- Email: [email protected]
Wind resource assessment for urban renewable energy application in Singapore
Renewable Energy 87 (2016) 403e414
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Wind resource assessment for urban renewable energy application in Singapore B.R. Karthikeya a, Prabal S. Negi b, N. Srikanth a a b
*
Energy Research Institute @ Nanyang Technological University, #06-04, 1 Cleantech Loop, Cleantech One, Singapore 637141, Singapore School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 February 2015 Received in revised form 5 August 2015 Accepted 7 October 2015 Available online xxx
In highly urbanized and energy intensive countries like Singapore all possible avenues for power generation need attention. In this context, rooftop installations of both solar and wind energy are of particular interest for Singapore, especially because of Singapore's condition of land limitation. Decentralized and distributed energy sources such as rooftop wind and solar installations have numerous advantages. However, the potential for wind energy is not fully understood in built-up areas and thus not fully exploited. Hence it is important to study wind flow patterns in built-up areas and also develop technologies tuned for these conditions. The demand for technologies that deliver energy for low flow wind conditions is of paramount importance to Southeast Asia region and especially to Singapore. In this paper, two measurement systems, namely stationary rooftop wind mast and mobile Light Detection and Ranging (LiDAR) profiler, have been discussed. Measured wind data from various sites across Singapore using have also been presented. Wind roses, Weibull distribution, roughness lengths and other statistical analyses were carried out to understand the prevailing wind characteristic, which is used for evolving the basic criteria for economic viability of roof top wind turbines in the tropical conditions of Singapore. © 2015 Published by Elsevier Ltd.
Keywords: Urban wind potential Wind energy Wind speed Urban wind turbines Weibull distribution Resource assessment
1. Introduction Singapore's installed electricity generation capacity is 12.5 GW, which is almost entirely derived from fossil fuels [1]. Household electricity consumption is 15% of the total electricity consumption and consumption by commercial and services sector is 37%. Singapore is committed to reducing its emissions by 7%e11% below 2020 business-as-usual levels [2]. Hence, decentralized and distributed energy sources such as rooftop wind and solar installations require due attention in terms of resource estimation and techno-economic evaluation. Based on the space availability in Singapore, maximum cumulative capacity of rooftop photovoltaic installations is estimated to be 5 GWp by 2030, with 80% of the installed capacity on rooftops and facades [3]. However, the potential for wind energy is not fully understood in built-up areas and hence not fully estimated. In order for Singapore to achieve this goal and to diversify the energy mix, several government agencies are working with Institutes of Higher Learning and local SMEs. National Environment Agency (NEA) owns and maintains several
* Corresponding author. E-mail address: [email protected] (N. Srikanth). http://dx.doi.org/10.1016/j.renene.2015.10.010 0960-1481/© 2015 Published by Elsevier Ltd.
met masts that measure the surface wind across the island nation. But this data is inadequate to estimate the wind potential for the installation of rooftop wind turbines in densely urbanized Singapore. Housing Development Board (HDB) of Singapore, a government body responsible for public housing in Singapore, has been working with the Energy Research Institute at Nanyang Technological University (ERI@N) to study the feasibility of rooftop wind turbines in Singapore. Hence, ERI@N has developed two wind measurement systems for Singapore, namely remote sensing and mobile LiDAR measurement system and rooftop standard anemometry measurement system with wireless data transfer capabilities. ERI@N is the first and so far the only institute in Singapore that conducts wind measurement campaigns in the region using SODAR (SOnic Detection and Ranging) and LiDAR wind profilers. Singapore has a wet equatorial climate with fairly uniform mean monthly temperatures between 26 C and 28 C throughout the year [4]. Singapore's weather can be classified into four seasons. These four seasons are the Northeast Monsoon (December to early March), the Southwest Monsoon (June to September) and two relatively short inter-monsoon seasons. Singapore experiences light and variable winds during the transition between these
404
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
seasons [5]. Wind power is affected by the monsoon weather pattern [6]. Singapore's weather is characterized by high humidity and frequent rainfall [7]. Singapore receives a considerable amount of rainfall throughout the year. The mean monthly rainfall can be between 150 mm and 275 mm depending on the season [4]. Fig. 1 indicates the locations where ERI@N either has permanent masts for long term wind measurements or has undertaken LiDAR campaigns for short-term measurements. Green ‘rhombus’ markers indicate the four HDB rooftops where wind measurement masts have been installed. In this paper, wind measurements of over two years (2012e2014) from the three sites viz. Woodlands Crescent, Pandan Gardens and Marine Drive have been presented and discussed. Results of short-term LiDAR campaigns undertaken at Sentosa and SSC Tanah Merah have been presented, especially to understand the roughness lengths in various direction sectors and turbulence intensity variation with increasing wind speeds. A stripped down techno-economic analysis has been presented for the case of small wind turbine installation. 2. Urban wind energy potential Urban areas include considerable turbulence and local aberrations. Urban wind energy problem has two main aspects: One, understanding of flow pattern within the lowest urban canopy layer where individual building affects the flow; Two, vertical extrapolation of wind in roughness and inertial sublayers. Wind characteristics such as surface drag, vertical shear profile and turbulence intensity are affected by the roughness lengths of the urban canopy. Estimation of spatially averaged wind profile for urban
surface sublayer and roughness sublayer has been discussed in Ref. [8]. In Ref. [9] Ishugah et al. discuss three methods to estimate surface roughness in urban areas viz. Davenport classification, morphometric and meteorological methods. Giovanni and Sauro [10] have discussed in detail the effect of roughness length and wind shear coefficients on the Annual Energy Yield (AEP) for three coastal sites in Southern Italy. Hee-Chang and Tae-Yoon [11] discuss the wind resource assessment of a wind energy site in Jeju Island and spectra analysis of wind data. There can be significant increase of wind speed at specific locations due to concentration effect of buildings [12]. Hence, estimation of wind parameters is more complicated in built up areas. Sara Louise Walker [13] has discussed in detail various methods of estimating urban wind resource. Wind potential in urban areas can be evaluated using standard anemometry at the site, computational fluid dynamics (CFD) simulation and wind tunnel experiments on the physical model of the building and the surrounding area [14]. A combination of two of these methods is employed to verify the results. A CFD simulation of wind flow around simple building and subsequent wind tunnel measurements to verify CFD simulation have been discussed in Ref. [15]. A method describing a combination of CFD and measurements from a single anemometer for wind energy assessment has been discussed in Ref. [16]. Subsequent to a thorough understanding of the flow patterns, various wind energy systems suitable for exploiting wind potential in that specific urban environment can be considered [17]. D. Elliott and D. Infield [18] have studied the effect of averaging time wind measurements on turbine energy capture prediction and turbulence intensity calculation. The AEP for low wind speed
Fig. 1. ERI@N’s wind measurement sites in Singapore.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
405
Table 1 Wind measurement locations. Location
Instruments
Elevation (m)
Woodlands Crescent Pandan Gardens Marine Drive Sentosa SSC Tanah Merah
WindSonic 2-axis anemometer, cup anemometer, wind vane WindMaster 3-axis anemometer, cup anemometer, wind vane WindSonic 2-axis anemometer Remote sensing LiDAR Remote sensing LiDAR
64.2 78 83 NA NA
regimes does not vary more than 1e2% for different averaging periods. However the calculated turbulence intensity changes significantly with the averaging time period [18]. We've chosen 10min average wind data has been used in this paper. Referring to various sources [19e21], a brief description of meteorological and technical nomenclature applicable for this paper is as follows. Weibull distribution is the probability distribution function used to describe the distribution of wind speeds over time duration. Weibull k (shape factor) parameter controls the width of the distribution and Weibull c (scale factor in m/s) parameter controls the average wind speed. The two-parameter Weibull probability function is given by
PðV; c; kÞ ¼
k k V k1 V exp c c c
(1)
(2)
There are various methods to estimate the Weibull parameters. In this paper, we have considered maximum likelihood, least squares and WAsP methods. Maximum likelihood method is the most widely used method. Shape factor k according to maximum likelihood method is given by Ref. [22].
2 31 P n 6 n vk ln v 7 1X 6 i¼1 i 7 i k¼6 P ln vi 7 n k 4 5 n v i¼1
TI ¼ s=Vav
(8)
where s ¼ 10-min standard deviation, Vav ¼ 10-min average wind speed. Wind speed variation with height above ground is given by logarithmic profile and power law profile. As per logarithmic profile, wind speed at height z1 is given by
ln zz10 ln zz20
(9)
where z0 ¼ roughness length, z2 ¼ reference height, v(z2) ¼ wind speed at the reference height. Power extracted by a wind turbine from the incident wind can be expressed as
P ¼ 0:5CP rV3av A
(10)
where A ¼ rotor area, CP ¼ power coefficient of the rotor, Vav ¼ wind speed and r ¼ air density [25]. AEP is given in Watt hour and over one year is calculated using the Weibull distribution and power curve of the wind turbine.
(3)
i¼1
i
(7)
Turbulence intensity is defined as
vðz1 Þ ¼ vðz2 Þ
Cumulative distribution function is given by
vo k pðv vo Þ ¼ 1 exp c
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u P 3 u Vi u 3 N c¼u u t NG 3k þ 1
Z∞ AEP ¼ 8; 760
PðV; c; kÞPðVÞdv
(11)
0
Scale factor c is given by
3. Wind measurement system
" c¼
n 1X vk n i¼1 i
#1=
k
(4)
In least squares method, the slope and intercept of the line of best fit given by the equation below are calculated [23].
ln ln
1 ¼ k ln V k ln c ½1 pðvÞ
(5)
WAsP algorithm was originally described by Troen and Petersen. According to [24], WAsP algorithm calculates c and k to fit the power density in the time series instead of the mean wind speed. k is given by the equation
lnX ¼ G
k 1 þ1 k
(6)
where X represents the proportion of the wind speeds that exceed mean wind speed. And c is given by
Table 1 shows the locations where wind parameters were measured and the set of instruments used for taking these measurements. There are three stationary wind measurement installations for long term measurements and two locations where the mobile LiDAR unit was deployed for short term measurements. Locations chosen for stationary measurement installation are on the rooftops of the tallest building in their respective vicinity. 3.1. Stationary installation: rooftop anemometry IEC61400 e Part 2 provides some general guidelines regarding the locations suitable for the installation of small wind turbines under inclined flow and turbulent wind conditions on rooftops [20]. Same guidelines, depicted in Fig. 2, have been adopted for the placement of anemometers on rooftops. Fig. 3 shows the measurement mast set up on one of the rooftops. Many commercial wind measurement systems are available. But these systems are less flexible in terms of the data transmission and types of sensors that can be installed.
406
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 2. General guidelines for the placement of anemometers on the rooftop to overcome turbulence conditions and inclined flow. Source: [20].
Fig. 4 shows the process flow diagram of the cost-effective measurement setup that was designed. Measured parameters are wind speed and direction. Different types of wind measurement instruments deployed are - wind vane, cup anemometers, 3-axis sonic anemometers, 2-axis sonic anemometers. Cup anemometers convert the rate of rotation into wind speed whereas sonic anemometers use ultrasonic pulses to measure the wind speed and direction. 2-axis anemometers measure ‘U’ and ‘V’ horizontal components of the wind and 3-axis anemometers measure an additional vertical wind vector ‘W’. Hence, 3-axis sonic anemometers have the ability to provide detailed information on the turbulence of the wind. Sonic anemometers are designated as primary sensors and cup anemometers, wherever used, are designated as secondary sensors. Missing or invalid data points from sonic anemometers are substituted by measurement data from cup anemometers [26]. From the measured 4 Hz data, 10 min averaged samples were derived. As documented in the calibration certification, the NRG cup anemometers were factory calibrated with maximum deviation of 0.035 m/s at 14e15 m/s. This complies with the MEASNET requirement for anemometers of absolute uncertainty less than 0.1 m/s at a mean wind velocity of 10 m/s [27]. 3axis sonic anemometers and 2-axis sonic anemometers have wind speed accuracy of ±2% and direction accuracy of ±20 at 12 m/s. No onsite calibration was carried out for any of the sensors. On building rooftops, the height of wind mast is limited to 4 m due to civil aviation and other regulatory requirements. Datalogger has integrated 3G modem that transmits 10-min average data to the FTP server. Using Python and Matlab scripts, FTP data is retrieved, stored locally and processed further. From Equation (3) it can be seen that air density has direct effect on the power generated by a wind turbine. However, for this study measurement masts have no mounted pressure and temperature measurement sensors.
and horizontal extrapolation and significantly reduce uncertainties in wind speed and energy yield prediction. For a site in the Netherlands, uncertainty in terms of wind speed was reduced by up to 4% and uncertainty of energy yield reduced by up to 7% [30]. Continuous wave, dual mode LiDAR profiler ZephIR 300 has been used for wind measurement campaigns at multiple locations to get
3.2. Roving installation: LiDAR unit LiDAR profiles work based on detection and processing of the Doppler shift of backscattered laser beam due to aerosols in the wind [28]. A LiDAR profiler is not a replacement for wind masts due to the costs involved [29]. But short term LiDAR campaigns significantly improve wind statistics, long term representation, vertical
Fig. 3. Measurement setup at Woodlands Crescent showing cup anemometer, 2-axis sonic anemometer and wind vane. DataTaker with integrated 3G modem is power by solar panels.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
407
Fig. 4. Process flow diagram from measurement to reporting for rooftop wind measurement installations.
holistic wind measurement data including turbulence intensity and wind shear profile. The LiDAR profiler can measure wind characteristics at ten user defined heights between 10 and 200 m at the sampling rate of 50 Hz [31]. Fig. 5 shows the mobile remote wind sensing system implemented with ZephIR 300. Gill 3-axis sonic anemometer was deployed on a mobile mast of height between 1
and 10 m. A Campbell Scientific CR800 datalogger is connected to ZephIR 300 and 3-axis anemometer, which transmits data over modem. This mobile sensing unit was deployed for short term measurements at several locations of interest to gain the complete understanding of prevailing wind characteristics. Wherever possible IEA recommended practices [32] for deploying and
Fig. 5. Remote sensing system using LiDAR profiler ZephIR 300 and Campbell Scientific CR800 datalogger.
408
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 6. A plot of daily mean wind speed at Marine Drive showing two instances of outage. Due to lack of a secondary sensor, missing data from the primary sensor could not be filled for this specific site.
operating the LiDAR profiler and data analysis have been followed. The LiDAR profiler was calibrated by the manufacturer and the calibration accuracy was validated against the measurements from a met mast. Calibration uncertainty of less than 0.1% and overall variation in calibration of less than ±0.5% is promised [33].
4. Results Our experience shows that 3-axis sonic anemometers are more susceptible to lightning strikes despite proper lightning protection schemes. There was some data loss due to outage of power supply
Fig. 7. Diurnal profile variation of mean wind speed and wind direction by month at Marine Drive. A diurnal variation of mean wind speed is weak in the month of August.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
409
Fig. 8. Wind direction frequency distribution and Weibull distribution for wind speeds at Marine Drive.
and anemometer faults. Data recovery is between 75 and 90% for various sensors at different locations (Fig. 6). At Woodlands Crescent and Pandan Gardens, cup anemometers were installed as secondary sensors. Due to secondary sensors, wind data is available
for two locations without any significant disruptions. However, at Marine Drive no secondary sensor was installed during the period of measuring considered for this paper. Southern shore of Singapore experiences better wind speeds
Fig. 9. Wind direction frequency distribution and Weibull distribution for wind speeds at Pandan Gardens.
Fig. 10. Wind direction frequency distribution and Weibull distribution for wind speeds at Woodlands Crescent.
410
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 11. Monthly average wind speeds measured from all the sensors at Marine Drive, Woodlands Crescent and Pandan Gardens.
due to open sea to the South. Hence LiDAR campaigns are mostly restricted to the southern shore of Singapore as can be seen in Fig. 1. Fig. 7 shows the monthly diurnal variation of mean wind speed and wind direction. Figs. 8e10 shows the Weibull distribution for the three measurement sites. Fig. 11 indicates the monthly variable of average wind speeds for all the sensors, separately, at all the three locations. From the wind measurements from stationary installations it was observed that southern coast of Singapore is better in terms of wind resources. So, LiDAR campaigns were undertaken at three locations - Tuas View Extension, SSC Tanah Merah and Sentosa, all located on the southern coast of Singapore. A thorough understanding of the Turbulence intensity of wind is important to understand the loadings on the turbine and hence also important for the optimum turbine design. The Normal Turbulence Model (NTM), defined in IEC61400-2, does not accurately estimate the turbulence in urban environments [34]. Figs. 13 and 14 are results from the LiDAR campaigns undertaken at these locations. Fig. 13 shows the variation of mean turbulence intensity, for different heights, according to varying wind speeds. Fig. 14 shows the 16-sector roughness roses for the three coastal locations.
wind speeds are observed in the month of January at all the locations. However, during Southwest Monsoon from June to September, Site B experiences high wind speeds in June and other two locations in the month of August. Turbulence intensities as the wind speed approaches 15 m/s are 0.15 at Site C and 0.25 at Site A. For Site B, there are not enough data points for wind speeds higher than 11 m/s. As can be seen in the Fig. 12, the turbulence intensity decreases with increasing wind speeds. The Turbulence Intensity versus the wind speeds at different heights is depicted in Fig. 13. It can be seen that the turbulence intensity grows in magnitude after 8 m/s. The turbulence intensity is fairly uniform for wind speeds between 2 m/s and 8 m/ s. Fig. 14 shows the 16-sector roughness rose obtain from the LiDAR measurements. Vertical shear profile of the wind indicates substantial surface roughness lengths at Sentosa and surrounding areas near the central business district. To the north of Sentosa island there is Singapore's business district
5. Discussion For the three sites where long term wind measurements were taken, Table 2 summarises the Weibull parameters estimated based on three algorithms viz. maximum likelihood method, least squares method and WAsP method. From the assessment of measured twoyear wind data for the three sites, originally chosen as prospective sites for small wind turbine installation, following outcomes can be listed out: Maximum wind power density at Site A, Site B and Site C are 45, 35 and 15 W/m2 respectively. The highest average diurnal wind speeds are observed between 3 pm and 4 pm. Based on monthly distribution of mean wind speed, two high wind seasons are observed. Even though Northeast Monsoon extends from December to March, as observed in Fig. 11, highest
Fig. 12. Turbulence intensity at Marine Drive, Woodlands Crescent and Pandan Gardens.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
411
Fig. 13. Turbulence Intensity and wind speed distribution at three heights (50 m, 80 m, and 100 m) from measurements at SSC Tanah Merah using LiDAR device. For low wind speeds, from 1 m/s to 8 m/s, the turbulence intensity gradually decreases.
with tall skyscrapers and to the south there are small islands. This explains significantly higher surface roughness lengths at Sentosa. The highest roughness length at Sentosa is 11.6 m in the direction sector 146.25 e168.75 . Changi airport lies to the north of SSC Tanah Merah, which is the reason why roughness lengths at SSC Tanah Merah are significantly lower than that of Sentosa. The highest roughness length at SSC Tanah Merah is 0.26 m in the direction sector 78.75 e101.25 . The roughness lengths at Tuas View Extension are 0.23 m in the direction sector 168.75 e191.25 and 0.127 m in the direction sector 348.75 e11.25 . However, roughness lengths in other direction sectors are found to be close to zero. Hence, Tuas View Extension is found to be the best location out of the three locations.
There is significant diurnal variability and seasonal variability that has to be taken into account to estimate wind energy potential.
5.1. Techno-economic analysis The installed costs per kW for small wind turbines are highly variable depending on the technology, country of origin and the market place conditions. The installed cost per kW for Skystream 3.7 is $7917 and annual operation and maintenance cost is estimated at $0.01/kWh [35]. In the USA, the cost per installed kW is between $2300 and $10,000. However, in China the cost per installed kW is as low as 12,000 Yuan (US $1900) [36]. The industry's cost target is to bring the cost per installed kW to
412
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 14. 16-sector roughness rose derived from measured vertical shear profiles at Tuas View Extension, Sentosa and SSC Tanah Merah using the LiDAR device. Axis is in logarithmic scale and six circles represent roughness length ZO values from 0.0001 m to 100 m.
$3000e$4500 [37]. Table 3 shows the basic tabulation to calculate some key techno-economic parameters [38]. It is difficult to understand the direct correlation between the cost of electricity, payback period and the turbine price, with too many variables at play. Hence, this calculation assumes no grant, incentive or loan. Only Site A and Site C are considered for economic analysis. The table indicates Annual Energy Production (AEP) and payback periods for a 2.5 kW turbine installed at Site A and Site C respectively. For various commercially available turbines, calculated Net Capacity Factor (NCF) is between 2 and 12% at Site C and between 3
and 15% at Site A. Due to better wind conditions, capacity factor at Site A is would be 25% higher than that of Site C. NCF is assumed to be 8.75% and 7% for Site A and Site C respectively. The cost of electricity in Singapore is directly dependent on the cost of imported natural gas. In the past 5 years, electricity tariff in Singapore has fluctuated between 0.25 and 0.30 S$/kWh [39]. Assuming 0.35 S$/kWh for exported electricity, expected payback period for Site A is 14 years and expected payback period for Site C is 19 years, longer than the payback period for Site C, due to relatively poor capacity factor. At both locations, payback period has to be less than
Table 2 Summary table. Algorithm
Weibull k (shape factor)
Weibull c (scale factor in m/s)
Mean (m/s)
Proportion above [Mean] m/s
Pandan Gardens (Site A)
Maximum likelihood Least squares WAsP Actual data
2.037 2.005 2.086
3.835 3.846 3.868
3.398 3.408 3.426 3.4
0.457 0.458 0.466 0.466
Woodlands Crescent (Site B)
Maximum likelihood Least squares WAsP Actual data
1.634 1.689 1.615
2.267 2.252 2.258
2.029 2.01 2.023 2.026
0.435 0.433 0.432 0.432
Marine Drive (Site C)
Maximum likelihood Least squares WAsP Actual data
2.312 2.174 2.489
3.793 3.83 3.854
3.361 3.391 3.419 3.367
0.468 0.47 0.489 0.489
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
413
Table 3 Payback period and AEP at Site A and Site C. #
Parameters
a) Estimating the value of generated electricity 1 Turbine capacity (TC) 2 Net capacity factor (CF) 3 Annual energy production (Y) 4 Export fraction (F) 5 Electricity exported (Eexp) 6 Electricity used directly (Edir) 7 Unit price of exported electricity (Pexp) 8 Unit cost of imported grid electricity (Pimp) 9 Value of exported electricity (Vexp) 10 Value of electricity used directly (Vdir) 11 Total value of electricity (Vtot) b) Estimating the simple payback 1 Total capital cost of turbine installation (CC) 2 Annual operating cost (Co) 3 Simple payback period
Formula
Site A
Site C
Unit
TC CF Y ¼ TC$CF$8760 h/year F Eexp ¼ Y $ F Edir ¼ Y $ (1eF) Pexp Pimp Vexp ¼ Eexp $ Pexp Vdir ¼ Edir $ Pimp Vtot ¼ Vexp þ Vdir
2.5 8.75 1916 100 1916 0 0.35 0.25 670.7 0 670.7
2.5 7 1533 100 1533 0 0.35 0.25 536.6 0 536.6
kW % kWh/year % kWh/year kWh/year S$/kWh S$/kWh S$ S$ S$/year
CC Co CC/(Vtot CO)
7500 150 14.4
7500 150 19.3
S$ S$/year years
20 years which is normally the life time of a wind turbine. This gives the precondition on the maximum cost per installed kW, which is S$ 3000. 6. Conclusions
[3] [4] [5]
In this paper the challenges and prior research on urban wind measurements and wind resource assessment have been examined. Two measurement systems and their implementation have been discussed-rooftop wind measurement systems and data integration for urban canopy and remote sensing, mobile wind profiler based on a LiDAR device. Key results of several wind measurement campaigns in Singapore are presented. Wind measurements from the three rooftop wind measurement installations have been discussed. Also, Turbulence Intensity and roughness rose derived from LiDAR campaigns have been presented. It is found that the southern shore of Singapore is ideal for wind turbines. Wind power densities at Pandan Gardens and Marine Drive are 45 and 35 W/m2 respectively. A CFD simulation approach would be ideal to understand the actual flow patterns in these localities to decide the best locations for wind turbine installations. For the cost per installed kW of less than S$ 3,000, it is possible to achieve simple payback period of less than 20 years. By employing materials that are cost-effective and locally sourced and unique design concepts, it is possible to achieve such low capital cost without substantially degrading the quality and performance. Since this the first study of its kind for Singapore, results presented are from the groundwork towards a comprehensive urban wind flow simulation for Singapore. Acknowledgements Authors would like to thank the Housing Development Board of Singapore for partially funding this research project. Authors would like to express their heartfelt gratitude to the Singapore Economic Development Board for their generous funding to facilitate further research under the Energy Innovation Research Programme (NRF2013EWT-EIRP003-032). Thanks to Do Dinh Tho David, Shu Guixin, P Mohan Kumar and Dr Giuseppe Cavallaro for their assistance in various matters related to the work. References [1] Energy Market Authority, Singapore Energy Statisctics 2014, Research and Statistics Unit, Energy Market Authority, Republic of Singapore, Republic of Singapore, 2014. [2] Sectoral Measures to Reduce Emissions (Up to 2020), NCCS, 13.11.2014
[6] [7]
[8]
[9]
[10]
[11] [12] [13]
[14]
[15] [16]
[17]
[18]
[19]
[20] [21] [22]
[23]
(Online). Available: http://app.nccs.gov.sg/nccs-2012/sectoral-measures-toreduce-emissions-up-to-2020.html?AspxAutoDetectCookieSupport¼1 (accessed 13.11.2014). J. Luther, T. Reindl, Solar Photovoltaic (PV) Roadmap for Singapore (A Summary), Solar Energy Research Institute of Singapore, Singapore, 2014. W.T.L. Chow, M. Roth, Temporal dynamics of the urban heat island of Singapore, Int. J. Climatol. 26 (15) (2006) 2243e2260. National Environment Agency, WEATHERwise Singapore, 2009 (Online). Available: http://www.nea.gov.sg/docs/default-source/training-knowledgehub/guidebook-on-climate-of-singapore.pdf?sfvrsn¼0 (accessed 20.01.2015). G.K. Nathan, T.N. Goh, Wind measurements in an equitorial region (Singapore), Sol. Energy 26 (1981) 275e278. Singapore's Climate Information & Data, 13.11.2014 (Online). Available: http://app2.nea.gov.sg/weather-climate/climate-information/localclimatology (accessed 14.11.2014). J.T. Millward-Hopkins, A. Tomlin, D. Ingham, M. Pourkashanian, The predictability of above roof wind resource in the urban roughness sublayer, Wind Energy 15 (2012) 225e243. T.F. Ishugah, Y. Li, R.Z. Wang, J.K. Kiplagat, Advances in wind energy resource exploitation in urban environment: a review, Renew. Sustain. Energy Rev. 37 (2014) 613e626. G. Gualtieri, S. Secci, Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy, Renew. Energy 36 (3) (2011) 1081e1094. H.-C. Lim, T.-Y. Jeong, Wind energy estimation of the Wol-Ryong coastal region, Energy 35 (12) (2010) 4700e4709. L. Lu, K. Sun, Wind power evaluation and utilization over a reference high-rise building in urban area, Energy Build. 68 (2014) 339e350. S.L. Walker, Building mounted wind turbines and their suitability for the urban scaleda review of methods of estimating urban wind resource, Energy Build. 43 (8) (2011) 1852e1862. A. Gagliano, F. Nocera, F. Patania, A. Capizzi, Assessment of micro-wind turbines performance in the urban environments: an aided methodology through geographical information systems, Int. J. Energy Environ. Eng. 4 (1) (2013) 43. M.A. Heath, J.D. Walshe, S.J. Watson, Estimating the potential yield of small building-mounted wind turbines, Wind Energy 10 (3) (2007) 271e287. M.X. Song, K. Chen, Z. He, X. Zhang, Wind resource assessment on complex terrain based on observations of a single anemometer, J. Wind Eng. Ind. Aerodyn. 125 (2014) 22e29. F. Toja-Silva, A. Colmenar-Santos, M. Castro-Gil, Urban wind energy exploitation systems: behaviour under multidirectional flow conditionsdopportunities and challenges, Renew. Sustain. Energy Rev. 24 (2013) 364e378. D. Elliott, D. Infield, An assessment of the impact of reduced averaging time on small wind turbine power curves, energy capture predictions and turbulence intensity measurements, Wind Energy 17 (2) (Feb. 2014) 337e342. E.L. Petersen, N.G. Mortensen, L. Landberg, J. Højstrup, H.P. Frank, Wind power meteorology. Part I: climate and turbulence, Wind Energy 1 (S1) (1998) 25e45. International Standard, Wind Turbines e Part 2 Small Wind Turbines, International Electrotechnical Commission, 2013. P.M. Pardalos, S. Rebennack, M.V.F. Pereira, N. Illiadis, V. Pappu, Handbook of Wind Power Systems, Springer Berlin Heidelberg, 2013. J.A. Carta, P. Ramirez, S. Velazquez, A review of wind speed probability distributions used in wind energy analysis case studies in the Canary Islands, Renew. Sustain. Energy Rev. 13 (5) (2009) 933e955. M.A. Al-Fawzan, Methods for Estimating the Parameters of the Weibull Distribution, 2000 (Online). Available: http://interstat.statjournals.net/YEAR/ 2000/articles/0010001.pdf (accessed 01.01.2015).
414
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
[24] H.S. Bagiorgas, M. Giouli, S. Rehman, L. Al-Hadhrami, Weibull parameters estimation using four different methods and most energy-carrying wind speed analysis, Int. J. Green Energy 8 (2011) 529e554. [25] A.P. Schaffarczyk, Introduction to Wind Turbine Aerodynamics, Springer Berlin Heidelberg, Berlin, 2014. [26] M.C. Brower, Wind Resource Assessment: a Practical Guide to Developing a Wind Project, John Wiley & Sons, Inc., May 2012. [27] MEASNET, Anemometer Calibration Procedure, October 2009 (Online). Available: http://www.measnet.com/documents/ (accessed June 2015). [28] J. Gottschall, M.S. Courtney, R. Wagner, H. Jørgensen, I. Antoniou, Lidar profilers in the context of wind energyea verification procedure for traceable measurements, Wind Energy 15 (1) (2012) 147e159. [29] P.J.M. Clive, Remote sensing best practice, in: EWEA Offshore, Amsterdam, 2011. [30] Erik Holtslag, Improved Bankability e the Ecofys Postion on LiDAR Use, ECOFYS, 2013. Available online: http://www.ecofys.com/files/files/ecofys2013-position-paper-on-lidar-use_02.pdf. [31] “zephirlidar.com,” ZephIR, (Online). Available: http://www.zephirlidar.com/ support/tech-specs/. (accessed 14.11.2014). [32] International Energy Agency, Ground-based Vertically-Profiling Remote Sensing for Wind Resource Assessment, International Energy Agency, 2013.
[33] T. Rutherford, M. Harris, W. Barker, E.B.D. Roziers, M. Pitter, R. Scullion, C. Slinge, Lidar Calibration and Performance Validation Process, Natural Power, Hollybush Ledbury, UK, 2014. [34] N. Carpman, Turbulence Intensity in Complex Environments and its Influence on Small Wind Turbines, Department of Earth Sciences Geotryckeriet, Uppsala University, Uppsala, 2011. [35] ICF International, “The Cost and Performance of Distributed Wind Turbines, 2010e35, June 2010. (Online). Available: http://www.eia.gov/analysis/ studies/distribgen/system/pdf/appendix-b.pdf. (accessed May 2015). [36] New Energy Husum, 2015 Small Wind World Report Summary, 2015 (Online). Available: http://small-wind.org/wp-content/uploads/2014/12/Summary_ SWWR2015_online.pdf (accessed May 2015). [37] DWEA, DWEA Distributed Wind Vision e 2015e2030, March 2015 (Online). Available: http://distributedwind.org/wp-content/uploads/2012/08/DWEADistributed-Wind-Vision.pdf (accessed May 2015). [38] Small-scale Wind Energy: Policy Insights and Practical Guidance, Carbon Trust, 2008. [39] Energy Market Authority. https://www.ema.gov.sg, 30 December 2014 (Online). Available: https://www.ema.gov.sg/Residential_Electricity_Tariffs.aspx (accessed 20.01.2015).
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Wind resource assessment for urban renewable energy application in Singapore B.R. Karthikeya a, Prabal S. Negi b, N. Srikanth a a b
*
Energy Research Institute @ Nanyang Technological University, #06-04, 1 Cleantech Loop, Cleantech One, Singapore 637141, Singapore School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 February 2015 Received in revised form 5 August 2015 Accepted 7 October 2015 Available online xxx
In highly urbanized and energy intensive countries like Singapore all possible avenues for power generation need attention. In this context, rooftop installations of both solar and wind energy are of particular interest for Singapore, especially because of Singapore's condition of land limitation. Decentralized and distributed energy sources such as rooftop wind and solar installations have numerous advantages. However, the potential for wind energy is not fully understood in built-up areas and thus not fully exploited. Hence it is important to study wind flow patterns in built-up areas and also develop technologies tuned for these conditions. The demand for technologies that deliver energy for low flow wind conditions is of paramount importance to Southeast Asia region and especially to Singapore. In this paper, two measurement systems, namely stationary rooftop wind mast and mobile Light Detection and Ranging (LiDAR) profiler, have been discussed. Measured wind data from various sites across Singapore using have also been presented. Wind roses, Weibull distribution, roughness lengths and other statistical analyses were carried out to understand the prevailing wind characteristic, which is used for evolving the basic criteria for economic viability of roof top wind turbines in the tropical conditions of Singapore. © 2015 Published by Elsevier Ltd.
Keywords: Urban wind potential Wind energy Wind speed Urban wind turbines Weibull distribution Resource assessment
1. Introduction Singapore's installed electricity generation capacity is 12.5 GW, which is almost entirely derived from fossil fuels [1]. Household electricity consumption is 15% of the total electricity consumption and consumption by commercial and services sector is 37%. Singapore is committed to reducing its emissions by 7%e11% below 2020 business-as-usual levels [2]. Hence, decentralized and distributed energy sources such as rooftop wind and solar installations require due attention in terms of resource estimation and techno-economic evaluation. Based on the space availability in Singapore, maximum cumulative capacity of rooftop photovoltaic installations is estimated to be 5 GWp by 2030, with 80% of the installed capacity on rooftops and facades [3]. However, the potential for wind energy is not fully understood in built-up areas and hence not fully estimated. In order for Singapore to achieve this goal and to diversify the energy mix, several government agencies are working with Institutes of Higher Learning and local SMEs. National Environment Agency (NEA) owns and maintains several
* Corresponding author. E-mail address: [email protected] (N. Srikanth). http://dx.doi.org/10.1016/j.renene.2015.10.010 0960-1481/© 2015 Published by Elsevier Ltd.
met masts that measure the surface wind across the island nation. But this data is inadequate to estimate the wind potential for the installation of rooftop wind turbines in densely urbanized Singapore. Housing Development Board (HDB) of Singapore, a government body responsible for public housing in Singapore, has been working with the Energy Research Institute at Nanyang Technological University (ERI@N) to study the feasibility of rooftop wind turbines in Singapore. Hence, ERI@N has developed two wind measurement systems for Singapore, namely remote sensing and mobile LiDAR measurement system and rooftop standard anemometry measurement system with wireless data transfer capabilities. ERI@N is the first and so far the only institute in Singapore that conducts wind measurement campaigns in the region using SODAR (SOnic Detection and Ranging) and LiDAR wind profilers. Singapore has a wet equatorial climate with fairly uniform mean monthly temperatures between 26 C and 28 C throughout the year [4]. Singapore's weather can be classified into four seasons. These four seasons are the Northeast Monsoon (December to early March), the Southwest Monsoon (June to September) and two relatively short inter-monsoon seasons. Singapore experiences light and variable winds during the transition between these
404
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
seasons [5]. Wind power is affected by the monsoon weather pattern [6]. Singapore's weather is characterized by high humidity and frequent rainfall [7]. Singapore receives a considerable amount of rainfall throughout the year. The mean monthly rainfall can be between 150 mm and 275 mm depending on the season [4]. Fig. 1 indicates the locations where ERI@N either has permanent masts for long term wind measurements or has undertaken LiDAR campaigns for short-term measurements. Green ‘rhombus’ markers indicate the four HDB rooftops where wind measurement masts have been installed. In this paper, wind measurements of over two years (2012e2014) from the three sites viz. Woodlands Crescent, Pandan Gardens and Marine Drive have been presented and discussed. Results of short-term LiDAR campaigns undertaken at Sentosa and SSC Tanah Merah have been presented, especially to understand the roughness lengths in various direction sectors and turbulence intensity variation with increasing wind speeds. A stripped down techno-economic analysis has been presented for the case of small wind turbine installation. 2. Urban wind energy potential Urban areas include considerable turbulence and local aberrations. Urban wind energy problem has two main aspects: One, understanding of flow pattern within the lowest urban canopy layer where individual building affects the flow; Two, vertical extrapolation of wind in roughness and inertial sublayers. Wind characteristics such as surface drag, vertical shear profile and turbulence intensity are affected by the roughness lengths of the urban canopy. Estimation of spatially averaged wind profile for urban
surface sublayer and roughness sublayer has been discussed in Ref. [8]. In Ref. [9] Ishugah et al. discuss three methods to estimate surface roughness in urban areas viz. Davenport classification, morphometric and meteorological methods. Giovanni and Sauro [10] have discussed in detail the effect of roughness length and wind shear coefficients on the Annual Energy Yield (AEP) for three coastal sites in Southern Italy. Hee-Chang and Tae-Yoon [11] discuss the wind resource assessment of a wind energy site in Jeju Island and spectra analysis of wind data. There can be significant increase of wind speed at specific locations due to concentration effect of buildings [12]. Hence, estimation of wind parameters is more complicated in built up areas. Sara Louise Walker [13] has discussed in detail various methods of estimating urban wind resource. Wind potential in urban areas can be evaluated using standard anemometry at the site, computational fluid dynamics (CFD) simulation and wind tunnel experiments on the physical model of the building and the surrounding area [14]. A combination of two of these methods is employed to verify the results. A CFD simulation of wind flow around simple building and subsequent wind tunnel measurements to verify CFD simulation have been discussed in Ref. [15]. A method describing a combination of CFD and measurements from a single anemometer for wind energy assessment has been discussed in Ref. [16]. Subsequent to a thorough understanding of the flow patterns, various wind energy systems suitable for exploiting wind potential in that specific urban environment can be considered [17]. D. Elliott and D. Infield [18] have studied the effect of averaging time wind measurements on turbine energy capture prediction and turbulence intensity calculation. The AEP for low wind speed
Fig. 1. ERI@N’s wind measurement sites in Singapore.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
405
Table 1 Wind measurement locations. Location
Instruments
Elevation (m)
Woodlands Crescent Pandan Gardens Marine Drive Sentosa SSC Tanah Merah
WindSonic 2-axis anemometer, cup anemometer, wind vane WindMaster 3-axis anemometer, cup anemometer, wind vane WindSonic 2-axis anemometer Remote sensing LiDAR Remote sensing LiDAR
64.2 78 83 NA NA
regimes does not vary more than 1e2% for different averaging periods. However the calculated turbulence intensity changes significantly with the averaging time period [18]. We've chosen 10min average wind data has been used in this paper. Referring to various sources [19e21], a brief description of meteorological and technical nomenclature applicable for this paper is as follows. Weibull distribution is the probability distribution function used to describe the distribution of wind speeds over time duration. Weibull k (shape factor) parameter controls the width of the distribution and Weibull c (scale factor in m/s) parameter controls the average wind speed. The two-parameter Weibull probability function is given by
PðV; c; kÞ ¼
k k V k1 V exp c c c
(1)
(2)
There are various methods to estimate the Weibull parameters. In this paper, we have considered maximum likelihood, least squares and WAsP methods. Maximum likelihood method is the most widely used method. Shape factor k according to maximum likelihood method is given by Ref. [22].
2 31 P n 6 n vk ln v 7 1X 6 i¼1 i 7 i k¼6 P ln vi 7 n k 4 5 n v i¼1
TI ¼ s=Vav
(8)
where s ¼ 10-min standard deviation, Vav ¼ 10-min average wind speed. Wind speed variation with height above ground is given by logarithmic profile and power law profile. As per logarithmic profile, wind speed at height z1 is given by
ln zz10 ln zz20
(9)
where z0 ¼ roughness length, z2 ¼ reference height, v(z2) ¼ wind speed at the reference height. Power extracted by a wind turbine from the incident wind can be expressed as
P ¼ 0:5CP rV3av A
(10)
where A ¼ rotor area, CP ¼ power coefficient of the rotor, Vav ¼ wind speed and r ¼ air density [25]. AEP is given in Watt hour and over one year is calculated using the Weibull distribution and power curve of the wind turbine.
(3)
i¼1
i
(7)
Turbulence intensity is defined as
vðz1 Þ ¼ vðz2 Þ
Cumulative distribution function is given by
vo k pðv vo Þ ¼ 1 exp c
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u P 3 u Vi u 3 N c¼u u t NG 3k þ 1
Z∞ AEP ¼ 8; 760
PðV; c; kÞPðVÞdv
(11)
0
Scale factor c is given by
3. Wind measurement system
" c¼
n 1X vk n i¼1 i
#1=
k
(4)
In least squares method, the slope and intercept of the line of best fit given by the equation below are calculated [23].
ln ln
1 ¼ k ln V k ln c ½1 pðvÞ
(5)
WAsP algorithm was originally described by Troen and Petersen. According to [24], WAsP algorithm calculates c and k to fit the power density in the time series instead of the mean wind speed. k is given by the equation
lnX ¼ G
k 1 þ1 k
(6)
where X represents the proportion of the wind speeds that exceed mean wind speed. And c is given by
Table 1 shows the locations where wind parameters were measured and the set of instruments used for taking these measurements. There are three stationary wind measurement installations for long term measurements and two locations where the mobile LiDAR unit was deployed for short term measurements. Locations chosen for stationary measurement installation are on the rooftops of the tallest building in their respective vicinity. 3.1. Stationary installation: rooftop anemometry IEC61400 e Part 2 provides some general guidelines regarding the locations suitable for the installation of small wind turbines under inclined flow and turbulent wind conditions on rooftops [20]. Same guidelines, depicted in Fig. 2, have been adopted for the placement of anemometers on rooftops. Fig. 3 shows the measurement mast set up on one of the rooftops. Many commercial wind measurement systems are available. But these systems are less flexible in terms of the data transmission and types of sensors that can be installed.
406
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 2. General guidelines for the placement of anemometers on the rooftop to overcome turbulence conditions and inclined flow. Source: [20].
Fig. 4 shows the process flow diagram of the cost-effective measurement setup that was designed. Measured parameters are wind speed and direction. Different types of wind measurement instruments deployed are - wind vane, cup anemometers, 3-axis sonic anemometers, 2-axis sonic anemometers. Cup anemometers convert the rate of rotation into wind speed whereas sonic anemometers use ultrasonic pulses to measure the wind speed and direction. 2-axis anemometers measure ‘U’ and ‘V’ horizontal components of the wind and 3-axis anemometers measure an additional vertical wind vector ‘W’. Hence, 3-axis sonic anemometers have the ability to provide detailed information on the turbulence of the wind. Sonic anemometers are designated as primary sensors and cup anemometers, wherever used, are designated as secondary sensors. Missing or invalid data points from sonic anemometers are substituted by measurement data from cup anemometers [26]. From the measured 4 Hz data, 10 min averaged samples were derived. As documented in the calibration certification, the NRG cup anemometers were factory calibrated with maximum deviation of 0.035 m/s at 14e15 m/s. This complies with the MEASNET requirement for anemometers of absolute uncertainty less than 0.1 m/s at a mean wind velocity of 10 m/s [27]. 3axis sonic anemometers and 2-axis sonic anemometers have wind speed accuracy of ±2% and direction accuracy of ±20 at 12 m/s. No onsite calibration was carried out for any of the sensors. On building rooftops, the height of wind mast is limited to 4 m due to civil aviation and other regulatory requirements. Datalogger has integrated 3G modem that transmits 10-min average data to the FTP server. Using Python and Matlab scripts, FTP data is retrieved, stored locally and processed further. From Equation (3) it can be seen that air density has direct effect on the power generated by a wind turbine. However, for this study measurement masts have no mounted pressure and temperature measurement sensors.
and horizontal extrapolation and significantly reduce uncertainties in wind speed and energy yield prediction. For a site in the Netherlands, uncertainty in terms of wind speed was reduced by up to 4% and uncertainty of energy yield reduced by up to 7% [30]. Continuous wave, dual mode LiDAR profiler ZephIR 300 has been used for wind measurement campaigns at multiple locations to get
3.2. Roving installation: LiDAR unit LiDAR profiles work based on detection and processing of the Doppler shift of backscattered laser beam due to aerosols in the wind [28]. A LiDAR profiler is not a replacement for wind masts due to the costs involved [29]. But short term LiDAR campaigns significantly improve wind statistics, long term representation, vertical
Fig. 3. Measurement setup at Woodlands Crescent showing cup anemometer, 2-axis sonic anemometer and wind vane. DataTaker with integrated 3G modem is power by solar panels.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
407
Fig. 4. Process flow diagram from measurement to reporting for rooftop wind measurement installations.
holistic wind measurement data including turbulence intensity and wind shear profile. The LiDAR profiler can measure wind characteristics at ten user defined heights between 10 and 200 m at the sampling rate of 50 Hz [31]. Fig. 5 shows the mobile remote wind sensing system implemented with ZephIR 300. Gill 3-axis sonic anemometer was deployed on a mobile mast of height between 1
and 10 m. A Campbell Scientific CR800 datalogger is connected to ZephIR 300 and 3-axis anemometer, which transmits data over modem. This mobile sensing unit was deployed for short term measurements at several locations of interest to gain the complete understanding of prevailing wind characteristics. Wherever possible IEA recommended practices [32] for deploying and
Fig. 5. Remote sensing system using LiDAR profiler ZephIR 300 and Campbell Scientific CR800 datalogger.
408
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 6. A plot of daily mean wind speed at Marine Drive showing two instances of outage. Due to lack of a secondary sensor, missing data from the primary sensor could not be filled for this specific site.
operating the LiDAR profiler and data analysis have been followed. The LiDAR profiler was calibrated by the manufacturer and the calibration accuracy was validated against the measurements from a met mast. Calibration uncertainty of less than 0.1% and overall variation in calibration of less than ±0.5% is promised [33].
4. Results Our experience shows that 3-axis sonic anemometers are more susceptible to lightning strikes despite proper lightning protection schemes. There was some data loss due to outage of power supply
Fig. 7. Diurnal profile variation of mean wind speed and wind direction by month at Marine Drive. A diurnal variation of mean wind speed is weak in the month of August.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
409
Fig. 8. Wind direction frequency distribution and Weibull distribution for wind speeds at Marine Drive.
and anemometer faults. Data recovery is between 75 and 90% for various sensors at different locations (Fig. 6). At Woodlands Crescent and Pandan Gardens, cup anemometers were installed as secondary sensors. Due to secondary sensors, wind data is available
for two locations without any significant disruptions. However, at Marine Drive no secondary sensor was installed during the period of measuring considered for this paper. Southern shore of Singapore experiences better wind speeds
Fig. 9. Wind direction frequency distribution and Weibull distribution for wind speeds at Pandan Gardens.
Fig. 10. Wind direction frequency distribution and Weibull distribution for wind speeds at Woodlands Crescent.
410
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 11. Monthly average wind speeds measured from all the sensors at Marine Drive, Woodlands Crescent and Pandan Gardens.
due to open sea to the South. Hence LiDAR campaigns are mostly restricted to the southern shore of Singapore as can be seen in Fig. 1. Fig. 7 shows the monthly diurnal variation of mean wind speed and wind direction. Figs. 8e10 shows the Weibull distribution for the three measurement sites. Fig. 11 indicates the monthly variable of average wind speeds for all the sensors, separately, at all the three locations. From the wind measurements from stationary installations it was observed that southern coast of Singapore is better in terms of wind resources. So, LiDAR campaigns were undertaken at three locations - Tuas View Extension, SSC Tanah Merah and Sentosa, all located on the southern coast of Singapore. A thorough understanding of the Turbulence intensity of wind is important to understand the loadings on the turbine and hence also important for the optimum turbine design. The Normal Turbulence Model (NTM), defined in IEC61400-2, does not accurately estimate the turbulence in urban environments [34]. Figs. 13 and 14 are results from the LiDAR campaigns undertaken at these locations. Fig. 13 shows the variation of mean turbulence intensity, for different heights, according to varying wind speeds. Fig. 14 shows the 16-sector roughness roses for the three coastal locations.
wind speeds are observed in the month of January at all the locations. However, during Southwest Monsoon from June to September, Site B experiences high wind speeds in June and other two locations in the month of August. Turbulence intensities as the wind speed approaches 15 m/s are 0.15 at Site C and 0.25 at Site A. For Site B, there are not enough data points for wind speeds higher than 11 m/s. As can be seen in the Fig. 12, the turbulence intensity decreases with increasing wind speeds. The Turbulence Intensity versus the wind speeds at different heights is depicted in Fig. 13. It can be seen that the turbulence intensity grows in magnitude after 8 m/s. The turbulence intensity is fairly uniform for wind speeds between 2 m/s and 8 m/ s. Fig. 14 shows the 16-sector roughness rose obtain from the LiDAR measurements. Vertical shear profile of the wind indicates substantial surface roughness lengths at Sentosa and surrounding areas near the central business district. To the north of Sentosa island there is Singapore's business district
5. Discussion For the three sites where long term wind measurements were taken, Table 2 summarises the Weibull parameters estimated based on three algorithms viz. maximum likelihood method, least squares method and WAsP method. From the assessment of measured twoyear wind data for the three sites, originally chosen as prospective sites for small wind turbine installation, following outcomes can be listed out: Maximum wind power density at Site A, Site B and Site C are 45, 35 and 15 W/m2 respectively. The highest average diurnal wind speeds are observed between 3 pm and 4 pm. Based on monthly distribution of mean wind speed, two high wind seasons are observed. Even though Northeast Monsoon extends from December to March, as observed in Fig. 11, highest
Fig. 12. Turbulence intensity at Marine Drive, Woodlands Crescent and Pandan Gardens.
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
411
Fig. 13. Turbulence Intensity and wind speed distribution at three heights (50 m, 80 m, and 100 m) from measurements at SSC Tanah Merah using LiDAR device. For low wind speeds, from 1 m/s to 8 m/s, the turbulence intensity gradually decreases.
with tall skyscrapers and to the south there are small islands. This explains significantly higher surface roughness lengths at Sentosa. The highest roughness length at Sentosa is 11.6 m in the direction sector 146.25 e168.75 . Changi airport lies to the north of SSC Tanah Merah, which is the reason why roughness lengths at SSC Tanah Merah are significantly lower than that of Sentosa. The highest roughness length at SSC Tanah Merah is 0.26 m in the direction sector 78.75 e101.25 . The roughness lengths at Tuas View Extension are 0.23 m in the direction sector 168.75 e191.25 and 0.127 m in the direction sector 348.75 e11.25 . However, roughness lengths in other direction sectors are found to be close to zero. Hence, Tuas View Extension is found to be the best location out of the three locations.
There is significant diurnal variability and seasonal variability that has to be taken into account to estimate wind energy potential.
5.1. Techno-economic analysis The installed costs per kW for small wind turbines are highly variable depending on the technology, country of origin and the market place conditions. The installed cost per kW for Skystream 3.7 is $7917 and annual operation and maintenance cost is estimated at $0.01/kWh [35]. In the USA, the cost per installed kW is between $2300 and $10,000. However, in China the cost per installed kW is as low as 12,000 Yuan (US $1900) [36]. The industry's cost target is to bring the cost per installed kW to
412
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
Fig. 14. 16-sector roughness rose derived from measured vertical shear profiles at Tuas View Extension, Sentosa and SSC Tanah Merah using the LiDAR device. Axis is in logarithmic scale and six circles represent roughness length ZO values from 0.0001 m to 100 m.
$3000e$4500 [37]. Table 3 shows the basic tabulation to calculate some key techno-economic parameters [38]. It is difficult to understand the direct correlation between the cost of electricity, payback period and the turbine price, with too many variables at play. Hence, this calculation assumes no grant, incentive or loan. Only Site A and Site C are considered for economic analysis. The table indicates Annual Energy Production (AEP) and payback periods for a 2.5 kW turbine installed at Site A and Site C respectively. For various commercially available turbines, calculated Net Capacity Factor (NCF) is between 2 and 12% at Site C and between 3
and 15% at Site A. Due to better wind conditions, capacity factor at Site A is would be 25% higher than that of Site C. NCF is assumed to be 8.75% and 7% for Site A and Site C respectively. The cost of electricity in Singapore is directly dependent on the cost of imported natural gas. In the past 5 years, electricity tariff in Singapore has fluctuated between 0.25 and 0.30 S$/kWh [39]. Assuming 0.35 S$/kWh for exported electricity, expected payback period for Site A is 14 years and expected payback period for Site C is 19 years, longer than the payback period for Site C, due to relatively poor capacity factor. At both locations, payback period has to be less than
Table 2 Summary table. Algorithm
Weibull k (shape factor)
Weibull c (scale factor in m/s)
Mean (m/s)
Proportion above [Mean] m/s
Pandan Gardens (Site A)
Maximum likelihood Least squares WAsP Actual data
2.037 2.005 2.086
3.835 3.846 3.868
3.398 3.408 3.426 3.4
0.457 0.458 0.466 0.466
Woodlands Crescent (Site B)
Maximum likelihood Least squares WAsP Actual data
1.634 1.689 1.615
2.267 2.252 2.258
2.029 2.01 2.023 2.026
0.435 0.433 0.432 0.432
Marine Drive (Site C)
Maximum likelihood Least squares WAsP Actual data
2.312 2.174 2.489
3.793 3.83 3.854
3.361 3.391 3.419 3.367
0.468 0.47 0.489 0.489
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
413
Table 3 Payback period and AEP at Site A and Site C. #
Parameters
a) Estimating the value of generated electricity 1 Turbine capacity (TC) 2 Net capacity factor (CF) 3 Annual energy production (Y) 4 Export fraction (F) 5 Electricity exported (Eexp) 6 Electricity used directly (Edir) 7 Unit price of exported electricity (Pexp) 8 Unit cost of imported grid electricity (Pimp) 9 Value of exported electricity (Vexp) 10 Value of electricity used directly (Vdir) 11 Total value of electricity (Vtot) b) Estimating the simple payback 1 Total capital cost of turbine installation (CC) 2 Annual operating cost (Co) 3 Simple payback period
Formula
Site A
Site C
Unit
TC CF Y ¼ TC$CF$8760 h/year F Eexp ¼ Y $ F Edir ¼ Y $ (1eF) Pexp Pimp Vexp ¼ Eexp $ Pexp Vdir ¼ Edir $ Pimp Vtot ¼ Vexp þ Vdir
2.5 8.75 1916 100 1916 0 0.35 0.25 670.7 0 670.7
2.5 7 1533 100 1533 0 0.35 0.25 536.6 0 536.6
kW % kWh/year % kWh/year kWh/year S$/kWh S$/kWh S$ S$ S$/year
CC Co CC/(Vtot CO)
7500 150 14.4
7500 150 19.3
S$ S$/year years
20 years which is normally the life time of a wind turbine. This gives the precondition on the maximum cost per installed kW, which is S$ 3000. 6. Conclusions
[3] [4] [5]
In this paper the challenges and prior research on urban wind measurements and wind resource assessment have been examined. Two measurement systems and their implementation have been discussed-rooftop wind measurement systems and data integration for urban canopy and remote sensing, mobile wind profiler based on a LiDAR device. Key results of several wind measurement campaigns in Singapore are presented. Wind measurements from the three rooftop wind measurement installations have been discussed. Also, Turbulence Intensity and roughness rose derived from LiDAR campaigns have been presented. It is found that the southern shore of Singapore is ideal for wind turbines. Wind power densities at Pandan Gardens and Marine Drive are 45 and 35 W/m2 respectively. A CFD simulation approach would be ideal to understand the actual flow patterns in these localities to decide the best locations for wind turbine installations. For the cost per installed kW of less than S$ 3,000, it is possible to achieve simple payback period of less than 20 years. By employing materials that are cost-effective and locally sourced and unique design concepts, it is possible to achieve such low capital cost without substantially degrading the quality and performance. Since this the first study of its kind for Singapore, results presented are from the groundwork towards a comprehensive urban wind flow simulation for Singapore. Acknowledgements Authors would like to thank the Housing Development Board of Singapore for partially funding this research project. Authors would like to express their heartfelt gratitude to the Singapore Economic Development Board for their generous funding to facilitate further research under the Energy Innovation Research Programme (NRF2013EWT-EIRP003-032). Thanks to Do Dinh Tho David, Shu Guixin, P Mohan Kumar and Dr Giuseppe Cavallaro for their assistance in various matters related to the work. References [1] Energy Market Authority, Singapore Energy Statisctics 2014, Research and Statistics Unit, Energy Market Authority, Republic of Singapore, Republic of Singapore, 2014. [2] Sectoral Measures to Reduce Emissions (Up to 2020), NCCS, 13.11.2014
[6] [7]
[8]
[9]
[10]
[11] [12] [13]
[14]
[15] [16]
[17]
[18]
[19]
[20] [21] [22]
[23]
(Online). Available: http://app.nccs.gov.sg/nccs-2012/sectoral-measures-toreduce-emissions-up-to-2020.html?AspxAutoDetectCookieSupport¼1 (accessed 13.11.2014). J. Luther, T. Reindl, Solar Photovoltaic (PV) Roadmap for Singapore (A Summary), Solar Energy Research Institute of Singapore, Singapore, 2014. W.T.L. Chow, M. Roth, Temporal dynamics of the urban heat island of Singapore, Int. J. Climatol. 26 (15) (2006) 2243e2260. National Environment Agency, WEATHERwise Singapore, 2009 (Online). Available: http://www.nea.gov.sg/docs/default-source/training-knowledgehub/guidebook-on-climate-of-singapore.pdf?sfvrsn¼0 (accessed 20.01.2015). G.K. Nathan, T.N. Goh, Wind measurements in an equitorial region (Singapore), Sol. Energy 26 (1981) 275e278. Singapore's Climate Information & Data, 13.11.2014 (Online). Available: http://app2.nea.gov.sg/weather-climate/climate-information/localclimatology (accessed 14.11.2014). J.T. Millward-Hopkins, A. Tomlin, D. Ingham, M. Pourkashanian, The predictability of above roof wind resource in the urban roughness sublayer, Wind Energy 15 (2012) 225e243. T.F. Ishugah, Y. Li, R.Z. Wang, J.K. Kiplagat, Advances in wind energy resource exploitation in urban environment: a review, Renew. Sustain. Energy Rev. 37 (2014) 613e626. G. Gualtieri, S. Secci, Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy, Renew. Energy 36 (3) (2011) 1081e1094. H.-C. Lim, T.-Y. Jeong, Wind energy estimation of the Wol-Ryong coastal region, Energy 35 (12) (2010) 4700e4709. L. Lu, K. Sun, Wind power evaluation and utilization over a reference high-rise building in urban area, Energy Build. 68 (2014) 339e350. S.L. Walker, Building mounted wind turbines and their suitability for the urban scaleda review of methods of estimating urban wind resource, Energy Build. 43 (8) (2011) 1852e1862. A. Gagliano, F. Nocera, F. Patania, A. Capizzi, Assessment of micro-wind turbines performance in the urban environments: an aided methodology through geographical information systems, Int. J. Energy Environ. Eng. 4 (1) (2013) 43. M.A. Heath, J.D. Walshe, S.J. Watson, Estimating the potential yield of small building-mounted wind turbines, Wind Energy 10 (3) (2007) 271e287. M.X. Song, K. Chen, Z. He, X. Zhang, Wind resource assessment on complex terrain based on observations of a single anemometer, J. Wind Eng. Ind. Aerodyn. 125 (2014) 22e29. F. Toja-Silva, A. Colmenar-Santos, M. Castro-Gil, Urban wind energy exploitation systems: behaviour under multidirectional flow conditionsdopportunities and challenges, Renew. Sustain. Energy Rev. 24 (2013) 364e378. D. Elliott, D. Infield, An assessment of the impact of reduced averaging time on small wind turbine power curves, energy capture predictions and turbulence intensity measurements, Wind Energy 17 (2) (Feb. 2014) 337e342. E.L. Petersen, N.G. Mortensen, L. Landberg, J. Højstrup, H.P. Frank, Wind power meteorology. Part I: climate and turbulence, Wind Energy 1 (S1) (1998) 25e45. International Standard, Wind Turbines e Part 2 Small Wind Turbines, International Electrotechnical Commission, 2013. P.M. Pardalos, S. Rebennack, M.V.F. Pereira, N. Illiadis, V. Pappu, Handbook of Wind Power Systems, Springer Berlin Heidelberg, 2013. J.A. Carta, P. Ramirez, S. Velazquez, A review of wind speed probability distributions used in wind energy analysis case studies in the Canary Islands, Renew. Sustain. Energy Rev. 13 (5) (2009) 933e955. M.A. Al-Fawzan, Methods for Estimating the Parameters of the Weibull Distribution, 2000 (Online). Available: http://interstat.statjournals.net/YEAR/ 2000/articles/0010001.pdf (accessed 01.01.2015).
414
B.R. Karthikeya et al. / Renewable Energy 87 (2016) 403e414
[24] H.S. Bagiorgas, M. Giouli, S. Rehman, L. Al-Hadhrami, Weibull parameters estimation using four different methods and most energy-carrying wind speed analysis, Int. J. Green Energy 8 (2011) 529e554. [25] A.P. Schaffarczyk, Introduction to Wind Turbine Aerodynamics, Springer Berlin Heidelberg, Berlin, 2014. [26] M.C. Brower, Wind Resource Assessment: a Practical Guide to Developing a Wind Project, John Wiley & Sons, Inc., May 2012. [27] MEASNET, Anemometer Calibration Procedure, October 2009 (Online). Available: http://www.measnet.com/documents/ (accessed June 2015). [28] J. Gottschall, M.S. Courtney, R. Wagner, H. Jørgensen, I. Antoniou, Lidar profilers in the context of wind energyea verification procedure for traceable measurements, Wind Energy 15 (1) (2012) 147e159. [29] P.J.M. Clive, Remote sensing best practice, in: EWEA Offshore, Amsterdam, 2011. [30] Erik Holtslag, Improved Bankability e the Ecofys Postion on LiDAR Use, ECOFYS, 2013. Available online: http://www.ecofys.com/files/files/ecofys2013-position-paper-on-lidar-use_02.pdf. [31] “zephirlidar.com,” ZephIR, (Online). Available: http://www.zephirlidar.com/ support/tech-specs/. (accessed 14.11.2014). [32] International Energy Agency, Ground-based Vertically-Profiling Remote Sensing for Wind Resource Assessment, International Energy Agency, 2013.
[33] T. Rutherford, M. Harris, W. Barker, E.B.D. Roziers, M. Pitter, R. Scullion, C. Slinge, Lidar Calibration and Performance Validation Process, Natural Power, Hollybush Ledbury, UK, 2014. [34] N. Carpman, Turbulence Intensity in Complex Environments and its Influence on Small Wind Turbines, Department of Earth Sciences Geotryckeriet, Uppsala University, Uppsala, 2011. [35] ICF International, “The Cost and Performance of Distributed Wind Turbines, 2010e35, June 2010. (Online). Available: http://www.eia.gov/analysis/ studies/distribgen/system/pdf/appendix-b.pdf. (accessed May 2015). [36] New Energy Husum, 2015 Small Wind World Report Summary, 2015 (Online). Available: http://small-wind.org/wp-content/uploads/2014/12/Summary_ SWWR2015_online.pdf (accessed May 2015). [37] DWEA, DWEA Distributed Wind Vision e 2015e2030, March 2015 (Online). Available: http://distributedwind.org/wp-content/uploads/2012/08/DWEADistributed-Wind-Vision.pdf (accessed May 2015). [38] Small-scale Wind Energy: Policy Insights and Practical Guidance, Carbon Trust, 2008. [39] Energy Market Authority. https://www.ema.gov.sg, 30 December 2014 (Online). Available: https://www.ema.gov.sg/Residential_Electricity_Tariffs.aspx (accessed 20.01.2015).