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Wind Resource Characterization in the Arabian Peninsula

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Wind Resource Characterization in the Arabian Peninsula Wind resource characterization in the Arabian Peninsula Item Type Article Authors Yip, Chak Man Andrew; Gunturu, Udaya; Stenchikov, Georgiy L. Citation Wind resource characterization in the Arabian Peninsula 2016, 164:826 Applied Energy Eprint version Post-print DOI 10.1016/j.apenergy.2015.11.074 Publisher Elsevier BV Journal Applied Energy Rights NOTICE: this is the author’s version of a work that was accepted for publication in Applied Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Energy, 28 December 2015. DOI: 10.1016/j.apenergy.2015.11.074 Download date 02/10/2021 22:34:00 Link to Item http://hdl.handle.net/10754/596964 Wind Resource Characterization in the Arabian Peninsula Chak Man Andrew Yipa,∗, Udaya Bhaskar Gunturua, Georgiy L. Stenchikova aKing Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia Abstract Wind energy is expected to contribute to alleviating the rise in energy demand in the Middle East that is driven by population growth and industrial development. However, variability and intermittency in the wind resource present significant challenges to grid integration of wind energy systems. These issues are rarely addressed in the literature of wind resource assessment in the Middle East due to sparse meteorological observations with varying record lengths. In this study, the wind field with consistent space-time resolution for over three decades at three hub heights (50 m, 80 m, 140 m) over the whole Arabian Peninsula is constructed using the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset. The wind resource is assessed at a higher spatial resolution with metrics of temporal variations in the wind than in prior studies. Previously unrecognized locations of interest with high wind abundance and low variability and intermittency have been identified in this study and confirmed by recent on-site observations. In particular, the western mountains of Saudi Arabia experience more abundant wind resource than most Red Sea coastal areas. The wind resource is more variable in coastal areas along the Arabian Gulf than their Red Sea counterparts at a similar latitude. Persistent wind is found along the coast of the Arabian Gulf. Keywords: Wind Energy, Variability, Intermittency, Middle East, Resource Assessment, Reanalysis 1 1. Introduction 22 the latest expansion of the renewable energy market 23 [5]. Among net oil importers such as Jordan, energy 2 The potential adverse impacts of climate change and 24 insecurity and dependence on expensive oil imports 3 energy insecurity have encouraged countries worldwide 25 have led to an expansion of the renewable energy 4 towards adopting renewable energy as an integral 26 program. Renewable energy has grown from 0.4 TW h 5 part of their future energy mix. Near-surface wind 27 in 2008 to 1.2 TW h in 2011 among net oil importers 6 energy has the potential to power the world; it allows 28 [4]. In the net oil exporting countries, renewable 7 extracting energy at a rate of at least 400 TW [1]. It 29 energy has grown from 0.8 TW h in 2008 to 1.6 TW h 8 is suggested that following a moderate wind energy 30 in 2011 [4]. This growth has been the result of rising 9 deployment plan by 2050 would delay the crossing 31 opportunity cost of oil and gas accompanied by an 10 of the 2 °C threshold for 1 to 6 years [2]. Wind 32 increase in urbanization and a rapid rise in domestic 11 energy provides a viable alternative energy source 33 demand for energy. These expansions are evident 12 to energy intensive countries such as China, where 34 in the countries’ recent large-scale procurement of 13 it is estimated to be sufficient to replace 23% of the 35 renewable energy systems to fulfill national renewable 14 electricity generated from coal [3]. In the Middle East 36 targets [5]. The surging interest in renewable energy 15 and North Africa (MENA), population growth has led 37 calls for a better understanding of the spatial and 16 to increases in demand for fuel and electricity for air- 38 temporal characteristics of the resource. This paper 17 conditioning and desalination. Regional annual Total 39 focuses on the wind energy resource, the most variable 18 Primary Energy Supply (TPES) increased by 14.9% to 40 and intermittent source of renewable energy in the 19 800 millions Mtoe (million tonnes of oil equivalent) in 41 Arabian Peninsula. 20 2010 compared to the TPES of 2007 [4]. These steady 21 increases in domestic consumption of energy drive 42 There are two key challenges in assessing the wind 43 resource in the Arabian Peninsula. Most of the 44 ∗Corresponding author observations available are sparse in space and in- Email address: [email protected] (Chak Man 45 consistent in time: spatially scattered observations Andrew Yip) 46 with varying record lengths come from meteorological November 15, 2015 47 stations that are located mainly in clustered coastal 101 Moreover, previous resource characterizations have 48 and inland settlements. Hourly wind speeds were 102 focused on average wind abundance and annual energy 49 collected by 293 weather stations in the Peninsula 103 production estimates. Wind power variability and 50 during our period of study from 1979 to 2013. Among 104 intermittency present significant challenges to grid 51 the stations, 42 collected data for at least half of the 105 integration of wind energy systems, as identified by 52 time. Only 17 stations have observations available 106 wind integration studies in the United States [17]. 53 for more than 80% of the record length [6]. Despite 107 Variability and intermittency have been considered, 54 these challenges, Ansari et al. [7] constructed the 108 most commonly using tower measurements where data 55 Saudi Arabian Wind Energy Atlas in 1986 using hourly 109 are limited in the spatial and temporal dimensions 56 observations from 20 airport weather stations from 110 [18–20]. Rehman and Halawani [8] provided a wind 57 1970 to 1982. They described diurnal and seasonal 111 persistence measure via auto-correlation and auto- 58 variations of wind speed at measurement height at 112 regression for ten weather stations. Rehman and 59 these locations and mapped prevailing wind directions. 113 Ahmad [21] presented a wind availability analysis 60 Rehman and Halawani [8] described diurnal, monthly, 114 for 5 coastal locations in Saudi Arabia in terms of 61 and inter-annual wind speed variations at 10 weather 115 frequency of wind speed within a specified interval. 62 stations. Most of the studies focused on prominent 116 Wind speed time series from meteorological stations 63 sites of assessment that are mainly coastal. A similar 117 were fitted by Weibull distributions to investigate the 64 tendency is observed for the MENA region [9–11], 118 monthly variation of wind speed and their changes 65 with the exception of Ohunakin et al. [12] where the 119 with hub height in Saudi Arabia [22, 23] and Bahrain 66 focus was inland. These analyses concentrated on 120 [24]. Ouarda et al. [25] fitted multiple distributions 67 wind speed time series from meteorological stations 121 and assessed their goodness-of-fit with wind speed 68 with different record periods. The wind resource is 122 measurements in the United Arab Emirates (UAE). 69 frequently characterized by average wind speed or 123 However, the variability and intermittency of the wind 70 wind power density (WPD) that is at measurement 124 resource have not been studied in the entire Peninsular 71 height or is adjusted to hub height. Recent works have 125 region. 72 attempted to study the spatial variation of the wind 126 The primary goal of this study is to overcome the 73 resource. Jervase and Al-Lawati [13] performed an 127 limitation of sparse station observations with varying 74 areal analysis of wind resource abundance in Oman 128 record lengths by constructing the wind field using a 75 using the NASA Surface Meteorology and Solar Energy 129 gridded reanalysis dataset with a multi-decadal record 76 (SSE) Release 6.0 dataset with a spatial resolution of 1° 130 period to arrive at a characterization of wind variability 77 × 1°. Al-Yahyai and Charabi assessed wind resource 131 and intermittency. We characterize the wind resource 78 in Oman using a nested ensemble numerical weather 132 using metrics proposed in Gunturu and Schlosser [26] 79 prediction (NWP) approach, where two global models 133 (United States), Cosseron et al. [27] (Europe), Fant 80 were used as boundary conditions to drive two local 134 and Gunturu [28] (South Africa), and Hallgren et al. 81 area models. The wind abundance has been assessed 135 [29] (Australia). 82 at the scale of a country [14] and a city [15]. Moreover, 83 Charabi et al. [16] demonstrated that NWP models 136 This work aims to answer the following questions: 84 at 7 km are effective in resolving finer structures such 85 as the sea breezes in this region. However, without 137 • What methodology can be used to assess the wind 86 well-formulated boundary conditions based on a long- 138 energy resource in a region where observational 87 term and spatially and temporally consistent dataset, 139 data are sparse and non-concurrent? (section2) 88 an NWP model would not capture the impact of 89 large-scale circulations such as the El Niño. Since 140 • Where are the areas with wind power potential 90 these circulations are of low frequency, they have 141 that were not previously located due to lack of 91 higher spectral power and, therefore, have a significant 142 observations? (section 3.1) 92 impact on the wind resource.
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