An Energy Efficiency Estimation Procedure for Small Wind Turbines

energies

Article An Energy Efﬁciency Estimation Procedure for Small Wind Turbines at Chosen Locations in Poland

Justyna Zalewska, Krzysztof Damaziak and Jerzy Malachowski *

Institute of Mechanics and Computational Engineering, Faculty of Mechanical Engineering, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland; [email protected] (J.Z.); [email protected] (K.D.) * Correspondence: [email protected]; Tel.: +48-261-839-140

Abstract: Contrary to the extensive amount of research on large wind turbines, substantial analyses of small wind turbines are still rare. In the present study, the wind energy potential of three locations in Poland is analyzed using real wind data from a five-year period and the parameters of the selected turbine model. Appropriate simulations are performed to assess the energy efficiency of the analyzed investments at a coastal, foothill, or lowland site. According to the results, the most favorable location for a small wind turbine is the coastal site (wind zone I). The payback time at this location is approximately 13 years, whereas the payback times at the other two analyzed are more than 3 times longer. The payback periods for the latter locations significantly exceed the estimated lifetime of the wind turbine, ruling out their economic viability. The cost of electricity generation varies greatly, from 0.16 EUR/kWh at the coastal location to 0.71 EUR/kWh at the lowland location. These results provide a reference for developing more efficient solutions, such as the use of a turbine with a shielded rotor, which can increase the power of the turbine by approximately 2.5 times.

Citation: Zalewska, J.; Damaziak, K.; Keywords: small wind turbine; economic evaluation; wind energy potential; payback period; Poland Malachowski, J. An Energy Efﬁciency Estimation Procedure for Small Wind Turbines at Chosen Locations in Poland. Energies 2021, 14, 3706. 1. Introduction https://doi.org/10.3390/en14123706 Tremendous advances have occurred in the renewable energy sector in recent decades, among which one of the most important is wind energy. As shown in Figure1, the rate Academic Editor: Miguel-Angel of growth of global total installed wind power capacity increased annually from 2013 to Tarancon 2019. Investments in onshore and offshore wind energy worldwide amounted to EUR 128 billion in 2019, compared with EUR 87 billion in 2010, an increase of 38.5% [1]. In Poland, Received: 26 April 2021 Accepted: 18 June 2021 wind energy is the most dynamically developing branch of the renewable energy sector. Published: 21 June 2021 According to the Energy Regulatory Ofﬁce (ERO), the total capacity of wind turbines in Poland at the end of 2019 was 5917.243 MW. There are several dozen active wind

Publisher’s Note: MDPI stays neutral farms in Poland, mostly located in the northern and central parts of Poland [2]. The ERO with regard to jurisdictional claims in regularly reports the installed capacity of each type of renewable energy source in Poland published maps and institutional afﬁl- (installations using biogas, biomass, solar energy, wind energy and hydropower). Table1 iations. shows the installed capacity of all renewable energy source (RES) installations in Poland in 2010, 2015 and 2019; all types increased in installed energy capacity during this period. Further details are shown in Figure2, which presents the installed capacity for wind power plants over the period 2005–2019. These increases in capacity reﬂect the expansion of wind installations, which, according to data from the ERO, increased from 981 at the beginning Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. of October 2015 to 1207 by the end of 2019 (single turbines and farms) [3]. Table1 clearly This article is an open access article shows that the highest share of installed capacity at each time point is from wind power distributed under the terms and plants. In 2019, wind power plants represented nearly 65% of total installed RES capacity conditions of the Creative Commons in Poland [3,4] and produced 13.9 TWh of electricity, an increase of almost 19.1% compared Attribution (CC BY) license (https:// with the previous year. However, when considered in relation to total domestic electricity creativecommons.org/licenses/by/ generation, wind energy was responsible for only 8.75% of total energy generated [5]. 4.0/).

Energies 2021, 14, 3706. https://doi.org/10.3390/en14123706 https://www.mdpi.com/journal/energies Energies 2021, 14, 3706 2 of 18

Energies 2021, 14, 3706 2 of 18

However, when considered in relation to total domestic electricity generation, wind Energies 2021, 14, 3706 energy was responsible for only 8.75% of total energy generated [5]. 2 of 18 However, when considered in relation to total domestic electricity generation, wind energy was responsible for only 8.75% of total energy generated [5].

Figure 1. Total installed wind power capacity in the world [6]. FigureFigure 1.1. TotalTotal installedinstalled windwind powerpowercapacity capacityin inthe theworld world[ 6[6].].

TableTable 1. 1.Installed Installed power power of of renewable renewable energy energy sources sources in Poland in Poland (years (years 2010, 2010, 2015, 2015, 2019) 2019) [3]. [3]. Table 1. Installed power of renewable energy sources in Poland (years 2010, 2015, 2019) [3]. InstalledInstalled Capacity Capacity [MW] [MW] TypeType of of RES RES Installation Installation Installed Capacity [MW] Type of RES Installation 20102010 2015 2015 2019 2019 2010 2015 2019 Biogas 82.884 212.497 245.366 BiogasBiogas 82.88482.884 212.497212.497 245.366245.366 BiomassBiomassBiomass 356.190356.190356.190 1122.6701122.6701122.670 1492.8751492.8751492.875 SolarSolarSolar energy energy energy 0.033 0.033 0.033 71.031 71.031 71.031 477.679 477.679 477.679 Wind energy 1180.272 4582.036 5917.243 WindWind energy energy 1180.272 1180.272 4582.036 4582.036 5917.243 5917.243 Hydropower 937.044 981.799 973.095 HydropowerHydropower 937.044 937.044 981.799981.799 973.095973.095 TotalTotalTotal 2556.4232556.4232556.423 6970.0336970.0336970.033 9106.2589106.2589106.258

Figure 2. Installed capacity of wind power plants in Poland, 2005–2019 [3]. Figure 2.2.Installed Installed capacity capacity of of wind wind power power plants plants in Poland,in Poland, 2005–2019 2005–2019 [3]. [3]. 1.1.1.1. SmallSmall WindWind TurbinesTurbines 1.1. Small Wind Turbines WithinWithin thethe wind wind energy energy sector, sector, small small wind wi turbinesnd turbines (SWTs) (SWTs) are a distinct are a anddistinct separate and Within the wind energy sector, small wind turbines (SWTs) are a distinct and groupseparate of group devices. of devices. According According to the IEC to the 61400-2 IEC 61400-2 standard, standard, SWTs SWTs are characterized are characterized by a rotorseparateby a rotor swept group swept area of area of devices. less of less than According than 200 200 m2 mand to2 andthe rated IECrated power 61400-2 power below standard,below 50 50 kW kWSWTs [7 [7].]. are Wind Wind characterized powerpower 2 plantsbyplants a rotor inin thisthis swept categorycategory area areareof less generallygenerally than 200 designeddesigned m and forfor rated smallsmall power andand individualindividual below 50 customers customerskW [7]. Wind suchsuch aspoweras households,plantshouseholds, in this farms,farms, category weatherweather are generally stations,stations, roadroad designed signalssignals for oror small advertisingadvertising and individual systemssystems [[8].8 ].customers SWTsSWTs areare such anan as excellenthouseholds,excellent energyenergy farms, solutionsolution weather forfor ruralruralstations, areasareas road oror poorlypoorly signals industrializedindustrialized or advertising areasareas systems wherewhere [8]. connectionconnection SWTs are an toexcellentto thethe electricityelectricity energy gridgrid solution isis problematicproblematic for rural butareasbut windswinds or poorly areare strong.strong. industrialized IndividualIndividual areas consumersconsumers where are areconnection alsoalso increasinglytoincreasingly the electricity investinginvesting grid inisin SWTsproblematicSWTs toto partiallypartially but winds meetmeet householdhousehold are strong. electricityelectricity Individual oror hot hotconsumers waterwater needs.needs. are also Thisincreasingly is conﬁrmed investing by data in from SWTs areport to partially from the meet Statista household portal [electricity9] showing or the hot capacity water ofneeds. small wind turbines in the world from 2010 to 2018. Undoubtedly, the values are rising from year to year, and the interest in the installation of small wind turbines is constantly growing.

Energies 2021, 14, 3706 3 of 18

However, many people who use home wind turbines are disappointed with the actual amount of energy produced, as small wind turbine manufacturers frequently overstate or provide inaccurate information on the generated amount of energy. Such discrepancies reduce public confidence in investing in SWTs and slow the pace of development [10]. Despite some hesitation among consumers, the number of operating SWTs is increasing each year. The growing interest in creating private wind farms may reflect increased awareness of upcoming climate change and the negative aspects of greenhouse gas emissions [11]. By the end of 2014, the total number of SWTs was approximately 945,000 worldwide, an increase of 8.3% compared with the previous year. The highest number of new installed units in 2014 was in China, where the majority of SWTs (approximately 72%) are located [12]. In 2018, the total global installed capacity of SWTs was over 1.73 GW [9]. The growing prevalence of SWTs and the emergence of so-called prosumers within the electricity grid (the smart grid concept) are expected to have major impacts on the way power companies deliver services over the next decade, especially in developing countries [13]. Experts agree that work on the design and architecture of the future grid is as important as efforts to develop the technologies and products, such as SWTs, needed to realize the smart grid vision [14]. Generating electricity using SWTs can be an economically viable solution, but unfortunately the market prices of these solutions are often overstated, especially for developing countries [11]. The publication [8] provides a broad overview of the performance, design, control and manufacturing of small wind turbines. This article shows the presence of research in various aspects of SWT. Among others, experiments were carried out to determine the best spot on the roof of a building to place a wind turbine. Small wind turbines are usually associated with mounting on buildings, but this is not a rule, as they can just as well be installed as stand-alone structures. SWT profitability is determined by the combination of wind turbine efficiency, cost and reliability. At the preliminary stage of the SWT design process, there is a need for an inexpensive, effective and reliable methodology for estimating these factors when consid- ering design solutions and variants. Given the relatively high cost and time-consuming nature of wind tunnel or natural environment experimental tests [15], the focus should be on modern modelling and simulation tools, which are widely used in fields such as mechanical engineering. Such techniques have already been used to analyze various aspects of SWT design [16,17] and have provided highly accurate results (especially in comparison analyses where the input data for all considered variants are the same) [18]. Of course, the energy produced by small wind turbines cannot always be predicted. This is the case when the turbine is installed close to buildings, where the air flow is disturbed, and in turbulent conditions where reliable measurements are not possible [19,20].

1.2. Motivation Economic analyses of wind turbines in the literature have adopted a variety of po- sitions but have mainly focused on large wind turbines. Prior to investing in large-scale power engineering projects, major corporations always perform detailed economic analyses, including evaluations of the availability of a convenient location for the construction of a wind farm or the attractiveness of the selected area [2,21,22]. Few such analyses are available for SWTs. SWTs are distinguished by their ability to be installed by individual investors. Given that SWTs are installed close to energy collection points and do not require advanced infrastructure, they may have a real impact on increasing the share of electricity production from RES and, consequently, delaying negative climate change. The method and time of collection of wind measurements are key factors in assessing the energy efﬁciency of an investment. Poland has a transitional climate, which indicates the possibility of large differences (deviations) in observations and necessitates long-term observations. Another important aspect is the analysis of multiple measurement locations, which allows comparisons and a more extensive evaluation. The ideal number of locations is not clear due to a lack of relevant publications [23,24]. Usually, one speciﬁc measuring Energies 2021, 14, 3706 4 of 18

station or several locations with a narrow range of tests are analyzed. The present article analyzes measurements in different wind zones at three measurement stations over a period of five years provided by the Institute of Meteorology and Water Management (IMWM) of Poland. Reliable data are essential for a more accurate depiction of turbine performance. Among previous efforts, sustainable investment in wind farms was examined in Italy [25,26] via a technical-energy-economic feasibility study of wind energy-based systems that took into account the importance of incentives to support wind energy. An in-depth analysis of local climatic conditions and the conditions of the immediate surroundings at the potential investment location is also necessary. In [27], regional heterogeneity in Spain was considered in relation to the aspect of sustainable development and, in particular, the implementation of wind energy. The results indicated that SWTs can work well as a power source for individual electricity consumers, especially in areas where access to the electricity grid is limited. Moreover, combining SWTs with other types of RES (so-called hybrid systems), such as solar power, can provide an uninterrupted power supply year round, regardless of the prevailing weather conditions. The idea of small wind turbines is very topical in Poland. Last year the National Centre for Research and Development announced the “Grand Challenge: Energy” competition for the design of a small wind turbine. The participants’ task is to design a device capable of capturing energy from the wind and converting it into electricity in the shortest possible time. Small wind turbines have a promising and wide range of applications. They are ideally suited to rural areas where there may be problems with access to the electricity grid, as well as in areas designated mainly for tourism, where electricity is needed to power individual devices and there is no possibility of supplying electricity in other ways, e.g., due to areas protected by the Natura 2000 programme. Therefore, there is a great need for research on small wind turbines as the idea is only just developing in Poland. In summary, despite the growing literature on SWTs, most studies are based on statistics or forecasts of wind power data, and the use of real wind data to test the effectiveness of SWTs is rare. This article seeks to address this gap by examining the real parameters of SWTs, especially data on actual electricity generation and the profitability of power plant installations. The results will be valuable for economic assessments of wind turbine investments and for determining the real energy potential of SWTs in Poland. Our contribution includes analysis of real locations in Poland based on current data from meteorological stations of the Institute of Meteorology and Water Management of Poland. The analyses are carried out with a view to maximizing the energy potential of SWTs, which are now strongly promoted in areas with very low levels of industrialization and population density. The innovation of this study is that real parameters and criteria are used to analyze the efficiency and payback period of investments in selected Polish locations. However, the same methods could be used to perform analyses in other countries with low population densities or high energy costs due to environmental conditions. The research carried out in our paper is a great novelty, as we do not know of any studies in the literature that compare the costs of wind resources in terms of installing small wind turbines in different regions of Poland.

2. Materials & Methods 2.1. Subject of the Analysis In this study, a speciﬁc turbine of a selected manufacturer that is recognizable in Poland as well as the European market is used. The company’s scope of activity includes, among others, the production of SWTs, which are commercial products established on the market. The turbine used in the analysis has a horizontal axis of rotation, a rotor diameter of 10.0 m, a nominal power of 12.0 kW and a rotor axis height above the ground of 15.0 m. Typically, the turbines of this manufacturer have blades with a variable angle of attack, multiple brake systems, remote monitoring and active wind direction tracking [28]. Figure3 provides a general view of the considered SWT and a photograph of the analyzed Energies 2021, 14, 3706 5 of 18

2. Materials & Methods 2.1. Subject of the Analysis In this study, a specific turbine of a selected manufacturer that is recognizable in Poland as well as the European market is used. The company’s scope of activity includes, among others, the production of SWTs, which are commercial products established on the market. The turbine used in the analysis has a horizontal axis of rotation, a rotor diameter of 10.0 m, a nominal power of 12.0 kW and a rotor axis height above the ground of 15.0 m. Energies 2021, 14, 3706 Typically, the turbines of this manufacturer have blades with a variable angle of5 attack, of 18 multiple brake systems, remote monitoring and active wind direction tracking [28]. Figure 3 provides a general view of the considered SWT and a photograph of the analyzed wind windturbine, turbine, which which is located is located at atthe the PAN PAN Research Research Centre forfor EnergyEnergy Conversion Conversion and and RenewableRenewable Sources.Sources.

FigureFigure 3.3.Analyzed Analyzed wind wind turbine, turbine, side side and and front front views views and and photograph photograph [28 [28].].

TableTable2 2provides provides basic basic technical technical data data on on the the selected selected wind wind turbine turbine taken taken directly directly from from thethe manufacturer.manufacturer. TheThe priceprice ofof the the turbine turbine is is expressed expressed in in euros euros based based on on the the average average exchangeexchange rate rate of of 1 1 EUR EUR = = 4.55 4.55 PLN PLN (data (data as as of of 4 4 October October 2020). 2020). The The net net price price of of the the turbine turbine includesincludes installation, installation, ﬁve-year five-year warranty, warranty, warranty warranty service service and remoteand remote monitoring monitoring but does but notdoes include not include transport transport and external and installations external installations required by therequired power by plant, the including power plant, the inverter,including foundation, the inverter, cable foundation, connection cable from conne the powerction from plant the to power the network, plant to transformer the network, station,transformer etc., as station, they are etc., assessed as they individually are assessed for individually each location. for each location.

Table 2. Table 2.Technical Technical speciﬁcations specifications of of the the selected selected turbine turbine [28 [28].].

ParametersParameters ValueValue MeasuredMeasured mast mast height height 15.0 15.0 m m RotorRotor diameter diameter 10.0 10.0 m m NominalNominal power power 12.0 12.0 kW kW NumberNumber of of blades blades 3 3 Cut-in speed 3.0 m/s↑ and 15.0 m/s↓ (10 min. average) Cut-in speed 3.0 m/s↑ and 15.0 m/s↓ (10 min. average) Cut-out speed 2.0 m/s↑ and 20.0 m/s↓ (10 min. average) Wind speed forCut-out nominal speed power (rated) 2.0 m/s↑ and 20.0 9.0 m/s↓ (10 min. average) Wind speedRotational for nominal speed range power (rated) 30–125 9.0 rpmm/s NetRotational price of the speed power range plant 173,000.00 PLN 30–125 = 38,022.00 rpm EUR NetTip price speed of the ratio power (TSR) plant 173,000.00 PLN 5.82 = 38,022.00 EUR Tip speed ratio (TSR) 5.82 The cut-in speed is the minimum wind speed at which the turbine starts delivering useful power, while the cut-out speed is the maximum wind speed at which the turbine can produce electric energy. The rated speed is the wind speed at which the nominal power is obtained, which is the maximum output from an electric generator [29]. An important parameter of a wind turbine is the tip speed ratio (TSR), which is the ratio of the linear speed of the turbine blade tip to the wind speed determined by the following formula (Equation (1)): ω·R λ = , (1) v where ω is the rotational speed, R is the rotor radius and v is the wind speed. To correctly calculate the TSR of a wind turbine, the same units must be used for all components; the resulting value is dimensionless. The TSR of the turbine considered here Energies 2021, 14, 3706 6 of 18

The cut-in speed is the minimum wind speed at which the turbine starts delivering useful power, while the cut-out speed is the maximum wind speed at which the turbine can produce electric energy. The rated speed is the wind speed at which the nominal power is obtained, which is the maximum output from an electric generator [29]. An important parameter of a wind turbine is the tip speed ratio (TSR), which is the ratio of the linear speed of the turbine blade tip to the wind speed determined by the following formula (Equation (1)): ∙ 𝜆 = , (1) Energies 2021, 14, 3706 6 of 18 where ω is the rotational speed, R is the rotor radius and v is the wind speed. To correctly calculate the TSR of a wind turbine, the same units must be used for all components; the resulting value is dimensionless. The TSR of the turbine considered here is 5.82.5.82. This is aa relativelyrelatively lowlow valuevalue andand indicatesindicates thatthat thethe turbineturbine isis sensitive sensitive to to weak weak winds andand suddensudden gustsgusts andand generatesgenerateslow low noise noise levels levels [ 30[30].]. Figure4 4 presents presents the the energy energy characteristics characteristics of of the the analyzed analyzed wind wind turbine turbine as as a a power power output curve, whichwhich showsshows thethe relationshiprelationship betweenbetween the the wind wind speed speed captured captured by by the the rotor rotor and itsits electricalelectrical output. output. The The power power curve curve allows allows the the amount amount of electricity of electricity generated generated by the by turbinethe turbine to be estimatedto be estimated [31] and [31] is an and essential is an component essential ofcomponent wind turbine of performancewind turbine assessment.performance When assessment. the detailed When characteristics the detailed char of windacteristics conditions of wind in conditions a given location in a given are known,location the are annual known, electricity the annual production electricity of a pr turbineoduction can beof forecasta turbine very can accurately be forecast on very the basisaccurately of the on power the basis curve, of and the the power economic curve, viability and the of economic the investment viability can of be the assessed investment [32]. Thiscan be type assessed of analysis [32]. isThis at thetype core of analysis of the present is at the paper. core of the present paper.

Figure 4.4. Power curve ofof thethe testedtested turbineturbine [[28].28].

2.2. Wind Energy PotentialPotential inin PolandPoland The bestbest windwind conditionsconditions inin PolandPoland areare foundfound onon thethe Baltic Baltic Sea Sea coast coast and and the the entire entire coastal zone, but but large large areas areas of of central central Poland Poland and and the the southern southern mountain mountain regions regions are arealso alsofavorable favorable [33]. [ 33The]. The technical technical potential potential of ofwind wind energy energy is is usually usually related toto thethe spatial spatial distribution of landland ofof anan openopen charactercharacter (characterized(characterized byby aalow low roughness roughness coefficient coefficient and laminar air flow, without interfering objects) [4]. Some wind conditions are not and laminar air flow, without interfering objects) [4]. Some wind conditions are not favorable for wind turbines, and zones of air turbulence should be avoided because they favorable for wind turbines, and zones of air turbulence should be avoided because they can adversely impact performance and weaken the energy potential. The rotor and blades can adversely impact performance and weaken the energy potential. The rotor and blades of the turbine should be in the laminar wind stream zone to avoid exposure to turbulence, of the turbine should be in the laminar wind stream zone to avoid exposure to turbulence, and the location of each turbine should be selected according to local conditions and wind and the location of each turbine should be selected according to local conditions and wind resources. The weather conditions at a given location should be surveyed continuously for resources. The weather conditions at a given location should be surveyed continuously at least one year, and actual data should be recorded, including any deviations. for atThe least wind one datayear, used and actual in the studydata shou coverld abe period recorded, of 5 yearsincluding (1 January any deviations. 2014 to 31 De- cemberThe 2018). wind Thedata input used data in the were study provided cover bya period the Institute of 5 years of Meteorology (1 January and2014, Water to 31 ManagementDecember 2018). of Poland.The input The data parameter were provided used in by the the analysis Institute is theof Meteorology wind speed and recorded Water atManagement hourly intervals, of Poland. expressed The parameter in meters used per second. in the analysis The measurements is the wind speed come fromrecorded three at synoptichourly intervals, stations inexpressed different partsin meters of Poland: per second. Location The 1, Locationmeasurements 2 and Locationcome from 3. three synopticBased stations on many in di yearsfferent of parts meteorological of Poland: Location observations 1, Location (the survey 2 and was Location carried 3. out duringBased the on 1971–2000 many years period), of meteorological the IMWM has ob developedservations a(the map survey of the was intensity carried and out magnitudeduring the of1971–2000 wind currents period), in Poland. the IMWM This maphas dividesdeveloped the countrya map of into the five intensity zones that and differ in attractiveness for wind energy power plants. Zone I, the most advantageous, is located in the coastal zone. Successive zone numbers indicate progressively weaker wind conditions. Figure5 presents a map of Poland marked with the five wind zones as well as the three locations at which the analyzed measurements were obtained. Energies 2021, 14, 3706 7 of 18

magnitude of wind currents in Poland. This map divides the country into five zones that differ in attractiveness for wind energy power plants. Zone I, the most advantageous, is Energies 2021, 14, 3706 located in the coastal zone. Successive zone numbers indicate progressively weaker7 of 18 wind conditions. Figure 5 presents a map of Poland marked with the five wind zones as well as the three locations at which the analyzed measurements were obtained.

FigureFigure 5. 5.Wind Wind energy energy zones zones in Poland in Poland and locations and location of thes measurement of the measurement stations (source: stations IMWM–PIB (source: ). IMWM–PIB). Information on the conditions at each measurement location and its immediate sur- roundingsInformation is important on forthe real conditions observations at andeach evaluating measurement the correctness location ofand the its measure- immediate ments.surroundings The synoptic is important station at for Location real observations 1 is located on and the evaluating outskirts of the a city correctness in the area of the ofmeasurements. the Carpathian The Foothills synoptic at 329.0 station m above at Location sea level 1 andis located is situated on the in windoutskirts zone of III. a Itcity in is located on a hill, approximately 70 m above the bottom of the river that flows through the area of the Carpathian Foothills at 329.0 m above sea level and is situated in wind zone the city. The surroundings are mainly farmlands and meadows. The nearest elevation III. It is located on a hill, approximately 70 m above the bottom of the river that flows is to the northeast (approximately 4 km)-OdrzykońCastle. To the south and north are thethrough Carpathian the city. Foothills, The surroundings which are approximately are mainly 20 farmlands to 50 km away. and meadows. The measurement The nearest deviceelevation of the is stationto the isnortheast located at(approximately a height of 10.0 4 m. km)-Odrzyko The station atń LocationCastle. To 2 isthe located south and innorth the strip are ofthe large Carpathian valleys in Foothills, the northern which part ofare the approximately South Podlasie 20 Lowland to 50 km (lowland away. The area),measurement 152.0 m above device sea of level. the station It is also is partlocated of wind at a height zone III, of and 10.0 the m.height The station of the at wind Location gauge2 is located is 12.0 in m. the The strip station of large is located valleys in in the the northwestern northern part part of of the a citySouth in aPodlasie completely Lowland open(lowland area. Immediatelyarea), 152.0 m to theabove north, seathere level. are It farmlandsis also part and of wastelands,wind zone III, and and at a the further height of distancethe wind of 45.0gauge m thereis 12.0 are m. single The trees;station towards is loca theted eastin the there northwestern are small clusters part of of treesa city in a andcompletely buildings. open There area. is aImmediately street to the south,to the no andrth, farther there onare there farmland are allotments and wastelands, gardens. and Fromat a westfurther to northdistance there of are 45.0 wastelands m there andare farmlands.single trees; The towards station the located east atthere Location are small 3clusters is in a coastal of trees area, and 3.0 buildings. m above sea There level. is Thisa street station to isthe located south, in and the mostfarther favorable on there are area,allotment wind zonegardens. I. The From meteorological west to north measurement there are wastelands station is located and farmlands. on a small sandyThe station dunelocated by theat Location city beach, 3 atis thein a mouth coastal of area, the river, 3.0 m near above the sea harbor level. channel. This station The station is located is in located in a lighthouse, approximately 60 m from the Baltic Sea coast; the distance from the the most favorable area, wind zone I. The meteorological measurement station is located coastline depends on the sea level at the given moment. The wind gauge is located at a on a small sandy dune by the city beach, at the mouth of the river, near the harbor channel. height of 23.0 m. The same model of measurement device is used at all three stations: a WINDCAPThe station WS425 is located (Vaisala in a Oyj, lighthouse, Helsinki, approx Finland).imately The device 60 m documentationfrom the Baltic indicatesSea coast; the thatdistance the wind from speed the coastline measurement depends accuracy on the is ± se0.135a level m/s at andthe thegiven wind moment. direction The accuracy wind gauge isis± located2◦ [34]. at All a threeheight synoptic of 23.0 m. stations The same meet mo thedel location of measurement requirements device recommended is used at byall three thestations: World Meteorologicala WINDCAP WS425 Organization (Vaisala (WMO). Oyj, Helsinki, Finland). The device documentation In the first stage of the study, wind speeds recorded during the 2014–2018 period were analyzed for the three locations. The raw data received from IMWM is in the form of hourly measurements of wind speeds. Average wind speeds for each month across all

Energies 2021, 14, 3706 8 of 18

indicates that the wind speed measurement accuracy is ±0.135 m/s and the wind direction accuracy is ±2° [34]. All three synoptic stations meet the location requirements recommended by the World Meteorological Organization (WMO). In the first stage of the study, wind speeds recorded during the 2014–2018 period were analyzed for the three locations. The raw data received from IMWM is in the form of hourly measurements of wind speeds. Average wind speeds for each month across all years studied were calculated separately for each location. Figure 6 presents the results to Energies 2021, 14, 3706 facilitate the analysis of trends, differences and relationships. A trend of windiness8 can of 18 be observed across all 3 locations: windiness is usually higher in the autumn-winter period than in the spring-summer period, and winter is the windiest season. The graphs also showyears studieda clear difference were calculated in monthly separately electricity for each generation, location. as Figure the vast6 presents majority the of results electricity to isfacilitate generated the analysisduring the of trends, windy differences winter months and relationships.. All necessary A trendmaintenance of windiness operations can be as wellobserved as routine across allchecks 3 locations: should windiness be carried is usuallyout during higher the in calm the autumn-winter (least windy) periodsummer months.than in theSeasonal spring-summer discrepancie period,s in and electrical winter is generation the windiest can season. cause The problems graphs also with continuousshow a clear supply difference [35]. in monthly electricity generation, as the vast majority of electricity is generatedFigure 7 duringpresents the the windy monthly winter wind months. speeds All averaged necessary among maintenance the three operationslocations for eachas well year as analyzed. routine checks The raw should data beare carried provided out in during Appendix the calm A and (least Appendix windy) B. summer Each year ismonths. different; Seasonal no two discrepancies diagrams inare electrical identical. generation Although can the cause individual problems diagrams with continuous intersect andsupply even [35 overlap,]. the discrepancies are rather significant and obvious.

6.5

5.5

4.5

3.5

Wind speed [m/s] 2.5

1.5

Location 1 Location 2 Location 3

FigureFigure 6. Distribution of of mean mean monthly monthly wind wind speeds speed acrosss across the period the period 2014–2018 2014–2018 for the for three the locations three . locations. Figure7 presents the monthly wind speeds averaged among the three locations for Energies 2021, 14, 3706 eachTo year more analyzed. accurately The depict raw data real areachievable provided wind in Appendices speeds in APola andnd,B. histograms Each year9 is wereof 18

prepareddifferent; noto twoillustrate diagrams how are frequently identical. specific Although wind the individualspeeds occur diagrams at each intersect location. and The histogramseven overlap, were the created discrepancies with instantaneous are rather signiﬁcant speeds andfrom obvious. raw data recorded at an hourly interval. The histograms in Figure 8 are for 2018 and clearly differ when Location 3 is compared6.5 with Locations 1 and 2. It can be seen from the graphs that throughout the study period, wind speeds in Location 1 and 2 mostly reached about 2 m/s, and for Location 3- about5.5 4 m/s. Wind speed analysis is the basis for calculating the annual electricity production in a given location by a specific wind turbine in this study. 4.5

3.5

Wind speed [m/s] 2.5

1.5

2014 2015 2016 2017 2018

FigureFigure 7. 7. DistributionDistribution of of average average monthly monthly wind wind spee speedd among among the the three three locations locations for for the the period period 2014–2018.2014–2018.

24 Location 1

0 Frequencyappearance of [%] 1234567891011121314151617181920 Wind speed [m/s] 28 24 Location 2 20 16 12

Frequency ofappearance [%] 0 1234567891011121314151617181920 Wind speed [m/s] 28

24 Location 3

20 16 12 8 4

Frequencyappearance of [%] 0 1234567891011121314151617181920 Wind speed [m/s]

Figure 8. Wind speed distribution in 2018.

Energies 2021, 14, 3706 9 of 18

6.5

5.5

4.5

3.5

Energies 2021, 14, 3706 9 of 18 Wind speed [m/s] 2.5

1.5 To more accurately depict real achievable wind speeds in Poland, histograms were prepared to illustrate how frequently speciﬁc wind speeds occur at each location. The histograms were created with instantaneous speeds from raw data recorded at an hourly interval. The histograms in Figure8 are for 2018 and clearly differ when Location 3 is compared with Locations2014 1 and 2. It can2015 be seen from2016 the graphs2017 that throughout2018 the study period, wind speeds in Location 1 and 2 mostly reached about 2 m/s, and for Location 3-aboutFigure 7. 4Distribution m/s. Wind of speedaverage analysis monthly is wind the basis speed for among calculating the three the locations annual for electricity the period production2014–2018. in a given location by a speciﬁc wind turbine in this study.

24 Location 1

0 Frequencyappearance of [%] 1234567891011121314151617181920 Wind speed [m/s] 28 24 Location 2 20 16 12

Frequency ofappearance [%] 0 1234567891011121314151617181920 Wind speed [m/s] 28

24 Location 3

20 16 12 8 4

Frequencyappearance of [%] 0 1234567891011121314151617181920 Wind speed [m/s]

Figure 8.8.Wind Wind speed speed distribution distribution in 2018.in 2018.

Figure9 shows the distribution of wind direction at the three locations in 2018. At Location 1, there were three dominant wind directions with similar frequencies of approximately 11%: northwestern, eastern and southern. At Location 2, the wind directions were less diverse, with no wind direction exceeding 10%, but were most often from the north and southeast. At the coastal Location 3, the most frequent wind direction was southern, with a frequency of 14.62%, reﬂecting the strong inﬂuence of the sea breeze. The average annual wind speeds for each location are shown in Table3 and Figure 10. At each location, the average wind conditions were similar throughout the study period, with slightly higher Energies 2021, 14, 3706 10 of 18 Energies 2021, 14, 3706 10 of 18

Figure 9 shows thethe distributiondistribution ofof windwind directiondirection atat thethe threethree locationslocations in in 2018. 2018. At At Location 1, there werewere threethree dominantdominant windwind directionsdirections withwith similarsimilar frequenciesfrequencies ofof approximately 11%:11%: northwesnorthwestern,tern, easterneastern andand southern.southern. AtAt LocationLocation 2,2, thethe windwind directions were lessless diverse,diverse, withwith nono windwind directiondirection exceedingexceeding 10%, 10%, but but were were most most often often from the north and southeast. At the coastal Location 3, the most frequent wind direction Energies 2021, 14, 3706 from the north and southeast. At the coastal Location 3, the most frequent wind direction10 of 18 was southern, with aa frequencyfrequency ofof 14.62%,14.62%, reflecreflectingting the the strong strong influence influence of of the the sea sea breeze. breeze. The average annual windwind speedsspeeds forfor eacheach locatilocationon are are shown shown in in Table Table 3 3 and and Figure Figure 10. 10. At At each location, the averageaverage windwind conditionsconditions werewere similarsimilar throughoutthroughout thethe studystudy period,period, withwind slightly speeds higher in 2017. windwind Compared speedsspeeds with inin 2017.2017. the ComparedotherCompared two stations, with with the the Location other other two two 3 clearlystations, stations, features Location Location 3much clearly more features favorable muchmuch wind moremore conditions, favorablefavorable consistent windwind conditions,conditions, with its location consistentconsistent in Poland’s withwith wind itsits location location zone I. in in Poland’sLocations wind 1 and zonezone 2 are I.I. located LocationsLocations in wind 11 andand zone 22 are IIIare and locatedlocated thus in exhibitin windwind similar zonezone III windIII andand speeds thusthus exhibit andexhibit similarsimilar wind disparities speedsspeeds compared andand similarsimilar with dispardispar Locationitiesities 3. comparedcompared with with Location Location 3. 3.

NN 16% NNW 16% NNENNE 14%14% NW 12%12% NENE 10%10% 8%8% WNW ENE WNW 6%6% ENE 4%4% LocationLocation 1 1 2%2% Location 2 W 0%0% EE Location 2 LocationLocation 3 3

WSW ESEESE

SW SESE

SSWSSW SSESSE SS FigureFigure 9. 9. WindWind frequencyfrequency distribution distributiondistribution at atat each eacheach location locationlocation in in in 2018. 2018. 2018.

TableTable 3. Average wind windwind speeds speedsspeeds (m/s) (m/s)(m/s) at atat the thethe tested testedtested locations locationslocations between between between 2014 2014 2014 and and and 2018. 2018. 2018.

201420142014 201520152015 201620162016 201720172017 201820182018 LocationLocation 1 2.992.992.99 3.073.073.07 2.982.982.98 3.143.143.14 2.952.952.95 LocationLocation 2 2.672.672.67 2.662.662.66 2.812.812.81 2.992.992.99 2.632.632.63 LocationLocation 3 4.814.814.81 5.245.245.24 5.015.015.01 5.395.395.39 4.734.734.73

6,00

5,00

4,00

3,00

2,00 Wind speed [m/s] Wind speed [m/s] 1,00

0,00 20142014 2015 2015 2016 2016 2017 2017 2018 2018

LocationLocation 11 LocationLocation 2 2 LocationLocation 3 3

FigureFigure 10. 10. AverageAverage windwindwind speeds speedsspeeds at atat the thethe tested testedtested locations locationslocations in in in 2014–2018. 2014–2018. 2014–2018. 2.3. Economic Analysis In the second stage of the study, the annual energy production (AEP) was calculated for

each surveyed year for each station based on the wind characteristics, namely individual wind speeds and their frequency in a given period, and parameters from the turbine power curves for speciﬁc wind speeds. The AEP is calculated by successively multiplying Energies 2021, 14, 3706 11 of 18

Energies 2021, 14, 3706 the power for each wind speed from the turbine power curve by the measured13 wind of 18 frequency distribution and the number of hours per year [36]. For calculating the AEP, a ﬁxed operating period was assumed, i.e., periods out of operation (e.g., due to repair and maintenance of equipment) were not taken into account. The results are summarized in Table 5 presents the economic parameters associated with the results of the Table4 and illustrated in Figure 11. simulation studies; the key indicator is SPBT. A wide range of investment payback periods isTable obtained, 4. Simulation highlighting results forthe energy importance production of a at proper, the tested in-depth locations analysis between of 2014 location and 2018. in the preliminary design of SWT installations and before the start of the investment. The table also shows the value of theAmount capacity of Electric factor, Energy calculated Produced for [kWh]the five-year study period for each case. It provides information about the performance of the turbine and the utilisationTotal 2014 2015 2016 2017 2018 of its potential under the given conditions. The value obtained for Location 3 2014–2018is optimum andLocation indicates 1 relatively6946.0 good 8089.0adaptation of 7287.0 the turbine 8661.0 to the wind 6831.0 conditions. 37,815.0 However, forLocation Locations 2 1 and5333.0 2 the indicator 5481.0 reached 5945.0 a very low 6898.0 level and it 4933.0can be concluded 28,590.0 that theLocation selected 3 turbine23,375.0 is not suitab 28,963.0le to use 26,519.0under these 31,403.0conditions. 23,416.0 133,676.0

32000

28000

24000

20000

16000

Energy [kWh] 12000

8000

4000

0 Location 1 Location 2 Location 3 2014 2015 2016 2017 2018

FigureFigure 11. 11. EnergyEnergy yield yield comparison comparison among the three locations.locations.

Table The5. Economic main object comparison of the of analysis selected locations–simulation is the distribution ofresults. wind speeds over the year. On its basis, the time of occurrence of winds with speciﬁc speeds during the year and, Payback Time for consequently, the energy productionCOE of a wind power plantCapacity is estimated. SPBT After dividing the Location AEP [kWh] s [EUR/Year] a Turbine with entire range of measured values[EUR/kWh] into intervals, the measuredFactor data are[Years] grouped, allowing a Diffuser [Years] for a number of classes ni to be determined, i.e., the number of wind speed occurrences in Location 1 7562.92 0.53 974.23 0.07 44.2 17.7 each i-th class. Related to the notion of abundance is the frequency fi, deﬁned as the ratio ofLocation the abundance 2 5718.02 to the total number0.70 of measured746.75 data N0.05(Equation (2)):57.5 23.0 Location 3 26,735.28 0.16 3338.18 0.25 12.9 5.2 n f = i . (2) i N Figure 12 shows the trends in the costs of electricity generation over the period 2014– The time t for one year (T–basic measurement period: one year), of the wind at a 2018 at the threei analyzed locations. Costs vary greatly depending on the location of the given speed in the i-th interval (Equation (3)): SWT, and Location 3 is clearly the most cost-effective. The cost is lowest at Location 3 for the five-year period analyzed, with ant i average= fi·T. cost of EUR 0.16/kWh, in contrast (3) to average costs of 0.53 EUR/kWh and 0.70 EUR/kWh at Locations 1 and 2, respectively. The To calculate the energy produced by the power plant in one year in the next i-th analysis shows that Location 2 is the most expensive-this can already be deduced from class interval Ewi, multiply the value ti by the power of the power plant expressed by the thecorresponding value of the turbineAEP index, power which curve isP the(Equation lowest of (4)): the three cases. We have the same cost assumptions for all locations, so if the least amount of electricity was produced in Location 2 during the year, then we get the highestEwi cost= P of·ti .electricity generation per kWh. (4) The total energy produced per year by the power plant will be the sum of the component energies of all class intervals c (Equation (5)):

Energies 2021, 14, 3706 12 of 18

= c Ei ∑i=1 EWi. (5)

In order to obtain the AEP, it is necessary to add up the energies produced Ei, consid- ering the ﬁve-year test period k [37] (Equation (6)):

∑k E AEP = i=1 i . (6) k In the next step, a number of calculations were performed for which the cost of electricity in Poland was required. The data were taken from Eurostat’s statistical database: the average retail electricity price in 2019 for households was 0.1343 EUR/kWh, and the market price was 0.1233 EUR/kWh [38]; prices quoted include all taxes and fees. In Poland, prosumers generating electricity for their own use sell excess produced energy to the grid, necessitating the use of two different electricity prices in the calculations. According to the Central Statistical Ofﬁce, the average household electricity consumption was 3797.0 kWh in Poland in 2018 [39]. This value was used with the average retail electricity price to calculate the price of 1.0 kWh for households; for the rest of the annual energy production, the market price was used. The cost of wind turbine construction (investment costs) obtained directly from the manufacturer was estimated at EUR 38,022. The cost of energy (COE) was also calculated for each considered case. Finally, the return of investment in the form of simple payback time (SPBT) was calculated for the three sites. The results of the calculations are shown in Table5.

Table 5. Economic comparison of selected locations–simulation results.

Payback Time for AEP COE Capacity SPBT Location s [EUR/Year] a Turbine with a [kWh] [EUR/kWh] Factor [Years] Diffuser [Years] Location 1 7562.92 0.53 974.23 0.07 44.2 17.7 Location 2 5718.02 0.70 746.75 0.05 57.5 23.0 Location 3 26,735.28 0.16 3338.18 0.25 12.9 5.2

COE is a metric used to assess the costs of electricity generation [40]. For one year of turbine operation, the formula for COE is as follows (Equation (7)): CRF·I TOM COE = + , (7) AEP AEP where COE is the cost of energy [EUR/kWh], I is investment wind turbine costs [EUR], AEP is annual energy production [kWh], TOM is total yearly operation and maintenance costs [EUR], and CRF is the capital recovery factor. CRF is the yearly interest [%/year], which depends on interest rate i and economic lifetime n (Equation (8)): i·(1 + i)n CRF = . (8) (1 + i)n − 1 Data related to the costs of maintenance and operation of SWTs are very difﬁcult to obtain, as the small companies or individuals operating SWTs do not publish such information or may not even record it. This lack of data hinders detailed and accurate economic analyses of SWTs. Values of parameters for which data were not available were therefore taken from the relevant literature concerning SWTs. The CRF value is derived from an article by G. J. W. van Bussel [40]. If we assume an interest rate of 6% per year and an economic lifetime of the turbine of 15 years, then CRF will be 0.103. We do not yet have information on the operating costs of the investment, so we use the claim by K. R. Rao [36] that the estimated TOM is 0.015 euros per 1.0 kWh of wind energy produced over the entire life of the turbine. Subsequently, the AEP was multiplied by the average retail electricity price according to Equation (4). The product is the monetary savings resulting from a year of operation of the SWT at the speciﬁc location (Equation (9)): Energies 2021, 14, 3706 13 of 18

s = AEP·c , (9) where s is the savings in one year and c is the price of 1.0 kWh; c = 0.1343 EUR/kWh and 0.1233 EUR/kWh. The last and key stage was the calculation of SPBT. SPBT describes the profitability of an investment in a simplified manner, as it does not take into account the entire lifetime of the project and the operating costs [41]. However, to obtain a more accurate assessment, we modified the formula by deducting the operating and maintenance costs of the turbine from the monetary savings earned in one year from the renewable energy system. Thus, in this study, SPBT is determined by the following formula (Equation (10)): I SPBT = . (10) s − TOM The result is the time (in years) after which the amount of money saved from the use of a small wind turbine will exceed the amount of funds invested in the project. To assess economic efficiency, appropriate calculations were used to determine whether the investment is profitable and after what period of use the wind power plant will start to generate profit.

3. Results The results of the simulations of energy production at the three selected locations for the period 2014–2018 are shown in Table4 and Figure 11. Based on the calculated total energy produced over a period of five years, Location 3 is definitely the most effective location, with more than four times greater energy production compared with either of the other two locations. Table5 presents the economic parameters associated with the results of the simulation studies; the key indicator is SPBT. A wide range of investment payback periods is obtained, highlighting the importance of a proper, in-depth analysis of location in the preliminary design of SWT installations and before the start of the investment. The table also shows the value of the capacity factor, calculated for the five-year study period for each case. It provides information about the performance of the turbine and the utilisation of its potential under the given conditions. The value obtained for Location 3 is optimum and indicates relatively good adaptation of the turbine to the wind conditions. However, for Locations 1 and 2 the indicator reached a very low level and it can be concluded that the selected turbine is not suitable to use under these conditions. Figure 12 shows the trends in the costs of electricity generation over the period 2014– 2018 at the three analyzed locations. Costs vary greatly depending on the location of the SWT, and Location 3 is clearly the most cost-effective. The cost is lowest at Location 3 for the five-year period analyzed, with an average cost of EUR 0.16/kWh, in contrast to average costs of 0.53 EUR/kWh and 0.70 EUR/kWh at Locations 1 and 2, respectively. The analysis shows that Location 2 is the most expensive-this can already be deduced from the value of the AEP index, which is the lowest of the three cases. We have the same cost assumptions for all locations, so if the least amount of electricity was produced in Location 2 during the year, then we get the highest cost of electricity generation per kWh. Energies 2021, 14, 3706 14 of 18 Energies 2021, 14, 3706 14 of 18

0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 Cost of Energy [EUR/kWh] Energy of Cost 0.10 0.00 2013 2014 2015 2016 2017 2018 2019

Location 1 Location 2 Location 3

Figure 12. Unit cost of energy production at the three locations in 2014–2018. Figure 12. Unit cost of energy production at the three locations in 2014–2018. 4. Discussion 4. DiscussionWhen choosing to invest in an alternative source of energy for a household, the most importantWhen considerationchoosing to invest is the in economic an alternative aspect. source The most of energy desirable for outcomea household, is return the most of importantinvestment, consideration followed by theis the time economic after which aspect the. investmentThe most desirable will start outcome to yield a is profit. return In of investment,an amendment followed to the RESby the Act, time the Polishafter which Ministry the of investment Energy (currently will start the to Ministry yield a ofprofit. State In anAssets) amendment assumes to an the average RES Act, rate the of returnPolish onMinistry prosumer of Energy PV installations (currently of the 10 yearsMinistry [42]. of Among the locations analyzed in this study, the return of investment for Location 3 is State Assets) assumes an average rate of return on prosumer PV installations of 10 years closest to that of PV panels. However, a period of 10 years before providing profit is still a [42]. Among the locations analyzed in this study, the return of investment for Location 3 long period of return of investment for the ordinary prosumer. If earning money was the is closest to that of PV panels. However, a period of 10 years before providing profit is still main objective, the construction of a small wind power plant is not the best option. On the a long period of return of investment for the ordinary prosumer. If earning money was other hand, if the aim of the investment is to reduce electricity bills and CO2 emissions, in thethe main long run,objective, an SWT the is construction a very beneficial of a small solution wind for power a household. plant is not the best option. On the otherHowever, hand, theif the investment aim of the return investment times is are to not reduce satisfactory electricity for bills all analyzed and CO2 locations, emissions, inwhich the long should run, motivate an SWT a is search a very for beneficial more efficient solution solutions. for a household. One such solution is a turbine withHowever, a shrouded the rotor investment (diffuser). return Despite times extensiveare not satisfactory study, shrouded for all analyzed turbines locations, are not whichyet widely should used motivate due to a their search complexity for more andeffici highent solutions. design and One manufacturing such solution costs. is a turbine The withdiffuser a shrouded acts as a windrotor gathering(diffuser). andDespite accelerating extensive device, study, allowing shrouded the turbines turbine to are achieve not yet widelyhigher aerodynamicused due to efficiencytheir complexity than allowed and byhigh the Betzdesign limit. and Reference manufacturing [43] describes costs. the The diffuseroccurring acts dependencies as a wind gathering related to theand Betz accelera limitting in more device, detail. allowing Subsequent the turbine work has to foundachieve higherthat it isaerodynamic possible to increaseefficiency the than efficiency allowed coefficient by the Betz by limit. approximately Reference 2.5 [43] times describes [44,45 ],the occurringwhich would dependencies increase the related power to of the the Betz turbine limit by in the more same detail. amount. Subsequent Using advanced work has foundnumerical that methodsit is possible to significantly to increase improve the efficiency turbine coefficient performance by and approximately reduce turbine 2.5 costs times [44,45],compared which with would existing increase designs, athe 1:2 power scale prototype of the turbine of an SWT by withthe same a diffuser amount. optimized Using advancedfor wind conditionsnumerical typicalmethods of Polandto significantly has been improve constructed. turbine The turbine’sperformance aerodynamics and reduce turbinewere optimized costs compared based on with wind existing tunnel testsdesigns, and reliablea 1:2 scale weather prototype data obtained of an SWT from with the a diffuserWeather optimized Research &for Forecasting wind conditions (WRF) numerical typical of model Poland [15 ,has46]. been These constructed. findings were The used in the present study to calculate the possible increase in efficiency of an SWT due to turbine’s aerodynamics were optimized based on wind tunnel tests and reliable weather rotor shielding and the expected payback period, which is shown in the last column of data obtained from the Weather Research & Forecasting (WRF) numerical model [15,46]. Table5. However, this assessment does not take into account the costs resulting from the These findings were used in the present study to calculate the possible increase in complexity of the new design, such as the need to redesign the hub structure, manufacture efficiencythe diffuser of oran add SWT or changedue to rotor the support shieldin structures,g and the and expected thus is notpayback fully reliable.period, which is shownThe in basicthe last knowledge column of necessary Table 5. for However, deciding this to invest assessment in a wind does turbine not take in a into given account area theis local costs wind resulting energy from resources. the complexity The results of of the the new present design, study such emphasize as the theneed potential to redesign for thelarge hub differences structure, in manufacture average annual the wind diffuser speeds or amongadd or measurementchange the support points andstructures, hence the and thusimportance is not fully of measurements reliable. of wind characteristics for decision-making. Wind potential clearlyThe differed basic knowledge not only among necessary the three for deciding selected locations to invest but in alsoa wind among turbine the analyzedin a given area is local wind energy resources. The results of the present study emphasize the

Energies 2021, 14, 3706 15 of 18

years, indicating that long-term wind measurements are necessary to avoid seasonality. The recorded measurements and calculations can also be used in further economic analyses. As shown in Figure 12, the unit cost of electricity generation clearly differs among the locations studied. This is an important parameter for assessing the energy efficiency of a turbine. The results show that the most favorable location for the installation of an SWT is Location 3, which is located in the coastal strip (wind zone I). The estimated service life of a wind turbine is usually 20–25 years [47]. Similar data are not available for SWTs, and therefore we use this estimated service life. According to the calculations, the return of the planned investment at Location 3 in the Baltic Sea will occur after approximately 13 years. This result is satisfactory compared with the cost-effectiveness of the other locations. The investment at Location 1 in the Carpathian Foothills will pay for itself after a period over 3 times longer (44 years and 2 months), and the least profitable place to build a wind turbine is Location 2, located in the lowlands of Poland, as the return of investment costs will not occur until 57 years and 5 months. The payback periods obtained for Location 1 and Location 2 significantly exceed the estimated wind turbine lifetime. Due to the poor wind conditions at these two locations, investments in SWTs in these areas are pointless, as the costs incurred during wind farm installation will far exceed the expected profits and payback time. Accordingly, these locations can be excluded from investment based on a lack of economic viability. The next stage of this research will be to analyze the operating costs of SWTs in cooperation with potential turbine manufacturers. The values from the literature used here will be replaced with actual price data of individual elements and exploitation stages in order to complement and extend the present overview of SWTs.

5. Conclusions Based on the results of this analysis, the following conclusions can be drawn: • The wind characteristics in the analysed locations have a similar windiness trend, as the windiness in autumn-winter period is higher than in spring-winter period in each location. • The unit cost of electricity generation is clearly different in each location studied in different regions of Poland. This proves the necessity of thorough verification of the surroundings before investing in a small wind turbine. • The most favourable location for SWT installation in Poland is Location 3, which is located in the coastal belt (I wind zone). • The conducted analysis gives an overview of the costs of wind resources in different parts of Poland, but it is not truly complete as the authors did not have all the required data for calculations, and therefore they partly used assumptions from the literature. • Current SWTs are promising solutions for use in sparsely populated areas where there is no access to electricity from the distribution grid. • The addition to SWTs of equipment such as diffusers to tunnel the rotor could increase SWT efficiency and promote further growth of the wind energy industry. Rotor tunnelling can also ensure efficient wind turbine operation, even in areas with less than ideal wind conditions.

Author Contributions: Conceptualization, J.Z. and J.M.; methodology, K.D. and J.M.; formal analysis, J.M. and K.D.; investigation, J.Z and K.D.; data curation. J.Z.; resources, J.Z. and J.M.; visualization, J.Z. and K.D.; writing—original draft preparation, J.Z.; writing—review and editing, J.Z., J.M. and K.D.; supervision, J.M. and K.D.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript. Funding: This work was ﬁnanced/co-ﬁnanced by Military University of Technology under research project UGB 878/2021. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Energies 2021, 14, 3706 16 of 18

Acknowledgments: The authors acknowledge the Institute of Meteorology and Water Management for providing the wind data used in the study. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table A1. Distribution of mean monthly wind speeds from 2014 to 2018 at the three locations.

Wind Speed [m/s] January February March April May June July August September October November December Location 1 3.59 3.23 3.26 3.24 2.77 2.68 2.55 2.40 2.69 2.96 3.25 3.71 Location 2 3.49 2.99 2.96 2.91 2.33 2.31 2.35 2.14 2.26 2.87 3.02 3.40 Location 3 5.44 4.84 4.80 5.45 4.62 4.79 4.81 4.44 4.87 5.32 4.98 6.07

Appendix B

Table A2. Distribution of average monthly wind speed at the three locations for the period 2014–2018.

Wind Speed [m/s] January February March April May June July August September October November December Location 1 3.92 3.32 3.37 2.82 3.02 2.74 2.32 2.40 2.43 2.67 3.28 3.60 Location 2 4.25 2.89 2.98 2.49 2.52 2.03 2.28 2.13 2.15 2.38 2.82 3.05 Location 3 5.28 4.61 5.24 4.86 5.20 4.80 4.58 4.78 4.75 4.15 3.62 5.78 2014 4.49 3.61 3.86 3.39 3.58 3.19 3.06 3.11 3.11 3.07 3.24 4.14 Location 1 3.97 2.90 3.44 3.41 2.74 2.54 2.55 2.34 3.13 2.73 3.16 3.90 Location 2 3.60 2.50 3.06 2.83 2.00 1.94 2.26 2.03 2.27 2.46 3.26 3.71 Location 3 6.52 5.01 5.26 6.27 4.94 4.71 5.29 3.91 5.08 3.70 5.86 6.35 2015 4.70 3.47 3.92 4.17 3.23 3.07 3.36 2.76 3.49 2.96 4.09 4.65 Location 1 3.34 3.86 2.79 2.93 2.63 2.47 2.74 2.66 2.03 3.04 3.78 3.52 Location 2 3.08 3.59 2.94 2.68 2.20 2.39 2.54 2.26 1.82 3.36 3.34 3.64 Location 3 4.94 5.41 4.42 5.03 4.29 4.07 4.65 4.67 4.04 5.86 6.02 6.71 2016 3.78 4.29 3.38 3.55 3.04 2.98 3.31 3.20 2.63 4.09 4.38 4.62 Location 1 2.94 3.51 3.53 3.45 2.80 3.20 2.58 2.53 3.12 3.31 2.98 3.80 Location 2 3.05 3.50 3.11 3.28 2.58 2.97 2.36 2.31 2.79 3.55 2.91 3.56 Location 3 5.52 5.06 4.72 5.91 5.04 5.60 5.08 4.25 4.96 7.02 5.37 6.18 2017 3.84 4.02 3.79 4.21 3.48 3.92 3.34 3.03 3.62 4.63 3.75 4.51 Location 1 3.76 2.58 3.17 3.60 2.63 2.42 2.58 2.09 2.71 3.04 3.05 3.72 Location 2 3.47 2.47 2.74 3.28 2.37 2.23 2.31 1.98 2.27 2.62 2.76 3.05 Location 3 4.96 4.12 4.35 5.18 3.64 4.78 4.44 4.59 5.53 5.85 4.01 5.32 2018 4.06 3.06 3.42 4.02 2.88 3.14 3.11 2.89 3.50 3.84 3.28 4.03

References 1. Lee, J.; Zhao, F. Global Wind Report; Global Wind Energy Council: Brussels, Belgium, 2019. 2. Gnatowska, R.; Was, A. Wind Energy in Poland—Economic Analysis of Wind Farm. In Proceedings of the E3S Web of Conferences, EDP Sciences, Cracow, Poland, 15 March 2017; Volume 14. 3. Moc Zainstalowana (MW)—Potencjał Krajowy OZE w Liczbach—Urz ˛adRegulacji Energetyki. Available online: https://www. ure.gov.pl/pl/oze/potencjal-krajowy-oze/5753,Moc-zainstalowana-MW.html (accessed on 8 February 2021). 4. Igliński,B.; Iglińska, A.; Koziński,G.; Skrzatek, M.; Buczkowski, R. Wind Energy in Poland—History, Current State, Surveys, Renewable Energy Sources Act, SWOT Analysis. Renew. Sustain. Energy Rev. 2016, 64, 19–33. [CrossRef] 5. Orkan Sabrina Nakr˛eciłPolskie Wiatraki. Rekordowa Generacja|GRAMwZIELONE.Pl. Available online: https://www. gramwzielone.pl/energia-wiatrowa/102441/orkan-sabina-nakrecil-polskie-wiatraki-rekordowa-generacja (accessed on 10 February 2021). 6. Global Wind Power Statistics—Wind Energy International Platform. Available online: https://library.wwindea.org/global- statistics/ (accessed on 8 February 2021). 7. International Standard IEC 61400-2. Wind Turbines-Part 2: Small Wind Turbines; International Electrotechnical Commission: Geneva, Switzerland, 2013; ISBN 9782832212844. 8. Tummala, A.; Velamati, R.K.; Sinha, D.K.; Indraja, V.; Krishna, V.H. A Review on Small Scale Wind Turbines. Renew. Sustain. Energy Rev. 2016, 56, 1351–1371. [CrossRef] 9. Sonnichsen, N. Capacity of Small Wind Turbines Worldwide 2018|Statista. Available online: https://www.statista.com/statistics/ 269885/capacity-of-small-wind-turbines-worldwide/ (accessed on 10 February 2021). 10. Bukala, J.; Damaziak, K.; Kroszczynski, K.; Krzeszowiec, M.; Malachowski, J. Investigation of Parameters Influencing the Efficiency of Small Wind Turbines. J. Wind Eng. Ind. Aerodyn. 2015, 146, 29–38. [CrossRef] 11. Bukala, J.; Damaziak, K.; Karimi, H.R.; Kroszczynski, K.; Krzeszowiec, M.; Malachowski, J. Modern Small Wind Turbine Design Solutions Comparison in Terms of Estimated Cost to Energy Output Ratio. Renew. Energy 2015, 83, 1166–1173. [CrossRef] Energies 2021, 14, 3706 17 of 18

12. Pitteloud, J.-D.; Gsänger, S. Small Wind World Report; World Wind Energy Association: Bonn, Germany, 2016. 13. Terrapon-Pfaff, J.; Dienst, C.; König, J.; Ortiz, W. A Cross-Sectional Review: Impacts and Sustainability of Small-Scale Renewable Energy Projects in Developing Countries. Renew. Sustain. Energy Rev. 2014, 40, 1–10. [CrossRef] 14. Grijalva, S.; Tariq, M.U. Prosumer-Based Smart Grid Architecture Enables a Flat, Sustainable Electricity Industry. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe, Anaheim, CA, USA, 17–19 January 2011. 15. Olasek, K.; Karczewski, M.; Lipian, M.; Wiklak, P.; Józwik, K. Wind Tunnel Experimental Investigations of a Diffuser Augmented Wind Turbine Model. Int. J. Numer. Methods Heat Fluid Flow 2016, 26, 2033–2047. [CrossRef] 16. Bukala, J.; Damaziak, K.; Karimi, H.R.; Malachowski, J. Aero-Elastic Coupled Numerical Analysis of Small Wind Turbine- Generator Modelling. Wind Struct. Int. J. 2016, 23, 577–594. [CrossRef] 17. Bukala, J.; Damaziak, K.; Karimi, H.R.; Malachowski, J.; Robbersmyr, K.G. Evolutionary Computing Methodology for Small Wind Turbine Supporting Structures. Int. J. Adv. Manuf. Technol. 2019, 100, 2741–2752. [CrossRef] 18. Kim, K.; Lim, C.; Oh, Y.O.; Kwon, I.; Yoo, N.; Paek, I. Time-Domain Dynamic Simulation of a Wind Turbine Including Yaw Motion for Power Prediction. Int. J. Precis. Eng. Manuf. 2014, 15, 2199–2203. [CrossRef] 19. Pagnini, L.C.; Burlando, M.; Repetto, M.P. Experimental power curve of small-size wind turbines in turbulent urban environment. Appl. Energy 2019, 154, 112–121. [CrossRef] 20. Vita, G.; Šarkić-Glumac,A.; Hemida, H.; Salvadori, S.; Baniotopoulos, C. On the Wind Energy Resource above High-Rise Buildings. Energies 2020, 13, 3641. [CrossRef] 21. Sakka, E.G.; Bilionis, D.V.; Vamvatsikos, D.; Gantes, C.J. Onshore Wind Farm Siting Prioritization Based on Investment Profitability for Greece. Renew. Energy 2020, 146, 2827–2839. [CrossRef] 22. Ayodele, T.R.; Ogunjuyigbe, A.S.O.; Amusan, T.O. Wind Power Utilization Assessment and Economic Analysis of Wind Turbines across Fifteen Locations in the Six Geographical Zones of Nigeria. J. Clean. Prod. 2016, 129, 341–349. [CrossRef] 23. Grieser, B.; Sunak, Y.; Madlener, R. Economics of Small Wind Turbines in Urban Settings: An Empirical Investigation for Germany. Renew. Energy 2015, 78, 334–350. [CrossRef] 24. Rodriguez-Hernandez, O.; Martinez, M.; Lopez-Villalobos, C.; Garcia, H.; Campos-Amezcua, R. Techno-Economic Feasibility Study of Small Wind Turbines in the Valley of Mexico Metropolitan Area. Energies 2019, 12, 890. [CrossRef] 25. Campisi, D.; Gitto, S.; Morea, D. Effectiveness of Incentives for Wind Energy: Models and Empirical Evidences from an Italian Case Study. J. Sustain. Sci. Manag. 2016, 11, 39–48. 26. Morea, D.; Poggi, L.A. An Innovative Model for the Sustainability of Investments in the Wind Energy Sector: The Use of Green Sukuk in an Italian Case Study. Int. J. Energy Econ. Policy 2017, 7, 53–60. 27. Gutiérrez-Pedrero, M.-J.; Ruiz-Fuensanta, M.J.; Tarancón, M.-Á. Regional Factors Driving the Deployment of Wind Energy in Spain. Energies 2020, 13, 3590. [CrossRef] 28. DR ZBER ZEFIR. Available online: http://zaber.com.pl/zefir/ (accessed on 2 February 2021). 29. Lydia, M.; Kumar, S.S.; Selvakumar, A.I.; Prem Kumar, G.E. A Comprehensive Review on Wind Turbine Power Curve Modeling Techniques. Renew. Sustain. Energy Rev. 2014, 30, 452–460. [CrossRef] 30. Bakırcı, M.; Yılmaz, S. Theoretical and Computational Investigations of the Optimal Tip-Speed Ratio of Horizontal-Axis Wind Turbines. Eng. Sci. Technol. Int. J. 2018, 21, 1128–1142. [CrossRef] 31. Karczewski, M.; Baszczynski, P.; Wiklak, P.; Sobczak, K.; Jozwik, K. Economic Analysis of Small Wind Turbines. J. Mach. Eng. 2017, 17, 112–123. 32. Saint-Drenan, Y.M.; Besseau, R.; Jansen, M.; Staffell, I.; Troccoli, A.; Dubus, L.; Schmidt, J.; Gruber, K.; Simões, S.G.; Heier, S. A Parametric Model for Wind Turbine Power Curves Incorporating Environmental Conditions. Renew. Energy 2020, 157, 754–768. [CrossRef] 33. Brzezińska-Rawa,A.; Go´zdziewicz-Biechońska,J. Recent Developments in the Wind Energy Sector in Poland. Renew. Sustain. Energy Rev. 2014, 38, 79–87. [CrossRef] 34. USER’S GUIDE Vaisala WINDCAP®Ultrasonic Wind Sensor WS425; Vaisala Oyj: Helsinki, Finland, 2010. 35. Bukala, J.; Damaziak, K.; Kroszczynski, K.; Malachowski, J.; Szafranski, T.; Tomaszewski, M.; Karimi, H.R.; Jozwik, K.; Karczewski, M.; Sobczak, K. Small Wind Turbines: Specification, Design, and Economic Evaluation. In Wind Turbines—Design, Control and Applications; InTech: Rijeka, Croatia, 2016; pp. 73–93. 36. Rao, K.R. Wind Energy Economics in Wind Energy for Power Generation; Springer: Cham, Switzerland, 2019; pp. 427–701, ISBN 9783319751344. 37. Michalak, P. Ocena zasobów energii wiatru na potrzeby małej energetyki wiatrowej. Elektrotechnika i Elektron. 2009, 28, 14–19. 38. Eurostat—Data Explorer. Available online: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_205&lang=en (accessed on 23 February 2021). 39. Statistical Analyses. Energy Consumption in Households in 2018; Główny Urz ˛adStatystyczny: Warsaw, Poland, 2019. 40. Bussel, G.J.W. Electricity Generation with Small Wind Turbines in Renewable Energy Systems; Springer: New York, NY, USA, 2013; pp. 696–727, ISBN 9781461458203. 41. Boczar, T. Wykorzystanie Energii Wiatru; PAK: Warszawa, Poland, 2010; ISBN 978-83-926319-6-5. 42. Uniewicz, C. 10 Czy 30 Lat? Jaka Jest Rzeczywista Stopa Zwrotu Mikroinstalacji?|GRAMwZIELONE.Pl. Available online: https: //www.gramwzielone.pl/energia-sloneczna/21908/10-czy-30-lat-jaka-jest-rzeczywista-stopa-zwrotu-mikroinstalacji (accessed on 25 February 2021). Energies 2021, 14, 3706 18 of 18

43. Jiang, H.; Li, Y.; Cheng, Z. Performances of Ideal Wind Turbine. Renew. Energy 2015, 83, 658–662. [CrossRef] 44. Karczewski, M.; Sobczak, K.; Lipian, M.; Jozwik, K. Numerical and experimental tools for small wind turbine load analysis. In Structural Control and Fault Detection of Wind Turbine Systems; The Institution of Engineering and Technology: London, UK, 2018; pp. 45–79, ISBN 9781785613944. 45. STOW Project Main Site. Available online: http://www.stow.wat.edu.pl/ (accessed on 25 February 2021). 46. Lipian, M.; Dobrev, I.; Karczewski, M.; Massouh, F.; Jozwik, K. Small Wind Turbine Augmentation: Experimental Investigations of Shrouded- and Twin-Rotor Wind Turbine Systems. Energy 2019, 186, 115855. [CrossRef] 47. Sliz-Szkliniarz, B.; Eberbach, J.; Hoffmann, B.; Fortin, M. Assessing the Cost of Onshore Wind Development Scenarios: Modelling of Spatial and Temporal Distribution of Wind Power for the Case of Poland. Renew. Sustain. Energy Rev. 2019, 109, 514–531. [CrossRef]