energies

Article Understanding the Potential of Wind Farm Exploitation in Tropical Island Countries: A Case for Indonesia

Annas Fauzy 1, Cheng-Dar Yue 2,*, Chien-Cheng Tu 3 and Ta-Hui Lin 1

1 Department of Mechanical Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan; [email protected] (A.F.); [email protected] (T.-H.L.) 2 Department of Landscape Architecture, National Chiayi University, No. 300, Syuefu Rd., Chiayi 600, Taiwan 3 Research Center for Energy Technology and Strategy, National Cheng Kung University, No. 25, Xiaodong Rd., North Dist., Tainan City 704, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-5-2717763

Abstract: Countries worldwide must dramatically reduce their emissions to achieve the goal of limiting temperature increases in line with the Paris Agreement. Involving developing countries in global actions on emission reduction will greatly enhance the effectiveness of global warming mitigation. This study investigated the feasibility of establishing a wind farm at four onshore and three offshore sites in Indonesia. Installing wind turbines with the highest hub height, largest rotor diameter, and lowest cut-in and rated wind speed in an identified area off Wetar Island presented the highest time-based availability and a capacity factor of 46%, as well as the highest power-based availability at 76%. The levelized cost of electricity at 0.082 USD/kWh was comparable to that of power generated from fossil fuels, which ranges from 0.07 to 0.15 USD/kWh in Indonesia. Increasing



the feed-in-tariff for wind power from the current 0.08 USD/kWh would provide sufficient incentive
 for investment. Moving subsidies from fossil fuels toward renewables would facilitate the transition

Citation: Fauzy, A.; Yue, C.-D.; Tu, to low-carbon renewables without increasing the financial burden on the country. C.-C.; Lin, T.-H. Understanding the Potential of Wind Farm Exploitation Keywords: wind energy potential; weather research and forecasting; wind farm layout optimization; in Tropical Island Countries: A Case levelized cost of electricity; fossil fuel subsidies for Indonesia. Energies 2021, 14, 2652. https://doi.org/10.3390/en14092652

Academic Editor: Jesús 1. Introduction Manuel Riquelme-Santos With climatic catastrophes, such as floods and wildfires occurring worldwide, the United Nations Environmental Programme announced that dramatic strengthening of Received: 19 March 2021 nationally determined contributions (NDCs) to emission cuts is required in 2020. Countries Accepted: 2 May 2021 must increase their NDC ambitions threefold to achieve the “well below 2 ◦C goal” and Published: 5 May 2021 more than fivefold to achieve the 1.5 ◦C goal. To be in line with the Paris Agreement, emissions must drop 7.6% per year from 2020 to 2030 to reach the 1.5 ◦C goal and 2.7% per Publisher’s Note: MDPI stays neutral ◦ with regard to jurisdictional claims in year to reach the 2 C goal. Greater cuts will be required the longer action is delayed [1]. published maps and institutional affil- These alarming estimates indicate that more active emission cuts are required worldwide. iations. However, energy demand and fossil fuel use are expected to grow in the coming decades due to population and economic growth in many developing countries [2]. Promoting the use of renewable energy in these countries is essential to the global effort to mitigate global warming. A part of the global trend in energy transition, the Association of Southeast Asian Copyright: © 2021 by the authors. Nations (ASEAN) is devoted to the development of renewable energy in this region. It Licensee MDPI, Basel, Switzerland. This article is an open access article set a target of renewable energy comprising 23% of total primary energy supply by 2025, distributed under the terms and compared with 9.4% in 2014. However, current policies are projected to reach only a conditions of the Creative Commons renewable energy share of 17% by 2025, creating a 6% “gap” [3]. Attribution (CC BY) license (https:// Indonesia is the most populated country in ASEAN and the largest archipelago creativecommons.org/licenses/by/ country in the world, with more than 17,000 islands. Many remote islands are untouched 4.0/). by the national electricity grid. The development of renewable energy is especially crucial

Energies 2021, 14, 2652. https://doi.org/10.3390/en14092652 https://www.mdpi.com/journal/energies Energies 2021, 14, 2652 2 of 26

to meet the energy needs of these islands. Micro hydroelectric power systems are a possible renewable energy source for more remote islands because they are simple and affordable. Wind and photovoltaic energy sources offer solutions on islands with few water sources. According to the Indonesian Ministry of Energy and Mineral Resources, the national target for renewable energy is a share of 23% by 2025 and 31% by 2050 [4]. Developing renewable energy on remote islands is necessary to reach this goal. Indonesia is a tropical country located near the equator. Sunlight produces a nearly uniform temperature throughout the year with little wind variation. The region where this phenomenon occurs, known as the doldrums and the Intertropical Convergence Zone, is located only near the equator. In the doldrums, the prevailing trade winds of the northern hemisphere blow to the southwest and collide with the southern hemisphere’s northeast trade winds. The winds blow up into the atmosphere and return to the northern and southern hemispheres, and velocity distribution near the equator becomes low to marginal. The doldrums are typically located within the five-degree latitude of the equator [5]. Wind energy utilization in Indonesia was recently realized with the establishment of the first wind farm, Sidrap Wind Farm, located on South Sulawesi Island. Hardianto et al. indicated that the potential of wind power at a height of 43.2 m reaches 277.03 W/m2 in Puger Beach, East Java [6]. Martosaputro and Murti performed a preliminary analysis to investigate wind energy potential for Indonesia. It showed that wind energy resources are available on the south coast of Java Island, the eastern part of Indonesia, and the south part of Sulawesi Island [7]. Some parts of Sumantera, Kalimantan, and Papua, especially the islands, also have resources for wind energy that can be utilized to generate electricity, especially in rural and remote areas where access to electricity facilities remains limited. The development of and investment in wind farms depend on accurate wind resource evaluation, which requires reliable wind measurement data and appropriate methodologies. One of the most widely used models for evaluating onshore and offshore wind resources is the Weather Research and Forecasting (WRF) model [8–10]. This mesoscale model enables the application of various numerical and physical options to a set of atmospheric and geographic scales [11,12]. These capabilities of the WRF model enable the simulation of wind fields worldwide [8,13], making it a useful tool for conducting wind prospecting studies where meteorological data are unavailable. The performance of the WRF model has been examined in numerous studies. Olaofe [14] analyzed the spatial distributions of historical wind conditions by using the WRF model to quantify long-term changes in surface wind conditions off the African coast. These long-term historical wind conditions in time and space revealed that the WRF model is a reliable tool for replicating the coastal wind conditions of Africa. Salvaçáo and Guedes Soares [15] simulated a 10-year wind hindcast for the Iberian Peninsula coast with the WRF model at a spatial resolution of 9 and 3 km and a 6-hourly output. The amount of energy that can be generated by an energy conversion device, as well as the annual operating hours and capacity factors, were estimated and presented as wind resource maps. Comparisons with observational data revealed that the WRF model is a proficient tool for use in wind-power generation surveys in both coastal waters and the open ocean, and even when the model is run at a low spatial resolution. Prósper et al. [16] conducted a production forecast and validation study for a real onshore wind farm in a complex region with abundant wind resources in Galicia, northwestern Spain, by using WRF model simulations with high horizontal and vertical resolution. The re- sults revealed that the WRF model yields favorable wind power operational predictions for the wind farms. In addition to the necessity of using a sophisticated tool for evaluating wind resources, the layout of a wind farm affects the efficacy of its power generation. The optimization of a wind farm layout involves identifying the optimal locations for wind turbines in a wind farm to maximize the total energy output of the farm. Bansal and Farswan [17] used a biogeography-based approach to optimize wind farm layout by maximizing energy production and reducing the wake effect. Patel et al. [18] used a geometric pattern-based Energies 2021, 14, 2652 3 of 26

Energies 2021, 14, 2652 3 of 26 turbines in a wind farm to maximize the total energy output of the farm. Bansal and Farswan [17] used a biogeography-based approach to optimize wind farm layout by maximizing energy production and reducing the wake effect. Patel et al. [18] used a geometricapproach topattern optimize-base windd approach farm layout to optimize by maximizing wind farm the layout total powerby maximizing output of the a wind total powerfarm. Parkoutput and of Lawa wind [19 farm.] employed Park and a mathematicalLaw [19] employ optimizationed a mathematical scheme optimization to efficiently schemeoptimize to the efficiently locations optimize of wind turbinesthe locations for maximizing of wind turbines wind farm for powermaximizing production. wind farm Con- powersidering production. the emerging Con trendsidering of replacing the emerging fossil fuels trend with renewable of replacing energy fossil for fuels mitigating with renewableclimate change, energy maximizing for mitigating wind farm climate power change, production maximizing has become wind a major farm element power of windproduction farm optimization.has become a major element of wind farm optimization. In this context, this study investigated the feasibility of establishing wind farms in four onshore (Banyuwangi, Baron, Lebak, and Sukabumi) Sukabumi) and and three three offshore offshore (Jeneponto, Wetar Island, Island, and and Banten) Banten) locations locations in Indonesia. in Indonesia. The The selection selection of these of these locations locations was based was basedon a preliminary on a preliminary wind assessment wind assessment for Indonesia for Indonesia [7]. The wind[7]. The properties wind properties in these locations in these werelocations characterized were characterized to select the to most select suitable the most site suitable with the site best with wind the speed best distribution wind speed distributionand power production and power estimation. production A estimation. wind farm A design wind wasfarm then design simulated was then and simulated analyzed andfor eachanalyzed location for each to determine location to the determine optimal farmthe opti sizemal and farm layout. size and Finally, layout. an Finally, economic an economicanalysis was analysis performed was performed using discounted using cashdiscounted flow models cash flow to determine models to the determine most feasible the mostinvestment feasible scenario. investment scenario.

2. Methodology 2.1. Sites for Evaluation 2.1. Sites for Evaluation The sites for evaluation in this study were onshore sites in Banyuwangi, Baron, Suk- The sites for evaluation in this study were onshore sites in Banyuwangi, Baron, abumi, and Lebak in the south of Java Island, and offshore sites in Banten, Jeneponto, and Sukabumi, and Lebak in the south of Java Island, and offshore sites in Banten, Jeneponto, Wetar Island (Figure1). and Wetar Island (Figure 1).

Figure 1.1. WRFWRF mapmap ofof Indonesia Indonesia generated generated using using the the ERA-Interim ERA-Interim data data map map (2004–2015) (2004–2015) [20]. [20 The]. T locationshe locations of onshore of onshore sites sites in Banyuwangi, Baron, Lebak, and Sukabumi (circular points) and offshore sites in Jeneponto, Wetar Island, and in Banyuwangi, Baron, Lebak, and Sukabumi (circular points) and offshore sites in Jeneponto, Wetar Island, and Banten Banten (square points) are indicated. The colored contours denote horizontal wind speeds at a height of 100 m. (square points) are indicated. The colored contours denote horizontal wind speeds at a height of 100 m. 2.2. Wind Dataset 2.2. Wind Dataset WRF mesoscale maps were used as the wind data source for this study. Established WRF mesoscale maps were used as the wind data source for this study. Established in 2017 by EMB International A/S, Denmark, they are financed by the Environmental in 2017 by EMB International A/S, Denmark, they are financed by the Environmental Support Programme (ESP3)/Daninda in collaboration with the Indonesian Ministry of Support Programme (ESP3)/Daninda in collaboration with the Indonesian Ministry of Energy and Mineral Resources. The WRF dataset has a resolution of 0.029° (ca. 3 km) and Energy and Mineral Resources. The WRF dataset has a resolution of 0.029◦ (ca. 3 km) and is driven by ERA-Interim global data for the period 2004–2015 (Figure1). The maps contain weather data for all of Indonesia. WRF data were used because of their relatively high

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spatial resolution as well as their availability and quality; actual high-quality wind data for Indonesia were unavailable. The measurement heights were 10, 25, 50, 75, 100, 150, and 200 m. WRF wind data at 1-h time intervals at heights of 100 m were used in this analysis in accordance with the hub height of modern wind turbines.

2.3. Wind Characteristics Wind speed trends were identified using monthly averaged wind speeds from 2004 to 2015 to include speeds in rainy and dry seasons. Speeds at 100 m were then analyzed using a Weibull fit distribution estimation to determine annual wind speed distributions. The Weibull fit distribution parameters were compared to determine the wind speed characteristics of each site so that optimal potential sites could be identified. The energy production of a wind turbine was calculated using wind speed and the turbine power curve. The energy production status of a wind turbine was classified as below the cut-in wind speed, between the cut-in and rated wind speed, the rated wind speed, and the cut-out wind speed as follows:

Vw < Vi, Pw = 0Vi ≤ Vw < Vr, Pw = PiVr ≤ Vw < Vo, Pw = PrVo < Vw, Pw = 0

where Vw is wind speed, Vi is cut-in wind speed, Pw is power output, Vr is rated wind speed, Pi is power output interpolated from the power curve, Vo is cut-out wind speed, and Pr is rated power output. The analysis was performed using the wind data input into the turbine power curve, after which the power production was interpolated using the power curve. The annual total power production was then aggregated. Three types of wind turbines built by different manufacturers in the wind energy market were selected. The turbines are widely used by wind energy researchers for the middle wind speed region. The availability of power curves of wind turbines, which are often commercially confidential, was also considered. The technical specifications of the three commercial wind turbines selected for the study indicated that wind turbine C has the highest hub height, the largest rotor diameter, and the lowest cut-in and rated wind speed (Table1).

Table 1. Technical specifications of selected wind turbines.

Rotor Cut-Out Rated Hub Cut-In Wind Rated Wind Turbine IEC Class Diameter Wind Speed Output (kW) Height (m) Speed (m/s) Speed (m/s) (m) (m/s) A IIIA 1800 90 100 4 12 20 B IIB 2300 80 90 4 13 25 C IIIA 2000 93 114 2 10 25

2.4. WindSim WindSim is a wind farm design tool based on computational fluid dynamics and used to solve flow equations in body-fitted geometry. It employs a 3D Reynolds Averaged Navier–Stokes (RANS) solver and is appropriate for sites with complex climatology and terrain. The input data for the WindSim simulation was a terrain model that included information about terrain orography, roughness, and objects. Meteorological data from one or more sites inside the modeled area were also required. This study was aimed at identifying the best-case scenario for a wind farm on Wetar Island. Turbulence intensity was not discussed because it was not a priority of the study. The main work focused on using the outputs of WindSim (wind speed, wind direction, and turbulence intensity) to establish the wind turbine layout for each scenario and further determine which scenario was both technically and economically viable. Energies 2021, 14, 2652 5 of 26

Energies 2021, 14, 2652 using the outputs of WindSim (wind speed, wind direction, and turbulence intensity)5 of 26to establish the wind turbine layout for each scenario and further determine which scenario was both technically and economically viable.

2.5. Wind Farm Performance The effectiveness of the wind farms was evaluated evaluated for for time time-based-based availability, availability, power production-basedproduction-based availability, and capacity factor. factor. The capacity factor represents the amount of electricit electricityy produced relative to its turbine-ratedturbine-rated power capacity. At times, the turbine stops operating due to low wind speed conditions or for maintenance. Time-based turbine stops operating due to low wind speed conditions or for maintenance. Time-based availability describes the effective operation time of the turbine, as represented below: availability describes the effective operation time of the turbine, as represented below:

To At = ××100%100% Tt

where At is time-basedtime-based availability, TToo is turbine operating hours, andand Ttt is total hourshours inin one year. PowerPower-based-based availability is the ratio of energy produced by a wind farm to expected power production. It can be expressed as follows: = E × 100% Ap = × 100% Ex where Ap is the power-based availability, E is the power production of the wind farm, and where Ap is the power-based availability, E is the power production of the wind farm, and Ex is the expected power production of the wind farm calculated from the estimated power E is the expected power production of the wind farm calculated from the estimated power productionx of a single turbine using the WRF dataset multiplied by the number of turbines production of a single turbine using the WRF dataset multiplied by the number of turbines in the farm. in the farm.

2.6. Economic Analysis of Wind Farm Design An economic evaluation was performed to determine the investment value of the wind farm farm and and the the feasibility feasibility of of the the project. project. In this In this study, study, the thewin windd farm farm cost costmodel model was wasderived derived from from Cali Caliet al. et [21 al.], [who21], whosplit splitthe model the model into intocapital capital expenditures expenditures (CapEx) (CapEx) and operationaland operational expenditures expenditures (OpEx). (OpEx). An An investment investment indicator indicator was was also also included included in in the investment scenario. Wind farm scenarios were obtainedobtained fromfrom WindSimWindSim results.results.

3. W Windind C Characteristicsharacteristics of the Studied Sites 3.1. Wind Speed and Direction Indonesia has two major seasons, rainy and dry. Monsoon wind is a dominant factor in weather changes in the country. The monthly averaged wind speed from 20042004 toto 20152015 (AppendixA A)) indicatedindicated thatthat thethe drydry seasonseason betweenbetween JuneJune andand OctoberOctober had had aa higherhigher wind wind speed thanthan thethe rainyrainy seasonseason between between November November and and March. March. These These phenomena phenomena occur occur in allin allonshore onshore and and offshore offshore sites sites (Figure (Figure2). April 2). April to May to May is the is transitionthe transition from from the rainy the rainy to dry to dryseason. season. The The weather weather is varied is varied during during this this time. time.

Figure 2. Average wind speed (2004–2015)(2004–2015) for dry and rainy seasons in onshore ((leftleft)) andand offshoreoffshore ((rightright)) sites.sites.

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The wind roses for the individual sites indicated that the wind direction was Energies 2021domin, 14, 2652ated by southeast and east–southeast winds, followed by south–southwest and east6 of 26 Energies 2021, 14, 2652 6 of 26 winds (Figure 3).

The wind wind roses roses for for the the individual individual sites sites indicated indicated that thatthe wind the wind direction direction was domi- was nateddomin byated southeast by south andeast east–southeast and east–southeast winds, winds, followed followed by south–southwest by south–southwest and east and winds east (Figurewinds (3Figure). 3).

Figure 3. Wind roses in 2015 for the onshore Banyuwangi, Baron, Lebak, and Sukabumi sites (top, left to right) and offshore Wetar, Jeneponto, and BantenFigureFigure sites 3. Wind (bottom roses in, left 2015 to for right) the onshore . Banyuwangi, Baron, Lebak, andand Sukabumi sitessites ((toptop,, leftleft toto right) and offshore Wetar,Wetar, Jeneponto, Jeneponto, and and Banten Banten sites sites ( bottom(bottom,, left left to to right). right).

3.2. Comparisons of Wind3.2. Comparisons Speeds Among of Wind Sites Speeds Among Sites 3.2. Comparisons of Wind Speeds Among Sites The wind speed distributionThe wind speed in 2015distribution revealed in 2015 that revealed Banyuwangi that Banyuwangi had the hadhighest the highest mean mean onshore wind speedonshore (6.09The wind windm/s; speedFigure speed (6.09 distribution 4) m/s;and Figure Jeneponto in 20154) and revealed theJeneponto highest that the Banyuwangi offshore highest offshore speed had thespeed (8.5 highest1 (8.5 1 mean onshore wind speed (6.09 m/s; Figure4) and Jeneponto the highest offshore speed m/s; Figure 5; Tablem/s; 2). Figure The offshore5; Table 2 ).wind The offshore potential wind in potential both Jeneponto in both Jeneponto and Wetar and WetarIsland Island (8.51appeared m/s; to Figure be excellent.5; Table 2). The offshore wind potential in both Jeneponto and Wetar appeared to be excellent.Island appeared to be excellent.

Figure 4. WRF wind speed at 100 m for onshore sites in 2015.

Figure 4. WRF wind speedFigure at 4. 100WRF m wind for onshore speed at 100 sites m forin onshore2015. sites in 2015.

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Figure 5. WRF wind speed at 100 m for offshore sites in 2015. Figure 5. WRF wind speed at 100 m for offshore sites in 2015. Figure 5. WRF windTable speed 2. Weib at 100ull fit m distribution for offshore parameters sites in 2015.for the studied sites (2015).

TableParameter 2. Weibull fit distributionTable 2.BanyuwangiWeibull parameters fit distribution forBaron the parameters studiedLebak for sites theSukabumi studied(2015). sites (2015).Banten Jeneponto Wetar ScaleParameter c (m/s) Banyuwangi6.78 Baron6.68 6.75 Lebak Sukabumi5.90 Banten7.04 Jeneponto9.60 Wetar9.05 Parameter ShapeBanyuwangi k Baron1.55 Lebak 2.39Sukabumi 2.39 Banten2.19 Jeneponto2.46 2.41Wetar 2.61 Scale c (m/s) 6.78 6.68 6.75 5.90 7.04 9.60 9.05 Scale c (m/s) VarianceShape 6.78k 6.6815.951.55 6.75 2.396.95 5.907.10 2.39 6.34 2.197.04 2.468.089.60 2.4114.089.05 2.6110.95 Shape k Mean windVariance speed1.55 (m/s) 2.39 15.956.09 2.39 6.955.92 2.195.98 7.10 5.23 6.342.46 8.086.562.41 14.088.512.61 10.95 8.04 Mean wind speed (m/s) 6.09 5.92 5.98 5.23 6.56 8.51 8.04 Variance 15.95 6.95 7.10 6.34 8.08 14.08 10.95 The shape parameter (k) controls the width of the distribution. Higher values of k Mean wind speed (m/s) 6.09 indicateThe5.92 a shapenarrower parameter5.98 frequency (k) controls distribution5.23 the width—a6.56 steadier, of the distribution. less 8.51variable Higher wind8.04 [ values22]. Variance of k refersindicate to ho a narrowerw much the frequency wind speed distribution—a in the wind steadier, dataset lessdiffers variable from windthe mean [22]. wind Variance speed The shape parameterandrefers thus to from how(k) muchcontrolsall other the wind windthe width speed indata of the the in wind the distribution. datasetdataset. differs Variance Higher from and the valuest meanhe shape wind of parameter k speed indicate a narrowerhaveand frequency a thus strong from relation, distribution all other and wind a high— speeda steadier,shape data parameter in the less dataset. variable k indicates Variance wind a andsmall [22 the variance]. shape Variance parameter in the wind k refers to how muchdata.have the However, awind strong speed relation, the values in andthe for awind high k and shape dataset variance parameter differs in Jeneponto indicatesfrom the and a mean small Wetar variance wind Island speed in did the not wind seem todata. conform However, with the this values relation. for k Bothand variance the k values in Jeneponto and variance and Wetar of IslandJeneponto did not and seem Wetar and thus from all otherto conform wind with speed this data relation. in the Both dataset. the k values Variance and variance and ofthe Jeneponto shape parameter and Wetar Island Island were high. The reason for this phenomenon was the different wind conditions in have a strong relation,were and high. a high The reason shape for parameter this phenomenon k indicates was thea small different variance wind conditions in the wind in rainy rainy and dry seasons. A high wind speed trend is evident in dry seasons, and a low one and dry seasons. A high wind speed trend is evident in dry seasons, and a low one is data. However, theis valuesevident forin rainy k and seasons. variance The in large Jeneponto difference and between Wetar them Island resulted did notin a seemhigh variance to conform with thisevident relation. in rainy Both seasons. the Thek values large difference and variance between themof Jeneponto resulted in anda high Wetar variance in inthese these regions. regions. The The wind wind speed speed distribution distribution in Banyuwangi, in Banyuwangi, Jeneponto, Jeneponto and Wetar, an Islandd Wetar Island were high. IslandTheindicated reason indicated that for each that this haseach phenomenon both has aboth higher a higher andwas a the and lower different a lower peak (Figure peak wind (Figure6), conditions indicating 6), indicating that in the that rainy and dry seasons.theWeibull Weibull A high distribution distribution wind didspeed notdid fittrendnot these fit isthese sites evident wellsites because wellin dry because the seasons, distinct the distinct windand a climates lowwind one climates in rainy in is evident in rainyrainy seasons.and dryand seasons dryThe seasons large are better difference are representedbetter representedbetween by a double-peaked them by a resulteddouble distribution.-peaked in a high distribution. Another variance possible Another in these regions. possibleThereason wind is reason that speed the is Weibull that distribution the curve Weibull is an in curve approximation Banyuwangi, is an approximation of Jeneponto the true wind of, thean speed d true Wetar frequency wind speed frequencydistribution. distribution. Although theAlthough real speed the distributionsreal speed distributions at many sites at fit many the Weibull sites fit curve the Weibull well, Island indicated that each has both a higher and a lower peak (Figure 6), indicating that curvethe fit well, is poor the in fit some is poor sites. in some sites. the Weibull distribution did not fit these sites well because the distinct wind climates in rainy and dry seasons are better represented by a double-peaked distribution. Another possible reason is that the Weibull curve is an approximation of the true wind speed frequency distribution. Although the real speed distributions at many sites fit the Weibull curve well, the fit is poor in some sites.

FigureFigure 6. 6. WindWind speed speed distribution distribution for Banyuwangi, Wetar, Wetar, and and Jeneponto. Jeneponto.

Differences in wind distribution in the rainy and dry seasons were supplementary to the main aims of the study. The average wind speeds in the rainy and dry seasons are discussed in Section 3.1, and the wind characteristics (i.e., wind speed distributions in rainy and dry seasons) of the proposed regions were discussed in the current section. The Figure 6. Wind speed distribution for Banyuwangi, Wetar, and Jeneponto.

Differences in wind distribution in the rainy and dry seasons were supplementary to the main aims of the study. The average wind speeds in the rainy and dry seasons are discussed in Section 3.1, and the wind characteristics (i.e., wind speed distributions in rainy and dry seasons) of the proposed regions were discussed in the current section. The

Energies 2021, 14, 2652 8 of 26

Differences in wind distribution in the rainy and dry seasons were supplementary

Energies 2021, 14, 2652 to the main aims of the study. The average wind speeds in the rainy and dry seasons8 of 26 are discussed in Section 3.1, and the wind characteristics (i.e., wind speed distributions in rainy and dry seasons) of the proposed regions were discussed in the current section. The energy production for each region was estimated as a preliminary result to select the region with energy production for each region was estimated as a preliminary result to select the the best prospects for the establishment of a wind farm. Therefore, a power production region with the best prospects for the establishment of a wind farm. Therefore, a power analysis was conducted. production analysis was conducted. 3.3. Estimated Power Production 3.3. Estimated Power Production Power production was estimated using the 2015 dataset and the power curves of the Power production was estimated using the 2015 dataset and the power curves of the selected turbines for each site (Figure7). Wind data at 100 m were input into the three selected turbines for each site (Figure 7). Wind data at 100 m were input into the three curves. The estimated monthly average power production is depicted in Figure8. curves. The estimated monthly average power production is depicted in Figure 8.

FigureFigure 7. Power 7. Power curves curves of selected of selected wind wind turbines turbines A (top A), ( topB (middle), B (middle), and ),C and(bottom C (bottom). ).

Energies 2021, 14, 2652 9 of 26 Energies 2021, 14, 2652 9 of 26

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Figure 8. MMonthlyonthly av averageerage power power production production estimated estimated using using the the power power curve of three selected wind turbinesturbines in Banyuwangi ( (leftleft)) and andFigure Wetar Wetar 8. M Island Islandonthly (right av(righterage) )(2015 (2015power data). data). production The The red red-coloredestimated-colored region using region theindicates indicatespower the curve therainy of rainy threeseason, season, selected blueblue indicates wind indi- turbine s in the dry season, and yellow indicates the transition seasons between the dry and rainy season and vice versa. cates the dry season,Banyuwangi and yellow (left indicates) and Wetar the transition Island (right seasons) (2015 betweendata). The the red dry-colored and region rainy seasonindicates and the vice rainy versa. season, blue indicates the dry season, and yellow indicates the transition seasons between the dry and rainy season and vice versa. The results were comparable to wind wind speed speed trends; trends; denser denser power power production production occurred in the dry Theseason results rather were than comparable in the rainy to season. wind speed W Windind trends; turbine denser C presented power production the occurred in the dry season rather than in the rainy season. Wind turbine C presented the highest power power production production and and capacity capacity factor factor in in all all sites, sites, followed followed by by turbine turbiness A Aand and B highest power production and capacity factor in all sites, followed by turbines A and B (FigureB (Figure 9).9 ).This This can can be beattributed attributed to the to the fact fact that that turbine turbine C has C hasthe thelargest largest diameter diameter (114 (Figure 9). This can be attributed to the fact that turbine C has the largest diameter (114 m) of the three (Table 1). Turbines with larger diameters have a larger turbine sweep area, (114 m) of the threem) of (Tablethe three1). (Table Turbines 1). Turbines with larger with diameterslarger diameters have have a larger a larger turbine turbine sweep sweep area, resultingarea, resulting in higher inresulting higher power in power higherproduction. production. power Anotherproduction. Another possible Another possible reason possible reason for reason the for difference thefor the difference difference is that is is that turbinethat turbine C has C the hasturbine lowest the C lowest hascut the-in cut-in lowestand rated andcut- inwind rated and speed,rated wind wind speed,enabling speed, enabling it enabling to produce it it to to produce producemore power. moremore power. Becausepower. Becausethe goalBecause of the the goal thestudy ofgoal thewas of studythe to studydetermine was was to to determinethe determine best scenario the the best best for scenario scenariobuilding for building fora wind building farma wind farm rathera wind than farm to rathercomparerather than the toto performances compare compare the the performances of performances wind turbines of wind ofwith turbines wind similar turbines with specifications, similar with specifications, similar the the resultsspecifications, averagedresults the for results theaveraged same averaged for rotor the for diameter same the rotor same and diameter rotor thediameter impact and the of and impact the the wind of impact the were wind of thenot were not discussed.wind were notdiscussed. discussed.

1200 80 1200 Turbine A energy production 80 Turbine A energy productionTurbine B energy production Turbine B energy production 70 1000 Turbine C energy production 70 1000 Turbine C energy productionTurbine A capacity factor Turbine A capacityTurbine factor B capacity factor 60 60 Turbine800 B capacityTurbine factor C capacity factor 800 Turbine C capacity factor 50 (%)

50 (%) 600 40 600 40 30

400 factorCapacity 30 Energy Energy production (MWh) 20

400 factorCapacity 200 Energy Energy production (MWh) 20 10 200 0 10 0 Banyuwangi Baron Sukabumi Lebak Wetar Jeneponto Banten 0 0 BanyuwangiFigureBaron 9. PowerFigure productionSukabumi 9. Power andproductionLebak capacity and factor capaciWetar ofty thefactorJeneponto turbines of the turbines at allBanten sites. at all sites.

Among the sites,Among Wetar the sites, Island Wetar had Island the highesthad the highest estimated estimated power power production production at at 1190 Figure 9. Power production and capacity factor of the turbines at all sites. 1190 MWh perMWh year andper year a capacity and a capacity factor factor of 67% of 67% with with turbine turbine C, C, meaning meaning that that it had the the greatest greatest potentialpotential for wind for energy wind energy production. production. Wind farmWind design farm design and analysis and analysis were thus were thus Among the sites, Wetar Island had the highest estimated power production at 1190 performed for Wetarperformed Island for Wetar by using Island WindSim. by using WindSim. MWh per year and a capacity factor of 67% with turbine C, meaning that it had the greatest potential4. Wind Farm for windDesign4. Wind energy andFarm Analysis D production.esign and for A Wetarnalysis Wind Island for farm Wetar design Island and analysis were thus performedIn this for section, Wetar the Island building by using of terrain WindSim. and wind field modules for Wetar Island by using WindSim is discussed, and the wind farm design and power production simulation 4. Wind Farm Design and Analysis for Wetar Island are explained.

Energies 2021, 14, 2652 10 of 26

4.1. Terrain Setup The site terrain was created in the WindSim Terrain Editor by using an orography map and a roughness map. The orography and roughness were described with counter lines on the maps, where each line provided information about the terrain. ASTER GDEM v2 Worldwide Elevation Data is a global orography map with an arc-second resolution (38.219 m). In this study, a Wetar Island orography map was derived from ASTER GDEM v2 Worldwide Elevation Data via Global Mapper software [23]. The terrain elevation of Wetar Island is depicted in AppendixB. The roughness map was created using WindSim Terrain Editor by classifying the terrain based on roughness length (AppendixC). Globeland30 is a land cover map dataset that covers the earth from 80 N to 80 S in the baseline year of 2010. Global Mapper software was used to classify the Globeland30 map into a roughness map that was then merged with the orography map to create a terrain file. The classified landscapes and roughness lengths from Globeland30 are listed in Table3.

Table 3. Roughness length provided by Globeland30.

Landscape Roughness Length (m) Cultivated Land 0.050 Forest 0.180 Grassland 0.150 Shrubland 0.040 Wetland 0.003 Water Bodies 0.003 Tundra 0.200 Artificial Surfaces 0.150 Bare land 0.030 Permanent Snow and Ice 0.001 Sea 0.002

Sentinel-2 is optical imagery provided by the European Space Agency with a high spatial resolution (up to 10 m) for land services. The Sentinel-2 maps of Wetar Island were used to classify land use based on roughness lengths (Table3). The orography and roughness maps were then combined into a GWS file for import into WindSim for wind farm design and analysis. Defining the simulation domain is necessary for WindSim settings to constrain the simulation. The aim of the Terrain module is to generate a 3D model of the terrain and define mesh settings for the terrain model. The mesh of the terrain calculated in this study Energies 2021, 14, 2652 11 of 26 is presented in Figure 10 and Table4. The grid was refined, and the refinement area was centered in the northeast of Wetar Island.

Figure 10. Terrain modelmodel gridgrid ofof WetarWetar IslandIsland withwith thethe windwind datadata sourcesource location.location.

Table 4. Mesh of terrain.

x y z Total Grid spacing, min–max (m) 54.1–1179.5 54.4–648.0 Variable - Number of cells 217 230 20 998,200

4.2. Boundary Conditions The Wind Fields module works by simulating wind fields by applying RANS equations to the 3D model mesh. The height of the boundary layer was set to 500 m, and the speed above the boundary layer was set to 10 m/s. Air density was set to 1.225 kg/m3, and the standard k- model was selected as the turbulence model [24]. The general collocated velocity method was used as the solver. For a more detailed result, convergence monitoring was set in the densest cell location because the default setting was set in the center of the terrain. The number of iterations required for convergence of the spot values varied between 80 and 150, as shown in Appendix D. Usually, increasing the number of iterations was required for the residual values to reach an acceptable convergence level, which occurred after approximately 80 to 120 iterations. The variables were scaled according to the minimum and maximum values listed to the right in Appendix D. The minimum (125) and maximum (206) number of spot value iterations for a whole sector are illustrated in Appendix D.

4.3. Wind Farm Layout Optimization The wind dataset was derived and processed from the WRF dataset at 100-m heights. The data contained the wind speed and direction, temperature, humidity, and other weather property values. The modified wind data were input into the object module as data gained from a virtual met mast located at −7.6° latitude and 126.75° longitude. The wind resource module provides results such as mean wind speed, power density, Weibull scale and shape parameters, and wake deficit. The Jensen wake model was used because of its simplicity and relatively high accuracy [25], and the heights were set to 80, 90, and 93 m according to wind turbine specifications. Energy maps were generated using the Park Optimizer module in WindSim by combining wind speed data with the power curves of the selected turbines, as illustrated in Figure 11. Energy maps were then used as input for wind farm layout optimization.

Energies 2021, 14, 2652 11 of 26

Table 4. Mesh of terrain.

x y z Total Grid spacing, min–max (m) 54.1–1179.5 54.4–648.0 Variable - Number of cells 217 230 20 998,200

4.2. Boundary Conditions The Wind Fields module works by simulating wind fields by applying RANS equa- tions to the 3D model mesh. The height of the boundary layer was set to 500 m, and the speed above the boundary layer was set to 10 m/s. Air density was set to 1.225 kg/m3, and the standard k-e model was selected as the turbulence model [24]. The general collocated velocity method was used as the solver. For a more detailed result, convergence monitoring was set in the densest cell location because the default setting was set in the center of the terrain. The number of iterations required for convergence of the spot values varied between 80 and 150, as shown in AppendixD. Usually, increasing the number of iterations was required for the residual values to reach an acceptable convergence level, which oc- curred after approximately 80 to 120 iterations. The variables were scaled according to the minimum and maximum values listed to the right in AppendixD. The minimum (125) and maximum (206) number of spot value iterations for a whole sector are illustrated in AppendixD.

4.3. Wind Farm Layout Optimization The wind dataset was derived and processed from the WRF dataset at 100-m heights. The data contained the wind speed and direction, temperature, humidity, and other weather property values. The modified wind data were input into the object module as data gained from a virtual met mast located at −7.6◦ latitude and 126.75◦ longitude. The wind resource module provides results such as mean wind speed, power density, Weibull scale and shape parameters, and wake deficit. The Jensen wake model was used because of its simplicity and relatively high accuracy [25], and the heights were set to 80, 90, and 93 m according to wind turbine specifications. Energy maps were generated using the Park Optimizer module in WindSim by combining wind speed data with the power curves of the selected turbines, as illustrated in Figure 11. Energy maps were then used as input for wind farm layout optimization. The wind farm layout was optimized using the module Park Optimizer of WindSim. Multiple wind farm sizes were tested to assess how wind farm size influenced power pro- duction and to determine the optimal size. Wind farm sizes were classified as three variant areas: Area-1 (8.174 × 3.35 km2), Area-2 (5.5 × 3.35 km2), and Area-3 (4.1 × 3.35 km2). The size decreased from the original size in Area-1 to half that in Area-2 and one-third in Area-3. The selected wind turbine power curves and wind resource maps were input into the wind resource module, and nine variations were simulated with three wind farm sizes and three types of wind turbines. The constraints were defined using parameters such as terrain inclination, flow incli- nation, shear exponent, turbulence intensity, and extreme wind. The minimal and maximal number of turbines was set to 1 and 80, respectively, with the area defined in Area-1 divided by the largest turbine diameter. The inter-turbine distance was set to 8 and 5 times the rotor diameter. The layout produced using WindSim Park Optimizer indicated that the layout typically produced a line perpendicular to the prevailing wind direction (Figure 12). An asymmetric layout order occurred because of the increase in the number of turbines. The number of turbines generated by the WindSim Park Optimizer is summarized in Table5. The turbine B required the highest number of turbines for each area. The optimized wind farm layouts are illustrated in Figure 11. EnergiesEnergies 20212021, 14, 14, 2652, 2652 12 of12 26 of 26

FigureFigure 11. 11. EnergyEnergy maps maps and and o optimizedptimized wind farmfarm layoutslayouts offoff Wetar Wetar Island Island using using turbine turbine A A (top (top), ), turbineturbine B B ( (middlemiddle), and turbineturbineC C ( bottom(bottom)) in in Area-1, Area-1 Area-2,, Area- and2, and Area-3, Area respectively.-3, respectively.

The wind farm layout was optimized using the module Park Optimizer of WindSim. Multiple wind farm sizes were tested to assess how wind farm size influenced power production and to determine the optimal size. Wind farm sizes were classified as three variant areas: Area-1 (8.174 × 3.35 km2), Area-2 (5.5 × 3.35 km2), and Area-3 (4.1 × 3.35 km2).

Energies 2021, 14, 2652 13 of 26

The size decreased from the original size in Area-1 to half that in Area-2 and one-third in Area-3. The selected wind turbine power curves and wind resource maps were input into the wind resource module, and nine variations were simulated with three wind farm sizes and three types of wind turbines. The constraints were defined using parameters such as terrain inclination, flow inclination, shear exponent, turbulence intensity, and extreme wind. The minimal and maximal number of turbines was set to 1 and 80, respectively, with the area defined in Area-1 divided by the largest turbine diameter. The inter-turbine distance was set to 8 and 5 times the rotor diameter. The layout produced using WindSim Park Optimizer indicated that the layout typically produced a line perpendicular to the prevailing wind direction (Figure 12). An asymmetric layout order occurred because of the increase in the number of turbines. The number of turbines generated by the WindSim Park Optimizer is Energies 2021, 14, 2652 summarized in Table 5. The turbine B required the highest number of13 ofturbines 26 for each area. The optimized wind farm layouts are illustrated in Figure 11.

FigureFigure 12.12.Symmetric Symmetric (left ()left and) asymmetricand asymmetric (right) wind (right farm) wind layout farm off Wetar layout Island. off Wetar Island.

TableTable 5. 5.Number Number of turbinesof turbines in a wind in a farm wind off farm Wetar off Island Wetar calculated Island by calculated WindSim Park by Optimizer. WindSim Park Optimizer.Area Turbine A Turbine B Turbine C Area1 61Turbine A 75Turbine B 48 Turbine C 2 42 50 35 31 3261 3975 27 48 2 42 50 35 4.4. Power Production3 Simulations 32 39 27 Power estimation refers to calculating power production by combining an hourly 4.4.average Power wind Production dataset with Simulations a power curve. Annual energy production (AEP) is the total hourly power production in 1 year. Power production was estimated by WindSim for all ninePower variations estimation based on refers layout tooptimizations. calculating Wind power farm production layouts for Area-1, by combining -2, and an hourly average-3 indicated wind that dataset turbine numberswith a power decreased curve. with farmAnnual area energy (Figure 11 production). The results (AEP) of is the total hourlythe power power production production estimation in 1 indicatedyear. Power no significant production difference was estimated in annual powerby WindSim for all production among the turbines. nine Thevariations wake losses based in each on layout layout calculated optimizations. by WindSim Wind indicated farm that layouts turbine Bfor consis- Area-1, -2, and -3 indicatedtently had thethat highest turbine wake number loss, withs decreased a higher wake with loss (14%)farm inarea every (Figure park layout 11). thanThe results of the powerthe other production turbines (9%). estimation Because turbine indicated B had the smallestno significant diameter, the difference Park Optimizer in annual power productionproduced a higher among number the turbines. of turbines, which increased wake loss in the area. Wind farm performance was evaluated for capacity factor and time-based availability, and power-basedThe wake losses availability. in each Turbine layout C had calculated the highest by time-based WindSim availability indicated and that turbine B consistentlycapacity factor had (46% the in highest Area-1), wake followed loss, by with turbine a higher A (42% wake in Area-3) loss and(14%) turbine in every B park layout than(27% in the Area-3). other Turbine turbines C also (9%). had the Because highest power-based turbine B had availability the smallest (76% in Area-1), diameter, the Park Optimizerfollowed by produced turbine A (73% a higher in Area-3) number and turbine of turbines, B (62% in which Area-3). increased Turbine C appearedwake loss in the area. as theWind most suitable farm wind performance turbine for the was wind evaluated farm on Wetar for Island. capacity The relatively factor low and time-based cut-in wind speed (2 m/s) and rated wind speed (10 m/s) of turbine C allow this turbine availabilityto work efficiently., and Using power the- turbinebased C availability. in Area-1 represented Turbine the best C had performance the highest from time-based availabilitythe perspective and of energycapacity production. factor (46% in Area-1), followed by turbine A (42% in Area-3) and turbine B (27% in Area-3). Turbine C also had the highest power-based availability 5. Economic Analysis of Wind Farm Investment (76% in Area-1), followed by turbine A (73% in Area-3) and turbine B (62% in Area-3). An economic evaluation of wind farm investment is conducted in this section through Turbinean analysis C of appeared investment as cost, the investment most suitable indicators, wind and turbine their implications for the forwind energy farm policy. on Wetar Island. The relatively low cut-in wind speed (2 m/s) and rated wind speed (10 m/s) of turbine C allow5.1. Wind this Farm turbine Investment to work Cost efficiently. Using the turbine C in Area-1 represented the best performanceThe analysis from of wind the farmperspective investment of cost energy in this production. study was based on the assumption that the facilities were imported from Europe because Indonesia does not yet have a wind turbine manufacturer. Using the European data may provide adequate initial values to estimate the cost of each part of the wind farm. In addition, the following cost estimations (based on European data) may provide a basis for determining which scenario will be more economically viable for Wetar Island. Investment cost comprises capital and operational expenditures. Capital expenditure involves the costs of turbines, the support system, the electrical system, and project devel- Energies 2021, 14, 2652 14 of 26

opment and management. The cost of a wind turbine with 2 to 5 MW capacity, including transportation and installation costs, can be estimated as follows [21]:

3 Cturbine = 3.245·10 ln(P) − 412.72 [k€]

where Cturbine is the cost of the wind turbine, and P is the capacity of the wind turbine. Support system (foundation and tower) costs include manufacturing, transportation, and installation costs. The cost of transportation and installation is assumed to be 50% of the manufacturing cost of the overall system [21,26]. The support system cost can be calculated as follows: D C = 480·P(1 + 0.02(d − 8))(1 + 0.8·10−6(h( )2 − 105)) [k€/turbine] support 2

where Csupport is the cost of the support system of the turbine, d is sea depth (m), h is hub height (m), and D is rotor diameter (m). The cost is valid for installing wind turbines to a sea depth of 45 m and when soil properties are neglected. The electrical system functions to distribute energy generated at the wind farm to the shore area. The system is built from various components. The cost of the electrical system component ranges from 10% to 30% of the manufacturing cost depending on the electrical design and distance to the shore [21]. Collection system costs include the cost of the submarine cable used to deliver power from turbines to onshore substations at medium and high voltages. The 3-core XLPE (cross-linked polyethylene) insulated cables were selected as the collection system cable. The cost of these cables usually changes with unit length (km). The XLPE cable calculation and costs can be calculated using the turbine power rating and system voltage level as follows [21]: γ·In 5 Ccable/km = α + β × e 10 [k€/km]

where Ccable/km is cable cost per kilometer, α = 52.08, β = 75.51, γ = 234.34 are coefficients of different cable voltage levels, and In is cable capacity. The radial, star, and circle are common electrical layouts for wind farms. In this case, the radial electrical layout was chosen due to the number of turbines, which varied from 4 to 12 for each line depending on the layout. A maximum of 12 turbines was determined by the maximum cable capacity of the XLPE cable type [21]. Total cable cost can be calculated as follows:

Ccable = Ccable/km mlcable [k€]

where lcable is cable length in kilometers. A 40-m cable is recommended as a supplementary cable extension [27]. Offshore cables must be buried for safety reasons and to prevent external damage. The cost of burying cable changes with unit length (km). Following Quinonez-Varela et al. [28], the cost of burying cable is as follows:

Cburying = 273 3 lcable [k€]

Onshore substation components mainly consist of transformers, high voltage busbars, switch gears, and backup generators. The cost of a substation is a function of wind farm capacity. The cost of a substation is 50 k€/MW [27]. Power factor correction devices, such as shunt reactors, static VAR compensators, and STATCOMs, are necessary to increase power efficiency during electricity distribution. The cost model was calculated as follows [27]:

 =  Cshunt−reactor 2.556 k€/MVAr CSVC = 6.39 k€ + 63.9 k€/MVAr  CSTATCOM = 128 k€/MVAr

Total substation cost can therefore be calculated as follows:

Csubstation = 50 [k€]/MW + Cshunt−reactor + CSVC + CSTATCOM Energies 2021, 14, 2652 15 of 26

The transmission line is the cable connecting the collection line to a substation. Because the cable must transfer electricity from the turbine, this price is different from that for the collection cable system. The transmission line cost is 673.6 k€/km for 630 − mm2 and 150 − kV based on Indonesia’s transmission line standards. Grid connection cost is considered a function of installed wind power [27] and was calculated as follows [21]:

1.66 CGC = 8.047 .0P

where CGC is grid connection cost and P is installed capacity. Project development, management, and other costs vary depending on project details. The estimation of this cost usually includes insurance, employee salaries, and engineering design of approximately 280.38 USD/MW [21]. The operational expenditure of a wind farm covers operational and maintenance costs, including administrative costs, insurance premiums, and royalties. This cost is usually estimated at 1.9% of the CapEx per year over a 20-year farm life-span [21]. The capital and operational expenditures for each scenario were calculated to deter- mine which scenario is the most economically viable. The cost breakdown of the wind farm indicated that the highest total CapEx for Area-1 was for the turbine B layout at approximately 539 million USD, whereas that for Area-2 and Area-3 was for the turbine Energies 2021, 14, 2652 16 of 26 B at 390 million USD and 286 million USD, respectively (Table6). Turbine and support system costs comprised most of the total CapEx costs, with values ranging from 32% to 42% for turbine cost and 28% to 29% for support system cost. The cost for the electrical system Buried cable 21,883.0was divided24,450.6 into 19,769.2 collection 15,861.2 system cost,20,703.9 onshore 15,004.5 substation 12,907.3 cost, transmission 11,106.7 12,275.8 line, and Onshore substation 11,199.3grid connection.15,427.0 The10,205.3 main cost7729 for.9the 10,320.6 electrical system7441.9 was the5902 cable.8 cost.8054 This.6 cost5741 can.9 be Transmission line 36,656.6divided 40,756.1 into the cable36,845.5 used for38,056.2 each turbine52,028.7 (collection 37,471.0 system 36,146.2 cable) and32,647.4 the transmission 36,759.8 Grid connection 28.5line. The42.8 cable length33.9 was estimated28.5 by calculating42.8 33.9 the distance28.5 for each42.8 turbine and33.9 then Project development 30,785.7adding 48,365.6 40 m for 26,916.5 supplementary 21,196.7 use. 32,243.7 The radial 19,626.6 layout was16,149.9 chosen 25,150.1 because it15,140.5 is more Total 348,508.2economical 538,996.7 than 326,886.7 the ring or252,299.6 star layout 389,921.4 [28]. The 248,397.4 wind farm 200,014.7 cabling 285,947.9 layout is presented199,681.3 OpEx (per year) 6.6in Figure10 13.2. The highest6.2 cost4.8 in the electrical7.4 system4.7 was the3.8 transmission 5.4 line,3.8 which dependeda Average on cable sea depth length was and set the at 25 diameter m for Wetar between Island. cables.

Figure 13. Wind farmfarm cablingcabling for for wind wind turbine turbine A A in in Area-1. Area-1. The The blue blue lines lines represent represent collection collection system cables,system andcables, the blackand the lines black represent lines represent transmission transmission lines. The lines. square The represents square represents the onshore the substation. onshore substation.

5.2. Investment Indicator The NPV (net present value), LCOE (Levelized Cost of Electricity), and DPBP (Discounted Payback Period) were used in this study to evaluate the wind farm investment. NPV represents the difference between the present value of cash inflows and the present value of cash outflows over a period of time. Positive NPV values represent economic viability [21]. NPV can be expressed by : ℎ() NPV = − + (1 + )

where is the total capital expenditure, is the lifespan of the wind farm, and is the annual discount rate. LCOE represents the offshore wind farm net present value divided by the wind farm lifetime. LCOE can be calculated as follows: () + (t) ∑ (1 + ) LCOE = AEP ∑ (1 + )

where () is the operational expenditure of wind farms for the year of , the netAEP is the total estimated energy production for one year. DPBP represents the time required for the cumulative profit to equal the cumulative cost, considering the discount rate. DPBP is applied when the discount rate is greater than zero. DPBP can be calculated as follows:

1 × 1 − ℎ DPBP = (1 + )

Energies 2021, 14, 2652 16 of 26

Table 6. Cost breakdown of wind farms off Wetar Island by area and turbine (all costs in thousands of USD).

Area 1 2 3 Type of Turbine A B C A B C A B C CapEx Wind turbine 121,716.6 229,293.3 117,685.6 83,804.9 152,862.2 85,812.5 63,851.4 119,232.5 66,198.2 Support system a 103,710.4 155,467.7 95,764.0 71,407.2 103,645.1 69,827.9 54,405.5 80,843.2 53,867.2 Electrical system Collection system Inner cable 22,528.1 25,193.7 19,666.6 14,215.0 18,074.3 13,179.2 10,623.2 8870.6 9663.9 Buried cable 21,883.0 24,450.6 19,769.2 15,861.2 20,703.9 15,004.5 12,907.3 11,106.7 12,275.8 Onshore substation 11,199.3 15,427.0 10,205.3 7729.9 10,320.6 7441.9 5902.8 8054.6 5741.9 Transmission line 36,656.6 40,756.1 36,845.5 38,056.2 52,028.7 37,471.0 36,146.2 32,647.4 36,759.8 Grid connection 28.5 42.8 33.9 28.5 42.8 33.9 28.5 42.8 33.9 Project development 30,785.7 48,365.6 26,916.5 21,196.7 32,243.7 19,626.6 16,149.9 25,150.1 15,140.5 Total 348,508.2 538,996.7 326,886.7 252,299.6 389,921.4 248,397.4 200,014.7 285,947.9 199,681.3 OpEx (per year) 6.6 10.2 6.2 4.8 7.4 4.7 3.8 5.4 3.8 a Average sea depth was set at 25 m for Wetar Island.

5.2. Investment Indicator The NPV (net present value), LCOE (Levelized Cost of Electricity), and DPBP (Dis- counted Payback Period) were used in this study to evaluate the wind farm investment. NPV represents the difference between the present value of cash inflows and the present value of cash outflows over a period of time. Positive NPV values represent economic viability [21]. NPV can be expressed by:

T netCashFlow(t) = − + NPV CCAPEX ∑ t t=1 (1 + r)

where CCAPEX is the total capital expenditure, T is the lifespan of the wind farm, and r is the annual discount rate. LCOE represents the offshore wind farm net present value divided by the wind farm lifetime. LCOE can be calculated as follows:

T CCapEx(t)+COpEx(t) ∑t=1 t = (1+r) LCOE T netAEP ∑ t t=1 (1+r)

where COpEx(t) is the operational expenditure of wind farms for the year of t, the netAEP is the total estimated energy production for one year. DPBP represents the time required for the cumulative profit to equal the cumulative cost, considering the discount rate. DPBP is applied when the discount rate is greater than zero. DPBP can be calculated as follows: ! 1 ln C ×r 1− CapEx DPBP = NetCashFlow ln(1 + r)

According to Indonesian regulations, the feed-in-tariff (FIT) for wind energy cannot be higher than the regional cost of provision [29]. The regional cost of provision near Sidrap Wind Farm was approximately 0.08 USD/kWh. The projected lifespan of the proposed wind farm was set at 20 years. Discount rates of 2%, 6%, and 10% were considered in the sensitivity analysis. The AEP of farms was calculated using the WindSim results. The annual revenue was the product of the multiplication of AEP and FIT. The annual revenue increased with AEP; Area-1 with wind turbine B produced the highest annual revenue. Energies 2021, 14, 2652 17 of 26

According to Indonesian regulations, the feed-in-tariff (FIT) for wind energy cannot be higher than the regional cost of provision [29]. The regional cost of provision near Sidrap Wind Farm was approximately 0.08 USD/kWh. The projected lifespan of the proposed wind farm was set at 20 years. Discount rates of 2%, 6%, and 10% were Energies 2021, 14, 2652 considered in the sensitivity analysis. 17 of 26 The AEP of farms was calculated using the WindSim results. The annual revenue was the product of the multiplication of AEP and FIT. The annual revenue increased with AEP; Area-1 with wind turbine B produced the highest annual revenue. NPV analysis indicated that wind turbine C always had a positive (i.e., feasible) NPV value, whereas turbine B always had a negative (infeasible) value. The highest NPV was for the wind turbine C in Area-1 Area-1 at at USDUSD 48.748.7 million. million. Variations in the discount rate revealed the sensitivity of NPV chang changee (Figure 1414).). Area Area-1-1 had a higher NPV compared with others with a discount rate lower than 6%. A 6% discount rate appeared to be the highest acceptable rate. A A discount discount rate rate higher higher than than 6% 6% led led to negative NPV, making the project infeasible.

350

300 Area-1 - Turbine C

250 Area-2 - Turbine C Area-3 - Turbine C 200 Area-2 - Turbine A 150 Area-3 - Turbine A 100

50 NPV NPV (million USD) 0

-50

-100 0% 2% 4% 6% 8% 10% Discount rate

Figure 14. SSensitivityensitivity of of NPV NPV for for different different discount rates at the proposed wind farms off Wetar Island.Island.

LCOE for various wind farm sizes off Wetar Island indicated that LCOE slightly increasedincreased as as farm farm size size dwindled. dwindled. The The lowest lowest LCOE LCOE was was 0.082 0.082 USD/kWh USD/kWh for the for turbine the tur- C inbine Area C in-1, Area-1,followed followed by 0.085 by USD/kWh 0.085 USD/kWh in Area in-2. Area-2.Turbine Turbine A had Aslightly had slightly higher higherLCOE thanLCOE turbine than turbine C did, whereas C did, whereas turbine turbine B had substantially B had substantially higher higherLCOE. LCOE.The lowest The lowestLCOE atLCOE 0.082 at USD/kWh 0.082 USD/kWh was still was higher still higher than the than FIT the for FIT wind for wind power power in Indonesia in Indonesia at 0.08 at USD/k0.08 USD/kWh.Wh. A higher A higher FIT would FIT would be required be required for Indonesia for Indonesia to draw to drawinvestment investment in wind in energy.wind energy. DPBP can be observed at the point where NPV starts to have a positive value, which means the farmfarm isis profitable. profitable. DPBPs DPBPs for for five five feasible feasible scenarios scenarios indicated indicated that that turbine turbine C had C hadthe shortestthe shortest DPBP DPBP at 16 at and 16 and 17 years 17 years in Area-1 in Area and-1 Area-2,and Area respectively-2, respectively (Figure (Figure 15). 15).

5.3. Implications for Energy Policy The LCOE of wind farms off Wetar Island was estimated as 0.082 USD/kWh, which is higher than the current FIT for wind power in Indonesia. When considering the external costs of fossil fuels, such as costs from global warming and health detriment, wind farms off Wetar Island may be a cost-effective and more sustainable option for power generation in comparison with power generation from fossil fuels with LCOE between 0.07 and 0.15 USD/kWh in Indonesia [30]. In addition, the 0.08 USD/kWh FIT for wind power in Indonesia is relatively low (Figure 16). Increasing the FIT may be required to promote offshore wind energy production in Indonesia. Total energy subsidies in Indonesia were estimated at 27.7 billion USD in 2014 (about 3% of GDP), almost 6% of the 493 billion USD in global subsidies to fossil fuels that same year [31]. Moving subsidies from fossil fuels toward renewables would be required to facilitate the use of low-carbon wind energy without increasing the financial burden on the country. Energies 2021, 14, 2652 18 of 26

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Figure 15. Discounted payback period for five feasible scenarios for wind farms off Wetar Island.

5.3. Implications for Energy Policy The LCOE of wind farms off Wetar Island was estimated as 0.082 USD/kWh, which is higher than the current FIT for wind power in Indonesia. When considering the external costs of fossil fuels, such as costs from global warming and health detriment, wind farms off Wetar Island may be a cost-effective and more sustainable option for power generation in comparison with power generation from fossil fuels with LCOE between 0.07 and 0.15 USD/kWh in Indonesia [30]. In addition, the 0.08 USD/kWh FIT for wind power in Indonesia is relatively low (Figure 16). Increasing the FIT may be required to promote offshore wind energy production in Indonesia. Total energy subsidies in Indonesia were estimated at 27.7 billion USD in 2014 (about 3% of GDP), almost 6% of the 493 billion USD in global subsidies to fossil fuels that same year [31]. Moving subsidies from fossil fuels Figure 15. Discountedtoward payback renewables period for would five feasiblebe required scenarios to facilitatefor wind farms the useoff Wetar of low Island.-carbon wind energy Figure 15. Discountedwithout payback increasing period the for financial five feasible burden scenarios on the for windcountry. farms off Wetar Island. 5.3. Implications for Energy Policy The LCOE of wind farms off Wetar Island was estimated as 0.082 USD/kWh, which China is higher than the current FIT for wind power in Indonesia. When considering the external costsIndonesia of fossil fuels, such as costs from global warming and health detriment, wind farms off WetarIndia Island may be a cost-effective and more sustainable option for power generation in comparisonGermany with power generation from fossil fuels with LCOE between 0.07 and 0.15 USD/kWhItaly in Indonesia [30]. In addition, the 0.08 USD/kWh FIT for wind power in Indonesia is relatively low (Figure 16). Increasing the FIT may be required to promote Taiwan offshore wind energy production in Indonesia. Total energy subsidies in Indonesia were estimatedJapan at 27.7 billion USD in 2014 (about 3% of GDP), almost 6% of the 493 billion USD in global subsidies0 to fossil 5fuels that same10 year [31]. Moving15 subsidies 20from fossil fuels toward renewables would be required to facilitate the use of low-carbon wind energy Feed-in-tariff (USCent/kWh) without increasing the financial burden on the country.

FigureFigure 16.16. Comparison of feed feed-in-in tariffs tariffs for for electricity electricity from from wind wind energy energy in in selected selected countries countries in in 20172017 [China[29,32,33]29,32,33].. Indonesia 6. Conclusions India In this study, wind power potential in Indonesia was evaluated at four onshore and Germany three offshore sites. The novel findings are as follows: Italy • The tropical regions of Southeast Asia are generally characterized by scarce wind Taiwanresources. However, the mean annual wind speeds offshore Jeneponto and Wetar IslandJapan reached 8.51 m/s and 8.04 m/s, respectively, in 2015. The capacity factor and power-based availability of a wind farm for each location reached 46% and 76%, 0 5 10 15 20 respectively, exhibiting an excellent potential for wind energy production; • Using wind turbine C withFeed the-in highest-tariff (USCent/kWh) hub height, largest rotor diameter, and lowest cut-in and rated wind speed achieved the largest capacity factor among the selected Figurecommercial 16. Comparison wind of turbines,feed-in tariffs indicating for electricity that these from wind features energy may in enhanceselected countries the efficiency in 2017 [29,32,33]of wind.power generation;

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• Wake loss was affected more by the performance of individual turbines rather than the size of the farm. Using turbines with smaller rotor diameters often leads to a layout with a higher number of turbines, increasing wake loss in the area; • The LCOE of various wind farm sizes indicated that LCOE slowly increased as farm size shrank. Increasing the size of a wind farm may enhance the economic effectiveness of investment; • The LCOE of offshore wind farms can be as low as 0.082 USD/kWh, which is compa- rable to that of power generation from fossil fuels (between 0.07 and 0.15 USD/kWh) in Indonesia. The current FIT for wind power (0.08 USD/kWh) would require en- hancement to attract investment. The cost of wind energy has considerably reduced to a level now comparable to that of fossil fuels. This study demonstrated that offshore wind energy has become a cost-effective low-carbon option for power generation. Moving subsidies from fossil fuels toward renewables would facilitate the use of low-carbon wind energy without increasing the financial burden on the country. This study provides vital findings for wind farm stakeholders. First, increasing the size of a wind farm may enhance its economic effectiveness. Second, using turbines with larger rotor diameters may help minimize turbine number and wake loss. In contrast to past findings indicating that the tropical regions of Southeast Asia have sparse wind energy resources, this study reveals that this region has considerable wind energy potential. This finding may inform regional and international low-carbon policy in addition to promoting further research activities and investment in wind power generation in this region.

Author Contributions: Conceptualization, A.F. and T.-H.L.; data curation, A.F., C.-D.Y. and T.- H.L.; formal analysis, A.F., C.-D.Y. and T.-H.L.; funding acquisition, T.-H.L.; investigation, A.F.; methodology, A.F., C.-D.Y. and T.-H.L.; project administration, T.-H.L.; resources, C.-C.T.; software, A.F. and C.-D.Y.; supervision, T.-H.L.; validation, C.-D.Y. and T.-H.L.; visualization, A.F. and C.-D.Y.; writing—original draft, A.F. and C.-D.Y.; writing—review and editing, C.-D.Y. All authors have read and agreed to the published version of the manuscript. Funding: This work was carried out under the support from the Ministry of Science and Technology of the Republic of China (MOST 108-3116-F-006-009-CC2) financed by the Ministry of Science and Technology of the Republic of China. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data sharing restrictions from funding agency. Acknowledgments: This study was conducted with financial support from the Ministry of Science and Technology of the Republic of China (MOST 108-3116-F-006-009-CC2). The authors appreciate the Ministry’s support of this study. This manuscript was edited by Wallace Academic Editing. Conflicts of Interest: The authors declare no conflict of interest. Energies 2021, 14, 2652 20 of 26

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Nomenclature Conflicts of Interest: The authors declare no conflicts of interest. AEP Annual energy production VNomenclaturew Wind speed ViAEP Cut-in wind speedAnnual energy production VrVw Rated wind speedWind speed VoVi Cut out windCut speed-in wind speed PwVr Power outputRated wind speed Pi Vo Power outputCut interpolated out wind speed frompower curve PrPw Rated power outputPower output AtPi Time-based availabilityPower output interpolated from power curve Pr Rated power output To Turbine operating hours At Time-based availability Tt Total hours in one year To Turbine operating hours Ap Power-based availability Tt Total hours in one year E Power production in the wind farm Ap Power-based availability E Expected power production in the wind farm xE Power production in the wind farm CapEx Capital expenditure Ex Expected power production in the wind farm OpExCapEx Operational expenditureCapital expenditure NPVOpEx Net present valueOperational expenditure LCOENPV Levelized costNet of electricitypresent value DPBPLCOE Discounted paybackLevelized period cost of electricity FITDPBP Feed-in-tariff Discounted payback period T FIT Total lifespanFeed of wind-in-tariff farm r T Annual discountTotal rate lifespan of wind farm r Annual discount rate Appendix A Appendix A

Figure A1. Cont.

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Figure A1. Cont.

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Figure A1. Cont.

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Figure A1. Monthly average wind speed between 2004 and 2015 in the seven evaluated sites.

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Figure A1. Monthly average wind speed between 2004 and 2015 in the seven evaluated sites. Figure A1. Monthly average wind speed between 2004 and 2015 in the seven evaluated sites. Appendix B Appendix B

Figure A2.A2. Terrain elevation of Wetar Island;Island; thethe selectedselected sitesite isis circled.circled. Figure A2. Terrain elevation of Wetar Island; the selected site is circled. Appendix C Appendix C

Figure A3. Roughness map of Wetar Island. FigureFigure A3. A3.Roughness Roughness map map of of Wetar Wetar Island. Island. Appendix D AppendixAppendix D D

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Figure A4. Convergence of the spot values in WindSim. Convergence was achieved after 125 iterations (top) to 206 iterations Figure A4. Convergence of the spot values in WindSim. Convergence was achieved after 125 bottom ( ). U1,iterations V1, and ( W1top are) to the206 velocities iterations in (bottom the x, y,). andU1, zV1, direction, and W1 respectively; are the velocities KE denotes in the thex, y, turbulent and z kinetic energy; EP refers todirection, the turbulent respectively; dissipation KE rate. denotes the turbulent kinetic energy; EP refers to the turbulent dissipation rate. References References 1. UNEP. Emissions Gap Report 2019; United Nations Environment Programme (UNEP): Nairobi, Kenya, 2019. 2. Lin, Y.R. Global Coal-Fired Power Generation is Increasing While European and American Investors Raise the Stakes, Awakening 1. UNEP. Emissions Gap Report 2019; United Nations Environment Programme (UNEP): Nairobi, Kenya, 2019. News Networks. Available online: https://udn.com/news/story/6811/4298905?from=udn-catelistnews_ch2 (accessed on 20 2. Lin, Y.R. Global Coal-Fired Power Generation is Increasing While European and American Investors Raise the Stakes, January 2020). Awakening News Networks. Available online: https://udn.com/news/story/6811/4298905?from=udn-catelistnews_ch2 3. IRENA; ACE. Renewable Energy Outlook for ASEAN: A REmap Analysis; International Renewable Energy Agency (IRENA): Abu (accessed on 20 January 2020). Dhabi, United Arab Emirates; ASEAN Centre for Energy (ACE): Jakarta, Indonesia, 2016. 3. IRENA & ACE. Renewable Energy Outlook for ASEAN: A REmap Analysis; International Renewable Energy Agency (IRENA): Abu 4. Yuliani, D. Is feed-in-tariff policy effective for increasing deployment of renewable energy in Indonesia? In The Political Economy Dhabi, United Arab Emirates; ASEAN Centre for Energy (ACE): Jakarta, Indonesia, 2016. of Clean Energy Transitions; Arent, D., Arndt, C., Miller, M., Tarp, F., Zinaman, O., Eds.; Oxford University Press: Oxford, UK, 4. Yuliani, D. Is feed-in-tariff policy effective for increasing deployment of renewable energy in Indonesia? In The Political Economy 2017. Available online: https://www.oxfordscholarship.com/view/10.1093/oso/9780198802242.001.0001/oso-9780198802242- of Clean Energy Transitions; Arent, D., Arndt, C., Miller, M., Tarp, F., Zinaman, O., Eds.; Oxford University Press; Oxford, UK, chapter-8?print=pdf (accessed on 2 April 2019). 2017; Available online: https://www.oxfordscholarship.com/view/10.1093/oso/9780198802242.001.0001/oso-9780198802242- 5. Lutgens, F.K.; Tarbuck, E.J. The Atmosphere: An Introduction to Meteorology; Prentice Hall: New York, NY, USA, 2001. chapter-8?print=pdf (accessed on 2 April 2019). 6. Hardianto, T.; Supeno, B.; Saleh, A.; Setiawan, D.K.; Gunawan; Indra, S. Potential of Wind Energy and Design Configuration of 5. Lutgens, F.K.; Tarbuck, E.J. The Atmosphere: An Introduction to Meteorology; Prentice Hall: New York, NY, USA, 2001. Wind Farm on Puger Beach at Jember Indonesia. Energy Procedia 2017, 143, 579–584. [CrossRef] 6. Hardianto, T.; Supeno, B.; Saleh, A.; Setiawan, D.K.; Gunawan; Indra, S. Potential of Wind Energy and Design Configuration of Wind Farm on Puger Beach at Jember Indonesia. Energy Procedia 2017, 143, 579–584, doi:10.1016/j.egypro.2017.12.730.

Energies 2021, 14, 2652 26 of 26

7. Martosaputro, S.; Murti, N. Blowing the Wind Energy in Indonesia. Energy Procedia 2014, 47, 273–282. [CrossRef] 8. Carvalho, D.; Rocha, A.; Gómez-Gesteira, M.; Silva-Santos, C. Off-shore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys e a comparative study for the Iberian Peninsula Atlantic coast. Renew. Energy 2017, 102, 433–444. [CrossRef] 9. Mattar, C.; Borvarán, D. Off-shore wind power simulation by using WRF in the central coast of Chile. Renew. Energy 2016, 94, 22–31. [CrossRef] 10. De Linaje, N.G.-A.; Mattar, C.; Borvarán, D. Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile. Energy 2019, 188, 116027. [CrossRef] 11. Grell, G.; Peckham, S.; Schmitz, R.; McKeen, S.; Frost, G.; Skamarock, W.; Eder, B. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 2005, 39, 6957–6975. [CrossRef] 12. Skamarock, W.; Klemp, J.; Dudhia, J.; Gill, D.; Barker, D.; Duda, M.; Huang, X.; Wanf, W.; Powers, J. A Description of the Advanced Research WRF Version 3 [Online]. 2008. Available online: http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf (accessed on 13 April 2021). 13. Ulazia, A.; Saenz, J.; Ibarra-Berastegui, G. Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating off-shore wind energy potential. A case study of the Iberian northern coastline. Appl. Energy 2016, 180, 617–627. [CrossRef] 14. Olaofe, Z. Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based). Energy 2019, 189, 116232. [CrossRef] 15. Salvação, N.; Soares, C.G. Wind resource assessment offshore the Atlantic Iberian coast with the WRF model. Energy 2018, 145, 276–287. [CrossRef] 16. Prósper, M.A.; Otero-Casal, C.; Fernández, F.C.; Miguez-Macho, G. Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations. Renew. Energy 2019, 135, 674–686. [CrossRef] 17. Bansal, J.C.; Farswan, P. Wind farm layout using biogeography based optimization. Renew. Energy 2017, 107, 386–402. [CrossRef] 18. Patel, J.; Savsani, V.; Patel, V.; Patel, R. Layout optimization of a wind farm using geometric pattern-based approach. Energy Procedia 2019, 158, 940–946. [CrossRef] 19. Park, J.; Law, K.H. Layout optimization for maximizing wind farm power production using sequential convex programming. Appl. Energy 2015, 151, 320–334. [CrossRef] 20. EMD. WindPROSPECTING: Wind Energy Resources of Indonesia. EMD International A/S. Available online: http://indonesia. windprospecting.com/ (accessed on 24 March 2016). 21. Cali, U.; Erdogan, N.; Kucuksari, S.; Argin, M. Techno-economic analysis of high potential offshore wind farm locations in Turkey. Energy Strategy Rev. 2018, 22, 325–336. [CrossRef] 22. Brower, M. Wind Resource Assessment: A Practical Guide to Developing a Wind Project; John Wiley & Sons: New York, NY, USA, 2012. 23. NASA/METI/AIST/Japan Space Systems; U.S./Japan ASTER Science Team. ASTER Global Digital Elevation Model [Data Set]. NASA EOSDIS Land Processes DAAC. 2009. Available online: https://doi.org/10.5067/ASTER/ASTGTM.002 (accessed on 6 December 2020). 24. Yakhot, V.; Orszag, S.A.; Thangam, S.; Gatski, T.B.; Speziale, C.G. Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids A Fluid Dyn. 1992, 4, 1510–1520. [CrossRef] 25. Shakoor, R.; Hassan, M.Y.; Raheem, A.; Wu, Y.-K. Wake effect modeling: A review of wind farm layout optimization using Jensen’s model. Renew. Sustain. Energy Rev. 2016, 58, 1048–1059. [CrossRef] 26. Dicorato, M.; Forte, G.; Pisani, M.; Trovato, M. Guidelines for assessment of investment cost for offshore wind generation. Renew. Energy 2011, 36, 2043–2051. [CrossRef] 27. Gonzalez-Rodriguez, A.G. Review of offshore wind farm cost components. Energy Sustain. Dev. 2017, 37, 10–19. [CrossRef] 28. Quinonez-Varela, G.; Ault, G.W.; Anaya-Lara, O.; McDonald, J.R. Electrical collector system options for large offshore wind farms. IET Renew. Power Gener. 2007, 1, 107–114. [CrossRef] 29. MMRI. Ministry of Mineral Resources of Indonesia Regulation No. 50/2017; Ministry of Mineral Resources of Indonesia (MMRI): Jakarta, Indonesia, 2017. 30. IRENA. Renewable Energy Prospects: Indonesia. International Renewable Energy Agency (IRENA). Available online: https: //irena.org/2017 (accessed on 24 April 2020). 31. IEA. Fossil Fuel Subsidy Database. Available online: http://www.worldenergyoutlook.org/resources/energysubsidies/ fossilfuelsubsidydatabase/ (accessed on 24 February 2016). 32. MEAROC. Feed-In Tariff of Electricity Generated from Renewable Energy Sources and its Calculation Formula; Ministry of Economic Affairs of the Republic of China (MEAROC): Taipei, Taiwan, 2019. 33. OECD. Renewable Energy Feed-In Tariffs. Available online: https://stats.oecd.org/Index.aspx?DataSetCode=RE_FIT (accessed on 24 April 2020).