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Dec 13, 2016 - building to be constructed at the center of Seoul, Korea. ... Keywords: building-integrated wind turbine; wind resource assessment; numerical ...
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Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD Hyun-Goo Kim 1, *, Wan-Ho Jeon 2 and Dong-Hyeok Kim 3 1 2 3

*

Korea Institute of Energy Research, Daejeon 34129, Korea CEDIC Co. Ltd., Seoul 08506, Korea; [email protected] Chungnam Institute, Gongju 32589, Korea; [email protected] Correspondence: [email protected]; Tel.: +82-42-860-3376

Academic Editors: Charlotte Bay Hasager, Alfredo Peña, Xiaofeng Li and Prasad S. Thenkabail Received: 29 July 2016; Accepted: 8 December 2016; Published: 13 December 2016

Abstract: In this paper, a new wind resource assessment procedure for building-integrated wind turbines (BIWTs) is proposed. The objective is to integrate wind turbines at a 555 m high-rise building to be constructed at the center of Seoul, Korea. Wind resource assessment at a high altitude was performed using ground-based remote sensing (RS); numerical weather prediction (NWP) modeling that includes an urban canopy model was evaluated using the remote sensing measurements. Given the high correlation between the model and the measurements, we use the model to produce a long-term wind climate by correlating the model results with the measurements for the short period of the campaign. The wind flow over the high-rise building was simulated using computational fluid dynamics (CFD). The wind resource in Seoul—one of the metropolitan cities located inland and populated by a large number of skyscrapers—was very poor, which results in a wind turbine capacity factor of only 7%. A new standard procedure combining RS, NWP, and CFD is proposed for feasibility studies on high-rise BIWTs in the future. Keywords: building-integrated wind turbine; wind resource assessment; numerical weather prediction; computational fluid dynamics; Seoul

1. Introduction In the Bible, the Tower of Babel was a symbol of the aspiration of humans to reach God’s realm. Pyramids built by the ancient Egyptians were also a reflection of such desire. The Great Pyramid of Giza, whose height is 145 m and which was built around BC 2560, was the tallest building in the world until it was replaced by the 160 m high Lincoln Cathedral in England in AD 1311. Since then the emergence of the highest buildings has been chronicled since then as follows: Eiffel Tower in Paris, France (300 m) in 1889; Empire State Building in New York, NY, USA (381 m) in 1930; Ostankino Tower in Moscow, Russia (537 m) in 1967; CN Tower in Toronto, Canada (553 m) in 1975; and Burj Khalifa in Dubai, UAE (830 m) in 2007 [1]. This landmark race continues in southeast and east Asia as well: Petronas Twin Towers in Kuala Lumpur, Malaysia (452 m) in 1996; Jin Mao Tower in Shanghai, China (421 m) in 1998; Taipei World Financial Center 101 in Taipei, Taiwan (509 m) in 2005; and Lotte World Tower in Seoul, Korea (555 m) in 2016. Note that the concept of sustainable architecture, which is contrary to ultra high-rise architectures, started in the 21st century. Sustainable architecture refers to the conscious minimization of the negative effects on the environment by making building materials, energy use, and space utilization efficient so that the actions and decisions we make today will not limit the opportunities for the next generations [2,3]. In other words, a new paradigm wherein buildings shall provide symbolism such as ecological design or sustainability—instead of concentrating on the tallest building race—has Remote Sens. 2016, 8, 1019; doi:10.3390/rs8121019

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tallest building race—has begun. As a result, the so-called building-integrated ideas, wherein begun. As aenergy result,as the so-called ideas, wherein renewable energy as a symbol of renewable a symbol ofbuilding-integrated sustainable and eco-friendly energy is integrated in buildings, have sustainable and eco-friendly energy is integrated in buildings, have been implemented. been implemented. The World Trade The Bahrain Bahrain World Trade Center, Center, which which was was completed completed in in 2008 2008 and and is is regarded regarded as as the the first first modern BIWT, is a 240 m-high, 50-story twin tower complex. It is a structure with symmetrical modern BIWT, is a 240 m-high, 50-story twin tower complex. It is a structure with symmetrical triangular-shaped shape andand layout werewere designed to make use of use windofenergy triangular-shaped twin twinbuildings buildingswhose whose shape layout designed to make wind beyond mere integration of wind turbine in the building. It isthe regarded as building-augmented energy the beyond the mere integration of wind turbine in building. It is regarded as wind turbine (BAWT),wind which is a more aggressive than BIWT [4].concept than BIWT [4]. building-augmented turbine (BAWT), whichconcept is a more aggressive As result via via CFD CFD (by (by the the authors; authors; see see Figure Figure 1), 1), As shown shown in in the the building building photo photo and and on on analysis analysis result 3-blade horizontal-axis wind turbines with diameter of 29 m and capacity of 225 kW were installed 3-blade horizontal-axis wind turbines with diameter of 29 m and capacity of 225 kW were installed at the bridges bridges connecting connectingthe thetriangular triangulartwin twintowers, towers,which whichare aresymmetrical symmetrical with interior angle at the with anan interior angle of ◦ of about 120 . Since the building is located at the coast, the Venturi effect occurs wherein in-between about 120°. Since the building is located at the coast, the Venturi effect occurs wherein in-between distance reduced by byan anangle anglemade madebybythe the twin towers when breeze blows. building distance reduced twin towers when thethe seasea breeze blows. The The building was was designed to increase the efficiency of wind power generation through the accelerated wind. designed to increase the efficiency of wind power generation through the accelerated wind. About About 1.3per GWh per year—which forthe 13% of theconsumption energy consumption of the building—is 1.3 GWh year—which accountsaccounts for 13% of energy of the building—is generated generated by wind power, for capacity factor of approximately 22%. by wind power, for capacity factor of approximately 22%.

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Figure 1. The Bahrain World Trade Center: (a) photo from the front of the wind turbines; and (b) Figure 1. The Bahrain World Trade Center: (a) photo from the front of the wind turbines; and (b) contour contour plot of wind speed on a horizontal cross-sectional plane at 100 m aboveground (CFD by the plot of wind speed on a horizontal cross-sectional plane at 100 m aboveground (CFD by the authors). authors).

The As aa 147 147 m-high, m-high, The Hess Hess Tower Tower in in Houston, Houston, Texas, Texas, USA USA was was completed completed in in 2009 2009 (Figure (Figure 2). 2). As 20-story building, it was regarded as BIWT wherein 10 vertical-axis wind turbines were installed 20-story building, it was regarded as BIWT wherein 10 vertical-axis wind turbines were installed on on top drew attention attention because because aa fragment fragment of of the the part part of top [5]. [5]. This This building building drew of aa wind wind turbine turbine damaged damaged by by strong event being covered in the community newspaper. The Hess strong wind windfell, fell,with withthe thedangerous dangerous event being covered in local the local community newspaper. The Tower case suggests that BIWTs installed in high-rise buildings should consider all accident factors Hess Tower case suggests that BIWTs installed in high-rise buildings should consider all accident including gusts and lightning. factors including gusts and lightning. The River Tower in Guangzhou, China is aChina typical example of BIWTs and building-integrated The Pearl Pearl River Tower in Guangzhou, is a typical example of BIWTs and photovoltaic (BIPV) in Asia. As a 309 m-high, 71-story building completed in 2011, it was designed to building-integrated photovoltaic (BIPV) in Asia. As a 309 m-high, 71-story building completed in increase the wind speed via the nozzle effect by making flow passages in the building as shown in 2011, it was designed to increase the wind speed via the nozzle effect by making flow passages in the Figure 3. A of four 8 m-high vertical-axis wind turbinesvertical-axis are installedwind insideturbines the building, each at building astotal shown in Figure 3. A total of four 8 m-high are installed the internal passage on the left and right sides [6]. inside the building, each at the internal passage on the left and right sides [6].

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Figure 2. The Hess Tower: (a) photo from the front of the wind turbines; and (b) contour plot of wind Figure 2. The Hess Tower: (a) photo from the front of the wind turbines; and (b) contour plot of wind Figureon 2. aThe Hess cross-sectional Tower: (a) photo from(CFD the front of authors). the wind turbines; and (b) contour plot of wind speed vertical plane by the speed on a vertical cross-sectional plane (CFD by the authors). speed on a vertical cross-sectional plane (CFD by the authors).

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Figure 3. The Pearl River Tower: (a) photo of front view; (b) wind penetration structure; and (c) Figure 3. The Pearl River Tower: (a) photo of front view; (b) wind structure; and (c) contour wind speed on a (a) horizontal plane (CFD penetration by thestructure; authors). Figure 3.plot Theof Pearl River Tower: photo ofcross-sectional front view; (b) wind penetration and (c) contour contour plot of wind speed on a horizontal cross-sectional plane (CFD by the authors). plot of wind speed on a horizontal cross-sectional plane (CFD by the authors).

Built in 2011, the Strata SE1 in London is a 148 m-high, 43-story residential building wherein in 2011, the Strata SE1 London with is a 148 m-high, 43-story wherein threeBuilt rooftop horizontal-axis windinturbines 19 kW capacity wereresidential integrated building (Figure 4). It has Built in 2011, the Strata SE1 in London is a 148 m-high, 43-story residential building wherein threeawarded rooftop horizontal-axis wind design turbines with but 19 kW capacity integrated claiming (Figure 4). Itthree has been several architectural prizes, the press waswere not impressed, that the rooftop horizontal-axis wind turbines with 19 kW capacity were integrated (Figure 4). It has been been awarded several architectural design prizes, but the press was not impressed, claiming that the wind turbines rarely rotated. The citizens believe the wind turbine should work continuously, but awarded several architectural design prizes,believe but thethe press wasturbine not impressed, claiming that the wind wind turbines rarely rotated. The citizens wind should work continuously, but the wind is generally low [7]. turbines rotated.low The[7]. citizens believe the wind turbine should work continuously, but the wind the windrarely is generally is generally low [7].

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Figure 4. The Strata SE1 Apartment: (a) photo from the rear of the wind turbines; and (b) contour Figure 4. The Strata SE1 Apartment: (a) photo from the rear of the wind turbines; and (b) contour plot of wind speed on a horizontal cross-sectional plane (top) and streamlines passing through holes plot of wind speed on a horizontal cross-sectional plane (top) and streamlines passing through holes (bottom; CFD by the authors). (bottom; CFD by the authors).

This study was conducted to integrate renewable energy in the Lotte World Tower, a 555 m This building study was conducted to integrate renewable in thebuilding Lotte World 555 m high-rise to be constructed in Seoul, Korea as theenergy sixth tallest in theTower, world.aSeoul is high-rise building to be constructed in Seoul, Korea as the sixth tallest building in the world. Seoul is one of the largest metropolitan cities with around 10 million citizens. Seoul is characterized by its one of the largest metropolitan citieswind with compared around 10 to million citizens. Seoul is characterized by its inland location as well as weakening onshore. inland location as well as weakening wind compared to onshore. In this study, wind resource assessment was performed to evaluate the performance of a 555 m In this study, resource was performed to evaluate the complicated performanceto of perform a 555 m high-rise BIWT to wind be built at the assessment center of Seoul. At such height, it is very high-rise BIWT to be built at the center of Seoul. At remote such height, it is such very complicated to perform in-situ measurements. Therefore, ground-based sensors as LIDAR and SODARin-situ were measurements. Therefore, ground-based remote sensors such as LIDAR and SODAR were employed employed to perform wind resource assessment. Furthermore, a numerical weather prediction to perform wind resource assessment. Furthermore, a numerical (NWP) model (NWP) model and measure-correlate-predict (MCP) method wereweather utilizedprediction to extend the short-term and measure-correlate-predict (MCP) method were utilized to extend the short-term data measured data measured via remote sensing to more than one-year data. A three-dimensional geographical via remote sensing moredatabase than one-year data. A three-dimensional information system information systemto(GIS) and CFD were also employed to geographical predict the effect of the building (GIS) on thedatabase flow. and CFD were also employed to predict the effect of the building on the flow. 2. Analysis Methods 2. Analysis Methods 2.1. Analysis Procedure 2.1. Analysis Procedure Figure 5 shows the procedure for wind resource assessment to implement BIWTs at the upper Figure 5 shows the procedure for wind resource assessment to implement BIWTs at the upper part of high-rise buildings. part of high-rise The remotebuildings. sensing campaign in this study lasted for two months only due to various The remote sensing campaign in this studyIn lasted for an twoNWP, months onlyWeather due toResearch various constraints. An MCP method is therefore applied. this study, namely constraints. An MCP method is therefore applied. In used this study, an NWP,against namelythe Weather Research Forecasting–Urban Canopy Model (WRF–UCM) was and evaluated RS data. The RS Forecasting–Urban Canopy Model (WRF–UCM) was used and evaluated against the RS data. The data were then utilized as reference data, and long-term wind climate data were reconstructed through RS data were then utilized as reference data, and long-term wind climate data were reconstructed an MCP method. through MCPstep method. The an second is to create a wind resource map that can show the spatial variability of the wind The second step is to area create wind resource map that can show the spatial variability of the resource at the installation to adetermine the turbines’ layout. windFor resource at the installation area to determine the turbines’ wind resource mapping, first, wind flow fields at thelayout. target area by wind direction are For wind mapping, first, wind flow are fields at the target area wind directions direction are are simulated usingresource CFD. If 16 wind directional sectors considered, a total of by 16 wind simulated using CFD. If 16 wind directional sectors are considered, a total of 16 wind directions are to be simulated. Next, 16 simulation results are synthesized by applying frequencies of occurrence to be simulated. Next, 16 simulation are synthesized by applying of occurrence by each of the wind directions and 1 results m/s wind speed intervals obtained frequencies from the wind climate as by each of the wind directions and 1 m/s wind speed intervals obtained from the wind climate as weighting factors. The procedure can be described as: weighting factors. The procedure can be described as: 16 25

V ( x, y, z) = ∑ ∑ Vij ( x, y, z)· f ij , , , , ∙ i =1 j =1

(1) (1)

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Remote Sens. 2016, 8, 1019 5 of 17 where, V ( x, y,, z,) is is the synthesized windatataadirectional directionalsector sector i and is the the wind i and where, the synthesizedwind wind speed, speed, Vij ( x,, y,, z) is wind speed bin j, and f is the frequency of occurrence by each directional and wind speed bin. ij synthesized wind speed ,bin is the frequency of occurrence directional wind speed bin. , by , each is the wind at aand directional sector i and where, , j, and is the wind speed, The third step is to select a type of wind turbine and determine an optimum layout. third is to select type of wind turbine and an optimum layout. Although windThe speed binstep j, and is thea frequency of occurrence bydetermine each directional and wind speed bin. Although energy production can be maximized if a wind turbine is installed at the highest wind energy can maximized a wind turbine is installedanatoptimum the highest wind power Theproduction third step is to be select a type of if wind turbine and determine layout. Although power density location, wake losses shall also be taken into account. density wake shall also beif taken intoturbine account. energy location, production canlosses be maximized a wind is installed at the highest wind power

density location, wake losses shall also be taken into account.

Figure 5. Procedure of wind resource assessment for a high-rise BIWT by using RS–NWP–CFD. Figure 5. Procedure of wind resource assessment for a high-rise BIWT by using RS–NWP–CFD. Figure 5. Procedure of wind resource assessment for a high-rise BIWT by using RS–NWP–CFD.

2.2. Remote Sensing Campaign 2.2. Remote Sensing Campaign 2.2. Remote Sensingsensing Campaign The remote campaign, which consisted on measuring the wind resource at 555 m The remote sensing campaign, which(yellow consisted on measuring the wind resource at 555 m270 altitude, altitude, was performed at the rooftop triangleon in measuring Figure 6) ofthe Lotte Hotel, whichatis 555 m The remote sensing campaign, which consisted wind resource was performed at the rooftop (yellow triangle in Figure 6) of Lotte Hotel, which is 270 m away from away from the lot (dashed line in Figure 6) of the Lotte World Tower in the west direction near the altitude, was performed at the rooftop (yellow triangle in Figure 6) of Lotte Hotel, which is 270 m theHan lot (dashed linecenter in Figure 6) ofAthe Lotte World TowerLIDAR in the west direction near MPAS the Han River at the of Seoul. Leosphere WLS70 Sintec SODAR awayRiver fromatthe lot (dashed line in Figure 6) ofWindCube the Lotte World Tower inand the awest direction near the thewere center of Seoul. A Leosphere WindCube LIDAR WLS70 and a Sintec MPAS SODAR were deployed deployed the rooftop of the 145 m-high Lotte Hotel, and WLS70 measurements wereMPAS conducted for Han River at theatcenter of Seoul. A Leosphere WindCube LIDAR and a Sintec SODAR at two the rooftop of at the 145 m-high Lotte measurements were conducted two months months from 9the March to 8ofMay 2009. were deployed rooftop the 145Hotel, m-highand Lotte Hotel, and measurements were for conducted for from 9 March to 8 May 2009. two months from 9 March to 8 May 2009.

Figure 6. Virtual landscape around the Lotte World Tower in Seoul (white dashed line indicates the Lotte Tower under construction, and yellow indicates the RSdashed campaign FigureWorld 6. Virtual landscape around the Lotte Worldtriangle Tower in Seoul (white linesite). indicates the Figure 6. Virtual landscape around the Lotte World Tower in Seoul (white dashed line indicates the Lotte World Tower under construction, and yellow triangle indicates the RS campaign site). World Weather Tower under construction, and yellow triangle indicates the RS campaign site). 2.3.Lotte Numerical Prediction

2.3. Numerical Weather Prediction Since wind resource assessment requires measurements of at least one year including seasonal 2.3. Numerical Weather Prediction variability, short-term remote sensingrequires measurements are extended through an including MCP method that Since wind resource assessment measurements of at least one year seasonal Since wind resource assessment requires measurements of at least one year including seasonal uses NWP data. NWP was conducted the urban canopy layer in order to secure reliable variability, short-term remote sensingconsidering measurements are extended through an MCP method that variability, sensing considering measurements are extended through an MCP method that reference data. uses NWPshort-term data. NWPremote was conducted the urban canopy layer in order to secure reliable uses NWP data. reference data. NWP was conducted considering the urban canopy layer in order to secure reliable reference data.

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WRF–UCM is that takes into account thethe interaction of the is aamesocale-microscale mesocale-microscalecoupled coupledmodel model that takes into account interaction of physical processes in aninurban canopy [8]. The assumes two-dimensional buildings and road the physical processes an urban canopy [8]. model The model assumes two-dimensional buildings and types for detailed city landuse. The single-layer land surface the lower boundary condition road types for detailed city landuse. The single-layer land model, surfacei.e. model, i.e. the lower boundary of WRF–UCM, employed the Seoul building height layerheight provided byprovided the National Spatial Information condition of WRF–UCM, employed the Seoul building layer by the National Spatial Clearinghouse of Korea. Here, the lower boundary condition was modified by applying the surface Information Clearinghouse of Korea. Here, the lower boundary condition was modified by applying roughness proposed byproposed Macdonald al. (1998) [9]. Specifically, displacement the surfaceestimation roughnessmethod estimation method byetMacdonald et al. (1998) [9].the Specifically, the height (do ) of the urban calculated to theand terrain elevation. In addition, surface displacement height (docanopy ) of the was urban canopy and was added calculated added to the terrain elevation. In roughness (zo ) was calculated; it was larger than the roughness height at the landdefined cover, the addition, surface roughness (zoif) was calculated; if it was larger than thedefined roughness height at value wascover, substituted. the land the value was substituted. 2.4. Computational Fluid Fluid Dynamics 2.4. Computational Dynamics The around thethe Lotte World Tower will bewill considerably distorted once the once building The wind windflow flowfield field around Lotte World Tower be considerably distorted the is complete. The wind flow field was simulated using the Reynolds-Averaged Navier-Stokes (RANS) building is complete. The wind flow field was simulated using the Reynolds-Averaged CFD software SC/Tetra a flow solver on aisfinite volume method Navier-Stokes (RANS) v12. CFDSC/Tetra softwareisSC/Tetra v12. based SC/Tetra a flow solver basedand onemploys a finite the SIMPLEC algorithm for pressure-velocity coupling and the QUICK upwind scheme for volume method and employs the SIMPLEC algorithm for 2nd-order pressure-velocity coupling and the convection terms. 2nd-order QUICK upwind scheme for convection terms. The draft of of the the Lotte Lotte World World Tower Tower is shape with with square square cross cross section, section, The design design draft is aa tapered tapered column column shape whose edges are round as shown in Figure 7. The cross-sectional area decreases in a stepwise manner whose edges are round as shown in Figure 7. The cross-sectional area decreases in a stepwise with increasing altitude. This designThis is intended to prevent by shifting the by Karman vortex manner with increasing altitude. design is intendedresonance to prevent resonance shifting the shedding frequency by altitude. Theby rooftop, where the windwhere turbines supposed besupposed installed, Karman vortex shedding frequency altitude. The rooftop, the are wind turbinestoare has a truss structure of a square cone at of theacenter 16 columns surrounding outer to be installed, has consisting a truss structure consisting squareand cone at the center and 16 the columns border. From the top view, the lower structures protruding on three sides excluding the left side are surrounding the outer border. From the top view, the lower structures protruding on three sides shelves to protect by preventing downwash fromby flowing down downwash the buildingfrom as a result of excluding the leftpedestrians side are shelves to protect pedestrians preventing flowing wind colliding with the building. down the building as a result of wind colliding with the building.

Figure 7. 7. Dimensions design and and rooftop rooftop structure: structure: (left) Figure Dimensions of of the the Lotte Lotte World World Tower Tower design (left) side side view; view; (right-top) perspective view of rooftop structure; and (right-bottom) top view. (right-top) perspective view of rooftop structure; and (right-bottom) top view.

The computational domain was set to 6 km × 6 km × 2.5 km around the Lotte World Tower and The computational domain was set to 6 km × 6 km × 2.5 km around the Lotte World Tower and Figure 8 shows the central part of the domain (3 km × 1.5 km area). The 90 million unstructured Figure 8 shows the central part of the domain (3 km × 1.5 km area). The 90 million unstructured hybrid hybrid mesh is used to model complex urban buildings. For a validation simulation, the main wind mesh is used to model complex urban buildings. For a validation simulation, the main wind direction direction (WNW) with/without the Lotte World Tower was simulated. Considering the periodic (WNW) with/without the Lotte World Tower was simulated. Considering the periodic geometrical geometrical characteristics of the urban canopy and a relatively short computational domain, characteristics of the urban canopy and a relatively short computational domain, periodic boundary periodic boundary conditions were imposed on inlet and outlet planes instead of an atmospheric conditions were imposed on inlet and outlet planes instead of an atmospheric boundary layer inlet boundary layer inlet condition. The constant pressure condition is imposed on the upper free condition. The constant pressure condition is imposed on the upper free boundary and slip conditions boundary and slip conditions are assigned to side boundaries. The WRF–UCM result was used as an are assigned to side boundaries. The WRF–UCM result was used as an initial condition. initial condition. The CFD analysis employed the modified production k–ε turbulence model, which predicts flow The CFD analysis employed the modified production k–ɛ turbulence model, which predicts separation in the cubic block structures as shown in urban streets [10,11]. flow separation in the cubic block structures as shown in urban streets [10,11].

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Figure 8. Computation domain around the Lotte World Tower (left); and the pedestrian-level wind Figure 8. Computation flow streamlines (right).domain around the Lotte World Tower (left); and the pedestrian-level wind Figure 8. Computation domain around the Lotte World Tower (left); and the pedestrian-level wind flow streamlines (right). flow streamlines (right).

3. Analysis Results 3. Analysis Results 3. Analysis Results 3.1. Remote Sensing Campaign 3.1. Remote Sensing Campaign 3.1. Remote FigureSensing 9 showsCampaign the mean wind speed profiles measured using LIDAR (from 10-min data) and Figure 9 shows the mean wind speed profiles measured using LIDAR (from 10-min data) and SODAR (from 30-min for the campaign period, where using the data recovery (DRR; dashed Figure 9 shows thedata) mean wind speed profiles measured LIDAR (fromrate 10-min data) and SODAR (from 30-min data) for the campaign period, where the data recovery rate (DRR; dashed lines) decreased with data) increasing Slight distortion of wind shear over m above ground SODAR (from 30-min for thealtitude. campaign period, where the data recovery rate400 (DRR; dashed lines) lines)isdecreased with increasing altitude. the Slight distortion of with windwhite shearcircles) over 400 m the above ground level observed in LIDAR data between raw data (lines and concurrent decreased with increasing altitude. Slight distortion of wind shear over 400 m above ground level level (lines is observedblack in LIDAR data between the rawof data with white circles) and the concurrent data which is about the(lines raw SODAR data, difference is observedwith in LIDAR circles) data between the raw 50% data (lines withdata. whiteIncircles) and theminor concurrent data data (lines with black circles) which is about 50% of the raw data. In SODAR data, minorisdifference is shown between the raw and concurrent data below 500 m aboveground where DRR over 50% (lines with black circles) which is about 50% of the raw data. In SODAR data, minor difference is is shown between the raw and concurrent data below 500 m aboveground where DRR is over 50% [12]. shown between the raw and concurrent data below 500 m aboveground where DRR is over 50% [12]. [12].

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Figure 9. Mean wind speed and data recovery rate profiles observed by the remote sensing Figure 9. Mean wind speed and data recovery rate profiles observed by the remote sensing equipment Figure 9. Mean and data recovery rate (10-min profiles readings); observed and by the remote (30-min sensing equipment (meanwind for thespeed campaign period): (a) LIDAR (b) SODAR (mean for the campaign period): (a) LIDAR (10-min readings); and (b) SODAR (30-min readings). equipment (mean for the campaign period): (a) LIDAR (10-min readings); and (b) SODAR (30-min readings). readings).

3.2. Numerical Weather Prediction 3.2. Numerical Weather Prediction 3.2. Numerical Prediction Figure 10 Weather shows the simulation results at 12:00 p.m. when the westward wind blew to the inland Figure 10 shows the simulation results at 12:00 p.m. when the westward wind blew to the fromFigure the west sea. The figure shows a results difference in wind speed between two cases, either applying the simulation 12:00 p.m. westward blew to the inland from 10 theshows west sea. The figure shows aatdifference in when wind the speed betweenwind two cases, either the urban canopy model or not. Wind speed in Seoul—marked by the dashed white circle—shows inland from westcanopy sea. The figureorshows a difference speed between twodashed cases, either applying thethe urban model not. Wind speed in in wind Seoul—marked by the white clear reduction. applying the urban canopy model or not. Wind speed in Seoul—marked by the dashed white circle—shows clear reduction. Figure 11 shows the comparison among mean wind speed profiles measured by the 1-h averaged circle—shows clear reduction. Figure 11 shows the comparison among mean wind speed profiles measured by the 1-h LIDAR and 11 SODAR and results using 1-h wind averaged WRF and WRF–UCM during Figure shows thesimulation comparison among mean profiles measured by the the 1-h averaged LIDAR and SODAR and simulation results usingspeed 1-h averaged WRF and WRF–UCM remote sensing campaign period. The LIDAR profiles agree better with the WRF–UCM results than averaged LIDAR and SODAR and simulation results using 1-h averaged WRF and WRF–UCM the SODAR profiles, while WRF results are noticeably the highest.

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during the remote sensing during the2016, remote sensing campaign campaign period. period. The The LIDAR LIDAR profiles profiles agree agree better better with with the the WRF–UCM WRF–UCM Remote Sens. 8, 1019 8 of 17 results than the SODAR profiles, while WRF results are noticeably the highest. results than the SODAR profiles, while WRF results are noticeably the highest.

10.Wind Wind speed difference between WRFWRF–UCM and WRF–UCM simulation at 12:00 p.m. Figure speed difference between WRF simulation at (negative Figure 10. 10. Wind speed difference between WRF and and WRF–UCM simulation at 12:00 12:00 p.m. p.m. (negative (negative value means that WRF over-predicted wind speed than WRF–UCM). value means that WRF over-predicted wind speed than WRF–UCM). value means that WRF over-predicted wind speed than WRF–UCM).

Comparison of of wind wind speed speed profiles profiles of of remote remote sensing sensing measurements (LIDAR Figure Figure 11. 11. Comparison Comparison of wind speed profiles of remote sensing measurements measurements (LIDAR (LIDAR and and (WRF and WRF–UCM: symbols). SODAR: lines) and numerical weather predictions SODAR: lines) and numerical weather predictions (WRF and WRF–UCM: symbols).

Figure 12 presents scatter plots comparing remote sensing measurements and Figure 12 12presents presents scatter plots comparing remote sensing measurements and numerical numerical Figure scatter plots comparing remote sensing measurements and numerical weather weather prediction during the campaign period at 400 m aboveground. The wind direction showed weather prediction during the campaign period at 400 m aboveground. The wind direction showed prediction during the campaign period at 400 m aboveground. The wind direction showed 22 2 goodness-of-fit and R = 0.84 for both 1-h averaged LIDAR and SODAR, whereas for the wind goodness-of-fit and forfor both 1-h1-h averaged LIDAR and and SODAR, whereas for thefor wind speed goodness-of-fit and RR = =0.84 0.84 both averaged LIDAR SODAR, whereas the speed wind 2 2 this == 0.67 for and SODAR, respectively. Through this was was Rwas 0.67 and 0.61 for LIDAR LIDAR andand SODAR, respectively. Through this comparison, since speed thisR R2 =and 0.670.61 and 0.61 for LIDAR SODAR, respectively. Throughthis thiscomparison, comparison, since since 222 = 0.67, LIDAR measurement data and WRF–UCM simulation data were matched at the level of R LIDAR measurement data and WRF–UCM simulation data were matched at the level of R = LIDAR measurement data and WRF–UCM simulation data were matched at the level of R = 0.67, 0.67, WRF–UCM data were considered to be in the correction of remote WRF–UCMdata datawere were considered to utilized be utilized utilized inlong-term the long-term long-term correction of short-term short-term remote WRF–UCM considered to be in the correction of short-term remote sensing sensing sensing measurement measurement data. measurement data. data.

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Figure 12. Comparison of numerical weather prediction and remote sensing measurements for the

Figure 12. Comparison of numerical weather prediction and remote sensing measurements for the (a) m aboveground: (a) wind speed; and (b) wind direction. (b) campaign period at 400 campaign period at 400 m aboveground: (a) wind speed; and (b) wind direction. Figure 12. Comparison of numerical weather prediction and remote sensing measurements for the campaignCorrection period at 400 m aboveground: (a) wind speed; and (b) wind direction. 3.3. Long-Term

3.3. Long-Term Correction

Short-term data were corrected to long-term data via MCP using statistical correlation in the Short-term data were corrected long-term data via MCP using correlation in the 3.3. Long-Termperiod Correction overlapped between the to reference data (WRF–UCM) and statistical the short-term LIDAR overlapped period between the reference data (WRF–UCM) and the consistently short-term LIDAR measurements. measurements. the variance ratio algorithm, which predictions Short-term Here, data were corrected to long-term data viaproduced MCP using statisticalreliable correlation in the Here, the variance ratio algorithm, which produced consistently reliable predictions among the MCP among the MCP algorithms, employed [13].data (WRF–UCM) and the short-term LIDAR overlapped period betweenwasthe reference algorithms, was employed [13].was extended The wind climate, which to one-year via MCP, at 555 m altitude the future measurements. Here, the variance ratio algorithm, whichdata produced consistently reliableat predictions the Lotte World Tower is as follows (see Figure 13): The wind climate, which was extended to one-year data via MCP, at 555 m altitude at the future among the MCP algorithms, was employed [13].

the•Lotte World Tower iswhich follows (see Figure 13): The wind climate, was extended to one-year data via MCP, at 555 m altitude at the future Mean wind speed =as4.9 m/s;

• • • •

2 (wind the Lotte World Tower as follows Figure 13): • Mean Wind power density = m/s; 160 W/m(see class 1, poor); wind speed = is4.9 • Weibull distribution scale factor c = 5.5 m/s, shape factor k = 1.72; and 2 • Wind Mean winddensity speed = =4.9160 m/s; power W/m (wind class 1, poor); • Prevailing wind direction WNW. 2 • Wind power density = 160 W/m (wind class 1, poor);

Weibull distribution scale factor c = 5.5 m/s, shape factor k = 1.72; and

• Weibull distribution scale factor c = 5.5 m/s, shape factor k = 1.72; and Prevailing wind direction WNW. • Prevailing wind direction WNW.

(a)

(b)

Figure 13. Wind climate at 555 m above ground level: (a) probability distribution of wind speed; and (a) (b) (b) wind rose. Figure 13. Wind climate at 555 m above ground level: (a) probability distribution of wind speed; and

Figure 13. Wind climate at 555 m above ground level: (a) probability distribution of wind speed; (b) wind rose. and (b) wind rose.

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3.4. Computational Fluid Dynamics 3.4. Computational Fluid Dynamics Figure 14 shows the comparison of simulation results of the main wind direction case, revealing Figurewind 14 shows thevariability comparison simulation results thevertical main wind direction case, revealing horizontal speed at of the pedestrian levelofand cross-sectional wind speed horizontal wind speed variability at the pedestrian level and vertical cross-sectional wind variability along the main wind direction (WNW) before and after the construction of the speed Lotte variability along the main wind direction (WNW) before and after the construction of the Lotte World Tower. World Tower. The CFD analysis results considering only the Lotte World Tower and including all The CFDbuildings analysis results only the Lotte WorldisTower and including all surrounding surrounding show considering that the rooftop of the Tower impacted by surrounding building buildings show that the rooftop of the Tower is impacted by surrounding building groups to a low groups to a low degree (less than 3% of wind speed), although the pedestrian level wind speeds are degree (less than 3% of wind speed), although the pedestrian level wind speeds are largely augmented largely augmented by the Lotte World Hotel (about 75% of area; left contour plots in Figure 14). This by the Lotte World Hotel (about 75% of area; left contour in Figure 14). Thisplots can also be seen can also be seen in the wind speed variability at the right plots vertical cross-sectional in Figure 14.in the wind speed variability thesignificantly right verticalreduced cross-sectional plots inthe Figure 14. The computation load canatbe by excluding surrounding buildings. As The computation load can be significantly reduced by excluding the surrounding buildings. shown in Figure 15, only three wind directions need to be simulated using the symmetry of the As shown in Figure 15, only three wind directions need to be simulated using the symmetry the cross-section (in the horizontal, vertical, and diagonal axes). With the CFD analysis on three of wind cross-section (in the horizontal, vertical, and diagonal axes). With the CFD analysis on three wind sectors and its projection to 13 other directional sectors, individual wind maps are obtained for a sectors its projection to 13 otherand directional sectors,using individual wind maps are obtained total of and 16 wind directional sectors then weighted the wind climate in Figure 13. for a total of 16Figure wind directional sectors and thenofweighted thewith wind climate in measurements Figure 13. 16 shows the evaluation the CFDusing results the LIDAR when the Figure 16 shows the evaluation of the CFD results with the LIDAR measurements when the good Lotte Lotte World Tower is not simulated. The wind speed and turbulence intensity profiles show World Tower is not simulated. The wind speed and turbulence intensity profiles show good agreement. agreement.

(a)

(b) Figure 14. Computation results of pedestrian-level wind speed around the Lotte World Tower: Figure 14. Computation results of pedestrian-level wind speed around the Lotte World Tower: horizontal (leftcolumn); column);and and vertical (right column) cross-sectional wind variability: speed variability: (a) horizontal (left vertical (right column) cross-sectional wind speed (a) without without the Tower; and (b) with the Tower. the Tower; and (b) with the Tower.

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Figure 15. Wind directional sectors around the Lotte World Tower (top view) for wind resource Figure 15. 15. Wind directional sectors around the Lotte World Tower (top view) for wind Figure mapping. Wind directional sectors around the Lotte World Tower (top view) for wind resource resource mapping.mapping.

(a) (a)

(b) (b)

Figure 16. Comparison of the CFD simulations and the LIDAR measurements at the Lotte World Figure 16. Comparison of the CFD simulations and LIDAR measurements at the Lotte World Tower position: (a) mean speed profiles; and and (b)the turbulence intensity profiles. Figure 16. Comparison of wind the CFD simulations the LIDAR measurements at the Lotte World Tower Tower position: (a) mean wind speed profiles; and (b) turbulence intensity profiles. position: (a) mean wind speed profiles; and (b) turbulence intensity profiles.

Figure 17 presents a wind resource map at 2 m above the rooftop horizontal plane of the Lotte Figure 17 revealing presents athe wind resourceofmap 2 m above rooftop the Lotte World Tower variability the at annual mean the wind speedhorizontal and wind plane powerof density. A Figure 17revealing presents athe wind resourceofmap at 2 m above the rooftop horizontal plane ofdensity. the Lotte World Tower variability the annual mean wind speed and wind power A low wind speed area is identified due to the wake from the square cone located at the center of the World Tower revealing variability annual mean wind speedcone and located wind power A low low wind speed area isthe identified dueofthe tothe the wake from the square atrooftop. thedensity. center of the rooftop; shelter is also observed from 16 columns at the external walls in the Thus, the wind speed area is identified due to the wake from the square cone located at the center of the rooftop; rooftop; shelter is also observed from the 16 columns at be theappropriate external walls in the rooftop. Thus, the four areas marked by the dashed circle are found to positions for wind turbine shelter is also observed fromdashed the 16 columns atfound the external walls in the rooftop. Thus, the four areas four areas marked by the circle arepower to be are appropriate positions for wind turbine installation because wind speeds and wind density some of the highest within a rooftop marked by the dashed circle are found to be appropriate positions for wind turbine installation installation wind speeds and west–east wind power density are some the highest within a because rooftop area. speeds Note because that the horizontal axes and south–north areoflabeled asarea. x-axis and y-axis, wind and wind power density are some of the highest within a rooftop Note that the area. Note that the horizontal axes west–east and south–north are labeled as x-axis and y-axis, respectively. The z-axis directs vertically upward from the ground. The origin is the center of the horizontal axes west–east and south–north are labeled as x-axis and y-axis, respectively. The z-axis respectively. z-axis directs vertically upward from the ground. The origin is the center of the Tower on theThe ground. directs vertically upward from the ground. The origin is the center of the Tower on the ground. Tower on the ground.

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(a) (b) (a) (b) Figure 17. Wind resource map at 2 m above the Lotte World Tower roof (dimensions in meters): (a)

Figure 17. Wind resource map at 2 m above the Lotte World Tower roof (dimensions in meters): mean wind speedresource map; and (b) at wind density map. Figure 17.wind Wind mpower above thedensity Lotte World (a) mean speed map;map and (b)2wind power map. Tower roof (dimensions in meters): (a) mean wind speed map; and (b) wind power density map.

The BIWT examples discussed in the introduction made efforts to change the building shapes to The BIWTspeed, examples discussed inthe the introduction made efforts to change the building shapes to increase but the cone atin in the rooftop of the Lotte World andshapes columns The wind BIWT examples discussed thecenter introduction made efforts to change theTower building to increase wind speed, but the cone at the center in the rooftop of the Lotte World Tower and columns installedwind around the external wallsatreduce the wind increase speed, but the cone the center in theinstead. rooftop of the Lotte World Tower and columns installed around the external walls reduce the wind instead. Figure 18 shows the CFD result for the main wind direction when changing the cross-sectional installed around the external walls reduce the wind instead. Figure 18 shows the CFD result for the main wind direction changing the cross-sectional square shape the cone of the center the rooftop a circle. when It is noticeable the low-wind Figure 18of shows the CFD result forin the main windtodirection when changing that the cross-sectional square shape of the cone of the center in the rooftop to a circle. It is noticeable that the low-wind speed speed wake decreases changing cross-section to a circle. square shapezone of the cone ofdramatically the center inwhen the rooftop to the a circle. It is noticeable that the low-wind wake zone decreases dramatically when changing the cross-section to a circle. speed wake zone decreases dramatically when changing the cross-section to a circle.

(a) (b) (a) (b) Figure 18. Wind speed contours at 2 m above the Lotte World Tower roof for the main wind direction (dimensions in meters): (a) square structure; and (b) circular cone structure. Figure 18. Wind speed contours at cone 2 m above the Lotte World Tower roof for the main wind direction Figure 18. Wind speed contours at 2 m above the Lotte World Tower roof for the main wind direction (dimensions in meters): (a) square cone structure; and (b) circular cone structure. (dimensions in meters): (a) square cone structure; and (b) circular cone structure.

Figure 19a presents a wind power density map when changing the cross section to circular cone.Figure Compared to the square cone case density in Figure 17b,when the wake area around thesection cone disappears, 19a presents a wind power map changing the cross to circular 19a presents a wind power density map17b, when changing the around cross section to circular cone. and aFigure relatively high wind power density is found. cone. Compared to the square cone case in Figure the wake area the cone disappears, Compared to the square casedensity in Figure 17b, the wake area around the cone disappears, and a and a relatively high windcone power is found. relatively high wind power density is found.

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(a) (a)

(b) (b)

Figure 19. Wind resource map at 2 m above the Lotte World Tower roof (dimensions in meters): (a) Figure 19. Wind resource map at 2 m above the Lotte World Tower roof (dimensions in meters): Figure 19. Wind resource at 2 m cone abovestructure; the Lotteand World roofmap (dimensions in meters): (a) wind power density map map for circular (b) Tower difference of wind power density (a) wind power density map for circular cone structure; and (b) difference map of wind power density wind power density map for circular cone structure; and (b) difference map of wind power density between the square and the circular cone cases. between the square and the circular cone cases. between the square and the circular cone cases.

Figure 19b illustrates the ratio in wind power density between the circular and square cone Figure 19b inin wind density between the circular and square cone cases. 19b illustrates illustrates theratio ratio wind power density between the circular and compared square cone cases. In particular, wind the power density inpower (+) shape layout is significantly improved to In particular, wind power density in (+) shape layout is significantly improved compared to ( × ) shape cases. In particular, wind power density in (+) shape layout is significantly improved compared to (×) shape layout of wind turbines. Using this result, the (+) shape layout of wind turbines for the layout ofcone wind turbines. Using this Using result, this the (+) shape of wind turbines for the circular (×) shape layout ofis wind turbines. result, thelayout (+) shape layout of wind turbines forcone the circular case determined. case is determined. circular cone20 case is determined. Figure shows the quantitative comparison of probability distribution of wind speed for all Figure shows quantitative comparison of probability distribution of wind wind speed for all Figure 20 shows the quantitative distribution of for sectors between the square and circularcomparison cone cases. of Forprobability the circle case, the frequency of speed wind speeds sectors between the square and circular cone cases. For the circle case, the frequency of wind speeds circle to the square case. below 2 m/s decreases, whereas it increases above 7For m/sthe compared m/sdecreases, decreases,whereas whereasititincreases increasesabove above77m/s m/scompared comparedtotothe thesquare squarecase. case. below 2 m/s

Figure 20. Probability distribution of wind speed for the square and circular cone cases. Figure 20. 20. Probability Probability distribution distribution of of wind wind speed speed for for the the square and circular cone cases. Figure

3.5. Energy Production Calcuations 3.5. Energy Energy Production Production Calcuations The reference wind turbines, 10 kW GWE-10KH (height: 4.2 m, diameter: 5.3 m) made by The wind 10 Korea) kW GWE-10KH m, diameter: 5.3 made by The reference 4.2 diameter:wind 5.3 m) m) madeand by Geum-Poong Energy, Inc.turbines, (Jeongeup, is selected,(height: which is4.2 a vertical-axis turbine, Geum-Poong Energy, Inc. (Jeongeup, Korea) is selected, which is a vertical-axis wind turbine, and Geum-Poong Energy, (Jeongeup, Korea) is selected, which is vertical-axis turbine, Excel-S 10 kW (rotor diameter 6.7 m) made by Bergey WindPower Co. (Norman, USA), a Excel-S 10kW kW (rotor diameter 6.7 m)curves Bergey WindPower Co. (Norman, USA),21. a 10 (rotor diameter 6.7 m)power made bymade Bergey WindPower Co. (Norman, USA), a horizontal-axis horizontal-axis wind turbine. The ofby the two wind turbines are presented in Figure horizontal-axis wind turbine. Theofpower curves the twoare wind turbinesinare presented wind turbine. The power curves the two windofturbines presented Figure 21. in Figure 21.

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Figure 21. Power curves of two small wind turbines (solid line: GWE-10KH; dashed line: Excel-S). Figure 21. Power curves of two small wind turbines (solid line: GWE-10KH; dashed line: Excel-S).

Tables 1 and 2 summarize the annual energy production (AEP) (AEP) and and capacity capacity factor factor results, results, respectively, when turbine and horizontal-axis wind turbine are used. NoteNote that respectively, whenthe thevertical-axis vertical-axiswind wind turbine and horizontal-axis wind turbine are used. the calculation assumed 100% 100% availability and 0%and of all of losses. AEPThe significantly increases that the calculation assumed availability 0%type of all type ofThe losses. AEP significantly with onlywith a change the cross-sectional shape ofshape the structure. More specifically, the AEPthe forAEP the increases only ainchange in the cross-sectional of the structure. More specifically, vertical-axis wind turbine was case increased to 28%, and forand thethat horizontal-axis wind turbinewind case, for the vertical-axis windcase turbine was increased tothat 28%, for the horizontal-axis to 27%. case, The capacity of the vertical-axis turbine iswind only turbine 2/3 of that of the turbine to 27%. factor The capacity factor of thewind vertical-axis is only 2/3horizontal-axis of that of the wind turbine. wind turbine. horizontal-axis The capacity factor of the simulated BIWTs can reach only 6.4%. The AEP corresponds to the inSouth SouthKorea). Korea). consumption of 17 persons (1.3 MWh/person MWh/person in Table 1. The AEP and capacity factor of the vertical-axis wind turbines. Table

Wind Turbine Wind Turbine GWE-10KH GWE-10KH WT#1 WT#1 WT#2 WT#2 WT#3 WT#3 TW#4 TW#4 Overall Overall

Square Cone Case Square Cone Case AEP (MWh) Capacity Factor AEP (MWh) Capacity Factor 2.81 3.21% 2.81 3.21% 3.14 3.58% 3.14 3.58% 3.28 3.74% 3.28 3.74% 2.18 2.49% 2.18 2.49% 11.4 3.26% 11.4 3.26%

Circular Cone Case Circular Cone Case AEP (MWh) Capacity Factor AEP (MWh) Capacity Factor 3.01 3.44% 3.01 3.44% 3.43 3.91% 3.43 3.91% 5.09 5.81% 5.09 5.81% 3.09 3.53% 3.09 3.53% 14.6 4.18% 14.6 4.18%

Table 2. The AEP and capacity factor of the horizontal-axis wind turbines. Table 2. The AEP and capacity factor of the horizontal-axis wind turbines. Square Cone Case Circular Cone Case Wind Turbine Excel-S AEP (MWh) Capacity Factor AEP (MWh) Capacity Factor Square Cone Case Circular Cone Case Wind Turbine WT#1 4.53 5.17% 4.89 5.47% Excel-S AEP (MWh) Capacity Factor AEP (MWh) Capacity Factor WT#2 4.63 5.28% 5.12 4.84% WT#1 4.53 5.17% 4.89 5.47% WT#3 4.98 5.68% 7.67 8.76% WT#2 4.63 5.28% 5.12 4.84% TW#4 3.59 4.10% 4.77 5.45% WT#3 4.98 5.68% 7.67 8.76% Overall 17.7 5.06% 22.4 6.38% TW#4 3.59 4.10% 4.77 5.45% Overall 17.7 5.06% 22.4 6.38%

4. Discussion

The wind resource in Seoul is weak mainly due to weak synoptic conditions. According to the 4. Discussion wind systems in the Korean Peninsula [14], a synoptic wind system is formed where WNW winds The wind resource in Seoul is weak mainly due to weak synoptic conditions. According to the blow to the Seoul region along Han River and northwesterly winds approximate from the west sea. wind systems in the Korean Peninsula [14], a synoptic wind system is formed where WNW winds As shown in the wind resource map [15] of Figure 22, as upwind in Seoul, which was wind blow to the Seoul region along Han River and northwesterly winds approximate from the west sea. blow from the west sea at 7 m/s or higher, was infiltrated into the inland, it was weakened into low As shown in the wind resource map [15] of Figure 22, as upwind in Seoul, which was wind blow wind speed due to geographical forcing caused by urban canopy in Seoul metropolitan in particular from the west sea at 7 m/s or higher, was infiltrated into the inland, it was weakened into low wind as well as friction with the land as shown in Figure 10. A mean wind speed and wind power density speed due to geographical forcing caused by urban canopy in Seoul metropolitan in particular as well

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as friction with the land as shown in Figure 10. A mean wind speed and wind power density were 2 at the location around the Lotte changed into weak resource as 4.1 m/s 2 at the location around the were changed intowind weakpower wind power resource as 4.1and m/s108 andW/m 108 W/m World LotteTower. World Tower.

Figure 22. Wind resource map of the Korean Peninsula by Korea Institute of Energy Research (mean Figure 22. Wind resource map of the Korean Peninsula by Korea Institute of Energy Research wind speed at 80 m aboveground). (mean wind speed at 80 m aboveground).

5. Conclusions 5. Conclusions Based on our findings, the BIWTs were not considered at the Lotte World Tower. However, we Based onaour findings, BIWTs wereassessment not considered at the Lotte World Tower. However, established procedure forthe wind resource for high-rise BIWTs by combining RS, NWP,we established and CFD. a procedure for wind resource assessment for high-rise BIWTs by combining RS, NWP, and CFD. The data recovery rate of ground-based remote sensing decreased dramatically. It is very important to recovery perform reconstruction of full wind climate data fordecreased at least one year using long-term The data rate of ground-based remote sensing dramatically. It is very correction NWP and MCP methods. Moreover, it is anticipated longer important tovia perform reconstruction of full wind climate data fortoatperform least one yearremote-sensing using long-term measurements to mitigate uncertainties caused by seasonal variation wind climate. correction via NWP and MCP methods. Moreover, it is anticipated toofperform longer remote-sensing Wind conditions can change considerably depending on the building shape, CFD simulation is measurements to mitigate uncertainties caused by seasonal variation of wind climate. therefore needed to analyze the effect of building shape accurately. Modifying the building shape is Wind conditions can change considerably depending on the building shape, CFD simulation results in up to 30% improvement in the density and large changes in spatialthe variability. therefore needed to analyze the effect of power building shape accurately. Modifying building shape The wind resource around the Lotte World Tower is very poor despite its high altitude (555 m) results in up to 30% improvement in the power density and large changes in spatial variability. because it is located inland of the Korean Peninsula. However, it should be noted that the The wind resource around the Lotte World Tower is very poor despite its high altitude (555 m) surrounding buildings of the Lotte World Tower do not directly influence the wind speed at 555 m because it is located inland of the Korean Peninsula. However, it should be noted that the surrounding according to the CFD simulation. buildings of the Lotte World Tower do not directly influence the wind speed at 555 m according to the Changes in building shape resulted in a capacity factor of only 7%, thus the BIWTs are not CFD simulation. economically feasible. Changes in building shape resulted in a capacity factor of only 7%, thus the BIWTs are not economically feasible.

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Acknowledgments: This work was conducted under the framework of Research and Development Program of the Korea Institute of Energy Research (KIER; GP2014-0030). Author Contributions: Hyun-Goo Kim assumed responsibility for the wind resource assessment of BIWT by compiling the RS, NWP, and CFD results and wrote the paper; Wan-Ho Jeon contributed to CFD simulation; and Dong-Hyeok Kim conceived and designed the LIDAR and SODAR remote-sensing campaign. All authors read and approved the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations The following abbreviations are used in this manuscript: AEP BAWT BIWT CFD DRR MCP NWP RANS RS WRF UCM

Annual Energy Production Building-Augmented Wind Turbine Building-Integrated Wind Turbine Computational Fluid Dynamics Data Recovery Rate Measure-Correlate-Predict Numerical Weather Prediction Reynolds-Averaged Navier-Stokes Remote Sensing Weather Research Forecasting Urban Canopy Model

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