An Assessment of Wind Energy Potential in the Beibu Gulf Considering the Energy Demands of the Beibu Gulf Economic Rim

An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim

Chen, X., Foley, A., Zhang, Z., Wang, K., & O'Driscoll, K. (2020). An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim. Renewable and Sustainable Energy Reviews, 119, [109605]. https://doi.org/10.1016/j.rser.2019.109605

Published in: Renewable and Sustainable Energy Reviews

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Download date:05. Oct. 2021 Elsevier Editorial System(tm) for Renewable & Sustainable Energy Reviews Manuscript Draft

Manuscript Number: RSER-D-18-03321R4

Title: An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim

Article Type: VSI:Bryden 2018

Section/Category: Wind

Keywords: Beibu Gulf; Wind energy; Renewable energy; Wind power; Wind climate

Corresponding Author: Dr. Xinping Chen, Ph.D.

Corresponding Author's Institution: National Marine Hazard Mitigation Service

First Author: Xinping Chen, Ph.D.

Order of Authors: Xinping Chen, Ph.D.; Aoife Foley; Zenghai Zhang; Kaimin Wang; Kieran O'Driscoll

Abstract: The Beibu Gulf Economic Rim of China is a key economic region in western China as demonstrated by the "Beibu Gulf Cities Development Project" (2017) that plans to build a "Blue Livable Gulf" to balance environmental protection while providing sustainable economic development. This region has significant energy needs and is predicted to exhibit rapid growth in the future. By means of meteorological observations located at seven islands, a comprehensive statistical analysis on wind energy potential in the northern coastal part of the Beibu Gulf is conducted in this study. Specifically, wind speed, Weibull parameters, wind power density, as well as wind directions on various timescales are analyzed. The analysis shows that annual mean wind power density during 2010-2017 at 100m above mean sea level was, respectively, 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5 Wm-2 at the seven sites, with average value of 353.2 Wm-2. Evidently, wind power potential demonstrates intra-annual variability, with greatest values occurring in December, while another peak value is observed in July. Wind speeds are lowest in May with another trough occurring in August. The data also display weak inter annual variability. The prevailing wind directions in the rim are mainly from opposing directions of N (winter and autumn) and S (summer).

Response to Reviewers: The authors wish to thank reviewers for their constructive comments, which have greatly helped improve the quality of the present manuscript. We have carefully considered all remarks. Below, please find detailed responses to the comments. We would also like to extend our appreciation to the editors for their efforts.

Reviewer comments and author responses: Reviewer #1: The authors have improved the manuscript in response to the reviewers' comments. This reviewer apologises to the author for confusing the Vietnamese island of "Dao Tran" with one of the islands within the study. The manuscript can be accepted in its current form, however I am not entirely convinced that wind speed and wind power variability estimates will not be affected by 30% data loss. The authors could include a comment to this effect, e.g. "Estimates of wind speed and wind power variability may be influenced by gaps in the data", I leave it to the authors to decide on this. Response: Thank you very much for your comments. The authors have taken this suggestion and comment on the influence of the data gap in the conclusion. Furthermore, a new subsection has been added to the paper to state the limitation of the present analysis due to this data loss, and also to recommend follow-up work and further analysis. Please see Subsection 3.5 ( P. 11-12) and the last paragraph of Conclusions (P. 20 Lines 28-30, P. 21 Lines 1-6).

Reviewer #2: Thank you for addressing my comments. The analysis is still limited by the number, location and height of the measurement locations, and subsequent analysis of collected data. The level of scientific merit is low, and therefore I do not believe that the study in its present form is suitable for publication. Response: The authors thank the reviewer for your comments. Concerning the limitation by number, location and height of measurement locations, a new subsection has been added to the paper to state the limitation of the present analysis due to the data loss and also recommending follow-up work and further analysis. Please see Subsection 3.5 (P. 11-12) and the last paragraph of Conclusions (P. 20 Lines 28-30, P. 21 Lines 1-6).

Editor in Chief: You are to add a subsection into the methodology called 'Limitations of the analysis,' which states that 'The authors wish to state for the record that analysis is meant as a first pass only at estimating the viable offshore wind resource in order to highlight the vast offshore wind resource available. There was a data loss of approximately 30%, which means that better and more reliable datasets are needed for the region. This is a recommendation of this work for follow- up work. Next the analysis is also limited by the hub height and measurement locations. However, once better datasets are available in the region, further analysis will be undertaken using the latest offshore wind turbine parameters.' Once this section has been added and the recommendation also added to the conclusion, please resubmit the paper. Response: Thank you very much for your comments and suggestion. We have revised the manuscript accordingly. The new subsection and the recommendation have been added to the paper. Please see Subsection 3.5 (P. 11-12) and the last paragraph of Conclusions (P. 20 Lines 28-30, P. 21 Lines 1-6).

Detailed Response to Reviewers

The authors wish to thank reviewers for their constructive comments, which have greatly helped improve the quality of the present manuscript. We have carefully considered all remarks. Below, please find detailed responses to the comments. We would also like to extend our appreciation to the editors for their efforts.

added and the recommendation also added to the conclusion, please resubmit the paper. Response: Thank you very much for your comments and suggestion. We have revised the manuscript accordingly. The new subsection and the recommendation have been added to the paper. Please see Subsection 3.5 (P. 11-12) and the last paragraph of Conclusions (P. 20 Lines 28-30, P. 21 Lines 1-6).

*Marked Version of revised manuscript

1 An assessment of wind energy potential in the Beibu Gulf considering the

2 energy demands of the Beibu Gulf Economic Rim

3 Xinping Chena,* , Aoife Foleyb, Zenghai Zhangc, Kaimin Wangd, Kieran O'Driscolle 4 a National Marine Hazard Mitigation Service of State Oceanic Administration, Beijing 100194, China. 5 b School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT9 5AH, 6 Northern Ireland, UK. 7 c National Meteorological Center of China Meteorological Administration, Beijing 100081, China. 8 d Shenzhen forecasting center, Shenzhen, Guangdong Province, China. 9 e School of Natural and Built Environment, Queen's University Belfast, Belfast BT9 5AH, Northern 10 Ireland, UK.

11 Abstract

12 The Beibu Gulf Economic Rim of China is a key economic region in China as

13 demonstrated by the “Beibu Gulf Cities Development Project” (2017) that plans to build a

14 “Blue Livable Gulf” to balance environmental protection while providing sustainable

15 economic development. This region has significant energy needs and is predicted to exhibit

16 rapid growth in the future.

17 By means of meteorological observations located at seven islands, a comprehensive

18 statistical analysis on wind energy potential in the northern coastal part of the Beibu Gulf is

19 conducted in this study. Specifically, wind speed, Weibull parameters, wind power density, as

20 well as wind directions on various timescales are analyzed. The analysis shows that annual

21 mean wind power density during 2010-2017 at 100m above mean sea level was, respectively,

22 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5 Wm-2 at the seven sites, with average value

23 of 353.2 Wm-2. Evidently, wind power potential demonstrates intra-annual variability, with

24 greatest values occurring in December, while another peak value is observed in July. Wind

25 speeds are lowest in May with another trough occurring in August. The data also display weak

26 inter annual variability. The prevailing wind directions in the rim are mainly from opposing

27 directions of N (winter and autumn) and S (summer).

28 Keywords: Beibu Gulf, Wind energy, Renewable energy, Wind power, Wind climate

* Corresponding author. Email address: [email protected] (Xinping Chen) 1

1 1. Introduction

2 The use of wind power as an alternative energy source, both on- and off-shore,

3 has increased rapidly over the last 10 years [1–4]. The offshore market is expected to

4 grow at a higher pace than the onshore market, representing about one quarter of the

5 total new wind power installations by 2020 [3]. One significant development in recent

6 years is the spectacular drop in offshore wind power prices, thus making offshore

7 wind power as competitive as fossil fuels and nuclear power [5]. Besides the cost of

8 wind power, renewables like wind can also provide solutions to relieve some key

9 environmental and social challenges, including improving energy security, creating

10 more jobs, reducing public health and air pollution problems, and mitigating against

11 greenhouse gas emissions (GHG) [3, 6].

12 When comparing development of wind energy sources, offshore sources have

13 been shown to be superior to onshore sources in a number of ways [7, 8]. For example,

14 offshore wind speeds are usually greater and more steady, leading to less turbulence

15 effects than those on land at nearby sites [9, 10]. A number of studies have assessed

16 offshore wind characteristics and wind power potential, either globally or at specific

17 sites [e.g., 11–35], providing fundamental information for offshore wind power

18 exploitation, for example, spatial and potential variability of wind energy potential

19 display for different offshore regions.

20 1.1 The development of wind energy in China

21 China possesses and can potentially harness abundant quantities of onshore and

22 offshore wind energy. It has been reported by the Global Wind Energy Council

23 (GWEC) [5] that as the driver of global market growth for most of the last decade,

24 China installed 21.2 GW of onshore wind and 1.8 GW of offshore wind in 2018,

25 representing 45% and 40% of global market share respectively. In 2018, the second

26 largest market was the US with 7.6 GW of new onshore installations, followed by

27 Germany (2.4 GW), India (2.2 GW) and Brazil (1.9 GW). For the offshore market in

28 2018, China took the lead for the first time, followed by the United Kingdom with 1.3

29 GW. Germany took the third place (0.9 GW) [5].

1 Although China is the world’s largest wind power market in both new and

2 cumulative installations, China also has a very large population of about 1.4 billion. In

3 particular, more than half the people in China live in the eastern coastal region, where

4 the economy is relatively developed, thereby leading to a huge energy demand.

5 Therefore, compared with other countries such as the USA, United Kingdom and

6 Germany, new installations in both onshore and offshore wind market is actually

7 much lower on a per capita basis, indicating that China still needs to strive to develop

8 wind energy exploitation, particularly for nearshore and offshore wind energy.

9 Additionally, China has a very long coastline, approximately 18,000 km running

10 from the Bohai Sea in the north to the Beibu Gulf in the south, adjacent to a

11 12-nautical-mile territorial sea with depths mostly less than 100m. This means that,

12 firstly, offshore wind is a very viable solution located close to the major coastal urban

13 areas of Tianjin, Shanghai, Guangzhou, Shenzhen and so on, with their many millions

14 of citizens. Secondly, wind power resources vary spatially and temporally: wind

−2 15 power densities along the coast of the Chinese Seas regions range from 200 Wm to

16 1000 Wm−2 [36–39], which can also be realized from the Global Wind Atlas

17 (https://globalwindatlas.info/). The potential for offshore wind power has been

18 examined for some coastal regions of China. For example, wind power potential was

19 assessed for the Pearl River Delta region [41] and for Hongkong [29, 42–44]; the

20 nearshore wind energy resources off the Shenzhen coast was carefully evaluated [45];

21 while a model-based climatology analysis was made for wind power resources over

22 the Bohai Sea and the Yellow Sea [46]. Furthermore, previous studies have shown that

23 even in relatively small offshore areas, wind energy potential can display spatial and

24 temporal differences [35, 45]. Therefore, before selecting a site to build wind power

25 plants, it is an essential and important first step to conduct a precise survey and

26 assessment of wind energy resources around the region, especially for the offshore

27 region where observational data are still rare and good quality information is limited.

28 The area of interest in this study is off the coast of Guangxi Zhuang Autonomous

29 Region, located in the Beibu Gulf Economic Rim (BGER) of China, located in the

30 South China Sea (see Figure 1). This is one of the least developed regions of China, 3

1 and especially for a coastal region. As an important part of the "China Western

2 Development Strategy", in 2017, the central government of China has sanctioned the

3 "Beibu Gulf Cities Development Project" (BGCD project). The project aims to build 3

4 "City Centre Circles", 2 "City Development Roles" and 1 "Blue Liveable Gulf" (the

5 Beibu Gulf). Various infrastructures will be built, and a large amount of manpower,

6 resources and capital investments and other invisible privileges needed are planned to

7 promote the development of the whole BGER and to thoroughly shake off the relative

8 poverty and backwardness of this region.

9 10 Figure 1 : Area of the study relative to the northern South China Sea (left) and geographic locations of the Formatted: Space After: 0.5 line 11 seven meteorological sites in red crosses (+ right).

1 1.2 Energy demands and policy changes in the Beibu Gulf Economic Rim

2 Formatted: Space Before: 0.5 line 3 Figure 2 : (Top) Electricity consumption of the Guangxi Zhuang Autonomous Region (Guangxi 4 province) for the period 2008-2017; (Bottom) monthly electricity consumption of Guangxi in 2015, 5 2016 and 2017. (Data resource: China Energy Statistical Yearbook (2008-2017) [47]). 6 Electricity consumption of Guangxi province in the past 11 years was analyzed

7 to reflect the energy demand for this rim. As shown in Figure 2, the electricity

8 consumption in Guangxi and Hainan provinces has been rising continuously for the

9 past 11 years [47]. Furthermore, since the BGCD project was sanctioned in 2017, the

10 growth rate of the electricity consumption in Guangxi province has risen rapidly,

11 exceeding expectations. In 2018, electricity consumption in Guangxi had reached

12 170.28TWh, with growth rate up 17.84% compared with the value of 2017 (144.23

13 TWh), which is much higher than the growth rate of China (8.5%). From the official

14 report, energy consumption in Zhanjiang municipal city in this rim was, 10.80 TWh

15 and 18.15 TWh in 2016 and 2017, respectively, and its year-on-year growth rate was

16 7.8% and 18.4%, respectively, with the latter leaping to the top in Guangdong

17 province (Zhanjiang is a municipal city of Guangdong province). Overall, electricity

18 consumption in this rim has rapidly grown, especially from 2017, reflecting the rapid

19 growth of energy demand in the rim.

1 However, air pollution and smog have already occurred in many regions of

2 China, especially in eastern coastal regions, including Beijing, Tianjin, and Hangzhou.

3 Development in Eastern China has followed the well-worn global routine of priority

4 for economic growth and expansion, inevitably resulting in air pollution, thus

5 sacrificing the environment without consideration of necessary steps to address the

6 resultant environmental and pollution problems. This approach has resulted in many

7 painful lessons for China, generating serious dialogue and deep thought, thereby

8 compelling China to express its resolute determination to avoid further pollution in

9 less developed regions such as the western region of China. China is now

10 implementing a sustained developmental strategy to accelerate the change of energy

11 structure, particularly with a view to increasing the share of clean sources in its

12 energy mix [48].

13 The BGER is one such new region to be developed in China, and has been

14 endowed to construct and provide non-polluting industry and tourism. For example,

15 Hainan Island (province), an important part of the BGER, recently officially

16 published the “Implementation plan of National pilot ecological zone (Hainan

17 Province) of China” in May 2019. This initiative clearly points out that Hainan plans

18 to build a “Clean Energy Island”, including measures leading to the step by step

19 prohibition of the selling of all petrol vehicles.

20 At the outset of action formulation and plan development for the BGER, clean

21 green energy, including wind energy, will be the primary consideration. However,

22 wind energy development in this zone has fallen far behind. In 2017, more than 1.16

23 GW of additional offshore wind power capacity was installed in Chinese coastal areas,

24 while there has been no installed capacity in the Beibu Gulf up to the end of 2017 [47].

25 Thus, especially because of inadequate knowledge and understanding of coastal wind

26 characteristics, the exploitation of coastal wind energy resources in the region is still

27 lacking.

28 To assist in the venture of optimal harvesting and utilization of wind power, this

29 paper presents a careful and comprehensive statistical assessment on wind field and

30 energy potential with detailed resource characterization for the period 2010 to 2017, 6

1 by means of measurement data collected from seven meteorological stations on

2 coastal islands in the Beibu Gulf. The present study aims to provide insight toward

3 understanding of wind energy potential required in exploring wind energy resources

4 in the study area, in terms of wind speed variation, Weibull parameters and wind

5 power density, as well as wind direction on various timescales. Additionally, to

6 discuss the specific ability of energy development within the region, this study also

7 provides the insight to meet the energy demand for the rim.

8 2. Wind data measurement and adjustment

9 In this study, data measured at seven meteorological stations on offshore islands

10 and acquired by the CMA, are available for the region. The geographic locations of

11 these seven meteorological stations are given in Figure 1. Before the stations were

12 built, the site selections were fully demonstrated, following related standards and

13 specifications, and data records were carefully checked by the data center of CMA, to

14 ensure that the data are no problem for further analyses. Data collected at the stations

15 are representative of wind climate for different areas of the study region. Data are

16 gathered mainly to monitor atmospheric changes in the region, including forecasting

17 and scrutiny of meteorological and environmental issues, etc. The stations are located

18 at different heights above mean sea level (MSL), while the anemometer for each

19 station is installed at 10m height above the surface of each station ground.

20 In China, each station for surface meteorological measurement must be followed

21 the standard of the “Specifications for surface meteorological observation” (QX/T

22 45-2007) that published in 2007 (the modified new version is published in 2017

23 (GB/T 35221-2017)), which specified that surface meteorological stations must be

24 located in the relatively flat and open terrain so as to be representative of relatively

25 large local meteorological characteristics. The stations used in this study meet the

26 standard. The wind monitors at the stations are high performance wind monitor

27 sensors (RM Yong 5106) with blade helicoid propellers, purchased from R.M. Young.

28 The wind data adapted in the present study include wind speed and direction

29 parameters over the period January 2010 to December 2017. Wind speed parameters 7

1 were sampled every 1s and averaged over 1 min. Wind direction data was recorded 6

2 times per minute, and also averaged over 1 min. The stations at QC, DM, SD, DY and

3 BH are located very close to shore, of which QC and DM are located in the mouth of

4 Qingzhou Bay; SD is located in Zhenzhu Bay; DY is located near Fangcheng Port.

5 BX is further offshore, and ZZ and XY are located furthest from shore.

6 The sensor height at which wind data is measured at the different stations is

7 shown in Table 1. We note the large variability in recorded wind speed, and also that

8 wind speed will vary as a function of recorded height. To extrapolate recorded wind

9 data to likely turbine hub height, hub height is set at 100m above MSL for this study,

10 unless otherwise stated. A number of earlier studies have employed mathematical

11 models representing height variability of wind speed, of which the log-law model has

12 been most commonly employed [49]. The simple power-law model is frequently

13 applied to offshore energy applications [14, 49–51], which is also employed in this

14 study. The value 0.13 for the power law exponent has been chosen for this assessment,

15 taken from the previous study of a Korean offshore wind farm, where the farm area is

16 more semi-closed than ocean areas without nearby obstacles and thereby substantially

17 affected by geomorphologic influences [35]. In comparison with the area in this

18 previous study, the geomorphologic condition of the present study area is closer to

19 that of the Korea wind farm. Formatted: Space Before: 0.5 line 20 Table 1 : List of measurement sites in the Guangxi coastal region with some fundamental mean wind 21 and energy characteristics. Hourly collected observational data covered for the period August 2009 to 22 the end of 2017. Table No. of observations represents the valid number of observations used in the 23 study for each station. Total number of the hours over the study period is 70128. Height (m) in the 24 table represents the wind sensor height above the mean sea level at each station. Main wind 25 characteristics were statistically calculated, including wind speed, (ms-1); Weibull parameters, -1 -1 -1 -2 26 and (ms ); (ms ); (ms ); wind power, (W m ). Site BX DY SD QC DM ZZ XY

No. of observations 53216 57908 61610 59657 50221 70001 54017

Height 17.1 13.5 20.6 21.0 28.0 65.7 42.8

7.6 7.2 6.1 6.0 5.8 4.0 5.7 1.8 1.7 1.6 1.9 1.8 1.9 1.5

8.6 8.0 6.8 6.8 6.6 4.6 6.4

7.8 7.1 5.8 6.1 5.8 3.9 5.3

9.4 8.8 7.7 7.6 7.3 5.3 7.3

605.6 542.0 368.0 282.0 265.6 87.6 321.5

1 3. Analytical methods

2 To provide the statistical analysis of wind characteristics and the assessment of

3 wind energy potential sources associated with wind energy exploitation, several

4 analytical methods are applied and briefly described below.

5 3.1 Weibull distribution function

6 Probability distribution models are commonly used to fit wind speed

7 distributions over a time period and to obtain a clear view of the available wind

8 potential for a region. A large number of different distribution models have been

9 proposed for statistically analysing both wind characteristics and potential wind as

10 energy assessment. Amongst all the available probability distribution functions, the

11 Weibull probability distribution functions, particularly the two-parameter Weibull

12 function, are commonly used and widely adopted for wind energy applications, not

13 only due to their great flexibility and simplicity but also because they can provide a

14 good representation of recorded wind data [29, 35, 50, 54–57]. Therefore, the

15 two-parameter Weibull distribution model was utilized for the assessment of wind

16 energy potential sources in this study.

17 The general form of the two-parameter Weibull function can be characterized by

18 the Probability Density Function (PDF), ( ), and

19 cumulative distribution function, . Here, is wind speed in ms-1, is the

20 dimensionless Weibull shape parameter, and is the Weibull scale parameter having

21 the same units as .

22 Several different numerical methods have been proposed and compared to

23 estimate the Weibull parameters in previous studies [56,57]. The performance of these

24 methods improves with increasing dataset size [56]. Following these studies, different

25 common numerical methods, including the moment, graphical, empirical, energy

26 pattern factor method, maximum likelihood and modified maximum likelihood, are

1 tested in determining the parameters of the Weibull distribution, so as to select the

2 most effective method by comparing performance for this study. The methods for

3 estimating Weibull parameters used in this study can be found in previous studies, e.g.,

4 [56,57].

5 6 Figure 3 : Weibull distributions based on the six methods for estimating Weibull parameters (i.e., the 7 moment, graphical, empirical, energy pattern factor method, maximum likelihood and modified 8 maximum likelihood) for the seven sites shown in Figure 1. The values in parentheses represent 9 RMSEs (root mean square errors).

10 The performance of the methods for estimating Weibull parameters listed above

11 (i.e., the moment, graphical, empirical, energy pattern factor method, maximum

12 likelihood and modified maximum likelihood) are compared to test which are

13 effective for this study. Figure 3 shows the PDFs (probability density functions) for

14 each of the seven sites located in the Beibu Gulf, and calculated from wind data for

15 the period 2010 to 2017. The difference between the curve calculated using the

16 graphical method and the other methods is most obvious. To further test the accuracy

17 of these methods, the judgment of accuracy of the RMSE (root mean square error)

18 used in [56,57] is adapted for the present study. The RMSE is defined as

19 , where is the frequency of observations, represents the

20 frequency of Weibull, and N is the number of observations. The RMSEs of the six

21 methods for the seven sites are listed in the parentheses of Figure 3. It can be seen

22 from these values that the performance of the maximum likelihood method is on

1 average best. Therefore, the study uses this method for determining the Weibull

2 parameters for further analyses in the following sections.

3 3.2 Most probable wind speed

4 The estimated Weibull parameters are directly used so as to obtain most probable

5 wind speeds, thereby indicating most frequent wind speed for a given PDF. The most

6 probable wind speed, , is computed in terms of Weibull distribution parameters

7 [29,51]:

8 (1)

9 3.3 Wind speed carrying maximum energy

10 The Weibull parameters are also used to compute wind speed containing

11 maximum energy, , for a wind turbine, which is also the speed that produces

12 the most energy. can also be adapted for estimating wind turbine rated wind 13 speeds, which can be defined as follows [29, 43, 50]:

14 (2)

15 3.4 Wind power density

16 Wind power density is an important parameter for evaluating available resources 17 at potential sites. Wind power, measured in Wm-2, per unit swept area of a turbine is 18 proportional to the cube of the wind speed. In terms of Weibull distribution 19 parameters, the wind power per unit area can be defined as follows [29, 50, 60–63]:

20 (3)

21 where and are still the Weibull parameters.

22 3.5 Limitations of the analysis of this study

23 We wish to state for the record that the analysis of the present study is meant as a 24 first pass only at estimating the viable offshore wind resource in order to highlight the 25 availability of this resource within the study region. 26 For the study period from August 2009 to December 2017, the valid number of 27 observations from all data records used in the study at each of the stations (i.e., BX, 28 DY, SD, QC, DM, ZZ, XY) was, respectively, about 76%, 83%, 88%, 85%, 72%, 29 99%, 77% (see Table 1). This means that there were data losses of approximately 24%, 30 17%, 12%, 15%, 28%, 1% and 23% for each station, respectively, indicating that 31 better and more reliable datasets are needed for the region, especially for stations BX,

1 DM and XY. This is a recommendation of the present study for follow-up work. 2 Moreover, the analysis is also limited by wind turbine hub height and measurement 3 locations. However, once better datasets become available for the region, further 4 analysis will be undertaken using the latest offshore wind turbine parameters.

6 4. Wind energy assessment in the Beibu Gulf

7 To interpret wind climate and potential wind energy characteristics at the stations

8 shown in Figure 1, wind features were analyzed based on detailed observational data,

9 including mean wind speed, prevailing direction, diurnal and monthly variations,

10 together with Weibull distributions, and the wind power density. The observed wind

11 speed data have been converted from anemometer height to wind turbine hub height

12 by use of the power-law approximation introduced in Section 2. Converted data have

13 been used in this study, whereas we assumed wind directions do not vary with height.

14 Moreover, for the present study, the four boreal seasons are winter

15 (December-February), spring (March-May), summer (June-August), and Autumn

16 (September-November).

1 2 Figure 4 : Intra- and inter-annual variability of wind speeds (ms-1) over the period 2010 to 2017 for the 3 seven sites shown in Figure 1. (A) Monthly wind speed; (B) Monthly climatology of wind speed; 4 (C1-C4) Year-to-year variability for different month (December, May, July and August) of the 5 wind speed.

6 4.1 Inter- and intra-annual wind speed variations

7 Monthly mean and seasonal wind speed characteristics were investigated for the

8 study sites, based on observations collected over the period 2010 to 2017, to show

9 inter- and intra-annual wind speed variability.

1 Figure 4(A) illustrates monthly wind speed for the seven sites. Intra- and

2 inter-annual variability is obvious. Monthly wind speed values at the sites ranged

-1 -1 3 from about 3.0 ms to 12.0 ms , with annual mean wind speed values varying

4 between 4.0 ms-1 and 7.6 ms-1 (see Table 1), although January 2011 values were

5 relatively higher at all sites, with a highest monthly wind speed value of about 15.6

6 ms-1 found at DY.

7 Figure 4(B) shows monthly wind speed climatologies for the seven sites.

8 Intra-annual wind sped variability is evident, with annual climatologies exhibiting two

9 cycles per year: largest wind speeds occurring in December with another peak value

10 in July, and lowest wind speeds apparent in May with another trough in August.

11 Figure 4(C1-C4) shows year-to-year wind speed variability for months

12 containing peak and trough wind speed values. Inter-annual variability seems weaker

13 than intra-annual variability. In addition, most of the seven sites display quite similar

14 inter-annual variations, especially for December and July.

15 The long-term trend of the wind speed at the seven sites was also checked,

16 however, no significant trend can be verified for the period of interest. This is because

17 the observational period is relatively short to for determining a conclusion on the

18 long-term trend in wind climate change for the study area, especially when

19 considering response to global climate change.

20 4.2 Diurnal wind speed variations

21 22 Figure 5 : Diurnal mean wind speed (ms-1) for the eight sites shown in Figure 1.

23 Besides monthly and seasonal probability wind speed distributions, diurnal mean

24 wind speed variations are also analyzed in this study to explain hourly wind speed

25 probabilities, and are presented in Figure 5 for each of the seven sites. A clear feature

1 at each site is that for sites BX, DY, ZZ and XY, which are the furthest from shore and

2 closer to the open sea, hourly mean wind speed is largest in day time (8:00-18:00);

3 while at SD, QC and DM, closer to the mainland, wind speeds are relatively small in

4 the afternoon.

5 4.3 Weibull distribution

6 7 Figure 6 : Annual Weibull probability distributions at all sites for the period 2010 to 2017. Formatted: Space After: 0.5 line

8 Annual wind speed Weibull distributions at all sites are highlighted in Figure 6,

9 and annual mean Weibull parameters for period 2010-2017 are listed in Table 1Table

10 1. As seen in Figure 6, the curve peak represents the most frequent wind speed.

11 The shape parameter, , was in the range 1.5-1.9 at all stations, while scale

12 parameter, , varied between 4.6 ms-1 and 8.6 ms-1. The average values of the shape

13 and scale parameters for all sites were, respectively, 1.7 ms-1 and 6.8 ms-1.

1 4.4 Variations of and

2 Formatted: Space After: 0.5 line 3 Figure 7 : Monthly variation of and at all sites for the period 2010 to 2017.

4 Figure 7 plots the variability in monthly mean values of most probable wind

5 speed, , and wind speed containing most energy, , observed at the seven -1 6 sites. Concerning , monthly values were mostly confined to the range 2-9 ms at

7 all sites. displayed seasonality similar to that of wind speed and Weibull

8 parameters: relatively high values in December and January, low values in April, May

9 and August, with increased values in June and July. Likewise, also exhibits

10 obvious seasonal changes. Annual mean values of and for the seven

11 sites over the period 2010 to 2017 are also given in Table 1. The largest annual mean

-1 12 values of and were found at BX and DY, with values of ~7.8 ms and

13 ~7.1 ms-1, respectively, while a smallest value of 3.9 ms-1 occurred at ZZ.

14 4.5 Seasonal wind speed and direction roses

15 Beside wind speed, wind direction is another important parameter for assessing

16 of wind energy resources. Seasonal distributions of incoming wind direction and wind

17 speed for the seven sites are presented in terms of a wind rose figure, Figure 8. It is

18 apparent from this figure that seasonal wind direction variability is occurring at the

19 seven locations, mainly reflecting the monsoon characteristics in this region.

1 In winter and autumn, northerly to northeasterly offshore winds, i.e. winds from

2 land, prevailed at each of the sites, accounting for greater than 50% of directional

3 winds, while onshore winds, i.e. winds from the open sea, from S, SW and SE,

4 provided the largest contribution to wind energy resources. In spring, winds from N to

5 E and to S accounted for most, mainly reflecting transition season characteristics in

6 wind direction. It is noted, but not surprising, that westerly to northwesterly winds

7 were rare in all seasons at all sites (on average less than 5%), reflecting South China

8 Sea monsoon characteristics.

9 Overall, prevailing wind directions in the rim are mainly from opposite N-S

10 directions, which might be good for energy exploitation since focus of wind direction

11 and concentration of energy benefit several types of wind turbines designed to convert

12 wind resources as fully as possible.

14 15 Figure 8 : Seasonal wind speed and direction roses based on the site measurements for the period 2010 to Formatted: Space After: 0.5 line

16 2017.

17 Figure 8 also displays wind speed range percentages at the sites over the period

18 2010 to 2017. The wind scale is in accordance with the Beaufort scale, as represented

19 by the colors. At BX and DY, wind speeds were mostly from 4 to 9 on the Beaufort

1 scale, i.e., from 5.5 ms-1 to 24.5 ms-1, reaching 10 on the Beaufort scale in autumn and

2 winter about 5% of the time. Wind speeds were mostly in the range of 3-8 Beaufort

3 scale at SD and QC, while mostly varied from 3 to 6 on the Beaufort scale at DM and

4 XY, with seasonal changes as mentioned above. Smallest wind speed scales were

5 found at BH and ZZ where over 90% of wind speeds were below 5 Beaufort scale

6 (less than 10.8 ms-1).

7 4.6 Wind power density

8 Wind power densities were calculated for the period 2010 to 2017 by use of eq. Formatted: Font: 12 pt 9 33. Wind power density, calculated based on the measurements, are, respectively,

10 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5Wm-2 at BX, DY, SD, QC, DM, ZZ

11 and XY stations (see Table 1). It is noted that the Global Wind Atlas

12 (https://globalwindatlas.info/) wind power range for the study region is 200-450Wm-2

13 at 100m above MSL. Wind power is higher than the Global Wind Atlas wind power

14 range only at BX and DY, while mean wind power averaged over the seven stations

15 is 353.2Wm-2, which is within the range of Global Wind Atlas.

16 Figure 9 describes the intra- and inter-annual variation in terms of monthly mean

17 wind power density for each site. Since wind power density is a function of wind

18 speed only, characteristics of its variability are in good agreement with the

19 corresponding wind speed variations. Intra- and inter-annual variability in wind power

20 patterns display quite similar characteristics at most of the seven sites. Intra-annual

21 variability was evident at all sites: wind power density values were largest in

22 December, while lowest in August; another peak/trough wind speed occurred in

23 July/May. This variability might somehow favour wind energy exploitation and usage:

24 monthly changes in electricity consumption for Guangxi province in the rim is

25 relatively high in summer time, while lowest in February and April (see Figure 2).

1 2 Figure 9 : Seasonal (left) and monthly (right) mean wind power density (Wm-2) for the sites shown in 3 Figure 1.

4 In accordance with mean wind-speed and Weibull parameters, for spatial

5 variation, the annual mean wind power density, (shown in Table 1), was

6 greatest at BX with a value of 605.6 Wm-2, and least at ZZ with a value of 87.6 Wm-2.

7 4.7 Estimation of the potential contribution of coastal wind in the study area

8 In 2017, more than 1.16 GW of additional offshore wind power capacity was

9 installed in Chinese coastal areas, while there has been no installed capacity in the

10 Beibu Gulf up to the end of 2017 [47]. If a turbine with 5 MW capacity is installed in

11 the study area, it would provide about 11 GWh in one year, with annual utilization

12 time of installed capacity in Guangxi being 2280 hours in 2017, based on official data

13 [47]. Thus, a wind farm with 100 such turbines would generate about 1.1 TWh

14 electricity, which can contribute about 0.8% and 3.6% of total electricity consumption

15 in 2017 for Guangxi and Hainan provinces, respectively.

16 5. Conclusions

17 The Beibu Gulf Economic Rim is critical for China's development, and

18 especially for the western China region. The “Beibu Gulf Cities Development Project”

19 sanctioned in 2017 plans to build a “Blue Livable Gulf” to avoid the well-worn

20 routine of priority for economic development, while sacrificing the environment.

21 Electricity consumption data of the rim shows a rising demand in the past ten years

22 and indicates rapid growth in the future, reflecting the huge amount of energy

23 required for developing these cities, with primary consideration given to clean energy.

1 In this study, a comprehensive statistical analysis and assessment of wind field

2 and associated energy potential with detailed resource characterization in the study

3 area for the period 2010 to 2017 was conducted at seven offshore coastal sites

4 mention the sites again here. Annual mean wind speed values for the period 2010 to

5 2017 for the seven sites at 100m ranged from 4.0 ms-1 to 7.6 ms-1, resulting in annual

6 mean wind power densities, respectively, of 605.6, 542.0, 368.0, 282.0, 265.6, 87.6

-2 7 and 321.5 Wm at the seven sites (BX, DY, SD, QC, DM, ZZ and XY), with average

8 value of 353.2 Wm-2. Intra-annual variability of wind power potential is evident at the

9 seven sites, with the greatest wind power occurring in December and another peak

10 value in July, while lowest wind speed values occur in May with another trough in

11 August. The data also display also shows weak inter-annual variations. However, no

12 significant annual trends were found over the period.

13 The annual mean Weibull shape parameter, κ, for period 2010-2017 varied from

14 1.5 to 1.9 at the sites, while the Weibull scale parameter, , was found to have values

-1 -1 15 between 4.6 ms and 8.6 ms . The annual mean and for the sites were,

16 respectively, in the range 3.9-7.8 ms-1 and 5.3-9.4 ms-1.

17 Seasonal variability is evident in wind direction, mainly reflecting monsoon

18 characteristics in the region: in winter and autumn, winds were predominantly from

19 the north, with a considerable northeasterly contribution, while southerly winds

20 prevailed in summer. The prevailing wind direction distribution in the rim is good for

21 energy exploiting and usage. Statistical estimates suggest that a wind farm with 100

22 turbines each of 5 MW capacity would contribute about 0.8% and 3.6% of the total

23 electricity consumption in 2017 for Guangxi and Hainan provinces, respectively.

24 Overall, this study suggests that China, including the Beibu Gulf, has relatively

25 large resources of offshore wind energy that can reduce its dependency on imported

26 fossil fuels, thus ensuring security of energy supply as well as reduced GHG

27 emissions in the Beibu Gulf Economic Rim, China and the world.

28 Finally, it should be stated that there was a data loss of more than 20% in the

29 measurement records at three of the seven stations (i.e., 24%, 28% and 23%,

30 respectively, for stations BX, DM and XY) used in this study. Estimates of wind 20

1 speed and wind power variability may be influenced by gaps in the data. We wish to

2 state, for the record, that the analysis of the present study is meant as a first pass only

3 at estimating the viable offshore wind resource in order to highlight the rich offshore

4 wind resource available for the study region. Better and more reliable datasets are

5 needed for follow-up work. Once these improved datasets become available for the

6 region, further analysis will be undertaken using the latest offshore wind turbine

7 parameters.

9 Acknowledgements

10 Xinping Chen was funded by the National Natural Science Foundation of China

11 (NSFC) grant 41506042. Aoife Foley was supported by the Northern Ireland

12 "Department for Economics USI 110 US & Ireland R&D Research Partnership" under

13 the US-Ireland Research and Development Partnership program Science Foundation

14 Ireland and the United States National Science Foundation Collaborative Research

15 Decentralisation, ElectrificatioN, Communications and Economics (CREDENCE)

16 Centre-to-Centre Award.

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*Clean Version of revised manuscript Click here to view linked References

1 1 An assessment of wind energy potential in the Beibu Gulf considering the 2 3 2 4 energy demands of the Beibu Gulf Economic Rim 5 a,* b c d e 6 3 Xinping Chen , Aoife Foley , Zenghai Zhang , Kaimin Wang , Kieran O'Driscoll 7 4 a National Marine Hazard Mitigation Service of State Oceanic Administration, Beijing 100194, China. 8 9 5 b School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT9 5AH, 10 11 6 Northern Ireland, UK. 12 7 c National Meteorological Center of China Meteorological Administration, Beijing 100081, China. 13 14 8 d Shenzhen forecasting center, Shenzhen, Guangdong Province, China. 15 16 9 e School of Natural and Built Environment, Queen's University Belfast, Belfast BT9 5AH, Northern 17 18 10 Ireland, UK. 19 11 Abstract 20 21 12 The Beibu Gulf Economic Rim of China is a key economic region in China as 22 23 13 demonstrated by the “Beibu Gulf Cities Development Project” (2017) that plans to build a 24 25 26 14 “Blue Livable Gulf” to balance environmental protection while providing sustainable 27 28 15 economic development. This region has significant energy needs and is predicted to exhibit 29 30 16 rapid growth in the future. 31 32 17 By means of meteorological observations located at seven islands, a comprehensive 33 34 18 statistical analysis on wind energy potential in the northern coastal part of the Beibu Gulf is 35 36 19 conducted in this study. Specifically, wind speed, Weibull parameters, wind power density, as 37 38 20 well as wind directions on various timescales are analyzed. The analysis shows that annual 39 40 21 mean wind power density during 2010-2017 at 100m above mean sea level was, respectively, 41 42 22 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5 Wm-2 at the seven sites, with average value 43 44 23 of 353.2 Wm-2. Evidently, wind power potential demonstrates intra-annual variability, with 45 46 24 greatest values occurring in December, while another peak value is observed in July. Wind 47 48 25 speeds are lowest in May with another trough occurring in August. The data also display weak 49 50 26 inter annual variability. The prevailing wind directions in the rim are mainly from opposing 51 52 27 directions of N (winter and autumn) and S (summer). 53 54 28 Keywords: Beibu Gulf, Wind energy, Renewable energy, Wind power, Wind climate 55 56 57 58 59 * Corresponding author. 60 Email address: [email protected] (Xinping Chen) 61 1 62 63 64 65

1 1 1. Introduction 2 3 4 2 The use of wind power as an alternative energy source, both on- and off-shore, 5 6 3 has increased rapidly over the last 10 years [1–4]. The offshore market is expected to 7 8 4 grow at a higher pace than the onshore market, representing about one quarter of the 9 10 5 total new wind power installations by 2020 [3]. One significant development in recent 11 12 6 years is the spectacular drop in offshore wind power prices, thus making offshore 13 14 7 wind power as competitive as fossil fuels and nuclear power [5]. Besides the cost of 15 16 8 wind power, renewables like wind can also provide solutions to relieve some key 17 18 9 environmental and social challenges, including improving energy security, creating 19 20 10 more jobs, reducing public health and air pollution problems, and mitigating against 21 22 11 greenhouse gas emissions (GHG) [3, 6]. 23 24 12 When comparing development of wind energy sources, offshore sources have 25 26 13 been shown to be superior to onshore sources in a number of ways [7, 8]. For example, 27 28 14 offshore wind speeds are usually greater and more steady, leading to less turbulence 29 30 15 effects than those on land at nearby sites [9, 10]. A number of studies have assessed 31 32 16 33 offshore wind characteristics and wind power potential, either globally or at specific 34 35 17 sites [e.g., 11–35], providing fundamental information for offshore wind power 36 37 18 exploitation, for example, spatial and potential variability of wind energy potential 38 39 19 display for different offshore regions. 40 41 20 1.1 The development of wind energy in China 42 43 21 China possesses and can potentially harness abundant quantities of onshore and 44 45 22 offshore wind energy. It has been reported by the Global Wind Energy Council 46 47 23 (GWEC) [5] that as the driver of global market growth for most of the last decade, 48 49 24 China installed 21.2 GW of onshore wind and 1.8 GW of offshore wind in 2018, 50 51 25 representing 45% and 40% of global market share respectively. In 2018, the second 52 53 26 largest market was the US with 7.6 GW of new onshore installations, followed by 54 55 27 Germany (2.4 GW), India (2.2 GW) and Brazil (1.9 GW). For the offshore market in 56 57 28 2018, China took the lead for the first time, followed by the United Kingdom with 1.3 58 59 29 GW. Germany took the third place (0.9 GW) [5]. 60 61 2 62 63 64 65

1 Although China is the world’s largest wind power market in both new and 1 2 2 cumulative installations, China also has a very large population of about 1.4 billion. In 3 4 3 particular, more than half the people in China live in the eastern coastal region, where 5 6 4 7 the economy is relatively developed, thereby leading to a huge energy demand. 8 9 5 Therefore, compared with other countries such as the USA, United Kingdom and 10 11 6 Germany, new installations in both onshore and offshore wind market is actually 12 13 7 much lower on a per capita basis, indicating that China still needs to strive to develop 14 15 8 wind energy exploitation, particularly for nearshore and offshore wind energy. 16 17 9 Additionally, China has a very long coastline, approximately 18,000 km running 18 19 10 from the Bohai Sea in the north to the Beibu Gulf in the south, adjacent to a 20 21 11 12-nautical-mile territorial sea with depths mostly less than 100m. This means that, 22 23 12 firstly, offshore wind is a very viable solution located close to the major coastal urban 24 25 13 areas of Tianjin, Shanghai, Guangzhou, Shenzhen and so on, with their many millions 26 27 14 of citizens. Secondly, wind power resources vary spatially and temporally: wind 28 29 15 power densities along the coast of the Chinese Seas regions range from 200 Wm−2 to 30 31 16 1000 Wm−2 [36–39], which can also be realized from the Global Wind Atlas 32 33 17 (https://globalwindatlas.info/). The potential for offshore wind power has been 34 35 18 examined for some coastal regions of China. For example, wind power potential was 36 37 19 38 assessed for the Pearl River Delta region [41] and for Hongkong [29, 42–44]; the 39 40 20 nearshore wind energy resources off the Shenzhen coast was carefully evaluated [45]; 41 42 21 while a model-based climatology analysis was made for wind power resources over 43 44 22 the Bohai Sea and the Yellow Sea [46]. Furthermore, previous studies have shown that 45 46 23 even in relatively small offshore areas, wind energy potential can display spatial and 47 48 24 temporal differences [35, 45]. Therefore, before selecting a site to build wind power 49 50 25 plants, it is an essential and important first step to conduct a precise survey and 51 52 26 assessment of wind energy resources around the region, especially for the offshore 53 54 27 region where observational data are still rare and good quality information is limited. 55 56 28 The area of interest in this study is off the coast of Guangxi Zhuang Autonomous 57 58 29 Region, located in the Beibu Gulf Economic Rim (BGER) of China, located in the 59 60 30 South China Sea (see Figure 1). This is one of the least developed regions of China, 61 3 62 63 64 65

1 and especially for a coastal region. As an important part of the "China Western 1 2 2 Development Strategy", in 2017, the central government of China has sanctioned the 3 4 3 "Beibu Gulf Cities Development Project" (BGCD project). The project aims to build 3 5 6 4 7 "City Centre Circles", 2 "City Development Roles" and 1 "Blue Liveable Gulf" (the 8 9 5 Beibu Gulf). Various infrastructures will be built, and a large amount of manpower, 10 11 6 resources and capital investments and other invisible privileges needed are planned to 12 13 7 promote the development of the whole BGER and to thoroughly shake off the relative 14 15 8 poverty and backwardness of this region. 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 9 38 10 Figure 1 : Area of the study relative to the northern South China Sea (left) and geographic locations of the 39 40 11 seven meteorological sites in red crosses (+ right). 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 4 62 63 64 65

1 1.2 Energy demands and policy changes in the Beibu Gulf Economic Rim 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2 26 27 3 Figure 2 : (Top) Electricity consumption of the Guangxi Zhuang Autonomous Region (Guangxi 28 29 4 province) for the period 2008-2017; (Bottom) monthly electricity consumption of Guangxi in 2015, 30 5 2016 and 2017. (Data resource: China Energy Statistical Yearbook (2008-2017) [47]). 31 32 6 Electricity consumption of Guangxi province in the past 11 years was analyzed 33 34 7 to reflect the energy demand for this rim. As shown in Figure 2, the electricity 35 36 8 consumption in Guangxi and Hainan provinces has been rising continuously for the 37 38 9 past 11 years [47]. Furthermore, since the BGCD project was sanctioned in 2017, the 39 40 10 growth rate of the electricity consumption in Guangxi province has risen rapidly, 41 42 11 exceeding expectations. In 2018, electricity consumption in Guangxi had reached 43 44 12 170.28TWh, with growth rate up 17.84% compared with the value of 2017 (144.23 45 46 13 TWh), which is much higher than the growth rate of China (8.5%). From the official 47 48 14 49 report, energy consumption in Zhanjiang municipal city in this rim was, 10.80 TWh 50 51 15 and 18.15 TWh in 2016 and 2017, respectively, and its year-on-year growth rate was 52 53 16 7.8% and 18.4%, respectively, with the latter leaping to the top in Guangdong 54 55 17 province (Zhanjiang is a municipal city of Guangdong province). Overall, electricity 56 57 18 consumption in this rim has rapidly grown, especially from 2017, reflecting the rapid 58 59 19 growth of energy demand in the rim. 60 61 5 62 63 64 65

1 However, air pollution and smog have already occurred in many regions of 1 2 2 China, especially in eastern coastal regions, including Beijing, Tianjin, and Hangzhou. 3 4 3 Development in Eastern China has followed the well-worn global routine of priority 5 6 4 7 for economic growth and expansion, inevitably resulting in air pollution, thus 8 9 5 sacrificing the environment without consideration of necessary steps to address the 10 11 6 resultant environmental and pollution problems. This approach has resulted in many 12 13 7 painful lessons for China, generating serious dialogue and deep thought, thereby 14 15 8 compelling China to express its resolute determination to avoid further pollution in 16 17 9 less developed regions such as the western region of China. China is now 18 19 10 implementing a sustained developmental strategy to accelerate the change of energy 20 21 11 structure, particularly with a view to increasing the share of clean sources in its 22 23 12 energy mix [48]. 24 25 13 The BGER is one such new region to be developed in China, and has been 26 27 14 endowed to construct and provide non-polluting industry and tourism. For example, 28 29 15 Hainan Island (province), an important part of the BGER, recently officially 30 31 16 published the “Implementation plan of National pilot ecological zone (Hainan 32 33 17 Province) of China” in May 2019. This initiative clearly points out that Hainan plans 34 35 18 to build a “Clean Energy Island”, including measures leading to the step by step 36 37 19 38 prohibition of the selling of all petrol vehicles. 39 40 20 At the outset of action formulation and plan development for the BGER, clean 41 42 21 green energy, including wind energy, will be the primary consideration. However, 43 44 22 wind energy development in this zone has fallen far behind. In 2017, more than 1.16 45 46 23 GW of additional offshore wind power capacity was installed in Chinese coastal areas, 47 48 24 while there has been no installed capacity in the Beibu Gulf up to the end of 2017 [47]. 49 50 25 Thus, especially because of inadequate knowledge and understanding of coastal wind 51 52 26 characteristics, the exploitation of coastal wind energy resources in the region is still 53 54 27 lacking. 55 56 28 To assist in the venture of optimal harvesting and utilization of wind power, this 57 58 29 paper presents a careful and comprehensive statistical assessment on wind field and 59 60 30 energy potential with detailed resource characterization for the period 2010 to 2017, 61 6 62 63 64 65

1 by means of measurement data collected from seven meteorological stations on 1 2 2 coastal islands in the Beibu Gulf. The present study aims to provide insight toward 3 4 3 understanding of wind energy potential required in exploring wind energy resources 5 6 4 7 in the study area, in terms of wind speed variation, Weibull parameters and wind 8 9 5 power density, as well as wind direction on various timescales. Additionally, to 10 11 6 discuss the specific ability of energy development within the region, this study also 12 13 7 provides the insight to meet the energy demand for the rim. 14 15 16 8 2. Wind data measurement and adjustment 17 18 19 9 In this study, data measured at seven meteorological stations on offshore islands 20 21 10 and acquired by the CMA, are available for the region. The geographic locations of 22 23 11 these seven meteorological stations are given in Figure 1. Before the stations were 24 25 12 built, the site selections were fully demonstrated, following related standards and 26 27 13 specifications, and data records were carefully checked by the data center of CMA, to 28 29 14 ensure that the data are no problem for further analyses. Data collected at the stations 30 31 15 are representative of wind climate for different areas of the study region. Data are 32 33 16 gathered mainly to monitor atmospheric changes in the region, including forecasting 34 35 17 and scrutiny of meteorological and environmental issues, etc. The stations are located 36 37 18 at different heights above mean sea level (MSL), while the anemometer for each 38 39 19 station is installed at 10m height above the surface of each station ground. 40 41 20 In China, each station for surface meteorological measurement must be followed 42 43 21 the standard of the “Specifications for surface meteorological observation” (QX/T 44 45 22 45-2007) that published in 2007 (the modified new version is published in 2017 46 47 23 48 (GB/T 35221-2017)), which specified that surface meteorological stations must be 49 50 24 located in the relatively flat and open terrain so as to be representative of relatively 51 52 25 large local meteorological characteristics. The stations used in this study meet the 53 54 26 standard. The wind monitors at the stations are high performance wind monitor 55 56 27 sensors (RM Yong 5106) with blade helicoid propellers, purchased from R.M. Young. 57 58 28 The wind data adapted in the present study include wind speed and direction 59 60 29 parameters over the period January 2010 to December 2017. Wind speed parameters 61 7 62 63 64 65

1 were sampled every 1s and averaged over 1 min. Wind direction data was recorded 6 1 2 2 times per minute, and also averaged over 1 min. The stations at QC, DM, SD, DY and 3 4 3 BH are located very close to shore, of which QC and DM are located in the mouth of 5 6 4 7 Qingzhou Bay; SD is located in Zhenzhu Bay; DY is located near Fangcheng Port. 8 9 5 BX is further offshore, and ZZ and XY are located furthest from shore. 10 11 6 The sensor height at which wind data is measured at the different stations is 12 13 7 shown in Table 1. We note the large variability in recorded wind speed, and also that 14 15 8 wind speed will vary as a function of recorded height. To extrapolate recorded wind 16 17 9 data to likely turbine hub height, hub height is set at 100m above MSL for this study, 18 19 10 unless otherwise stated. A number of earlier studies have employed mathematical 20 21 11 models representing height variability of wind speed, of which the log-law model has 22 23 12 been most commonly employed [49]. The simple power-law model is frequently 24 25 13 applied to offshore energy applications [14, 49–51], which is also employed in this 26 27 14 study. The value 0.13 for the power law exponent has been chosen for this assessment, 28 29 15 taken from the previous study of a Korean offshore wind farm, where the farm area is 30 31 16 more semi-closed than ocean areas without nearby obstacles and thereby substantially 32 33 17 affected by geomorphologic influences [35]. In comparison with the area in this 34 35 18 previous study, the geomorphologic condition of the present study area is closer to 36 37 19 38 that of the Korea wind farm. 39 40 20 Table 1 : List of measurement sites in the Guangxi coastal region with some fundamental mean wind 41 21 and energy characteristics. Hourly collected observational data covered for the period August 2009 to 42 43 22 the end of 2017. Table No. of observations represents the valid number of observations used in the 44 23 study for each station. Total number of the hours over the study period is 70128. Height (m) in the 45 24 table represents the wind sensor height above the mean sea level at each station. Main wind 46 -1 47 25 characteristics were statistically calculated, including wind speed, (ms ); Weibull parameters, -1 -1 -1 -2 48 26 and (ms ); (ms ); (ms ); wind power, (W m ). 49 50 Site BX DY SD QC DM ZZ XY 51 No. of observations 53216 57908 61610 59657 50221 70001 54017 52 53 Height 17.1 13.5 20.6 21.0 28.0 65.7 42.8 54 55 7.6 7.2 6.1 6.0 5.8 4.0 5.7 56 57 1.8 1.7 1.6 1.9 1.8 1.9 1.5 58 59 8.6 8.0 6.8 6.8 6.6 4.6 6.4 60 61 8 62 63 64 65

7.8 7.1 5.8 6.1 5.8 3.9 5.3 1 2 9.4 8.8 7.7 7.6 7.3 5.3 7.3 3 4 605.6 542.0 368.0 282.0 265.6 87.6 321.5 5 6 7 1 3. Analytical methods 8 9 2 To provide the statistical analysis of wind characteristics and the assessment of 10 11 12 3 wind energy potential sources associated with wind energy exploitation, several 13 14 4 analytical methods are applied and briefly described below. 15 16 5 3.1 Weibull distribution function 17 18 6 Probability distribution models are commonly used to fit wind speed 19 20 7 distributions over a time period and to obtain a clear view of the available wind 21 22 8 potential for a region. A large number of different distribution models have been 23 24 9 proposed for statistically analysing both wind characteristics and potential wind as 25 26 10 energy assessment. Amongst all the available probability distribution functions, the 27 28 11 Weibull probability distribution functions, particularly the two-parameter Weibull 29 30 12 function, are commonly used and widely adopted for wind energy applications, not 31 32 13 only due to their great flexibility and simplicity but also because they can provide a 33 34 14 good representation of recorded wind data [29, 35, 50, 54–57]. Therefore, the 35 36 15 two-parameter Weibull distribution model was utilized for the assessment of wind 37 38 16 energy potential sources in this study. 39 40 17 The general form of the two-parameter Weibull function can be characterized by 41

42 18 the Probability Density Function (PDF), ( ), and 43 44 45 19 cumulative distribution function, . Here, is wind speed in ms-1, is the 46 47 20 dimensionless Weibull shape parameter, and is the Weibull scale parameter having 48 49 21 the same units as . 50 51 22 Several different numerical methods have been proposed and compared to 52 53 54 23 estimate the Weibull parameters in previous studies [56,57]. The performance of these 55 56 24 methods improves with increasing dataset size [56]. Following these studies, different 57 58 25 common numerical methods, including the moment, graphical, empirical, energy 59 60 26 pattern factor method, maximum likelihood and modified maximum likelihood, are 61 9 62 63 64 65

1 tested in determining the parameters of the Weibull distribution, so as to select the 1 2 2 most effective method by comparing performance for this study. The methods for 3 4 3 estimating Weibull parameters used in this study can be found in previous studies, e.g., 5 6 4 7 [56,57]. 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 5 24 25 6 Figure 3 : Weibull distributions based on the six methods for estimating Weibull parameters (i.e., the 26 7 moment, graphical, empirical, energy pattern factor method, maximum likelihood and modified 27 8 maximum likelihood) for the seven sites shown in Figure 1. The values in parentheses represent 28 29 9 RMSEs (root mean square errors). 30 31 10 The performance of the methods for estimating Weibull parameters listed above 32 33 11 (i.e., the moment, graphical, empirical, energy pattern factor method, maximum 34 35 12 likelihood and modified maximum likelihood) are compared to test which are 36 37 13 38 effective for this study. Figure 3 shows the PDFs (probability density functions) for 39 40 14 each of the seven sites located in the Beibu Gulf, and calculated from wind data for 41 42 15 the period 2010 to 2017. The difference between the curve calculated using the 43 44 16 graphical method and the other methods is most obvious. To further test the accuracy 45 46 17 of these methods, the judgment of accuracy of the RMSE (root mean square error) 47 48 18 used in [56,57] is adapted for the present study. The RMSE is defined as 49

50 19 , where is the frequency of observations, represents the 51 52 53 20 frequency of Weibull, and N is the number of observations. The RMSEs of the six 54 55 21 methods for the seven sites are listed in the parentheses of Figure 3. It can be seen 56 57 22 from these values that the performance of the maximum likelihood method is on 58 59 60 61 10 62 63 64 65

1 average best. Therefore, the study uses this method for determining the Weibull 1 2 2 parameters for further analyses in the following sections. 3 4 3 3.2 Most probable wind speed 5 6 4 7 The estimated Weibull parameters are directly used so as to obtain most probable 8 9 5 wind speeds, thereby indicating most frequent wind speed for a given PDF. The most 10 11 6 probable wind speed, , is computed in terms of Weibull distribution parameters 12 13 7 [29,51]:

14 15 8 (1) 16 17 18 9 3.3 Wind speed carrying maximum energy 19 10 The Weibull parameters are also used to compute wind speed containing 20 21 11 maximum energy, , for a wind turbine, which is also the speed that produces 22 23 12 the most energy. can also be adapted for estimating wind turbine rated wind 24 25 13 speeds, which can be defined as follows [29, 43, 50]: 26 27 14 (2)

28 29 15 3.4 Wind power density 30 31 16 Wind power density is an important parameter for evaluating available resources 32 -2 33 17 at potential sites. Wind power, measured in Wm , per unit swept area of a turbine is 34 35 18 proportional to the cube of the wind speed. In terms of Weibull distribution 36 19 parameters, the wind power per unit area can be defined as follows [29, 50, 60–63]: 37 38 20 (3) 39 40 41 21 where and are still the Weibull parameters. 42 43 22 3.5 Limitations of the analysis of this study 44 45 23 We wish to state for the record that the analysis of the present study is meant as a 46 47 24 first pass only at estimating the viable offshore wind resource in order to highlight the 48 25 availability of this resource within the study region. 49 50 26 For the study period from August 2009 to December 2017, the valid number of 51 52 27 observations from all data records used in the study at each of the stations (i.e., BX, 53 28 54 DY, SD, QC, DM, ZZ, XY) was, respectively, about 76%, 83%, 88%, 85%, 72%, 55 29 99%, 77% (see Table 1). This means that there were data losses of approximately 24%, 56 57 30 17%, 12%, 15%, 28%, 1% and 23% for each station, respectively, indicating that 58 59 31 better and more reliable datasets are needed for the region, especially for stations BX, 60 61 11 62 63 64 65

1 DM and XY. This is a recommendation of the present study for follow-up work. 1 2 2 Moreover, the analysis is also limited by wind turbine hub height and measurement 3 4 3 locations. However, once better datasets become available for the region, further 5 4 analysis will be undertaken using the latest offshore wind turbine parameters. 6 7 8 5 4. Wind energy assessment in the Beibu Gulf 9 10 11 6 To interpret wind climate and potential wind energy characteristics at the stations 12 13 7 shown in Figure 1, wind features were analyzed based on detailed observational data, 14 15 8 including mean wind speed, prevailing direction, diurnal and monthly variations, 16 17 9 together with Weibull distributions, and the wind power density. The observed wind 18 19 10 speed data have been converted from anemometer height to wind turbine hub height 20 21 11 by use of the power-law approximation introduced in Section 2. Converted data have 22 23 12 been used in this study, whereas we assumed wind directions do not vary with height. 24 25 13 Moreover, for the present study, the four boreal seasons are winter 26 27 28 14 (December-February), spring (March-May), summer (June-August), and Autumn 29 30 15 (September-November). 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 12 62 63 64 65

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 1 43 44 2 Figure 4 : Intra- and inter-annual variability of wind speeds (ms-1) over the period 2010 to 2017 for the 45 3 seven sites shown in Figure 1. (A) Monthly wind speed; (B) Monthly climatology of wind speed; 46 47 4 (C1-C4) Year-to-year variability for different month (December, May, July and August) of the 48 5 wind speed. 49 50 51 6 4.1 Inter- and intra-annual wind speed variations 52 53 7 Monthly mean and seasonal wind speed characteristics were investigated for the 54 55 8 study sites, based on observations collected over the period 2010 to 2017, to show 56 57 9 inter- and intra-annual wind speed variability. 58 59 60 61 13 62 63 64 65

1 Figure 4(A) illustrates monthly wind speed for the seven sites. Intra- and 1 2 2 inter-annual variability is obvious. Monthly wind speed values at the sites ranged 3 4 -1 -1 3 from about 3.0 ms to 12.0 ms , with annual mean wind speed values varying 5 6 4 -1 -1 7 between 4.0 ms and 7.6 ms (see Table 1), although January 2011 values were 8 9 5 relatively higher at all sites, with a highest monthly wind speed value of about 15.6 10 -1 11 6 ms found at DY. 12 13 7 Figure 4(B) shows monthly wind speed climatologies for the seven sites. 14 15 8 Intra-annual wind sped variability is evident, with annual climatologies exhibiting two 16 17 9 cycles per year: largest wind speeds occurring in December with another peak value 18 19 10 in July, and lowest wind speeds apparent in May with another trough in August. 20 21 11 Figure 4(C1-C4) shows year-to-year wind speed variability for months 22 23 12 containing peak and trough wind speed values. Inter-annual variability seems weaker 24 25 13 than intra-annual variability. In addition, most of the seven sites display quite similar 26 27 14 inter-annual variations, especially for December and July. 28 29 15 The long-term trend of the wind speed at the seven sites was also checked, 30 31 16 however, no significant trend can be verified for the period of interest. This is because 32 33 17 the observational period is relatively short to for determining a conclusion on the 34 35 18 long-term trend in wind climate change for the study area, especially when 36 37 19 38 considering response to global climate change. 39 40 20 4.2 Diurnal wind speed variations 41 42 43 44 45 46 47 48 49 50 51 21 52 22 Figure 5 : Diurnal mean wind speed (ms-1) for the eight sites shown in Figure 1. 53 54 55 23 Besides monthly and seasonal probability wind speed distributions, diurnal mean 56 57 24 wind speed variations are also analyzed in this study to explain hourly wind speed 58 59 25 probabilities, and are presented in Figure 5 for each of the seven sites. A clear feature 60 61 14 62 63 64 65

1 at each site is that for sites BX, DY, ZZ and XY, which are the furthest from shore and 1 2 2 closer to the open sea, hourly mean wind speed is largest in day time (8:00-18:00); 3 4 3 while at SD, QC and DM, closer to the mainland, wind speeds are relatively small in 5 6 4 7 the afternoon. 8 9 5 4.3 Weibull distribution 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 6 26 27 7 Figure 6 : Annual Weibull probability distributions at all sites for the period 2010 to 2017. 28 29 8 Annual wind speed Weibull distributions at all sites are highlighted in Figure 6, 30 31 9 and annual mean Weibull parameters for period 2010-2017 are listed in Table 1Table 32 33 10 1. As seen in Figure 6, the curve peak represents the most frequent wind speed. 34 35 11 The shape parameter, , was in the range 1.5-1.9 at all stations, while scale 36 37 12 -1 -1 38 parameter, , varied between 4.6 ms and 8.6 ms . The average values of the shape 39 -1 -1 40 13 and scale parameters for all sites were, respectively, 1.7 ms and 6.8 ms . 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 15 62 63 64 65

1 1 4.4 Variations of and 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2 24 25 3 Figure 7 : Monthly variation of and at all sites for the period 2010 to 2017. 26 27 28 4 Figure 7 plots the variability in monthly mean values of most probable wind 29 30 5 speed, , and wind speed containing most energy, , observed at the seven 31 -1 32 6 sites. Concerning , monthly values were mostly confined to the range 2-9 ms at 33 34 7 all sites. displayed seasonality similar to that of wind speed and Weibull 35 36 8 parameters: relatively high values in December and January, low values in April, May 37 38 9 and August, with increased values in June and July. Likewise, also exhibits 39 40 10 obvious seasonal changes. Annual mean values of and for the seven 41 42 11 sites over the period 2010 to 2017 are also given in Table 1. The largest annual mean 43 44 12 values of and were found at BX and DY, with values of ~7.8 ms-1 and 45 46 -1 -1 13 ~7.1 ms , respectively, while a smallest value of 3.9 ms occurred at ZZ. 47 48 14 49 4.5 Seasonal wind speed and direction roses 50 51 15 Beside wind speed, wind direction is another important parameter for assessing 52 53 16 of wind energy resources. Seasonal distributions of incoming wind direction and wind 54 55 17 speed for the seven sites are presented in terms of a wind rose figure, Figure 8. It is 56 57 18 apparent from this figure that seasonal wind direction variability is occurring at the 58 59 19 seven locations, mainly reflecting the monsoon characteristics in this region. 60 61 16 62 63 64 65

1 In winter and autumn, northerly to northeasterly offshore winds, i.e. winds from 1 2 2 land, prevailed at each of the sites, accounting for greater than 50% of directional 3 4 3 winds, while onshore winds, i.e. winds from the open sea, from S, SW and SE, 5 6 4 7 provided the largest contribution to wind energy resources. In spring, winds from N to 8 9 5 E and to S accounted for most, mainly reflecting transition season characteristics in 10 11 6 wind direction. It is noted, but not surprising, that westerly to northwesterly winds 12 13 7 were rare in all seasons at all sites (on average less than 5%), reflecting South China 14 15 8 Sea monsoon characteristics. 16 17 9 Overall, prevailing wind directions in the rim are mainly from opposite N-S 18 19 10 directions, which might be good for energy exploitation since focus of wind direction 20 21 11 and concentration of energy benefit several types of wind turbines designed to convert 22 23 12 wind resources as fully as possible. 24 25 13 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 14 50 15 Figure 8 : Seasonal wind speed and direction roses based on the site measurements for the period 2010 to 51 52 53 16 2017. 54 55 17 Figure 8 also displays wind speed range percentages at the sites over the period 56 57 18 2010 to 2017. The wind scale is in accordance with the Beaufort scale, as represented 58 59 60 19 by the colors. At BX and DY, wind speeds were mostly from 4 to 9 on the Beaufort 61 17 62 63 64 65

1 scale, i.e., from 5.5 ms-1 to 24.5 ms-1, reaching 10 on the Beaufort scale in autumn and 1 2 2 winter about 5% of the time. Wind speeds were mostly in the range of 3-8 Beaufort 3 4 3 scale at SD and QC, while mostly varied from 3 to 6 on the Beaufort scale at DM and 5 6 4 7 XY, with seasonal changes as mentioned above. Smallest wind speed scales were 8 9 5 found at BH and ZZ where over 90% of wind speeds were below 5 Beaufort scale 10 -1 11 6 (less than 10.8 ms ). 12 13 7 4.6 Wind power density 14 15 8 Wind power densities were calculated for the period 2010 to 2017 by use of eq. 3. 16 17 9 Wind power density, calculated based on the measurements, are, respectively, 605.6, 18 -2 19 10 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5Wm at BX, DY, SD, QC, DM, ZZ and XY 20 21 11 stations (see Table 1). It is noted that the Global Wind Atlas 22 23 12 (https://globalwindatlas.info/) wind power range for the study region is 200-450Wm-2 24 25 13 at 100m above MSL. Wind power is higher than the Global Wind Atlas wind power 26 27 28 14 range only at BX and DY, while mean wind power averaged over the seven stations 29 -2 30 15 is 353.2Wm , which is within the range of Global Wind Atlas. 31 32 16 Figure 9 describes the intra- and inter-annual variation in terms of monthly mean 33 34 17 wind power density for each site. Since wind power density is a function of wind 35 36 18 speed only, characteristics of its variability are in good agreement with the 37 38 19 corresponding wind speed variations. Intra- and inter-annual variability in wind power 39 40 20 patterns display quite similar characteristics at most of the seven sites. Intra-annual 41 42 21 variability was evident at all sites: wind power density values were largest in 43 44 22 December, while lowest in August; another peak/trough wind speed occurred in 45 46 23 July/May. This variability might somehow favour wind energy exploitation and usage: 47 48 24 49 monthly changes in electricity consumption for Guangxi province in the rim is 50 51 25 relatively high in summer time, while lowest in February and April (see Figure 2). 52 53 54 55 56 57 58 59 60 61 18 62 63 64 65

1 2 3 4 5 6 7 8 9 10 11 1 12 2 Figure 9 : Seasonal (left) and monthly (right) mean wind power density (Wm-2) for the sites shown in 13 14 3 Figure 1. 15 16 4 In accordance with mean wind-speed and Weibull parameters, for spatial 17 18 5 variation, the annual mean wind power density, (shown in Table 1), was 19 20 6 greatest at BX with a value of 605.6 Wm-2, and least at ZZ with a value of 87.6 Wm-2. 21 22 7 4.7 Estimation of the potential contribution of coastal wind in the study area 23 24 8 In 2017, more than 1.16 GW of additional offshore wind power capacity was 25 26 9 installed in Chinese coastal areas, while there has been no installed capacity in the 27 28 29 10 Beibu Gulf up to the end of 2017 [47]. If a turbine with 5 MW capacity is installed in 30 31 11 the study area, it would provide about 11 GWh in one year, with annual utilization 32 33 12 time of installed capacity in Guangxi being 2280 hours in 2017, based on official data 34 35 13 [47]. Thus, a wind farm with 100 such turbines would generate about 1.1 TWh 36 37 14 electricity, which can contribute about 0.8% and 3.6% of total electricity consumption 38 39 15 in 2017 for Guangxi and Hainan provinces, respectively. 40 41 42 16 5. Conclusions 43 44 45 17 The Beibu Gulf Economic Rim is critical for China's development, and 46 47 18 especially for the western China region. The “Beibu Gulf Cities Development Project” 48 49 19 sanctioned in 2017 plans to build a “Blue Livable Gulf” to avoid the well-worn 50 51 20 routine of priority for economic development, while sacrificing the environment. 52 53 21 Electricity consumption data of the rim shows a rising demand in the past ten years 54 55 22 and indicates rapid growth in the future, reflecting the huge amount of energy 56 57 23 required for developing these cities, with primary consideration given to clean energy. 58 59 60 61 19 62 63 64 65

1 In this study, a comprehensive statistical analysis and assessment of wind field 1 2 2 and associated energy potential with detailed resource characterization in the study 3 4 3 area for the period 2010 to 2017 was conducted at seven offshore coastal sites 5 6 4 7 mention the sites again here. Annual mean wind speed values for the period 2010 to 8 -1 -1 9 5 2017 for the seven sites at 100m ranged from 4.0 ms to 7.6 ms , resulting in annual 10 11 6 mean wind power densities, respectively, of 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 12 -2 13 7 and 321.5 Wm at the seven sites (BX, DY, SD, QC, DM, ZZ and XY), with average 14 -2 15 8 value of 353.2 Wm . Intra-annual variability of wind power potential is evident at the 16 17 9 seven sites, with the greatest wind power occurring in December and another peak 18 19 10 value in July, while lowest wind speed values occur in May with another trough in 20 21 11 August. The data also display also shows weak inter-annual variations. However, no 22 23 12 significant annual trends were found over the period. 24 25 13 The annual mean Weibull shape parameter, κ, for period 2010-2017 varied from 26 27 14 1.5 to 1.9 at the sites, while the Weibull scale parameter, , was found to have values 28 29 -1 -1 15 between 4.6 ms and 8.6 ms . The annual mean and for the sites were, 30 31 16 respectively, in the range 3.9-7.8 ms-1 and 5.3-9.4 ms-1. 32 33 17 Seasonal variability is evident in wind direction, mainly reflecting monsoon 34 35 18 characteristics in the region: in winter and autumn, winds were predominantly from 36 37 19 38 the north, with a considerable northeasterly contribution, while southerly winds 39 40 20 prevailed in summer. The prevailing wind direction distribution in the rim is good for 41 42 21 energy exploiting and usage. Statistical estimates suggest that a wind farm with 100 43 44 22 turbines each of 5 MW capacity would contribute about 0.8% and 3.6% of the total 45 46 23 electricity consumption in 2017 for Guangxi and Hainan provinces, respectively. 47 48 24 Overall, this study suggests that China, including the Beibu Gulf, has relatively 49 50 25 large resources of offshore wind energy that can reduce its dependency on imported 51 52 26 fossil fuels, thus ensuring security of energy supply as well as reduced GHG 53 54 27 emissions in the Beibu Gulf Economic Rim, China and the world. 55 56 28 Finally, it should be stated that there was a data loss of more than 20% in the 57 58 29 measurement records at three of the seven stations (i.e., 24%, 28% and 23%, 59 60 30 respectively, for stations BX, DM and XY) used in this study. Estimates of wind 61 20 62 63 64 65

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1. In this study, a comprehensive statistical assessment on wind energy potential in the Beibu Gulf region of the South China Sea was conducted for the period 2010 to 2017.

2. Wind power development in the study area has its own unique advantages, including policy and geographic advantages.

3. Results display evidence of seasonal variability in wind speed and direction, while no significant interannual variability or trends were identified for the period 2010-2017.

4. The Beibu Gulf evidently has vast resources of offshore wind energy that will reduce its dependency on imported fossil fuels such as natural gas.