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Article

Projected Wind Energy Maximum Potential in Lithuania

by
Justė Jankevičienė
* and
Arvydas Kanapickas
Lithuanian Energy Institute, Smart Grids and Renewable Energy Laboratory, 44403 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(1), 364; https://doi.org/10.3390/app13010364
Submission received: 5 December 2022 / Revised: 16 December 2022 / Accepted: 21 December 2022 / Published: 27 December 2022
(This article belongs to the Special Issue Selected Papers from the 18th International Conference of Young Scientists on Energy and Natural Sciences Issues (CYSENI 2022))
Browse Figures
Versions Notes

Abstract

:
Harnessing wind energy in Lithuania is one of the most important ways to implement climate mitigation measures. This study aims to assess whether it is feasible to implement an energy greening plan in Lithuania in the 21st century, hypothetically by using only wind farms, where the entire permitted area is developed with them. The wind turbine chosen for the study is a 3 MW wind turbine, installed at a height of 100 m. Wind speeds were estimated using the most up-to-date generation of shared socioeconomic pathway (SSP) climate scenario projections. The most likely climate model, SSP2-4.5, shows that the wind speed in Lithuania is more likely to decrease slightly over the 21st century. The decrease could be as high as 3% in the coastal region, while in the eastern part of the country, which is the furthest from the sea, the decrease will likely be around 1.5%. Analysis of the projected data shows that the number of days with wind speeds below the cut-in speed is decreasing, while the number of days with wind speeds higher than the cut-off speed is increasing slightly. However, the number of days on which the operating conditions of the wind farm are met has changed only slightly. The results show that the potential maximum wind power generation exceeds Lithuania’s renewable energy needs by at least three times, suggesting that the development of onshore wind farms in Lithuania can help to achieve the energy greening plans.

1. Introduction

The nature of energy production has changed considerably since the need to mitigate climate change was recognized. The Intergovernmental Panel on Climate Change (IPCC) states that climatological change is mainly caused by human activities, specifically the heavy use of fossil fuels [1]. The Paris Agreement, which aims to make the world carbon-neutral by the middle of this century, has had a significant influence [2,3]. Following the Paris Agreement, countries have made various decisions regarding reducing CO2 and increasing renewable energy sources. For example, the European Union has an overall goal to reduce CO2 emissions by 40% and increase the share of renewable energy sources (RES) to at least 27% by 2030 [4].
Future worldwide electricity demand is expected to reach 3377–6020 TWh [5]. Part of this electricity demand will need to be generated from renewable energy sources (RES). The share of energy produced by RES has increased significantly over the past decade [6]. For example, the International Renewable Energy Agency (IRENA) states that in 2019, 1412 TWh of electricity was generated using wind power globally [7]. This is twice as much as was produced in 2012, and three times the power generated using solar energy [7]. Although wind energy is the most popular and fastest-growing type of renewable energy, most authors agree that wind speed is highly sensitive to climate change [8,9,10]. Therefore, to maintain the right balance, a recommendation has been made for the RES mix: 60% of the energy should be generated by wind, and the remaining 40% by solar irradiation [11]. This makes the analysis of the actual wind potential very important.
Lithuania produces ~25.8% of the green energy out of the country’s total consumption, which is 10.76 TWh [12]. The use of wind as one of the RESs is, therefore, considered a good alternative to fossil fuel combustion and climate change mitigation [13]. Wind energy is currently the most widely used and most suitable renewable energy source for achieving the carbon-neutral target [14]. Wind energy offers the opportunity to meet climate change mitigation targets, as power plants can be built both onshore and offshore. In addition, it is worth noting that wind power plants have the lowest failure rate compared to other renewable energy technologies [15,16]. Wind energy generation is popular and widely used because of its ability to produce large amounts of energy, and because onshore wind farms require far less agricultural land than solar power plants [17]. In addition, the energy density of sunlight in the territory of Lithuania is relatively low, which limits the potential of solar power plants.
In order to assess the wind potential, the most significant factor is the wind speed, the change of which in the near future is mainly decided according to the IPCC projection data [18]. Previous studies on wind potential for this century have shown quite different results. For example, some studies have shown that wind speeds will increase in the future in the northern parts of central Europe, but will decrease in the south of the continent [8,19,20]. Carvalho et al. argue that a significant decrease in wind speed is also very possible in Eastern Europe [21]. As Solaun et al. [22] note, wind speed variations in Europe can be up to ± 12%. These estimates can vary considerably, as analysis of wind trends shows, and this does not allow us to determine the actual magnitude of the wind speed change [8].
Recently, climate-related data were obtained from the simulations based on Representative Concentration Pathways (RCP) scenarios (IPCC AR5 WG 2013) [1], [23,24,25]. The RCP scenarios approved by the IPCC indicate the likely total radiative forcing at the end of the century and the projected change in temperature. RCP scenarios have been updated, and new shared socio-economic pathways (SSP) have been introduced [25]. SSP scenarios are very similar to RCP, but new analysis elements are included, such as the social, economic and technological development to derive greenhouse gas emission scenarios with different climate policies [26].
Previous studies have mostly assessed trends in average wind speed or wind potential on a large scale [5,7,27,28]. Such studies rarely consider areas that are unsuitable for wind energy, such as mountains, forests, roads, road protection zones, etc. There are only a few studies that have included areas that are unsuitable for wind energy. For example, Enevoldsen et al. have carried out a rather detailed analysis that assesses the current wind potential in Europe in terms of the suitable area for the construction of wind farms [29].
The Lithuanian government prepared a map of wind power development areas (Figure 1, [30]). This map takes into account not only socially and technically important territories, but also military and politically prohibited territories. These restrictions significantly reduce the area suitable for the installation of wind farms and, at the same time, reduce the potential of wind energy. In the works of other authors, for example [31], it is noted that such territories must be taken into account, but similar studies did not use the detailed wind power regulation map prepared in those countries [31,32,33].
This study aims to cover the following objectives: firstly, to assess the impact of a changing climate on wind speed over Lithuanian territory for the 21st century, based on the SSP2-4.5 scenario; secondly, to evaluate the available area for wind farm development in Lithuania based on the wind power regulation map; and finally, to evaluate how much energy could hypothetically be generated if wind turbines were built in all permitted locations, and whether this would help meet Europe’s green deal commitments [34].

2. Methodology

2.1. Area Identification and Turbine Description

Wind turbines can be built in many different locations. However, some areas are not suitable. In Lithuania, the locations prohibited for wind energy development are regulated by law. The list of forbidden sites includes border protection zones, protected landscapes, sites reserved for infrastructure development, roads, military areas, airports, etc. Lithuania has a regulatory map for wind turbines, which visually outlines the areas where the construction of wind turbines is prohibited. As a free area is needed for further study, the authors have marked this area in magenta on the map (Figure 1). The available area for wind farm development in Lithuania is 23,948.74 km2. In this article, an available area is defined as a place where no restrictions on wind energy development are implied.
For this study, an existing wind turbine was chosen for analysis, the same size as a standard wind turbine described in the literature. The Enercon E-101 wind turbine, with a height of 99 m, a rotor diameter of 101 m and a rated capacity of 3 MW, was analyzed. The turbine’s cut-in speed is 3 m/s, and its cut-out speed is 25 m/s. The power curve of the plant can be found here [35].

2.2. Climate Data Sources

Observed wind speed data were taken from the National Oceanic and Atmospheric Administration (NOAA) for the time period of 2015–2020, since this period is covered by projections and has been used for bias adjustment. Climatological projections are simulations of the future climate. The last generation of climate projections is based on five SSP (shared socioeconomic pathway) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5. The number of each scenario defines specific radiative forcing in the year 2100, namely 2.6, 4.5, 6, 7 and 8.5 W/m2, respectively [26].
Simulation results obtained using the main scenarios and implemented by various models provide the primary data for the assessment of climate change and related impacts until the end of the current century, and for some models until 2300 [1,36]. In this study, three different models were used: MPI-ESM-LR (Max Planck Institute for Meteorology Earth System, MPI), HadGem2-ES (the Hadley Centre Global Environmental Model, HAD) and IPSL-CM5A-MR (Institute Pierre Simon Laplace, IPSL) (Table 1). The "middle of the road" SSP2 scenario was analyzed. The daily mean near-surface wind speed for the period of 2015–2100 was obtained from the portal of the Earth System Grid (https://esgf-node.llnl.gov/projects/esgf-llnl/, accessed on 20 December 2022).

2.3. Energy Analysis

The resolution used in the models is usually too rough (~70–400 km) to be used for the comparison of meteorological observations and projected data [37]. Every model has a different resolution. For example, IPSL-CM5A-MR’s resolution is 1.9° × 3.75°. As projected wind speed data cannot usually be extracted for the desired point, the closest grid point was found, and the data were extracted. Wind potential evaluation models require finer-scale climate data to assess wind speed trends induced by climate change. Downscaling is required in any impact studies due to varying agricultural and natural landscapes across small distances [37]. Therefore, downscaling was performed using the Inverse Distance Weighted (IDW) interpolation method.
After both observed and projected wind speed data had been collected, bias adjustment was performed. Bias adjustment is usually needed, as substantial biases are uncovered by comparing observed and projected wind speed data.
The percentage change in wind speed from the reference period of 2015–2020 was calculated for every year until the end of the century using the equation below:
a i = R F p − w s i · 100 R F p
where ai is the projected annual wind speed change (i represents the day since the start of the observations) in %, RFp is the mean wind speed over the reference period using projected data in m/s and wsi is the projected annual mean wind speed (i represents day since the start of the observations) in m/s.
After the conversion of wind speed to a percentage is performed, the projected wind speed was calculated using the historical data on average wind speed, which were obtained for the same period of 2015–2020. The equation below shows how projected wind speed is calculated:
w i = 100 − a i · R F o 100
where wi is the expected annual wind speed (i represents the day since the start of the observations) in m/s, and RFo is the mean wind speed over the reference period [38]. Wind speed is usually measured at the height of 2 m or 10 m at meteorological stations. The data used in this study, both historical and projected, are presented at 10 m. However, the most common wind turbine heights vary between 100 m and 120 m [21,39]. Therefore, for a proper assessment of the wind speed potential, the near-surface wind speed is converted to the average height of the wind turbines (100 m in this study) using the following equation:
V h = V h 0 · h h 0 ∝
where V(h)—wind speed at the specified height, m/s; V(h0)—near-surface wind speed, m/s; h—specified height, m; h0 —near-surface height, m; α—wind shear exponent, 1/7, in this study [40].
Wind potential calculations are based on realizable wind turbine installations using wind speed conversion to energy. This has been performed using the equation below [41].
P = 1 2 C p ρ A V 3
where P—wind potential, W; Cp—power coefficient of a turbine (dimensionless); ρ—air density (1.225 kg/m3 is the standard value, which is assumed in this study [21]); A—rotor swept area, m2; V—wind speed, m/s.
Enevoldsen et al. suggested a method to calculate how many turbines can be installed in a specific region [29]:
N = P W T G s C = A s 2 · d 2
where N—number of turbines; PWTGs—potential, MW; C—turbine’s nameplate capacity, MW; A—available area, m2, s—mean European spacing density (median minimum is 3.45 and median maximum 5.3, dimensionless); d—rotor diameter, m.
The changes in the total wind energy potential available for electricity production in Lithuania in upcoming years were evaluated using these equations.

2.4. Software

Data analysis and calculations were performed using the open-source software Rstudio. Data cleaning and analysis were performed using various standard R packages, such as data.table, dplyr, reshape, etc. Prior to data analysis and calculations, projected wind speed data were extracted from downloaded files using special R packages for climate data ncdf4 and cmsaf. Though R can be used for map building, QGIS was preferred. QGIS is an open-source application that supports composing maps, viewing maps and analyzing geospatial data. This software requires no additional coding, and, therefore, saves time. Two main functions used in QGIS were inverse distance weighted (IDW) interpolation and raster extraction. In the IDW interpolation method, the points with known wind speed values are weighted during interpolation, such that the influence of one point relative to another declines with distance from the point at which the wind speed value must be calculated. Raster extraction clips the interpolated area by a vector mask layer (within the defined area) [42].

3. Results

3.1. Wind Speed

Three regions were chosen for wind speed analysis in the territory of Lithuania: coastal (Figure 2b), central (Figure 2c) and eastern (Figure 2d). Klaipėda HMS was selected for the observed wind speed of the coastal region, the central region was analyzed using the data of the Kaunas HMS and Utena HMS was used for the wind description in the eastern region. Figure 2a shows the observed annual mean speed in these three areas during 1980–2020 and the projected annual mean speed of wind, according to the IPSL model, during 2020–2100. From these data, we can see that the average speed in the coastal region is the strongest, about 1.5 m/s higher than in the central region, while the wind in the eastern area is about 1.5 m/s lower. It can also be observed that both observational data and projected data indicate that the wind speed is slightly decreasing. In order to assess the rate at which the wind speed decreased, Figure 2b–d show the linear trends of wind speed variation. The coefficients of the linear equations show that the observed wind speed decreases more rapidly than the wind speed projected by all the models according to the selected scenario SSP2-4.5. However, the data indicate that wind speed may slightly increase in later decades of the 21st century.
As mentioned in the previous section, the selected wind turbine has a cut-in wind speed of 3 m/s and a cut-off wind speed of 25 m/s. An analysis was carried out on how the number of windless days per decade and per year affects energy production (Table 2 and Figure 3). For the HAD model, the number of windless days per year in different parts of Lithuania varies from 31 (in Laukuva) to 215 (in Utena); for the IPSL model, from 37 (in Laukuva) to 210 (in Utena); for the MPI scenario, from 15 (in Klaipėda) to 100 (in Utena). For the MPI model, it was found that there are about two times fewer days with wind speeds that are not suitable (comparing the maximum number of days) for electricity generation as for the other models. This observation explains why the map in Figure 4 shows the highest energy production compared to the other models.
An analysis of the days when the wind is not predicted to be suitable for renewable wind energy production showed no trend over the next four decades. However, these trends emerged from an analysis of the current century, as shown in Figure 3, wherein the number of days when the wind was unsuitable (<3 m/s or >25 m/s) for the operation of wind farms for the SSP2-4.5 scenario was assessed. By the end of the century, the maximum number of ineligible days is predicted to decrease from an average of 91 days to 87 days. However, the MPI model is the most optimistic in windy areas (e.g., Klaipėda), as the number of unsuitable days will not exceed 30 days per year. According to the HAD model data analysis, the maximum number of such days will vary between 31 and 71. The lowest among all models is MPI, where the minimum number will remain stable throughout the century at around 18 days. The analysis of both HAD maximum and IPSL maximum unsuitable days showed very similar results. Windless days could account for 131 to 215 days per year.

3.2. Energy Potential

In order to assess the energy potential of wind, according to the data in Figure 1, the maximum area of the territory of Lithuania where, at least hypothetically, it would be possible to build wind power plants of the selected size was estimated. The analysis of the wind power regulation map (Figure 1) showed that Lithuania has 23,949 km2 of land that could be used for wind energy development. According to equation (4), this area could accommodate approximately 12,023 wind turbines of the size described in the previous section. We assume that electricity production in Lithuania is expected to change similarly to wind speed projected for each of the IPCC models over the next 40 years. This period was chosen because it is approximately the lifetime of wind turbines.
The next four decades (2020–2060) should be the most energy-efficient in the MPI model. Figure 4 shows that energy production could reach up to 150 GWh per decade in the western part of Lithuania. Some decrease in energy production would be expected between 2020 and 2040 under the IPSL and HAD models, but a slight increase is possible between 2040 and 2060—those two decades should be the most energy-effective throughout the century.
The analysis of the HAD model shows that in 2020–2040, wind generation will be the lowest compared to any other of the four decades (Figure 4). Over the next two decades, the amount of energy produced will increase. An evident change is expected in the western and central parts of the country. This change will lead to an increase in the energy output of up to two times compared to 2020–2040 (Table 3).
The IPSL model should be similar to the HAD model. The first two decades of the analyzed period should be the worst in terms of energy production, followed by an increase in green energy production and greater stability in the middle of the century (2040–2060) (Figure 4). An ongoing decrease, from the middle of the century until the end of the century, will follow the increase. In the upcoming 20 years, the maximum annual energy production of the country should be around 57.78 TWh, and in the middle of the century, it will increase by around 2.3% (Table 3).
The MPI model is the most favorable for the wind energy sector compared to the other examined scenarios. The highest electricity production is projected in this scenario; it could reach up to 150 GWh per decade per turbine in the coastal region. One wind turbine’s average annual electricity production will be up to 9 GWh. The last two decades have been the least energy-efficient. In addition, the analysis shows that this scenario shows a much larger energy-useful area of the country (with an energy production between 30–45 GWh).

3.3. Energy Potential with Alternative Wind Turbines

Four more wind turbines from Enercon were selected for additional analysis. Three of them had a capacity of ~3 MW (E-82, E-101 and E-115). The other two were 4.2 MW (E-126 EP4) and 7.6 MW. Table 4 shows how much energy each of these plants would generate in different regions, based on the average wind speed prevailing in that region.
Figure 5, Figure 6 and Figure 7 show the energy profile in different regions. It should be noted that the wind in eastern Lithuania is not very favorable for wind power generation. Here, compared to the coastal or central regions, wind generation is almost two times lower. The most powerful (E-126 and E-126 EP4) of the selected plants are too powerful for any region, taking into account energy profiles. The graphs show that in Lithuania, there are many suitable wind power plants for any region, with a capacity of around 3 MW. The eastern region is the exception. Here, even a 3 MW plant does not reach peak capacity due to the low average wind speed at hub height (3.38 m/s), which makes wind energy development in this region questionable.
For all regions, E-115, which, like E-82 and E-101 (Table 5), has a capacity of 3 MW, is the best choice. It is best suited for this purpose because it will exploit the potential of the plant. Meanwhile, E-126 is too powerful for Lithuania, as the peak power is either unavailable or rarely available. It should be noted that in the central part of the country, the power generation is 2.2 times lower than in the coastal part, and in the eastern part, it is 12.5 times lower. This means that it is not advisable to develop wind energy in the east of the country.

4. Discussion

Analyzing the wind speed in Lithuania’s territory, according to observational data over 1980–2020 and projected according to the most likely SSP2-4.5 scenario over 2020–2100, the wind speed is predicted to decrease throughout the country. Additionally, it should be noted that the projected wind speed decreases at a lower rate than the observed wind speed. This is an understandable phenomenon if we take into account the fact that as the climate warms, the equatorial temperature increases at a lower rate than in the polar regions. As a consequence, the temperature difference between the equator and the polar regions decreases, and as a result, the global atmospheric circulation weakens. The wind speed comparison for the most severe SSP5-8.5 scenario and the most likely SSP2-4.5 scenario meets this assumption [21]: the decrease in wind speed for the first, most severe scenario is bigger than for the second. The global temperature for the SSP5-8.5 scenario is projected to increase by more than 4 °C by the end of the 21st century, while the temperature will increase by 2.7 °C for the SSP2-4.5 scenario compared to pre-industrial levels.
The results show that wind speeds will decrease across the entire territory of Lithuania during the ongoing century. The coastal region is projected to experience the greatest decrease in wind speed, with some models predicting a 50% decrease, while in the eastern, more continental part of the country, the decrease is only predicted to be 10% (the distance from the Baltic Sea is about 300 km). These results are in line with previous authors’ studies which have used wind projection data from RCP scenarios [38,43]. Comparison with other authors’ studies on 21st century windiness yields ambiguous results. For example, Tobin et al. [19] found an increase in wind speed in Northern and Central Europe and a decrease in Southern Europe [19]. However, more studies have shown a general trend of decreasing wind speeds in Europe [44], but in the Baltic region, the data from RCP projections have shown a slight increase in wind speeds [45]. To conclude this part of the discussion, it should be noted that the spatial resolution of global models is too coarse to estimate windiness reliably at the regional level [37].
Another feature of the analyzed wind speed data is that the different global circulation models produce a smaller spread in wind speed trends. Studies by other researchers using data from RCP scenarios [18,24,38], among which this review [22] should likely be noted, have shown that both the Baltic region and Northern Europe show different trends in wind speeds. Some authors state that wind will increase in this region, while others predict that it will decrease slightly, and there are also studies that show little change [38]. Such differences are probably related to the fact that different GCM models were analyzed. The projected wind speed data used for this study and other investigations [17] are characterized by stability of variation, so it can be said that the wind speed data under SSP scenarios are better suited for solving applied problems. Of course, a more detailed analysis of the wind data should be performed for a more reliable conclusion.
The impact of future changes on wind speed, which is relevant for wind energy production, is analyzed as the occurrence of wind speeds at the turbine hub height, which is below the cut-in velocity and above the cut-out velocity of the chosen wind turbine [18,21,24]. Although climate models predict an increase in extreme weather events, the results show that the number of days with wind speeds too high for the wind farm has not increased significantly. Other studies have shown similar results; for example, [46] found that the number of days with wind speeds above 20 m/s is projected to decline over the Mediterranean Sea and the North Atlantic, while the number will remain stable for most of continental Europe. However, a slight increase in windy days was detected for the Baltic Sea. It was also revealed that an increase in days per year with wind speeds below 3 m/s would occur for large parts of Europe. Only for the Baltic Sea and some other regions was a decrease in calm days detected [46]. In conclusion, it can be noted that similar trends were found in the present study for Lithuania, as an eastern part of the Baltic region.
The analysis of the "middle of the road," or SSP2-4.5 scenario, shows that a changing climate will not significantly impact Lithuania’s renewable energy sector. Current renewable energy capacity, which generates around a quarter of the electricity needed, is not sufficient to meet and fulfill our targets [4]. According to the analysis, Lithuania could generate up to almost four times more electricity than it currently needs. Even if not all the available space was filled, Lithuania could become a zero-emission country and trade in green electricity.
Table 2 shows the maximum energy values that wind farms could produce if they were to cover the whole of Lithuania, except in unsuitable locations. The values obtained for each model vary significantly. This can be explained by the differences between the models. This would require the use of more models in the study.
The significance of the wind energy potential can be compared with the consumption level of the European Union. Electricity consumption in the European Union is 6.2 MWh/per capita [47], and in Lithuania, it is 4182 KWh/per capita [48]. A total of 2865 TWh of electricity was consumed in the European Union [49]. The potential of wind energy in the European Union is estimated at 138,090 TWh by Enevoldsen [29], which is almost 50 times the amount of electricity consumed in the EU. Meanwhile, the authors in this investigation have found that the annual potential of wind energy, according to three climate projection models in the 21st century, is only 3–9 times higher than the amount of electricity consumed. The obtained difference can be explained primarily by the fact that the wind speed in the territory of Lithuania is low compared to most EU countries [50]. Another reason is related to the methodology used to assign the suitable area for wind energy development. Using the wind power regulation map of Lithuania, it was found that 36.7% of the territory is suitable for the installation of wind power plants, while in other studies, the area suitable for wind energy was found to be larger. For example, Sliz [32] found that this area was about 42% (Kujawsko–Pomorskie Voivodeship, Poland); and Enevoldsen [29] estimated that 45% of the European territory was suitable for the installation of wind farms. The third reason is calculating the amount of energy produced by wind farms. In this work, the wind speed was estimated for each element of the area suitable for wind farms, then the number of wind farms that could be located in that area was evaluated, and then the electricity produced by the farms in that area was estimated. Meanwhile, other authors estimated the average wind speed throughout the country’s territory, counted the number of wind plants that can be placed in the permitted territory and then obtained the total wind energy potential. According to the results of this study, as well as in wind atlas [50] show that the wind speed depends quite strongly on the distance to the sea. Since wind energy depends on the cube of wind speed, v3, using the mean wind speed over the entire area can unreasonably increase the total energy potential. In summary, we can state that the methodology used in this study was more accurate, and as a result, we obtained a lower wind energy potential compared to the results presented in other studies.

5. Conclusions

In this study, wind energy potential in Lithuania in the 21st century was analyzed based on the wind power regulation map and IPCC climate projection data. After analyzing the area that allowed for the installation of wind power plants, it was found that it constituted about 37% of the territory of Lithuania. A significant portion of the territorial restrictions is due to political and military reasons.
An analysis has been made of the amount of wind electricity generated in Lithuania using updated wind speed data, projected according to the newest generation of the SSP scenario. It was found that in the most likely SSP2-4.5 scenario, the wind speed will slightly decrease over the entire territory of Lithuania during the current century. The changes in the projected wind speed correspond to the trends observed at the meteorological station. The wind speed in the eastern part of Lithuania, farthest from the Baltic Sea, is about 3.5 m/s, remaining about two times lower than in the coastal region, where it is about 7.0 m/s. The decrease in wind speed in the coastal region is small in absolute terms, amounting to 0.03 m/s per decade, while in the eastern part of the Baltic Sea, farthest away, it is vanishingly small, about 0.015 m/s per decade.
Using Lithuania’s wind power regulation map, the maximum amount of energy produced by wind power plants was estimated if wind power plants of the selected type were installed across the entire permitted territory. It was found that the amount of electricity produced in hypothetical wind power plants would be at least three times higher than Lithuania’s annual electricity demand of about 11 TWh. In the SSP2-4.5 climate change scenario, the amount of wind energy produced by these wind farms decreases, but the decrease does not significantly affect the amount of energy produced. The increasing number of days that the wind farm would not be suitable for the operation would reduce the amount of energy produced. Therefore, it is necessary to take into account the trend of such days in the selected area when constructing wind farms.
A comparison of the obtained results with similar studies shows that this type of work mainly considers the social, economic and technical limitations caused by the development of wind power plants. The authors believe that the potential of wind energy could be assessed much more accurately if the limitations of political and military purposes were taken into account, which can be summarized on a regulation map. This study was limited only to the territory of Lithuania, as it was not possible to obtain similar wind power regulation maps from other neighbouring countries.

Author Contributions

Conceptualization, J.J. and A.K.; methodology, J.J. and A.K.; software, J.J.; validation, J.J.; formal analysis, J.J.; investigation, J.J. and A.K.; resources, J.J. and A.K.; data curation, J.J. and A.K.; writing—original draft preparation, J.J. and A.K.; writing—review and editing, J.J. and A.K.; visualization, J.J.; supervision, A.K.; project administration, J.J. and A.K.; funding acquisition, J.J. and A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 13 00364 g001 550
Figure 1. Wind power regulation map. The available area is marked in magenta. Full legend can be found here [30].
Figure 1. Wind power regulation map. The available area is marked in magenta. Full legend can be found here [30].
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Figure 2. Analysis of wind speed over Lithuanian territory. (a): the change in mean annual speed according to the observed and bias-corrected projection wind speed data (IPSL model, SSP2-4.5 scenario). (b): wind speed change in the coastal region ("-o") according to the data of Klaipeda HMS and three projection models. (c): wind speed change in the central region ("-c") according to the data of Kaunas HMS and three projection models. (d): wind speed change in the central region ("-e") according to the data of Utena HMS and the same three projection models.
Figure 2. Analysis of wind speed over Lithuanian territory. (a): the change in mean annual speed according to the observed and bias-corrected projection wind speed data (IPSL model, SSP2-4.5 scenario). (b): wind speed change in the coastal region ("-o") according to the data of Klaipeda HMS and three projection models. (c): wind speed change in the central region ("-c") according to the data of Kaunas HMS and three projection models. (d): wind speed change in the central region ("-e") according to the data of Utena HMS and the same three projection models.
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Figure 3. Days per year with unsuitable wind speed for energy production (<3 m/s and >25 m/s) and moving average (MA) for 20 years.
Figure 3. Days per year with unsuitable wind speed for energy production (<3 m/s and >25 m/s) and moving average (MA) for 20 years.
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Applsci 13 00364 g004a 550Applsci 13 00364 g004b 550
Figure 4. Maps show how much energy can be produced over a particular decade in any location using a selected wind turbine for the SSP2-4.5 scenario. Units—GWh.
Figure 4. Maps show how much energy can be produced over a particular decade in any location using a selected wind turbine for the SSP2-4.5 scenario. Units—GWh.
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Figure 5. Energy profile of different wind turbines in the coastal region.
Figure 5. Energy profile of different wind turbines in the coastal region.
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Figure 6. Energy profile of different wind turbines in the central region.
Figure 6. Energy profile of different wind turbines in the central region.
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Figure 7. Energy profile of different wind turbines in the eastern region.
Figure 7. Energy profile of different wind turbines in the eastern region.
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Table
Table 1. Models used in this research.
Table 1. Models used in this research.
ModelInstitutionCountryHorizontal Resolution
MPI-ESM-LRMax Planck Institute for Meteorology Earth SystemGermany1.875° × 1.875°
HadGem2-ESHadley Centre Global Environmental ModelUnited Kingdom1.25° × 1.875°
IPSL-CM5A-MRInstitute Pierre Simon LaplaceFrance1.9° × 3.75°
Table
Table 2. The number of days per decade when the wind is predicted to be unsuitable (<3 m/s or >25 m/s) for the operation of wind farms for the SSP2-4.5 scenario.
Table 2. The number of days per decade when the wind is predicted to be unsuitable (<3 m/s or >25 m/s) for the operation of wind farms for the SSP2-4.5 scenario.
HADIPSLMPI
MinMaxMinMaxMinMax
2021–203046816735601716176766
2031–204045416376261762192750
2041–205046916845961623195755
2051–206050317776541797163750
2061–207049517956841835159714
2071–208049518047031865170760
2081–209045415936621844180792
2091–210042716215761660195771
Table
Table 3. Estimated average energy production per year, TWh, for SSP2-4.5 scenario, with the assumption that wind power plants will be built in every available area.
Table 3. Estimated average energy production per year, TWh, for SSP2-4.5 scenario, with the assumption that wind power plants will be built in every available area.
HADIPSLMPI
2021–204013.92–31.9639.74–57.7878.46–96.50
2041–206038.32–56.3521.04–59.0844.06–62.09
2061–208028.50–46.5424.22–42.2640.03–58.06
2081–210024.14–42.1819.33–37.3728.63–46.67
Limits are set according to Figure 4.
Table
Table 4. Turbine comparison by rated power and energy production at different regions.
Table 4. Turbine comparison by rated power and energy production at different regions.
TurbineE-82E-115E-101E-126 EP4E-126
Rated power, kW30203000305042007580
Energy production (coastal), kWh321627479745760
Energy production (central), kWh144281214332340
Energy production (eastern), kWh2650396057
Table
Table 5. Comparison of average yearly energy output (MWh) by a single turbine.
Table 5. Comparison of average yearly energy output (MWh) by a single turbine.
CoastalCentralEastern
E-821 926864156
E-1153 7621 686300
E-1012 8741 284234
E-1264 4701 992360
E-126 EP44 5602 040342
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Jankevičienė, J.; Kanapickas, A. Projected Wind Energy Maximum Potential in Lithuania. Appl. Sci. 2023, 13, 364. https://doi.org/10.3390/app13010364

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Jankevičienė J, Kanapickas A. Projected Wind Energy Maximum Potential in Lithuania. Applied Sciences. 2023; 13(1):364. https://doi.org/10.3390/app13010364

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Jankevičienė, Justė, and Arvydas Kanapickas. 2023. "Projected Wind Energy Maximum Potential in Lithuania" Applied Sciences 13, no. 1: 364. https://doi.org/10.3390/app13010364

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