Royal Institute of Technology
Department of Energy Technology
Division of Energy Systems Analysis (KTH-DESA)
Master’s Thesis (Draft)
Estimation of Un-electrified Households & Electricity Demand for Planning
Electrification of Un-electrified Areas – Using South Africa as Case”.
Supervised by
Prof: Mark I. Howells
Head of Division of Energy System Analysis
(
mark.howells@energy.kth.se
)
Performed By
Usman Hassan Syed
(usyed@kth.se)
MS – Sustainable Technology (2010)
1
Contents
Abstract ... 2
Acknowledgments ... 3
Preface ... 5
Introduction ... 7
Aims & Objectives ... 7
Methodology ... 7
South Africa ... 8
Discussion ... 12
Input Parameters: ... 12
Transformed Parameters: ... 17
Results ... 22
Estimates for South Africa ... Error! Bookmark not defined.
Preliminary Results: ... 27
Useful Parameters and Estimation Methodology ... 31
Conclusion... 33
Proposed Further Work ... 34
Bibliography... 36
Annexes ... 39
Annex-A: Estimation Methodology... 40
2
Abstract
“We emphasize the need to address the challenge of access to sustainable modern energy services
for all, in particular for the poor, who are unable to afford these services even when they are
available.”
Section 126: The Future We Want (Out Come Document of Rio+20-United Nations Conference on Sustainable
Development
June 20-22, 2012).
The lack of energy access has been identified as a hurdle in achieving the United Nations’ Millennium
Development Goals, leading towards the urge to set a goal for universal electrification till 2030. With
around 600 million people in Africa without access to electricity, effective and efficient electrification
programs and policy framework is required to achieve this goal sustainably.
South Africa is an example in the continent for initiating intense electrification programs and policies
like “Free Basic Electricity”, increasing its electrification rate from 30% in 1993 to 75% in 2010 and a
claimed 82% in 2011. The case of South Africa has been analysed from the perspective of universal
electrification in the coming years. The aim was to estimate the un-electrified households for each
area of South Africa in order to provide the basis for electrification planning. The idea was to use
available electrification statistics with GIS (Geographic Information System) maps for grid lines and
identifying the suitability of on-grid or off-grid electrification options, which may help in planning the
electrification of these areas in the near future. However, due to lack of readily available data, the
present work has been able to estimate the un-electrified households & their possible electrical load.
The estimates have been distributed in different income groups for each province and district
municipality of South Africa, which can be used for electrification planning at national, provincial and
municipal level. As a result, some simple and useful data parameters have been identified and an
estimation methodology has been developed, which may be employed to obtain similar estimates at
lower administrative levels i.e. local municipalities and wards.
3
Acknowledgments
It is my utmost pleasure to express due acknowledgement and gratitude to the many wonderful
individuals without whom I would have not been able to reach this point in life.
Beginning with this thesis work, I am really grateful to Professor Mark Howells for providing me this
opportunity and introducing me to the fascinating world of energy planning and modelling. His
patience, guidance and positive approach made this work a great learning experience for me. Both
his technical and soft skills are inspirational.
Besides him, my fellows at KTH-DESA, Manuel Welsch, Rebecka Segerstöm, Oliver Broad & Ahmad
Zulifqar always provided useful support for data collection and analysis. Rochelle Morrison &
Sebastian Hermann (the GIS Guru) deserve a big “thank you” for their efforts and help with the GIS
work. Special thanks to Francesco Fuso Nerini for reviewing and improving this report.
A sincere gratitude is due to a number of persons at the Energy Research Centre of University of
Cape Town, especially to Mamahloko Senatla who provided a lot of support, information and
valuable feedback.
Temidayo Adegbaiye, Constantinos Taliotis, Brijesh Mainali, Alessandro Sanches Pereira, Dilip
Khatiwada & María F. Gómez and everyone at energy department and KTH had always been really
friendly & inspirational with their works.
I would also like to thank Monika Olsson Director of Studies at the Unit of Industrial Ecology-KTH, for
her support and guidance during the entire masters.
Last but never the least, my family and friends whom I owe everything I have for their love and
support. My mother, my sister, my brothers and my fiancée have been always caring and supporting
and are the reason for everything I have been able to achieve so far.
4
Abbreviations
CN2001
Census 2001
CS2007
Community Survey 2007
DoE SA
Department of Energy, South Africa (formerly known as Department of Minerals and
Energy, DME)
ERC
Energy Research Centre (at University of Cape Town)
GIS
Geographic Information System
GDP
Gross Domestic Product
HDI
Human Development Index
KTH
Kungliga Tekniska Högskolan (The Royal Institute of Technology).
KTH-dESA
Division of System Analysis, (at KTH)
MDB
Municipal Demarcation Board, South Africa
MYPE
Mid-Year population Estimates
Stats SA
Statistics South Africa
IEA
International Energy Agency
UN
The United Nations
SA
South Africa
SE4ALL
Sustainable energy for all (UN’s initiative for universal energy access)
ZAR
South African Rand (South African currency)
5
Preface
Like the whole world, Africa and African nations are also looking forward towards development and
prosperity in the future. However, lack of access to energy has been identified as one of the major
hurdles to reach the United Nation’s (UN) Millennium Development Goals (Bogdansk, et al., 2010).
Therefore, in 2010, the UN Secretary-General’s Advisory Group on Energy and Climate Change
suggested the international community to set a target of “providing access to sustainable energy to
all by year 2030” (AGECC, 2010) known as the “SE4ALL
1” initiative . The UN also declared year 2012
as the “International Year of Sustainable Energy for All” and further commitments to this goal were
made in the “Rio+20 - United Nations Conference on Sustainable Development
2” held in Rio de
Janeiro, Brazil, on June 20-22, 2012. According to Section 127 of the conferences’ outcome document
“The Future we want” (United Nations, 2012)
“We commit to promoting sustainable modern energy services for all through national and
sub-national efforts”
An estimated 1.3 million people, mainly women & children, die every year due to the health hazards
caused by the use of solid fuels like wood, charcoal and dung (IEA, 2012). There are an estimated 1.4
billion people in the world without access to electricity and around 0.6 billion of this un-electrified
population lives in Africa, especially in the rural and remote areas (OECD/IEA, 2010). The people in
un-electrified areas spent three to four times more money on energy than the people in electrified
areas (DoE,SA, 2009). In order to resolve such issues, focus should be emphasized on designing a plan
to improve access to clean and modern energy (e.g. electricity) in a favourable and timely manner.
Achieving universal access to clean and modern energy services requires institutional supports and
taking concrete steps and developing a strong policy framework. Effective electrification program,
initiated with smart planning, is the key to achieve universal electrification targets on time and
assuring economical and environmental sustainability.
Background:
6
Figure 1: Electrification Rate-Lighting (Left hand) and Households (Right) presented on GIS maps of Local Municipalities of South Africa. Sources: Community Survey 2007 (Stats SA, 2007). Merged GIS Maps (MDB-SA, 2012)It was intended to suggest on-grid or off-grid solution for each area by using similar GIS maps for
finding the distance of the areas from the Medium Voltage distribution grid. The idea was to analyze
the data at the lowest administrative levels of South Africa (i.e. wards, being smaller geographical
areas) for coming up with more precise results. However, relevant data for all the wards and the grid
maps weren’t readily available which created hurdles in following the approach. Communications
were made to acquire the latest desired data for wards but unfortunately it will be available
sometime in 2013. Therefore, the data acquisition and analysis could only be done for higher
administrative levels and the future estimates could only be made for provinces and district
municipalities. Efforts were also made to obtain the latest GIS maps for the grids networks, which
remained unfruitful. Also the names of areas in map files were mostly different than that in the data
files and matching the correct data had been a frequently encountered problem. Most of these
problems were identified and sorted out. However, even in the present shape, the final results file
doesn’t show separate results for Buffalo City Municipality, where as City of Tshwane (TSH) &
Motheo municipalities (DC17) are shown separately (which were merged after the boundary
adjustments in 2007). These shortcomings can be eliminated if the results are presented for local
municipalities.
7
Introduction
All kinds of development plans are based on the existing conditions and possible future situations.
Without knowing the current and future demands correctly, the plan is not likely to sustain or
succeed technically and economically. The necessity of electrification programs and policies is
determined by the current electrification rates and the possible households increase in future. In
order to plan appropriate capacity extensions, it is essential to know how many households require
electricity and how much electricity demand they can impose on the system. After collecting and
analyzing the data for South Africa, no such data or estimates were found which can be used for
household electrification planning through energy modelling. Therefore, it was felt important to
estimate the current and future un-electrified households for South Africa, along with their possible
electrical load. The approach may be helpful in finding similar estimates for planning household
electrification in other countries of Africa.
Aims & Objectives
The aim of this work is to estimate the number of un-electrified households and possible electricity
demand of different income groups within different regions of South Africa. The aim may be
achieved using the data & statistics available for South Africa with the following objectives:
Estimation of possible population & number of households for all areas/regions of South Africa.
Estimation of the possible number of electrified and un-electrified households.
Estimation of the distribution of un-electrified households in different income groups.
Estimation of average monthly electricity consumption for each income group.
This may lead to a general load estimation methodology that can be useful in electrification
planning of any area or region of a country in future years
Methodology
8
South Africa
As the name suggest, South Africa is located in south of the African continent. The country has a
geographical area of around 1.2 million km
2. According to official website of the government, the
total population is estimated to be over 50.5 million in 2011 with an estimated 13.8 million
households (GCIS-SA, 2012). The national motto of South Africa means “unity in diversity” and gives a
true representation of South Africa’s diversity. The country has population with variety of ethnicities,
3 different capitals and 11 official languages. The country has been divided into four administrative
levels i.e. Provinces, metropolitan and district municipalities, local municipalities and electoral wards,
as shown in below figure.
South Africa
Figure 2: Administrative levels of South Africa. (Source: (MDB-SA, 2012)
To briefly introduce about the provinces of South Africa, the share of each province to the
population, households, land area and GDP of South Africa are presented in the following figure.
Level 1: Province
9 Provinces
Eastern Cape (EC)
Free State (FS)
Gauteng (GT)
KwaZulu-Natal (KZN)
Limpopo (LIM)
Mpumalanga (MP)
Northern Cape (NC)
North West (NW)
Western Cape (WC)
Level 2: District & Metropoliton Municipalities
8 Metropoliton Municipalities
Buffalo City (BUF), Nelson Mandela Bay (NMA),
Mangaung (MAN), Johannesburg (JHB),
Tshwane (TSH), Ekurhuleni (EKU),
Ethekwini (ETH), Cape Town (CPT
44 District Municipalities
District municipality have their
name and are identified by
number from 1 to 48 (e.g. DC1,
DC 42)
Level 3: Local Municipalities
226 Local Municipalities
Each District municipality
is further dividedinto local
municipalities which have
their name and are
identified by combining a
number with the
respective provinicial code
and the district
municipalitiy's two digit
code. (e.g. EC104 is the
"fourth" local municipality
in Eastern Cape 's DC10.
Level 4: Eloctral Wards
4277 Electoral Wards
Each Metropoliton and
Local Muncipalality is
divided electora wards
which are only identified
by their number e.g
Ward # 3. The number of
wards in each
9
Figure 3: Share of each province to the population, households, land area and GDP of South Africa. Sources: (Stats SA). (DoE,SA)
Economic Conditions
The South African economy has long been the largest, and one of the most diversified, economies in
Africa. The economy is backed by the enormous mineral wealth besides a developed manufacturing
industry, agriculture, and tourism and service sector.
The country’s GDP enjoyed good growth from 2000 until the global financial crisis in 2008. It
although experienced a negative growth in 2009 but recovered back 2010 & 2011 (OECD/United
Nations, 2011) and is reported to value 363 Trillion US$ in 2010 (IMF, 2012). South Africa’s Human
Development Index (HDI) value for 2011 is 0.619 i.e. in the medium human development category
and the country ranked 123 out of 187 countries and territories (UNDP, 2011). The Official Currency
of South Africa is Rand (ZAR) which is approximately equivalent to 0.12 US$.
South Africa is a key member of the Southern African Development Community (SADC) and enjoys an
important position as a driver of economic development and diversification in Africa. Its commercial
reach across the continent and its integration in global supply chains is helping it to increasingly
become an economic hub for Sub Saharan Africa. It’s economic growth and position in the continent
has made it the part of the association of emerging economies in 2010, now known as the BRICS
-acronym for Brazil, Russia, India, China and South Africa (Stats SA, 2011).
However, diversification also exists in the income levels and economic conditions of the people
across the country. The GINI index (which indicates the extent of deviation in income distribution
from a perfectly equal distribution among individuals within an economy) is reported to be 63.1 for
10
South Africa in 2009 (The World Bank, 2012), indicating a high income inequality. The Stats SA data
for 2007 reveals that roughly 50 % of the population has no income. Also most of the high income
population lives in the urban and metropolitan areas with 50% of the total high income (considered
for this work) lives in the Gauteng region. This income diversification results in diversification of
consumption behaviour and preferences in all purchasing decisions, including energy sources and
electricity.
Electrification in South Africa
Like economic growth, South Africa is also an example for other countries in the region because of its
remarkable initiatives and policy reforms for initiating and implementing successful electrification
programs. The government declared electricity as basic service and initiated intense electrification
programs involving key stakeholders. It facilitated & empowered many institutions such as the
national power company, Eskom, through various policy and structural reforms. There had been two
successful and fruitful electrification programs from Eskom, which have shown impressive results by
connecting more than 5 million households between 1994 and 2010 (OECD/IEA, 2010). President
Thabo Mbeki committed during the “State of the Nation” address on the 21 May 2004 that each
household should have access to electricity by 2012 (Bekker, et al., 2008).
The extent of commitment for remote rural and poor households is evident from initiatives like the
off-grid photovoltaic program and the free basic electricity (FBE). A large-scale off-grid electrification
program was launched in March 1999, which aimed to provide 350,000 Solar Household
electrification Systems. The government subsidized the installation cost and the users had to pay a
nominal monthly fee (Winkler, et al., 2011). To support the affordability of electricity for poor
customers, the government introduced free supply of electricity for basic needs (named as the
electricity basic support services tariff - EBSST) giving 50 kWh/month for free to all consumers. The
purpose was to increase the “social benefits” of electrification and support the vast majority with low
or no income (Winkler, et al., 2011).
The results of governments and Eskom’s effort are clearly visible. The electrification rate of the
country increased from 34% in 1994 to a reported 75% in 2009 (DoE,SA, 2009) (OECD/IEA, 2010) and
a claimed 82% in 2011
(DoE-SA, 2012)
The total electricity consumption of the country was reported
to be 240 TWh in 2010 with electricity consumption per capita of 4803 KWh/capita (IEA, 2012). Most
of the energy in South Africa comes from coal, but the government has set a target to obtain 15% of
its energy from renewable sources by 2020. (Bekker, et al., 2008) (Winkler, et al., 2011)
11
Figure 4: Electrification rates of the provinces in first quarter of 2010. (Source: Electrification Statistics for March 2010 - (DoE,SA, 2010)).The DoE SA explains that the current backlog is in remote and outskirt areas where the geography,
high cost per connection and lack of infrastructure poses challenges (DoE,SA, 2009). Its recent plans
aims to provide 150,000 new electricity connections and an additional 10,000 off-grid connections
each year, in order assure to electricity access to 15.2 million households by March 2025 (DoE-SA,
2012) (DoE-SA, 2012). However, it doesn’t appear to achieve universal electrification in coming years
despite of setting & revising targets.
The current electrification statistics and the lacking in the ambitious universal access target set in
2004, (highlighted in “Uncertainties within South Africa’s goal of universal access to electricity by
2012” by Bekker et al - 2008), reveals that serious financial and capacity obstacles made it impossible
to achieve 100% electrification of South African households by 2012. The lack of bulk infrastructure
was identified as a major obstacle to universal electrification by DoE, as early as in 2004, and it began
to plan and fund infrastructure development (Bekker, et al., 2008). It is also believed after the recent
power outages that the electricity generation capacity didn’t grow in parallel with the demand, which
grew much faster due to South Africa’s high economic growth rates in the recent years and a lot of
investment is still needed for in electricity infrastructure (OECD/United Nations, 2011). Thus there
may be a need to review and revise universal electrification targets which should be more realistic
and achievable according to the current situation.
20%
30%
40%
50%
60%
70%
80%
90%
100%
Electrification Rate of Provinces - 2010
12
Discussion
In light of the discussion presented in the preceding sections, the availability of relevant data and
past research works, we opted for South Africa to assess the available data and identify those data
parameters that could be useful in initiating rural household electrification planning. With the aim in
mind, a number of data sources, data sets and research works related to South Africa were reviewed
and the data for some parameters was found useful. Working with these data sets ultimately led us
towards our desired goals of establishing reasonable estimates of un-electrified households and their
electrical loads in future for South Africa, its 9 provinces and & 52 district and metropolitan
municipalities. The initial intention was to estimate the possible power load according to the
suitability of on-grid or off-grid supply options. However, the present work only provides the
estimates of un-electrified households and their possible future load, if it is planned to be completely
electrified in any future year. The estimates are also distributed into different income groups, which
may be helpful in planning subsidies or assessing feasibility of low cost & low power electrification
options over high cost & high electrification options.
The parameters found useful are briefly discussed below along with the observations regarding their
available data. The details about data sources used, the transformations and calculations performed
are described in Appendix A. The method, which led to development of results, may be applicable for
estimating the number of un-electrified household (along with their possible load demand) for the
purpose of universal electrification planning in other areas of Africa.
Input Parameters:
Population:
The population is perhaps the most important parameter to consider for all kinds of future plans.
Various national & international sources, from The World Bank to the Population Department of
South Africa, provide population data or estimates for South Africa. We opted to use the population
data available from Stats SA, since most of the data used for this work was available from Stats SA.
The actual population data for 2001 & 2007 was available from the Census 2001 (CN2001),
Community Survey 2007 (CS2007) for all the provinces and municipalities. Also, Midyear population
estimates (MYPE) of South Africa & its provinces for each year up to 2011. The data was found to be
similar among all sources and didn’t have considerable variations, as shown in below table.
Year
CN2001 & CS2007
MYPE-Stats SA
United Nations
2001
44 819 778
44 801 352
45 389 577
2002
45 401 096
46 015 405
2003
45 997 195
46 631 364
2004
46 589 256
47 226 670
2005
47 176 879
47 792 787
2006
47 759 647
48 330 914
2007
48 502 049
48 337 181
48 842 462
2008
48 909 060
49 319 363
2009
49 474 876
49 751 503
2010
50 034 244
50 132 817
13
The population for some provinces (i.e. Free State, Mpumalanga, North West and Western Cape) &
district municipalities given in CS2007 is higher than even MYPE 2010 (though it’s a consistent with
MYPE 2007). Also the MYPE for some years, for the provinces & the district municipalities, had
slightly different total from one another. In such cases the share % of population (for the provinces &
district municipalities) has been found using their respective sum instead of the country’s total
population. It’s felt that the variation in the data may be due to the change in administrative
boundaries of some district and local councils in 2005, which might have not been incorporated in
the MYPE tool of Stats SA.
Number of Households:
Since the electricity connections are provided to a whole household and not to individuals, the data
related to number of households is an important parameter to ascertain different things for instance,
the number of electrified and un-electrified households, the household density etc.
The data for number of households in all the provinces, metropolitans, district municipalities, local
municipalities of South Africa for the year 2001 & 2007 was available in CN2001 and CS2007. The
number of total households, in all the provinces for the year 2009 & 2010, were available in the
“Electrification Statistics 2009” (DoE,SA, 2009) & “Energy Synopsis 2010” (DoE,SA, 2010) published by
the Department of Energy (DoE SA), South Africa. The available data for total households in all the
provinces is shown in table 2.
PROVINCE
2001
2007
2009
2010
Eastern Cape
1 506 540
1 586 740
1 667 435
1 683 420
Free State
758 115
802 870
823 972
834 337
Gauteng
2 889 678
3 175 578
3 127 991
3 185 858
Kwazulu Natal
2 233 499
2 234 129
2 405 165
2 439 751
Limpopo
1 194 038
1 215 934
1 250 716
1 264 792
Mpumalanga
830 986
940 401
879 082
889 958
North West
898 737
911 118
914 070
923 954
Northern Cape
259 632
264 653
272 958
276 265
Western Cape
1 211 412
1 369 181
1 333 886
1 355 952
Total
11 782 637
12 500 604
12 675 275
12 854 287
Table 2: Total number of households in all provinces from CN2001, CS2007 (Stats-SA) & DoE SA 2009 & 2010.
Like the population, the number of total households for some provinces (i.e. Gauteng, Mpumalanga,
Western Cape) given in CN2007 is higher than that of DoE SA 2009 & 2010. Also the numbers of
households for the municipalities were available only till 2007.
The Number of Electrified Households
14
Therefore, another approach was applied and the number of average of all households using
electricity for lighting, cooking and heating was considered as electrified. The resulting numbers for
2001 & 2007 were found consistent with the 2009 & 2010 figures of electrified household as shown
in table 3.
PROVINCE
2001
(Lighting)
2007
(Lighting)
2001
(Average)
2007
(Average)
2009
2010
Eastern Cape
756 596
1 045 720
518 355
760 751
998 014
1 035 827
Free State
567 386
695 219
416 299
579 219
622 053
634 712
Gauteng
2 326 287
2 646 397
2 149 844
2 554 544
2 387 422
2 406 104
Kwazulu Natal
1 376 919
1 596 351
1 169 651
1 409 757
1 586 457
1 623 397
Limpopo
754 220
987 417
468 893
641 339
921 267
942 620
Mpumalanga
574 304
772 635
405 522
573 302
647 597
662 479
North West
661 204
751 347
514 722
629 170
717 465
728 152
Northern Cape
190 368
229 641
155 399
201 912
222 553
226 471
Western Cape
1 067 170
1 285 544
970 191
1 198 661
1 142 520
1 153 827
Total
8 274 454
10 010 271
6 768 875
8 548 654
9 245 357
9 413 589
Table 3: Total number of electrified households in all provinces from CN2001, CS2007 (Stats-SA) & DoE SA 2009 & 2010.
Baker et al (2008) also highlighted different numbers for electrified households from different
governmental sources. It may be due to the fact that the 2007 data comes from public survey and
some of the households may be using electricity for lighting from their own sources e.g. PV panels,
generators or batteries charged from other locations. It can also be assumed that some of the
households may be using electricity illegally. Therefore we kept both approaches under
consideration when estimating electrified households and termed them as “electrified
household-lighting” and “electrified household-average”
Number of Annual Connections:
The data of new electrical connections completed each year in South Africa (from 1996 to 2008) was
obtained from the “IEA Comparative Analysis Report” (OECD/IEA, 2010). It was intended to be used
with available data for electrified household of 2001 & 2007 to observe the trend. It was observed
that if the number of annual connections reported by both DoE and IEA are added with the Census
2001 data, they did not match convincingly with the Census 2007 data and further more with the
DoE 2010 & DoE 2009 data. The data of annual connection available from the DoE was observed to
be slightly different from the other sources like IEA. This may be due the fact of not considering the
number of households disconnected each year most likely due to non-payment. The data has also
been reported of having discrepancies & lacking (Bekker, et al., 2008).
15
Population in Different Income groups
The population distribution in different income groups was available in the CN2001 & CS2007 data.
Keeping focus towards the low income levels, the populations were grouped in the following monthly
income ranges (in South Africa Rand).
o Group 1 = No Income (R0)
o Group 2 = Very Low income (R1-R800. R800 is the limit for Free Basic Electricity also)
o Group 3 = Low Income (R801-R1600)
o Group 4 = Medium Income (R1601-R12800)
o Group 5 = High Income (R12801 +)
The CS2007 data also indicates that income data for around 7% of the population wasn’t available
(including 1% institutions). Therefore, the rest of 93% household with income data were considered
as the total no. of households when calculating the share % of households in each income group.
Even then, a shift towards higher income groups has been observed from 2001 to 2007, as evident
from table 4.
Income Group No Income (R0) Very Low Income (R1- R800) Low Income (R801-1600) Medium Income (R1601 - R12800) High Income (12801+ )
Year
2001
2007
2001
2007
2001
2007
2001
2007
2001
2007
South Africa 56,22% 49,24% 15,43% 22,47% 9,54% 12,67% 16,86% 12,94% 1,95% 2,68% Provinces Eastern Cape 74,91% 50,53% 16,46% 28,23% 2,88% 13,49% 5,32% 6,69% 0,44% 1,07% Free State 68,06% 46,28% 19,46% 25,39% 4,34% 14,55% 7,60% 12,26% 0,55% 1,54% Gauteng 60,45% 48,08% 11,54% 16,00% 8,66% 11,37% 16,63% 19,09% 2,71% 5,45% KwaZulu-Natal 73,06% 52,39% 14,61% 24,34% 4,11% 12,38% 7,54% 9,44% 0,68% 1,45% Limpopo 74,44% 48,24% 18,20% 32,23% 2,38% 12,17% 4,68% 6,54% 0,30% 0,82% Mpumalanga 71,89% 49,10% 16,81% 27,75% 4,34% 11,39% 6,44% 10,06% 0,52% 1,70% Northern Cape 64,71% 48,28% 21,52% 21,92% 4,24% 15,92% 8,83% 11,94% 0,70% 1,93% North West 68,34% 50,58% 16,11% 22,22% 5,78% 13,05% 9,18% 13,02% 0,59% 1,12% Western Cape 56,22% 47,34% 15,43% 13,98% 9,54% 14,52% 16,86% 19,85% 1,95% 4,31%Table 4: Percentage of Population in defined income groups from CN2001 & CS2007 data
.
This shift towards higher income may be mainly related to the GDP and economic growth in these
provinces; however some studies suggest that increased electrification can also be considered to
contribute in this prosperity (Prasad & Dieden, 2007).
Since the 2007 data was more recent, it was used to estimate the number of households in different
income groups for each area, assuming the same proportion as population.
Percentage of Electrified Households in Income Groups:
The percentages of electrified households in income quintiles, for rural and urban areas of South
Africa, were in given in Prasad (2006) as shown in table 5.
Electrification %
Q1
Q2
Q3
Q4
Q5
Rural
41%
45%
59%
68%
76%
Urban
63%
78%
87%
91%
98%
16
The data was given for quintiles and our first two income groups together constitutes about 60-80%
of the total population i.e. up to the 3
rd& 4
thquintile. However, matching the quintiles would have
lead towards different income groups for each province. Therefore, we associated the “n
th” quintile
with our “n
th” income groups and adjusted the percentages for each province to match the total
number of electrified households in that province, described in detail in the annex.
Electricity Consumption for different Income groups
The electricity consumption data
for
households can provide an idea of the increasing demand in
future for planning appropriate capacity additions and conducting a cost-benefit analysis. The
electricity consumption in newly electrified households was reported to be lower than anticipated, in
the initial years of electrification (Winkler, et al., 2011). Prasad (2006) stated an average monthly
consumption of 132 KWh / household against an expected 350 KWh. The reason reported was the
consumption of most low income households being less than the expected 50 KWh. Such situations
can lead to difficulties in recovery of investment and operational costs. (Louwa, et al., 2008)
The electricity consumption data is not explicitly available for the provinces, income groups or
households. The DoE SA provides electricity consumption data for the residential sector for the
whole South Africa only and it was difficult to find data for each province and income group. Using
the reported residential electricity consumption for South Africa for 2007 and the number of
electrified household (average), the average monthly consumption per electrified household was
found to be 401 KWh. Stats SA only provides the total monthly electricity consumption for all the
provinces, with no indication to the household or residential sectors contribution to it. Some data
collected by researcher and consultant Markus Dekenah was received from the UCT, which state
average monthly consumption of households with monthly incomes from 500-13500 ZAR. However,
there were no details available about the spatial & temporal settings of the recordings. Also the
averages obtained from the data were found giving very low annual consumptions. The annual
consumption obtained were 16 TWh for all the households of 2007, which was much less than the
reported 41 TWh for the same year.
Therefore the available data sets were used with different assumptions along with excel equation
solver to estimate the average monthly consumption of households in different provinces and
different income groups as shown in the below table. The details of estimation are described in the
annex.
Geographical Areas of Provinces & Municipalities:
The geographical area helps in determining the population and household density. The areas of all
the provinces and municipalities were found with help of GIS maps. The GIS maps for provinces,
municipalities and wards of South Africa are available from a number of sources. However, there
exists contradiction in them mainly due to the changing of boundaries in 2005 or 2007. Therefore,
the GIS maps, available from the Municipal Demarcation Board of South Africa, were used.
Future Population Projections:
17
Affairs/Population Division (United Nations, 2010), for different countries and different scenarios
(low, medium and high variant). The projections for South Africa used for this work are based on the
medium variant scenario, which assumes neither very high population increase nor decrease. Modest
population growth is opted to avoid exaggeration of future estimates. Also it gives a fair chance to
see whether the existing electrification program will be sufficient or not, even if modesty in
population growth is considered.
Transformed Parameters:
In order to come up with future estimates for the provinces and municipalities and distribute them to
the proposed income grouping, most of above mentioned data sets were transformed into relative
parameters e.g. share % in total. The details of transformation steps are described in the annex and
are briefly discussed below.
Number of Persons per Household:
The future population projections were available but future estimates related to electrification
planning were required in terms of households. This parameter helps to estimate the possible no. of
future households using the population projections. The average number of persons per households
was simply calculating by dividing the total population of an area with the total number of
households from the available data and taking their average. The average persons per household
obtained from the available data for South Africa are shown in table 6.
South Africa
2001
2007
2009
2010
Average
Average Persons / Household
3,80
3,88
3,90
3,89
3,87
Table 6:Average Persons Per Household obtained from the available data for South Africa
.
Though, the values obtained showed an increasing trend from 3,80 in 2001 to 3,89 in 2010, following
the trend gives a less numbers of households than that obtained through the average.
In reality, the number of persons living in a household in South Africa varies from 2 till more than 12.
The number of households, categorized according to the number of persons living in them, is given in
CN2001 & CS 2007 data. However, transforming this data would simply give us share % of such
households and would again require the total number of households. Also, the number of persons
per household may marginally effect the electricity consumption of household due to shared
activities e.g. lighting, watching TV (Louwa, et al., 2008). It can only affect the consumption if the
area of the house is bigger, which is mainly influenced by income, not the number of people.
Total Households Share (%):
Based on population projections, the total household were estimated for South Africa using the
above. However, if this parameter is used to estimate the number of households for each area, the
sum of total households for provinces & municipalities was observed to be different than that of the
whole South Africa. Therefore the average number of persons per households was only used to
estimate the total number of households for whole South Africa.
18
Africa. The share in total household was found to be virtually constant, for almost all the areas, with
very minor increase or decrease of less than 0,5% for the provinces.
Provincial
2001
2007
2009
2010
Average
Difference
2001-2010
Eastern Cape
12,79%
12,69%
13,16%
13,10%
12,93%
0,31%
Free State
6,43%
6,42%
6,50%
6,49%
6,46%
0,06%
Gauteng
24,52%
25,40%
24,68%
24,78%
24,85%
0,26%
KwaZulu-Natal
18,96%
17,87%
18,98%
18,98%
18,70%
0,02%
Limpopo
10,13%
9,73%
9,87%
9,84%
9,89%
-0,29%
Mpumalanga
7,05%
7,52%
6,94%
6,92%
7,11%
-0,13%
Northern Cape
2,20%
2,12%
2,15%
2,15%
2,16%
-0,05%
North West
7,63%
7,29%
7,21%
7,19%
7,33%
-0,44%
Western Cape
10,28%
10,95%
10,52%
10,55%
10,58%
0,27%
Table 7: Share of Provinces in Total Household in South Africa.
The share % of local municipalities were only available from the CN2001 & CS 2007 data and the
difference is even minor except for Cape Town, where the household share% had an increase of
0,61%. Therefore, the average share % of households of an area can be assumed to result in a safe
estimate of future households in an area.
Electrified Households Share (%):
Like the total households, the available data for electrified household for the provinces and
municipalities were also converted in terms of share % of total electrified households for the
respective year. (Please note that this is different from the electrification rate which is the share of
electrified household in total households of an area). The share % of electrified household was found
to be increasing for some provinces and for some provinces it was found to decreasing, as shown in
table 8 below.
Provincial
2001
2007
2009
2010
Eastern Cape
7,66%
8,90%
10,79%
11,00%
Free State
6,15%
6,78%
6,73%
6,74%
Gauteng
31,76%
29,88%
25,82%
25,56%
KwaZulu-Natal
17,28%
16,49%
17,16%
17,25%
Limpopo
6,93%
7,50%
9,96%
10,01%
Mpumalanga
5,99%
6,71%
7,00%
7,04%
Northern Cape
2,30%
2,36%
2,41%
2,41%
North West
7,60%
7,36%
7,76%
7,74%
Western Cape
14,33%
14,02%
12,36%
12,26%
Table 8: Share of each province in total Electrified (average) Households.
19
PROVINCE
Increase In Total Households
Increase In Electrified Households
2001-2007 2007-2009 2009-2010 2001-2007 2007-2009 2009-2010
Eastern Cape
80 200
80 695
15 985
242 396
237 263
37 813
Free State
44 755
21 102
10 365
162 921
42 834
12 659
Gauteng
285 900
- 47 587
57 867
404 699
-167 122
18 682
Kwazulu Natal
630
171 036
34 586
240 106
176 700
36 940
Limpopo
21 896
34 782
14 076
172 446
279 928
21 353
Mpumalanga
109 415
-61 319
10 876
167 780
74 295
14 882
North West
12 381
2 952
9 884
114 448
88 295
10 687
Northern Cape
5 021
8 305
3 307
46 514
20 641
3 918
Western Cape
157 769
-35 295
22 066
228 470
-56 141
11 307
Total
717 967
174 671
179 012
1 779 780
696 694
168 241
Table 9: Comparison of increase in Total Household and Electrified Households for all provinces.
As seen above, it is obvious that with a focus on increasing electrification and controlling the
population, the number of electrified households is likely to increase faster than the total number of
households. However, it is difficult to predict a trend for the increase in electrified households for
each area in the future years. Considering the fact that the future connections can be provided more
conveniently in the already electrified areas due to existing infrastructure, it may be assumed that
the share % of electrified household of an area (province or municipality) will not drop from the last
known levels and will gradually increase as other areas achieve complete electrification. With this
assumption, the share % of electrified household (in total household of the area) can be used to
provide the basis for estimating the electrified households of an area. It is understandable to be not
the accurate estimate, as the connections distribution in an area in a year may be more than the
other areas. However, the difference may not be significant in long range planning perspective,
which usually involves a broader spatial & temporal extent. Thus it can be assumed that the numbers
may be reasonably supportive for estimating future plans for electrifications.
Number of Households in Different Income groups
Since data giving the distribution of households in different income groups weren’t available, We
assumed the distribution to be same as the population distribution in income groups. The data
related to population distribution in income group was available through CN2001 & CS2007. The
latest available data for population share % from CS2007 was used to estimate the number of
households in different income groups for the year 2007, as presented in table 10.
No
Income
Very Low
Income
Low
Income
Medium
Income
High
Income
Total
Eastern Cape
801 738
447 872
214 125
106 097
16 908
1 586 740
Free State
371 544
203 825
116 779
98 392
12 330
802 870
Gauteng
1 526 886
508 184
361 104
606 303
173 101
3 175 578
KwaZulu-Natal
1 170 480
543 795
276 641
210 822
32 392
2 234 129
Limpopo
586 624
391 882
147 921
79 544
9 964
1 215 934
Mpumalanga
461 734
260 957
107 121
94 627
15 962
940 401
North West
460 876
202 439
118 940
118 629
10 234
911 118
Northern Cape
127 783
58 024
42 145
31 590
5 111
264 653
Western Cape
648 116
191 381
198 816
271 844
59 024
1 369 181
Total
6 155 780
2 808 357
1 583 593
1 617 847
335 027
12 500 604
20
Number of Electrified Households in Different Income groups:
The number of electrified households in each income group wasn’t available from any data source.
We used the percentages of electrified households in income quintiles for urban & rural areas given
in Prasad & Visagie (2006) and multiplied it with the number of households in respective income
groups for each province from the 2007 data. The calculated numbers of electrified households gave
slightly different total than the total number of electrified households for each province in CS2007.
Using the excel equation solver, the percentages were adjusted and the sum of resulting numbers of
electrified households in different income groups was made same as the total no. of electrified
households in that province for the year 2007.
No Income
Very Low
Income
Low
Income
Medium
Income
High
Income
Total
Eastern Cape
340 894
206 122
128 133
72 733
12 869
760 751
Free State
224 591
154 609
99 812
88 149
12 058
579 219
Gauteng
1 060 125
413 056
324 634
584 037
172 693
2 554 544
KwaZulu-Natal
594 015
376 718
225 403
182 142
31 479
1 409 757
Limpopo
285 321
199 119
93 036
56 245
7 617
641 339
Mpumalanga
221 908
169 770
86 114
80 066
15 443
573 302
North West
264 399
150 226
100 182
104 365
9 998
629 170
Northern Cape
83 965
46 353
37 384
29 188
5 022
201 912
Western Cape
512 522
164 053
191 218
271 844
59 024
1 198 661
Total
3 587 740
1 880 026
1 285 915
1 468 769
326 204
8 548 654
Table 11: Estimated Electrified Households in defined income groups for all the provinces of South Africa- 2007
Number of Un-Electrified Households in Different Income groups:
The number of un-electrified households in different income groups for each province in 2007 was
estimated by subtracting the electrified households form the total households for that income group
and province.
No
Income
Very Low
Income
Low
Income
Medium
Income
High
Income
Total
Eastern Cape
460 843
241 750
85 992
33 364
4 039
825 989
Free State
146 953
49 216
16 968
10 243
272
223 651
Gauteng
466 761
95 128
36 471
22 266
408
621 034
KwaZulu-Natal
576 465
167 077
51 238
28 679
914
824 372
Limpopo
301 303
192 763
54 884
23 299
2 347
574 595
Mpumalanga
239 826
91 187
21 006
14 561
519
367 099
North West
196 478
52 212
18 759
14 264
236
281 948
Northern Cape
43 818
11 671
4 761
2 402
89
62 741
Western Cape
135 594
27 328
7 599
-
-
170 520
Total
2 568 040
928 331
297 677
149 078
8 824
3 951 950
21
Share % of Un-Electrified Households in Different Income groups:
Ultimately the number of un-electrified households in different income groups in each province for
2007 was converted in terms of share% of total un-electrified household of that province as shown in
the following table.
No Income
Very Low
Income
Low
Income
Medium
Income
High Income
Eastern Cape
55,79%
29,27%
10,41%
4,04%
0,49%
Free State
65,71%
22,01%
7,59%
4,58%
0,12%
Gauteng
75,16%
15,32%
5,87%
3,59%
0,07%
KwaZulu-Natal
69,93%
20,27%
6,22%
3,48%
0,11%
Limpopo
52,44%
33,55%
9,55%
4,05%
0,41%
Mpumalanga
65,33%
24,84%
5,72%
3,97%
0,14%
North West
69,69%
18,52%
6,65%
5,06%
0,08%
Northern Cape
69,84%
18,60%
7,59%
3,83%
0,14%
Western Cape
79,52%
16,03%
4,46%
0,00%
0,00%
South Africa
64,98%
23,49%
7,53%
3,77%
0,22%
Table 13: Share of Un-electrified Households in defined income groups for all the provinces of South Africa- 2007
