*3.2. Wind Data—Mesoscale Modeling*

Simultaneous information on wind statistics over every potential area for wind farm development is required for this analysis. Even if a large number of wind measurements are available, it is practically difficult to represent simultaneous data series and cover every potential area of interest. Installation of a mast network for this purpose could lead to rather prohibitive technical and economic restrictions. Additionally, existing wind monitoring networks are relatively large and can provide large spatial coverage but not necessarily high resolution [37]. On the other hand, use of wind potential maps is not a solution since they only provide an estimation of the spatial distribution of the mean wind speed without any information on its temporal variation. Application of a Numerical Weather Prediction (NWP) model can effectively provide the information required.

In this connection, high resolution analytical wind data timeseries for typical wind year are used. These data have been produced by the systematic application of a numerical weather prediction model. Analytical presentation and description of the approach was given in [38]. In Figure 2, the relative high resolution wind atlas of Greece is presented in terms of power density and parameter c of Weibull distribution, which are the most common ways to present Aeolian maps. The wind atlas of Greece was based on a typical wind year and 12 months of weather model simulations for grid boxes 2 <sup>×</sup> 2 km<sup>2</sup> in size. The numerical weather prediction model used is "MM5" which is run operationally at the

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cast skill over the area of interest.

National Observatory of Athens since 2002 [39] and has been verified [40,41] for its forecast skill over the area of interest. Weibull distribution (m/s) are presented. Both are widely used in Aeolian maps for representation of the wind potential [42,43]

was given in [38]. In Figure 2, the relative high resolution wind atlas of Greece is presented in terms of power density and parameter c of Weibull distribution, which are the most common ways to present Aeolian maps. The wind atlas of Greece was based on a typical wind year and 12 months of weather model simulations for grid boxes 2 × 2 km2 in size. The numerical weather prediction model used is "MM5" which is run operationally at the National Observatory of Athens since 2002 [39] and has been verified [40,41] for its fore-

The selection of 90 representative points in the Greek territory is based on the location of wind farms as it is expressed by investors' interest and it is depicted in the Regulatory Authority for Energy (RAE) geographical information system in the Greek territory (Figure 3). For future scenarios, spatial dispersion of wind farms and existing plans for interconnections of Greek islands with the power system of mainland Greece are considered. Data for the selected 90 points are presented in Table 2. The duration curve of wind power output is dependent on the spatial distribution of wind farms. Historical data of single wind turbine power production typically show extended time periods with zero or rated production. However, as the spatial dispersion is increased and more wind farms are introduced, the time periods with cumulative zero or rated production are reduced [34]. The aggregated hourly wind power output is calculated on the basis of the mesoscale wind data, and the selected points in the whole Greek territory for the current wind energy development (2019) and the scenarios of installed capacity in each point under consideration for 2030 and 2050 (Figure 4). In Figure 2, high resolution wind atlas are presented for the typical wind year [38]. The indexes of Power density (W/m2) and Parameter c of

**Figure 2.** High resolution wind atlas for a typical wind year [38]: (**a**) power density (W/m2), (**b**) parameter c (m/s) of Weibull distribution. **Figure 2.** High resolution wind atlas for a typical wind year [38]: (**a**) power density (W/m<sup>2</sup> ), (**b**) parameter c (m/s) of Weibull distribution.

Figure 3 shows the overview of wind farms applications in the Greek territory and selection of 90 representative points in the whole Greek territory. The selection of 90 representative points in the Greek territory is based on the location of wind farms as it is expressed by investors' interest and it is depicted in the Regulatory Authority for Energy (RAE) geographical information system in the Greek territory (Figure 3). For future scenarios, spatial dispersion of wind farms and existing plans for interconnections of Greek islands with the power system of mainland Greece are considered. Data for the selected 90 points are presented in Table 2. The duration curve of wind power output is dependent on the spatial distribution of wind farms. Historical data of single wind turbine power production typically show extended time periods with zero or rated production. However, as the spatial dispersion is increased and more wind farms are introduced, the time periods with cumulative zero or rated production are reduced [34]. The aggregated hourly wind power output is calculated on the basis of the mesoscale wind data, and the selected points in the whole Greek territory for the current wind energy development (2019) and the scenarios of installed capacity in each point under consideration for 2030 and 2050 (Figure 4). In Figure 2, high resolution wind atlas are presented for the typical wind year [38]. The indexes of Power density (W/m<sup>2</sup> ) and Parameter c of Weibull distribution (m/s) are presented. Both are widely used in Aeolian maps for representation of the wind potential [42,43]

Figure 3 shows the overview of wind farms applications in the Greek territory and selection of 90 representative points in the whole Greek territory.

In Table 2 the details of the 90 representative points are presented (name, location, k and c of the Weibull distribution and wind power density) [38].

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**Figure 3.** Overview of wind farms applications in the Greek territory and selection of representative points in the whole Greek territory. **Figure 3.** Overview of wind farms applications in the Greek territory and selection of representative points in the whole Greek territory. **Figure 3.** Overview of wind farms applications in the Greek territory and selection of representative points in the whole Greek territory.

In Table 2 the details of the 90 representative points are presented (name, location, k

In Table 2 the details of the 90 representative points are presented (name, location, k

**Figure 4.** Location of wind farms by 2019, 2030 and 2050. **Figure 4.** Location of wind farms by 2019, 2030 and 2050. **Figure 4.** Location of wind farms by 2019, 2030 and 2050.

and c of the Weibull distribution and wind power density) [38].

and c of the Weibull distribution and wind power density) [38].

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**Point Site Lat (** ◦ **) Long (** ◦ **) k (-) c (m/s) Wind Power Density (W/m2 ) Point Site Lat (** ◦ **) Long (** ◦ **) k (Weibull Coefficient) c (m/s) Wind Power Density (W/m2 )** 1 Othonoi 39.786 19.422 1.59 5.99 232 46 Kozani 40.368 21.545 1.58 4.78 116 2 Lefkada 38.867 20.652 1.71 5.37 146 47 Chalkidiki 39.947 23.972 1.73 5.35 143 3 Krioneri 38.292 21.602 1.21 5.78 339 48 Serres 41.296 23.317 1.49 4.63 116 4 Petalioi 38.103 24.133 1.8 7.41 369 49 Veroia 40.127 22.214 1.59 4.47 92 5 Kimi 38.599 24.221 1.75 6.68 278 50 Kilkis 41.134 22.663 1.39 4.53 127 6 Karpathos 35.417 27.039 1.91 8.92 592 51 Trikeri 39.126 23.237 1.77 5.33 137 7 Lemnos 39.951 25.573 1.69 7.62 431 52 Trikala 39.312 21.525 1.51 6.16 273 8 Ai-Stratis 39.536 25.071 1.8 7.25 344 53 Anavra 39.017 22.705 1.65 5.06 128 9 Samothraki 40.575 25.714 1.5 5.96 251 54 Ioannina 40.327 20.837 1.72 5.8 186 10 Alexandroupoli 40.726 25.977 1.63 6.4 272 55 Perdika 39.278 20.328 1.52 4.21 83 11 Fanari 40.867 25.205 1.52 5.07 150 56 Kalavrita 38.751 20.933 1.5 5.18 164 12 Thasos 40.733 24.946 1.55 5.09 145 57 Mitikas 38.417 21.773 1.75 5.2 129 13 Makronisos 37.732 24.150 1.66 7.22 379 58 Naupaktos 38.174 21.856 1.55 5.81 221 14 Ai-Giorgis 37.465 23.942 1.68 6.77 307 59 Kissamos 35.455 23.587 1.63 6.55 290 15 Gyaros 37.609 24.723 1.61 7.4 422 60 Sfakia 35.258 24.225 1.43 7.78 554 16 Erithres 38.151 23.525 1.56 6.23 268 61 Psiloreitis 35.250 24.697 1.59 7.39 429 17 Lavrio 37.755 24.058 1.7 7.25 372 62 Elounta 35.312 25.740 1.7 8.2 526 18 Geraneia 37.985 23.067 1.43 5.87 261 63 Toplou 35.239 26.250 1.74 7.34 374 19 Lemnos (Mirina) 39.983 25.043 1.73 5.95 199 64 Ziros 35.078 26.188 1.59 8.6 638 20 Lesvos (Eresos) 39.183 25.947 1.86 6.52 239 65 Malia 35.090 25.492 1.54 8.24 594 21 Chios 38.447 26.126 1.85 7.45 363 66 Irakleio (south) 35.117 25.068 1.59 6.52 299 22 Ikaria 37.559 26.096 1.37 8.26 662 67 Mochlos 35.080 25.896 1.49 8.73 699 23 Lesvos (Mantamados) 39.331 26.228 1.76 6.57 263 68 Alexandroupoli 41.063 25.942 1.78 6.74 280

**Table 2.** Mesoscale typical wind year data of the representative points [38].




### *3.3. Load Demand Data 3.3. Load Demand Data*

Actual time series of load demand data for the interconnected power system have been used (Power Public Corporation S.A. data, https://www.dei.gr/en, accessed on 15 January 2021). Corresponding adjustments to the demand time series were realized in order to formulate the corresponding timeseries for the years 2030 and 2050. The base year for the load demand time-series is 2006. This is the last year before the start-up of PV development in Greece. The forecasts for annual electricity demand and peak power demand are based on relevant studies that have been conducted for the power system of Greece. The comparative study of the researches carried out for Greece's future demand concludes that, in 2030, a modest estimation is considered to be 57.2 TWh with a peak demand of 10.5 GW, while for 2050, 74 TWh with a peak demand of 13 GW. The studies that were taken into consideration were conducted by the Ministry of Environment and Energy [27], the European Commission [28] and WWF [29]. A comparative representation of the electricity demand forecasts is presented in Figure 5. Actual time series of load demand data for the interconnected power system have been used (Power Public Corporation S.A. data, https://www.dei.gr/en). Corresponding adjustments to the demand time series were realized in order to formulate the corresponding timeseries for the years 2030 and 2050. The base year for the load demand time-series is 2006. This is the last year before the start-up of PV development in Greece. The forecasts for annual electricity demand and peak power demand are based on relevant studies that have been conducted for the power system of Greece. The comparative study of the researches carried out for Greece's future demand concludes that, in 2030, a modest estimation is considered to be 57.2 TWh with a peak demand of 10.5 GW, while for 2050, 74 TWh with a peak demand of 13 GW. The studies that were taken into consideration were conducted by the Ministry of Environment and Energy [27], the European Commission [28] and WWF [29]. A comparative representation of the electricity demand forecasts is presented in Figure 5.

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**Figure 5.** Electricity demand development scenarios [27–29]. **Figure 5.** Electricity demand development scenarios [27–29].

### *3.4. PV Data 3.4. PV Data*

In terms of PV production, actual time series provided by CRES (Centre of Renewable Energy Sources) were used with the appropriate adjustments. The adjustment is based on PVGIS estimated annual output [44]. Table 3 present the information used from PVGIS and representative duration curves of PV power output for four cases. In terms of PV production, actual time series provided by CRES (Centre of Renewable Energy Sources) were used with the appropriate adjustments. The adjustment is based on PVGIS estimated annual output [44]. Table 3 present the information used from PVGIS and representative duration curves of PV power output for four cases.

**Table 3.** Annual PV production [44]. **Table 3.** Annual PV production [44].


### **4. Application—Results 4. Application—Results**

### *4.1. Scenarios 4.1. Scenarios*

By 2030, the electricity demand in the country is expected to reach 57.15 TWh, with a peak of 10 GW and by 2050, 74 TWh with a peak of 11.3 GW [27]. By 2030, the electricity demand in the country is expected to reach 57.15 TWh, with a peak of 10 GW and by 2050, 74 TWh with a peak of 11.3 GW [27].

Lignite power plants are expected to be decommissioned by 2028, in view of the achievement of decarbonization targets. The nominal output of natural gas units for reference years 2030 and 2050 is expected to reach 6.97 GW [27] and 7.1 GW [29]. Lignite power plants are expected to be decommissioned by 2028, in view of the achievement of decarbonization targets. The nominal output of natural gas units for reference years 2030 and 2050 is expected to reach 6.97 GW [27] and 7.1 GW [29].

In this connection, the reference scenario by 2030 for wind and PV capacity refers to a cumulative capacity of up to 16 GW (8 GW wind, 8 GW PV). By 2050, it is assumed that the

renewable energy sources' capacity, i.e., wind and PV, will account for 18 GW aggregated, with wind installations of 9 GW and photovoltaic of 9 GW. The normalized wind and PV capacities (by the average annual demand) are 1.83 by 2030 and 2.48 by 2050, close to the relative normalized capacities in the corresponding studies discussed and presented in Figure 1. gated, with wind installations of 9 GW and photovoltaic of 9 GW. The normalized wind and PV capacities (by the average annual demand) are 1.83 by 2030 and 2.48 by 2050, close to the relative normalized capacities in the corresponding studies discussed and presented in Figure 1. The percentage of instantaneous wind penetration (δ) is considered to be 50% for

In this connection, the reference scenario by 2030 for wind and PV capacity refers to a cumulative capacity of up to 16 GW (8 GW wind, 8 GW PV). By 2050, it is assumed that the renewable energy sources' capacity, i.e., wind and PV, will account for 18 GW aggre-

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The percentage of instantaneous wind penetration (δ) is considered to be 50% for 2030 and 60% for 2050. The increase of the instantaneous penetration (δ) in 2050 is based on the fact that the management of the grid renewable energy will have matured, and weather load forecast models will be widely used operationally. However, both figures are considered as very conservative approaches, which will keep the results on the safe side. 2030 and 60% for 2050. The increase of the instantaneous penetration (δ) in 2050 is based on the fact that the management of the grid renewable energy will have matured, and weather load forecast models will be widely used operationally. However, both figures are considered as very conservative approaches, which will keep the results on the safe side.

According to IPTO adequacy study for 2020–2030, as of December 2019, 700 MW of hydroelectric plants have been licensed, including 590 MW of hydro-pumped storage facilities [45]. According to IPTO adequacy study for 2020–2030, as of December 2019, 700 MW of hydroelectric plants have been licensed, including 590 MW of hydro-pumped storage facilities [45].

The nominal output of power plants using as feedstock biomass is considered to reach 300 MW by 2030 and 600 MW by 2050 [27]. The nominal output of power plants using as feedstock biomass is considered to reach 300 MW by 2030 and 600 MW by 2050 [27].

### *4.2. Energy Mix by 2030 and 2050 4.2. Energy Mix by 2030 and 2050*

By 2030 and 2050, considering the developments and assumptions mentioned above, the final energy mix could be formed as depicted in Figure 6. By 2030 and 2050, considering the developments and assumptions mentioned above, the final energy mix could be formed as depicted in Figure 6.

**Figure 6.** Energy share by technology for reference years 2030 and 2050.

The estimated nominal output of hydro-pumped storage units reaches 1500 MW for the reference year 2030. The capacity factor of pumped-storage units, expressed by the rated power of the turbines, reaches 19%. This value of capacity factor is below the lower acceptable limit which renders the investment economically viable (a benchmark of the capacity factor could be 25% [6]). In this study, this value is considered acceptable, since, according to current data for Greece, large hydro units' capacity factor varies to similar levels (15–17%); therefore, this value could also be considered for reverse hydro-pumped storage projects. The capacity factor of hydropower units may be lower than benchmark, because they are used as a safety net, for cases of emergency, so they are over-dimensioned in order to be able to support the system when a deficit occurs.

The energy contribution of wind and PV installations reaches 49%, and the energy contribution of conventional units is lowered to 31%. The electricity sector is characterized by a higher renewable share, and electricity production is partially decarbonized, compared to the current situation. Hydro-pumped storage units contribute with 6% to the annual electricity demand, by exploiting 70% of curtailed energy.

In 2050, the installed capacity of wind and photovoltaic installations is considered to be 24 GW in total, which results in a reduction of the conventional power plants energy contribution to 25%. Renewable energy sources possess the highest share of production, accounting for approximately 60%. The energy surplus increases due to the higher integration of renewables, and the hydro-pumped storage capacity is estimated at 2700 MW. The share of hydro-pumped storage projects reaches 7% of the total energy produced. The capacity factor of hydro-pumped storage units is estimated at 23%. This higher capacity factor could be attributed to the higher integration of RES, which results in the increase of curtailed power. Consequently, the higher integration of RES contributes to the enhancement of the economically viable operation of hydro-pumped storage units.
