Wind Resource Assessment on Puná Island

applied sciences

Article Wind Resource Assessment on Puná Island

Yuri Merizalde 1, Luis Hernández-Callejo 2,* , Javier Gracia Bernal 3, Enrique Telmo Martínez 4, Oscar Duque-Perez 5 , Francisco Sánchez 4 and Andrés Llombart Estpopiñán 4

1 Faculty of Chemical Engineering, University of Guayaquil, Ph.D. School of University of Valladolid (UVA), Clemente Ballen 2709 and Ismael Perez Pazmiño, Guayaquil 593, Ecuador 2 Department of Agricultural Engineering and Forestry, University of Valladolid (UVA), Campus Universitario Duques de Soria, 42004 Soria, Spain 3 Compañan Eólica de Tierras Altas S.A. (CETASA), Calle Diputación 1, Ediﬁcio Caja Rural, 42002 Soria, Spain 4 Department of Electrical Systems, Group Analysis and Optimization of Renewable Generation, Center for Energy Resources and Consumption Research (CIRCE), Avda. Ranillas 3D, 1ª Planta, 50018 Zaragoza, Spain 5 Department of Electrical Engineering, University of Valladolid (UVA), Escuela de Ingenierías Industriales, Paseo del Cauce 59, 47011 Valladolid, Spain * Correspondence: [email protected]; Tel.: +34-975-129-213

Received: 18 May 2019; Accepted: 17 July 2019; Published: 22 July 2019

Abstract: Puná Island, located in the Pacific Ocean off the southern coast of Ecuador, has a population of approximately 3344 inhabitants. However, not all inhabitants have access to electricity, which is largely supplied by diesel generators. Therefore, to identify a renewable, sustainable, environmentally friendly and low-cost alternative, a 40-m-high anemometer tower was installed for wind resource assessment and to determine the possibility of generating electricity from wind energy. Based on mathematical models for electricity generation from wind energy, data were analyzed using the software Windographer and WAsP, to determine a long-term wind speed of 4.8 m/s and a mean wind power density of 272 W/m2. By simulating the use of a 3.3-MW wind turbine, we demonstrated that as much as 800 kWh could be generated during the hours when the wind reaches its highest speed. In addition to demonstrating the technical feasibility of meeting the electricity demands of Puná Island through wind power, this study exemplifies a method that can be used for wind resource assessment in any location.

Keywords: renewable energy; wind energy potential; electricity generation; wind turbines

1. Introduction Belonging to the Guayaquil canton, Guayas province, Ecuador, the rural parish of Puná is a group of more than 30 islands and islets with 35 human settlements, of which 22 are located on the largest island (919 km2), Puná Island. Located in the Gulf of Guayaquil, Puná Island is 3 km oﬀ the coast of the Posorja precinct, which is the mainland town closest to the island (see Figure1). The Universal Transverse Mercator (UTM) coordinate is NS98 and its Joint Operations Graphics grid reference is SA17-11. The time zone of the island is Coordinated Universal Time (UTC)/Greenwich Mean Time (GMT)-5. The geographical coordinates of the points that deﬁne the location of the island in its entirety are outlined in Table1[1,2].

Appl. Sci. 2019, 9, 2923; doi:10.3390/app9142923 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 2923 2 of 20 Appl. Sci. 2019, 9, x FOR PEER REVIEW 2 of 21

FigureFigure 1. 1.Satellite Satellite viewview of Puná Puná Island.Island. Source: Source: [3]. [3].

TableTable 1. 1.Geographical Geographical coordinates coordinates ofof thethe perimeterperimeter of Puná Puná Island.Island. Source: Source: [1]. [1 ].

Geographical Coordinates Geographical Coordinates Point Geographical Point Geographical Point XYXYCoordinates Point Coordinates 1 571,331.99X 9,705,877.72Y 6 X 589,765.93Y 9,702,878.50 21 572,464.23571,331.99 9,705,608.009,705,877.72 6 7589,765.93 585,466.92 9,702,878.50 9,698,762.99 32 576,440.60572,464.23 9,706,019.369,705,608.00 7 8585,466.92 584,241.35 9,698,762.99 9,694,975.13 4 578,109.74 9,706,338.38 9 577,967.60 9,694,979.36 3 576,440.60 9,706,019.36 8 584,241.35 9,694,975.13 5 583,024.35 9,707,088.13 10 577,968.02 9,698,883.58 4 578,109.74 9,706,338.38 9 577,967.60 9,694,979.36 5 583,024.35 9,707,088.13 10 577,968.02 9,698,883.58 According to the latest census conducted in 2010, the island has a population of approximately 3344 inhabitants,According to divided the latest into census 22 human conducted settlements in 2010, ortheprecincts. island has Onlya population 50.78% of of approximately the population has3344 completed inhabitants, primary divided education, into 22 human and the settlements illiteracy rateor precin is 11.73%.cts. Only Although 50.78% of part the of population the population has ofcompleted Puná Island primary has access education, to piped and the water, illiteracy the island rate is lacks11.73%. basic Although services: part drinking of the population water (when of necessary,Puná Island drinking has access water to ispiped transported water, the from island the lacks mainland basic toservices the island),: drinking sewage water treatment, (when necessary, a sanitary landfill,drinking a hospital, water is transported and access roads from the in satisfactory mainland to conditions. the island), Thesewage local treatment, electricity a companysanitary landfill, supplies electricitya hospital, to 66%and ofaccess the population roads in satisfactory through three conditions. generators The with local a totalelectricity power company of 2.2 MW, supplies whereas 20%electricity of the inhabitantsto 66% of the have population their own through electric three generator. generators Water with a pumps total power used of in 2.2 shrimp MW, pondswhereas are the20% predominant of the inhabitants energy have demand their own of the electric island, gene whichrator. is Water met using pumps diesel used generators in shrimp ponds (over are 200 the HP). Currently,predominant approximately energy demand 80% ofof thethe populationisland, which is engagedis met using in activities diesel generators related to (over shrimp 200 farming, HP). fishing,Currently, forestry, approximately agriculture, 80% and of the animal population husbandry. is engaged Other in economicactivities related activities to shrimp include farming, tourism, commerce,fishing, forestry, and construction. agriculture, Despite and animal the wealth husbandry. generated Other from economic shrimp farming, activities the include economic tourism, benefits arecommerce, not invested and in construction. the island because Despite the the owners wealth of the generated shrimp pondsfrom areshrimp neither farming, native the to nor economic residents ofbenefits the island. are not In addition,invested in oil the spills, island exhaust because gas the emissions,owners of the garbage, shrimp plastic ponds wasteare neither washed native ashore, to nor residents of the island. In addition, oil spills, exhaust gas emissions, garbage, plastic waste domestic sewage discharges, and shrimp farms cause pollution. This environment of unmet needs not washed ashore, domestic sewage discharges, and shrimp farms cause pollution. This environment of only prevents economic growth but also causes migration, in the absence of the necessary conditions unmet needs not only prevents economic growth but also causes migration, in the absence of the for development, thereby aggravating the island’s problems [4,5]. necessary conditions for development, thereby aggravating the island’s problems [4,5]. One of the primary factors for the development of towns and their economic growth is the One of the primary factors for the development of towns and their economic growth is the availability of energy resources that supply the electricity necessary for society’s activities in the 21st availability of energy resources that supply the electricity necessary for society’s activities in the 21st century [6,7]. Therefore, the main objective of this study is to determine the wind energy potential to century [6,7]. Therefore, the main objective of this study is to determine the wind energy potential to generategenerate the the electricity electricity Pun Punáá Island Island requiresrequires forfor itsits sustainable development. development. Furthermore, Furthermore, this this study study aimsaims to to apply apply existing existing knowledge knowledge of of wind wind resource resource assessment assessment in in a a manner manner that that maymay bebe replicatedreplicated in anyin location.any location. The remainingThe remaining manuscript manuscript is organized is organized as follows: as follows: the method, the method, materials, materials, and costsand costs of this researchof this areresearch described are described in Section in2; Section and some 2; and theoretical some theoretical foundations foundations of electricity of electricity generation generation from wind Appl. Sci. 2019, 9, 2923 3 of 20 Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 21 energyfrom wind are addressed energy are in addressed Section3. The in Section results 3. from The the results data from analysis the usingdata analysis the software using Windographerthe software andWindographer WAsP are included and WAsP in Section are included4. The studyin Section ends 4. with The conclusions study ends and with recommendations conclusions and in recommendations in Section 5. Section5.

2.2. Materials Materials and and Methods Methods InIn general, general, based based on on the the experience experience gained gained in in this this research research project, project, the the methodology methodology andand phasesphases of of wind resource assessment studies follow the flowchart of Figure 2, whereas the specific data wind resource assessment studies follow the flowchart of Figure2, whereas the specific data processing processing method follows the flowchart presented in [8]. As electricity supply is essential for method follows the flowchart presented in [8]. As electricity supply is essential for modern human modern human activities and that wind energy is an increasingly developed alternative worldwide, activities and that wind energy is an increasingly developed alternative worldwide, wind resource wind resource assessment is crucial for testing the feasibility of this type of initiative in a specific assessment is crucial for testing the feasibility of this type of initiative in a specific location. For this location. For this purpose, the first step should be to determine whether wind resource assessment purpose, the first step should be to determine whether wind resource assessment studies have already studies have already been performed at the site; otherwise, a study with a minimum duration of one beenyear performed must be conducted. at the site; otherwise, a study with a minimum duration of one year must be conducted.

FigureFigure 2. 2.Methodology Methodology for studies on on wind wind resource resource assessment. assessment. Appl. Sci. 2019, 9, x FOR PEER REVIEW 4 of 21

Appl. Sci. 2019, 9, 2923 4 of 20 Whether a study is performed depends on several factors. The economic factor may be the most important because it determines not only whether the study is feasible as well as its magnitude, i.e., its duration,Whether number a study isof performedanemometer depends towers on to several be installed, factors. tower The economic characteristics, factor mayand beequipment the most availabilityimportant because [9]. Another it determines key issue not to onlyconsider whether is technical the study and is feasiblelegal feasibility, as well asi.e., its whether magnitude, a site’s i.e., conditionsits duration, make number it possible of anemometer to build and towers transport to be a tower, installed, obtaining tower landowners’ characteristics, permission and equipment to erect aavailability tower and [legal9]. Another requirements key issue for its to considerinstallation, is technical such as municipal and legal and feasibility, civil aviation i.e., whether permits. a site’s conditionsAfter makeensuring it possible the availability to build and of transport economic a tower, resources, obtaining important landowners’ decisions permission are torequired erect a regardingtower and aspects, legal requirements such as purcha for itssing installation, a prefabricated such as tower municipal or cons andtructing civil aviation a tower permits. from scratch. The formerAfter ensuring alternative the availabilityis typically ofexpensive, economic ther resources,efore leaving important the decisionsoption to aredesign required and regardingbuild the toweraspects, from such scratch. as purchasing In both cases, a prefabricated experienced tower staff or are constructing required. Some a tower system from component scratch. The (sensors former andalternative data loggers) is typically options expensive, for wind therefore resource leavingassessm theent optionare available to design on the and market. build theTheir tower selection from dependsscratch. Inon both technical cases, characteristics, experienced sta costs,ff are required.availability, Some and systemwarranties, component among (sensors other factors. and data In additionloggers) optionsto storing for records wind resource on memory assessment cards, data are available loggers can on thesend market. data by Their email, selection as long depends as they haveon technical access to characteristics, the Internet. costs, availability, and warranties, among other factors. In addition to storingAfter records installing on memory the tower cards, and data initiating loggers the can device send datas, the by data email, logger as long must as theybe tested have to access assess to whetherthe Internet. the data from all installed instruments are recorded and whether the values recorded are withinAfter the installingexpected range the tower or abnormal, and initiating possibly the du devices,e to equipment the data loggerfailure mustor installation be tested problems. to assess whetherFrequent the equipment data from allfailures installed are instruments often due areto recordedimproper and wind whether vane the installation values recorded or data are withinlogger programmingthe expected range or battery or abnormal, discharge. possibly The following due to equipment measures failure aim to or avoid installation these setbacks: problems. accurately Frequent programmingequipment failures the data are oftenlogger due before to improper installing wind the device, vane installation installing a or solar data panel logger large programming enough to preventor battery battery discharge. discharge, The following and periodically measures visiting aim to th avoide tower these site setbacks: to test the accurately conditions programming of the entire system.the data After logger a beforeyear of installingmeasurements the device, or when installing a report a solarmust panelbe submitted, large enough the data to prevent are processed. battery Thus,discharge, depending and periodically on the objectives visiting and the availability tower site toof testfinancial the conditions resources, of different the entire tools system. may be After used a yearto process of measurements information or from when the a report data mustloggers, be submitted,ranging from the datadesktop areprocessed. applications Thus, such depending as Excel onor MATLAB,the objectives which and availabilityare easily accessible of financial programs, resources, to di ffsoftwareerent tools such may as be WAsP, used to Windographer, process information and windPRO.from the data loggers, ranging from desktop applications such as Excel or MATLAB, which are easily accessibleFor this programs, study, toan software anemometer such as towe WAsP,r was Windographer, installed at and coordinates windPRO. 2°43′58.06″ south by 80°11For′56.97 this″ west study, (see an anemometerFigure 3). On tower the tower, was installed wind vane at coordinatess were installed 2◦430 58.06”at 40 and south 20 bym, 80in◦ 11addition056.97” westto a thermometer, (see Figure3). Ona barometer, the tower, windand a vanes relative were hu installedmidity sensor. at 40 and All 20 instruments m, in addition used, to a including thermometer, the dataa barometer, logger, were and apurchased relative humidity from NRG sensor. Systems All [9]. instruments All equipment used, and including respective the datacosts logger,are outlined were purchasedin Table 2, fromand the NRG design Systems of the [9 ].tower All equipment is presented and in respectiveFigure 4. costs are outlined in Table2, and the design of the tower is presented in Figure4.

Figure 3. Location of the anemometric towertower on PunPunáá island. Source: [3]. [3]. Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 21

Table 2. Costs of equipment and materials used. Appl. Sci. 2019, 9, 2923 5 of 20 Total Unit of Unit Value ITEM Number Value Table 2. Costs of equipment andMeasurement materials used. (USD) (USD) Meters of anemometer tower 40 Unit meters of Unit Value 100 Total Value4000 ITEM Number Tower assembly 1 Measurement overall (USD) 1000 (USD) 1000 ConstructionMeters of anemometerthe concrete tower foundation 40 meters 100 4000 4 units 250 1000 toTower erect assemblythe tower 1 overall 1000 1000 LeaseConstruction of the land of thewhere concrete the foundationanemometer 412 month units 250 400 10004800 towerto erect was the installed tower LeaseTransportation of the land and where food the 1 overall 500 500 12 month 400 4800 Internetanemometer for towerdata transmission was installed 12 month 20 240 Data loggerTransportation NRG Symphonie and food Plus 3 1 1 overall units 500 2000 500 2000 AnemometersInternet for(WindSensor data transmission P2546-OPR) 12 2 month units 20 381.3 240 762.6 Data logger NRG Symphonie Plus 3 1 units 2000 2000 Wind vane NRG 200P 2 units 262 524 Anemometers (WindSensor 2 units 381.3 762.6 TemperatureP2546-OPR) sensor 10S 1 units 250 250 BarometricWind pressure vane NRG sensor 200P BP20 2 1 units units 262 402 524 402 RelativeTemperature humidity sensor sensor 10S RH5X 1 1 units units 250 445 250 445 Barometric20-Watt pressure solar sensorpanel BP20 1 1 units units 402 50 402 50 Relative humidity sensor RH5X 1 units 445 445 70-Ah battery 1 units 100 100 20-Watt solar panel 1 units 50 50 Franklin’s70-Ah lightning battery rod 1 1 units units 100 150 100 150 3Franklin’s × 16 concentric lightning cable rod 1 2 units roll 150 60 150 120 3 16 concentric cable 2 roll 60 120 × Electric board 1 units 60 60 6-mmElectric steel board cable 1 300 units m 60 1 60 300 6-mm steel cable 300 m 1 300 3/43/4”″ turnbuckles turnbuckles 21 21 units units 10 10 210 210 3/43/4”″ ground ground rodrod 70 70 units units 1 1 70 70 TOTALTOTAL 16,983.6016,983.60

Figure 4. DetailsDetails of of design and assembly of the anemometer tower. Appl. Sci. 2019, 9, 2923 6 of 20

The project requires not only the approval and resources of the sponsoring entities but also the permission of the landowners of the tower site. After installing the equipment and activating the Internet, the data logger records the variables every 10 min and stores hourly means on its memory card. The data logger is conﬁgured to send data every day at 07h00 a.m. (Ecuador time) in an email with a report of the measurements from the previous day. According to the standards, the studies on evaluation of wind resources should last a minimum of one year, however, due to the gap between the date of approval by the funding institution and the availability of resources necessary for the study, monitoring could only be performed for nine months. The data collected during this period were analyzed using the software Windographer to determine the following parameters: wind speed variation as a function of time of day and month, wind speed histogram, wind direction (compass rose), theoretical mean power a particular wind turbine model would deliver, and daily and annual energy production depending on the selected wind turbine model. The contour and wind resource maps were drawn using WAsP and Google Earth.

3. Fundamentals of Electricity Production from Wind For thousands of years, wind has been an important resource from which humans have harnessed the necessary energy to perform various activities such as sailing, grain milling, water pumping, and electricity generation [10]. The energy that can be harnessed from wind is proportional to its speed, which is not affected by terrain conditions at high altitudes. However, in the lower layers of Earth’s atmosphere, wind varies as a function of surface friction or ground surface roughness (affected by vegetation cover and buildings, among other factors). In short, wind speed is proportional to the height above ground. As anemometer towers cannot always be erected at the height at which wind turbines operate, wind speed is extrapolated using functions such as Equation (1), known as the Hellman function or power-law profile and Equation (2), called logarithmic wind profile [11,12].

!α v z = , (1) v1 z1 v z v = ∗ ln , (2) k z0 where: v = wind speed at height z; v1 = wind speed at height z1; and v = friction velocity; ∗ k = the von Karmann constant; z0 = roughness length; and α = friction or Hellman coeﬃcient.

As the amount of available wind energy varies as a function of wind speed, which changes constantly, statistical models and probability distributions, such as Gaussian, Rayleigh, and Weibull distributions, are used to quantify the performance of this variable at a specific location over time. The Gaussian or normal distribution is the probability density function that best describes wind variations. This model is represented according to Equation (3), whereas the probability of a specific wind speed at a given interval is provided by Equation (4). In the wind industry, Rayleigh and Weibull are the most commonly used probability distributions [13]. The equations of these models and their respective cumulative distribution functions (probability of a wind speed lower than or equal to a specific value) are presented in Equations (5)–(8) [14,15].

 2  1  (v v)  p(v) = exp − , (3)  2  σv √2π − 2σv Appl. Sci. 2019, 9, 2923 7 of 20

Z vb p (va v vb) = p(v)dv, (4) ≤ ≤ va !  !2 π v  π v  p(v)1 = exp , (5) 2 v2 − 4 v´   !2  π v  F(v)1 = 1 exp , (6) − − 4 v´  " # k v k 1 v k p(v) = ( )( ) − exp ( ) , (7) 2 c c − c " # v k F(v) = 1 exp ( ) , (8) 2 − − c where: v = wind speed; v = mean wind speed; p(v) = probability density function of wind speed;

σv = standard deviation of wind speed; p(v)1 = Rayleigh distribution; F(v)1 = cumulative distribution function; p(v)2 = Weibull distribution; F(v)2 = cumulative distribution function; k = shape factor; and c = scale factor.

The wind power per unit area or wind power density may be determined using Equation (9), whereas the hourly mean is calculated according to Equations (10) and (11). In practice, for increased reliability in predicting the production of wind power using wind turbines, the mean speed determined in our study was not used; instead, the value calculated by applying a correction factor was used. This correction factor is a percentage of the diﬀerences between the mean wind speed of our study and those provided by databases with wind speed records from several years [14–16]. In this research study, databases such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Climate Forecast System Reanalysis (CFSR2) were used for comparison purposes. The coincidence that exists between the magnitude of the wind speed obtained with the measurements made in this study and the values obtained from the aforementioned databases, in addition to validating the study, allows for more accurate projections.

P 1 = ρv3, (9) A 2

P 1 = ρv3K , (10) A 2 e N 1 X K = U 3, (11) e 3 i Nv i=1 where:

P A = Power per unit area or wind power density (power that can be extracted from the wind by a wind turbine having the rotor swept area A). P = power (watts); ρ = air density (1.225 kg/m3); Appl. Sci. 2019, 9, 2923 8 of 20 v = wind speed (m/s); P = mean wind power (watts); v = annual mean wind speed (m/s);

Ke = energy pattern factor; N = 8760 hours in a year; and

Ui = wind hours available.

For example, assuming that two hours of data were missing daily (it would have only 22 h of wind) and annual mean wind speed is 5 m/s, when replacing values in Equations (9) through (11), a wind power density of 272 watts per square meter is assessed. Conversely, the higher the wind speed and the larger the diameters of the wind turbine blades, the higher the harnessed amount of wind power. 365 1 X K = (22)3, e 3 (8760)(5) i=1

Ke = 3.55,

P 1 3 = ρV K , A 2 e ! P 1 kg = 1225 (5 m/s)3(3.55), A 2 m3 P = 272 watts/m2, A The theoretical energy production of the selected turbines may be calculated using several methods, including [13,14]:

direct use of the mean data during an interval; • wind speed data binning (bins); • construction of a wind power curve using the data; and • statistical analysis. • Using the first method, the mean power assessed in a wind turbine of a specific power Pw is given by Equation (12), whereas when using probability and statistical methods, the mean wind power is given by Equation (13). The wind power curve may be constructed using the available wind power and the rotor power coefficient Cp, according to Equations (14) and (15). By combining Equations (13) through (15), the mean power of a wind turbine is given by Equation (16), which may be expressed as a function of the cumulative distribution function, according to (18). Replacing Equation (8) in Equation (18) and solving the resulting equation, P may be calculated using the Weibull distribution, according to Equation (19) [14,15]. N 1 X P = P (v), (12) N w i=1 Z ∞ P = Pw(v)p(v)dv, (13) 0 1 P(v) = ρAC ηv3, (14) 2 p

Rotor power Protor Cp = = , (15) Wind power 1 3 2 ρAv Z 1 2 ∞ 3 P = ρηπR Cp(λ)v p(v)dv, (16) 2 0 Appl. Sci. 2019, 9, 2923 9 of 20

Blade tip speed ΩR λ = = , (17) Wind speed v Z ∞ P = Pw(v)dF(v), (18) 0    NB  !k " k# + ! X  vj 1  vj vj 1 vj P = exp −  exp Pw − , (19)  − c  − − c 2 j=1 where:

P(v) = power calculated using the wind turbine power curve as a function of v; p(v) = probability distribution of v;

CP = rotor power coeﬃcient; η = drive train eﬃciency; λ = tip speed ratio; Ω = angular speed (radians/sec); and R = rotor radius.

4. Results In wind resource assessment studies, wind variation is analyzed using frequency distributions of wind speed, wind direction (compass rose), wind speed as a function of height, and mean wind speed according to time of day [17]. Based on these and other variables, the appropriate wind turbine for a speciﬁc location is designed or selected while determining the optical location for the wind turbine and predicting the power or electricity that may be harnessed. In the following subsections, the records are processed to analyze the wind variation on Puná Island, especially at the anemometer tower site.

4.1. Frequency Distributions One of the primary characteristics of wind resources is the frequency distribution of different values of wind speed at a given location. For analysis purposes, the speed spectrum is typically divided into different ranges, such as 0.5 or 1 m/s termed bins. The frequency histograms with several frequency distributions of wind speed for Puná Island are presented in Figure5a,b. The mean wind speed is approximately 4.8 m/s, which reflects a class II wind resource with marginal potential [18]. The lowest values occur from 6 to 7 a.m., after which time wind speed increases again, peaking at 3 and 5 p.m. (see Figure6). During the study period (9 months), availability was 88%; extrapolating this value to a full year, availability declines to 60%. Wind speed varies similarly at both 40 and 20 m, as shown in Figure6, which is mathematically corroborated by the correlation between anemometer measurements (see Figure7). Appl. Sci. 2019, 9, 2923 10 of 20

Appl. Sci. 2019, 9, x FOR PEER REVIEW 10 of 21

(a)

(b)

Figure 5. (a) Frequency histogram and Weibull distribution of wind speed for Puná Island. Source: generated by the software. (b). Frequency histogram and several frequency distributions of wind speed for Puná Island. Source: generated by the software. Appl. Sci. 2019, 9, x FOR PEER REVIEW 11 of 21

Figure 5. (a) Frequency histogram and Weibull distribution of wind speed for Puná Island. Source: Appl. Sci.generated2019, 9, 2923 by the software. (b). Frequency histogram and several frequency distributions of wind 11 of 20 speed for Puná Island. Source: generated by the software.

Appl. Sci. 2019, 9Figure, xFigure FOR 6.PEER 6.Mean Mean REVIEW diurnal diurnal proﬁle profile ofof windwind speed.speed. Source: Source: generated generated by by the the software. software. 12 of 21

FigureFigure 7. 7.Correlation Correlation between wind wind speed speed values values from from different diﬀerent anemometers anemometers at 40 at and 40 and 20 m 20 height. m height. Source:Source: generated generated byby thethe software.

4.2. Wind Direction A compass rose is a circle through which the horizon is divided according to the direction of the four cardinal points, typically into 8, 12, or 16 sectors (45, 30, or 22.5 degrees per sector, respectively), upon which the percentage of wind blowing in a specific direction is plotted. A primary use of a compass rose is the determination of the optimal location for a wind turbine. Wind direction as a function of frequency and energy as a function of direction are shown in Figures 8 and 9, respectively, revealing predominantly eastward winds according to the data logger. As we can see in the compass rose of Figure 8, 80% of the highest wind speeds occur within the interval from 67.5° to 112.5°. The same range encompasses approximately 90% of the available wind energy (see Table 3). Wind direction uniformity is beneficial for both the individual production of a wind turbine and the installation of several wind turbines in the case of a wind farm. A comparison of the compass rose results with those provided by the databases MERRA-2 and CFSR2 reveals a 180° difference because, according to the aforementioned databases, the wind blows predominantly westward in the study area. Nevertheless, the mean wind speed of the present study agrees completely with those of the referenced databases. Appl. Sci. 2019, 9, 2923 12 of 20

4.2. Wind Direction A compass rose is a circle through which the horizon is divided according to the direction of the four cardinal points, typically into 8, 12, or 16 sectors (45, 30, or 22.5 degrees per sector, respectively), upon which the percentage of wind blowing in a specific direction is plotted. A primary use of a compass rose is the determination of the optimal location for a wind turbine. Wind direction as a function of frequency and energy as a function of direction are shown in Figures8 and9, respectively, revealing predominantly eastward winds according to the data logger. As we can see in the compass rose of Figure8, 80% of the highest wind speeds occur within the interval from 67.5 ◦ to 112.5◦. The same range encompasses approximately 90% of the available wind energy (see Table3). Wind direction uniformity is beneficial for both the individual production of a wind turbine and the installation of several wind turbines in the case of a wind farm. A comparison of the compass rose results with those provided by the databases MERRA-2 and CFSR2 reveals a 180◦ difference because, according to the aforementioned databases, the wind blows predominantly westward in the study area. Nevertheless, Appl.the mean Sci. 2019 wind, 9, x FOR speed PEER of REVIEW the present study agrees completely with those of the referenced databases.13 of 21

Figure 8. WindWind direction frequency. Sour Source:ce: generated by the software.

Figure 9. Wind direction as a function of mean speed. Source: generated by the software. Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 21

Appl. Sci. 2019, 9, 2923 13 of 20 Figure 8. Wind direction frequency. Source: generated by the software.

Figure 9. Wind direction as a function ofof meanmean speed.speed. Source: generated by the software. Table 3. Mean wind speed, frequency, and energy as function of direction. Source: the authors.

Mean (m/s) vs. Proportion of Total Wind Frequency (%) Sector Midpoint ‘Direction 0 A’ Energy (%) Direction 0 A Direction 0 B Speed 40 m Speed 20 m Speed 40 m Speed 20 m

1 0◦ 1.77 1.75 2.625 1.963 0.53 0.34 2 22.5◦ 2.43 2.29 2.729 1.933 0.74 0.42 3 45◦ 3.4 2.6 3.006 2.301 1.29 0.93 4 67.5◦ 21.33 5.14 4.661 4.007 24.21 24.39 5 90◦ 40.77 39.65 5.003 4.357 55.62 56.89 6 112.5◦ 13.54 25.54 4.122 3.456 10.88 9.8 7 135◦ 4.71 8.37 3.534 3.086 2.56 2.63 8 157.5◦ 2.74 3.58 2.797 2.575 0.81 1 9 180◦ 1.77 2.42 2.567 2.286 0.45 0.5 10 202.5◦ 1.63 1.81 2.866 2.681 0.51 0.64 11 225◦ 1.77 1.87 3.762 3.495 1.33 1.57 12 247.5◦ 0.61 1.19 3.02 2.72 0.27 0.31 13 270◦ 0.59 0.47 2.455 2.085 0.17 0.16 14 292.5◦ 0.94 0,71 2.297 1.824 0,17 0,13 15 315◦ 0,94 1.23 2.196 1.572 0.17 0.09 16 337.5◦ 1.06 1.36 2.613 1.935 0.29 0.19

4.3. Vertical Wind Profile as a Function of Height The vertical wind profile as a function of height, determined using exponential and logarithmic functions, according to Equations (1) and (2), is presented in Figure 10. Although the wind resources measured at 40 m may be considered scarce, wind speeds reach values of approximately 6 m/s at 80 and 100 m, heights at which most modern wind turbines, whose power can be several MWs, operate. The variation of the exponential coefficient of Hellman’s law, according to the time of day and month, is presented in Figure 11. Appl. Sci. 2019, 9, x FOR PEER REVIEW 14 of 21

Table 3. Mean wind speed, frequency, and energy as function of direction. Source: the authors.

Mean (m/s) vs. ‘Direction Proportion of Total Frequency (%) 0 A’ Wind Energy (%) Sector Midpoint Speed 40 Speed 20 Direction 0 A Direction 0 B Speed 40 m Speed 20 m m m 1 0° 1.77 1.75 2.625 1.963 0.53 0.34 2 22.5° 2.43 2.29 2.729 1.933 0.74 0.42 3 45° 3.4 2.6 3.006 2.301 1.29 0.93 4 67.5° 21.33 5.14 4.661 4.007 24.21 24.39 5 90° 40.77 39.65 5.003 4.357 55.62 56.89 6 112.5° 13.54 25.54 4.122 3.456 10.88 9.8 7 135° 4.71 8.37 3.534 3.086 2.56 2.63 8 157.5° 2.74 3.58 2.797 2.575 0.81 1 9 180° 1.77 2.42 2.567 2.286 0.45 0.5 10 202.5° 1.63 1.81 2.866 2.681 0.51 0.64 11 225° 1.77 1.87 3.762 3.495 1.33 1.57 12 247.5° 0.61 1.19 3.02 2.72 0.27 0.31 13 270° 0.59 0.47 2.455 2.085 0.17 0.16 14 292.5° 0.94 0,71 2.297 1.824 0,17 0,13 15 315° 0,94 1.23 2.196 1.572 0.17 0.09 16 337.5° 1.06 1.36 2.613 1.935 0.29 0.19

4.3. Vertical Wind Profile as a Function of Height The vertical wind profile as a function of height, determined using exponential and logarithmic functions, according to Equations (1) and (2), is presented in Figure 10. Although the wind resources measured at 40 m may be considered scarce, wind speeds reach values of approximately 6 m/s at 80 and 100 m, heights at which most modern wind turbines, whose power can be several MWs, operate. TheAppl. variation Sci. 2019, 9 ,of 2923 the exponential coefficient of Hellman’s law, according to the time of day and month,14 of 20 is presented in Figure 11.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 15 of 21

FigureFigure 10.10 WindWind variation as a function of height above ground. Source: generated generated by by the the software. software.

Figure 11.11. Alpha coecoefficient,ﬃcient, accordingaccording to to the the month month of of the the year year and and time time of of day. day. Source: Source: generated generated by theby the software. software.

4.4. Short-Term Wind Variation, Gusts, and Turbulence Turbulence peaks in the early hours of the morning (see Figure 12), but the magnitude of this variable is nonsignificant because the wind speed is lower than the values found in areas considered turbulent (see Figure 13). The small values of turbulence occur in the opposite direction to the region where the highest values of wind speed are observed (see Figure 14). No short-term, monthly, or seasonal variations in wind direction were detected. As the maximum value observed during the study period was 8 m/s, no extreme wind speed calculations were made. Appl. Sci. 2019, 9, 2923 15 of 20

4.4. Short-Term Wind Variation, Gusts, and Turbulence Turbulence peaks in the early hours of the morning (see Figure 12), but the magnitude of this variable is nonsigniﬁcant because the wind speed is lower than the values found in areas considered turbulent (see Figure 13). The small values of turbulence occur in the opposite direction to the region where the highest values of wind speed are observed (see Figure 14). No short-term, monthly, or seasonal variations in wind direction were detected. As the maximum value observed during the study period was 8 m/s, no extreme wind speed calculations were made. Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 21 Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 21

Figure 12. Wind turbulence as a function of time of day. Source: generated by the software. FigureFigure 12. 12.Wind Wind turbulence turbulence as as a a function function ofof timetime ofof day. Source: generated generated by by the the software. software.

Figure 13. Comparison between wind speed and wind turbulence intensity. Source: generated by the Figure 13. Comparison between wind speed and wind turbulence intensity. Source: generated by the Figuresoftware. 13. Comparison between wind speed and wind turbulence intensity. Source: generated by software. the software. Appl. Sci. 2019, 9, 2923 16 of 20 Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 21 Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 21

FigureFigure 14. Representative14. Representative turbulence turbulence intensity intensity vs.vs. wind direction. direction. Source: Source: generated generated by the by thesoftware. software. Figure 14. Representative turbulence intensity vs. wind direction. Source: generated by the software. 4.5.4.5. Contour Contour Line Line Maps, Maps, Isotach Isotach Maps, Maps,and and WindWind TurbineTurbine Siting 4.5. Contour Line Maps, Isotach Maps, and Wind Turbine Siting A contourA contour line line map, map, isotach isotach mapmap atat 120120 m and contour lines lines spaced spaced every every 5 m 5 mfor for the the entire entire island,island, areA arecontour shown shown line in in Figures map,Figures isotach 15 15 and and map 16 16,, respectively. respectively.at 120 m and The contour topographical topographical lines spaced maps maps every of of the the5 site,m site, for in inwhichthe which entire the the island, are shown in Figures 15 and 16, respectively. The topographical maps of the site, in which the contourcontour lines lines and and the the available available measurement measurement device are are represented, represented, were were obtained obtained using using WindPRO WindPRO contour lines and the available measurement device are represented, were obtained using WindPRO 3.23.2 software software (downloaded (downloaded from from SRTM SRTM server). server). 3.2 software (downloaded from SRTM server).

Figure 15. Contour line and isotach map of Puná Island. Figure 15. Contour line and isotach map of Puná Island. Figure 15. Contour line and isotach map of Puná Island. Appl. Sci. 2019, 9, 2923 17 of 20

Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 21

9705000

Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 21 9700000 T.M. Puna 9705000

9695000

9700000 T.M. Puna 9690000

9695000

9685000 9690000

9680000 9685000

9675000 9680000

9670000 9675000

9665000 9670000 580000 585000 590000 595000 600000 605000 610000 615000 620000 Figure 16. Contour lines every 5 m. 9665000 Figure 16. Contour lines every 5 m. 580000 585000 590000 595000 600000 605000 610000 615000 620000 4.6.4.6. Wind Wind Turbine Turbine Selection Selection and and Assessed Assessed Wind Wind Power Power Figure 16. Contour lines every 5 m. AsAs the the wind wind resource resource has has a a low low speed, speed, low-speedlow-speed wind turbines turbines with with considerable considerable diameter diameter 4.6. Wind Turbine Selection and Assessed Wind Power sweepssweeps and and as as high high as as possible possible should should be be used used forfor energyenergy production. The The following following wind wind turbine turbine modelsmodelsAs meetthe meet wind these these resource criteria: criteria: has Gamesa Gamesaa low speed, G126-2.5G126-2.5 low-spee MW, MW,d windVestas Vestas turbines V110-2.0 V110-2.0 with MW, MW, considerable and and Vestas Vestas diameter V126-3.3 V126-3.3 MW. MW. Considering that a higher power wind turbine equates to a higher power generated at low wind Consideringsweeps and thatas high a higher as possible power should wind be turbine used for equates energy to production. a higher power The following generated wind at turbine low wind speeds, modelsspeeds, meet the these last ofcriteria: the aforementioned Gamesa G126-2.5 models MW, Vestaswas used V110-2.0 for the MW, simulation. and Vestas According V126-3.3 MW.to its power the last of the aforementioned models was used for the simulation. According to its power curve (see Consideringcurve (see thatFigure a higher 17), 179 power and 711wind kW turbine could eq beuates gene torated a higher at 5 andpower 6 m/s, generated respectively. at low Accordingwind to Figure 17), 179 and 711 kW could be generated at 5 and 6 m/s, respectively. According to simulations speeds,simulations the last based of the aforementionedon the software models results wa ofs usedthe grossfor the mean simulation. output According curve shown to its powerin Figure 18, basedcurvedepending on (see the Figure software on 17),wind 179 results speeds and 711 of and thekW grosscouldtime meanbeof geneday, outputrated this atwind curve 5 and turbine shown6 m/s, respectively. incould Figure generate 18 According, depending minimum to on and wind speedssimulationsmaximum and time based powers of on day, of the 200 this software and wind 850 turbine resultskW, respectively. of could the gross generate Ta meanble minimum4 outlinesoutput curve the and net shown maximum power in andFigure powers annual 18, of energy 200 and 850dependingproduction kW, respectively. on ofwind five Tablespeedswind4 turbineoutlinesand time models. the of netday, As power this the wind anddiameter annualturbine and energycould height generate production of the minimumturbine of ﬁve increases, and wind turbine the models.maximumproduction As powers the of diameterelectric of 200 andpower and 850 heightwillkW, berespectively. improved. of the turbine Ta ble 4 increases, outlines the the net production power and annual of electric energy power will beproduction improved. of five wind turbine models. As the diameter and height of the turbine increases, the production of electric power will be improved.

Figure 17. Power curve of the Vestas V126-3.3 MW wind turbine. Source: [15]. Figure 17. Power curve of the Vestas V126-3.3 MW wind turbine. Source: [15]. Figure 17. Power curve of the Vestas V126-3.3 MW wind turbine. Source: [15]. Appl. Sci. 2019, 9, 2923 18 of 20 Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 21

FigureFigure 18. 18. GrossGross mean mean power power output output of of the the Vestas Vestas V126- V126-3.33.3 MW wind turbine. Source: Source: generated generated by by thethe software. software.

TableTable 4. Power and energy of several wind turbine models. Source: Source: the the authors. authors.

Hub PercentagePercentage of of SimpleSimple Mean Mean Mean Mean of Monthly of Monthly Means Means Valid Hub HeightHeight Time atTime at Valid Time TurbineTurbine Time Wind SpeedWind Net Net AEP NCF Net Net AEP NCF Samples Zero ZeroRated RatedNet Power Net AEP NCF Net Power Net AEP NCF Samples (m/s) Speed Power Power PowerPowerPower Power(kW) (kWh/yr) (%) (kW) (kWh/yr) (%) (m/s) (kW) (kWh/yr) (%)(kW) (kWh/yr) (%) VestasVestas V126-3.3 MW V126-3.3 3191 4.94 8.26 0 466.2 4,083,967 14.13 466.2 4,083,960 14.13 IEC IIIA (90 m) 3191 4.94 8.26 0 466.2 4,083,967 14.13 466.2 4,083,960 14.13 GamesaMW G80-2.0 IEC MW 3191 4.82 13.33 0 175.1 1,533,961 8.76 175.1 1,533,962 8.76 IIIA(80 (90 m) m) GE 2.75-100Gamesa (80 m) 3191 4.82 13.99 0 212 1,856,809 7.71 212 1,856,807 7.71 SuzlonG80-2.0 S66-1.25 MW MW 3191 4.82 13.33 0 175.1 1,533,961 8.76 175.1 1,533,962 8.76 3191 4.82 13.89 0 104.5 915,088 8.36 104.5 915,084 8.36 (80(80 m) m) VestasGE 2.75-100V100-1.8 MW 31913191 4.824.82 13.998.83 00 212 292.3 1,856,8092,560,8487.71 16.11 212292.3 1,856,8072,560,851 7.7116.11 60(80 Hz m) (80 m) Suzlon S66-1.25 3191 4.82 13.89 0 104.5 915,088 8.36 104.5 915,084 8.36 5. ConclusionsMW (80 m) and Recommendations SeveralVestas software programs are publicly available for use, as are a significant number of studies V100-1.8 3191 4.82 8.83 0 292.3 2,560,848 16.11 292.3 2,560,851 16.11 on windMW 60 Hzresource assessment. Accordingly, in this type of project and especially because the study site was(80 m)an island, the most complex task is the installation of an anemometer tower. On Puná Island, the low-density wind resource is classified as marginal, with a well-defined variation by time of day and5. Conclusions a long-term andmean Recommendations speed of 4.8 m/s. Wind availability peaks in the afternoon hours, and the lowest valuesSeveral occur softwarein the morning programs hours. are Electricity publicly available production for use, might as arebe possible; a significant however, number due of to studies the low on meanwind wind resource speed, assessment. wind turbines Accordingly, with a considerable in this type ofblade project diameter, and especially equal to or because greater the than study 100 sitem, wouldwas an have island, to thebe used. most complexTherefore, task the is power the installation of such turbines of an anemometer would be on tower. the order On Pun of ámegawatts.Island, the Inlow-density this study, wind the use resource of the isVestas classified V126-3.3 as marginal, MW wind with turbine a well-defined model was variation simulated. by time However, of day this and typea long-term of wind meanturbine speed has an of estimated 4.8 m/s. Wind cost of availability one million peaks US dollars in the per afternoon megawatt, hours, which and might the lowest limit possible funding. Appl. Sci. 2019, 9, 2923 19 of 20 values occur in the morning hours. Electricity production might be possible; however, due to the low mean wind speed, wind turbines with a considerable blade diameter, equal to or greater than 100 m, would have to be used. Therefore, the power of such turbines would be on the order of megawatts. In this study, the use of the Vestas V126-3.3 MW wind turbine model was simulated. However, this type of wind turbine has an estimated cost of one million US dollars per megawatt, which might limit possible funding. Based on the mean power assessed in the simulation of this wind turbine model at low wind speed hours (see Figure5), to generate 2.2 MW, which is currently the power installed on the island, approximately 10 wind turbines would be necessary. An alternative would be to concurrently operate a lesser number of wind turbines with diesel generators that are currently installed on the island, particularly during hours of low wind availability. In addition, because solar radiation peaks during the midday hours and subsequently decreases to zero at night, energy could be generated in the morning using a solar farm, whereas wind turbines would operate in the afternoon, evening, and night. For all alternatives, an accumulator system capable of storing excess wind or solar energy would ideally be coupled to the electrical grid to supply electricity during peak demand hours, maintaining the diesel generators as backup. The use of wind resources would make it possible to replace not only diesel generators that provide electricity to the island but also a considerable number of stationary engines used for water pumping in shrimp farms. These machines contaminate the island environment not only with CO2 emissions but also with lubricant waste and discarded spare parts. For some of the island’s inhabitants, some of which have no access to electricity due to their distance from the electrical grid or lack of power installed, an alternative would be to install individual wind turbines. This option would also be valid for smaller islands in the area. To complete the research project, a feasibility study should be conducted to determine the following:

electricity demand of the island; • wind resources using an 80-m anemometer tower in several locations on the island, especially in • the region closest to the Pacific Ocean (Punta Carnero); civil engineering considerations (foundations for wind turbines, access roads, electrical grids); • type of coupling to the wind turbine grid (direct coupling or via accumulators); and • costs and funding. • Another key initiative would be to promote this study among different institutions to obtain financing for the next phase of the study and perhaps the development of a wind farm. The following institutions could be targeted: The Ministry of Energy, Ministry of the Environment, electric companies, and Guayaquil’s government. Development of a wind farm would not only supply clean and renewable energy to Puná Island but also create a research and training center.

Author Contributions: Conceptualization and methodology: Y.M., L.H.-C. and J.G.B.; software, validation and formal analysis: E.T.M., F.S. and A.L.E.; investigation: Y.M. and L.H.-C.; resources: J.G.B. and Y.M.; data curation: E.T.M. and Y.M.; writing—original draft preparation: Y.M.; writing—review and editing: L.H.-C.; supervision: O.D.-P. and L.H.-C.; project administration: Y.M. Funding: This research was funded by the University of Guayaquil (Universidad de Guayaquil—UG), by Compañia Eólica de Tierras Altas (CETASA), the University of Valladolid (Universidad de Valladolid—UVA) and the Research Center for Energy Resources and Consumption Research (Centro de Investigación de Recursos y Consumos Energéticos—CIRCE). Acknowledgments: The authors express their gratitude to Martha Heras and Miguel Ángel Hernández, CETASA, for their support in providing us with the equipment used in the tower. We also thank the anonymous reviewers who contributed to publishing the study. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appl. Sci. 2019, 9, 2923 20 of 20

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