An assessment of wind power generation potential of 2 Built Environment Wind Turbine ( BEWT ) systems in 3 Fort Beaufort , South Africa

The physical and economic sustainability of using Built Environment Wind Turbine 10 (BEWT) systems depends on the wind resource potential of the candidate site. Therefore, it is crucial 11 to carry out a wind resource assessment prior to deployment of the BEWT. The assessment results 12 can be used as a referral tool for predicting the performance and lifespan of the BEWT in the given 13 built environment. To date, there is limited research output on BEWTs in South Africa with available 14 literature showing a bias towards utility-scale or conventional ground based wind energy systems. 15 This study aimed to assess wind power generation potential of BEWT systems in Fort Beaufort using 16 the Weibull distribution function. The results show that Fort Beaufort wind patterns can be 17 classified as fairly good and that BEWTs can best be deployed at 15m for a fairer power output as 18 roof height wind speeds require BEWT of very low cut-in speed of at most 1.2ms. 19


Introduction 23
Eskom, the custodian of South Africa's national grid, is saddled with the government's optimism 24 to triple the contribution by renewable energy from the current 4% national generating capacity to 25 about 6000MW by 2020 [1]. This comes against Eskom's occasional failure to meet demand that 26 compels the energy regulatory authority to impose strict load shedding schedules so as to ease 27 pressure on the grid. The pressure in turn hampers Eskom's drive towards renewable energy use as 28 it will be forced to focus more on meeting demand through traditional non-renewable technologies 29 rather than promoting new renewable ones. One way of easing pressure on the national grid without 30 the need of scheduling load shedding is promoting the use of distributed wind power systems. The 31 major advantage of distributed wind power systems, as is the case with other distributed systems, is 32 their proximity to end users. Distributed wind power systems can protect consumers from dearths 33 due to technicalities associated with grid failure, transportation or capacity shortfalls since the system 34 can be installed within the consumer's locality. Of particular interest in this study is the Built 35 Environment Wind Turbine (BEWT) technology that [2] identified as a developing and less mature 36 innovation than the utility-scale or conventional ground based distributed wind power systems.

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BEWT refers to wind projects that are constructed on, in or near buildings. One of the main factors 38 to consider when choosing a wind turbine for deployment as a BEWT is its performance, in terms of 39 power output, within the given built environment. The power output of a wind turbine depends on wind speed that in turn is a spatiotemporal 47 variable. Therefore it is important to carry out a wind resource assessment of the candidate site prior 48 to deployment of the BEWT. This is crucial in assessing the physical and economic sustainability of 49 deploying a particular wind turbine in the given environment. Carrying out site specific wind 50 resource assessment gives the most reliable estimation of the wind resource potential but this may 51 increase installation costs and even delay the deployment exercise. Knowledge of the wind resource 52 potential of the host region for the candidate site(s) is therefore important as it can be used as a referral 53 tool for predicting the performance and lifespan of the BEWT in the given built environment. 54 Wind speed is a random variable hence it can be represented statistically with Weibull 55 distribution being recommended by most authors due to its flexibility, simplicity and capability to fit 56 a wide range of wind data [5]-[7]. This paper is aimed at using the Weibull distribution function to 57 assess the wind resource potential of Fort Beaufort, South Africa for the purpose of deploying BEWT 58 systems. This may go a long way in promoting the adoption of BEWTs in South Africa and ease 59 pressure on the national grid. South Africa is yet to adopt BEWT with available literature on wind 60 power projects in the country (as is the case with other African countries) showing a bias towards 61 wind resource potential assessment for establishing large-scale wind farms.   (2) 80 (3) 86 Equation (3) was successfully used to estimate power output of a turbine operating within the built 87 environment at 15m height where building geometry was assumed not to influence wind speed. 88 However, for a BEWT operating in/and on a building, building orientation with respect to the wind 89 profile should be catered for when recalculating wind speed. Reference [13] used equation (4) to 90 extrapolate a velocity profile from the meteorological station to the building while studying wind 91 induced natural ventilation in residential areas; Thus, equation (2) can be modified into (5); 94 where ℎ is the building height and , are constants for terrain conditions. Considering Fort 96 Beaufort's peripheral zone that can be classified as sub-urban, the constants were assumed to be 0.

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Its operational specifications are presented in Table 1; 102 fundamental to estimating the potential of the preferred choice of a BEWT in the given environment. 120 A large (and hence large power output) can support a turbine with a large cut-in speed and 121 conversely. The probability density function, ( , , ) is then given by; 122 (9) 123 The maximum wind speed corresponding to maximum power output is obtained from and 124 using the formular; 125 (10) 126

Power density 127
Wind power density ( ) is generally considered a better indicator of wind resource potential 128 than wind speed [6]. It is a measure of the power available per unit square area ( ) swept by the wind 129 turbine. The wind power density can be estimated using the Weibull distribution as; 130 Thus, wind resource potential can be rated using a magnitude-based assessment categorisation in 132  Fairly good 100 ≤ < 300 Good 300 ≤ < 700 Very good 700 ≤

Wind and power density distribution 136
Wind speed ranges from 0 to 14.8 −1 for the ten year period that was considered.  It can be observed from Table 1 that a BEWT deployed at 3 gives a less power density than one 141 deployed at 15 as is expected since wind speed increases with altitude. The unimodal seasonal 142 probability densities for wind speed are presented graphically. 143

Summer 144
The wind speed distribution for summer is presented on Figure 3; 147 Figure 3 shows that the distribution of wind speed in summer is slightly skewed towards lower wind 148 speeds hence the probability of having above average wind speeds is relatively low. Considering 149 Figure 4 in conjunction with Table 4, it can be realized that both and for summer are both 150 less than the cut-in speed of the Power Tree at a 3 height. This shows that the Psiclone Power Tree 151 cannot be supported at this height. On the other hand, both and at 15 for summer are 152 greater than the cut-in speed hence the Power Tree can be supported as a BEWT at this height. Thus, 153 with reference to the summer wind distribution, a BEWT can be deployed at 3 if its cut-in speed 154 is at most 1.2ms −1 and such technologies are generally expensive considering the returns in terms 155 of power output and production costs. Using the categorization on Table 2, the most probable power 156 density at 3 is 35.2 −2 while at 15 it is 192.3 −2 as shown on Table 5. The power 157 densities can therefore be categorized as fair and fairly good respectively. Table 6 also shows that the 158 maximum power densities achievable in summer are 5.6 −2 and 192.3 −2 at 3 and 15 159 respectively.  The probability of having average or higher wind speeds in winter is relatively low since the 173 probability distribution for winter is again slightly skewed towards low wind speeds as shown on 174 The distribution for wind speeds in spring is shown on Figure 6; 183 184

Conclusion 201
The most probable seasonal power density for Fort Beaufort is in the range of 0.8 −2 to 202 1.7 −2 at 3 height. At 15 height, the most probable seasonal power density ranges from 203 28.4 −2 to 57.6 −2 . Thus, seasonal wind conditions for Fort Beaufort can be categorized as fair 204 to fairly good for operating a BEWT with maximum power densities achievable being 3.6 −2 at 205 3 and 123.1 −2 at 15 . However, the BEWTs can best be deployed at 15 for a fairer power 206 output as roof height wind speeds require BEWT of very low cut-in speed of 1.2 −1 that are not 207 readily available on the market. Therefore, it is recommended to install BEWTs at 15 otherwise 208 low cut-in speed BEWTs should be used on rooftops 209 Acknowledgments: I am grateful to the South African Weather Services for freely providing me with data that 210 was used in this study.

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Conflicts of Interest: The authors declare no conflict of interest.