Int. J. Renewable Energy Technology, Vol. 4, No. 1, 2013
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Wind resource assessment using numerical weather prediction models and multi-criteria decision making technique: case study (Masirah Island, Oman) Sultan Al-Yahyai* Department of Electrical & Computer Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khodh, Muscat, Oman E-mail:
[email protected] *Corresponding author
Yassine Charabi Department of Geography, Sultan Qaboos University, P.O. Box 33, Al-Khodh, Muscat, Oman E-mail:
[email protected]
Abdullah Al-Badi and Adel Gastli Department of Electrical & Computer Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khodh, Muscat, Oman E-mail:
[email protected] E-mail:
[email protected] Abstract: The Authority for Electricity Regulation in Oman has recently announced the implementation of a 500 kW wind farm pilot project in Masirah Island. Detailed wind resource assessment is then required to identify the most suitable location for this project. This paper presents wind resource assessment using nested ensemble numerical weather prediction (NWP) model’s approach at 2.8 km resolution and multi-criteria decision making (MCDM) technique. A case study based on the proposed approach is conducted over Masirah Island, Oman. The resource assessment over the island was based on the mean wind speed and wind power distribution over the entire island at different heights. In addition, important criteria such as turbulence intensity and peak hour matching are also considered. The NWP model results were verified against the available 10 m wind data observations from the meteorological station in the northern part of the island. The resource assessment criteria were evaluated using MCDM technique to score the locations over the island based on their suitability for wind energy applications. Two MCDM approaches namely equally weighted and differently weighted criteria were implemented in this paper. Keywords: numerical weather prediction; NWP; wind farm; resource assessment; Masirah Island; Oman.
Copyright © 2013 Inderscience Enterprises Ltd.
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S. Al-Yahyai et al. Reference to this paper should be made as follows: Al-Yahyai, S., Charabi, Y., Al-Badi, A. and Gastli, A. (2013) ‘Wind resource assessment using numerical weather prediction models and multi-criteria decision making technique: case study (Masirah Island, Oman)’, Int. J. Renewable Energy Technology, Vol. 4, No. 1, pp.17–33. Biographical notes: Sultan Al-Yahyai is the Chief of Numerical Weather Prediction (NWP) section at Oman Meteorological Service. He received his BSc and MSc from the Computer Science Department at SQU. He is currently a PhD student at the Department of Electrical and Computer Engineering (SQU). Since 2000, he is leading the NWP Group at Oman Meteorological Service to develop and maintain both operational and research NWP models. His research interest is to develop approached and methodologies for better use of NWP models in renewable energy applications including resource assessment and power forecast. He is also interested on NWP model verification and validation. Yassine Charabi received his MSc (1997) and PhD (2001) in Applied Climatology and Meteorology from Lille University, Sciences and Technology, France. He joined the Department of Geography at Sultan Qaboos University in 2006. He is working as an expert in group 1 IPCC 2010-2014. His main research interests are focused on understanding the dynamical processes in the atmosphere to address atmospheric problems, such as climate change and urban air pollution. He is also interested in renewable energy (wind, solar) resource assessment and power forecast. Abdullah Al-Badi obtained his BSc in Electrical Engineering from Sultan Qaboos University, Oman, in 1991. He received his MSc and PhD from UMIST, UK, in 1993 and 1998, respectively. In September 1991, he joined Sultan Qaboos University as a Demonstrator and in 2008, he became an Associate Professor. His main research area is electrical machines, drives, interference, renewable energy and high voltage. He is a Senior Member IEEE, and a Consultant in Oman Society of Engineers. In 2009, he was appointed as a Dean for the deanship of Admissions and Registration in Sultan Qaboos University. Adel Gastli received his BSc in Electrical Engineering from National School of Engineers of Tunis, in 1985. He received his MSc and PhD degrees from Nagoya Institute of Technology, Japan in 1990 and 1993 respectively. He joined the R&D Department at Inazawa Works of Mitsubishi Electric Corporation in Japan from April 1993 to August 1995. He joined Sultan Qaboos University in August 1995. He is currently a Professor of Electrical Engineering at SQU, Oman. His current research interests include electrical energy conversion, power electronics, drives, power quality, renewable energy, and power system analysis.
1
Introduction
Masirah Island is located on the East Southern Coast of Oman. It forms one of the provinces of Sharqiyah region as shown in Figure 1. According to census of 2003, Masirah is inhabited by 9,292 people. Due to its seasonal climate, load demand has also seasonal behaviour. Maximum load are typically recorded during April to June. Figure 2 shows the daily and monthly maximum load at Masirah for year 2008.
Wind resource assessment using NWP models and MCDM technique Figure 1
Oman map and Masirah Island elevation (see online version for colours)
Figure 2
Daily and monthly maximum load (kW) for Masirah during 2008 (see online version for colours)
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Recently, Masirah Island was selected for the implementation of the first wind farm in the country. The wind regime of Masirah is mainly dominated by the summer monsoon due to its position in the northern part of the Arabian Sea and in the proximity of the eastern coastal line of Oman. Highly accurate meteorological data is essential for wind resource assessment including site selection, wind farm design and power forecast. With respect to data availability, there is only one meteorological station installed in the northern part of the island, which provide wind data at only 10 m above the ground. Usually, wind farm site selection requires a deep knowledge about the wind regime at different heights which are normally much higher than 10 m. Based on the data from the meteorological weather station, Masirah wind data was investigated in some studies. Data analysis in (Atsu and Ampratwum, 2002) showed that the annual mean wind speed in Masirah is higher than the annual mean wind speed of the other stations except those for Sur and Thumrait. Weibull distribution was used (Yusof Sulaiman et al., 2002) to analyse five-year monthly/annual means Masirah and other three stations. The results showed a good fit of Weibull distribution to the observed data. It showed that Masirah had average annual wind speed exceeding 5 m/s and annual wind power density of 167.44 W/m2. Moreover, it was also found that Masirah is among the places which has higher wind energy potential in Oman through the analysis of ten years of wind data (1985–1994) in (Al-Ismaily and Probert, 1996) Detailed analysis of five-year hourly data was presented in Al-Yahyai et al. (2010a) for 31 weather stations including Masirah. Masirah wind data showed seasonal variation of wind speed from 6.7 m/s during summer to 4.3 m/s, 4 m/s and 3.6 m/s during spring, winter and autumn respectively. Wind speed at different heights above the ground was also calculated using the logarithmic law. Wind speed was calculated to be 7 m/s at 50 m above the ground and it reaches 7.4 m/s and 7.6 m/s at 80 m and 100 m above the ground respectively. Other locations in the island were not investigated due to the absence of the wind measurements. Therefore it was required to find other source of wind data to conduct detailed investigation over the entire island. On this respect, numerical weather prediction (NWP) models were recently used as alternative source to provide data needed for wind energy resource assessment. They simulate the dynamic of the atmosphere and evaluate the evolution of different atmospheric parameters including air temperature, air pressure and wind with time at different heights above the ground. Details review of the use of NWP models in wind resource assessment is presented in (Al-Yahyai et al., 2010b). As a matter of fact, that NWP models have limitations to simulate the reality. Therefore, wind resource assessment studies need to consider the limitations of such models. Different uncertainties sources such as model formulation and assumptions contribute to the biased data generated from NWP models. Recently new capabilities and methodologies were studies to highlight and reduce the uncertainties in the NWP models. An ensemble nested approach was presented in Al-Yahyai et al. (2012a). Selecting the best site for wind farm projects can be seen as decision making problem. Alternative sites are compared with each other based on different criteria and then the best site is selected. Different criteria including technical, economical, social and environmental are considered during the selection process. Wind farm land suitability indexing based on multi-criteria decision making (MCDM) has been applied using different aggregation methods namely linear weighting average (LWA) over the UK in
Wind resource assessment using NWP models and MCDM technique
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Serwan and Parry (2001) and over the Baltic Sea in Henning (2005), AHP over Thailand in Bennui et al. (2007) and OWA over Turkey in Nazil et al. (2010). Recently, the new aggregation function AHP-OWA combination was used in Al-Yahyai et al. (2012b) for wind resource assessment. The main objective of this paper is to apply high resolution meso-scale NWP models and MCDM technique based on the new aggregation function AHP-OWA combination to assist the wind resource assessment over the entire Masirah Island. The rest of the paper is organised as follows. Section 2 describes the methodology. Model validation is presented in Section 3. Section 4 presents the resource assessment criteria based on the model simulation. Section 5 presents the resource assessment results based on the MCDM technique. Section 6 concludes the paper.
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Methodology
To reduce the uncertainly of NWP model simulations, the nested ensemble approach presented in Al-Yahyai et al. (2012a) was applied for this case study. Four ensemble model members were used to provide wind data at 7 km resolution. The resulting ensemble mean is then utilised to initialise the 2.8 km resolution model. NWP model simulations were run for year 2009. Each model simulation was initialised at 00 GMT and generated output for 30 hours. The first six hours were discarded due to the spin-up of the model. This provided data for a total of 8,760 hours. The 7 km resolution ensemble members consist of two regional models namely high resolution model (HRM) (Majewski, 2009) and consortium for small scale modelling (COSMO) (Doms and Schattler, 2008). The two regional models were initialised by the data provided from the German global model (GME) which runs at 40 km resolution using two different initial states of the atmosphere. One initial state is provided by the 3Dvar data assimilation system at the Deutscher Wetterdienst DWD and the other is provided from the reanalysis data (ERA-Interim) (ECMWF, 2006) from the European Centre for Medium-Range Weather Forecasts (ECMWF). Finally, the mean of the 7 km (regional scale) ensemble system was used to run COSMO model at 2.8 km (local scale) resolution over the local domain. This NWP modelling approach is needed to quantify and reduce the uncertainty of wind data generated by single model. It is also useful to generate probabilistic forecast once the site is selected. Model simulation results are used to generate the required assessment criteria including wind speed, wind power density, steadiness of wind flow, turbulence intensity and peak hours load demand matching. After the data generation, MCDM technique under geographical information system (GIS) environment (Soheil and Malczewski, 2008) was used to evaluate the criteria and rank the sites based on their suitability for wind farm applications. Other parameters such as distance to road, sand dunes which may affect the turbines performance, terrain slope and urban and sensitivity areas were also considered in this part. Two methods were implemented in this case study. First method considers that all criteria are equally weighted. The second method assigned different weights for each criterion. The assigned weights were based on expert’s judgment as in Al-Yahyai et al. (2012b). To evaluate the effect of the importance value of each criterion, sensitivity analysis of wind related criteria was conducted. In this regards, each criterion importance value was varied while
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the other criteria importance values were equally fixed. Figure 3 summarises the whole approach. Figure 3
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Data flow diagram of the approach used in this study (see online version for colours)
Model validation
Directorate General of Meteorology and Air Navigation (DGMAN) is operating one meteorological station in the north part of the Masirah Island. This station is providing an hourly wind data at 10m above the ground. Due to its availability, this data was used to validate the 2.8 km resolution model. Figure 4 shows the monthly mean wind speed for both observed [Figure 5(a)] and modelled [Figure 5(b)]. It shows that the model was able to simulate the seasonal behaviour of the wind speed over the station. It also shows that the model underestimated the wind speed by 1 m/s in June. Smaller underestimation is also observed at both the beginning and the end of the year. Due to the model resolution, dynamics and formulation this negative bias is understood and acceptable for the surface parameters. Model output statistic approach could be used to correct the systematic errors on the model results. Wind roses for both observed [Figure 5(a)] and modelled [Figure 5(b)] are shown in Figure 5. The model was able to simulate the dominating south-westerly wind direction. More southerly wind was forecasted than observed.
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Figure 4
Observed (blue), modelled (red) monthly mean for Masirah weather station (see online version for colours)
Figure 5
Wind rose for (a) observed and (b) modelled wind data for Masirah weather station (see online version for colours)
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Resource assessment criteria
This section presents and discusses resource assessment criteria based on the model simulation over Masirah Island. Different criteria are presented including wind speed and wind power at different heights, wind flow steadiness, peak hours load matching and turbulence intensity.
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4.1 Mean wind speed Figure 6 shows the simulated wind speed over Masirah at different heights: 50 m, 100 m, 150 m and 200 m above the ground. It shows that the mean wind speed ranges between 3.8 and 5.2 m/s at 50 m, 4.4–5.7 m/s at 100 m, 4.7–5.9 m/s at 150 m and 5.1–6.0 m/s at 200 m. It shows also that the eastern parts of the northern half of the island (around Al Ghayray village) is affected by relatively stronger wind. The northern and middle parts of the island have the lowest wind speed distribution. Unlike the northern half, the southern half of the island has uniform wind speed distribution of 4.5 m/s at 50 m, 5.3 m/s and 5.8 at 100 m and 150 m respectively. It is noticed that the simulated mean wind speed at 200 m above the ground in the northern half of the island is lower than the mean wind speed at lower heights. This phenomenon is explained by the results of (Pedgley, 1970) analysis of the monsoonal flow which shows that the summer monsoonal makes it turn off the mainland over the southern half of the island. Pedgley’s analysis shows that the monsoonal flow minimum depth is located over this region and therefore this explains the lower mean wind speed at 200 m.
4.2 Wind power density Theoretical wind power or wind power density, which combines the effect of site's wind speed distribution and its air density, is an important factor during wind resource assessment. This quantity describes the total power available in the wind that can be extracted by the wind turbine. Figure 7 shows the wind power (W/m2) over the island at different heights above the ground. As expected, Figure 7 shows that the wind power distribution agrees with wind speed distribution shown in Figure 6. It also shows that a maximum wind power of 514 W/m2 is available at 50 m in the western part (around Al Ghayray village) of the northern half of the island. The maximum wind power increases to 600 W/m2 at 100 m above the ground.
4.3 Wind flow steadiness It is important for the daily wind farm operation to analyse the occurrence frequency of certain wind speed thresholds around the typical cut-in and cut-off wind speeds. The cut-in speed which is the minimum speed at which the wind turbine will generate usable power is typically between 3 and 5 m/s (DBD Energy Associates, 2010). On the other hand, the cut-off speed at which the wind turbine is shut down (20–35 m/s) (AWS Scientific, 1997; DBD Energy Associates, 2010). Figure 8 shows the occurrence frequency for wind speed greater than or equal 5 m/s [Figure 8(a)] and the occurrence frequency for wind speed greater than or equal 20 m/s [Figure 8(b)] at 50 m above the ground. It is clearly shown that 63% of the time, the wind speed is greater than 5m/s over the island. This occurrence frequency increases to 76% over the middle parts of the northern half of the island. On the other hand, the occurrence frequency of very high wind speed which may cause wind turbines to shut down is very low over the island. Model simulation shows that the occurrence frequency of wind speed greater than 20 m/s is 0.2% over most parts of the island and it reaches only 1% over the area around Al-Ghayray village.
Wind resource assessment using NWP models and MCDM technique Figure 6
Wind speed at different heights, (a) 50 m, (b) 100 m, (c) 150 m and (d) 200 m (see online version for colours)
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S. Al-Yahyai et al. Wind power at different heights, (a) 50 m, (b) 100 m, (c) 150 m and (d) 200 m (see online version for colours)
Wind resource assessment using NWP models and MCDM technique Figure 8
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Wind speed frequency of (a) ≥5 m/s and (b) ≥20 m/s at 50 m above the ground (see online version for colours)
4.4 Turbulence intensity and peak hour matching Turbulence intensity is the rapid disturbance in the wind speed, direction or vertical component. It may affect the power output of the wind turbines in case of high turbulence levels and may cause extreme loading on turbine components (AWS Scientific, 1997) Turbulence intensity is the wind speed standard deviation normalised with the mean wind speed. Turbulence intensity at 50 m above the ground is shown in Figure 9(b). Due to the continuous flow during summer monsoon, model simulation shows that the island is subject to turbulence intensity ranging from low to moderate values except the most northern part. The lowest turbulence intensity value of 0.2 is found in the middle of both halves of the island. Wider area of least turbulence intensity values are located in the northern half of the island. Due to the seasonal climatology of Oman, higher load power demand is normally experienced during summer. Peak hours are typically between 3 pm and 5 pm and again between 11 pm and 4 am during summer (Al-Badi et al., 2009). Figure 9(a) shows the mean wind speed during peak hours. Model simulation shows that the mean wind speed distribution during peak hours is similar to the mean wind speed distribution for the whole data set. It also shows that the maximum mean wind speed (4.25 m/s) during peak hours is located around Al-Ghayray village and is lower than the mean wind speed for the whole data set by 1 m/s.
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Figure 9
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Mean wind speed during (a) peak hours and (b) turbulence intensity at 50 m above the ground (see online version for colours)
Wind resource assessment results
After creating the resource assessment criteria from the NWP model simulation, these criteria were exported to GIS environment. Then, the multi-criteria analysis system was used based on analytical hierarchy process with ordered weigh averaging (AHP-OWA) aggregation function to derive wind farm land suitability ranking.
5.1 Equally weighted criteria The first implemented method in this part is considering all criteria are equally weighted. Figure 10 shows the ranking scores of land suitability for wind farm application at different heights above the ground. Rank 4 is the best and Rank 1 is the worst. It can be seen that at 50 m and 80 m above the ground, the best sites are located at the eastern side of the top half of the island. On the other hand, new good sites are appearing at the middle of the bottom half of the island at 100 m above the ground. Finally, as the wind power moves more to the south and becoming more steady at 150 m above the ground, wider areas with best score are found at the bottom half of the island. As of the current technical feasibility, wind turbines are typically installed at 50–100 m above the ground. Land suitability map for heights 150 m above the ground can be used once such installation is technically feasible.
Wind resource assessment using NWP models and MCDM technique Figure 10 Wind farm land suitability ranking, (a) 50 m, (b) 80 m, (c) 100 m and (d) 150 m (see online version for colours)
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5.2 Differently weighted criteria The second approach in this analysis considered different weights for each criterion based on its perceived importance. Criteria weights were calculated using the Pairwise comparison technique as described in (Al-Yahyai et al., 2012b). Based on the preserved criteria importance, the calculated weights (Table 1), were used in the MCDM GIS environment. Figure 11 shows a comparison between land suitability areas using equally weighted and differently weighted criteria analysis for different suitability classes. Figure 11 Comparison of the land suitability area using equally weighted and differently weighted criteria analysis (see online version for colours)
Table 1
Weight
Pairwise comparison matrix for the technical attributes and the calculated weights Wind power density
Peak hours matching
Wind occurrence ≥5
Turbulence intensity
Wind occurrence ≥20
0.3
0.092
0.154
0.154
0.3
It can be seen that both approaches showed similar results especially for the mostly suitable and moderately suitable classes. Higher differences can be seen in the marginally suitable class. Marginally suitable areas obtained using equally weighted criteria were 2% smaller than those obtained using differently weighted criteria at 80m and were 3% higher at 50 m. To understand the effect of the importance of wind related parameter in the total area of each class, sensitivity study was conducted. Therefore, each criterion importance (weight) was varied while the other criteria importance was equally fixed. Figure 12 shows the normalised area of mostly suitable class using different importance values for wind related criteria.
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Figure 12 Normalised land area of mostly suitable class using different importance values for wind related criteria (see online version for colours)
To understand the effect of the importance of each parameter in Figure 12, it is useful to consider Figure 7 which showed that areas with high power density values are very limited over the island. Therefore, increasing the importance of wind power density criterion did not have significant impact on the mostly suitable land area. Unlike wind power density, with the increase of the importance of wind speed occurrence ≥5 m/s and peak hour matching criteria, new sites area were included in the mostly suitable class. The largest impact on land area was observed for wind speed occurrence ≥20 m/s and turbulence intensity criteria. Both criteria are minimisation criteria where large areas over the island are preferred according to these two criteria. Therefore, increasing their preserved importance resulted in an increase of the mostly suitable land area.
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Conclusions
This paper presented a wind resource assessment using nested ensemble NWP models’ approach. A study case over Masirah Island in Oman was presented. The approach captures the cascade effect of the wind from global to regional and then to local scales over the island. Detailed analysis of wind speed and wind power distribution at different heights above the ground was also presented. A good agreement was achieved between the model wind forecast and the actual wind data observations, which validated this modelling approach for wind resource assessment. The resource assessment was based on multi-criteria analysis in GIS environment. Two aggregation methods namely equally weighted and differently weighted criteria were implemented. In addition sensitivity analysis of the importance of each criterion was also conducted. For the Masirah Island case study, the results of the model simulation and multi-criteria analysis showed that, at 50 m and 80 m heights above the ground, the eastern part of the northern half of the island (around Al-Ghayray village) has higher
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potential for wind farm project application than the other locations in the island. This location is characterised by higher mean wind speed, higher wind power, steady wind flow and lower turbulence intensity factor. In addition, the mean wind speed during the peak hours over this location is the highest.
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