Research article Topical Sections

Assessment of offshore wind power potential in the Aegean and Ionian Seas based on high-resolution hindcast model results

  • 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.

    Citation: Takvor Soukissian, Anastasios Papadopoulos, Panagiotis Skrimizeas, Flora Karathanasi, Panagiotis Axaopoulos, Evripides Avgoustoglou, Hara Kyriakidou, Christos Tsalis, Antigoni Voudouri, Flora Gofa, Petros Katsafados. Assessment of offshore wind power potential in the Aegean and Ionian Seas based on high-resolution hindcast model results[J]. AIMS Energy, 2017, 5(2): 268-289. doi: 10.3934/energy.2017.2.268

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  • 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.


    Fractional differential equations rise in many fields, such as biology, physics and engineering. There are many results about the existence of solutions and control problems (see [1,2,3,4,5,6]).

    It is well known that the nonexistence of nonconstant periodic solutions of fractional differential equations was shown in [7,8,11] and the existence of asymptotically periodic solutions was derived in [8,9,10,11]. Thus it gives rise to study the periodic solutions of fractional differential equations with periodic impulses.

    Recently, Fečkan and Wang [12] studied the existence of periodic solutions of fractional ordinary differential equations with impulses periodic condition and obtained many existence and asymptotic stability results for the Caputo's fractional derivative with fixed and varying lower limits. In this paper, we study the Caputo's fractional evolution equations with varying lower limits and we prove the existence of periodic mild solutions to this problem with the case of general periodic impulses as well as small equidistant and shifted impulses. We also study the Caputo's fractional evolution equations with fixed lower limits and small nonlinearities and derive the existence of its periodic mild solutions. The current results extend some results in [12].

    Set ξq(θ)=1qθ11qϖq(θ1q)0, ϖq(θ)=1πn=1(1)n1θnq1Γ(nq+1)n!sin(nπq), θ(0,). Note that ξq(θ) is a probability density function defined on (0,), namely ξq(θ)0, θ(0,) and 0ξq(θ)dθ=1.

    Define T:XX and S:XX given by

    T(t)=0ξq(θ)S(tqθ)dθ,  S(t)=q0θξq(θ)S(tqθ)dθ.

    Lemma 2.1. ([13,Lemmas 3.2,3.3]) The operators T(t) and S(t),t0 have following properties:

    (1) Suppose that supt0S(t)M. For any fixed t0, T() and S() are linear and bounded operators, i.e., for any uX,

    T(t)uMu and S(t)uMΓ(q)u.

    (2) {T(t),t0} and {S(t),t0} are strongly continuous.

    (3) {T(t),t>0} and {S(t),t>0} are compact, if {S(t),t>0} is compact.

    Let N0={0,1,,}. We consider the following impulsive fractional equations

    {cDqtk,tu(t)=Au(t)+f(t,u(t)), q(0,1), t(tk,tk+1), kN0,u(t+k)=u(tk)+Δk(u(tk)), kN,u(0)=u0, (2.1)

    where cDqtk,t denotes the Caputo's fractional time derivative of order q with the lower limit at tk, A:D(A)XX is the generator of a C0-semigroup {S(t),t0} on a Banach space X, f:R×XX satisfies some assumptions. We suppose the following conditions:

    (Ⅰ) f is continuous and T-periodic in t.

    (Ⅱ) There exist constants a>0, bk>0 such that

    {f(t,u)f(t,v)auv, tR, u,vX,uv+Δk(u)Δk(v)bkuv, kN, u,vX.

    (Ⅲ) There exists NN such that T=tN+1,tk+N+1=tk+T and Δk+N+1=Δk for any kN.

    It is well known [3] that (2.1) has a unique solution on R+ if the conditions (Ⅰ) and (Ⅱ) hold. So we can consider the Poincaré mapping

    P(u0)=u(T)+ΔN+1(u(T)).

    By [14,Lemma 2.2] we know that the fixed points of P determine T-periodic mild solutions of (2.1).

    Theorem 2.2. Assume that (I)-(III) hold. Let Ξ:=Nk=0MbkEq(Ma(tk+1tk)q), where Eq is the Mittag-Leffler function (see [3, p.40]), then there holds

    P(u)P(v)Ξuv, u,vX. (2.2)

    If Ξ<1, then (2.1) has a unique T-periodic mild solution, which is also asymptotically stable.

    Proof. By the mild solution of (2.1), we mean that uC((tk,tk+1),X) satisfying

    u(t)=T(ttk)u(t+k)+ttkS(ts)f(s,u(s))ds. (2.3)

    Let u and v be two solutions of (2.3) with u(0)=u0 and v(0)=v0, respectively. By (2.3) and (II), we can derive

    u(t)v(t)T(ttk)(u(t+k)v(t+k))+ttk(ts)q1S(ts)(f(s,u(s)f(s,v(s))dsMu(t+k)v(t+k)+MaΓ(q)ttk(ts)q1f(s,u(s)f(s,v(s))ds. (2.4)

    Applying Gronwall inequality [15, Corollary 2] to (2.4), we derive

    u(t)v(t)Mu(t+k)v(t+k)Eq(Ma(ttk)q), t(tk,tk+1), (2.5)

    which implies

    u(tk+1)v(tk+1)MEq(Ma(tk+1tk)q)u(t+k)v(t+k),k=0,1,,N. (2.6)

    By (2.6) and (Ⅱ), we derive

    P(u0)P(v0)=u(tN+1)v(tN+1)+ΔN+1(u(tN+1))ΔN+1(v(tN+1))bN+1u(tN+1)v(tN+1)(Nk=0MbkEq(Ma(tk+1tk)q))u0v0=Ξu0v0, (2.7)

    which implies that (2.2) is satisfied. Thus P:XX is a contraction if Ξ<1. Using Banach fixed point theorem, we obtain that P has a unique fixed point u0 if Ξ<1. In addition, since

    Pn(u0)Pn(v0)Ξnu0v0, v0X,

    we get that the corresponding periodic mild solution is asymptotically stable.

    We study

    {cDqkhu(t)=Au(t)+f(u(t)), q(0,1), t(kh,(k+1)h), kN0,u(kh+)=u(kh)+ˉΔhq, kN,u(0)=u0, (2.8)

    where h>0, ˉΔX, and f:XX is Lipschitz. We know [3] that under above assumptions, (2.8) has a unique mild solution u(u0,t) on R+, which is continuous in u0X, tR+{kh|kN} and left continuous in t ant impulsive points {kh|kN}. We can consider the Poincaré mapping

    Ph(u0)=u(u0,h+).

    Theorem 2.3. Let w(t) be a solution of following equations

    {w(t)=ˉΔ+1Γ(q+1)f(w(t)), t[0,T],w(0)=u0. (2.9)

    Then there exists a mild solution u(u0,t) of (2.8) on [0,T], satisfying

    u(u0,t)=w(tqq1)+O(hq).

    If w(t) is a stable periodic solution, then there exists a stable invariant curve of Poincaré mapping of (2.8) in a neighborhood of w(t). Note that h is sufficiently small.

    Proof. For any t(kh,(k+1)h),kN0, the mild solution of (2.8) is equivalent to

    u(u0,t)=T(tkh)u(kh+)+tkh(ts)q1S(ts)f(u(u0,s))ds=T(tkh)u(kh+)+tkh0(tkhs)q1S(tkhs)f(u(u(kh+),s))ds. (2.10)

    So

    u((k+1)h+)=T(h)u(kh+)+ˉΔhq+h0(hs)q1S(hs)f(u(u(kh+),s))ds=Ph(u(kh+)), (2.11)

    and

    Ph(u0)=u(u0,h+)=T(h)u0+ˉΔhq+h0(hs)q1S(hs)f(u(u0,s))ds. (2.12)

    Inserting

    u(u0,t)=T(t)u0+hqv(u0,t), t[0,h],

    into (2.10), we obtain

    v(u0,t)=1hqt0(ts)q1S(ts)f(T(t)u0+hqv(u0,t))ds=1hqt0(ts)q1S(ts)f(T(t)u0)ds+1hqt0(ts)q1S(ts)(f(T(t)u0+hqv(u0,t))f(T(t)u0))ds=1hqt0(ts)q1S(ts)f(T(t)u0)ds+O(hq),

    since

    t0(ts)q1S(ts)(f(T(t)u0+hqv(u0,t))f(T(t)u0))dst0(ts)q1S(ts)f(T(t)u0+hqv(u0,t))f(T(t)u0)dsMLlochqtqΓ(q+1)maxt[0,h]{v(u0,t)}h2qMLlocΓ(q+1)maxt[0,h]{v(u0,t)},

    where Lloc is a local Lipschitz constant of f. Thus we get

    u(u0,t)=T(t)u0+t0(ts)q1S(ts)f(T(t)u0)ds+O(h2q), t[0,h], (2.13)

    and (2.12) gives

    Ph(u0)=T(h)u0+ˉΔhq+h0(hs)q1S(hs)f(T(h)u0)ds+O(h2q).

    So (2.11) becomes

    u((k+1)h+)=T(h)u(kh+)+ˉΔhq+(k+1)hkh((k+1)hs)q1S((k+1)hs)f(T(h)u(kh+))ds+O(h2q). (2.14)

    Since T(t) and S(t) are strongly continuous,

    limt0T(t)=I and limt0S(t)=1Γ(q)I. (2.15)

    Thus (2.14) leads to its approximation

    w((k+1)h+)=w(kh+)+ˉΔhq+hqΓ(q+1)f(w(kh+)),

    which is the Euler numerical approximation of

    w(t)=ˉΔ+1Γ(q+1)f(w(t)).

    Note that (2.10) implies

    u(u0,t)T(tkh)u(kh+)=O(hq), t[kh,(k+1)h]. (2.16)

    Applying (2.15), (2.16) and the already known results about Euler approximation method in [16], we obtain the result of Theorem 2.3.

    Corollary 2.4. We can extend (2.8) for periodic impulses of following form

    {cDqkhu(t)=Au(t)+f(u(t)), t(kh,(k+1)h), kN0,u(kh+)=u(kh)+ˉΔkhq, kN,u(0)=u0, (2.17)

    where ˉΔkX satisfy ˉΔk+N+1=ˉΔk for any kN. Then Theorem 2.3 can directly extend to (2.17) with

    {w(t)=N+1k=1ˉΔkN+1+1Γ(q+1)f(w(t)), t[0,T], kN,w(0)=u0 (2.18)

    instead of (2.9).

    Proof. We can consider the Poincaré mapping

    Ph(u0)=u(u0,(N+1)h+),

    with a form of

    Ph=PN+1,hP1,h

    where

    Pk,h(u0)=ˉΔkhq+u(u0,h).

    By (2.13), we can derive

    Pk,h(u0)=ˉΔkhq+u(u0,h)=T(h)u0+ˉΔkhq+h0(hs)q1S(hs)f(T(h)u0)ds+O(h2q).

    Then we get

    Ph(u0)=T(h)u0+N+1k=1ˉΔkhq+(N+1)h0(hs)q1S(hs)f(T(h)u0)ds+O(h2q).

    By (2.15), we obtain that Ph(u0) leads to its approximation

    u0+N+1k=1ˉΔkhq+(N+1)hqΓ(q+1)f(u0). (2.19)

    Moreover, equations

    w(t)=N+1k=1ˉΔkN+1+1Γ(q+1)f(w(t))

    has the Euler numerical approximation

    u0+hq(N+1k=1ˉΔkN+1+1Γ(q+1)f(u0))

    with the step size hq, and its approximation of N+1 iteration is (2.19), the approximation of Ph. Thus Theorem 2.3 can directly extend to (2.17) with (2.18).

    Now we consider following equations with small nonlinearities of the form

    {cDq0u(t)=Au(t)+ϵf(t,u(t)), q(0,1), t(tk,tk+1), kN0,u(t+k)=u(tk)+ϵΔk(u(tk)), kN,u(0)=u0, (3.1)

    where ϵ is a small parameter, cDq0 is the generalized Caputo fractional derivative with lower limit at 0. Then (3.1) has a unique mild solution u(ϵ,t). Give the Poincaré mapping

    P(ϵ,u0)=u(ϵ,T)+ϵΔN+1(u(ϵ,T)).

    Assume that

    (H1) f and Δk are C2-smooth.

    Then P(ϵ,u0) is also C2-smooth. In addition, we have

    u(ϵ,t)=T(t)u0+ϵω(t)+O(ϵ2),

    where ω(t) satisfies

    {cDq0ω(t)=Aω(t)+f(t,T(t)u0), t(tk,tk+1), k=0,1,,N,ω(t+k)=ω(tk)+Δk(T(tk)u0), k=1,2,,N+1,ω(0)=0,

    and

    ω(T)=Nk=1T(Ttk)Δk(T(tk)u0)+T0(Ts)q1S(Ts)f(s,T(s)u0)ds.

    Thus we derive

    {P(ϵ,u0)=u0+M(ϵ,u0)+O(ϵ2)M(ϵ,u0)=(T(T)I)u0+ϵω(T)+ϵΔN+1(T(T)u0). (3.2)

    Theorem 3.1. Suppose that (I), (III) and (H1) hold.

    1). If (T(T)I) has a continuous inverse, i.e. (T(T)I)1 exists and continuous, then (3.1) has a unique T-periodic mild solution located near 0 for any ϵ0 small.

    2). If (T(T)I) is not invertible, we suppose that ker(T(T)I)=[u1,,uk] and X=im(T(T)I)X1 for a closed subspace X1 with dimX1=k. If there is v0[u1,,uk] such that B(0,v0)=0 (see (3.7)) and the k×k-matrix DB(0,v0) is invertible, then (3.1) has a unique T-periodic mild solution located near T(t)v0 for any ϵ0 small.

    3). If rσ(Du0M(ϵ,u0))<0, then the T-periodic mild solution is asymptotically stable. If rσ(Du0M(ϵ,u0))(0,+), then the T-periodic mild solution is unstable.

    Proof. The fixed point u0 of P(ϵ,x0) determines the T-periodic mild solution of (3.1), which is equivalent to

    M(ϵ,u0)+O(ϵ2)=0. (3.3)

    Note that M(0,u0)=(T(T)I)u0. If (T(T)I) has a continuous inverse, then (3.3) can be solved by the implicit function theorem to get its solution u0(ϵ) with u0(0)=0.

    If (T(T)I) is not invertible, then we take a decomposition u0=v+w, v[u1,,uk], take bounded projections Q1:Xim(T(T)I), Q2:XX1, I=Q1+Q2 and decompose (3.3) to

    Q1M(ϵ,v+w)+Q1O(ϵ2)=0, (3.4)

    and

    Q2M(ϵ,v+w)+Q2O(ϵ2)=0. (3.5)

    Now Q1M(0,v+w)=(T(T)I)w, so we can solve by implicit function theorem from (3.4), w=w(ϵ,v) with w(0,v)=0. Inserting this solution into (3.5), we get

    B(ϵ,v)=1ϵ(Q2M(ϵ,v+w)+Q2O(ϵ2))=Q2ω(T)+Q2ΔN+1(T(t)v+w(ϵ,v))+O(ϵ). (3.6)

    So

    B(0,v)=Nk=1Q2T(Ttk)Δk(T(tk)v)+Q2T0(Ts)q1S(Ts)f(s,T(s)v)ds. (3.7)

    Consequently we get, if there is v0[u1,,uk] such that B(0,v0)=0 and the k×k-matrix DB(0,v0) is invertible, then (3.1) has a unique T-periodic mild solution located near T(t)v0 for any ϵ0 small.

    In addition, Du0P(ϵ,u0(ϵ))=I+Du0M(ϵ,u0)+O(ϵ2). Thus we can directly derive the stability and instability results by the arguments in [17].

    In this section, we give an example to demonstrate Theorem 2.2.

    Example 4.1. Consider the following impulsive fractional partial differential equation:

    { cD12tk,tu(t,y)=2y2u(t,y)+sinu(t,y)+cos2πt,  t(tk,tk+1), kN0,  y[0,π], Δk(u(tk,y))=u(t+k,y)u(tk,y)=ξu(tk,y),  kN,  y[0,π], u(t,0)=u(t,π)=0,  t(tk,tk+1),  kN0, u(0,y)=u0(y),  y[0,π], (4.1)

    for ξR, tk=k3. Let X=L2[0,π]. Define the operator A:D(A)XX by Au=d2udy2 with the domain

    D(A)={uXdudy,d2udy2X, u(0)=u(π)=0}.

    Then A is the infinitesimal generator of a C0-semigroup {S(t),t0} on X and S(t)M=1 for any t0. Denote u(,y)=u()(y) and define f:[0,)×XX by

    f(t,u)(y)=sinu(y)+cos2πt.

    Set T=t3=1, tk+3=tk+1, Δk+3=Δk, a=1, bk=|1+ξ|. Obviously, conditions (I)-(III) hold. Note that

    Ξ=2k=0|1+ξ|E12(13)=|1+ξ|3(E12(13))3.

    Letting Ξ<1, we get E12(13)1<ξ<E12(13)1. Now all assumptions of Theorem 2.2 hold. Hence, if E12(13)1<ξ<E12(13)1, (4.1) has a unique 1-periodic mild solution, which is also asymptotically stable.

    This paper deals with the existence and stability of periodic solutions of impulsive fractional evolution equations with the case of varying lower limits and fixed lower limits. Although, Fečkan and Wang [12] prove the existence of periodic solutions of impulsive fractional ordinary differential equations in finite dimensional Euclidean space, we extend some results to impulsive fractional evolution equation on Banach space by involving operator semigroup theory. Our results can be applied to some impulsive fractional partial differential equations and the proposed approach can be extended to study the similar problem for periodic impulsive fractional evolution inclusions.

    The authors are grateful to the referees for their careful reading of the manuscript and valuable comments. This research is supported by the National Natural Science Foundation of China (11661016), Training Object of High Level and Innovative Talents of Guizhou Province ((2016)4006), Major Research Project of Innovative Group in Guizhou Education Department ([2018]012), Foundation of Postgraduate of Guizhou Province (YJSCXJH[2019]031), the Slovak Research and Development Agency under the contract No. APVV-18-0308, and the Slovak Grant Agency VEGA No. 2/0153/16 and No. 1/0078/17.

    All authors declare no conflicts of interest in this paper.

    [1] European Wind Energy Association, The European offshore wind industry-key trends and statistics 2015. European Wind Energy Association, 2016. Available from: https://www.ewea.org/fileadmin/files/library/publications/statistics/EWEA-European-Offshore-Statistics- 2015.pdf.
    [2] Bilgili M, Yasar A, Simsek E (2011) Offshore wind power development in Europe and its comparison with onshore counterpart. Renew Sust Energ Rev 15: 905–915. doi: 10.1016/j.rser.2010.11.006
    [3] Perveen R, Kishor N, Mohanty SR (2014) Off-shore wind farm development: present status and challenges. Renew Sust Energ Rev 29: 780–792.
    [4] Soukissian TH, Papadopoulos A (2015) Effects of different wind data sources in offshore wind power assessment. Renew Energ 77: 101–114. doi: 10.1016/j.renene.2014.12.009
    [5] Colmenar SA, Perera PJ, Borge DD, et al. (2016) Offshore wind energy: a review of the current status, challenges and future development in Spain. Renew Sust Energ Rev 64: 1–18. doi: 10.1016/j.rser.2016.05.087
    [6] European Wind Energy Association, Wind in power: 2015 European statistics. European Wind Energy Association, 2016. Available from: http://www.ewea.org/fileadmin/files/library/ publications/ statistics/ EWEA-Annual-Statistics-2015.pdf.
    [7] Soukissian T, Reizopoulou S, Drakopoulou P, et al. (2016) Greening offshore wind with the smart wind chart evaluation tool. Web Ecol 16: 73–80. doi: 10.5194/we-16-73-2016
    [8] Kaldellis JK, Apostolou D, Kapsali M, et al. (2016) Environmental and social footprint of offshore wind energy. Comparison with onshore counterpart. Renew Energ 92: 543–556.
    [9] Brownlee MTJ, Hallo JC, Jodice LW, et al. (2015) Attitudes toward offshore wind energy development. J Leisure Res 47: 263–284.
    [10] Westerberg V, Jacobsen JB, Lifran R (2013) The case for offshore wind farms, artificial reefs and sustainable tourism in the French Mediterranean. Tourism Manag 34: 172–183. doi: 10.1016/j.tourman.2012.04.008
    [11] DIRM Méditerranée, Document de planification: Le développement de l'éolien en mer Méditerranée. France: Préfecture maritime de la Méditerranée, Préfecture de région Provence Alpes Côte d'Azur, 2015. Available from: http://www.dirm.mediterranee.developpement-durable.gouv.fr/ IMG/pdf/ Document_de_planification_pour_transmission.pdf.
    [12] 4C Offshore, Two more French Floaters get approved! 4C Offshore, 2016. Available from: http://www.4coffshore.com/windfarms/two-more-french-floaters-get-approved!-nid4813.html.
    [13] Rodrigues S, Restrepo C, Kontos E, et al. (2015) Trends of offshore wind projects. Renew Sust Energ Rev 49: 1114–1135. doi: 10.1016/j.rser.2015.04.092
    [14] Westerberg V, Jacobsen JB, Lifran R (2015) Offshore wind farms in Southern Europe-determining tourist preference and social acceptance. Energ Res Soc Sci 10: 165–179. doi: 10.1016/j.erss.2015.07.005
    [15] Zountouridou EI, Kiokes GC, Chakalis S, et al. (2015) Offshore floating wind parks in the deep waters of Mediterranean Sea. Renew Sust Energ Rev 51: 433–448. doi: 10.1016/j.rser.2015.06.027
    [16] Onea F, Deleanu L, Rusu L, et al. (2016) Evaluation of the wind energy potential along the Mediterranean Sea coasts. Energ Explor Exploit 34: 766–792. doi: 10.1177/0144598716659592
    [17] Balog I, Ruti PM, Tobin I, et al. (2016) A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean. Renew Energ 85: 395–405. doi: 10.1016/j.renene.2015.06.038
    [18] Kotroni V, Lagouvardos K, Lykoudis S (2014) High-resolution model-based wind atlas for Greece. Renew Sust Energ Rev 30: 479–489. doi: 10.1016/j.rser.2013.10.016
    [19] Emmanouil G, Galanis G, Kalogeri C, et al. (2016) 10-year high resolution study of wind, sea waves and wave energy assessment in the Greek offshore areas. Renew Energ 90: 399–419. doi: 10.1016/j.renene.2016.01.031
    [20] Soukissian T, Karathanasi F, Axaopoulos P (2017) Satellite-based offshore wind resource assessment in the Mediterranean Sea. IEEE J Oceanic Eng 42: 73–86.
    [21] Soukissian TH (2014) Probabilistic modeling of directional and linear characteristics of wind and sea states. Ocean Eng 91: 91–110. doi: 10.1016/j.oceaneng.2014.08.018
    [22] Song M, Chen K, Zhang X, et al. (2016) Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction. Renew Energ 85: 57–65. doi: 10.1016/j.renene.2015.06.033
    [23] Watson SJ (2014) Quantifying the variability of wind energy. Wires Energ Environ 3: 330–342. doi: 10.1002/wene.95
    [24] EMODnet, EMODnet Bathymetry portal. EMODnet, 2016. Available from: http://www.emodnet-hydrography.eu/.
    [25] Caralis G, Chaviaropoulos P, Ruiz Albacete V, et al. (2016) Lessons learnt from the evaluation of the feed-in tariff scheme for offshore wind farms in Greece using a Monte Carlo approach. J Wind Eng Ind Aerod 157: 63–75. doi: 10.1016/j.jweia.2016.08.008
    [26] Greek Parliament, Governmental Gazette, A' No. 149/9-8-2016, L. 4414/2016. Official Government Gazette of the Hellenic Republic.
    [27] European Commission, Official Journal of the European Union, Guidelines on State aid for environmental protection and energy 2014-2020 (2014/C 200/01). European Commission, 2014. Available from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52014XC 0628(01)&from=EN.
    [28] Karathanasi FE, Soukissian TH, Axaopoulos PG (2016) Calibration of wind directions in the Mediterranean Sea. In: Proceedings of the 26th International Ocean and Polar Engineering Conference; 2016; Rhodes, Greece, 491–497.
    [29] Fisher N (1995) Statistical analysis of circular data. 1st ed. Cambridge: Cambridge University Press, 294.
    [30] Jammalamadaka R, SenGupta A (2001) Topics in circular statistics. Singapore: World Scientific Publishing Co. Pte. Ltd., 334.
    [31] Hansen FV (1993) Surface roughness lengths. White Sands Missile Range, New Mexico: U.S. Army Research Laboratory, 1–40.
    [32] Shu ZR, Li QS, He YC, et al. (2016) Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications. Appl Energ 169: 150–163. doi: 10.1016/j.apenergy.2016.01.135
    [33] Papadopoulos A, Katsafados P (2009) Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean. Nat Hazards Earth Syst Sci 9: 1299–1306. doi: 10.5194/nhess-9-1299-2009
    [34] Papadopoulos A, Korres G, Katsafados P, et al. (2011) Dynamic downscaling of the ERA-40 data using a mesoscale meteorological model. Mediterranean Mar Sci 12: 183–198.
    [35] Ferrier BS, Jin Y, Lin Y, et al. (2002) Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta Model. 19th Conference on weather analysis and forecasting/15th Conference on numerical weather prediction. San Antonio: Am Meteorol Soc, 280–283.
    [36] Janjic ZI, Gerrity JP, Nickovic S (2001) An alternative approach to nonhydrostatic modeling. Mon Weather Rev 129: 1164–1178.
    [37] Janjić ZI (1994) The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122.
    [38] Chen F, Janjić Z, Mitchell K (1997) Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale eta model. Bound Lay Meteorol 85: 391–421. doi: 10.1023/A:1000531001463
    [39] Lacis AA, Hansen J (1974) A parameterization for the absorption of solar radiation in the earth's atmosphere. J Atmos Sci 31: 118–133.
    [40] Schwarzkopf MD, Fels SB (1991) The simplified exchange method revisited: an accurate, rapid method for computation of infrared cooling rates and fluxes. J Geophys Res 96: 9075–9096. doi: 10.1029/89JD01598
    [41] Soukissian T, Chronis G (2000) Poseidon: a marine environmental monitoring, forecasting and information system for the Greek Seas. Mediterranean Mar Sci 1: 71–78.
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