Evaluating the Energy Potential of Solar PV Located on Mining Properties in the Northern Cape Province of South Africa

Abstract

The lauded Renewable Energy Independent Power Producer Procurement Program (REIPPPP) has achieved much in stimulating private sector investment in the renewable energy market in South Africa. Yet, 95% of electricity generated is still from a single source, the state-owned utility Eskom. This paper set out to explore the policy sphere governing electricity generation and identifying possible avenues that can contribute to a more vibrant solar energy market in the most solar abundant province of South Africa, the Northern Cape Province. Licensed mines were identified as low hanging fruit due to a large policy overlap and leeway within existing mining policy. A solar audit of these areas was performed, based on accepted multi-criteria decision analysis techniques, and found that a potential 369 TWh to 679 TWh per annum can be generated, exceeding South Africa’s current electricity usage.

Full text

sustainability
Article
Evaluating the Energy Potential of Solar PV Located
on Mining Properties in the Northern Cape Province
of South Africa
Waldo van der Merwe 1,* and Alan C. Brent 1,2
1Department of Industrial Engineering and the Centre for Renewable and Sustainable Energy Studies,
Stellenbosch University, Stellenbosch 7602, South Africa; acb@sun.ac.za or alan.brent@vuw.ac.nz
2
Sustainable Energy Systems, School of Engineering and Computer Science, Victoria University of Wellington,
Wellington 6140, New Zealand
*Correspondence: waldovdm@gmail.com
Received: 18 June 2020; Accepted: 17 July 2020; Published: 21 July 2020


Abstract:
The lauded Renewable Energy Independent Power Producer Procurement Program
(REIPPPP) has achieved much in stimulating private sector investment in the renewable energy
market in South Africa. Yet, 95% of electricity generated is still from a single source, the state-owned
utility Eskom. This paper set out to explore the policy sphere governing electricity generation and
identifying possible avenues that can contribute to a more vibrant solar energy market in the most
solar abundant province of South Africa, the Northern Cape Province. Licensed mines were identified
as low hanging fruit due to a large policy overlap and leeway within existing mining policy. A solar
audit of these areas was performed, based on accepted multi-criteria decision analysis techniques,
and found that a potential 369 TWh to 679 TWh per annum can be generated, exceeding South Africa’s
current electricity usage.
Keywords: REIPPPP; policy; mining; solar energy; GIS; multi-criteria decision analysis
1. Introduction
1.1. Mining as a Consumer of Energy and as a Potential New Renewable Enegy Generator
Mining and energy are intrinsically linked within the South African economy in what has become
known as the Minerals-Energy Complex [
1
], and mining alone accounted for as much as 18% of GDP,
and 60% of all exports, at its peak during the turn of the century [
2
]. Because 93% (during 2014) of all
electricity in the country is generated from coal sources [
3
], and the mining sector is responsible for
15% of South Africa’s entire electricity demand [
2
]—40% of members of the Energy Intensive Users
Group (EIUG) of Southern Africa are involved in downstream beneficiation [
4
,
5
]—this relationship
becomes partly circular. Electricity production supplied by coal mined within the country’s borders
supplies 29% of South Africa’s total energy demand (including transportation fuels) and is responsible
for 50% of all local carbon emissions [5], making it the largest energy sub-sector.
While mines could traditionally rely on relatively cheap electricity supply from the national utility
Eskom, this trend seems to be coming to an end with Eskom increasing the price of electricity by 26%
from 2007 to 2012 [
2
,
6
] and continuing the trend of above inflation increases on average by 9.75%
(tariffs as reported by Eskom SOC for the mining sector) p.a. from 2013 to 2019 [
7
], while the other
major energy source, diesel used in generators, has seen a 15.7% increase during the same period [
2
],
resulting in a combined increase of 7% to 20% in the seven-year period from 2008 to 2014 for 47% of
the companies in the mining sector who are part of the EIUG [
8
]. A looming carbon tax can potentially
increase the price of diesel from a further 11.4 cents per litre to 28.6 cents per litre [
9
]. These factors
Sustainability 2020,12, 5857; doi:10.3390/su12145857 www.mdpi.com/journal/sustainability

Sustainability 2020,12, 5857 2 of 14
all contribute to a market conducive to the adoption of alternative forms of energy, not for altruistic
reasons, but driven purely by market forces.
Solar photovoltaic (PV) seems the most likely candidate to succeed in the mining environment,
due to it having one of the lowest prices per installed power unit, and it has one of the most
predictable and steady power supply curves of the renewables stable, which is a good fit with the
almost constant demand of mines. Furthermore, while South Africa has some of the world’s largest
coal reserves, it pales in comparison to its solar resources, ranking together with Chile as having
some of the highest solar insolation figures in the world [
10
,
11
]. Nowhere in the country is this as
true as in the Northern Cape Province, and subsequently developers of utility-scale solar energy
sources have overwhelmingly settled here first [
12
]. While the development of utility-scale projects,
through the government Renewable Energy Independent Power Producer Procurement Program
(REIPPPP), feeding the national grid has been an internationally lauded success [
11
], the uptake of
renewables in other private sector markets has been lagging. Even after commissioning in excess of
2900 MW of renewable energy through privately funded projects worth US$13.1 billion (exchange rate
of 15.37 ZAR:1 USD as per South African Reserve Bank data for the 1st quarter of the 2020 fiscal year)
of investment [
11
], Eskom remains the largest generator of electricity in the country at 95%, through an
installed capacity of 47 GW, of which 39 GW is from coal-based sources [
13
]. The government has
also been slow with passing new policy to supplement the REIPPPP success, such as the proposed
Independent System and Market Operator (ISMO) bill, which is meant to establish a single buyer and
wholesale distributor of electricity, separate from Eskom [14].
1.2. Objective of the Paper
The objective of this paper is to evaluate the potential, in terms of energy extraction, of the land
licensed to the mining industry in the Northern Cape Province of South Africa. This is quantified in
terms of a simulated audit of the solar energy potential and qualified within the policy framework
that exists currently within the mining and energy sectors. Section 2describes the steps followed to
achieve each sub-objective as described in Figure 1below. The research literature review covers the
first two sub-objectives. All the studies found to be relevant to energy audits, which initially included
a number of studies which included other technologies such as wind and biomass energy, were used
to identify the first sub-objective. The next step was to exclude studies of other technologies and
only focus on solar PV audits or where solar PV was included as part of a multi-technology audit.
This was done to determine the specific framework used, down to the fine detail of selection criteria,
in order to formulate a customized framework to apply to this research. Data gathering was achieved
by assembling a database of all the resources used in the literature study and adding further studies to
this which focus on locally relevant content. This was followed by exploratory fieldwork to obtain
access to quality local data. Lastly, all of the above was applied practically to build and execute a model
to input data and calculate meaningful information from it. To achieve this, additional research had to
be done qualitatively outside of the literature study sphere in order to gain the required knowledge on
the specialized subject of GIS systems.
Sustainability 2020, 12, x FOR PEER REVIEW 2 of 15
can potentially increase the price of diesel from a further 11.4 cents per litre to 28.6 cents per litre [9].
These factors all contribute to a market conducive to the adoption of alternative forms of energy, not
for altruistic reasons, but driven purely by market forces.
Solar photovoltaic (PV) seems the most likely candidate to succeed in the mining environment,
due to it having one of the lowest prices per installed power unit, and it has one of the most
predictable and steady power supply curves of the renewables stable, which is a good fit with the
almost constant demand of mines. Furthermore, while South Africa has some of the world’s largest
coal reserves, it pales in comparison to its solar resources, ranking together with Chile as having some
of the highest solar insolation figures in the world [10,11]. Nowhere in the country is this as true as
in the Northern Cape Province, and subsequently developers of utility-scale solar energy sources
have overwhelmingly settled here first [12]. While the development of utility-scale projects, through
the government Renewable Energy Independent Power Producer Procurement Program (REIPPPP),
feeding the national grid has been an internationally lauded success [11], the uptake of renewables
in other private sector markets has been lagging. Even after commissioning in excess of 2900 MW of
renewable energy through privately funded projects worth US$13.1 billion (exchange rate of 15.37
ZAR:1 USD as per South African Reserve Bank data for the 1st quarter of the 2020 fiscal year) of
investment [11], Eskom remains the largest generator of electricity in the country at 95%, through an
installed capacity of 47 GW, of which 39 GW is from coal-based sources [13]. The government has
also been slow with passing new policy to supplement the REIPPPP success, such as the proposed
Independent System and Market Operator (ISMO) bill, which is meant to establish a single buyer and
wholesale distributor of electricity, separate from Eskom [14].
1.2. Objective of the Paper
The objective of this paper is to evaluate the potential, in terms of energy extraction, of the land
licensed to the mining industry in the Northern Cape Province of South Africa. This is quantified in
terms of a simulated audit of the solar energy potential and qualified within the policy framework
that exists currently within the mining and energy sectors. Section 2 describes the steps followed to
achieve each sub-objective as described in Figure 1 below. The research literature review covers the
first two sub-objectives. All the studies found to be relevant to energy audits, which initially included
a number of studies which included other technologies such as wind and biomass energy, were used
to identify the first sub-objective. The next step was to exclude studies of other technologies and only
focus on solar PV audits or where solar PV was included as part of a multi-technology audit. This
was done to determine the specific framework used, down to the fine detail of selection criteria, in
order to formulate a customized framework to apply to this research. Data gathering was achieved
by assembling a database of all the resources used in the literature study and adding further studies
to this which focus on locally relevant content. This was followed by exploratory fieldwork to obtain
access to quality local data. Lastly, all of the above was applied practically to build and execute a
model to input data and calculate meaningful information from it. To achieve this, additional
research had to be done qualitatively outside of the literature study sphere in order to gain the
required knowledge on the specialized subject of GIS systems.
Main objective: Calculate
the energy potential of
solar PV located on
Northern Cape province
mining properties
Sub-objective 1: Identify the
most suitable decision
making system
Sub-objective 2: Identify
and analyse qualifying
decision criteria
Sub-objective 3: Gather the
required data from public
available sources
Sub-objective 4: Develop
procedures and perform
calculation in an open-
source environment to
promote reproducibility
Figure 1. Research objectives.

Sustainability 2020,12, 5857 3 of 14
2. Literature Review and Model Design
2.1. Multi-Criteria Decision Analysis
In the GIS-based (Geographic Information System) PV potential studies in literature, most authors
use some form of Multi-Criteria Decision Analysis (MCDA) with weighted selection criteria, preceded
with a Boolean filter to eliminate areas completely unsuitable, such as holy grounds or bodies of
water. The weighted selection criteria take many forms. The most common method of assigning
weights is the Analytical Hierarchy Process (AHP) [
15
] with variants, such as using fuzzy logic [
16
].
Two studies have combined the Eliminating Choice and Translating Reality (ELECTRE) method [
17
]
with their MCDA, and another used the Technique for Order Preference by Similarity to Ideal Solution
(TOPSIS) method [
18
]. One of the most interesting cases was by Borgogno Mondino et al. [
19
],
who trained an Artificial Neural Network (ANN) in what the conditions around existing solar plants
in the investigated area are and let it come up with the criteria and associated weights for the MCDA
itself. Five studies [
20
–
24
] used Boolean logic as the basis of their MCDA. The latter two of these five
did not explicitly specify their MCDA as using Boolean logic, but this could be inferred after a careful
look at their methodology. A specific type of MCDA, known as an Environmental Decision Support
System (EDSS) [
25
], requires large amounts of spatial information to facilitate the decision-making
process, as well as visualizing the results. It is for this reason that GIS systems are such a good match
for this customized type of MCDA.
2.2. External Influence on Model Design
This study differs from those in the literature on one specific point. Where those studies attempted
to classify a large area according to suitability, this study was limited to very specific smaller areas,
or pockets, due to pre-determined criteria. This flows from the decision to limit the study area to
licensed mines in the Northern Cape Province of South Africa. This allowed for some generalization of
many of the criteria, which will be detailed in the following sections, but for the purpose of the MCDA,
it had the consequence of allowing the MCDA to be vastly simplified to that of Boolean choices only.
2.3. Model Criteria Selection
The Northern Cape Province was deliberately chosen as the starting point of the study, as this
province can be generalized in terms of solar radiation in exactly the way that the geographic regions
of the literature could not. If the nine provinces of South Africa were to be ranked on average solar
radiation received, including quality, then this province would be ranked the best [
26
]. In addition,
it would not be meaningful to use a ranking system to order the different mining areas on solar
insolation, as the objective is to reveal the collective solar insolation that these areas hold. Additionally,
if it is assumed that the majority of mines must have some type of road or rail infrastructure in place
to haul equipment and people to the mine and extract ore from the mine, then there is no reason to
build in weighted criteria to exclude areas far away from roads. The same argument can be applied
to electricity infrastructure given the energy intensity of mines, and the finding that all the mines
investigated as part of the study by Votteler & Brent [
27
] were connected to the grid. Lastly, it can also
be assumed with relative confidence that mining rights would not be granted for a specific area if that
area contained any known historical landmarks, paleontological significant sites, archaeological sites,
and protected wildlife sanctuaries. The remaining exclusions that were included in the model were
the following:
•Rivers (not including dry riverbeds)
•Areas where the defined mining area included a portion of the ocean along a coastline.
•Farm sheds
•Farm housing
•Mining housing

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•Mining machine sheds
•Open grooved mines
•Mine earth dumps (if discernible)
•National and regional tarred roads
•Airfields
•Railway tracks
These exclusions were all identified manually through human input due to a lack of consistency
in the datasets at hand. Inclusion was based on a system of positive identification, which implies that
the final result for net insolation might not represent the worst-case scenario. On the other side of
the spectrum are items that can also be positively identified but were deliberately not included as
exclusions. These are:
•Gravel/sand roads
•Agricultural activity
These were seen as non-permanent and can be rerouted or removed without a significant cost
compared to the overall cost of development. Furthermore, as agriculture does not alter or change
the landscape topography dramatically, it would not be something that poses a great limitation to a
developer. Figure 2shows an example of the identified exclusions for one specific mining area.
Sustainability 2020, 12, x FOR PEER REVIEW 5 of 15
Figure 2. GID 97373 outline in black and identified exclusion areas in red.
2.4. Model Application
When unfolding a three-dimensional sphere like earth onto a two-dimensional piece of paper,
there will inevitably be some type of distortion. An example is the manner in which the Mercator
projection distorts the size of the land area in an attempt to preserve shape. Equal area projections,
on the other hand, distort shape while preserving area. The equal area projection selected is from the
conical family of projections named Albers, having the characteristic that visual distortion is limited
between the so-called standard parallels, which means that an Albers projection can be adapted to a
localized area in order to be accurate while also having nearly no distortion when compared to a
Mercator projection in that same area. The South African Albers NGI Aerials (reported to have 0.001%
error when reporting projected area compared to actual area (source: Waywell, T. The effect of
various map projections on surface area. PositionIT, 2009)) projection is exactly customized for this
Figure 2. GID 97373 outline in black and identified exclusion areas in red.

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Slope and Aspect
Many of the studies in literature note that there is no definite number at which a slope is considered
to be unfeasible to build a utility-scale PV plant, as opposed to what is the case for concentrating solar
power (CSP). This caused the studies to have a wide range of cut-offvalues beginning as low as 3%
(commonly including 5%, 10% and 15%) up to 30% and even 50%. When slope was combined with an
aspect, there were usually two numbers, such as 3% for north facing land (the majority of the studies
are northern hemisphere based) and 5% for south facing land, therefore including the factor that south
facing land is more preferable to north facing land in the northern hemisphere. This is in line with
findings of studies such as that of Le Roux [
28
] studying the optimal tilt angle of solar panels for local
South African conditions, which implies that a tilted solar panel (north aspect) is preferred to a flat
panel as one moves further south of the equator. By extension, this implies that a greater amount of
leeway should be allowed for naturally north aspect sloping ground. To this end, a cut-offof three per
cent was chosen, while for slopes with a northern aspect (being defined as from 315◦to 45◦) the limit
was increased to five per cent.
2.4. Model Application
When unfolding a three-dimensional sphere like earth onto a two-dimensional piece of paper,
there will inevitably be some type of distortion. An example is the manner in which the Mercator
projection distorts the size of the land area in an attempt to preserve shape. Equal area projections,
on the other hand, distort shape while preserving area. The equal area projection selected is from the
conical family of projections named Albers, having the characteristic that visual distortion is limited
between the so-called standard parallels, which means that an Albers projection can be adapted to
a localized area in order to be accurate while also having nearly no distortion when compared to a
Mercator projection in that same area. The South African Albers NGI Aerials (reported to have 0.001%
error when reporting projected area compared to actual area (source: Waywell, T. The effect of various
map projections on surface area. PositionIT, 2009)) projection is exactly customized for this purpose,
and as such this was the de facto projection favored in all calculations unless otherwise stated in
the research.
2.4.1. Solar Resource Data
Solar resource maps can be created from mainly two sources: ground measurements, and satellite-
derived data. The latter typically covers very large areas and are, therefore, suitable to exploratory
work, and the datasets available usually offer much longer time periods, which alleviates at least
some investor fears, as solar plants are built to be operated for decades. For ground measurements,
a measurement device, such as a pyranometer or a pyrheliometer is required. Both these devices are
extremely accurate, but as a consequence, are also very expensive. They also require deployment in
many locations and this, combined with the high cost, makes it understandable why Fluri [
29
] reported
only 11 weather stations, managed by the South African Weather Services (SAWS), equipped with
such devices. For the purposes of this study, a satellite-derived solar resource map was best suited
and the available solar resource maps for South Africa are discussed by three papers [
29
–
31
] in the
local context with a summary of these given in Table 1. Only the NASA dataset was found to grant
access to the full raw dataset before any variables are included through manipulations in an unknown
software environment.

Sustainability 2020,12, 5857 6 of 14
Table 1.
Various sources of solar maps relevant to local conditions (Source: Fluri [
29
], Zawilska et al. [
30
],
Winkler et al. [31]).
Map Name Author Year Availability Data Source
Solar radiation data
handbook for
Southern Africa
- 1990 - Ground
measurements
South African Renewable
Energy Resource Database CSIR, Eskom, DME 1999 Public Ground
measurements
Solar and Wind Energy
Resource Assessment
(SWERA)
USDE, NREL & DLR 2006 Public Satellite
Surface Meteorology and
Solar Energy (NASA SMSE
or SSE)
NASA 2008 Public Satellite
Photovoltaic Geographical
Information System
European Commission
Joint Research Centre 2007 Public Satellite
Meteonorm. Meteotest AG. 2009 Commercial Satellite & ground
measurements
SolarGIS. Solargis. - Commercial -
3Tier. Vaisala. - Commercial -
2.4.2. Data Sources
According to Regulation 2, sub-section (2), paragraph (f) of the Mineral and Petroleum Resources
Development Regulations, as enacted by the Mineral and Petroleum Resources Development
Act (2002) [
32
,
33
], to obtain a mining permit the application should include a plan that includes
“the boundaries of the land to which the application relates.” Within data sourced from the Directorate:
National Geo-Spatial Information (NGI), a branch of Department of Rural Development and Land
Reform, data defining these boundaries were found and assumed to be the verified product of the
above-mentioned requirement. In general, a preference was enforced for publicly available data in
order to promote reproducibility and, therefore, it was not only the mining boundary data, but also the
aerial photographs and topographical data, sourced from the NGI.
2.4.3. Simulation Tool
The seat-cost for GIS work in the academic community is prohibitive [
34
], especially in the African
context, and for this reason, open-source software was considered a prerequisite. Since the start of
the millennium, the uptake of R [
35
] (R is a free software environment for statistical computing and
graphics) in the underfunded teaching and research sphere of developing countries has been noticeably
high [
36
]. Bivand [
36
] reported in 2006 that a search on the R website for the term “spatial” yielded
1219 results, which was considerably more than the 447 results during the year of 2002. Repeating this
in 2018 yielded 7280 results.
On recommendation by Endel and Filzmoser [
37
], RStudio was selected as the preferred graphical
user interface. The basic R distribution is capable of computing mathematical and statistical data
functions as reported by Grunsky [
38
], but to perform true GIS operations extra libraries need to be
installed. A summary of these is given in Table 2.

Sustainability 2020,12, 5857 7 of 14
Table 2. Summary of all additional libraries added to the base R.
Library Author
cleangeo Blondel (2017) [39]
dplyr Wickham & Francois (2016) [40]
gstat Graler et al. (2016) [41]
maptools Bivand & Lewin-Koh (2017) [42]
sp Pebesma & Bivand (2005) [43]
raster Hijmans (2016) [44]
rgdal Bivand et al. (2016) [45]
rgeos Bivand & Rundel (2017) [46]
2.4.4. Data Manipulation and Calculations
The requirement to exclude areas exceeding a pre-determined slope limit creates the need for
datasets that include data for both aspect and slope. A digital elevation model (DEM) is merely a
raster object with each cell containing a single value: height. Interpolation provides a way of assigning
values to these areas that exist between two topographical lines. In general, nearby data is selected
with a weighted average to calculate the missing values at a given location. According to Babak and
Deutsch [
47
], there are two main branches of interpolation. Either a statistical criterion can be used
to select the nearby weighted values, or a simpler deterministic method can be used. For the former,
techniques such as kriging (including but not limited to: simple-, ordinary-, universal- and simple
cokriging) can be implemented, while for the latter, inverse distance weighted (IDW) interpolation is
the most commonly implemented technique. IDW especially takes advantage of the principle of spatial
autocorrelation, which assumes that values closer to the missing value should be more similar to it than
values that are far away [
48
]. Simply stated, when taking one step away from your current position on
earth, you never really find yourself in a very different state (with the exception of stepping over a
cliff). The implementation of this is based on assigning a higher weight to points that are closer [
47
] or,
as the name suggests, using the inverse distance.
The function requires the user to choose between two techniques, namely: Fleming & Hoffer, or Horn.
A thorough comparison of these two techniques was made by Jones [
49
] who found that Fleming &
Hoffer performed better for smooth surfaces, and Horn performed better for noisy surfaces. Jones [
49
]
warns that when topographical lines are digitized and converted to a DEM through interpolation,
it might create step artefacts, and thus Horn was selected.
Figure 3shows the intermediate steps toward calculating the aspect and slope data. In Figure 3a,b
the original cropped topographical data is shown next to the same data that has been converted from
lines to points. Below that Figure 3c shows what the result of the interpolation process looks like.
The aspect and slope plots are shown in Figure 3d,e, respectively. It should be noted that the values for
aspect are seemingly random, or noisy, in areas that are not close to the original topographical lines.

Sustainability 2020,12, 5857 8 of 14
Sustainability 2020, 12, x FOR PEER REVIEW 8 of 15
(a)
(b)
(c)
Figure 3. Cont.

Sustainability 2020,12, 5857 9 of 14
Sustainability 2020, 12, x FOR PEER REVIEW 9 of 15
(d)
(e)
Figure 3. (a) Original topographical data; (b) converted to point, or raster, data; (c) result of
interpolation; (d) the slope and aspect data and (e), respectively.
3. Results
The sum of the areas under investigation was calculated as being 2572 km2, which represents
0.69% of the total land area of the Northern Cape Province, with a combined net solar insolation of
12,057 GWh/day. Because the assessed areas vary wildly in size, the net insolation results also vary
with the largest value for a single mine at 1,609,358 MWh/day, and the minimum a mere 2 MWh/day.
The lower limit is typical of a licensed mining area that is smaller than average, combined with a high
level of area utilization dedicated toward mining activities. To provide some insight, the results
shown in Table 3 are summarized according to order of magnitude in a pseudo-logarithmic style.
The majority of mines fall in the 10–100 GWh/day category, while the broader 1–100 GWh/day
category holds the outright majority share.
Table 3. Ranges of classification and total number of investigated areas within each.
Classification Range
Number of Areas
0–10 MWh/day
3
10–100 MWh/day
7
100–1000 MWh/day
18
1–10 GWh/day
27
10–100 GWh/day
33
100–1000 GWh/day
21
1–10 TWh/day
4
These numbers should be understood in context, as this is far from the realistic levels of energy
that can be expected to be evacuated. The factors that play the largest role were found to be panel
efficiency and area-factor. From the literature, three studies [20,22,50] reported their applied panel
efficiency, with values ranging between 8.8% and 37.9%. Only the study by Yushchenko et al. [51]
reported ranges, which were mostly referenced to meta-studies on the subject at hand and can,
therefore, be seen as the most reliable. The reported efficiencies were between 13.3% and 22% for
monocrystalline cells and between 12% and 15.67% for polycrystalline cells. For this study, the
simplest case was to assume a high and low case with values set at 12% and 22%, respectively,
enveloping both aforementioned ranges.
Figure 3.
(
a
) Original topographical data; (
b
) converted to point, or raster, data; (
c
) result of interpolation;
(d) the slope and aspect data and (e), respectively.
3. Results
The sum of the areas under investigation was calculated as being 2572 km
2
, which represents
0.69% of the total land area of the Northern Cape Province, with a combined net solar insolation of
12,057 GWh/day. Because the assessed areas vary wildly in size, the net insolation results also vary
with the largest value for a single mine at 1,609,358 MWh/day, and the minimum a mere 2 MWh/day.
The lower limit is typical of a licensed mining area that is smaller than average, combined with a high
level of area utilization dedicated toward mining activities. To provide some insight, the results shown
in Table 3are summarized according to order of magnitude in a pseudo-logarithmic style. The majority
of mines fall in the 10–100 GWh/day category, while the broader 1–100 GWh/day category holds the
outright majority share.
Table 3. Ranges of classification and total number of investigated areas within each.
Classification Range Number of Areas
0–10 MWh/day 3
10–100 MWh/day 7
100–1000 MWh/day 18
1–10 GWh/day 27
10–100 GWh/day 33
100–1000 GWh/day 21
1–10 TWh/day 4
These numbers should be understood in context, as this is far from the realistic levels of energy that
can be expected to be evacuated. The factors that play the largest role were found to be panel efficiency
and area-factor. From the literature, three studies [
20
,
22
,
50
] reported their applied panel efficiency,
with values ranging between 8.8% and 37.9%. Only the study by Yushchenko et al. [
51
] reported ranges,
which were mostly referenced to meta-studies on the subject at hand and can, therefore, be seen as the
most reliable. The reported efficiencies were between 13.3% and 22% for monocrystalline cells and
between 12% and 15.67% for polycrystalline cells. For this study, the simplest case was to assume a high
and low case with values set at 12% and 22%, respectively, enveloping both aforementioned ranges.

Sustainability 2020,12, 5857 10 of 14
Area factors are an indication of what fraction of a specific area can be covered with panels since
it is inevitable that space has to be left open between rows and mitigate shading and to leave room for
service roads. The study of Gastli and Charabi [
52
] is the only one found that included an area factor of
0.7 when calculating electric power generation potential, and, therefore, in the absence of alternatives,
this study proceeded with the same value. The formula utilized was similar to that implemented by
Carrión et al. [24], and the adaptation used in this study is given by Equation (1):
E=G×A×ε(1)
where:
E=Electric power generation per day (MWh/day)
G=Solar radiation received (MWh/day)
A=Area factor
ε=Panel efficiency
Table 4. summarizes the results after the effect of Equation (1) is included. Table 5provides the
headline numbers.
Table 4. High and Low case after performing Equation (1) on Table 3.
From Table 3Low Case High Case
0<10 MWh/day 3 10 6
10 <100 MWh/day 7 21 17
100 <1000 MWh/day
18 24 28
1<10 GWh/day 27 34 29
10 <100 GWh/day 33 23 27
100 <1000 GWh/day 21 1 6
1<10 TWh/day 4 0 0
Total 113 113 113
Table 5. Summary of headline values.
Total Insolation-Gross
[TWh/day]
Total Insolation-Minus
Exclusions [TWh/day]
Energy Production-High
Case [TWh/day]
Energy Production-Low
Case [TWh/day]
14.88 12.06 1.86 1.01
Finally, dispatchability remains the last link in the energy chain. Figure 4shows the locations of
where a broad selection of the REIPPPP projects are located. It is clear that solar resources are located in
areas that were previously net receivers of energy, with the Northern Cape Province under-developed
in terms of large-scale grid infrastructure. This disconnect between the locations of generation and
consumption, in essence, is the reason why solar-generated power, at present, will not be able to satisfy
the entire country’s electricity needs, even if the resource exceeds the demand.

Sustainability 2020,12, 5857 11 of 14
Sustainability 2020, 12, x FOR PEER REVIEW 11 of 15
Figure 4. National electricity grid within the borders of South Africa. Solid lines show transmission
lines of different carrying capacity. Black, red and yellow dots show current Eskom infrastructure
(sub-stations) and purple dots around crosshairs show REIPPPP projects. (Source: Eskom
Transmission Division [53]).
4. Conclusions
South Africa uses over 220 TWh of electricity annually [6], which is exceeded by even the low-
case of 369 TWh per annum, and certainly by the high-case of 679 TWh per annum; implying that
even on a worst-case base estimation, using only the identified mines to commission utility-scale solar
PV plants, would satisfy the entire country’s electricity (energy, not power) demand requirements.
To achieve this, the national grid would have to be reorganized. The fact that the national grid
operator is also the single largest generator of coal-derived electricity is a major conflict of interest in
achieving this goal and, again, highlights the importance of the need for the ISMO bill to be
promulgated. However, depending on the interpretation of existing policy frameworks,
opportunities exist to export energy after mine closures through the policies that govern
environmental rehabilitation. This would open various permutations of co-ownership and
commercial relationships between mining companies and solar energy developers.
All the options that exist are dependent on either small policy changes, or leniency in execution.
It would also require inter-departmental cooperation. This has already been proven successful
between the Departments of Energy, Environmental Affairs and Water Affairs during the
development and execution of the REIPPPP, and would only require a single department, the
Department of Mineral Resources, to be aligned. Agile policy could also play a large role in load-
generation matching by shifting or flattening peak load times through mechanisms such as time-
dependent pricing strategies on the demand-side. On the generation-side, incorporating a favorable
pricing strategy in the negotiated power purchase agreement (PPA) for supply during current peak
times could allow the financial impetus required to make incorporated energy storage financially
Figure 4.
National electricity grid within the borders of South Africa. Solid lines show transmission
lines of different carrying capacity. Black, red and yellow dots show current Eskom infrastructure
(sub-stations) and purple dots around crosshairs show REIPPPP projects. (Source: Eskom Transmission
Division [53]).
4. Conclusions
South Africa uses over 220 TWh of electricity annually [
6
], which is exceeded by even the low-case
of 369 TWh per annum, and certainly by the high-case of 679 TWh per annum; implying that even on a
worst-case base estimation, using only the identified mines to commission utility-scale solar PV plants,
would satisfy the entire country’s electricity (energy, not power) demand requirements. To achieve
this, the national grid would have to be reorganized. The fact that the national grid operator is also
the single largest generator of coal-derived electricity is a major conflict of interest in achieving this
goal and, again, highlights the importance of the need for the ISMO bill to be promulgated. However,
depending on the interpretation of existing policy frameworks, opportunities exist to export energy
after mine closures through the policies that govern environmental rehabilitation. This would open
various permutations of co-ownership and commercial relationships between mining companies and
solar energy developers.
All the options that exist are dependent on either small policy changes, or leniency in execution.
It would also require inter-departmental cooperation. This has already been proven successful between
the Departments of Energy, Environmental Affairs and Water Affairs during the development and
execution of the REIPPPP, and would only require a single department, the Department of Mineral
Resources, to be aligned. Agile policy could also play a large role in load-generation matching by
shifting or flattening peak load times through mechanisms such as time-dependent pricing strategies on
the demand-side. On the generation-side, incorporating a favorable pricing strategy in the negotiated
power purchase agreement (PPA) for supply during current peak times could allow the financial
impetus required to make incorporated energy storage financially viable for energy developers, as in
the widely publicized Tesla battery project in southern Australia [
54
]. In addition, investing in further

Sustainability 2020,12, 5857 12 of 14
research into storage technologies, such as is currently the case between the government and private
sector on a vanadium redox flow battery project [
55
], could help alleviate peaking supply problems
and stimulate GDP growth by making South Africa a serious player in a global market where demand
for storage solutions will only get bigger in the future.
The own-consumption model is by far the simplest and presents the least number of variables
to analyze in terms of its business sense to a company mainly concerned with profiting from mining
activities. Possible variables can relate to the cost of obtaining a grid connection to a previously
unconnected area or to avoid loss of services by an increasingly unstable grid utility. The first question
should be whether enough reliable solar power can be generated, and this study found that the annual
electricity generation potential varied between 36.5 GWh and 3650 GWh (100 MWh–10 GWh per day)
for the majority of the mines analyzed. This compares favorably to the annual consumption per mine,
reported as 4.2 GWh to 2752 GWh in the sample of the study by Votteler & Brent [
27
]. It was also
found that the Acts regulating the mining industry and mining rights/permit holders provide the
freedom to bring onto the land being mined any equipment and erecting any structure which is used
for, or is incidental to, mining activities. While the final decision would be in the hands of the Minister,
it can be stated that there should be no reason, insofar as generation potential or policy blockades,
to prevent solar PV plant development for our own-consumption. Whether by own-consumption or as
a grid supplier, market forces alone cannot guarantee uptake, leaving government policy as the largest
common denominator that can halt or stimulate a vibrant privately funded solar energy market in the
mining sector.
Author Contributions:
Conceptualisation, W.v.d.M.; Formal analysis, W.v.d.M.; Methodology, W.v.d.M.;
Supervision, A.C.B.; Validation, W.v.d.M.; Writing—original draft, W.v.d.M.; Writing—review and editing,
A.C.B. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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