A case study of climate variability effects on wind resources in South Africa

June 30th, 2015, Published in Articles: Energize

 

South Africa is a signatory to the United Nations Framework Convention on Climate Change (UNFCCC); and is committed to reducing greenhouse gas (GHG) emissions by 42% by 2025 [1]. Apart from the climate change question, the necessity for the expansion and tweaking of South Africa’s energy resource industry has been marked by the electricity crisis of 2008 [2]. The Department of Science and Technology has acknowledged the exigency of utilising additional resources [3]. A target of 3725 MW from renewable resources has been established by the Department of Energy (DoE) [4].

The Renewable Energy Independent Power Producer Procurement Program (REIPPPP) has been created to support this target and to encourage growth of the renewable energy industry in South Africa. In the first round of the REIPPPP, 634 MW of wind power was awarded to eight Preferred Bidders at an average R1,14/kWh, and in the second round, 562,4 MW was awarded by the DoE to seven planned wind farms at R0,89/kWh. The REIPPPP aims to have 1850 MW wind power capacity connected to the grid by the end of 2016 [5].

The sources of renewable energies are locally available and can contribute to moderating fossil fuel dependency [6]. In support of GHG reduction targets wind power requires no fossil fuels to continue operation, does not emit GHGs directly when producing electricity, uses basic materials in construction and transportation, and does not require the circulation of large amounts of water for cooling during the generation process [7,8].

Feasibility of wind energy technology

Securing financial support for wind energy projects is often harder than for conventional power projects. It is therefore imperative to have a thorough knowledge of a country’s complete wind resource, as well as access to dependable methods for wind farm siting [9]. Wind characteristics must be investigated exhaustively at potential sites [10].

Fig. 1: The WASA domain,demarcated by a black line. The two selected sites are located within this region and are indicated b boxes. Source: Adapted from WASA (2012).

Fig. 1: The WASA domain,demarcated by a black line. The two selected sites are located within this region and are indicated b boxes. Source: Adapted from WASA (2012).

One of the most crucial hindrances in the exploitation of global wind power is the lack of steadfast wind resource data. This is vital for government and industry to establish wind power potential [11]. Mitigating this problem will help determine whether wind power is worth consideration as a large-scale contributor to gridded electricity, especially in a country like South Africa, where renewables are primarily restricted to the off-grid sector [12].

Climate variability and wind resources

Weather patterns shift yearly between successive decades. Variability is an inherent component of climate which must be taken into consideration when assessing the fiscal viability of wind power [9]. If South Africa is to reach its goal of GHG mitigations between 2020 and 2025, then 27% of the electricity supply should be contributed by renewable energy sources, with wind energy contributing a capacity of 14 GW by 2030 [13].

A number of wind characteristics could change as a result of natural or anthropogenic climate change, of which speed and direction are most commonly modelled. Shifting probabilities of extreme wind speeds have also been evident in some studies [14, 15]. In the Southern African region, a number of studies indicate possible changes in circulatory systems or the components impacting them [16, 17, 18]. For large parts of South Africa (including the Northern Cape and Western Cape), a 10% increase in wind speed can be expected relative to a 1981 to 2100 control period at 10 m above ground level [19].

Fig. 2: Weibull probability distribution at Alexander Bay (WM01).

Fig. 2: Weibull probability distribution at Alexander Bay (WM01).

To be able to supply electricity reliably and at affordable rates, one needs to assess wind characteristics and wind energy in detail, which translates to collecting information on wind distribution and wind power potential in South Africa [20].

Considerations in wind resource assessment

The power available from the wind is dependent on cubed wind speed and is proportional to air density:

P = ½ x ρ x A x v3    (1)

where:

P is electrical power, ρ is air density, A is the surface area of the wind turbine rotor and v is the wind speed.
A number of wind resource assessment studies mention this equation as an indicator of wind energy [21, 22, 23]. However, this equation only describes the amount of mechanical power in moving air [22], and not the electricity that could be extracted from moving air.

Fig. 3: Weibull probability distribution at Calvinia (WM02).

Fig. 3: Weibull probability distribution at Calvinia (WM02).

In fact, numerous studies that test the effect of climate change on wind energy resources focus solely on wind speed changes, and thus only the total potential available energy in wind [24, 25, 26, 27], without taking into consideration other factors important in understanding how much of this potential is really available for transmission as electricity, such as wind turbine capacity factors.

The amount of electricity generated from the potential energy in wind is a function of the wind turbine’s power curve (showing wind power generation as a function of wind speed) as well as wind shear or the wind speed distribution, described by Weibull probability distribution functions (PDF) [25].

When assessing an area’s wind resource, one must, therefore, consider the influence of wind turbines themselves on the amount of electrical energy that can be extracted from the kinetic energy available in the wind. Wind turbine generator efficiency and mechanical transmission efficiency, for instance, also play a role in the electrical power output of a particular turbine [28].

Fig. 4: Alexander Bay "cut" 0,4º x 0,4º tile in (a) raster and (b) vector formats.

Fig. 4: Alexander Bay “cut” 0,4º x 0,4º tile in (a) raster and (b) vector formats.

Changes in climate could possibly have two main effects on wind power plants: a change in mean wind speed could influence electricity produced as well as the timing and the period for which a plant can operate; and increased maximum wind speeds could affect the safety and reliability of turbines [27]. The aim of this study in particular was to determine whether energy production from wind resources can change over time due to climate change itself with the objective of quantifying the AEP and PD using data on two randomly selected locations in South Africa, assuming the 10% wind speed increase mentioned before. The two randomly selected locations represent the two case studies alluded to previously.

    Fig. 5: Calvinia's "cut" 0,4º x 0,4º tile in (a) raster and (b) vector formats.

Fig. 5: Calvinia’s “cut” 0,4º x 0,4º tile in (a) raster and (b) vector formats.

At this point we should point out that an inherent discrepancy exists between turbine lifespan (>20 years) and climate change projection horizons (>100 years). To reiterate the objective of the study: the effects of climate variability on wind speed and consequently wind energy are tested here. Wind turbine characteristics are included in this study purely as a tool to describe energy output from particular wind resources, not to test how a given turbine will perform many years from now.

Literature review

A number of studies based on circulation models have found changes in wind speeds over an extended period. In France’s north-west region, for instance, increases of up to 2,6% and decreases of up to 5,8% in its Mediterranean region can be expected between 2046 and 2065 [29]. Nolan [26] assessed the impact of climate change on Ireland’s wind resource using a regional climate model (RCM) simulation ensemble for 2021 to 2060. For this study, winter wind speeds were projected to increase by up to 3,5% and summer wind speeds decrease by up to 5%. Pašičko found a significant change in wind speeds during summer in coastal and neighbouring areas of Croatia: an increase of 20% in the mean wind speed is projected for 2011 to 2040, and more than 50% for 2041 to 2070 [27].

Fig. 6: Power curve for Vestas V90 2 MW wind turbine.  Source: Adapted from WASP (2012).

Fig. 6: Power curve for Vestas V90 2 MW wind turbine.
Source: Adapted from WASP (2012).

Breslow and Sailor used general circulation models (GCMs) to predict wind speed reductions of 1,0% to 3,2% in the next 40 years [23]; and 1,4% to 4,5% over the next 90 years in the United States. A similar study by Sailor using statistically downscaled output from four GCMs found that summertime wind speeds in the region may decrease by 5 to 10%, while wintertime wind speeds may decrease by relatively little, or possibly increase slightly in the north-western United States [21]. Pereira de Lucena projected wind speed increases of more than 20% over north-eastern Brazil, and decreases of more than 20% in a smaller part of north-western Brazil in 2071 to 2100 [25].

A paper by Pryor predicts that near-surface wind speeds for 2071 to 2100 are expected to increase in most parts of northern Europe by 5 to 10% [30]. Diamond discusses how climate change induced wind patterns and turbine productivity could affect financial risk mitigation measures and how these may have to be re-evaluated [8]. She recommends that wind project developers take the effects of climate change into account if wind farms are to remain profitable for their entire lifetimes. If turbines are shut down to avoid damage from extreme winds, less energy is produced from utility scale wind turbines. She further warns that wind farm developers may have unrealistic expectations of turbine output if they are unaware of future wind patterns. Similar studies have not been performed for South Africa. The study described by this paper represents the first results of what promises to become an expanded research project.

Methodology

In this study, WAsP was employed as a modelling and simulation tool to combine meteorological data with digital surface roughness and height contour data to determine potential AEP and PD in two regions at specified heights. Similarly, potential AEP and PD under different wind speed conditions were determined after wind speeds were modified based on previous work on wind speed changes according to 19 GCMs [19].

Two locations on the west coast of South Africa within the WASA domain (Fig. 1) were selected for analysis in the latest version of WAsP. Their Weibull PDFs are shown in
Figs. 2 and 3. Observed wind data collected close to these areas were used in the study to model the current and projected situation regarding energy production from wind. This part of the method is similar to that of Hocaoglu and Kurban [22] where data measured at a site was employed to estimate a wind resource at that exact same site. Projected wind speeds were determined by modifying observed wind data according to projected changes in wind speeds.

Wind data

The use of raw wind data is preferred in WAsP, as it allows for the detection of errors in the data which may be indiscernible in data summaries [39]. It is also recommended that data of at least one year, with ten minute averages of wind data is selected.

Table 1: AEP and PD in Calvinia and Alexander Bay.
Site AEP (GWh) PD
(W/m2)
Calvinia Reference 4,670 234
Future 5,644 306
Change +17% +24%
Alexander Bay Reference 5,563 392
Future 6,381 518
Change +13% +24%

Raw wind data was therefore downloaded from the Wind Atlas of South Africa’s website for WM01 Alexander Bay and WM02 Calvinia [40]. “WM01 Alexander Bay” and
“WM02 Calvinia” refers to the names of the masts from where data was collected, and are located at 28°36’06,7”S; 16°39’51,9”E and 31°31’29,7”S; 19°21’38,7”E respectively.

The elevations of WM01 and WM02 are 2 m and 543 m respectively. The data employed in the study was collected between October 2010 and September 2012 (at most only two years’ worth of data was available at the time that the study took place). To create data files for at least one whole year, the data from subsequent months were concatenated into a single file by importing the data as text files into the starting month’s (October) file in MS Excel.

Four such files were created – October 2010 to September 2011 and October 2011 to September 2012 files for both Alexander Bay and Calvinia. In order to make said modifications to the observed data for the future scenario, winds measured at 10 m height were employed throughout. Measured wind speeds at 10 m were used because projections were only available for this height above ground level.

Wind speeds at 60 m were calculated from 10 m winds so as to provide measurements at a height that is as close as possible to average wind turbine hub heights, but also at a height comparable to the observed data.

The common power law was employed in the calculation of wind speed at 60 m:

VH = Vref x (H/Href)μ    (2)

where VH denotes the wind speed (in m/s) at a given height H (in m), Vref is the wind speed (m/s) at a reference height Href (usually of 10 m), and v is the wind shear coefficient with 1,7 used in this case as the masts are located on fairly flat terrain [39, 40].

Fig. 7: Wind power conversion process, [28].

Fig. 7: Wind power conversion process, [28].

A wind shear coefficient of 1,7 was used as is often approximated by the European Wind Atlas for open, flat sites [41].

The wind shear coefficient changes with time of day, wind direction and atmospheric stability, and should therefore be kept in mind when interpreting the results. An increase in 10 m wind speeds was assumed based on the work of McInnes [42]. They found that, in the region including the Alexander Bay and Calvinia anemometers, an increase of at least 10% in mean wind speeds at 10 m could be expected in 2081 to 2100 (66% of the data from the GCMs agreed upon the sign of change in wind speed).

Dominant wind directions are also predicted to change; this aspect will be addressed in ongoing research. To create the modified/future data sets, 10 m winds were increased by 10% and consequently converted to 60 m winds using Eq. (2).

This process included the creation of a so-called “protocol”, for which time stamps and only necessary wind speed and direction data were extracted from the raw data. Observed wind climate files were created using data from October 2010 to September 2012, and then exported for use in WAsP after the generation report was scrutinised for possible errors in the importation process.

Orography and roughness data

“Orography” refers to terrain height (elevation) variations, such as mountainous areas or smooth hills, whereas “roughness” refers to terrain surface characteristics, such as vegetation, water or buildings [43]. Orography is represented by height contour lines, indicating the elevation above mean sea level. These files are provided as 10 x 10 tiles of digital elevation models (DEMs) and are therefore in raster format (see Figs. 4 and 5 a).

Tiles including Alexander Bay and Calvinia were processed in the SAGA (system for automated geo-scientific analyses) GIS. Firstly, the tiles were “cut” into smaller areas of 0,4º x 0,4º (Figs. 4a and 5a). They were then converted to vector format (Figs. 4b and 5b) for use in WAsP Map Editor 10. The area was then located in Google Earth (GE). GE-images could then be used as background images for the demarcation of areas of different land cover (roughness).

The areas of different land cover were demarcated as polygons, and internal and external roughness lengths were specified for each polygon (WASA, 2012). After checking and correcting for errors (cross-points of lines, for instance), the map could be exported for use in WAsP. Hence, a single map contained both elevation and roughness data.

Wind turbine generator files

Wind turbine generator files were downloaded from WAsP’s Power curve download site (WAsP, 2012). The files provide information on wind turbine generator performance for particular makes and capacities of wind turbines, such as the amount of power generated at certain wind speeds (Fig. 6). In this study, a Vestas V90 2 MW turbine was selected as a typical wind turbine representation.

Modelling the wind resource

The elevation/roughness map, observed wind climate file and wind turbine generator file could then be combined in WAsP to calculate AEP and PD for a particular resource grid. The resource grid includes the complete area to be assessed.

Results

Table 1 shows the overall AEP and PD of the two locations under current and projected conditions, as produced by WAsP. Relative changes in the two variables are also indicated. Note that relative changes in AEP differ for the two locations assessed, a constant 10% wind speed increase notwithstanding. Such a difference can be explained by considering the relative wind speed distribution functions of the two locations. Calvinia provides a generally weaker wind resource than Alexander Bay, but can expect a more dramatic increase in AEP even if a similar wind speed increase is projected.

Discussion

In this study, AEP and PD were modelled at 60 m heights based on wind speed increases. These indicators take more factors into account than traditional studies that only provide indications of wind speed changes.

Electrical power output is given as follows:

Pe = ½ ρ ACρ ηm ηg v3    (3)

where ρ, A and v are the same as in Eqn. (1), Cp is the coefficient of performance of the turbine (of which the maximum value is the Betz limit of 0,59), ηm is the mechanical transmission efficiency and ηg is the generator efficiency.

Fig. 7 demonstrates the importance of calculating Pe for wind resource assessment rather than using only the power available in wind as an indicator thereof. It has been observed that considerable variability exists between wind speed and wind power [43] and it is therefore important to bear this type of uncertainty in mind when interpreting the results of wind speed projections in the wind energy industry [44].

The overall AEP and PD are projected to increase by 17% and 24% in Calvinia respectively; and by 13% and 24% in Alexander Bay. The different magnitudes of change are related to the manner in which electricity is generated by wind turbines: there’s not a linear relationship between wind speed and power generated (Fig. 6). The two locations have different wind speed distributions (Figs. 2 and 3), translating to different output by the same turbine.

Conclusion

Climate change is projected to have a substantial effect on wind speeds globally. Furthermore, the geographic distribution and variability of wind resources may shift as a result of variability in climate. The wind resources of a region is mainly dependent on wind speed and therefore the amount of energy available in the wind, as well as local features of the area in which a particular turbine is located. The wind resource should be determined on a small scale in order to avoid incorrect placement of wind turbines, and the possibility of changing weather patterns should also be taken into account when planning wind farm projects. This is especially important for a long-term view on correct wind farm positioning.

Studies analysing the impact of climate change on wind resources usually only model changes in wind speed. This study took it a step further by determining how different wind speeds affect AEP and PD. It provides a more accurate description of how altered wind speeds could affect that which will be most important when determining a project’s feasibility: the electrical energy output. Determining the AEP takes into consideration the power curve of whichever turbine(s) occur in the resource grid.
The work carried out in this study is a preliminary look at possible effects of changes in climate behaviour on electricity generation. In future it can be significantly expanded to include larger geographical areas of the country. Analyses of wind velocity distribution and/or wind shear may be included in future work as opposed to only wind power generation potential as a function of wind speed. Such expansion is required before the results of a study such as this one can be used to generalise for the entire country or region. Nevertheless, its value lies in quantifying some of the increases in wind energy resources that could potentially be encountered. It should therefore serve as motivation for further investigation into energy sector diversification and expansion into areas without grid supply.

Acknowledgements

The authors are grateful to Steve Szewczuk and Eugene Mabille from the CSIR’s Wind Atlas initiative for assistance on the use of WAsP. We are also thankful to DTU Wind Energy for the temporary WAsP licence that enabled this work to be carried out.
This article was published in the Journal of Energy in South Africa, August 2014, and republished here with permission.

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Contact Lynette Herbst, University of Pretoria, Tel 012 420-5840, lynette.herbst@up.ac.za

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