 9, the main findings and conclusions of this work are summarized.

2. Wind Data Sources

2.1. In Situ Wind Data

Long-term-measured wind speed data from six buoys are analyzed and used for the evaluation of the CERRA wind dataset: (i) two (2) buoys 68422-Pylos and 61277-Crete, which provide ocean in situ data from the Copernicus Marine Service (https://marineinsitu.eu/dashboard/, accessed on 29 January 2025) and (ii) four (4) floating oceanographic buoys, located near the islands of Mykonos, Lesvos, and Santorini, and Athos Peninsula. These buoys are part of the POSEIDON network [9,10], which started operating in 1999 by the Hellenic Centre for Marine Research (HCMR). In

Table 1. Station names, geographical coordinates, overlapping time periods, and common sample sizes between collocated CERRA and buoy wind data.

Buoy Name/Code	Latitude–Longitude	Overlapping Time Periods	Sample Size
68422 (PYL)	[36.83° N, 21.61° E]	2007–2020	24,445
61277 (CRE)	[35.73° N, 25.13° E]	2007–2020	24,741
ATH (Athos)	[39.97° N, 24.72° E]	2000–2015	31,849
LES (Lesvos)	[39.17° N, 25.81° E]	2000–2012	27,418
MΥΚ (Mykonos)	[37.51° N, 25.46° E]	2000–2012	27,209
SAN (Santorini)	[36.26° N, 25.50° E]	2000–2012	30,831

, the locations of the 6 buoys are listed, along with the overlapping time periods and the common sample sizes of buoy measurements and CERRA model results (see also Figure 1). All wind speed and direction measurements are conducted at 3 m asl with a recording interval of 3 h and a recording period of 10 min.

2.2. CERRA Reanalysis System

Since there is currently a lack of offshore wind measurements in the OWFODA, the only way for the preliminary wind energy assessment is to use high spatial and temporal resolution reanalysis wind data. Accurate and high-resolution wind speed data are essential for a variety of applications that depend on the reliable estimation of the atmospheric state. Taking into consideration the scarcity of wind speed data over sea areas where the deployment of site-specific instruments (e.g., fixed or floating masts, buoys, LIDARs, etc.) is more difficult and cost-intensive than onshore, the use of reanalysis/hindcast products [3,7,11] has gained a lot of attention in wind energy studies among related methods, such as satellite [12,13,14,15] and gridded products constructed by interpolating observational data [16,17].

Wind reanalysis datasets are a combination of atmospheric models and wind observational data spanning back several decades using an assimilation scheme. Specifically, over a long-time span, these reanalysis datasets can provide wind speed estimates for every grid point of the model domain at each assimilation time and level. Thus, the provision of wind information over extended areas at multiple vertical atmospheric levels [18,19], regardless of the coverage of observational networks, is an improved tool for better understanding the wind patterns. Some of the advantages of the reanalysis data are summarized as follows:

• The great lengths of the relevant time series, without gaps, allow for the estimation of interannual variability, long-term/climatic trends, etc.
• The spatial coverage extends to remote and offshore locations.
• Reanalysis data are usually provided for free.

The Copernicus European Regional Reanalysis (CERRA) is a high spatial resolution reanalysis product that was released in August 2022 [20]. The CERRA dataset has been validated in terms of air temperature, relative humidity, 10 m wind speed, and global solar radiation in Greece by using up to 11-year-long ground-based observations. It has also been compared to the ERA5-Land dataset [21]. The results of the latter study revealed that the CERRA dataset outperformed significantly the ERA5-Land reanalysis data with respect to the measured meteorological variables.

CERRA was initially developed by a collaboration of the Swedish Meteorological and Hydrological Institute (project leader) with Météo-France and the Norwegian Meteorological Institute. The CERRA reanalysis system is based on the “Hirlam Aladin Research Mesoscale Operational NWP In Europe” (HARMONIE) numerical weather prediction system with ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) physics and dynamics. For the surface and upper-air analyses, it uses an optimal interpolation assimilation scheme and a 3D variational data assimilation scheme, respectively, with a horizontal resolution of 5.5 km and a vertical one of 106 levels. The ERA5 reanalysis is used as input for the boundary conditions of CERRA. Apart from the high spatial resolution, additional improvements of the CERRA system include the following:

• The assimilation of additional observations, available from the observing system, throughout the reanalysis period in order to represent the atmospheric conditions more accurately. These observations are obtained from ECMWF’s Meteorological Archival and Retrieval System (MARS) and European Centre File Storage system and include conventional (e.g., synoptic surface observations, drifting buoys, ships) and other observations, such as scatterometer and radiance observations. In this respect, let it be noted that the buoy measurements used in this work for the evaluation of the CERRA data have not been used in the assimilation procedure [22]. See also El-Said et al. [23] where a detailed description of the CERRA assimilation procedure is provided.
• A coupling between the Ensemble Data Assimilation system with the CERRA system to estimate the background error covariance matrix (B-Matrix) with flow-dependency updates to sufficiently represent errors when changes in weather regime are detected [23].

The model domain spans from northern Africa beyond the northern tip of Scandinavia and from the Atlantic Ocean up to the Ural Mountains. The model grid is Lambert conformal conic, comprising 1069 × 1069 grid points. CERRA covers a period of 36 years (1985–2020), which is constantly updated, and assimilates eight times per day (i.e., 3 h temporal resolution). The dataset can be obtained through the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/#!/home, accessed on 13 August 2023).

3. Methodology and Theoretical Background

3.1. Information on the OWFODA

From the 23 OWFODA that were identified, 10 and 13 OWFODA were prioritized for the medium- and long-term horizon, respectively, i.e., 23 OWFODA in total. Most of the OWFODA are mainly designated for floating wind turbine deployment, with a total estimated capacity of 10.4 GW. For fixed-bottom OWF, the estimated capacity is 1.4 GW. Two additional locations (Pilot1 and Pilot2) were also defined for the development of pilot OWF projects, with a potential capacity of up to 600 MW and a total area of 353 km². Note, however, that the final boundaries of the pilot projects will be determined after the approval of the Hellenic National Defense General Staff and Civil Aviation Authority. The total span of OWFODA is 2712 km², and the minimum foreseen capacity is 12.4 GW, including the two pilot areas. The locations of the potential floating and bottom-fixed OWFs for the medium-term horizon are shown in Figure 2, and for the long-term horizon in Figure 3. It should be noted, however, that OWF installation area(s) within each OWFODA will be proposed at the next stage of technical and SEIA studies, and their boundaries will be finalized after the monitoring campaigns. The areas that have been selected for development at the medium-term horizon, along with the foreseen maximum capacities, are the following:

• Crete1 and Crete2 (800 MW),
• Rhodes (300 MW–550 MW),
• Gyaros and Donousa (200 MW–450 MW),
• Ag. Apostoli and Chios (300 MW), and
• Diapontia and Patras (450 MW).

Figure 2. OWFODA planned to be developed in the medium-term horizon according to [6]. Bathymetric data obtained from [24].

Figure 2. OWFODA planned to be developed in the medium-term horizon according to [6]. Bathymetric data obtained from [24].

Figure 3. OWFODA planned to be developed in the long-term horizon according to [6]. Bathymetric data obtained from [24].

Figure 3. OWFODA planned to be developed in the long-term horizon according to [6]. Bathymetric data obtained from [24].

The potential OWFODA planned to be developed in the medium-term horizon are summarized in

Table 2. List of potential OWFODA and corresponding short names.

Full OWFODA Name	Short Name
Ag. Apostoli	O1
Chios	O2
Crete1	O3
Crete2A	O4
Crete2B	O5
Diapontia	O6
Donousa2	O7
Patras	O8
GyarosA	O9
GyarosB	O10
GyarosC	O11
Pilot1A	O12
Pilot1B	O13
Rhodes	O14

, along with the short names that will be used in the numerical results sections.

3.2. Statistical Analysis of Wind Speed and Wind Power Density

For the spatial analysis of wind speed and wind power density and their variability at the annual, seasonal, or monthly, and daily times scales, the terminology of Soukissian et al. [2] is adopted.

For the linear trend identification of wind speed $w_S$ and the estimation of the corresponding slope, the Theil–Sen (TS) estimator is used [25,26]. This estimator is the median of the slopes of all possible combinations of pairs of points $\left(w_S,t\right)$, where $t$ is the time variable. The TS estimator is resistant to outliers and very robust compared to the estimator obtained from the ordinary least squares approach [27]. The non-parametric Mann–Kendall (MK) test is also used to detect any monotonic trends in time series. A detailed description of the MK test can be found in Appendix B of [2].

The estimation of wind speed $n-$year design values is based on the block maxima approach since the available wind speed time series length (36 years) is adequate for this purpose [28,29]. The estimation of the parameters of the generalized extreme value (GEV) distribution can be performed using a variety of methods, such as the maximum likelihood (which is used here), the probability weighted moments, the L-moments, the maximum product spacings, etc. An analytic description of these methods is provided in [28].

The mean wind power density $\overline{P}$ (W/m²) is estimated directly by utilizing the long time series of wind speed at the location of interest. For a particular location (in our case, a grid point), $\overline{P}$ is estimated by the following equation:

$$\overline{P}={\displaystyle \frac{1}{2N}}{\sum }_{i=1}^N\rho {w_S}_i^3,$$

(1)

where $N$ is the sample size of the time series; $\rho$ is the air density, considered in this work constant and equal to 1.2258 kg/m³, see e.g., [30,31]; and ${w_S}_i=w_S\left(t_i\right), i=\mathrm{1,2},\dots ,N$, is the value of wind speed at the time instant $t_i$. The constant value of 1.2258 kg/m³ is valid at standard conditions with a temperature of 15 °C and pressure of 1013 hPa.

A wind steadiness metric will also be used in the wind speed analysis of the OWFODA. The most commonly used and useful for practical offshore wind applications is the wind speed persistence (duration). In this context, the procedure suggested in [32] will be followed. Specifically, the examined wind speed time series ${w_S}_i, i=1, 2,\dots ,N$ will be first sorted in ascending order, and the sorted time series ${w_S}_{\left(i\right)}, i=1, 2,\dots ,N$ will be obtained. Note that, in this case, $i$ denotes the $i$th observation in the ascending series. Then the percentage of time for which ${w_S}_i\ge {w_S}_{\left(i\right)}$ (or the probability of occurrence of the event $A=\left[{w_S}_i\ge {w_S}_{\left(i\right)}\right]$) is given as follows:

$$P\left({w_S}_{\left(i\right)}\right)=100\times {\displaystyle \frac{N+1-\mathrm{i}}{N+1}}.$$

(2)

Note that similar results can also be obtained for the operational range of any wind turbine, i.e., of the event $A=\left[u_{cut-in}\le {w_S}_i\le u_{cut-out}\right]$, where $u_{cut-in}$ and $u_{cut-out}$ are the cut-in and cut-out speeds of the wind turbine, respectively. See also [33,34].

3.3. Offshore Wind Turbine and Annual Energy Production

Consider a wind turbine with a power curve $P_T\left(w\right)$; then, the energy output $E_T\left(w\right)$ is obtained as follows:

$$E_T=T_r{\int }_{u_{cut-in}}^{u_{cut-out}}{P}_T\left(w\right)f_W\left(w\right)dw,$$

(3)

where $u_{cut-in}$ and $u_{cut-out}$ are the cut-in and cut-out speeds, respectively, of the particular turbine; $f_W\left(w\right)$ is the probability density function of wind speed; and $T_r$ is the reference time period. When long time series of wind speed is available, the annual energy production $\left(AEP\right)$ for a particular year $j$ of a single wind turbine can be estimated as follows:

$${AEP}_T\left(j\right)={\sum }_{i=1}^n{P}_T\left(w_{i,j}\right)\delta \left(t_i\right),i=1,2,\dots ,n,$$

(4)

where $w_{i,j}$, $i=1, 2,\dots ,n$, is wind speed for year $j$, and $\delta \left(t_i\right)$ is the time step of the wind speed time series (in the CERRA case $\delta \left(t_i\right)$= 3 h). The mean $AEP$ is easily estimated as follows:

$${\overline{AEP}}_T={\displaystyle \frac{1}{J}}{\sum }_{j=1}^J{AEP}_T\left(j\right),$$

(5)

where $J$ is the total number of years.

The capacity factor of an offshore wind turbine is the average energy produced by the wind turbine during a year divided by the energy available in wind during the same year:

$${CF}_T={\displaystyle \frac{{AEP}_T}{T_{ry}{P}_{T,NOM}}},$$

(6)

where ${AEP}_T$ is the energy produced during a year, $P_{T,NOM}$ is the wind turbine nominal power, and $T_{ry}$ is the number of hours in that year.

A simplified estimation of the annual energy production of an OWF, from a starting year (say 1) to a year (say $N$) is provided through the following relation:

$${AEP}_{OWF}={\displaystyle \frac{1}{N}}\left[\alpha \times \eta \times {\sum }_{j=1}^{j=N}{E}_j\right],$$

(7)

where $E_j$ is the overall energy generated from the OWF in year $j$, $\alpha$ is the farm availability [35], and $\eta$ is the overall energy efficiency of the farm, taking into consideration the energy losses (mainly electrical and aerodynamic). In this paper, the energy availability $\alpha$ for an OWF is considered equal to 94%, and $\eta$ is equal to 90.5%, assuming 1.8% of electrical losses and 7.7% of aerodynamics losses, as is suggested in [36]. Note also that the technical efficiency of an OWF does not decline with age [37].

3.4. The IEA 15-MW Offshore Wind Turbine

The IEA 15-MW upwind offshore wind turbine of the Renewable Energy Laboratory (NREL) is considered in this work. This is an IEC Class 1B wind turbine. Its main characteristics are provided in

Table 3. Main characteristics of the IEA 15-MW wind turbine.

Parameter	Value
$P_{TR}$	15 MW
$S_P$	332 W/m2
$u_{cut-in}$	3 m/s
$u_R$	10.59 m/s
$u_{cut-out}$	25 m/s
$D$	240
$H$	150 m
$N_B$	3

[38], where $P_{TR}$ is the rated power, $S_P$ is the specific power (i.e., the ratio of the rated power to the swept area), $D$ is the diameter of the rotor, $H$ is the hub height, and $N_B$ is the number of blades. The corresponding power curve is provided in Figure 4.

3.5. Collocation of Datasets in Space and Time

The CERRA wind speed and wind direction are spatially and temporally co-located with in situ wind measurements at the selected locations by using the following approach.

Since wind direction is provided using the meteorological convention (i.e., direction from), the $u$ and $v$ components of wind speed are estimated as follows:

$$\begin{gathered} u_{i,j}=-\left|{w_S}_{i,j}\right| \mathrm{s}\mathrm{i}\mathrm{n}\left({w_D}_{i,j}\right),v_{i,j}=-\left|{w_S}_{i,j}\right| \mathrm{c}\mathrm{o}\mathrm{s}\left({w_D}_{i,j}\right), \\ i=1,2,\dots ,n,j=1,2,3,4, \end{gathered}$$

(8)

where $w_S$ and $w_D$ are the wind speed and direction, respectively, derived from CERRA at the four grid points closest to the location (say L) of interest, $j=1, 2, 3, 4$ denotes the location around L, and $i=1, 2,\dots ,n$ denotes the particular observation of the time series. Then, the $u_L$ and $v_L$ components at the location of interest L are estimated as follows:

$$u_{i,L}={\displaystyle \frac{\sum _{j=1}^4\frac{u_{i,j}}{d_j^2}}{\sum _{j=1}^4\frac{1}{d_j^2}}},v_{i,L}={\displaystyle \frac{\sum _{j=1}^4\frac{v_{i,j}}{d_j^2}}{\sum _{j=1}^4\frac{1}{d_j^2}}},i=1,2,\dots ,n,$$

(9)

where $d_1,d_2,d_3,d_4$, are the corresponding distances from L. The corresponding wind speed and direction time series can then be estimated as follows:

$${w_S}_{i,L}=\sqrt{{u_{i,L}}^2+{v_{i,L}}^2},i=1,2,\dots ,n,$$

(10)

and

$${w_D}_{i,L}=\mathrm{m}\mathrm{o}\mathrm{d}\left(180+{\displaystyle \frac{180}{\pi }}\mathrm{a}\mathrm{t}\mathrm{a}\mathrm{n}2\left(u_{i,L},v_{i,L}\right),360\right),i=1,2,\dots ,n,$$

(11)

respectively (https://confluence.ecmwf.int/pages/viewpage.action?pageId=133262398, accessed on 17 January 2025).

4. Evaluation of the CERRA Wind Dataset

4.1. Evaluation of Wind Speed

For wind speed comparison and evaluation purposes, the common reference height was set at 10 m asl for both wind data sources, corresponding to the CERRA reference height. Thus, the buoy wind speed data (originally provided at 3 m asl) were adjusted to 10 m asl by using the log-law with roughness length equal to 0.0001 m, considering that the atmospheric conditions are neutral. Let it be noted that the commonly used WAsP software (Version 2) for wind speed estimation used $z_0=0.0002 \mathrm{m}$ [39] (see also [40]), while as is concluded in [41], an inaccurate surface roughness length does not play a significant role in wind speed prediction. Moreover, the relative difference between the wind speeds adjusted from 3 m asl to 10 m asl using the simplified log-law with $z_0=0.0001 \mathrm{m}$ and $z_0=0.001 \mathrm{m}$ is only 3%.

For the evaluation of the CERRA wind speed, the following statistics are used: mean bias ($b$), mean absolute error ($MAE$), mean relative absolute error ($MRAE$), root-mean-squared error ($RMSE$), and normalized root-mean-squared error ($NRMSE$). Moreover, the Pearson (product moment) correlation coefficient $\rho$ will also be estimated.

The evaluation statistics with regard to the 3 h CERRA wind speed performance compared to buoy measurements are presented in

Table 4. Evaluation statistics of CERRA wind speed with respect to measured wind speed.

Buoy Name	$\mathit{b}$ (m/s)	$\mathit{M}\mathit{A}\mathit{E}$ (m/s)	$\mathit{R}\mathit{M}\mathit{S}\mathit{E}$ (m/s)	$\mathit{N}\mathit{R}\mathit{M}\mathit{S}\mathit{E}$ (%)	${\widehat{\mathit{\rho }}}_{\mathit{B}\mathit{C}}$
PYL	−0.108	1.373	1.843	8.921	0.824
CRE	0.122	1.319	1.821	8.699	0.824
ATH	0.463	1.494	1.951	9.338	0.880
LES	−0.506	1.553	2.163	8.820	0.840
MΥΚ	0.456	1.803	2.381	7.968	0.788
SAN	0.176	1.515	1.986	9.347	0.813

, (see also Figure 1). CERRA wind speeds are overall in good agreement with wind speed measurements. The smallest absolute bias (−0.108 m/s) is found for PYL, and then for CRE (0.122 m/s) and SAN (0.176 m/s), whereas the highest absolute value (−0.506 m/s) is observed for LES. The smallest values of $MAE$ (1.319 m/s) and $RMSE$ (1.821 m/s) are observed for CRE, while all $MAE$ values are smaller than 1.553 m/s (except for MYK) and all $RMSE$ values are between 1.82 and 2.16 m/s (except for MYK). $NRMSE$ fluctuates between 7.97% and 9.35% for all locations. Finally, the largest values of ${\widehat{\rho }}_{BC}$ correspond to ATH (0.88), LES (0.84), and CRE and PYL (0.824). Based on the results for bias, in general, CERRA underestimates the mean wind speeds (for 4 out of 6 locations) up to 0.463 m/s, while CERRA overestimates wind speed for PYL and LES. Overall, it seems that the performance of CERRA is good at PYL, CRE, and ATH, and it is of lower quality at MYK and LES. For comparison purposes, in

Table 5. Evaluation statistics of ERA5 wind speed with respect to measured wind speed.

Buoy Name	$\mathit{b}$ (m/s)	$\mathit{M}\mathit{A}\mathit{E}$ (m/s)	$\mathit{R}\mathit{M}\mathit{S}\mathit{E}$ (m/s)	$\mathit{N}\mathit{R}\mathit{M}\mathit{S}\mathit{E}$ (%)	$\widehat{\mathit{\rho }}$
PYL	−0.476	1.634	2.136	10.339	0.768
CRE	0.236	1.333	1.813	8.660	0.824
ATH	−0.484	1.573	2.180	8.888	0.833
LES	0.951	2.005	2.545	8.516	0.782
MΥΚ	0.132	1.575	2.049	9.807	0.857
SAN	−0.575	1.621	2.120	9.976	0.800

, the corresponding results for ERA5 are also provided. ERA5 reanalysis data have been produced by the European Centre for Medium-Range Weather Forecasts [42,43].

As can be seen from Table 4 and Table 5, CERRA performs better than ERA5 for the majority of the evaluation statistics and examined locations.

Let it be noted that in [44], where the evaluation of the ERA5 wind data against the New European Wind Atlas (NEWA, https://map.neweuropeanwindatlas.eu/, accessed on 24 September 2023) has been performed, the estimated $RMSE$ fluctuated between 1.81 m/s and 2.34 m/s, and these values have been considered acceptable. Fernandes et al. [40], evaluated ERA5 hourly wind data at 100 m asl in Brazil. $RMSE$ fluctuated between 1.63 m/s and 2.75 m/s, and the correlation coefficient was greater than 0.7; the authors concluded that the ERA5 database can be used in preliminary wind energy studies. Zhai et al. [45] evaluated the ERA5 wind and wave data based on buoy measurements from 15 locations in the South China Sea. Wind speed evaluations have presumably been performed at 10 m asl. It was found that the $RMSE$ fluctuated between 1.14 m/s and 2.46 m/s, and the mean bias fluctuated between −0.78 m/s and −0.99 m/s. They concluded that “the ERA5 wind speed … data show inspiring accordance with the buoy-measured data under non-extreme conditions”. Sifnioti et al. [46] evaluated the ERA-Interim reanalysis wind dataset at 10 m asl (with a temporal resolution of six hours) using in situ wind measurements from eight oceanographic buoys. The $RMSE$ fluctuated between 2.57 m/s and 2.14 m/s, the mean bias fluctuated between −0.38 m/s and 1.18 m/s, and the correlation coefficient between 0.71 and 0.83. They concluded that ERA-Interim wind data “could be regarded as representative for the Greek Seas, although their application should be made with caution regarding the assessment of extreme conditions”. Finally, in a very recent publication [47], the authors evaluated the ERA5 wind speed data across 19 offshore locations of the Chinese coastal waters. They used the wind speed obtained from offshore meteorological stations at 10 m and 100 m asl as reference measurements. For the wind speed at 10 m asl, they found that the mean bias ranged between −1.62 m/s and 3.18 m/s, and the mean absolute error ranged between 1.74 m/s and 3.25 m/s. They concluded that “when observational data are not available, ERA5 reanalysis wind data is very useful”.

4.2. Evaluation of Wind Direction

Regarding the evaluation of CERRA wind direction, the root-mean-squared error and the mean bias are used along with the circular-circular correlation coefficient ($r_{D_B{D}_C}$), where $D_B$ denotes the wind direction from the buoy and $D_C$ the wind direction from CERRA. See also [48]. In

Table 6. Evaluation statistics of CERRA wind direction with respect to measured wind direction.

Buoy Name	$\mathit{b}(°)$	$\mathit{R}\mathit{M}\mathit{S}\mathit{E}(°)$	${\mathit{r}}_{{\mathit{D}}_{\mathit{B}}{\mathit{D}}_{\mathit{C}}}$
PYL	−3.226	45.240	0.655
CRE	−5.684	43.576	0.758
ATH	−8.547	37.859	0.730
LES	−5.433	61.468	0.399
MΥΚ	−4.847	41.307	0.723
SAN	−6.911	39.327	0.772

, the evaluation statistics are presented.

CERRA wind directions are overall in good agreement with wind directions provided from all buoys. Specifically, the absolute bias ranges between 3.23° (for PYL) and 8.55° (for ATH). $RMSE$ also takes rather low values ranging between 37.86° and 61.47°, while $r_{D_B{D}_C}$ takes rather high values, greater than 0.65, except for LES. The low value of $r_{D_B{D}_C}$ and the high value of $RMSE$ in LES may be attributed to the effects of the complicated topography of the neighbouring coasts on wind speed patterns, which the existing model setup (an in particular the spatial resolution) cannot accurately resolve.

5. Offshore Wind Speed Assessment

5.1. Annual Time Scale

All the results provided in the next sections refer to the wind speed at 150 m asl which is directly provided by the CERRA reanalysis product. The following statistics are obtained: the mean annual value and variability ($MAV$), the interannual variability ($IAV$), the TS slopes of the mean annual values and the corresponding $p$-values, and the TS slopes of the annual 95th and 99th percentiles and corresponding $p$-values (at 95% confidence level). For wind direction, the mean annual value and the circular variance are estimated (not presented here).

The spatial distributions of the mean annual wind speed (upper row), the interannual variability (lower row left), and the mean annual variability (lower row right) are depicted in Figure 5. These results are qualitatively aligned with the wind patterns that have been identified in previous studies, e.g., see [7]. In this case, however, the high spatial resolution of CERRA reveals wind patterns that were hidden from previous studies, such as the wind tunnelling effect at the southern coasts of Crete Island.

The areas with the most intense wind climate of the Greek Seas are located along the northeast–southwest axis of the Aegean Sea, the area offshore the eastern coasts of Crete Island. (between Crete Island and Kasos Island), as well as the area offshore the southwestern coasts of Crete Island. $IAV$ and $MAV$ are overall characterized by low values (similar results with [7]), except for some coastal areas. The highest mean annual value (9.93 m/s) is encountered offshore the eastern coasts of Crete Island. (35.12° N–26.53° E). $IAV$ takes low values (fluctuating around 4%), while $MAV$ fluctuates around 58%.

The spatial distributions of the statistically significant (at 95% confidence level) TS slopes for the mean annual wind speed and the 95th and 99th annual percentile points are shown in Figure 6. The statistically significant slopes are marked with an “X”.

The highest positive statistically significant value of the TS slope (0.014 m/s/year) occurs offshore the southwestern coasts of Crete Island, and the lowest negative (−0.023 m/s/year) in the area between the eastern coasts of Rhodes Island and the Turkish coast. Overall, for most of the Greek Seas, the TS slopes take negative values, suggesting a decreasing trend with regard to wind speed. Assuming that the identified trends will persist in the future, this must be taken into account, along with potential climate change effects, in the design of any offshore wind energy project. See also Section 6.3.1, where the trends for the OWFODA are presented analytically. The statistically significant TS slopes of the 95th annual percentile points of wind speed take negative values overall, suggesting a decreasing trend, with the highest positive value (0.026 m/s/year) being presented in the offshore area of the Ionian Sea and the lowest negative value (−0.065 m/s/year) in the Gulf of Patras. On the other hand, for the 99th annual percentile points, the statistically significant TS slope takes positive values at the northwestern and southwestern coasts of Crete Island and the western coast of the Aegean Sea, and negative values at the southeastern and eastern coasts of Crete Island. Specifically, the highest positive value (0.054 m/s/year) occurs offshore the southwestern coasts of Crete Island, and the lowest negative value (−0.05 m/s/year) on the southeastern coasts of Crete Island. Results from previous studies, using datasets with different spatial and temporal resolutions, were similar to the ones discussed herein, indicating the decrease in the central Aegean and the increasing trend in the Ionian [2]. Soukissian and Sotiriou [1] observed the overall highest slope in the SW coasts of Crete and the highest negative slope in Rhodes Island, which is in line with the results of the present study. Overall, for the areas of the southwestern Aegean Sea–Levantine basin, there is a negative trend signal for both annual and extreme wind speeds, while for the rest of the Aegean and the Ionian Seas, the trend signal is positive.

5.2. Seasonal Time Scale

The spatial distribution of the mean seasonal wind speed is depicted in Figure 7.

For winter, high wind speed values are observed for most of the Greek offshore areas, ranging from 7 to 12 m/s. The highest values are encountered in the northern Aegean Sea, exceeding 11 m/s. The most energetic areas are the eastern parts of Lemnos Island and the surrounding areas up to Samothraki and down to Ag. Efstratios Islands, with E and NE directions, and the offshore areas of the western and eastern coasts of Crete Island, with wind speeds exceeding 9 and 8 m/s, with NNW and W directions, respectively. In the eastern—central Aegean, wind speeds are between 9 and 10 m/s. In spring, there is a notable wind speed decrease, with values below 10 m/s. The highest wind speeds are observed near the western and eastern coasts of Crete Island. Summer is the windiest season of the year, with the highest wind speed values observed in the Aegean Sea. The windiest areas are the eastern offshore areas of Crete Island, and the straits between Kasos, southeast Karpathos, and southwest Rhodes Island, with NW winds exceeding 13 m/s. In the northern, central, and eastern Aegean, wind speeds exceed 11 m/s, with the highest values observed in the straits between Andros and Evia Island, at the offshore areas of northern Cyclades, as well as, in Ikaria and Amorgos islands. The Northern Aegean Sea is dominated by north wind directions with lower wind speed values, while in the central and southeastern Aegean Sea, NNW and NNW directions are observed, respectively. Decreased wind speeds are observed in the Ionian Sea, except for the areas near Diapontia Islands and the western coasts of Corfu Island. Autumn is characterized by low wind speeds, with the highest values observed in the central Aegean, the eastern and western coasts of Crete Island, and the northern Aegean. The lowest values are observed in the offshore area east of Rhodes Island and near the coast of Chalkidiki.

5.3. n-Year Return Levels (Design Values)

The $n$-year wind speed design values are provided in this section, using the block maxima approach.

The most sensitive and important parameter of the GEV distribution is the shape parameter $\xi$. In Figure 8, the spatial distribution of this parameter for the annual maximum wind speed is provided. No systematic spatial pattern with regard to the behavior of $\xi$ can be identified. The majority of the areas are characterized by negative values of $\xi$, suggesting a reversed Weibull extreme value distribution, while the most probable value of $\xi$ over the entire domain is around −0.17.

The spatial distribution of the 50- and 100-year design values of wind speed is depicted in Figure 9. The highest design values occur in the northern and central Aegean Sea, the southern part of the Ionian Sea, the southern coast of Crete Island, and the area between Rhodes Island and the Turkish coast. As far as we are aware, the only studies that present similar results for the Greek Seas (i.e., 50-year design value of wind speed at 10 m asl) are [49,50]. In [49], the RegCM model was used for the period 1980–2000, with a spatial resolution of 10 km × 10 km. The results provided there are qualitatively fairly similar to the present results, but the 50-year design values are clearly lower. In [50], the SKIRON-Eta model was used for the period 1995–2004, with a spatial resolution of 0.1° × 0.1°. In this case, there is a fair similarity between the provided results.

6. Offshore Wind Speed Characteristics and Wind Power Density in the OWFODA

The local statistical analysis of wind speed and wind power density is performed in this section for each medium-term OWFODA. In this connection, for each OWFODA, the centroid is considered as a reference (representative) point. The calculation of the centroid of a non-intersecting polygon is presented in Appendix A.

Two sites are available for developing pilot projects (Pilot1 and Pilot2), with a capacity of up to 600 MW [6]. For the sake of this analysis, it is assumed that a 600 MW project will be developed in Pilot1 (mainly due to its proximity to the necessary infrastructure), and thus, in the following analysis, Pilot2 is neglected.

6.1. Statistics of the 3-Hour Wind Speed

The results for the wind speed $w_S$ that is provided by the CERRA dataset at a 3 h time step for each of the examined OWFODA are summarized in

Table A1. Statistical parameters of the 3-hour wind speed.

Polygon	Parameter
	$\overline{{\mathit{w}}_{\mathit{S}}}$ m/s	${{\mathit{w}}_{\mathit{S}}}_{0.5}$ m/s	${\mathit{s}}_{{\mathit{w}}_{\mathit{Y}}}$ m/s	${{\mathit{w}}_{\mathit{S}}}_{\mathit{m}\mathit{a}\mathit{x}}$ m/s	$\mathit{C}\mathit{V}$ %	${{\mathit{w}}_{\mathit{S}}}_{0.95}$ m/s	${{\mathit{w}}_{\mathit{S}}}_{0.99}$ m/s
O1	7.58	7.48	4.06	29.61	53.60	14.38	17.42
O2	7.89	7.56	4.21	29.52	53.32	15.30	19.38
O3	9.12	8.83	5.04	29.03	55.21	17.56	19.86
O4	7.82	7.93	3.89	25.96	49.75	13.88	16.20
O5	8.01	8.16	3.86	26.01	48.15	14.01	16.59
O6	6.60	5.94	4.09	29.30	62.03	14.07	17.39
O7	8.84	8.94	4.16	28.79	47.09	15.44	17.93
O8	6.00	5.31	4.12	30.18	68.66	13.54	18.28
O9	8.32	8.09	4.58	28.63	55.08	15.81	18.21
O10	8.36	8.13	4.65	29.54	55.64	16.05	18.65
O11	8.49	8.06	4.85	28.65	57.15	16.76	19.28
O12	6.17	5.69	3.73	28.55	60.39	12.88	17.20
O13	6.97	6.59	4.06	28.59	58.26	14.16	18.64
O14	8.28	8.24	3.90	29.61	47.08	14.82	17.60

(see Appendix B.1). The statistics include the mean value $\overline{w_S}$, the median ${w_S}_{0.5}$, the standard deviation $s_{w_S}$, the maximum $w_{S_{max}}$, $CV$, and the 95 and 99 percentile points ${w_S}_{0.95}$ and ${w_S}_{0.99}$, respectively.

The mean value and the coefficient of variation of the 3 h wind speed are very close to the mean annual value and $MAV$, which are presented and commented on in the next subsection. The maximum values of wind speed are observed in the Gulf of Patras (30.18 m/s), Rhodes, and Ag. Apostoli (29.61 m/s). Although the wind climate in Patras (O8) is a milder climate in terms of both mean and median values compared to the rest areas, the most extreme wind speeds on an annual basis are depicted there, resulting in increased design values (see also

Table A5. Wind speed 20-, 30-, and 50-year design values (DV) and 95% confidence intervals (CI).

Polygon	Design Values and 95% Confidence Intervals
	${\mathit{D}\mathit{V}}_{20}$	$\mathit{C}\mathit{I} \mathbf{o}\mathbf{f} {\mathit{D}\mathit{V}}_{20}$		${\mathit{D}\mathit{V}}_{30}$	$\mathit{C}\mathit{I} \mathbf{o}\mathbf{f} {\mathit{D}\mathit{V}}_{30}$		${\mathit{D}\mathit{V}}_{50}$	$\mathit{C}\mathit{I} \mathbf{o}\mathbf{f} {\mathit{D}\mathit{V}}_{50}$
O1	26.954	25.439	28.468	27.510	25.773	29.248	28.159	26.096	30.222
O2	28.444	27.430	29.458	28.794	28.008	31.217	29.177	27.854	30.500
O3	27.525	26.213	28.837	27.982	26.409	29.554	28.530	26.574	30.485
O4	24.741	23.704	25.778	25.118	23.961	26.276	25.542	24.208	26.876
O5	25.195	24.200	26.191	25.545	24.433	26.657	25.930	24.646	27.214
O6	26.693	25.402	27.983	27.174	25.670	28.678	27.749	25.932	29.565
O7	26.569	25.472	27.667	26.990	25.767	28.214	27.476	26.071	28.881
O8	30.296	26.751	33.841	31.435	26.796	36.073	32.927	26.588	39.267
O9	26.679	24.986	28.373	27.289	25.234	29.343	28.045	25.455	30.635
O10	26.725	24.952	28.498	27.383	25.215	29.551	28.218	25.461	30.976
O11	27.501	25.361	29.64	28.196	25.464	30.928	29.078	25.444	32.712
O12	26.919	25.728	28.109	27.338	25.975	28.7	27.812	26.195	29.429
O13	27.175	26.273	28.077	27.508	26.511	28.505	27.879	26.741	29.016
O14	27.451	25.597	29.304	28.100	25.883	30.317	28.884	26.134	31.634

). The highest 95th and 99th percentiles are observed in Crete1 (17.56 m/s and 19.86 m/s), GyarosC (16.76 m/s) for ${w_S}_{0.95}$, and Chios (19.38 m/s) for ${w_S}_{0.99}$. It should be noted that the most favorable areas for the development of OWF are the ones that are characterized by strong wind and low variability values. In this respect, as can be seen from the numerical results, there are several options among the OWFODA, such as Rhodes, Donousa2, and Crete2, which exhibit these characteristics and could be initially prioritized over others for the development of OWF.

6.2. Statistics of the Annual Wind Speed

The same statistics as above for the annual wind speed $w_{SY}$ for each OWFODA are summarized in

Table A2. Annual wind speed statistics.

Polygon	Parameter
	$\overline{{\mathit{w}}_{\mathit{S}\mathit{Y}}}$ m/s	${{\mathit{w}}_{\mathit{S}\mathit{Y}}}_{0.5}$ m/s	${\mathit{s}}_{{\mathit{w}}_{\mathit{S}\mathit{Y}}}$ m/s	${{\mathit{w}}_{\mathit{S}\mathit{Y}}}_{\mathit{m}\mathit{a}\mathit{x}}$ m/s	$\mathit{M}\mathit{A}\mathit{V}$ %	$\mathit{I}\mathit{A}\mathit{V}$ %	${{\mathit{w}}_{\mathit{S}\mathit{Y}}}_{0.95}$ m/s	${{\mathit{w}}_{\mathit{S}\mathit{Y}}}_{0.99}$ m/s
O1	7.58	7.68	0.33	8.16	53.39	4.35	8.00	8.16
O2	7.89	7.89	0.24	8.37	53.19	3.01	8.28	8.37
O3	9.12	9.13	0.40	9.96	55.04	4.39	9.85	9.96
O4	7.82	7.79	0.32	8.64	49.62	4.13	8.37	8.64
O5	8.01	8.00	0.32	8.87	48.02	3.99	8.54	8.87
O6	6.60	6.59	0.29	7.25	61.92	4.40	7.07	7.25
O7	8.84	8.87	0.37	9.82	46.94	4.21	9.33	9.82
O8	6.00	5.96	0.31	6.74	68.41	5.15	6.57	6.74
O9	8.32	8.37	0.41	9.00	54.90	4.91	8.93	9.00
O10	8.36	8.43	0.42	9.08	55.44	5.06	9.00	9.08
O11	8.49	8.58	0.44	9.27	56.93	5.19	9.15	9.27
O12	6.17	6.16	0.25	6.66	60.25	4.02	6.63	6.66
O13	6.97	7.00	0.27	7.53	58.12	3.86	7.44	7.53
O14	8.28	8.30	0.37	9.20	46.91	4.42	8.92	9.20

.

The areas that are characterized by the highest mean annual wind speeds are located in the central and southeastern Aegean, namely, Crete1 (9.12 m/s), Donousa2 (8.84 m/s), GyarosC (8.49 m/s), and GyarosB (8.36 m/s). The areas with the lowest annual wind speeds are Patras (6.00 m/s), Pilot1A (6.17 m/s), and Diapontia (6.60 m/s). The highest mean annual wind speeds are observed in Crete1 (9.96 m/s), Donousa2 (9.82 m/s), and GyarosC (9.27 m/s). The OWFODA with the lowest $MAV$ values are Rhodes (46.91%), Donousa2 (46.94%), and Crete2B (48.02%); the lowest $IAV$ values are observed in Chios (3.01%), Pilot1B (3.86%), and Crete2B (3.99%). The highest annual 95th and 99th extreme percentiles are observed at Crete1 (9.85 m/s and 9.96 m/s), Donousa2 (9.33 m/s and 9.82 m/s), and GyarosC (9.15 m/s and 9.27 m/s), respectively. Overall, the wind climate of the Ionian OWFODA is clearly milder than the OWFODA in the Aegean. Regarding the Pilot1A area, the short wind fetches from the N and NE directions are the main reason for the rather low speed values of the prevailing winds.

6.3. Statistics of Extreme Wind Speeds and Trends

6.3.1. Long-Term Trends of Wind Speed

The slopes of the annual mean wind speed, along with the corresponding $p$-values, are presented in

Table A3. Slopes and extreme percentiles of annual wind speed.

Polygon	Parameter
	$\mathit{b}\left(\overline{{\mathit{w}}_{\mathit{S}\mathit{j}}}\right)$ m/s/y	$\mathit{p}$-Value	$\mathit{b}\left({{\mathit{w}}_{\mathit{S}\mathit{j}}}_{0.95}\right)$ m/s/y	$\mathit{p}$-Value	$\mathit{b}\left({{\mathit{w}}_{\mathit{S}\mathit{j}}}_{0.99}\right)$ m/s/y	$\mathit{p}$-Value
O1	−0.004	0.505	−0.012	0.215	−0.014	0.376
O2	−0.001	0.902	0.016	0.048	0.036	0.048
O3	−0.011	0.051	−0.022	0.016	−0.019	0.028
O4	−0.003	0.505	−0.006	0.334	0.000	0.967
O5	−0.004	0.391	−0.006	0.470	0.003	0.775
O6	0.005	0.294	0.012	0.178	0.014	0.307
O7	−0.004	0.540	−0.004	0.614	0.003	0.754
O8	−0.007	0.138	−0.030	0.037	−0.007	0.924
O9	−0.002	0.634	−0.006	0.438	0.003	0.859
O10	−0.003	0.673	−0.010	0.307	−0.003	0.634
O11	−0.006	0.247	−0.015	0.215	−0.002	0.859
O12	0.004	0.307	0.004	0.754	0.021	0.186
O13	0.002	0.796	−0.003	0.838	0.013	0.470
O14	−0.018	0.002	−0.027	0.001	0.000	0.946

. The boldface numbers indicate statistically significant trends (at the 95% confidence level). A statistically significant slope (−0.018 m/s/year) appeared in Rhodes, which also represents the overall maximum wind speed decrease for all OWFODA. In Chios and Crete1, there is a significant trend for the annual 95th and 99th percentiles, namely, 0.016 m/s/y and −0.022 m/s/y, respectively. In Patras and Rhodes, there is a significant decreasing trend for the 95th percentile points, namely, −0.030 m/s/y and −0.027 m/s/y, respectively. Overall, most of the mean annual wind speed trends are decreasing except for Diapontia, Pilot1A, and Pilot1B.

6.3.2. Wind Speed Extremes

An offshore wind turbine should be capable of withstanding environmental loads (wind, waves, and currents), and the environmental load-resistant design of the turbine requires the design values of the corresponding environmental parameters [51]. A detailed assessment of the most important metocean parameters and their effects on the development of OWF at two Norwegian sites can be found in [52].

The parameters of the GEV distribution, along with the corresponding 95% confidence intervals ($CI$), are presented in

Table A4. GEV parameters and 95% confidence intervals of wind speed.

	Parameters and 95% Confidence Intervals
Polygon	$\mathit{\xi }$	$\mathit{C}\mathit{I}-\mathit{\xi }$		$\mathit{\sigma }$	$\mathit{C}\mathit{I}-\mathit{\sigma }$		$\mathit{\mu }$	$\mathit{C}\mathit{I}-\mathit{\mu }$
O1	−0.149	−0.345	0.047	2.155	1.677	2.770	21.780	21.003	22.556
O2	−0.285	−0.512	−0.059	2.093	1.619	2.705	24.253	23.494	25.012
O3	−0.087	−0.331	0.157	1.453	1.116	1.891	23.721	23.186	24.257
O4	−0.230	−0.415	−0.045	1.890	1.483	2.407	20.673	19.998	21.348
O5	−0.274	−0.481	−0.066	2.014	1.571	2.582	21.100	20.376	21.825
O6	−0.095	−0.291	0.100	1.571	1.220	2.023	22.629	22.061	23.196
O7	−0.176	−0.333	−0.019	1.778	1.403	2.253	22.455	21.825	23.085
O8	0.102	−0.250	0.454	1.989	1.465	2.700	23.397	22.619	24.174
O9	−0.015	−0.242	0.212	1.544	1.189	2.006	22.194	21.630	22.758
O10	0.036	−0.175	0.248	1.415	1.090	1.837	22.288	21.777	22.800
O11	0.031	−0.297	0.359	1.521	1.134	2.042	22.767	22.178	23.355
O12	−0.211	−0.438	0.017	1.974	1.521	2.562	22.559	21.839	23.279
O13	−0.244	−0.427	−0.062	1.745	1.365	2.229	23.490	22.867	24.113
O14	−0.077	−0.309	0.155	2.004	1.547	2.594	22.131	21.399	22.863

. As is seen from this table, for all areas (except for Patras, GyarosB, and GyarosC), the $\xi$ parameter of the GEV distribution takes negative values, and for some cases (Crete1, Diapontia, Patras, GyarosA,B,C, and Rhodes), values are very close to zero. This behavior suggests that the maximum annual wind speed at the OWFODA follows, in general, an FT-III (reversed Weibull) or FT-I (Gumbel) distribution.

The $n-$year design values of wind speed for $n=20, 30, 50$ years, along with the corresponding 95% confidence intervals are shown in Table A5. The confidence intervals have been estimated using the normal approximation. The highest 20, 30, and 50 years return levels appear in the Gulf of Patras (30.30 m/s, 31.43 m/s, 32.92 m/s), Chios (28.44 m/s, 28.79 m/s, 29.18 m/s), and GyarosC (27.50 m/s, 28.20 m/s, 29.08 m/s), respectively.

6.4. Wind Power Density at the OWFODA

The results for the medium-term OWFODA with regard to wind power density are summarized in

Table A6. Statistics of annual wind power density.

Polygon	Parameter
	${\mathit{m}}_{\mathit{W}\mathit{P}\mathit{D}}$, W/m2	${\mathit{W}\mathit{P}\mathit{D}}_{0.5}$, W/m2	${\mathit{s}}_{\mathit{W}\mathit{P}\mathit{D}}$, W/m2	$\mathit{M}\mathit{A}\mathit{V}$%	$\mathit{I}\mathit{A}\mathit{V}$
O1	509.75	522.36	61.31	136.03	12.03
O2	584.26	578.75	58.07	151.90	9.94
O3	908.60	918.99	102.60	126.12	11.29
O4	513.21	511.48	50.35	117.40	9.81
O5	536.56	537.21	51.14	116.51	9.53
O6	408.14	402.51	43.43	168.18	10.64
O7	708.16	712.73	69.35	114.27	9.79
O8	359.56	348.26	53.62	208.20	14.91
O9	688.13	701.69	78.44	126.57	11.40
O10	706.45	723.48	84.34	128.40	11.94
O11	765.16	773.10	96.54	132.81	12.62
O12	328.03	323.31	36.01	188.80	10.98
O13	448.99	452.65	49.25	172.57	10.97
O14	588.56	594.78	64.68	124.02	10.99

. Crete1 is the most energetic area (909 $\mathrm{W}/{\mathrm{m}}^2$), followed by GyarosC (765 $\mathrm{W}/{\mathrm{m}}^2$) and Donousa2 (708 $\mathrm{W}/{\mathrm{m}}^2$). Moderate to high values of $WPD$ are encountered for most of the areas, whereas the minimum $WPD$ value is observed in the Pilot1A area (328 $\mathrm{W}/{\mathrm{m}}^2$). The highest variability is observed in Patras ($MAV=208\mathrm{\%}$ and $IAV=15\mathrm{\%}$), while the lowest $MAV$ values are encountered in Donousa2 ($114\mathrm{\%}$) and Crete2B ($117\mathrm{\%}$) and the lowest $IAV$ values in Crete2A, Crete2B, Chios, and Donousa2 (10%).

In order to compare the wind power densities of the Greek OWFODA with other areas of the Mediterranean basin, two offshore locations have been selected, namely, (1) the Beleolico offshore wind farm in Taranto, Italy, comprising 10 offshore wind turbines with 3 MW nominal power and (2) the Eolmed offshore wind farm in the Gulf of Lyon, France, consisting of 3 offshore wind turbines with 10 MW nominal power. Beleolico is an operational wind farm, while Eolmed is under construction and is expected to be commissioned in 2025. The analysis of the CERRA wind speeds and wind power density at these locations provided the following results:

• For Beleolico, the annual mean wind speed at 150 m asl is 5.09 m/s and the wind power density is 203.31 W/m². Based on the results presented in Table A2 and Table A6, the annual wind speeds and wind power densities i all Greek OWFODA are clearly higher than the corresponding values for Beleolico.
• For Eolmed, the annual mean wind speed at 150 m asl is 9.02 m/s and the corresponding wind power density is 877.78 W/m². Based on the results presented in Table A2 and Table A6, it can be concluded that Eolmed exhibits higher wind speed and wind power density than the Greek OWFODA, except for O3 (Crete1). Specifically, O3 exhibits a mean annual wind speed of 9.12 m/s and a wind power density of 908.60 W/m². Moreover, the summer means for O3 are 10.946 m/s and 1389.31 W/m² compared to 7.87 m/s and 603.75 W/m² for Eolmed. The effect of the Etesian winds in the area of Crete1 is evident.

7. Offshore Wind Energy Production

A preliminary estimation of the wind energy production at different time scales from the medium-term OWFODA is presented in this section. For each OWFODA, the reference wind speed time series is the corresponding one to the OWFODA centroid.

7.1. Number of Wind Turbines and Capacity

For the analysis of the next sections, the following three capacity density settings (CDS) are considered for each OWFODA and, consequently, the theoretical number of wind turbines that can be installed under the ideal condition where the entire OWFODA is free from any other restriction and there are no limitations for the grid space:

• CDS_3: This setting refers to a capacity density of 3.0 MW/km² that is almost corresponding to the capacity density of the Pilot1 area. Specifically, assuming that the Pilot1 will have a capacity of 600 MW and taking into consideration that it has a total surface of 219.28 km² and that the IEA 15-MW offshore wind turbines will be used, this is translated to the installation of 40 wind turbines (e.g., 14 wind turbines for the Pilot1A and 26 wind turbines for the Pilot1B). Therefore, the capacity density of the Pilot1 is 2.74 MW/km²;
• CDS_5: This setting refers to a capacity density of 5.0 MW/km²;
• CDS_7: This setting refers to a capacity density of 7.0 MW/km².

The setting CDS_3 is the conservative one, the setting CDS_7 is, according to the authors’ opinion, optimistic, and CDS_5 is the balanced setting, while it is also close to the future capacity density trends in the US and Europe.

The number of offshore wind turbines and the corresponding available capacities are provided for the above settings in

Table 7. Number and foundation type of wind turbines and corresponding capacity for the OWFODA.

			Settings
			CDS_3	CDS_5	CDS_7	CDS_3	CDS_5	CDS_7
Polygon	Surface [km2]	Foundation	No. of Wind Turbines			Capacity (MW)
O1	133.9	FL	26	44	62	402	670	937
O2	65.54	FL	13	21	24	197	328	360
O3	118.0	FL	23	39	55	354	590	826
O4	40.06	FL	8	13	14	120	200	220
O5	187.26	FL	37	62	87	562	936	1311
O6	54.34	FB	10	18	19	163	272	299
O7	65.03	FL	13	21	30	195	325	455
O8	138.83	FB	27	46	50	416	694	764
O9	43.44	FL	8	14	20	130	217	304
O10	14.90	FL	2	4	5	45	75	82
O11	41.41	FL	8	13	19	124	207	290
O12	77.39	FB	14	14	14	210	210	210
O13	141.89	FB	26	26	26	390	390	390
O14	74.86	FL	14	24	27	225	374	412
Total	1196.85		229	365	452	3131	5488	6860

. As can be seen from this table, CDS_5 provides almost 4.9 GW of (theoretically) available installed capacity (excluding the Pilot1A and Pilot1B areas), which corresponds to the foreseen medium-term installed capacity in Greece [6], p. 122.

Evidently, the lowest total number of offshore 15 MW wind turbines corresponds to CDS_3 with a total capacity of 3.13 GW. Under setting CDS_5, 365 wind turbines can be installed, with a total capacity of 5.49 GW. Under the optimistic setting CDS_7, 452 wind turbines can be installed, with a total capacity of almost 6.9 GW.

Note also that the actual number of wind turbines is dependent on the final (approved) offshore wind development area(s) within each OWFODA and the available grid space that can absorb the energy produced by the OWF, among others. In this respect, capacity densities of the order of 9 MW/km² and even more, may also be feasible, but to the authors’ opinion, such a development is unlikely. In this connection, in the recently published report [53], where a review of the current capacity factors of OWF is made, the authors conclude that capacities between 4.9 and 5.9 MW/km² are reasonable and consistent with the practice of European offshore wind projects; see also [54]. Nevertheless, they adopted a capacity factor of 4.0 MW/km² for OWF in the US that has not been developed yet.

7.2. Annual Wind Energy Production

The annual energy production (AEP) for each OWFODA is provided in

Table 8. Annual energy production.

Polygon	AEP (GWh)
	Settings
	CDS_3	CDS_5	CDS_7
O1	1260.70	2133.49	3006.29
O2	645.48	1042.70	1191.65
O3	1384.36	2347.39	3310.43
O4	418.02	679.28	731.53
O5	1999.71	3350.86	4702.01
O6	377.27	679.09	716.81
O7	797.95	1288.99	1841.41
O8	867.69	1478.29	1606.83
O9	441.25	772.18	1103.12
O10	110.73	221.45	276.82
O11	441.04	716.69	1047.46
O12	452.41	452.41	452.41
O13	1058.54	1058.54	1058.54
O14	773.79	1326.50	1492.31
Total	11,028.93	17,547.86	22,537.64

.

Crete2B provides the highest value of annual energy production (4702 GWh) for CDS_7, followed by Crete1 (3310 GWh) and Ag. Apostoli (3006 GWh). Under this setting, the offshore wind energy sector can contribute 22.54 TWh/year to the energy mix of the country. Under CDS_5, Crete2B provides 3351 GWh, Crete1 2347 GWh, and Ag. Apostoli 2133 GWh. In this case, the offshore wind energy sector can contribute 17.55 TWh/year to the energy mix of the country.

The annual electricity consumption in Greece for 2022 was 52 TWh (of which 3.4 TWh was imported), while for 2030, the foreseen consumption is 61.1 TWh (of which 1.8 TWh will be imported). In the case where CDS_5 will be realized up to 2030–2032, offshore wind farms will contribute 28.7% to the total electricity consumption, while under CDS_7, offshore wind farms will contribute 36.9% to the total electricity consumption. The amounts of 17,548 GWh and 22,538 GWh annual energy production are equivalent to 1509.1 ktoe and 1938.2 ktoe, respectively. Moreover, even under the conservative setting CDS_3, there will be no need for energy imports in the country.

The major energy contributors are Crete1, Crete2A, Crete2B, Donousa2, GyarosA, GyarosB, GyarosC, and Rhodes. The rest of the OWFODA belong to the north–central Aegean (Pilot1A, Pilot1B, Chios, and Ag. Apostoli) and the Ionian Sea (Diapontia and Patras). The relevant energy contribution of each of these extended areas to the total energy production under CDS_7 is the following: north–central Aegean 25.3%, central–southern Aegean 64.4%, and Ionian Sea 10.3%. Evidently, the maximum contribution per year comes from the central–southern Aegean OWFODA.

Regarding the optimum utilization of the available space and the relative value of the installed wind turbines, we introduce two variables: (1) the energy yield per unit area of the OWFODA: $AS=AEP/S$, where $S$ denotes the surface of the OWFODA in km², and (2) the ratio of $AEP$ to the number of installed wind turbines $N_{WT}$, i.e., $AN=AEP/N_{WT}$. The values of these variables are summarized in

Table 9. $AS$ and $AN$ for the OWFODA.

Polygon	$\mathit{A}\mathit{S}$ GWh/km2			$\mathit{A}\mathit{N}$ GWh/Num. of Turbines
	Settings
	CDS_3	CDS_5	CDS_7	For all CDS
O1	9.42	15.93	22.45	48.49
O2	9.85	15.91	18.18	49.65
O3	11.73	19.89	28.05	60.19
O4	10.43	16.96	18.26	52.25
O5	10.68	17.89	25.11	54.05
O6	6.94	12.50	13.19	37.73
O7	12.27	19.82	28.32	61.38
O8	6.25	10.65	11.57	32.14
O9	10.16	17.78	25.40	55.16
O10	7.43	14.86	18.57	55.36
O11	10.65	17.31	25.29	55.13
O12	5.85	5.85	5.85	32.32
O13	7.46	7.46	7.46	40.71
O14	10.34	17.72	19.93	55.27
Overall	9.21	14.66	18.83

.

For CDS_3, the optimum space utilization is achieved in Donousa2 (12.3 GWh/km²), Crete1 (11.7 GWh/km²), and Crete2B (10.7 GWh/km²). For CDS_7, the optimum space utilization is achieved in Donousa2 (28.3 GWh/km²), Crete1 (28.0 GWh/km²), and GyarosA (25.4 GWh/km²). Finally, for the most probable setting CDS_5, the optimum space utilization is achieved in Crete1 (19.9 GWh/km²), Donousa2 (19.8 GWh/km²), and Crete2B (17.9 GWh/km²). Regarding $AN$, the optimal values are observed for Donousa2 (61.4 GWh/WT), Crete1 (60.2 GWh/WT), and GyarosB (55.4 GWh/WT).

In the next sections, the analysis is focused on the balanced and, according to authors’ opinion, most probable setting CDS_5.

7.3. Monthly Wind Energy Production

The mean monthly energy production under setting CDS_5 for the medium-term OWFODA is shown in Figure 10 (see also

Table 10. Mean monthly energy production (in GWh) at the OWFODA.

Polygon	Months
	1	2	3	4	5	6	7	8	9	10	11	12
O1	197	180	180	146	140	144	212	232	163	182	167	192
O2	100	96	97	76	65	68	102	102	72	78	84	101
O3	199	180	176	154	148	186	277	271	205	188	172	193
O4	49	47	49	48	49	65	93	91	57	44	40	46
O5	248	239	247	236	247	326	451	437	272	209	203	236
O6	65	64	62	55	48	51	61	51	43	49	65	65
O7	103	99	98	86	88	106	161	157	109	95	87	100
O8	162	143	143	121	100	78	75	86	97	150	160	163
O9	68	63	62	50	47	52	84	88	63	66	62	67
O10	20	18	18	14	14	15	24	26	18	19	18	19
O11	64	59	58	46	44	47	75	79	58	63	59	64
O12	49	45	45	31	27	20	30	37	32	41	44	51
O13	116	103	103	73	63	48	70	87	75	99	103	120
O14	106	103	103	93	98	129	170	153	115	80	79	97
TOTAL	1546	1439	1441	1229	1178	1335	1885	1897	1379	1363	1343	1514

). The maximum total energy throughout the year is produced during August and July (1897 GWh and 1885 GWh, respectively), and the minimum during May and April (1178 GWh and 1229 GWh, respectively). The behavior of the monthly energy production curves is different for Patras and Diapontia compared to the rest of the locations. It is evident that the effect of the Etesian winds plays a major role in the OWFODA of the central and southern Aegean Sea and a smaller role in the OWFODA of the Ionian Sea. On the other hand, Diapontia, Patras, Pilot1 and Pilot2 OWFODA provide the highest energy during the winter months (November, December, and January).

From the above table, it can be seen that the OWFODA of the central–southern Aegean Sea provide the highest share of energy during July and August, with 70.8% and 68.6%, respectively. During January, February, March, April, October, November, and December, the OWFODA of the north–central Aegean and the Ionian Seas contribute together almost 80% of the amount of energy provided by the central–southern Aegean OWFODA. For the rest of the year, the contribution of the central–southern Aegean fluctuates between 60 and 70%. Thus, the OWFODA of the central–southern Aegean play a major role in the 2030–2032 targets of Greece, while the rest of the OWFODA seem to play an important complementary role during the autumn and winter seasons.

7.4. Daily Energy Production

The statistical parameters of the daily energy production (in MWh) for the medium-term OWFODA are presented in

Table 11. Statistics of the daily energy production (in MWh).

Polygon	Parameter
	${\mathit{m}}_{\mathit{E}\mathit{P}}$ MWh	${\mathit{s}}_{\mathit{E}\mathit{P}}$ MWh	$\mathit{C}\mathit{V}$ %
O1	5841.19	4466.18	76.46
O2	2854.75	2070.58	72.53
O3	6426.81	4315.44	67.15
O4	1859.77	1369.11	73.62
O5	9174.15	6428.38	70.07
O6	1859.24	1639.75	88.19
O7	3529.06	2266.24	64.22
O8	4047.33	4101.49	101.34
O9	2114.13	1571.60	74.34
O10	606.30	450.48	74.30
O11	1962.18	1447.54	73.77
O12	1238.64	1238.66	100.00
O13	2898.12	2548.92	87.95
O14	3631.75	2482.43	68.35

.

The highest variability is observed for Patras (101.34%), Pilot1A (100.00%), and Diapontia (88.19%). The lowest variability is observed for Donousa2 (64.22%), Crete1 (67.15%), and Rhodes (68.35%).

The total mean daily energy production, along with a Fourier smooth time series, in order to reveal clearly the intra-annual features and especially the seasonal ones, is shown in Figure 11. As seen from this figure, the total energy production roughly increases from October up to the end of January, then it decreases during the period of February–May, attains its highest values during summer, and during autumn, the energy decreases again. The three peaks are encountered during December, the end of January, and July, while the two troughs are observed for April–May and September.

7.5. Hourly Energy Production

The theoretical capacity factors (i.e., assuming a continuous wind turbine operation), the percentage of operating hours ($PoH$) per year, and the statistical parameters of the hourly energy production are provided according to CDS_5 in

Table 12. Statistics of the hourly energy production.

Polygon Name	Parameter
	$\mathit{C}\mathit{F}$ (%)	$\mathit{P}\mathit{o}\mathit{H}$ (%)	${\mathit{m}}_{\mathit{E}\mathit{P}}$ MWh	${\mathit{s}}_{\mathit{E}\mathit{P}}$ MWh	$\mathit{C}\mathit{V}$ %	${\mathit{E}\mathit{P}}_{\mathit{p}\mathit{e}\mathit{a}\mathit{k}}$ (Hours in UTC)
O1	43.35	76.9	243.38	220.32	90.52	12:00–15:00
O2	44.39	79.9	118.95	104.79	88.09	18:00–21:00
O3	53.81	80.1	267.78	209.85	78.37	03:00–06:00
O4	46.71	79.4	77.49	65.60	84.66	12:00–15:00
O5	48.32	81.2	382.26	311.56	81.51	12:00–15:00
O6	33.73	67.5	77.47	86.73	111.96	18:00–21:00
O7	54.87	85.1	147.04	107.82	73.33	15:00–18:00
O8	28.73	60.5	168.64	208.08	123.39	15:00–18:00
O9	49.31	77.9	88.09	74.97	85.10	15:00–18:00
O10	49.49	77.7	25.26	21.45	84.90	15:00–18:00
O11	49.29	77.7	81.76	69.99	85.61	15:00–18:00
O12	28.89	66.3	51.61	60.71	117.64	21:00–00:00
O13	36.40	71.7	120.76	123.96	102.66	03:00–06:00
O14	49.41	84.4	151.32	119.23	78.79	15:00–18:00

. Evidently, $CF$ and $PoH$ remain the same for any of the examined settings.

The highest capacity factors are observed for Donousa2 (54.87%), Crete1 (53.81%), and GyarosB (49.49%), and the lowest for Patras (28.73%), Pilot1A (28.89%), and Diapontia (33.73%). The highest percentage of operating time per year for the IEA 15 MW wind turbine is observed for Donousa2 (85.1%), Rhodes (84.4%), and Crete2B (81.2%).

The highest variability is observed for Patras (123.39%), Pilot1A (117.64%), and Diapontia (111.96%), and the lowest variability is encountered for Donousa2 (73.33%), Crete1 (78.37%), and Rhodes (78.79%). High variability values, in general, imply a greater uncertainty with regard to the hourly energy production. Let us also note that in [55], where a wind energy analysis performed for four Mediterranean islands (including Chios and Crete Islands at locations close to the present OWFODA) resulted in the following values for $CF$: 46.4% (for Chios Island., i.e., a relative difference of 4.3%) and 50.2% (for Crete Island, i.e., a relative difference of ~2.0%) for the same type of offshore wind turbines.

The wind speed duration curves are presented in Figure 12. The curves reveal the persistence of wind speed above a threshold level expressed as a percentage of the total time. For example, for 30% of the time, wind speed in O8 (Patras) is approximately above 8 m/s, while for O3 (Crete1) and for the same percentage of time, wind speed is approximately above 12.5 m/s.

Based on the above results and assumptions, Donousa2 is characterized by the highest values of $CF$, $PoH$, and $AN$, the lowest value for $CV$, and a high value for $AS$. In this respect, this area is ideal for the development of OWF, compared to the other OWFODA.

The time period of the day when the peak energy is produced varies among the OWFODA. During the morning, the highest energy productivity is observed for Crete1 and Pilot1B (06:00–09:00 LT) (Local time), during the afternoon for Ag. Apostoli, Crete2A, and Crete2B (15:00–18:00 LT), during early evening for Donousa2, Patras, GyarosA, B, C and Rhodes (18:00–21:00 LT), and during the night for Chios, Diapontia (21:00–00:00 LT), and Pilot1A (00:00–03:00 LT).

8. Correlation, Synergies, and Complementarity of Wind Energy

The correlation coefficients between the examined OWFODA with regard to the energy produced is assessed on an annual, monthly, and short-term time scale. As noted in [11], positive values of the correlation coefficient suggest (a degree) of synergy, while negative values suggest (a degree) of complementarity between the energy produced by the corresponding areas. The degrees of synergy and complementarity are important information regarding the transmission network design and operation.

The correlation coefficients of wind energy produced by each OWFODA are shown in

Table A7. Correlation coefficient of wind energy production (per 3 h).

Polygon Name (Short Names)
	O1	O2	O3	O4	O5	O6	O7	O8	O9	O10	O11	O12	O13	O14
O1	1.000
O2	0.581	1.000
O3	0.350	0.398	1.000
O4	0.371	0.347	0.659	1.000
O5	0.368	0.356	0.647	0.971	1.000
O6	0.019	0.108	−0.051	−0.028	−0.012	1.000
O7	0.489	0.560	0.706	0.651	0.628	−0.012	1.000
O8	0.352	0.239	0.087	0.044	0.032	0.050	0.158	1.000
O9	0.769	0.626	0.521	0.498	0.485	−0.014	0.689	0.328	1.000
O10	0.776	0.613	0.518	0.500	0.486	−0.015	0.691	0.323	0.987	1.000
O11	0.763	0.608	0.488	0.454	0.440	−0.021	0.637	0.373	0.958	0.946	1.000
O12	0.456	0.371	0.131	0.097	0.111	0.044	0.207	0.389	0.390	0.391	0.399	1.000
O13	0.514	0.409	0.134	0.094	0.108	0.050	0.222	0.383	0.429	0.430	0.436	0.888	1.000
O14	0.118	0.204	0.528	0.504	0.508	0.025	0.510	−0.040	0.230	0.234	0.203	0.018	−0.010	1.000

. The values of the correlation coefficient above 0.6 are shown in boldface. As can be seen from this table, the highest correlation coefficients are encountered for the following areas: GyarosA—GyarosB: 0.987, Crete2A—Crete2B: 0.971, GyarosA—GyarosC: 0.958, GyarosB—GyarosC: 0.946, Pilot1A—Pilot1B: 0.888, Ag. Apostoli—GyarosB: 0.776, Ag. Apostoli—GyarosA: 0.769, Ag. Apostoli—GyarosC: 0.763, Crete1—Donousa2: 0.706, Donousa2—GyarosB: 0.691, Donousa2—GyarosA: 0.689, Crete1—Crete2A: 0.659, Crete2A –Donousa2: 0.651. Evidently, the highest correlation coefficients are encountered for OWFODA that are very close (such as the neighboring areas in Gyaros, Crete 2, and Pilot1 areas).

The lowest correlation coefficients (very close to zero) are observed for Diapontia, which seems to be, in statistical terms, a completely “isolated” area with respect to the others. Patras exhibits a low degree of correlation with Ag. Apostoli (0.352), GyarosA,B,C (0.328, 0323, and 0.373, respectively), and Pilot 1A, B (0.389, 0.383, respectively). According to these results, there is an increased degree of synergy between some of the OWFODA, but there is a complete lack of complementarity.

The correlation coefficients of the monthly wind energy production are shown in

Table 13. Correlation coefficient of monthly wind energy production.

Polygon
	O1	O2	O3	O4	O5	O6	O7	O8	O9	O10	O11	O12	O13	O14
O1	1
O2	0.753	1
O3	0.684	0.595	1
O4	0.560	0.430	0.854	1
O5	0.545	0.443	0.836	0.994	1
O6	−0.108	0.231	−0.143	−0.146	−0.103	1
O7	0.720	0.615	0.923	0.898	0.884	−0.139	1
O8	0.272	0.251	−0.109	−0.358	−0.372	0.054	−0.161	1
O9	0.926	0.736	0.813	0.677	0.653	−0.134	0.848	0.202	1
O10	0.924	0.718	0.816	0.685	0.660	−0.148	0.854	0.189	0.998	1
O11	0.923	0.725	0.778	0.612	0.585	−0.149	0.798	0.288	0.989	0.986	1
O12	0.538	0.558	0.099	−0.127	−0.126	0.175	0.091	0.679	0.449	0.437	0.499	1
O13	0.564	0.584	0.100	−0.143	−0.141	0.182	0.090	0.691	0.464	0.451	0.514	0.978	1
O14	0.296	0.319	0.735	0.811	0.825	0.020	0.755	−0.392	0.416	0.424	0.353	−0.195	−0.219	1

. The highest correlation coefficients, suggesting an almost perfect correlation, are encountered for the following areas: GyarosA—GyarosB: 0.998, GyarosA—GyarosC: 0.989, GyarosB–GyarosC: 0.986, Pilot1A–Pilot1B: 0.978, Ag. Apostoli–GyarosA, B, C: 0.926, 0.924 and 0.923, respectively, and Crete1-Donousa2: 0.923. Overall, the correlation coefficients were significantly increased compared to the hourly energy production. The increased values suggest a very strong synergy between the corresponding OWFODA.

On an annual basis, the corresponding results are shown in

Table 14. The correlation coefficient of annual wind energy production.

Polygon
	O1	O2	O3	O4	O5	O6	O7	O8	O9	O10	O11	O12	O13	O14
O1	1
O2	0.613	1
O3	0.668	0.660	1
O4	0.596	0.641	0.818	1
O5	0.537	0.626	0.796	0.990	1
O6	−0.330	0.015	−0.073	0.047	0.106	1
O7	0.720	0.773	0.897	0.764	0.736	−0.104	1
O8	0.346	0.349	0.259	0.047	0.021	−0.355	0.344	1
O9	0.896	0.698	0.826	0.711	0.657	−0.324	0.881	0.391	1
O10	0.903	0.683	0.814	0.697	0.642	−0.334	0.877	0.407	0.998	1
O11	0.884	0.689	0.816	0.680	0.623	−0.363	0.867	0.463	0.990	0.988	1
O12	0.468	0.383	0.270	0.199	0.173	−0.032	0.431	0.454	0.453	0.474	0.452	1
O13	0.575	0.427	0.350	0.297	0.262	−0.168	0.513	0.478	0.573	0.596	0.574	0.948	1
O14	0.144	0.401	0.578	0.553	0.583	0.254	0.497	0.164	0.279	0.281	0.305	0.079	0.122	1

. The synergy between the OWFODA is decreased compared to the monthly energy production. The highest values are observed for the GyarosA,B,C OWFODA (ranging between 0.988 and 0.998), the Pilot areas (0.948), GyarosA,B,C with Donousa2 (ranging between 0.867 and 0.881), Gyaros A,B,C with Ag. Apostoli (ranging between 0.884–0.903), and GyarosA,B,C with Crete1 (ranging between 0.814 and 0.826). On this time scale, some signs of complementarity also appear, such as in Diapontia–Ag. Apostoli (−0.330), Diapontia–Patras (−0.355), and Diapontia with GyarosA,B,C (ranging between −0.363 and −0.324).

Based on the above results, the following conclusions can be drawn:

• At all the examined time scales, there is a rather high degree of synergy for most of the OWFODA, while for the hourly and monthly scales, there is no complementarity present.
• For all time scales, GyarosA,B,C and Donousa2 seem to play a crucial role in this framework since they exhibit increased synergy with most of the OWFODA in the Aegean Sea.
• Diapontia exhibits very low synergy and complementarity with respect to the rest of OWFODA at all time scales. Nevertheless, at the annual scale, it exhibits some signs of (reduced) complementarity with some of the rest areas.

Another result that is presented here is obtained through the cross-correlation function of the wind energy produced every three hours for each OWFODA. In

Table 15. Maximum correlation from cross-correlation analysis.

Polygon
	O1	O2	O3	O4	O5	O6	O7	O8	O9	O10	O11	O12	O13	O14
O1	1
O2	0.592 (1)	1
O3	0.425 (4)	0.440 (2)	1
O4	0.413 (4)	0.379 (3)	0.664 (−1)	1
O5	0.399 (3)	0.382 (3)	0.648 (−1)	0.971 (0)	1
O6	0.095 (−4)	0.187 (−8)	0.063 (−9)	0.061 (−7)	0.076 (−7)	1
O7	0.525 (2)	0.562 (1)	0.723 (−1)	0.651 (0)	0.628 (0)	0.083 (8)	1
O8	0.352 (0)	0.242 (−1)	0.144 (−4)	0.094 (−4)	0.087 (−4)	0.055 (−81)	0.187 (−3)	1
O9	0.775 (1)	0.626 (0)	0.558 (−2)	0.518 (−2)	0.501 (−2)	0.079 (6)	0.702 (−1)	0.337 (1)	1
O10	0.780 (1)	0.613 (0)	0.555 (−2)	0.518 (−2)	0.500 (−2)	0.075 (5)	0.703 (−1)	0.331 (1)	0.987 (0)	1
O11	0.767 (1)	0.608 (0)	0.530 (−3)	0.476 (−2)	0.458 (−2)	0.076 (6)	0.653 (−1)	0.378 (1)	0.958 (0)	0.946 (0)	1
O12	0.472 (−2)	0.371 (0)	0.147 (−3)	0.140 (−6)	0.141 (−4)	0.088 (4)	0.227 (−3)	0.389 (0)	0.410 (−2)	0.411 (−2)	0.418 (−2)	1
O13	0.532 (−2)	0.409 (0)	0.176 (−7)	0.158 (−6)	0.155 (−6)	0.106 (4)	0.261 (−4)	0.390 (−2)	0.459 (−2)	0.461 (−2)	0.467 (−2)	0.888 (0)	1
O14	0.184 (5)	0.217 (3)	0.537 (1)	0.519 (1)	0.523 (1)	0.119 (8)	0.526 (1)	0.049 (906)	0.275 (3)	0.280 (3)	0.247 (3)	0.054 (7)	0.061 (7)	1

, the lag (provided in parentheses) where the correlation coefficient takes its maximum value and the value of the correlation coefficient are presented. The correlations at lag 0 have already been discussed in relation to Table A7. From Table 15, the following additional conclusions can be drawn:

• Correlation coefficients above 0.7 are encountered for Ag. Apostoli-GyarosA,B,C (at lag 1), Crete1-Donousa2 (at lag −1), Donousa2-GyarosA,B (at lag −1), and Pilot1A-Pilot1B (at lag 0).
• Correlation coefficients above 0.6 are encountered for Crete1-Crete2A, Crete2B (at lag −1), and Donousa2-GyarosC (at lag −1).
• Fair correlation values are encountered at different lags (which are always less than 2 in the absolute sense).

9. Conclusions

The aim of this work is of great interest to the global offshore wind energy industry that plans to invest in Greece since it provides baseline information regarding the wind climate of the Greek Seas and the energy characteristics of the OWFODA. The Greek Seas are among the most favorable areas in the Mediterranean basin for the development of OWF. Since there is currently a lack of offshore wind measurements in the OWFODA, the only way for the preliminary wind energy assessment is by using reanalysis wind data that present high spatial and temporal resolution. In order to demonstrate the high accuracy of the CERRA dataset, the wind speed and direction have been evaluated for six offshore locations in the Greek Seas at 10 m asl. The evaluation revealed that CERRA reanalysis wind speeds are very close to the wind speeds measured from buoys.

The evaluation of the CERRA dataset at 150 m asl is not performed in this work due to a lack of reference measurements at this height. Note, however, that for the aforementioned evaluation, the use of specialized instruments capable of providing wind measurements at such heights (e.g., LIDARs) is essential, a process that typically takes place at later stages of an OWF development.

By utilizing the CERRA wind speed data at 150 m asl, an extensive statistical analysis on the offshore wind potential is performed, providing baseline information regarding the wind climate of the Greek Seas. The numerical results clearly indicate that the offshore wind potential remains high throughout the entire year, including summer, during which wind speeds tend to decrease in the rest Mediterranean regions, ensuring a steadier energy production throughout the year.

After the preliminary planning of OWFs is complete (which includes the selection of the appropriate sites), assessments related to wind, wave, and seabed conditions should be conducted. In this respect, the preliminary assessment on the offshore wind energy at the 10 medium-term OWFODA from the Greek NDP-OWF is performed based on some assumptions: (i) the wind turbine hub height is set at 150 m asl; (ii) the turbine nominal capacity is considered to be 15 MW; (iii) the air density is considered constant. Since only the (draft) delimitation of the OWFODA is already available and the final polygons’ shapes are not known yet, only an approximate evaluation of the wind energy production can be made at this stage. The analysis is based on three different settings with regard to the capacity density of the OWFODA: CDS_3 (3.0 MW/km²), CDS_5 (5.0 MW/km²), and CDS_7 (7.0 MW/km²), while further results have been provided for CDS_5.

Crete1 and Donousa2 are characterized by the most intense wind climates between the OWFODA, with wind speeds reaching 9.12 m/s and 8.84 m/s, respectively, while Crete1 and GyarosC exhibit the highest values of wind power densities with 908.6 W/m² and 765.16 W/m², respectively. The lowest wind speeds and wind power densities are encountered in the Gulf of Patras (6.00 m/s and 359.56 W/m²) and Pilot1 (6.17 m/s and 328.03 W/m²). The highest variabilities of wind power density are observed in the Gulf of Patras, and the lowest values are encountered in Donousa2. The Gulf of Patras exhibits the highest 30-year design value of wind speed (31.4 m/s). This result may be justified by the extreme wind speeds that occur in this location, despite that the corresponding wind climate is rather mild compared to the rest of OWFODA. There are several options among the OWFODA, such as Rhodes, Donousa2, and Crete2, which exhibit high values of wind speed and wind power density combined with relatively low variability values, and could be initially prioritized for the development of OWF.

For the preliminary wind energy production estimation, three different settings regarding the available capacity density are considered: one conservative (CDS_3, with capacity density 3 MW/km²), one optimistic (CDS_7, with capacity density 7.0 MW/km²), and one balanced (CDS_5, with capacity density 5.0 MW/km²). According to the forecasts with regard to electricity consumption in Greece for 2030, under the settings CDS_3, CDS_5, and CDS_7, the contribution of the OWF to the energy mix of the country is 18.05%, 28.7%, and 36.89%, respectively. It should be noted that even under the conservative setting CDS_3, there will be no need for energy imports in the country.

The OWFODA of the central–southern Aegean Sea provide the highest share of energy during July and August, with 70.8% and 68.6%, respectively. During January, February, March, April, October, November, and December, the OWFODA of the north–central Aegean and the Ionian Seas contribute together almost 80% of the amount of energy provided by the central–southern Aegean OWFODA. For the rest of the year, the contribution of the central–southern Aegean fluctuates between 60 and 70%. Thus, the OWFODA of the central–southern Aegean play a major role in the 2030–2032 targets of Greece, while the rest of the OWFODA seem to play an important complementary role during the autumn and winter seasons.

For CDS_5, the highest values for $AS$ are observed for Crete1 (19.9%) and Donousa2 (19.8%), and the highest values for $AN$ for Donousa2 (61.4%) and Crete1 (60.2%). Donousa2 and Crete1 are also characterized by the highest capacity factors (54.9% and 53.8%, respectively) for the IEA 15 MW wind turbine, while the lowest values correspond to Patras (28.7%) and Pilot1A (28.9%). The highest percentage of operating time is observed for Donousa2 (85.1%) and Rhodes (84.4%). Taking into consideration the above results, it seems that Donousa2 is an ideal location for the potential development of OWF.

An exceptional characteristic of the OWFODA of the Greek Seas compared to the other Mediterranean areas is that, under the CDS_5 setting, the total energy production from all OWFODA becomes maximum during July (1885 GWh) and August (1897 GWh). To a large extent, this is due to the Etesian winds that blow during the summer season in the Aegean Sea. On the other hand, the central–southern Aegean Sea OWFODA (i.e., Ag. Apostoli, Chios, Crete1, Crete2A,2B, Donousa2, GyarosA,B,C, and Rhodes) contribute more than 72% of the total energy produced for each month, while for June, July and August, the corresponding energy share is 85%, 87%, and 86%, respectively. In this regard, the role of these OWFODA is of utmost importance for the accomplishment of the medium-term energy goals of the country.

In order to identify potential synergies and complementarities between the examined OWFODA, a spatial correlation and cross-correlation analysis was performed at various time scales. The analysis identified strong synergies for most of the OWFODA, while Diapontia is characterized by very low degrees of synergy and complementarity with respect to all the rest OWFODA. Nevertheless, for a systematic approach to this problem, the onshore wind farm features should also be considered. Overall, there is a lack of complementarity at the hourly and monthly scales, and this may be a future problem. As noted in [11], collocation of OWF with floating offshore solar panels might be a reasonable approach to this end.

Overall, the present study demonstrated that the Greek Seas are one of the most favorable areas for the development of OWF compared to other Mediterranean regions, while the examined OWFODA hold the potential to cover a significant share of the country’s annual energy demand and minimize or eliminate the need for energy imports.