Hybrid-Renewable Energy: a Methodology for Identifying Communities That Can Benefit from Off-Grid Systems

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How to cite this thesis

Surname, Initial(s). (2012). Title of the thesis or dissertation (Doctoral Thesis / Master’s Dissertation). Johannesburg: University of Johannesburg. Available from: http://hdl.handle.net/102000/0002 (Accessed: 22 August 2017).

Master’s Research Proposal

For

Hybrid-Renewable Energy: A methodology for identifying communities that can benefit from off-grid systems

Submitted to

The Faculty of Engineering and the Built Environment

The Department of Mechanical Engineering Science

University of Johannesburg

Master of Engineering Degree

HLOLOGELO MAESELA KEKANA

201048853

Supervisor: Prof. Esther T. Akinlabi

Co-supervisor: Mr Gregory Landwehr

May 2020

Plagiarism Declaration

I, Hlologelo Maesela Kekana, hereby declare that this report is entirely my own work; and it has not been used anywhere else for acquiring academic credit.

I understand what plagiarism implies; and I hereby declare that this report is my own ideas, words, phrases, figures, results and organisation – except where reference is explicitly made to another person’s work.

I understand that any wrong academic behaviour, which includes plagiarism, results in prosecutable penalties by the University of Johannesburg; and consequently, it is punishable by disciplinary action.

Thus, as a student of the University of Johannesburg, I adhere to the rules and regulations of this institution.

Signature......

Student number ...... 201048853......

Date ......

Abstract

In a world of increasing demand for electrical energy, renewable energy is Africa’s calling- card to come to the forefront of renewable energy generation. Solar energy can be harnessed in different ways; and the technology to increase the energy output efficiency continues to develop daily, growing exponentially towards achieving efficiency levels, which can substantiate its greater use in everyday lives. This study has attempted to model a hybrid-energy power plant that utilises solar PV and wind energy to power the electricity demand of a community in South Africa. The hybrid-energy system operates with the support of a Battery Energy Storage (BES) system, together with a diesel generator, in order to access an energy supply whenever solar or wind energy are not harnessed.

This research work focuses on optimizing the methodology of determining the benefit of an off-grid system. South Africa is one of the most gifted nations in terms of natural resources, providing solar energy and wind energy in abundance from the coastlines to the northern desert areas; renewable energy has the potential of becoming a reliable and sustainable resource with the capacity to power up the remote and rural parts of South

Africa, which are currently too costly to connect to the grid and to electrify.

This study provides an in-depth look into the methodology of determining a community that can benefit from an off-grid hybrid-energy system. The Ntabankulu local municipality was the opted choice for its remote and rural communities, together with high levels of lack of access to electricity. From the municipality, the Buwani, Mdin and Xopo communities were chosen based on their size and topography. The Mdini community in the Ntabankulu local municipality of the Eastern Cape Province has been shown to be the most suitable community on the basis of the determined selected criteria. This was also based on the

DREI costing tool’s levelized cost of energy results. The Derisking Renewable Energy

Investment (DREI) costing tool, factored in the size of the community, the daily energy consumption and the available wind and solar resources in the area. The costing tool is detailed; and it provides the user with project investment options, interest rates and subsidised financing.

Keywords: Battery Energy Storage System (BESS), De-risking Renewable Energy

Investment (DREI) costing tool, Eastern Cape, Levellised Cost of Energy (LCOE),

Ntabankulu local municipality, Off-grid,.

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Table of Contents Plagiarism Declaration ...... i Abstract ...... ii List of Figures ...... viii List of Tables ...... xii ACRONYMS ...... i Nomenclature ...... ii CHAPTER ONE ...... 1 1.0 Introduction ...... 1 1.1 Background ...... 1 1.2 Motivation ...... 2 1.3 Problem statement ...... 2 1.4 Aim of the Research ...... 3 1.5 Research Objectives ...... 3 1.6 Hypothesis Statements ...... 4 1.7 Significance of research ...... 6 1. 8 Project plan ...... 6 1. 9 Summary ...... 8 CHAPTER TWO ...... 9 2 Literature Survey ...... 9 2.1 Introduction ...... 9 2.2 Renewable energy ...... 9 2.2.1 Types of renewable energy ...... 9 2.2.2 Global standings of renewable energy ...... 10 2.3 An Overview of Electricity in South Africa ...... 14 2.3.1 South Africa’s GDP on Energy ...... 23 2.3.2 Gini Coefficient ...... 25 2.3.3 Integrated Resource Plan for Electricity (IRP) ...... 26 2.4 Access to electricity and State of renewable energy in South Africa ...... 29 2.5 Critical Review of Off-grid systems ...... 35 2.5.1 Case Study: Off-grid Micro-grid for Universal Electricity Access in the Eastern Cape, South Africa ...... 35 2.5.2 Case Study: Upper Blinkwater off-grid system in Eastern Cape, South Africa ..... 36

2.5.3 Case Study: Potential of Hybrids for rural off-grids in the Eastern Cape, South Africa ...... 36 2.5.4 Case Study: Thabazimbi Chrome Mine in Limpopo, South Africa ...... 38 2.5.5 Hybrid energy projects in Africa ...... 39 2.5.6 Mini-grid projects in Africa ...... 40 2.5.7 Case study: Grid parity analysis of a PV- BESS hybrid ...... 46 2.5.8 Global view on Wind energy ...... 46 2.5.9 Case Study: World’s biggest lithium-ion battery in South Australia...... 47 2.6 South Africa’s solar profile ...... 48 2.6.1 Solar PV energy transfer methods ...... 50 2.6.2 PV-direct system (Grid-tied) ...... 51 2.6.3 Off-grid system ...... 52 2.6.4 Grid-tied systems with battery back-up ...... 53 2.6.5 Grid-tied system without battery back-up ...... 53 2.6.6 Global Horizontal Irradiance (GHI) & Direct Normal Irradiance (DNI) systems .... 53 2.6.7 Solar PV system model ...... 54 2.6.8 Solar Capacity factor ...... 55 2.7 Solar Energy Storage ...... 56 2.7.1 Lithium-ion battery storage system ...... 57 2.7.2 Flow batteries ...... 59 2.8 Wind Energy...... 62 2.8.1 Wind turbine ...... 63 2.8.2 Power Curve ...... 64 2.8.3 Weibull curve distribution ...... 65 2.8.4 Wind measurement ...... 65 2.8.5 Capacity factor vs Efficiency ...... 66 2.8.6 South Africa’s wind profile ...... 67 2.9 Hybrid-energy system ...... 68 2.9.1 The Hybrid-Energy Model ...... 72 2.9.2 Solar PV in Hybrid model ...... 72 2.9.3 Small-scale wind turbines in a hybrid model ...... 72 2.9.4 Battery storage and Diesel generators in Hybrid models ...... 77 2.10 Off-grid systems ...... 77 2.10.1 Off-grid financing systems ...... 82 2.10.2 Willingness to Pay (WTP) for Off-grids...... 89 2.10.3 Demand and Supply estimation for Off-grids ...... 90 2.11 Stand-alone solutions ...... 90 2.12 Mini-grids / Micro-grids ...... 91

2.12.1 Community micro-grid vs Traditional micro-grid...... 95 2.12.2 Rural electrification in Africa ...... 97 2.13 Mini-grids compared to Solar-House systems (SHS) ...... 99 2.14 Smart-grids and Smart meters...... 101 2.15 Small-Scale Embedded Generation ...... 104 2.16 Rural Development opportunities...... 109 2.17 Electricity Tariffs and Prices ...... 111 2.17.1 South Africa’s electricity tariff from a global perspective ...... 112 2.17.2 South Africa’s tariffs compared to other African countries ...... 115 2.18 Levellised Cost of Energy (LCOE) ...... 115 2.18.1 Load Factor ...... 117 2.18.2 Demand Factor ...... 117 2.18.3 Capacity Factor ...... 118 2.19 Off-grid system sizing ...... 118 2.19.1 Hybrid type 1: ...... 120 2.19.2 Modelling the expected Solar PV power output ...... 120 2.19.3 Mini-grid sizing criteria...... 120 2.20 Summary ...... 122 CHAPTER THREE ...... 124 3.0 The Research Methodology ...... 124 3.1 Introduction ...... 124 3.2 Hypothesis 1 ...... 124 Hypothesis 1 ...... 124 3.2.1 Data Collection ...... 129 3.2.2 Data Analysis ...... 130 3.3 Hypothesis 2 ...... 130 Hypothesis 2 ...... 131 3.3.1 Criterion Categories ...... 132 3.3.2 Calculating the Percentage ratio ...... 133 3.3.3 Calculating the household energy consumption ...... 134 3.3.4 Calculating the energy demand ...... 134 3.3.5 Community selection ...... 134 3.3.6 Calculating the available renewable energy resources ...... 135 3.3.6 DREI Costing Tool ...... 135 3.4 Summary ...... 137 CHAPTER 4 ...... 138 4.0 RESULTS AND DISCUSSION ...... 138 4.1 Introduction ...... 138

4.2 Hypothesis 1 ...... 138 4.2.1 National Criteria ...... 138 4.2.2 Eastern Cape Selection criteria ...... 139 4.2.3 District Criteria ...... 142 4.3 Summary ...... 148 4.4 Kwa-Zulu Natal Selection criteria ...... 148 4.4.1 District Criteria ...... 149 Umzinyathi District ...... 149 iLembe District ...... 150 uThukela District ...... 151 Zululand District ...... 151 Harry Gwala District ...... 152 uMkhanyakude District ...... 153 4.4.2 Hypothesis 1 feedback...... 156 4.5 Hypothesis 2 ...... 156 4.5.1 Ntabankulu Municipal-resource profile ...... 157 4.5.2 Upper Blinkwater ...... 162 4.5.3 Comparing East London Load profile to Profiled villages ...... 163 4.5.4 Electricity consumption calculation based on Upper Blinkwater...... 165 4.5.5 Profiling of Mdini-community ...... 166 4.5.6 Profiling of Buwani community ...... 174 4.5.7 Profiling of Xopo community ...... 176 4.6 Obtained results ...... 178 4.6.1 Mdini Village results ...... 179 4.6.2 Buwani Village results ...... 184 4.6.3 Xopo Village results ...... 188 4.6.4 Summary of the results ...... 192 4.7 Off-grid Costing ...... 197 4.7.1 Xopo Community’s LCOE ...... 198 4.7.2 Buwani Community’s LCOE ...... 202 4.7.3 Mdini Community’s LCOE ...... 204 4.7.4 Summary ...... 207 4.8 Discussion ...... 209 CHAPTER 5 ...... 214 5.0 CONCLUSIONS AND RECOMMENDATIONS ...... 214 5.1 Introduction ...... 214 5.2 Conclusions ...... 214 5.3 Recommendations and Future work ...... 218

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References ...... 220 APPENDIX A: RESULTS ...... 237 APPENDIX B: PROVINCIAL ADDITONAL DATA...... 244 Existing Projects in Eastern Cape ...... 244 Projects according to Eskom, to be established in the East London Area...... 247

List of Figures FIGURE 1. 1 RESEARCH METHODOLOGY FLOW CHART ...... 5 FIGURE 1. 2 PROJECT PLAN FLOW CHART ...... 7 FIGURE 1. 3 FLOW AND STRUCTURE OF DISSERTATION ...... 8

FIGURE 2. 1 ACCESS TO ELECTRICITY, 1990 -2017 [16] 15 FIGURE 2. 2 ACCESS TO ELECTRICITY, 1992 -2017 [17] 16 FIGURE 2. 3 PLANNED POWER STATION CAPACITY 2027 [21] 18 FIGURE 2. 4 A COMPARATIVE STUDY OF LOAD-SHEDDING BETWEEN 2015 AND 2019 [24] 19 FIGURE 2. 5 MUNICIPALITIES THAT EARN OVER 40 PERCENT FROM ELECTRICITY [27] 20 FIGURE 2. 6 ELECTRICITY SOLD BY ESKOM (2015/2016) AND THE CONSUMER BASE [26] 21 FIGURE 2. 7 KEY STATS FOR SOUTH AFRICA, 1990-2016 [28] 22 FIGURE 2. 8 RELATIONSHIP BETWEEN GROWTH IN REAL GDP AND ELECTRICITY CONSUMPTION (GWH), 1997- 2016 [29] 23 FIGURE 2. 9 GLOBAL BOTTOM 20 COUNTRIES' GDP PER UNIT OF ENERGY [30] 24 FIGURE 2. 10 SOUTH AFRICAN GDP BETWEEN 2017-2019 [31] 25 FIGURE 2. 11 ELECTRICITY DEMAND FORECAST FOR 2017-2050 [18] 29 FIGURE 2. 12 MUNICIPAL BACKLOG IN ELECTRICITY SERVICES [45] 31 FIGURE 2. 13 ACCESS TO ELECTRICITY IN URBAN AREAS [44] 32 FIGURE 2. 14 POTENTIAL DISTRIBUTION OF JOBS WITHIN DIFFERENT RESOURCES [46] 33 FIGURE 2. 15 RENEWABLE ENERGY PROGRESSION (ESKOM, DOE) [24] 34 FIGURE 2. 16 MONTHLY ELECTRICITY OF SOUTH AFRICA'S WIND, SOLAR PV & CSP (2016) [52] 35 FIGURE 2. 17 SOLAR PV-WIND HYBRID AT HLULEKA NATURE RESERVE [55] 37 FIGURE 2. 18 LUCINGWENI 86 KW HYBRID MINI-GRID [55] 37 FIGURE 2. 19 THABAZIMBI CHROME MINE SUPPLEMENTED BY SOLAR PV’S 39 FIGURE 2. 20 COUNTRIES WITHOUT ACCESS TO ELECTRICITY 2014 [139] 41 FIGURE 2. 21 GRAPHIC DISPLAY OF COUNTRIES WITHOUT ACCESS TO ELECTRICITY [139] 42 FIGURE 2. 22 AFRICAN COUNTRIES WHICH NEED INVESTMENT FOR ACCESS TO ENERGY [141] 42 FIGURE 2. 23 MORE THAN HALF OF THE PEOPLE WHO LIVE WITHOUT ELECTRICITY ARE IN AFRICA [142] 43 FIGURE 2. 24 POWER CONSUMPTION PER CAPITA, ESPECIALLY IN SUB-SAHARAN AFRICA, REMAINS LOW. [72]) 45 FIGURE 2. 25 GLOBAL WEIGHTED AVERAGE TOTAL INSTALLED COSTS, CAPACITY FACTORS AND LCOE FOR ONSHORE WIND (2010-2017) [100] 47 FIGURE 2. 26 SOLAR RADIATION RECEIVED ON HORIZONTAL SURFACE IN SOUTH AFRICA [84] [85] 50 FIGURE 2. 27 SOLAR POWER PLANT TECHNOLOGIES 51 FIGURE 2. 28 PV DIRECT SYSTEM 52 FIGURE 2. 29 GHI VS. DHI AND THEIR TECHNOLOGIES 54 FIGURE 2. 30 ELECTRICITY DEMAND IN SUB-SAHARAN AFRICA [98] 58 FIGURE 2. 31 MARKET SEGMENT IN AFRICA FOR GRID AND OFF-GRID CONNECTION [15] 59 FIGURE 2. 32 FLOW BATTERY 60 FIGURE 2. 33 VANADIUM IN A CIRCULAR ECONOMY [102] 61 FIGURE 2. 34 WIND POWER CURVE [61] 65

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FIGURE 2. 35 HIGH RESOLUTION WIND RESOURCE MAP OF THE WASA DOMAIN. (MEAN SPEED (M/S)) AT 100 M AGL IN A GRID SPACING OF 250 M [119] 68 FIGURE 2. 36 HYBRID ENERGY PROCESSES 69 FIGURE 2. 37 OFF-GRID HYBRID SYSTEMS SUPPLIED BY SOLAR PV, WIND, BATTERIES AND BACK-UP GENERATOR [79] 70 FIGURE 2. 38 HYBRID SYSTEM TECHNICAL OVERVIEW [81] 71 FIGURE 2. 39 RURAL SITE WIND TURBINE [86] 73 FIGURE 2. 40 URBAN SITE WIND TURBINES [86] 74 FIGURE 2. 41 WORLD MARKET FORECAST 2020 [88] 75 FIGURE 2. 42 SMALL-SCALE TURBINES IEC CERTIFIED 2019. [91] 77 FIGURE 2. 43 CASE FOR OFF-GRID RENEWABLE ENERGY SOLUTIONS [105] 79 FIGURE 2. 44 ACCESS TO ELECTRICITY DOE, 2012 80 FIGURE 2. 45 OFF-GRID IMPLEMENTATION FLOW-CHART 82 FIGURE 2. 46 KEY PERFORMANCE INDICATORS OF MICRO-GRIDS DESIGNED WITHOUT DATA [150] [63] 84 FIGURE 2. 47 KEY PERFORMANCE OF MICRO-GRIDS DESIGNED WITH DATA [150] [63] 84 FIGURE 2. 48 OFF-GRID ENERGY ACCESS COMPANIES HAVE ABSORBED JUST SHY OF $1.7 BILLION IN DISCLOSED INVESTMENT 85 FIGURE 2. 49 SUB-SAHARAN AFRICA BY REGION, DISPLAYING THE POPULATION WITHOUT ACCESS TO ELECTRICITY (%) [122] 86 FIGURE 2. 50 SUB-SAHARAN AFRICA NEW ELECTRICITY GENERATION FOR UNIVERSAL ENERGY ACCESS (2018-2030) % OF ADDITIONAL TWH [122] 86 FIGURE 2. 51 RENEWABLE ENERGY SHARE IN TOTAL FINAL ENERGY CONSUMPTION, 1990 – 2015, [125] 87 FIGURE 2. 52 DIGITAL GRID ROADMAP IN SUB-SAHARAN AFRICA [126] 88 FIGURE 2. 53 CONTRAST BETWEEN MINI-GRID AND STAND-ALONE SOLUTIONS [131] 91 FIGURE 2. 54 RENEWABLE TECHNOLOGIES EXPANDING ELECTRICITY ACCESS [135] [167] 92 FIGURE 2. 55 TYPES OF MICRO-GRIDS [148] 95 FIGURE 2. 56 COMMUNITY MICRO-GRID KEY STAKEHOLDERS [172] 96 FIGURE 2. 57 EASE OF DOING BUSINESS INDEX FOR SOUTH AFRICA (2010-2019) [174] 98 FIGURE 2. 58 CUMULATIVE POPULATION GAINING ACCESS TO ELECTRICITY BY 2030 [175] 99 FIGURE 2. 59MINI-GRIDS AND STAND-ALONE SYSTEMS [155] 100 FIGURE 2. 60 THE CONVERGING ELEMENTS OF SMART GRIDS [166] 103 FIGURE 2. 61 OPPORTUNITIES FOR SMART-GRID INVESTMENT IN SUB-SAHARAN AFRICA [166] 104 FIGURE 2. 62 OFF-GRID SOLAR FINANCIERS ACROSS THE START-UP DEVELOPMENT CYCLE [177] 107 FIGURE 2. 63 EVOLUTION OF FRACTION OF POPULATION WITH ELECTRICITY ACCESS IN SUB SAHARAN AFRICA [201] 110 FIGURE 2. 64 TECHNO-ECONOMIC VIABILITY OF HYBRID PV-WIND-DIESEL-BATTERY STORAGE ENERGY SYSTEM [202] 111 FIGURE 2. 65 ELECTRICITY PRICES IN SELECTED COUNTRIES IN 2014 ($/KWH) [203] 113 FIGURE 2. 66 INTERNATIONAL ELECTRICITY PRICE SURVEY -2013/2014 SOUTH AFRICA WITH THE BIGGEST CHANGE [203] 114 FIGURE 2. 67 AFRICAN COUNTRIES' PRICES IN 2015/2016 [203] 115 FIGURE 2. 68 FLOW CHART OF SYSTEM SIZING [76] 119 FIGURE 2. 69 MINI-GRID MULTI-TIER FRAMEWORK FOR ACCESS TO HOUSEHOLDS [211] 121 FIGURE 2. 70 ENERGY CONSUMPTION 122

FIGURE 3. 1 OFF-GRID SYSTEMS FLOWCHART 125 FIGURE 3. 2 HYPOTHESIS 1 CRITERION. 127 FIGURE 3. 3 HYPOTHESIS 2 CRITERION 127 FIGURE 3. 4 DATA COLLECTION FLOWCHART 130 FIGURE 3. 5 DATA ANALYSIS FLOWCHART 131 FIGURE 3. 6 METHODOLOGY FLOWCHART 137

FIGURE 4. 1 NATIONAL ANALYSIS OF HOUSEHOLDS WITHOUT ACCESS TO ELECTRICITY ...... 138 FIGURE 4. 2 EASTERN CAPE RANKING OF DISTRICTS ...... 141 FIGURE 4. 3 NUMBER OF HOUSEHOLDS WITHOUT ELECTRICITY PER DISTRICT IN EASTERN CAPE ...... 142 FIGURE 4. 4 ALFRED NZO DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY ...... 143 FIGURE 4. 5 AMATHOLE DISTRICTS WITHOUT ACCESS TO ELECTRICITY ...... 144 FIGURE 4. 6 JOE GQABI DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY ...... 145 FIGURE 4. 7 O.R TAMBO DISTRICT PERCENTAGE WITHOUT ELECTRICITY ...... 146 FIGURE 4. 8 RANKING OF MUNICIPALITIES OF EASTERN CAPE ...... 147 FIGURE 4. 9 RANKING THE MUNICIPALITIES WITH GREATER THAN 30% NEED FOR ENERGY ...... 148 FIGURE 4. 10 UMZINYATHI DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY ...... 150 FIGURE 4. 11 ILEMBE DISTRICT PERCENTAGE WITHOUT ELECTRICITY ...... 150 FIGURE 4. 12 UTHUKELA DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY ...... 151 FIGURE 4. 13 ZULULAND DISTRICT PERCENTAGE WITHOUT ELECTRICITY ...... 152 FIGURE 4. 14 HARRY GWALA DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY...... 152 FIGURE 4. 15 UMKHANYAKUDE DISTRICT PERCENTAGE WITHOUT ACCESS TO ELECTRICITY ...... 153 FIGURE 4. 16 RANKING OF MUNICIPALITIES PERCENTAGE WITHOUT ACCESS TO ELECTRICITY...... 154 FIGURE 4. 17 RANKING MUNICIPALITIES WITH NEED GREATER THAN 30% ...... 154 FIGURE 4. 18 TEMPERATURE PROFILE OF NTABANKULU [213] ...... 159 FIGURE 4. 19 WIND SPEED PROFILE OF NTABANKULU MUNICIPALITY [213] ...... 160 FIGURE 4. 20 UV INDEX PROFILE IN NTABANKULU MUNICIPALITY [213] ...... 161 FIGURE 4. 21 SUN HOURS AND SUN DAYS IN NTABANKULU MUNICIPALITY [213] ...... 162 FIGURE 4. 22 ELECTRIC ENERGY APPLICATION SCENARIOS [54] ...... 163 FIGURE 4. 23 UPPER-BLINKWATER PEAK DEMAND FORECAST - DAILY PROFILE ...... 164 FIGURE 4. 24 UPPER-BLINKWATER DATA- POWER DEMAND PROFILE (2009-2010) [216] ...... 165 FIGURE 4. 25 CURRENT EASTERN CAPE PROVINCE NETWORK DIAGRAM [21] ...... 167 FIGURE 4. 26 EASTERN CAPE PROVINCE LOAD FORECAST [21] ...... 168 FIGURE 4. 27 NTABANKULU MUNICIPALITY [217] ...... 169 FIGURE 4. 28 ELECTRICAL GRID LINES AND REGIONAL ROUTES...... 170 FIGURE 4. 29 DETAILED VILLAGES IN NTABANKULU MUNICIPALITY ...... 171 FIGURE 4. 30 NTABANKULU MUNICIPALITY, WARD 1 [217] ...... 172 FIGURE 4. 31 MDINI VILLAGE AERIAL VIEW...... 173 FIGURE 4. 32 MDINI VILLAGE DEMARCATION AND TOPOGRAPHY...... 174 FIGURE 4. 33 BUWANI DEMARCATION AND TOPOGRAPHY ...... 175 FIGURE 4. 34 BUWANI ARIEL VIEW AND COORDINATES ...... 176 FIGURE 4. 35 XOPO DEMARCATION AND TOPOGRAPHY...... 177 FIGURE 4. 36 XOPO ARIAL VIEW AND COORDINATES ...... 178 FIGURE 4. 37 MDINI TYPICAL SUMMER DAY - SOLAR PV ...... 179 FIGURE 4. 38 MDINI TYPICAL WINTER DAY - SOLAR PV ...... 180 FIGURE 4. 39 MDINI TYPICAL WINTER DAY - WIND ...... 181 FIGURE 4. 40 MDINI TYPICAL SUMMER DAY - WIND ...... 182 FIGURE 4. 41 MDINI TYPICAL WINTER DAY (SUPPLY VS DEMAND) ...... 183 FIGURE 4. 42 MDINI DEMAND PROFILE (2009-2010) ...... 184 FIGURE 4. 43 BUWANI TYPICAL SUMMER DAY – WIND ...... 185 FIGURE 4. 44 BUWANI TYPICAL WINTER DAY - WIND ...... 185 FIGURE 4. 45 BUWANI TYPICAL SUMMER DAY - SOLAR PV ...... 186 FIGURE 4. 46 BUWANI TYPICAL WINTER DAY - SOLAR PV ...... 187 FIGURE 4. 47 BUWANI TYPICAL WINTER DAY (SUPPLY VS DEMAND) ...... 187 FIGURE 4. 48 BUWANI DEMAND PROFILE ...... 188 FIGURE 4. 49 XOPO TYPICAL SUMMER DAY - SOLAR PV ...... 189 FIGURE 4. 50 XOPO TYPICAL WINTER DAY - SOLAR PV ...... 190

FIGURE 4. 51 XOPO TYPICAL WINTER DAY – WIND ...... 190 FIGURE 4. 52 XOPO TYPICAL SUMMER DAY - WIND ...... 191 FIGURE 4. 53 XOPO TYPICAL DAY (SUPPLY VS DEMAND) ...... 191 FIGURE 4. 54 XOPO ENERGY PROFILE ...... 192 FIGURE 4. 55 COMMUNITY RESOURCE AVAILABILITY ANALYSIS ...... 194 FIGURE 4. 56 COMMUNITY ENERGY DEMAND PROFILE ...... 195 FIGURE 4. 57 LOAD FACTOR VS DEMAND FACTOR ...... 196 FIGURE 4. 58 ENERGY DEMAND ANALYSIS...... 196 FIGURE 4. 59 LEVELIZED COST OF ENERGY OF XOPO...... 199 FIGURE 4. 60 LEVELIZED COST OF ENERGY OF XOPO, INCLUDING DIESEL GENERATOR ...... 199 FIGURE 4. 61 LEVELIZED COST OF ENERGY OF BUWANI ...... 202 FIGURE 4. 62 LEVELIZED COST OF ENERGY OF BUWANI, INCLUDING DIESEL GENERATOR ...... 202 FIGURE 4. 63 LEVELIZED COST OF ENERGY OF MDINI ...... 205 FIGURE 4. 64 LEVELIZED COST OF ENERGY OF MDINI, INCLUDING DIESEL GENERATOR ...... 205 FIGURE 4. 65 SUMMARY OF LCOE OF ANALYSED COMMUNITIES ...... 208

List of Tables TABLE 2. 1 GINI COEFFICIENT RANKINGS [36] ...... 26 TABLE 2. 2 PROPOSED UPDATED PLAN FOR THE 2019-2030 (MW) [37] ...... 27 TABLE 2. 3 ACCESS TO ELECTRICITY, BASED ON THE TOTAL POPULATION PERCENTAGE [44] ...... 30 TABLE 2. 4 COMPARING CAPACITY FACTORS OF DIFFERENT ENERGY SOURCES [91] ...... 56 TABLE 2. 5 COMPARISON OF SMALL- AND LARGE-SCALE WIND TURBINES [133] ...... 75 TABLE 2. 6 RATING POWERFUL SMALL-SCALE WIND TURBINES [134] ...... 76 TABLE 2. 7 NUMBER OF CONSUMER UNITS RECEIVING ELECTRICITY AND FREE BASIC ELECTRICITY SERVICES FROM MUNICIPALITIES BETWEEN 2012 AND 2013...... 81 TABLE 2. 8 COMMUNITY MICRO-GRID VS TRADITIONAL MICRO-GRID [172] ...... 97 TABLE 2. 9 COMPARISON OF OPERATIONS DONE BY MINI-GRID VS. SHS [177] [155] ...... 100 TABLE 2. 10 COMMERCIAL BANKS ON SOLAR-ENERGY PROJECT FINANCING STRUCTURE [193] [192]...... 108 TABLE 2. 11 ESKOM ELECTRICITY TARIFFS FROM APRIL 2016 [26]...... 111 TABLE 2. 12 TYPES OF HYBRIDS ...... 119

TABLE 4. 1 NATIONAL CRITERIA AND PROVINCIAL CRITERIA ...... 139 TABLE 4. 2 EASTERN CAPE DISTRICT CRITERIA ...... 140 TABLE 4. 3 KWA-ZULU NATAL DISTRICT CRITERIA ...... 149 TABLE 4. 4 DISTRICT CRITERIA ...... 155 TABLE 4. 5 MUNICIPALITY CRITERIA ...... 155 TABLE 4. 6 TOPOGRAPHICAL CRITERIA ...... 157 TABLE 4. 7 BOUNDARY LINE OF NTABANKULU LOCAL MUNICIPALITY...... ERROR! BOOKMARK NOT DEFINED. TABLE 4. 8 TYPICAL HOUSEHOLD POWER USAGE...... 166 TABLE 4. 9 POTENTIAL RESOURCE ANALYSIS OF COMMUNITIES ...... 193 TABLE 4. 10 WARD CRITERIA ...... 197 TABLE 4. 11 DEMAND PROJECTIONS BASED ON TARIFF PRICE ...... 198 TABLE 4. 12 TECHNICAL SUMMARY OF XOPO ANALYSIS...... 200 TABLE 4. 13 COST SUMMARY OF XOPO OFF-GRID SYSTEM ...... 201 TABLE 4. 14 TECHNICAL SUMMARY OF BUWANI OFF-GRID SYSTEM ...... 203 TABLE 4. 15 COST SUMMARY OF BUWANI OFF-GRID SYSTEM ...... 204 TABLE 4. 16 TECHNICAL SUMMARY OF A MDINI OFF-GRID SYSTEM ...... 206 TABLE 4. 17 COST SUMMARY OF MDINI OFF-GRID SYSTEM ...... 207 TABLE 4. 18 COMMUNITY CRITERIA...... 208

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ACRONYMS AEP Annual Energy Production ATM Automated Teller Machine BESS Battery Energy Storage System CAPEX Capital Expenditure COE Cost of Energy CSP Concentrated Solar Power DBSA Development Bank of Southern Africa DC Direct Current DNI Direct Normal Irradiation DoE Department of Energy DREI Derisking Renewable Energy Investment ETPSG European Technology Platform Smart Grid FIRST Facility for Investment in Renewable Small Transactions GDP Gross Domestic Product GHI Global Horizontal Irradiation G.I.Z Gesellschaft f r Internationale usammenar eit HOMER Hybrid Optimization Model for Electric Renewables IEA International Energy Agency IPP Independent Power Producer IRENA International Resource Energy National Agency IRP Integrated Resource Plan LED Light emitting diode LCOE Levelized Cost of Energy Mtoe Million tonne NERSA National Energy Regulator of South Africa NPC Net Present Cost OPEX Operating Expenditure OCGT Open Cycle Gas Turbines PAYG Pay-As-You-Go PV Photovoltaic REDs Regional Electricity Distributors RBS Revised Balanced Scenario RMB Rand Merchant Bank SANEDI South African National Energy Development Institute SHS Solar House System SMME’s Small Medium Micro Enterprises SPIPP Small Project Independent Power Producers SSA Sub-Saharan Africa SSEG Small Scale Embedded Generation USD United States Dollar UV Ultra-Violet VRFB Vanadium Redox Flow battery WACC Weighted Average Cost of Capital WTP Willingness to Pay

Nomenclature

℃ Degrees centigrade

W Watts kW Kilowatts

GW Gigawatts

Average global solar irradiation of site

Temperature-correction factor

Solar radiation on tilted plane module

Air Density

Area swept by rotor blades

Velocity of wind

Coefficient of performance

Weibull distribution

Weibull-shaped parameter

Weibull -scale parameter

Power

Lifetime of project

Weighted average cost of capital

Annual Energy Production in year t

Fuel expenditure in year t

Operation and maintenance cost

Capital expenditure in year t

Period of operation (hrs/day)

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CHAPTER ONE 1.0 Introduction The overall aim of this dissertation is to outline the process of determining the village or community of need for off-grid electrification in South Africa. This will include the analysis of local renewable resources and the topography and remoteness of the village. South

Africa, although being one of the leading electrified countries in Africa, still has challenges with electrifying its rural communities. Thus, the model of rural off-grid hybrid energy solutions will be the focus of this research, in addition to the possible feasibility of the implementation of such projects.

1.1 Background South Africa’s economy is heavily dependent on its State-owned utility, Eskom, to generate electricity. During the 2019 state of the nation address, President Cyril

Ramaphosa, announced that the country’s utility company is struggling; and if changes are not rapidly implemented, the default on its R 419 Billion debt could cripple the country’s economy [1] [2]. This then is the precursor to the President’s executive order to unbundle

Eskom into three separate entities: Generation, Transmission and Distribution – during

(and even after) such time, certain remote communities in South Africa would have to wait to be connected to the electricity grid.

This announcement has initiated a process that could take many years, despite the 14% of national population not yet being electrified [2]. Globally, off-grid solutions have become the most suitable form of aid for the generation of electricity to such remote communities and municipalities. This thesis focuses on developing a method to determine the communities that could benefit the most from a hybrid energy system to be implemented as an off-grid solution.

1.2 Motivation

One of the key challenges that arise from implementing electricity in different locations in

South Africa is that the farther away one moves from the urban areas, the more difficult it becomes – both financially and practically – for that community to acquire electricity. The proposed investigation begins by quantifying how many of such communities still exist in

South Africa. This then leads into the cost-benefit analysis of implementing a mini-grid, off- grid hybrid energy system for specifically chosen examples of such communities.

Using the Council for Scientific and Industrial Research (CSIR) Upper Blinkwater energy demand study dataset, a high level off-grid energy supply system was employed to model the hybrid energy systems that formed the basis of the renewable energy off-grid solution.

The hybrid energy system focuses on South Africa’s a undant renewa le resources, namely wind and solar energy. The model targets the relevant designated regions, which have no access to electricity; and it outlines the supply process, the Levelized Cost of

Energy (LCOE), the energy demand; and it finally looks at the investment potential (private or municipal).

1.3 Problem statement

The aim of this research study is to design a model, which can be implemented in various provinces, in order to reduce the dependency on coal and fossil fuels; and to increase the implementation of renewable energy in the form of hybrid-energy farms. The need to develop hybrid-energy farms is also recognised by the Government, as indicated in the

2010-2030 integrated resource plan. This research investigates the implementation of a grand model, which can generate renewable energy at the same rate as the conventional coal-powered station; it also focuses on the storage of such energy and the distribution methods to provide a steady output.

Hybrid-energy systems will become a dependable way forward for areas and regions, which cannot be sustained by a single source of renewable energy. The model considers wind energy, or solar PV, as the primary forms of energy sources depending on the most efficient off-grid hybrid model, which is the aim of the research.

1.4 Aim of the Research

The aim of this research is to determine the off-grid possibilities that can be explored in

South African rural communities. These are communities without grid connection; and they are often located in remote and rural areas of the country. This dissertation develops a method of identifying and connecting those communities that are assessed through a set of criteria that will justify the best hybrid energy system for that community.

The system also outlines the critical points that justify the implementation, and in turn, the size of the hybrid-energy system. The hybrid energy system will be focused on using the available renewable energy in and around a community, combining both wind and solar, together with a storage battery energy storage system and a diesel generator for back-up.

1.5 Research Objectives The following are the objectives of this research study:

. To optimize and simplify the design of an off-grid system.

. To model a hybrid system for rural communities in South Africa.

. As part of the study, the financial structuring of hybrid and off-grid projects was

investigated, both to determine and to analyse the effects of a growing favour

towards off-grid systems as a sustainable solution for the power needs of the

country.

. To quantify the need for off-grid electrification in South Africa.

1.6 Hypothesis Statements

Hypothesis 1 - There are a number of Municipalities in SA that would not be connected to the grid within the next 5-10 years, thus warranting off-grid solutions.

The basis of investigating the first hypothesis is to determine whether the number of existing municipalities and communities in South Africa without electricity can justify the need for off-grid solutions.

Determining whether there are enough municipalities without access to electricity in South

Africa is a primary issue. This can be determined by analysing the number of households that are without access to electricity in South Africa from each province; and then proceeding to highlight those provinces that demonstrate the greatest number of households without access to electricity; this can be done by using a reasonable cut-off point of > 15% of households that are currently without any access to electricity.

The next step is to break down each province into Districts and Municipalities, and finally into actual communities that need electrical connection. Each step to higher resolution uses a set of selection criteria. The aggregation of these data for South Africa will provide evidence to resolve hypothesis 1.

The flow chart in Figure 1.1 displays the different criteria, which filtrate from national level to community and ward level. Once the community has been identified, the selection criteria can be ascertained, which determine whether the community can have an off-grid system implemented. The percentage of households without electricity is the most significant determinant throughout the study; since it provides critical information about the state of the province, the district and the municipality, as well as the ward.

National Criteria

Provincial Criteria

District Criteria

Municipal Criteria

Community Criteria

Ward Criteria

Figure 1. 1 Research Methodology flow chart

Hypothesis 2: If Hypothesis 1 is satisfactorily proven true, can an off-grid community be connected easily to an off-grid supply?

Using the www.renewables.ninja dataset [3] and taking the most suitable off-grid communities to implement a simplified energy supply and mini-grid costing model is developed, to incentivise the investment in off-grid systems. Hypothesis 2 must also prove

whether rural off-grid communities are willing to contribute financially to implement such projects, based on the analysis of similar instances and communities. Furthermore, it is necessary to outline what would be the socio-economic effects of implementing such a system.

1.7 Significance of research The significance of this research lies largely in the feasibility of its implementation. The rural off-grid hybrid energy system model would lay the ground-work for the potential of hybrid systems for remote and often rural areas, which have no access to electricity. Any questions of renewable energy being unsustainable or unreliable would be demolished by also optimizing the methods of achieving energy distribution.

1. 8 Project plan

Optimizing the model entails the following:

 Determining the current statistics regarding the demand for electricity in South

Africa per province, per month, and annually.

 Analyzing the demand profile, taking note of peaks and troughs, and their causes;

and also how they can be managed.

 From the demand profile, determine the annual solar PV energy supply.

 Then determine the wind-energy supply.

 Measure the hybrid-energy profile, and how to store the energy over time, and how

to deliver a constant energy output.

 Optimizing the rural off-grid hybrid system to achieve an efficient operation. Analyse

for losses in sending energy to the closest grid.

Model the supply for an off- grid community in South Africa through hybrid energy

Problem Statement

Aim of the project Objectives of the project

Literature review, Experimental design, and Modelling of system

Mechanical design

Data acquisition and Modelling system test results analysis evaluation

Model the operating system to optimize the energy generation system.

Output/ Solutions and recommendations

Figure 1. 2 Project plan flow chart

1. 9 Summary

This chapter has covered the introduction of the research investigation and the background information required to understand what this project entails. The research is focused on modelling the rural off-grid hybrid-energy system and its implementation for a community with no less than 50 households, bringing enlightenment to the possibilities around off-grids in South Africa and in Africa in general. The following chapter will present an intensive literature review, introducing and discussing different concepts, which contribute to the understanding of off-grid systems.

Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 •Introduction •Literature •Research •Results and •Conclusions and Survey Methodology Discussion Recommendation

Figure 1. 3 Flow and structure of dissertation

CHAPTER TWO

2 Literature Survey

2.1 Introduction

This chapter discusses the overview of the literature survey, which extensively investigates renewable energy and hybrid-energy solutions and their relevance to off-grid communities in South Africa. The chapter begins with an overview of renewable energy and the different types of renewable energy sources, before looking into the state of renewable energy in

South Africa, compared to the rest of the world. Furthermore, the literature survey delves into the critical review of supporting research in rural hybrid off-grid systems.

2.2 Renewable energy

Renewa le energy is the inexhausti le, infinite energy that is derived from the earth’s resources, such as wind and sunlight [4]. It is often referred to as clean energy, unlike the use of fossil fuels, which emit greenhouse gases, resulting in contaminated and polluted air, which harms the environment and affects climate change [5] [6].

2.2.1 Types of renewable energy There are seven types of renewable energy currently in use today, and these are described as follows, with the first four being the most common [4]:

 Solar;  Wind;  Biomass;  Hydro-electric;  Geothermal;  Ocean; and  Hydrogen.

The use of these forms of energy will be further described in detail as the hybrid-energy systems. These systems are formed from the combination of two or more of these forms of energy, as well as the option to include storage and a fossil-fuel energy source to supplement the renewable energy sources. These are the six reasons why renewable energy is the future of electrical generation in South Africa [7]:

1. Renewable energy is currently the cheapest source of electricity generation to

install.

2. The projected future cost of electrical generation continues to fall as the technology

improves and is scaled.

3. Renewable energy will create new jobs for the modern economy.

4. Their use means a great reduction in production and the use of

fossil fuels and water consumption.

5. The risk of load shedding is reduced.

6. There is a direct community benefit where the electricity is generated.

2.2.2 Global standings of renewable energy

The current iteration of the Integrated Resource Plan (IRP) for South Africa, initiated by the

Department of Energy (DoE), after a first round of public participation in June 2010, led to the Revised Balanced Scenario (RBS) that was published in October 2010. It laid out the proposed generation of a newly built fleet for South Africa for the period 2010 to 2030. This scenario was derived, based on the cost-optimal solution for new build options

(considering the direct costs of building new power plants), which was then “ alanced”, in accordance with qualitative measures, such as local job creation. In addition to all the existing and committed power plants, the RBS included a nuclear fleet of 9.6 GW; 6.3 GW of coal; 11.4 GW of renewable energy; and 11.0 GW of other generated sources.

The continent of Asia has led the renewable energy capacity deployment throughout the world, with two major contributors from China and India. Between 2008 and 2017, the continent experienced exponential growth in the off-grid sector, as the capacity grew from

1.3 GW to 4.3 GW [8].

China, being the next global super-power, has become the front-runner in Solar PV renewable energy generation. The country has become the largest manufacturer and producer of Solar PV panels, their aluminium mounting stands, as well as other essential equipment, like inverters and energy-storage facilities. As a country, their plans include the closing down of polluting and inefficient units, controlling new constructions and upgrading efficiency and emissions of the performance of existing stations.

In 2016, they experienced an 82% growth in Solar PV installation, to achieve 34.54 GW

[9]. India, on the other hand, has also seen tremendous growth focused on its agricultural sector, with solar pumps becoming a primary form of irrigation and drinking water [8].

Germany, the Netherlands and several other countries, including the United States of

America, are investing heavily in solar and wind, as primary forms of renewable energy.

The hybrid energy system may not alleviate the use of fossil fuels today; but the process of developing sustainable renewable energy sources should begin today.

In order for South Africa, a third world country, with considerable potential and a beacon of light in Africa, to achieve any substantial growth in renewable energy, a host of policies and regulations, together with various cost-saving methods, will need to come into the fold, in order to stream-line this type of growth, and to show its economic influence. For this reason, the hybrid-energy systems have become more appealing and reliable throughout the country and continent, in order to integrate the different forms of energy available.

Since South Africa has desert lands in the Karoo and North Western parts of the country,

with rain-forest type terrain in Johannesburg and tropical coastal weather in the Northern

Eastern parts of Kwa-Zulu Natal, the country’s diversified lands make it prime for hy rid- energy systems. Hybrid-energy systems of renewable energy can be implemented throughout the country, in different forms and sizes, in order to achieve the desired energy output.

Sub-Saharan Africa (SSA) is mostly undeveloped and rural, with most people on the continent living below the global minimum standard of living. With regard to the African continent’s growing off-grid renewable energy, the growth in capacity has ranged from 231

MW to 1.2 GW between 2008 and 2017 [8]. The lack of development, economic growth, political cohesion and infrastructure has held the continent back from competitive growth in areas, such as renewable energy, in which it has so much potential.

The lack of rural households electrified affects rural migration and over-population of the major cities. This over-population creates concentrated areas of peak load demand [10].

According to the CNBC Africa report [10], which states that in order to reduce urban migration future investment in the continent should be geared towards rural development; as this would see an improvement in the standard of living. The socio-economic inclination in rural and remote areas in SSA should initiate-small-to-medium economic hubs, where villagers can buy and sell amongst themselves. Agricultural work is made easier with machinery. Furthermore, education also benefits from electricity, as well as the daily standard of living for all.

The improvements that off-grid electrification can bring to small and medium communities in rural and remote areas, remain an untapped market [10] [11]. According to the World

Bank [12], Africa’s electrification challenges lie in the failure of electricity tariffs being feasible to ensure profitability in the process of delivery, as well as the lack of proper

governance of national utility companies in most countries. The World Bank also states that investment in mini-grids in SSA is becoming appealing; since USD 50 billion is required year-on-year to achieve universal electrification in SA by 2030 through traditional grid expansion; and this would still not improve the entire value-chain, as far as technological innovation, strategies and regulatory innovation are concerned [12].

For these reasons, mini-grids and off-grid systems bring more than electricity to a community; they bring the opportunity for technological innovation, which could accelerate rural development throughout the continent.

Looking at East Africa, Ethiopia has begun utilising their wind resource by building grid- connected wind parks all around the country. The country has also begun to look into off- grids; as they have discovered the use of e-commerce pay-as-you-go repayment systems

[13]. These payment systems make technological innovation critical and integral in the growth of interconnected sectors, such as telecommunications, which form the bedrock for a rural economy to emerge.

In Bangladesh, a case study was done to determine the feasibility of stand-alone PV/Wind/

Battery-Hybrid energy systems in rural areas for a community of about 220 people living in

51 households. The community had no access to electricity; and it was fully dependent on kerosene and candles for light. The feasibility study looked into the solar and wind profile of the selected location, in order to determine the capacity to meet the load demand from the community by using renewable energy sources, and a diesel generator to supplement the basic load [14].

Essentially, there is a global shift towards renewable energy; and the first-world countries are firmly in the lead, with multi-national companies finding their feet in SSA, and South

Africa in particular. This research will narrow down the options for an investor, regarding

an off-grid system being deployed in South Africa. The country’s landscape, renewa le energy resources, and most importantly, the vast number of households in need of access to electricity, will all be factored into the investment profile of a community.

2.3 An Overview of Electricity in South Africa

There are several challenges facing South Africa’s energy industry and its power utility,

Eskom. The continuous rate of load-shedding in South Africa from 2015 has staggered the economic growth of the country [2], while billions of Rands have been spent on diesel as a substitute for Open-cycle Gas Turbines. Eskom is also operating below its spinning reserve margin of 15 per cent (6000 MW) at 2000 MW, currently [15]. This has been due to resistance in coal-market depreciation, while renewable energy is being sourced; and

Eskom’s power stations are faulty and una le to perform optimally – particularly Medupi and Kusile, which means that the heavily relied upon coal-fired stations are not reliable enough to meet the growing electrical needs of the country.

The challenge that Eskom continues to face is the issue of electrifying households that are frequently in rural areas. The use of wind and solar energy, together with the option of battery storage and diesel generator, is well suited for off-grid communities in South Africa; and as the number of households grows without access to electricity, the need for electrification grows with it. The cost of electrifying a remote community is greater than that of those communities, which surround a city. This challenge can be resolved through an off-grid solution, funded by any number of willing participants.

The model being investigated in this research requires the analysis of the different aspects, which affect and influence the electrical tariff cost, and how that model can be

translated into a business model for investment, and in turn, could achieve the actual project implementation.

The dissertation also investigates how the off-grid solution might be used to sell some of its generated power to Eskom, or the municipalities, or a neighbouring community. Figure

2.1 displays the graphical distribution of the population with access to electricity, relative to the rate of population growth and the population without any access to electricity, over a period of 27 years, from 1990 to 2017 in South Africa. Although most of the population has been electrified, there remain millions more without any access to electricity in their households.

Figure 2. 1 Access to Electricity, 1990 -2017 [16]

Figure 2.2 displays the electrical power consumption in South Africa between 1992 and

2017. The Figure also demonstrates that the lowest power consumption was in 2013; while the peak consumption year was in 2014 [17]. Between 2006 and 2011, the electricity consumption rate was steadily growing in a linear projection, before experiencing volatile consumption rates, with peaks and troughs that heavily affected the economic environment negatively.

Figure 2. 2 Access to Electricity, 1992 -2017 [17]

The IRP from 2010-2030 [18] assumed and averaged the existing Eskom fleet plant performance of 86%; however, the actual performance has, in the recent past, declined to less than 70%. Eskom has since adopted a new operation and a maintenance strategy, which has seen this performance improve significantly; and this is reflected in the Eskom

submission for the IRP update assumptions dated January 2016. Electricity distribution has also declined by over 4% since 2017, which clearly demonstrates the growing demand that this utility company is facing [19].

South Africa’s growing population and economy has resulted in a growing demand for electrical power. A secure supply of electricity, at an affordable cost is essential; if the economy is to sustain faster rates of investment and economic growth, as well as to provide access to electricity for all [1].

South Africa is energy-intensive; and it needs to find ways to meet these growing demands for energy, without compromising the economic growth. This need can be fulfilled by investment in renewable energy, which would push the economy forward in every way.

The generation allocation and planned capacity network for 2027 is displayed in Figure

2.3. A key point of note is the cluster of coal power stations in Mpumalanga, which has resulted in the Emalahleni region becoming a greenhouse gas-emission concentration point [20]. This is after the country’s energy sector had increased the intensity of its emissions by 5 per cent, between 2012 and 2017 [20].

Figure 2. 3 Planned power station capacity 2027 [21] According to Goldberg [22], the devastating impact of load shedding in 2015, has resulted in a R13.72 billion rand lost in revenue during the first half of the year, while a conservative estimate of R 716 million was invested in back-up generators during that period.

Eskom has been rolling out load shedding throughout the country, most recently from

December 2018, and most noticeably in the first quarter of 2019. The effects of load shedding have been devastating for Small and Medium Micro-Enterprises (SMMEs); and the economy at large, which has further made the need for off-grid solutions even more urgent [2] [23].

When comparing the extent of load shedding from 2015, when it was at its most prolonged, to the first quarter of 2019, where it was at its most intense during stage 4, where the energy demand reached 138GWh. The knock-on effect of load shedding on the

South African economy has made alternative sources of energy a greater concern for business and the general public. Figure 2.4 displays the comparison in the type of load- shedding etween 2015 and 2019’s first quarter [24]. The figure demonstrates the growing energy demand versus supply gap observed in the market, and these gaps will need renewable energy sources to fill them swiftly.

Figure 2. 4 A comparative study of load-shedding between 2015 and 2019 [24]

After the Presidential election of 2019, the government decided that Eskom would need to actively focus its resources on producing sustainable energy and moving away from fossil fuels; and from coal, in particular [25]. The minister of Trade and Industry, Mr Ebrahim

Patel said that Eskom needs to embrace renewable sources, in order to cover its debt.

This approach would see the utility company looking into diverse methods of distributing and generating electricity, in order to eliminate those remote locations relying on wind and solar primarily; as well as looking into policies around pricing and tariffs [25].

The Figure 2.5 shows some of the top achievers, earning over 40 per cent from electricity sales. These municipalities demonstrate their dependence on a grid-connected electrical

supply. A total of 137 municipalities to date have purchased 40 per cent of the generated electricity from Eskom, in order to sell this power to their end-users [26].

Figure 2. 5 Municipalities that earn over 40 percent from electricity [27]

In 2015/2016, Eskom had a peak demand of 34 481 MW, while transmitting and distributing 214 487 GWh to its South African consumers. A slow economy resulted in a less than one per cent growth in the energy demand. Figure 2.6 displays the breakdown of the electricity Eskom sold between 2015 and 2016, as well as the different sectors, which purchased and used that electricity.

Figure 2. 6 Electricity sold by Eskom (2015/2016) and the consumer base [26]

Through the analysis of South Africa’s energy statistics, the following highlights can e observed and presented, as in Figure 2.7 [27]:

 Between 1990 and 2016, the population in South Africa grew by almost 20 million

people, which resulted in an almost linear 0.74 population growth per annum.

 The energy production over the 26-year period resulted in 1.86 Million tonnes (Mt)

of coal consumption growth per annum.

 The electricity consumption was 2.67 Terra Watt hours (TWh) over the 26-year

period.

 The carbon dioxide emission during the 26-year period grew by 6.56 Metric tonnes

(Mt) per annum.

Figure 2. 7 Key stats for South Africa, 1990-2016 [28]

From these statistics, the demonstration of South Africa as a growing economy post- apartheid, can be seen. Three things can be noted: that (i) the relationship between energy consumption and a growing economy is an interdependent and crucial one. (ii) load-shedding stagnates growth in an economy. (iii) a country’s GDP is indirectly affected by its electricity consumption; and it is directly affected by the poor growth rate of the economy.

2.3.1 South Africa’s GDP and Energy One of the key performance indicators is a country’s GDP per unit of energy use. China has grown over time to exceed South Africa, even though it has a population 20 times greater than that of South Africa.

Between 1997 and 2016, Eskom’s growth in sales has een similar to the GDP growth rate in South Africa, which indicates a correlation between the GDP and the electricity consumption, as may be observed in Figure 2.8. According to a report by Deloitte and

Eskom [29], the correlation coefficient between the two stands at 0.93, which indicates a strong correlation, from which it can be deduced the effects of no access to electricity or load-shedding would have towards the country’s GDP.

Figure 2. 8 Relationship between growth in real GDP and electricity consumption (GWh), 1997-2016 [29]

Figure 2.9 displays the ottom 20 performing GDP’s glo ally in 2014; while South Africa ranked 118th from 128 countries surveyed at the time. This demonstrates the lack of urgency to ensure the provision of electrical services to households in the country, as compared to the rest of the world. When combining Figures 2.8 and 2.9, the sharp decline in Eskom’s electricity sales from 2010 to 2013, shows South Africa being in a negative

growth position, before declining again to a point below 2.0 GWh y/y% in electricity sales by the end of 2014.

The relativity of the GDP to the decline in electricity sales between 2010 and 2016, shows more volatility in Eskom’s growth in sales; and this has affected the GDP.

Figure 2. 9 Global bottom 20 countries' GDP per unit of energy [30] In 2019, the South African GDP grew by only 0.1 per cent year-on-year ,during the third quarter, which resulted in a missed forecasted growth rate of an additional 0.3, to make it 0.4 per cent, after growing by 0.9 during the second quarter of 2019 [31].

The stunted growth rate in the third quarter could be attributed to the nationwide load-shedding, which had severe ramifications in the top-performing sectors, like

mining and agriculture. Figure 2.10 displays the graphical data of the GDP between

2017 and 2019, expressed in percentages on the vertical plane. This further demonstrates the effect that load-shedding has on the economy, and the need for renewable energy generation, and particularly mini-grid and off-grid systems, in order to su sidize SMME’s and production nationwide.

Figure 2. 10 South African GDP between 2017-2019 [31]

2.3.2 Gini Coefficient The Gini coefficient, also known as the Gini Index, was developed by an Italian statistician in 1912; and it was called the Corrado Gini, in order to measure the statistical distribution

[32]. This coefficient is used to measure economic inequality over a region or country by measuring its income distribution, or the lack of wealth distribution in a population. Using this coefficient, the variable measure ranges between 0 and 1, with 0 being the perfect equality, and 1 being perfect inequality [32] [33].

South Africa has been considered the most unequal society in the world, with a Gini coefficient ranging from 0.63 in 2014, and growing to range between 0.660 and 0.696 in

2015 [33] [34]. Table 2.1 displays the top 10 world Gini-coefficient rankings, according to the World Bank Group [35].

Table 2. 1 Gini Coefficient rankings [36]

Country Most Recent Year Most Recent value (%)

South Africa 2014 63

Namibia 2015 59.1

Suriname 1999 57.6

Zambia 2015 57.1

Central African Republic 2008 56.2

Lesotho 2010 54.2

Mozambique 2014 54.0

Belize 1999 53.3

Brazil 2017 53.3

Botswana 2015 53.3

2.3.3 Integrated Resource Plan for Electricity (IRP)

The Integrated Resource Plan (IRP) [37] for electricity has been active from 2018 -2030; and it is subject to future changes, based on the unforeseen future requirements, which need to be met. Table 2.2 indicates the various resources, on which the country of South

Africa is dependent, in addition to their different contributions and the intent to grow each resource. The 2019 IRP had to be adjusted by the end of August of 2018, after realising that due to certain key factors, the electrical demand was lower than projected. The updated 2019 IRP showed that due to the slow economic growth in the country, the improved energy efficiency of products y large consumers reduced the rising tariffs’ leverage, together with the relocation and/or the closing down of some energy-intensive

mining smelters. These have all contributed to the projected electricity demand not being

realized [38].

This, coupled with the declining costs over the past decade, has seen the bidding tariff

price for wind being cut by 55 per cent from R 1.51 to R0.62 per kWh; while solar PV has

seen even greater price cuts, from R3.65 to R 0.62 per kWh, to become cost competitive

with coal [39].

The proposed updated energy plan for the needs in SA, for the period ending 2030, is

presented in Table 2.2. All units in the table are in Mega watts.

Table 2. 2 Proposed Updated Plan for the 2019-2030 (MW) [37]

Other Coal (Distributed Decom Gas/ Generation, Coal Nuclear Hydro Storage Solar PV Wind CSP mission Diesel Co-Gen, ing Biomass, Landfill)

Current Base 39126 1860 2100 2912 1474 1980 300 3830 499 (MW)

Allocation to the extent 2019 2155 -2373 244 300 of short- erm capacity and energy 2020 1433 -557 114 300 gap 2021 1433 -1403 300 818 4 2022 711 -844 513 0 1000 1600 0 2023 750 -555 1000 1600 500 2024 1860 1600 1000 500 2025 1000 1600 500 2026 -1219 1600 500 2027 750 -847 1600 2000 500

2028 -475 1000 1600 500 2029 -1694 1575 1000 1600 500 2030 -1050 2500 1000 1600 500 TOTAL Installed Capacit y by 33364 1860 4600 5000 8288 17742 600 6380 2030 (MW) Total Installe d 43 2.36 5.84 6.35 10.52 22.53 0.76 8.1 capacit y(% MW) Annual Energy Contr. 58.8 4.5 8.4 1.2* 6.3 17.8 0.60 1.3 (% MWh) Installed Capacity Committed/ Already Contracted capacity Capacity Decommissioned New Additional Capacity Extension of Koeberg Design Life Includes Distributed Generation for own Use

Critical to the 2019 IRP report was the electricity demand forecasts for 2050, as displayed

in Figure 2.11. The DoE has projected the electrical demand of South Africa, based on its

GDP. The current GDP in 2018/9 stands at 0.6 per cent of the world economy, after

growing by 0.8 per cent in 2018 and 0.9 per cent year-on-year in 2019 [40] [41].

Based on the assumption that the economy remains at a steady growth rate, the DoE

projects an electrical demand growth of 2 per cent and 4.26 per cent of the annual GDP by

2030, using the upper forecast; while the lower forecast predicts 1.33 per cent annual GDP

growth, and an increase of 1.21 per cent in the electricity demand by 2030 [18].

Figure 2. 11 Electricity demand forecast for 2017-2050 [18]

2.4 Access to electricity and State of renewable energy in South Africa

In 2010, South Africa became one of the first developing countries to pledge to reduce its greenhouse emissions, in order to achieve its set target by 34 per cent by 2020, and by 42 per cent by 2025 under the ‘ usiness as usual’ levels [42]. Despite these pledges, South

Africa’s utility company, Eskom has had to overcome several stumbling blocks.

The state of renewable energy in South Africa is far behind that of many first-world countries; and the rate of its growth is even slower. According to Greenbyte data [43],

South Africa is lagging far behind its BRICS compatriot country, China, which has installed over 188 232 MW of wind energy and 106 921 MW of solar energy.

There are two key challenges, which the South African Department of energy must resolve, in order to ensure the growth of the renewable energy industry reaches its full potential. The first challenge is in the policy-implementation process, which handles the

IPPs and the long-term contracts with Eskom, which, should Eskom decide not to endorse

them, will halt the entire implementation process, as was the case between 2015-2018

[28].

Access to electricity for the rural population of South Africa is above 66.89 per cent, which indicates that there is still a 33.1 per cent market available for mini-grid and off-grid systems. These forms of decentralized electrification have the potential to create sustainable development and innovation in villages around the country [44]. The access to electricity, based on the total population percentage is presented in Table 2.3.

A point worth noting is that the percentage of total rural population electrified may be 66.89 percent; but the actual number of rural dwellers accounts for most of country, which puts that number in the tens of millions.

Table 2. 3 Access to electricity, based on the total population percentage [44] Access to electricity (2016) % of the population Electrification total % 84.2 Electrification Urban Areas % 92.85 Electrification Rural Area % 66.89 Access to clean fuels/ technology for cooking 84.75

The municipality backlog in electrical services has shown that Kwa- ulu Natal’s

Umhlabuyalingana (81.5%) and Jozini (58.4%) municipalities lead the ranks of those municipalities without access to electricity based on the total population living in the region, followed by the Eastern Cape’s Nta ankulu (47.2%); and then, K N’s Maphumulo

Emadlageni (42.8%) and Msinga (42.7%) round up the top-five municipalities without electricity [45], as displayed in Figure 2.12.

Figure 2. 12 Municipal backlog in Electricity Services [45] Figure 2.13 displays the increase in access to electricity for urban areas/populations. The linear projection demonstrates the steady incline to electrify areas where commerce aggregates. This trend can benefit the rural population; as the growth of households surrounding urban areas, in small-to-medium-sized communities continues. This should improve the ease of delivery in basic needs, such as electricity, to the rural population of

South Africa.

Figure 2. 13 Access to electricity in Urban Areas [44] The second challenge is the job losses, which will occur, as caused by the fossil-fuel utilities; if there is no system to ensure skills transfer from one industry to the next. In that way, the growth of the renewable energy industry does not threaten the jobs and the livelihoods of the working-class within the fossil-fuel industry [28].

According to the Department of Energy’s (DoE) 2018 Draft IRP, the net jo decrease in coal will be 100 000 jobs in 10 years; while gas will grow to 55 000 jobs in the same period, as demonstrated by Figure 2.14 [46]. According to the South African Renewable

Energy Council, the potential job loss amounts to 15 000 annually, according to their 2017 predictions [47].

This is due to the already R100 billion plus invested in the construction of coal stations, which then pulls back the investment and the progress made in renewable energy [47].

Both sources have estimated heavy job losses in the coal-fired utility sector; but the

CSIR’s analysis of the DoE’s 2018 Draft report also shows that an estimated 110 000 jobs will be taken on by the renewable energy sector by 2030.

Figure 2. 14 Potential distribution of jobs within different resources [46] There is an irrefutable link between the availability of sustainable energy and the developmental growth of a group of people, who benefit from that energy. The lack of electrical energy to a community further makes it difficult for the people to facilitate social and economic activities, which stimulate growth and development [48].

South Africa’s Department of Energy hopes to achieve a 42 per cent of electricity generation through renewable energy by 2030. There are seven Concentrated Solar

Power (CSP) power plants, which have been established, or are under development in the country, all of which can be found in the Northern Cape Province. The primary reason for this, is the annual average Direct Normal Incident (DNI) in the region, which ranges above

2816 , which is much higher than the Spanish and American CSP power plant’s capacity [49].

A summary of renewable energy from the Council for Scientific and Industrial Research

(CSIR) in South Africa for 2018/9, demonstrates the following outputs for wind, solar PV

and CSP. By March of 2019, a total of 2078 MW wind, 1479 MW of solar PV and 500 MW of CSP capacity were in operation [46]. The three main types of renewable energy sources in operation by the end of the first quarter of 2019 amounted to only 4.057 GW of the renewa le energy compared to China’s 750-plus GW of wind and solar, after adding 5.2

GW of solar PV at the end of the first quarter of 2019 [50] [51]. The 4057 MW of renewable energy only account for 5 per cent of the total generation, of which 42 000 MW is still generated from coal [39].

The annual operational capacity growth for different renewable energy sources is displayed in Figure 2.15, focusing on solar PV, wind and CSP.

Figure 2. 15 Renewable energy progression (Eskom, DoE) [24] Figure 2.16 displays the monthly electricity production throughout 2016, the contribution of solar PV, CSP and wind, showing the addition of operational capacity and the effect of each resource’s availa ility during different seasons throughout the year.

Figure 2. 16 Monthly electricity of South Africa's wind, solar PV & CSP (2016) [52] 2.5 Critical Review of Off-grid systems

2.5.1 Case Study: Off-grid Micro-grid for Universal Electricity Access in the Eastern Cape, South Africa

Longe et al [53] showed through their research that micro-grids are progressively becoming the most feasible off-grid solution. The case study investigates the feasibility of micro-grid solutions and details the costs involved in implementing such a system in

Ntabankulu Local Municipality, Eastern Cape province. The simulation and optimization software used to analyse the hybrid-energy system with regard to the renewable energy resources available in that region was HOMER. The case study comes to the following conclusions [53]:

1. A Wind-Diesel generator-BESS powered micro-grid has the lowest cost.

2. The break-even cost is achieved with the grid extension of 45.38 km.

3. The electrical energy supply = $ 0.32/kWh and 0.0057 kg/kWh

4. The hybrid outputs renewable energy fraction at a lower rate than grid extension at

90.5%

2.5.2 Case Study: Upper Blinkwater off-grid system in Eastern Cape, South Africa

Blinkwater is a rural community in the Raymond Mhlaba municipality in the Eastern Cape, with 67 households which comprise 254 people. The community was found to have an average income of R 1237 per month in 2018, most of which comes from various social grants or pensions. The community relies heavily on wood, paraffin for cooking, lighting and heating; while the candles and small batteries and car batteries, LPG stoves and generators are the other forms of energy carriers on which the community relies [54].

2.5.3 Case Study: Potential of Hybrids for rural off-grids in the Eastern Cape, South Africa

The Eastern Cape has garnered much interest in recent years for its excellent wind profile, and the quality of wind flowing through the mountainous topography, which led to the first off-grid hybrid-energy systems in South Africa being established in that province. In the early 2000’s a hy rid mini-grid system project led by the CSIR, was implemented at the

Hluleka Nature Reserve on the Wild Coast [55]. The project was implemented with socio- economic development as a priority for rural areas, as demonstrated by the inclusion of nearby villages to the nature reserve, such as the Lucingweni village [55]. The project generated electricity from two small-scale wind turbines, which produce 2.5 kW each, together with 48 (100 W) solar panels, a battery storage system and a diesel generator, housed with the Control system, as can be observed in Figure 2.17 [55].

Figure 2. 17 Solar PV-Wind Hybrid at Hluleka Nature Reserve [55]

The Lucingweni village, which is located 10kms away from Hluleka Nature Reserve was the site for a hybrid-energy system, which was intended to supply energy to 220 households. The system comprised 6 (6 kW) wind turbines, which stood 6m tall, together with an array of 560 (100 W) solar panels with a battery storage system, as displayed in

Figure 2.18.

Figure 2. 18 Lucingweni 86 kW hybrid mini-grid [55]

In a different study, which focused on the sustainability of mini-hybrid off-grid technological systems in rural areas, Brent [56] found that among the key reasons that led to the

Lucingweni project failing was the high electrical costs. The costs associated with supplying electricity to villagers using renewable energy sources, proved economically unfeasible; since village grids were too expensive to be supported by the available subsidies [56].

The project faced various complex problems, which today may be overcome, as rural areas are more open to socio-economic development and much more adaptable to the benefits of technology; however, more work remains to be done [55] [56]. This project was implemented in a time when solar energy was still an unfamiliar concept to rural villagers and even to the global markets, which still saw it as a niche innovation.

The Hybrid system had high costs; and the attempt to get a return on investment on such projects was unheard of; and it still remains a challenge today. Off-grid hybrid-energy systems require municipal support in their deployment; as they carry high risks of failure if not correctly implemented.

2.5.4 Case Study: Thabazimbi Chrome Mine in Limpopo, South Africa

Mining companies are investing in off-grid renewable energy systems and reaping the rewards of grid-parity costs and the long-term benefit of reducing their electrical costs [57].

The Thabazimbi Mine uses a hybrid system supplied by solar PV and diesel generators.

Thabazimbi is a remote area with only limited grid connection and high transportation costs for diesel. The area has high solar irradiation, making it ideally suited for the use of

PV. The PV system generates up to 1.8GWh per year; and this minimises the fuel consumption during the day. It saves the mine up to 450 000 litres of diesel and it reduces

CO₂ emissions by up to 1200 tonnes per year.

Considering the estimated installation costs of R20 million and the estimated annual savings for diesel of R4m, this hybrid system is financially viable [57] [58] [59]. Figure 2.19 displays the Limpopo Chrome mine in Thabazimbi and its use of solar PV to subsidize its energy consumption and proof grid-parity.

Solar PV has become the go-to renewable energy source to supplement the high energy consumption levels of a mine. When considering off-grid solar PV systems, the cost advantage of solar is often seen, especially when the price of diesel is high, and the quantity of diesel used is in high volumes. Since solar is currently only limited to day-time generation, the other 12-14 hours in a day, when the mine remains in operation, need power. For this reason, off-grid mining operations will opt for a solar-diesel hybrid-energy system [60].

Figure 2. 19 Thabazimbi chrome mine supplemented by solar PV’s 2.5.5 Hybrid energy projects in Africa

As the exposure of off-grid hybrid-energy systems continue to grow, more and more impoverished governments in third-world countries are finding that the solution of rural

electrification lies not in singular source or fossil fuel utility stations, but more in hybrid renewable energy. Mali has shown progress in this regard, through its release of tenders in September 2019 for two 1.3 MW solar PV and battery-storage hybrid systems. These off-grid power plants will have a combined capacity of 2.6 MW; and they will service 24 villages in the Macina circle, coupled with a 1.5 MW battery-storage system. The project is set to range in the USD 15 million region [61].

Another African country that is stepping up to commission a solar hybrid-energy plant is at

Bayero University in Nigeria; it is set to generate 7.1 MW of renewable off-grid power. The project is set to be the second of nine projects in the first phase to be commissioned under the Energising Education Programme that aims to provide its federal universities with energy over the next four years.

The country’s Rural Electrification Agency (REA) is charged with implementing the power grids to the 37 federal universities, with 7 other affiliated universities that have teaching hospitals [62]. The system will consist of 3.5 MW solar PV, 8.1 MWh of battery-energy storage and 2.4 MW from back-up generators [62].

2.5.6 Mini-grid projects in Africa

Africa has currently over 2000 mini-grids, where 40% of those projects are sourced by solar PV. The number of mini-grids is set to increase to 16000 by 2023. According to the

International Energy Agency (IEA), the minimum of 40 per cent of the new power generated over the next decade will come from mini-grids [63]. Globally, the need for access to electricity continues to be a concerning impediment towards social development and community development, as well as the overall improving quality of life [64].

The annual increasing rate of electrification lay between 2-3 per cent in 2014, in countries like Kenya, Malawi, Sudan and Uganda, while Angola and the Democratic Republic of

Congo both fell by 1 per cent during that time [64]. Figure 2.20 shows the severity of

African countries compared to India for population without access to electricity in 2014, and the darker-shaded country, indicates the people that are without electricity.

Figure 2. 20 Countries without access to electricity 2014 [139]

Across Africa, there is an estimated 600 million people within the Sub-Saharan Africa region, living without access to electricity [65]. The challenges that renewable energy interventions have to overcome are frequently to be found in the policy framework of the country and the financing of the project. Figure 2.21 displays the graphical measure of

African countries without access to electricity relative to India.

Figure 2. 21 Graphic display of countries without access to electricity [139]

One of the key barriers to energy access for most developing and third-world countries continues to be the poor policy regulations surrounding their energy. Figure

2.22 shows the different levels at which SSA countries score, based on their electrification policy.

Figure 2. 22 African countries which need investment for access to energy [141] There are several opportunities for being an early investor in PV mini-grid systems, for rural electrification, among which is the long-term income that a company can acquire from such a system by providing electricity to villages and communities, which do not have the

economical capacity to invest in such a system independently [66]. Mini-grids have the advantage of being a centralized source of power for a community; and they have a more inclusive system that allows for energy management systems to be involved, such as smart meters, billing systems and other supply-and-demand management systems, which render the implementation of such a system economically viable [66].

According to the World Bank, the majority of the already planned mini-grids in Africa will be developed in Nigeria and Senegal, at 879 and 1217 mini-grids respectively [67]. Some of

West Africa’s ooming economies are reporting massive losses due to power outages; and they are therefore turning to mini-grids as feasible and scalable solutions. The need to provide over a half a billion people with electricity through the continent can only be met through mini-grids, due to the falling costs, and the complement to grid extension and solar house systems, as seen in Figure 2.23 [67].

Figure 2. 23 More than half of the people who live without electricity are in Africa [142] East Africa has also shown itself to be a region of future expounded growth in mini and off- grids, in order to cover the gap in power generation [68]. East Africa (Tanzania, Kenya,

Ethiopia, Zambia) has also shown interest in developing solar PV and river-based hydro- hybrid mini-grids, utilizing a greater scope of renewable energy resources [68] [69]. As of

2016, Tanzania has installed over 109 mini-grids, with the total capacity exceeding 157.7

MW, to provide power to an estimated 184 000 customers. Approximately 14.7 per cent of their mini-grids supply directly to the national grid; while the remaining mini-grids are isolated [70].

According to a study done by IRENA [70], off-grid systems have begun having a middle- class appeal, in developing countries. The study looked into four operational structures, namely:

1. Community-based model: the community owns the plant and the network, which

supplies it its electricity, while being managed by the elected village committee over

a three-year period.

2. Private-business model: generated power is sold at a fee to local customers; as

the investor(s) recoup their investment over time.

3. Utility-based model: the utility company owns and operates the mini-grids, which

usually use diesel or natural-gas as the primary energy source.

4. Faith-based model: these plants are owned by ministries and churches, to supply

their own electricity; and they sell all the excess generated power to villagers at a

high subsidy, if not free.

All the models described above are exemplified in Tanzania and have found relevance in some form, with each project’s successful deployment eing dependent upon the region of its implementation [70].

There is no doubt that the impact of mini-and off-grid power systems will be beneficial to the African continent. However, the development of mini-grids faces several challenges in

terms of policy, regulation and the lack of sufficient market data, which substantiate the successful and sustainable implementation of mini-grids [71]. These gaps are specific to what this dissertation aims to prove with regard to South Africa. Figure 2.24 displays the major global energy-consumption markets, while also comparing them to some selected

SSA countries.

Figure 2. 24 Power consumption per capita, especially in sub-Saharan Africa, remains low. [72]) On the other hand, the success of micro-grids is dependent on their energy service reliability and schedule reliability, which can be defined by a set load supply that services a particular need, such as lighting, cell-phone charging or cooking, which then restricts the use of electricity and is often insufficient. The schedule’s reliability is determined by the micro-grid’s performance during the scheduled period of operation; and since it does not supply energy on a 24-hour a day basis, the level of consistency in the supply of energy during the scheduled period would be the key indicator of successful performance [73].

Much emphasis is placed on micro-grids; as they are the base model for mini grids. Mini- grids cost more; and therefore, they require a much more concrete cost-recouping model, which would reduce the investor’s risk y ensuring that the community eing provided electricity can handle the financial burden that comes with pre-paid electricity.

At the mini-grid scale, the risk being taken is greater; and therefore, greater understanding of the needs of the community, and its plan to repay the investment long-term are needed.

This process will require municipal involvement to keep the financial burden to recoup their

udget expenditure on the municipality’s side, rather than on the investor.

2.5.7 Case study: Grid parity analysis of a PV- BESS hybrid

Juwi [74] has modelled the grid parity system, which consists of a PV-hybrid system that also relies on a battery-energy storage system in order to supply electrical power to a mine. The mine would use an energy mix to meet the needs of the electrical power demand, which is sourced from renewable energy. By definition, grid parity is the ability for an alternative or renewable energy system to generate power at the levelized cost of electricity (LCOE). Grid parity is the point at which an energy system can operate without any subsidy or support from the national grid [74].

The technological advance in battery-energy storage systems and the cost reductions in

PV systems continue to further justify off-grid systems and their reliability as sustainable long-term solutions for consumers and industrial business complexes [75].

2.5.8 Global view on Wind energy

The renewable energy industry globally continues to grow at different rates, with first-world countries leading the way, and Europe dominating the market in manufacturing and capacity, encouraged by the favourable wind resources available to the continent, as

displayed in Figure 2.25. Renewable energy is not without its own challenges; but the growth in the industry has been due to three main cost-reduction drivers, ;listed as follows

[76]:

1. Technological improvements;

2. Competitive procurement;

3. Large base of experienced, skilled and internationally active project developers.

Figure 2. 25 Global weighted average total installed costs, capacity factors and LCOE for onshore wind (2010- 2017) [100]

2.5.9 Case Study: World’s biggest lithium-ion battery in South Australia.

Tesla has found perfect economic and environmental conditions to showcase its Lithium- ion battery storage capabilities by installing a 100 MW/129MWh battery in South Australia.

The battery began operation on the 1st December 2017, following black-outs from the previous year [77]. The battery is paired with a wind farm close by, providing greater reliability and stability to the utility grid. The battery can supply 30 000 homes for an hour,

in the case of a black-out; but it will also be used in cases of peak demand. Only a year later and the battery has already proven itself to be profitable for Tesla, with the company already seeing a third of its total investment within the first year of operation [78]. Many more developments in the BES sector continue to be spear-headed by South Australia, with plans for a new 200 MW BESS that uses solar PV already approved, and in the process of being constructed [79].

2.6 South Africa’s solar profile The southern African region, and South Africa in particular, has sunshine all year round, with 24-hour global solar radiation averaging 220 that is twice as good as the global solar radiation exposure received in Europe at about 100 W/m2 [80]. Solar energy has two main technologies, solar PV and Concentrated Solar Power (CSP). Both of these have elite investment attractiveness, particularly with South Africa ranking third for CSPs and seventh for solar PV, out of 40 countries globally, according to Ernst and Young’s renewable energy country index [81].

South Africa’s Northern Cape Province has een the most attractive location for IPPs that intend implementing solar farms, because of its desert terrain and high temperatures.

The growing appeal of renewable energy is closely tied to its growing efficiency levels and declining implementation costs. This becomes interesting for future investment in the solar

PV energy market by raising the feasibility in developing countries. This can be seen by the power produced by the older, fossil-fuel power plants that are much more expensive to operate and to maintain, due to natural degradation and losses in efficiency; backed up with the previous high investment costs, which were part of the building costs.

The growth in the solar PV energy market can also be seen in the over 50% drop in solar panel prices over the last decade, which was a previous hindrance, due to the high initial

investment costs and the uncertain returns in efficiency, operations and sustainability [82].

The prices of solar panels will continue to drop, provided there is continued progress in product development, manufacturing processes and global co-operation in renewable energy sources.

As a country, South Africa receives some of the best quality solar radiation in the entire world. South Africa’s solar industry continues to grow; and the Northern Cape remains the prime location for IPPs to achieve peak solar radiation and power generation, such as the

Kathu Solar Park, which is the largest solar farm in South Africa, with a generation capacity of 100 MW. The farm began operating from January 2019.

In second place is the Jasper Solar Power Project, which has been operating since

October 2014, with 325 000 solar panels to produce a maximum capacity of 96 MW.

Finally, the Solar Capital De Aar Projects 1 and 2, which produce 85 MW and 90 MW, respectively, over 500 hectares of land are in the Northern Cape [83]. Figure 2.26 displays the annual solar radiation received in South Africa, which has the highest radiation levels in the Northern Cape Province.

Figure 2. 26 Solar radiation received on horizontal surface in South Africa [84] [85]

2.6.1 Solar PV energy transfer methods There are four main types of energy-transfer methods, namely:

 PV-direct system,

 Off-grid system,

 Grid-tied system with battery storage,

 Batteryless grid-tied systems.

Figure 2.27 demonstrates the categories that are used in the solar-power generation system; and which technologies can be used.

Solar Power

1. Grid-Direct (Grid –tied)

2. Grid Interactive (Grid with battery storage)

3. Off-grid with storage Grid 4. Grid-tied with battery storage

Solar PV Solar Thermal Power Technologies Plants

c-Si Thin film 1. Parabolic Trough (Silicon 2. Solar Tower )

3. Parabolic Dishes

4. Linear Freshnel reflectors

Figure 2. 27 Solar power plant technologies 2.6.2 PV-direct system (Grid-tied) These are the simplest form of solar-harnessing systems; and they have the fewest components; and since they do not have batteries and they are not hooked up to the utility, they only power the loads when the sun is shining. These types can be used to power up a home, a business centre and commercial locations, like malls and airports, as can be seen in Figure 2.28; this is because they work to subsidise the overall power demand and to reduce it. They can be directly located on rooftops. This type of system would not be suitable for power generation purposes due to the increasing demand for power today.

The greatest load demand for energy occurs when the sun is least effective; during the early morning and the evening. The type of system is often used as a support, in order to reduce the electrical costs for a household [86].

Figure 2. 28 PV direct system 2.6.3 Off-grid system Although they are most commonly used in remote locations and regions without any utility service, off-grid solar systems can work anywhere to meet the desired energy demand.

The off-grid system operates independently from the grid; but it requires batteries to store the energy, in order to maintain their function in the absence of sunlight, be it night-time or cloudy days. The challenge with this type of system is that it needs to charge its battery, while also providing energy simultaneously; otherwise it needs to be designed to generate electricity directly [86]. In the process of supplying electricity directly, the system is only expected to function under ideal conditions, when there is no cloud coverage and it is daytime. In a typical off-grid system, there need to be the following components:

 Solar PV modules – to absorb light and convert it to electricity;

 Charge controllers – to manage the charging and discharging of the batteries, in

order to maximize their lifetime and to reduce operational problems;

 Battery bank – to store the energy generated;

 Inverter – This converts the Direct current (DC) generated by the solar PV to

Alternating current (AC) for consumer needs.

Off-grid systems work well for households and small business supplies, particularly in solar-weather conducive regions.

2.6.4 Grid-tied systems with battery back-up For the sake of this thesis, the focus will be the storage of energy after the energy has been generated. The energy generated by a solar power plant, is converted into electricity which is directly supplied to the grid, while also storing energy to supply after the sunset, or when there is no solar energy being received by the solar modules.

2.6.5 Grid-tied system without battery back-up A grid-tied system that operates without any battery storage would probably become an inefficient system for power generation. This is true because of two simple reasons, being that a power generating plant must be both sustainable and reliable, which cannot be the case when there is no power supply, as in a cloudy day, or at night.

2.6.6 Global Horizontal Irradiance (GHI) & Direct Normal Irradiance (DNI) systems

The solar radiation has critical parameters, such as Global Horizontal Irradiance (GHI) and

Direct Normal irradiance (DNI), which are derived from advanced satellite models. These are computer atmospheric models designed to attain the most accurate outputs. The solar input parameters include cloud index, water vapour database, the atmospheric optical depth, the horizontal profile, as well as altitude, among other things [87]. Figure 2.29 displays the difference in the GHI and the DNI over different regions of South Africa.

From the Figure, it can be deduced that GHI serves solar PV and SHS systems; while the

DNI serves only the CSP and the CPV systems.

Figure 2. 29 GHI vs. DHI and their technologies Global Horizontal Irradiation (GHI): the combination of direct and diffuse irradiance measured on a horizontal plane. (GHI applies to solar PV and Solar WH).

Direct Normal Irradiation (DNI): this is the only direct-beam component of the sun

(excluding diffuse irradiance); and it is measured in a plane normal to the rays of the sun

(and it applies to CSP and CPV). [88]

2.6.7 Solar PV system model The solar PV system is modeled as follows [89]:

1) To determine the area of PV module needed to meet the energy demand

The PV generator model is described as follows [90]:

The PV generator efficiency is described as follows [90]:

1) The amount of PV modules needed to achieve the solar energy demand [89]

2.6.8 Solar Capacity factor The capacity factor of a solar PV system, operates on the basis of the ratio between the actual energy generated over a period of time, divided by the ideal installed capacity [91].

When comparing the capacity factor across different energy sources, and focusing on renewable energy sources, we find the following [91]:

The capacity factor is often calculated on an annual basis; therefore where the time period is considered, it can be considered on a per month or even on a per year period. This holds true for the ratio, provided the time period is consistent throughout [92]. Table 2.4 displays the different capacity factors, which can be expected when using different technologies.

Table 2. 4 Comparing capacity factors of different energy sources [91] Generation Type Capacity factor Solar Panels 10 -27 % Wind turbines 25 % Hydroelectric power stations 40 % Coal fired power plants 70 % Nuclear Power plants 89 % Combined cycle gas turbine 38 %

An alternative method of calculating the capacity factor can be achieved as follows [92]:

2.7 Solar Energy Storage By 2050, solar power could e the world’s largest source of energy; and according to the

International energy agency (IEA) [93], more power will come from the sun than from fossil fuel, wind, hydro and nuclear. While this may be a huge step towards supplying the planet, there is still much work to be done in finding ways to store that energy, particularly for buildings using their own solar panels. Today, solar power is mostly stored in batteries; although some don’t last too long; and they can contain poisonous materials, making it difficult to dispose of them safely.

Solar energy storage in rural African households and communities, as well as most of the world, is done by Lithium batteries, which can hold about nine hours of stored energy.

These Lithium batteries are not classified as hazardous waste; and they can last up to ten years; but they are expensive to acquire. In rural locations, the usual model is that of roof- solar panels, which supplies directly into the household, while also charging its battery system. Then it recycles the surplus and unused energy into the mini-grid system for the household.

Scientists in the London City University believe that the future of solar storage does not lie in Lithium battery-storage systems; because of the poisonous material used to manufacture the batteries [94]. A team of scientists has been working on developing a fly- wheel, which could be used to store solar energy in households and small businesses, rather than continuing to use a battery.

2.7.1 Lithium-ion battery storage system

In 1991, Sony commercialized lithium-ion batteries and popularised the distinctive internal operational structure of lithium-ion batteries, which allowed for the electrolytes that comprised lithium salts, to be broken down into organic carbonates [95] [96]. The battery- charging process includes lithium particles in the cathode anode becoming ions and migrating through the electrolyte towards the carbon anode. At the carbon anode, the ions join the external electrons; and they are collected between the carbon layers as lithium atoms. The process is reversed through discharging [96].

Lithium-Ion batteries use lithium compounds; and they are rechargeable. The main principles of operating a lithium-ion battery are the same as those for a lead-acid battery.

The positive lithium-ions in the anode travel through the electrolyte towards the cathode; while the electrons flow from the anode to the cathode simultaneously; thus, the battery charges itself, when the electron flow from the anode to the cathode is reversed [97].

Sub-Saharan Africa continues to show market growth by the forecasting from the US

Energy information Administration [98]. Figure 2.30 displays the actual electricity demand in 2010; while 2020-2024 is the projected forecast. Additionally, the industrial/commercial auto-generation and back-up power supply is estimated to operate at an annual compound rate.

Figure 2. 30 Electricity demand in Sub-Saharan Africa [98]

Bushveld Minerals shows that the possible market segment in grid and off-grid storage in SSA can be seen in Figure 2.31. The future of Vanadium Redox-Flow

Batteries (VRFB) should combine well with off-grid systems throughout the African continent.

Figure 2. 31 Market segment in Africa for Grid and Off-grid connection [15]

2.7.2 Flow batteries Flow batteries are the new development in large-scale battery energy storage systems for renewable energy. Although lithium-ion batteries have dominated the market and have been able to provide a back-up energy supply, Redox flow batteries systems have the capacity to supply energy for cities, according to Michael Perry, the associate director for electrochemical energy systems at United Technologies Research Centre in East Hartford,

Connecticut [99].

Figure 2. 32 Flow battery

The flow battery is essentially an ongoing chemical reaction, where electrical charge is stored in tanks of liquid electrolyte, which is pumped through the electrodes, in order to extract the electrons, after which the spent electrolyte returns to the tank [99].

Flow-batteries are instantly rechargeable by replacing the electrolyte liquid, while simultaneously recouping the new material to be re-energized [100]. The volume of the electrolyte determines the capacity of the flow-battery.

Bushveld Minerals is a South African Vanadium mining company, which has an energy- focused subsidiary called Bushveld Energy. The parent company mines the mineral, which is used in the steel-making process, as well as the making of the Vanadium redox flow batteries (VRFB). Bushveld have paired their VRFB with a mini-grid at their Vametco mining and processing facility. The mini-grid will supply 1 MW of power to the facility, as permitted by NERSA, without needing a generation licence.

The power-generation process will be a combination of solar PV generation of 1MW and

4MWh of VRFB [101]. Figure 2.33 displays the role of vanadium in a circular economy,

which begins with the mineral being mined and used in steel production and VRFBs, among other things.

Figure 2. 33 Vanadium in a circular economy [102]

According to Bushveld Energy [103], VRFBs will capture the stationary energy-storage market due to their scalability, long lifespan and their lengthy usefulness, which remains after being used for 20 years in the flow battery. This will make VRFBs top contenders in the stationary storage market, should they transition well to mini-grids and off-grid systems, in the future.

2.8 Wind Energy

Wind energy is one of the earth’s most sustaina le natural resources; and it has the ability to harness a natural resource into clean energy to generate electricity, which makes it one of the most rewarding returns on investment for renewable energy [104].

Wind power is the conversion of wind energy into useful energy, which can be regarded as electrical energy. This wind power is generated from wind turbines, which operate in a similar manner as do windmills, except that the output is not mechanical energy, but electrical energy [105]. The variable nature of wind speeds caused by different climatology, surface roughness, landscapes and topography results in the need for localized data collection techniques for screening purposes.

Wind turbines are essentially machines that transform kinetic energy into mechanical and electrical energy. Through the kinetic energy harnessed from the wind, which causes the rotor blades to rotate, the wind turbine is much like the wind mill or wind pump; since it transforms the harnessed wind energy into mechanical energy. The same mechanical energy then goes through the gears within the nacelle, which is connected to the wind rotor. The gears which are connected to the electrical generator, generate electricity, which is then transferred through the cables that run along the inside of the tower [106].

Wind energy is measured by its speed and direction, through meteorological stations, which collect the measured wind data. Traditionally, wind speeds are measured by using an anemometer, which is composed of three cups that capture the wind rotating in a vertical axis; while the wind direction is measured through weather vanes [106]. The process of determining whether a site produces enough wind resource capacity factor must last for the minimum of a full year.

Most wind turbines begin operating at wind speeds between 4- 6 m/s; and they can reach a maximum power output at around 15m/s and have safety cut-off speeds at around

25m/s. The average electrical production from a wind turbine occurs for 70 -85 % of the time, but it does not necessarily generate the maximum electrical power all the time, due to varying wind speeds [106]. In comparison the solar energy received by the solar panels tends to increase throughout the morning; and it usually peaks between 11 am and 1 pm; and therefore, even in the case of the sun being available the entire day; there are nevertheless peaks.

The wind is similar; except that the peak of the electrical energy is not as consistent over a particular time period; as the wind is available throughout the day and the night.

The power which can be extracted from the wind turbine is given by the formula:

2.8.1 Wind turbine Modelling a wind turbine [107]: The energy output of wind turbines can be calculated as follows:

Where,

Wind turbines generally consist of three-blades that are attached to a rotor, a tower hub and a nacelle, which carries in it a gearbox and a drive train [108]. The wind turbines rotor is responsi le for converting the wind’s kinetic energy into rotational energy, which transfers the mechanical energy to a generator in the nacelle [109].

According to the Bertz limit, the maximum efficiency attainable by a wind turbine is 59%; therefore, the maximum power that can be extracted from airflow is no more than 60%.

The capacity factor of a region suitable for a wind farm, should be at least 30% or more

[110].

2.8.2 Power Curve The wind power curve describes the energy conversion rate of a wind turbine. The curve has three distinctive operating speeds, described as follows: (i) Cut-in wind speed – which is the minimum wind speed required to start the turbine blades rotating. (ii) Rated wind speed/power output – this is the wind speed at which the wind turbine generates maximum power output and produces its maximum capacity. (iii) Cut-out speed – the speed at which the turbine blades are stopped from rotating due to excessive wind speeds [109].

Figure 2. 34 Wind power curve [61] 2.8.3 Weibull curve distribution Weibull distribution can generally be expressed in terms of the wind speed. The Weibull shape parameter (k) and the Weibull scale parameter (c) describe the Weibull distribution.

2.8.4 Wind measurement The measurement of wind resources and their analysis is critical in the feasibility study of a potential wind-farm project construct. The following technical factors must be considered, when locating a wind-energy facility [108]:

 Predominant wind direction and frequency,

 Closeness to the coast, as the land mass reduces the wind energy available at a

site,

 Topographical features, as they affect the air flow, turbulence and density.

 Effect of adjacent turbines on wind flow and speed, spacing in between turbines is

thus important,

 Capacity factor of the production process.

2.8.5 Capacity factor vs Efficiency Capacity factors are more in relation to the utilization of how the energy conversion system is used; while the efficiency is a measure of how well the energy is converted. Therefore, the efficiency of the wind turbine is not indicative of the capacity factor; but it does have a significant role to pay in achieving it. This is because the normalized efficiency is measured against the productivity per square metre for each turbine; while the capacity factor considers the energy power output [111].

The capacity factor is calculated by the Annual Energy Production (AEP) over the theoretical maximum output. The capacity factor is calculated as a percentage [112].

The capacity factor is the measure of the amount of electricity generated over the ideal theoretical maximum production, which is also expressed as a percentage [113]. The wind turbine capacity factor will be influenced by the average wind speed. In the place of efficiency, the capacity factor takes prominence, when considering the maximum AEP from a production site [114].

When measuring the wind capacity factor, the region of it being considerably good lies between 0.25 and 0.3; while the very good wind capacity factor would range around 0.4

[115].

The wind-capacity factor is determined by the following factors [116]:

 Average Wind speed

 Rotor diameter

 Wind-turbine technological specs

 Hub height.

The capacity factor of a wind farm will have limitations; and it will not achieve 100%. These are the following limitations:

1. Wind turbines may be out of service due to routine maintenance or equipment

failures. The unexpected equipment failures create the biggest losses to the base

load.

2. The supply of electricity, when there is no demand for it, will create losses in the

energy generation of the wind farm.

2.8.6 South Africa’s wind profile South Africa is a unique country, gifted with both an excellent wind profile in the southernmost parts of the country, as well as good solar resources in the North Western part of the country. The country’s coastline stretches over 2 800 km; and it becomes the point of convergence for the Indian and Atlantic Ocean [117], as displayed in Figure 2.35.

The wind profile is most prolific in the Eastern, Western and Northern Cape provinces, which are the southern coastline provinces that can also experience gale-force winds

[117].

There is an estimated 410 000 km2 of land exposed to wind speeds greater than 6.5 m/s; but only 1174 km2 would be available for wind farms [118].

Figure 2. 35 High resolution wind resource map of the WASA domain. (Mean speed (m/s)) at 100 m agl in a grid spacing of 250 m [119] 2.9 Hybrid-energy system

A hybrid-energy system is different from a co-generation system in their two ways of operation. The co-generation system will often support a renewable power plant through gas turbines, diesel, or coal. A hybrid-energy system is the combination of two separate energy systems, which complement one another, in order to achieve a greater power- output efficiency. There are a few types of hybrid energy systems; and these are listed below:

 Solar PV and Wind

The simulation of the hybrid model would be conducted by using MS Excel to analyse and optimize the data. The simulation process requires an understanding of the parameters, as well as the definition of solar and wind conditions.

Solar PV Diesel energy Generator system

Hybrid Battery Wind stored energy turbine energy energy system system

Figure 2. 36 Hybrid energy processes

Hybrid-energy systems are the combination of two or more renewable energy sources.

This is due to the unpredictable and intermittent nature of renewable energy sources, such that frequently, a single stand-alone source of energy would not be a reliable source for the end-user. The optimized integration of solar and wind, together with battery-energy storage as sustainable off-grid solutions, will be the focus of this dissertation [120].

Hybrid-energy systems work well together to complement each other’s need for a reliable energy supply. Solar energy can be considered more consistent than wind, as regards the numbert of days per year that it is available; but it is limited in the number of hours in which it can be harnessed; while wind energy can be harnessed both during the day and night

[121].

The reliability of an energy supply is generally greater from a hybrid mini-grid than from a single renewable energy resource. This also reduces the net costs over the project’s lifetime; but it also allows for the availability of power, even when one system is not generating power [122].

Figure 2. 37 Off-grid hybrid systems supplied by solar PV, wind, batteries and back-up generator [79]

Hybrid-energy systems that rely heavily on solar PV as the main contributor to the power generation are also known as PV hybrid systems. These systems are fast becoming the cornerstone of off-grid rural electrification. Their appeal precedes their economic viability with the cost of solar PV modules falling significantly in recent years. The PV modules have advanced to now come in different topologies, in order to serve different power- generation capacities [123]. These topologies are categorized, according to their performance and efficiency; and they are described as follows:

 < 0.5 kW (P) power output (DC Solar Home System)

 0.5 kW < P < 4 kW pure stand-alone (Inverter systems)

 4 kW < P < 20 kW DC -coupled (Single-phase hybrid system)

 20 kW < P < 50 kW AC- coupled (3-phase systems)

All four of these topologies can be coupled with battery-energy storage systems (B.E.S.S) to improve their efficiency [123].

The techno-economic analysis of a hybrid system is critical for the effective and efficient implementation of renewable energy resources. The hybrid-energy system design process must account for the effective technological adaptation by the community. in order to optimize the utilization and the reception of the renewable energy technology, leading to economic sustainability models, which justify the implementation of these hybrid-energy systems in rural off-grid remote locations [124].

In this research dissertation, the focus of hybrid-energy systems will be that of renewable energy sources, leaving out generators (diesel or otherwise), but including battery-energy storage systems. There are two basic electrical topologies, which are currently being used; these are the DC or the AC coupling. In DC coupling, the hybrid system generates energy, and supplies directly the inverter before being fed to the consumer. In AC coupling, the

BESS needs an inverter, as well as the solar PV modules; while the wind feeds directlyas seen in Figure 2.38.

Figure 2. 38 Hybrid System Technical Overview [81]

The complexity of designing a hybrid-energy system is eased by the comprehension of the local resources available, the cost of storage systems, as well as the size of the community for which it is being generated. The following aspects summarise the key aspects for hybrid energy systems [125]:

1. The configurations of hybrid system are determined by the availability of local

resources and the utilization constraints.

2. The optimization of reliable power generation.

3. The maximum power generation is achieved from resource exploitation, which in

turn, optimizes the system.

2.9.1 The Hybrid-Energy Model The following components are the essential components of an off-grid hybrid-energy system and their analysis:

1) Solar PV

2) Small-scale wind turbines

3) Battery storage and a Diesel generator

2.9.2 Solar PV in Hybrid model The most common type of solar PV installations in urban areas are rooftop installations; and while this model has been effective for SME’s and uildings with large enough surface areas on their roofs, rural areas require a different approach to PV installations. Rural and off-grid communities benefit better from mini-grids; since the power source is centred and electricity is generated and distributed from that single source.

2.9.3 Small-scale wind turbines in a hybrid model Small-scale wind turbines are designed to operate on residential or small business/ industrial sites. The economics supporting small-scale wind turbines continue to show promise, although still overlooking the large-scale wind turbines, with over 806 000 units

being installed globally at the end of 2012 [126]. By definition, there are two main types of wind turbines, Horizontal Axis and Vertical Axis [127]; and for the sake of this research, only the horizontal-axis wind turbine will be considered.

M. Predeescu’s study of small-wind turbines and hybrid systems for residential use resulted in some interesting observations. The applications for small-scale wind turbines vary from residential, telecommunication towers, small commercial businesses, farms and rural communities, as seen in Figure 2.39 and 2.40. The energy output for small wind turbines can be categorized as follows [128]:

 Micro-wind turbine < 50 W

 Household/ residential <20 kW

 Small-scale wind turbine <50kW (some countries consider anything < 100kW)

Figure 2. 39 Rural site wind turbine [86]

Figure 2. 40 Urban site wind turbines [86]

The best rural sites for small-wind turbines would be areas without any obstacles to free- flowing wind, while allowing for good exposure to the wind at the height of the wind turbine.

The same wind turbines used in rural sites can be used in off-grid rural villages and communities. The only major difference in rated wind turbines would be their determined use and the height of establishment, as well as the minimum rotational speed [129].

Small wind turbines are also gaining favour with farmers globally, as the need for renewable energy sources to power their off-grid residences and to reduce their operating costs and electricity bills [130]. The small-scale wind turbine industry is forecasted to reach 270 MW of newly installed turbines by 2020 with proceeds from the 12% growth per annum and reaching the estimated capacity of 1,9GW by 2020, as displayed in Figure

2.41 [131].

Figure 2. 41 World Market Forecast 2020 [88] The wind speed flowing through a small wind turbine has the same effect as that of a large-scale wind turbine. A small increase in wind speed can increase the power production; and this can also be influenced by installing a taller turbine tower [132].

Wind energy is abundant in the Eastern Cape; and off-grid communities which are also in elevated locations, mountainous regions without obstacles would be suitable for small- scale wind turbines [127]. Off-grid systems, which utilize small wind turbines need battery storage to store any excess energy generated, as well as an inverter to transform the DC- power to AC, in order to deliver it to households for use [127].

Table 2.5 displays a comparative study of small- and large-scale wind turbines; and it analyses the different attributes or parameters between small- and large-scale wind turbines.

Table 2. 5 Comparison of small- and large-scale wind turbines [133]

No Parameter Small-Scale Large-Scale

1 Blade length (rotor radius) < 5- 8m 20-50m

2 Rated power < 100 kW 1-3MW

3 Turbine speed 100+ rpm 15-30 rpm

4 Transmission type Direct driven Gear box

5 Generator type DC or PM synchronies Induction / synchronies

6 Speed control Blade stall, Furling Blade pitch

7 Application Stand-alone/ Grid connected Grid connected

The wind turbine types 30-60 and 30-100, have a rotor diameter of 30m, sweeping an area of 705m2 to produce an Annual Energy Production (AEP) rating displayed in Table 2.6.

These ratings were IEC-Norm certified in 2019 [134].

Table 2. 6 Rating powerful small-scale wind turbines [134]

Average wind speed (m/s) AEP: Type (30-60) AEP: Type (30-100)

5 216 MWh 265 MWh

6 275 MWh 361 MWh

7 311 MWh 443 MWh

There are many types of SSWT built for household needs; and these are able to generate enough electricity to power a few light bulbs. But Figure 2.42 displays the above average configuration of a small-scale wind turbine, which can generate up to 300 kW; and this is enough to supply a small community.

Figure 2. 42 Small-scale turbines IEC certified 2019. [91]

2.9.4 Battery storage and Diesel generators in Hybrid models Battery storage and diesel generators, both play critical roles in supplementing the generated energy from wind and solar PV. Battery energy storage has more limitations than diesel generators; as they are dependent on recharging through a renewable source; while they are also able to discharge 50 per cent of their capacity, without eroding their long-term benefits. In off-grid systems diesel may become a challenge, depending on the remoteness of the community where the system is deployed.

2.10 Off-grid systems Off-grid renewable energy systems can be described as the decentralizing of power generation. It is the source of power, which does not come from the main utility power grid.

A centralized electric utility system is becoming both economically unfeasible and

environmentally detrimental, particularly when it needs fossil fuels to extend the grid system. Therefore, since the growth of fossil fuel power stations without renewable energy sources is deemed inefficient and costly, the need for off-grid solutions and decentralised power-generation systems becomes a priority [135]. While off-grid solutions were previously too expensive, currently they are feasible. According to the IEA estimates, mini- grids will be the most suitable and financially viable solution for over 45 per cent of the global non-connected population, while stand-alone systems could contribute a further 25 per cent [136].

Figure 2.43 displays the four factors that justify the impact and need for off-grid renewable energy. These factors dive into the multi-layered impact that off-grid systems have on communities. When compared to fossil fuels and off-grid systems, coal-fired powered stations in particular lack the environmental impact; since there is limited room for technology to advance, thereby fulfilling only the Economic and Human Development factor.

Figure 2. 43 Case for off-grid renewable energy solutions [105] In 2012, the Department of Energy (DoE) reported that 86 per cent of South African household were electrified, which meant an estimated 14 per cent of households nationwide were without access to electricity [137]. In 2014, the DoE again reported some levels of improvements by reducing the number of households without access to electricity to 11 per cent. Figure 2.44 demonstrates the different types and levels of electricity accessibility in South Africa in the year 2012, according to the Department of Energy.

Figure 2. 44 Access to electricity DoE, 2012 One key factor affecting the access to electricity is how it is received. In 2014, a report from StatsSA displayed the access to free electricity as a basic need [138]. Some communities will not make the effort to privatize their electricity system; nor will they accept the burden of repaying the investment made towards the community through the implementation of the off-grid system; since they would prefer to wait for free electricity from the municipality and the government. Table 2.7 displays the number of consumer units receiving electricity and free basic electricity services from the municipalities between

2012 and 2013.

Table 2. 7 Number of consumer units receiving electricity and free basic electricity services from municipalities between 2012 and 2013.

Province 2012 2013 Number of Number of Proportion Number of Number of Proportion consumer consumer benefiting consumer consumer benefiting units units (%) units units (%) receiving receiving receiving receiving basic free basic basic free basic electricity electricity electricity electricity services services services services Western Cape 1 236 228 542 230 43.9 1 266 161 560 877 44.3 Eastern Cape 1 116 022 303 707 27.2 1 146 447 325 429 28.4 Northern Cape 263 969 96 914 36.7 270 283 68 292 25.3 Free State 667 310 210 373 31.5 691 914 171 847 24.8 Kwa-Zulu 1 539 986 215 287 14.0 1 566 638 182 156 11.6 Natal North West 834 074 162 724 19.5 856 531 158 970 18.6 Gauteng 2 137 638 548 372 25.7 219 0415 677 341 30.9 Mpumalanga 784 485 276 172 35.2 804 408 262 848 32.7 Limpopo 1 169 008 199 398 17.1 1 183 304 141 913 12.0 South Africa 9 748 720 2 555 177 28.2 9 976 101 2 549 673 25.6

The challenges in off-grid systems need to be overcome before implementation can be fully achieved. One of the key obstacles to overcome is affordability. When intending to implement an off-grid system for rural areas, the initial investment required is often outside the affordability of the community, and the individual’s capa ility to purchase electricity at a rate that would justify the off-grid system, even when it is well below market price [139].

For this reason, su sidies in the project’s implementation will e crucial in reducing interest rates and improving the affordability of electricity for the community.

Figure 2. 45 Off-grid implementation flow-chart [139] Another challenge to off-grid systems will be the provision of electricity to remote locations with limited or no infrastructure. With limited infrastructure, the cost of establishing distribution increases the cost of implementation [140]. Poor infrastructure also increases the difficulty of recouping the initial investment, such as no bank ATM machines, and such systems that would allow the end-user to buy access to the generated electricity [139].

Lastly, the lack of awareness can hinder the distribution of energy; and any benefits would go unused, or as lost.

There may be a need for some basic education in the process of community engagement before off-grid systems can be implemented [140].

Off-grid renewable energy solutions, including stand-alone systems and mini-grids, give a different perspective to energy access, expanding to rural areas. Hybrid-energy systems offer the versatility of serving a wider customer base, with the combination of different renewable energy-based sources. [141]

2.10.1 Off-grid financing systems According to a report by the IEA in 2018, the required investment to achieve universal electrification in Sub-Saharan Africa would be in the region of USD 27 billion per annum

(2018-2030), which is currently more than double the existing levels of financing. This

figure highlights the need for increased priority for domestic and international sources of financing [142].

However, investment in off-grid systems has seen positive growth over the past few years to the excess of 400 per cent reaching USD 284 million globally by 2017 [143]. However, off-grids are still regarded as a niche market, attracting no more than 1 per cent of the market USD 30 billion used between 2015 and 2016 for access to electricity. Perhaps the most difficult aspect of off-grid project implementation has been financial viability and the scalability thereof. The scalability of this market will hinge on the accessibility of commercial debt financing, using tailored payment methods, such as PAYG [144], and the maintenance of grant funding, crowd-funding and non-profit organisations.

Africa, unlike Europe, has no common currency recognized and used throughout the entire continent, such as the Euro or the European dollar (€) [145]. This, in part, speaks to the variation in the economies in Africa – most of which are poor or non-existent. For this reason, providing electricity in Africa becomes challenging; as the continent shows that it requires a more complex and detailed solution that surpasses the current one-size-fits-all energy solution [146] [147].

Off-grid systems are niche markets, which can use innovative finance and impact investment to fund the implementation of off-grid and mini-grid projects.

Nigeria is said to have the most comprehensive mini-grid regulation system in Africa, according to the World Bank [148]. This comes after a push from government to comply with mini-grid and off-grid policies, in order to achieve the mass scaling that would boost its economy [149] [148]. There have been positive signs that have pulled in overseas private investments and collaborations that perceive Nigeria to be the pinnacle break-out point for the West African region and a mini-grid frontrunner on the continent [148] [67].

Before a company makes an investment, there are key performance indicators that are critical to the community survey. These are as follows:

1. Energy consumption, peak and base loads;

2. Average number of households to be supplied;

3. Daily power usage from the available appliances; and

4. The average number of appliances, in addition to the appliances’ efficiency.

The influence of reliable data is critical in the implementation of the project. The key indicators of performance for micro-grids, as demonstrated in Figure 2.45 and Figure 2.46, are heavily reliant on the analysis of the real data, which comes before the system design.

This then leads to a suitable system and a sustainable business. This comes from the understanding that micro-grids and off-grid systems require specialization, which comes from the data that indicate whether or not the design system would lead to a sustainable solution or business. Consequently Figure 2.46 demonstrates the design process without real data, which leads to a different outcome.

Process without Reliable Data

System Underutilization of Demand System Site Survey Oversized or assests or Costomers Assessment Design Undersized Unhappy

Figure 2. 46 Key performance indicators of micro-grids designed without data [150] [63]

Process with Reliable Data

Demand Analysis Site System Suitable Sustainable Assessmen based on Survey Design system business t real data

Figure 2. 47 Key performance of micro-grids designed with data [150] [63]

Figure 2.48 demonstrates the off-grid energy corporate-level investment into off-grid energy access companies according to the year and type of financing through year-end 2018.

Figure 2. 48 Off-grid energy access companies have absorbed just shy of $1.7 billion in disclosed investment Studies from India show that household income is no longer a primary determinant or barrier in households receiving solar home-lighting systems [151]. The same study goes on to conclude that most off-grid solar products are purchased by using micro-financing institutions; and these automatically side-line traditional commercial banks. This practice works well in rural areas where knowledge and awareness of such purchase methods exist. The willingness to pay varies with each region; and areas with higher incomes would be more willing to pay for off-grid products [151] [152]. Figure 2.49 displays the lack of access to electricity in different African regions over a 30 year projection. Figure 2.50 demonstrates through a pie-chart diagram the fuel-source of the generating electricity versus the type of connection in Sub-Saharan Africa.

African energy landscape

Figure 2. 49 Sub-Saharan Africa by Region, displaying the population without access to electricity (%) [122]

Figure 2. 50 Sub-Saharan Africa New Electricity Generation for Universal Energy Access (2018-2030) % of additional TWh [122] Kenya is becoming the fastest growing renewable energy-implementing country in Sub-

Saharan African and a leader in East Africa. The country is set to host Africa’s first large- scale hybrid wind-energy power plant [153]. The project will be set up at the Meru Country

Energy Park, producing 80 MW of renewable energy, powering over 200 000 households

and set to cost an estimated USD 150 million [154]. The large-scale hybrid energy power plant will be sourced by using solar PV panels, wind turbines and battery-energy storage

[153] [154] . Figure 2.51 demonstrates the Renewable Energy share in total final energy consumption between 1990 and 2015 in Sub-Saharan Africa.

Figure 2. 51 Renewable Energy share in Total Final Energy Consumption, 1990 – 2015, [125]

Figure 2.52 demonstrates the digital grid roadmap in Sub-Saharan Africa, where the timeline coincides with the type of renewable energy smart metering and distributed energy over the next decade.

Figure 2. 52 Digital Grid roadmap in Sub-Saharan Africa [126]

Among the challenges of off-grid solutions is in the form of mini-grids and stand-alone systems remains, as well as the financing of multiple small projects for small communities, which is quite unlike the risk associated with one large utility-scale project. The second challenge lies in the client’s willingness to pay ack the investment for the off-grid long- term. The challenge of licensing mini-grids remains a hurdle to be overcome, as can be seen with small embedded generation in South Africa [155].

According to a study done by Ernst and Young [156] [157] on mergers and acquisition in the renewable energy space, the findings indicated positive growth in off-grid systems being a reliable solution to bridge the power-demand and the supply gap, particularly in remote and rural locations, in Africa [157]. Africa as a continent, and South Africa in particular, continue to attract foreign and government investment in renewable energy; as the economic reforms challenge the economic environment [157].

2.10.2 Willingness to Pay (WTP) for Off-grids. The implementation of off-grids and hybrid mini-grids is often challenging to finance; as they are not privately owned; and they are built to serve the energy needs of a community, which in most cases, live in low-income households, rural areas and informal housing

[158]. It is for this reason that the customer’s willingness to pay (WTP) is a key indicator in any feasibility study, which seeks to implement renewable energy-power generation in off- grid or decentralized markets [159].

According to a UK based solar-power venture company Bboxx, their sustainable building materials and design for future rural homes in Rwanda will include a 50 W solar PV panel.

This will be mounted on the roof; and it will be payable over a duration of three-years

[160]. The system will be designed is such a way that each unit will connect remotely to the central database, which supports the automatic switch-off, thereby protecting maintenance and repairs, as well as assisting with upgrades [160]. The company further goes to explore the service provider’s role in rural areas, with such systems as PAYG, and the future development of banking systems to allow for long-term payment plans to become a norm in remote and rural areas.

A study was done to assess the feasibility of renewable rural off-grids in South Africa; and the results indicated that the willingness to pay depends on the options of payment and the offerings which cater to their financial capability, in addition to the amount of energy they can generate. The study compared two neighbourhoods, which are similar in their demographics, housing types, current fuel-use patterns, livelihoods and access to basic services [161]. These rural villages are in the Eastern Cape’s M andana and Dumsi areas.

The study then showed that even in the most impoverished communities, the options for payment can be tailored to meet the needs of the community over a period of sufficient time.

When the World Bank researched possible electrification projects for Cameroon for their

Rural Electricity Access Project for Undeserved Regions programme, they found that on average the WTP relative to the average income per household in each part of the country converged toward 9%; but the actual costs were within 5,2% of their income.

2.10.3 Demand and Supply estimation for Off-grids According to Boait et al. [162], the potential of mini-and micro-grids powering rural development can be realized in developing countries, provided the off-grid systems are designed and managed to provide energy with the same level of reliability and economic sustainability as grid-tied systems [162]. In his feasibility study, Boait noted that as the number of households or businesses served increases, the variability of aggregate demand is reduced exponentially [162].

Therefore, the more households that are being served, at least 50 households, the greater the diversity between households, the lower the demand variability; and thus the more sustainable the mini-grid system then becomes [162].

2.11 Stand-alone solutions Stand-alone systems are considered to be small electricity-generating systems, which do not distribute energy from a central point, but they supply energy to individual appliances, households and small business, or to industrial areas [163]. These systems can be differentiated into, Pico systems, which describe the individual appliances, such as lights, radio and television; Home systems, which describe the use of individual households and

Productive systems, which are associated with hotels, clinics, shopping malls and factories

[163].

Figure 2.53 displays the differences between mini-grid solutions and stand-alone solutions.

Figure 2. 53 Contrast between mini-grid and stand-alone solutions [131]

2.12 Mini-grids / Micro-grids Micro-grids (1- 10kW) and Mini-grids describe the same system, and the terms can be used interchangeably. Mini-grids are essentially small-scale (from 10kW to 10MW) decentralized power generation and distribution systems, which can be built from different renewable energy sources or non-renewable sources, as further displayed in Figure 2.54

[164]. Sub-Saharan Africa could electrify over 140 million rural Africans by 2040 if mini- grids become the predominant source of rural and off-grid electrification, thus resulting in the erecting of 100 000 – 200 000 mini-grids [165].

The best operational conditions for a micro-grid are in dense areas and communities close to one another; while the grid is located some distance away, serving a limited number of consumers through the distribution grid that can operate in isolation from the national utility grid network [166]. Mini-grids can have more than one energy source from solar PV, Wind,

Hydro or biomass to form a hybrid-energy system. In most cases, micro-grids require a storage battery, in order to sustain the power supply during periods of no-power generation, provided they operate from an off-grid system. Where the mini-grid is grid-tied, then the energy supply balance is subsidised by a utility grid [164].

Figure 2. 54 Renewable technologies expanding electricity access [135] [167]

Mini-grids must be perceived as localized power networks, designed and implemented to supply generated power to a specific number of households. These are also referred to as distributed-energy resources (DER) [168]. Mini-grids should be seen as a cost-effective solution to providing access to electricity, while also being easier to implement. [169] .

Mini-grids are becoming the main solution to electricity access for remote and rural and under-served areas throughout Africa, and South Africa, in particular. As with most progressive concepts in developing countries, the access to funding often begins in private investments before gaining mainstream popularity [168].

The deployment of micro-grids can fall victim to theft, poor tariff collection, customer overuse, unreliable operation; while poor maintenance can lead to the failure of micro-

grids [73]. Other challenges facing micro-grids, excluding the up-scaling process, is their exclusion in the country’s regulatory framework. This makes the rolling out of micro-grid systems challenging because of the uncertain equipment standards, the ability to charge cost-reflective tariffs and the implications of the central grid reaching an area served by a micro-grid [150]. The scalability of micro-grids is constrained by the cost thereof.

Both mini/micro-grids and stand-alone systems have become the most cost-competitive off-grid solutions for power generation. With the stand-alone system, the solution serves small electricity systems powering individual appliances, homes or small business enterprises [166].

There are essentially two types of mini-grids in operation today; these are the Alternating

Current (AC) and Direct Current (DC) mini-grids. The AC mini-grids require an inverter to connect the main grid to the households to which it supplies energy. The AC mini-grid is the most suited to replace a pre-paid electricity supply; as it can deliver power to high- energy demanding appliances, such as washing machines, heaters and electric stoves.

On the other hand, with regard to the DC mini-grid, the system operates efficiently over short distances; and since it does not use an inverter, it is less complex. DC mi0ni-grids are best suited for appliances with low energy demand, such as lighting and cell-phone charging [170].

Mini-grids have the option to connect to the national or municipal centralized grid. They can be designed to operate autonomously in remote locations; but they are also able to be connected to the centralized grid during grid extension [122].

There are four models, which describe mini-grid ownership; these are as follows [171]:

 Private sector: Private investor(s); here a private company has ownership over the

system and its proceeds.

 Utility/ government: The municipality or government, in this case Eskom, owns the

system.

 Community-led: The community raises its own funds, implements the system, and

has complete ownership, or the majority ownership of the system.

 Hybrids: The combination of public and private partnership. This could be the

municipality and a private company collaborating to implement the system.

Figure 2.55 displays the two categories, which describe mini-grids and off-grid solutions and their types as:

 Autonomous systems

 Interconnected systems

Within the category of separation there further exist: Lower and High tiers of quality, which separate the two systems. The Autonomous basic service and the Interconnected community application fall under the lower tier of quality, while the Autonomous full service and the Interconnected large industrial application fall under the higher tier of quality. An off-grid system could be developed by complying with one of these criteria.

Figure 2. 55 Types of micro-grids [148]

2.12.1 Community micro-grid vs Traditional micro-grid Figure 2.56 displays the inclusiveness involved in community micro-grids and their different stakeholders. The concept of community micro-grids is a term used internationally; but it refers to the same system used to power rural and remote households from a centralized generation point. However, it spans over a larger region, as can be seen in Table 2.20. [168] [172]. The image demonstrates the different stakeholders, which must be engaged in the deployment of micro-grids, which would ensure that they are deployed correctly.

Figure 2. 56 Community micro-grid key stakeholders [172] Table 2.8 displays the differences between community micro-grids and traditional micro-grids, in summary.

Table 2. 8 Community micro-grid vs Traditional micro-grid [172] Feature Community Micro-grid Traditional micro-grid

Scale Spans an entire substation grid Covers a single customer area, benefitting thousands of location or a small number of customers. adjacent locations.

DER Usually installed in front of the Usually installed behind the location meter (on the side of the electric meter (on the owner’s property) grid).

Cost Lowers costs by identifying optimal Maximizes benefits for a single DER locations, deploying DER customer and does little for the more broadly, and providing grid. Replacing it is very scalability. expensive.

Resilience, Provides indefinite back-up power Provides limited back-up power security to prioritized loads that are critical to only a single location or to an entire community. customer.

Scalability Enables easy replication and Requires tedious work to scaling across any distribution grid implement at each individual area. location.

2.12.2 Rural electrification in Africa Rural electrification in Africa is seeking to become a socio-economic development trigger- point for villagers and rural dwellers throughout the continent [173]. In South Africa, the

Solar Home System has seen more prominence than the hybrid mini-grid. The Lucingweni village hy rid system’s failure has rought some caution to potential investors, due to the limitations involved in instalment costs, lack of certainty of the return on investment, in addition to other variables, which contribute to the generation of electricity [173].

South Africa has faced some challenges in past years in attracting international investment; and consequently, it has seen its Ease-of-Doing-Business Index drop further down the rankings, as seen in Figure 2.57. The ease of doing business determines the perception that international money weighs the risk profile of the economy and the country.

Some of the key factors affecting this index, include the regulatory conducive environment for business, the GDP index and the growth rate, as well as the political steadfastness of a country [174]. Currently, South Africa is ranked 84th out of 190 countries, which means that investors may not identify it as the most ideal location to invest in its rural electrification.

However, it remains in Africa’s top ten countries with an a ove average index.

Figure 2. 57 Ease of doing business index for South Africa (2010-2019) [174]

Electrifying SSA has taken centre stage globally and different solutions have unfolded in the past decade, most of which are in favour of renewable energy.

Decentralized energy-generation systems, which include mini-grid and off-grids, have been identified as the most suitable solutions for the continent [175]. According to the IEA [176], decentralized renewable energy systems are becoming the new form of centralized energy systems, in the form of community-based grids, which are

built to service the needs of the community, while also being cost-effective. Mini- grids are still facing obstacles with regard to universal electrification – even after being a proven and well-suited option for electricity access in remote rural areas

[175]. Figure 2.58 displays the types of solutions classified as either Central

Scenario or Energy-for-all case, which indicates the exponential growth in population with access to energy, which can be achieved from renewable energy technologies, particularly solar PV.

Figure 2. 58 Cumulative population gaining access to electricity by 2030 [175]

2.13 Mini-grids compared to Solar-House systems (SHS) The comparison between the installation of mini-grids and solar-home systems (SHS), as methods of electrical generation continues to be worth looking into regarding the type of electrical generation being pursued and the renewable resources available. Figure 2.59 displays the difference between a mini-grid and a stand-alone system. Table 2.9 summarises the comparison between mini-grids and solar-home systems by comparing the attributes and the operational strengths and load types. From these, the economic contribution can be determined, when deploying a system in a community.

Mini-grids would serve a greater number of people for longer, while also being supported by BES and diesel generators, to achieve a 24-hour power supply.

Figure 2. 59Mini-grids and Stand-alone systems [155]

Table 2. 9 Comparison of operations done by Mini-grid vs. SHS [177] [155]

Energy Source Definition Load Type Supported Energy Economic Availability Activities SHS/ Stand- An energy Black & white / Small retail 3 -4 hours per alone system solution for low resolution TV, (Spaza/ day individual electric bulbs, cell Shebeen) shops. users/applications phone charging Lights for typically without and small radio workshops, street utility grid use. barbers. connection

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Mini-grid A small grid Coloured TV, Agricultural use Up to 24 hours system electric bulbs, through irrigation, per day electrifying a electric iron, carpentry, cyber number of users refrigerator, air- cafe, ice-making or a community conditioner, cell and butcheries, typically without phone charger, water pumping, utility grid water pump, welding, schools, connection and milling machine, clinics and hair requiring some welding set and salons, form of battery grinder, drilling storage with and other work power supply. tools. Office equipment (printers, scanners etc) Salon equipment (hair-drier, hair clippers)

2.14 Smart-grids and Smart meters According to the European Technology Platform Smart Grid (ETPSG) [178], the standing definition for smart grids refers to “an electricity network that can intelligently integrate the actions of all users connected to it – generators, consumers and those that do both – in order to efficiently deliver sustainable, economic and shared electricity supplies” [178].

Essentially, smart-grids are becoming the most robust energy-efficient economy boosters.

Smart-grids are the energy networks needed to better manage the energy usage in communities, expanding the use of renewable energy, greater cost-benefits for both the municipalities and the individual households [179]. The following are the three key areas of influence in which smart-grids can play a critical role:

 Energy efficiency

 Energy security

 Distributed generation

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It is through smart-grids that all three key areas are achieved, as smart grids incorporate smart-metering, which allows for inter-communication and the use of control technologies to transmission lines, sub-stations, households and individual appliances [179] [180].

Smart metering is defined as a system that fulfils the following functions [178]:

 Measurement of the energy consumption of individual appliances and total

consumption in a time interval.

 It allows for two-way communication between the licensee or user of the smart-

meter and the customer.

 It stores data on a time-interval basis; and it then transfers it to the licensee.

 It allows for remote load management.

Smart-grids and smart meters are the future of off-grid and mini-grid electrical appliances; since they allow for individual appliance control. This system has been used by different smart-home development companies, which focus on optimizing the use of appliances and their on-off wireless switches. A Swiss-German company called, DigitalSTROM, is implementing digital network communication between applications, which will allow for networks of communication to be built off-grid and also to self-regulate, being able to switch on-off individual appliances and therefore increasing control of the system [181].

Such a system introduces a new dynamic for off-grids; since the long-term payment systems that are currently being used, such as PAYG and others would increase their chances of recouping on their investment and making the system much more sustainable and worth the money for private investors and commercial banks. What further makes smart-grids appealing for off-grids will be the size of the community and its ability to form an undisturbed network, which will be easier to manage and design for its testing period;

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as the technology is adapted from urban European countries to rural Africa villages, and particularly for South African villages.

Lastly, the LED lighting systems, for which these systems are designed, increase the efficient use of appliances, thereby reducing costs and improving the period of operation.

Figure 2. 60 The converging elements of smart grids [166] The Figure 2.60 demonstrates the interconnected relation between Transmission and

Distribution Equipment, Information and communication Technologies, together with

Energy-Storage Systems. The network created from smart-grids improves the energy- delivery system; and it creates an interactive system, which is also interdependent; and consequently, it optimizes how energy electricity is managed, distributed and stored in the future.

One of the positives of smart grids is that there are opportunities for jobs to be created in such a system. Smart grids can definitely make a case for off-grid systems, particularly if

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more than one such network can be formed closely to another, like a collective network of off-grid communities that can be controlled by one main smart-grid hub.

Figure 2. 61 Opportunities for smart-grid investment in Sub-Saharan Africa [166] In 2011, when the International Energy Agency (IEA) published a roadmap describing the deployment of smart-grids, the concept was built for cities that could become smart cities

[182]. Almost ten years later, the potential for smart-grids to make off-grid hybrids systems financially viable has become apparent, as can be seen in Figure 2.61.

2.15 Small-Scale Embedded Generation Small-scale embedded generation (SSEG) refers to a system that generates power, while located at a residential, commercial or industrial site, ranging below the 1 MW power output. SSEG’s usually service their site of location with the power generated [183]. This is known as self-consumption; while in other markets around the world it allows consumers the option to feed any access energy generated back into the grid.

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According to an article by Chris Yelland et al. [184] on present and future tax incentives, it reveals the South African government’s intention to improve conditions for small and medium businesses to cut costs and ensure the stability of their power supply during load shedding [184]. From the 1st of January 2016, the amendment to Section 12B of the

Income Tax (Act 58 of 1996) has given precedence and provision for companies to claim capital depreciation allowances for businesses using solar PV grid-tied systems generating electricity at less than 1MW per annum [184].

Recently, business models have had to evolve to cater for households with different incomes, and for those living in different regions and developing countries. The growing potential of off-grid systems becoming more commercially viable has led to interesting financing models that are showing promise in Africa. The following four financing models are currently in use [185]:

 Retail: Customers buy the products off the shelf from retailers and distributors

 PAYG: This system uses mobile telephones and smart meters.

 Consumer financing: Consumer partners with a commercial bank or financial

institution, which collects the repayments.

 Fee-for-service: Ownership of systems is not transferred to customers; but it

charges a fee for the use, and for recharging products.

Due to varying markets and cultural proclivities, the investor confidence in certain countries and the economic priorities of developing countries in Africa, PAYG (Pay-As-

You-Go) and Fee-for-service have surpassed retail in most parts of the continent. These systems have been proven to be the systems of least resistance in rural developing countries [185]. PAYG has steadily ecome Africa’s most technologically advanced innovative client payment instrument. With solar PV house systems (SHS) becoming the

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leading form of access to electricity using renewable energy, the potential for this long- term payment scheme/ instrument could also be applied to rural off-grid hybrid systems

[186].

PAYG allows its customers to pay off their solar kits, be they lights, panels, radio, or any other domestic appliance that uses renewable energy as a power source; preferably solar energy [187]. Companies like M-Kopa, BBoXX, Solar’s, and iShack have found that this micro-financing tool has become revolutionary with regard to the implementation of rural off-grid systems [188]; since it integrates the use of mobile cell-phones and the ability for rural communities to participate in the digital economy, while also developing communities and giving them the opportunity to contribute to innovation and their local economy [186]

[187].

D.light solar is an Africa consortium, which has sought to attract international investment in excess of USD 18 million, to secure debt funding, which would enable the company to provide reliable solar solutions for off-grids in rural areas [189]. Debt funding is what allows micro-financing schemes to lend money – and to carry the credit until the debt is paid off, at sub-prime interest rates.

In 2017, Rand Merchant Bank (RMB) and KfW Development Bank (KfW) closed on a merger that brought the Facility for Investment in Renewable Small Transactions (FIRST), with the intention to fund small renewable energy projects and other projects that fall within the Department of Energy’s criteria (less than 5MW) for Small Projects and Independent

Power Producers (SPIPP) programme [190] [191]. This further demonstrates that where small-to-medium-sized projects are identified, funding within South Africa exists. Figure

2.62 displays the off-grid financers, according to the stage of project deployment. This is

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further highlighted in detail in Table 2.10, which shows the stance from commercial banks on off-grid systems.

Figure 2. 62 Off-grid solar financiers across the start-up development cycle [177] Commercial Banks are becoming accustomed to the type of long-term loans, on which the renewable energy industry needs to operate, in order to make their business models feasible, particularly regarding solar PV installation. With the Department of Energy already giving tax incentives, other incentives that could see the widespread use of solar

PV that requires no upfront capital from the end-user, are the 12-to-15 year loans [192].

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Currently, the installation cost (without battery storage) is well above the affordability of rural households, which are the focus of this research, While solar PV prices have declined significantly, the next control measure will be tariff prices, which currently range in the following regions [192]:

 Commercial tariffs (R0, 85 –R1, 70 per kWh)

 Residential tariffs (R2, 70 per kWh)

Table 2. 10 Commercial banks on solar-energy project financing structure [193] [192].

Absa Nedbank Standard First National Bank Bank Loan Offered Scheduled -60% company - Depending on Up to R50 repayments at debt and 40% orrower’s million for a 10 up to 100% loan- equity from bank financial year period. at-cost - Asset based position. Bank finance and term can consider loans up to funding up to 100% of project 100% installation cost costs - Scheduled repayments Security Project-specific Security can be Collateral-based Utilising equity in Required and additional taken against contractual commercial security may not the asset but agreement property as be required. The often is taken collateral installation is against the seen as part of underlying the collateral balance sheet of the client Limitations Individual No minimum Typical finance None projects size, but for projects up to between (30kWp Nedbank must 999kWp. Larger -1MW) be sole primary projects are banker evaluated on a case by case basis. Term of Debt 5 -10 years Up to 10 years Up to 10 years Up to 10 years Interest rate Risk dependent Risk dependent Risk dependent Risk dependent

Commercial banks are also shying away from financing coal-fired power stations, of which

Nedbank Group Ltd and the Development Bank of Southern Africa (DBSA), remain poised

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to implement their tailored business models after the Integrated Resource Plan (IRP) is finalised [193].

2.16 Rural Development opportunities. Rural development in Africa has begun showing promise with potential markets, which emerge from off-grid solutions and internet connectivity. While in past times, energy generation and the broadband industries have remained separate entities of development, the opportunities of boosting economic growth are tying these two industries together to improve service delivery [194] [195]. According to the 2016 World Development Report, the digital economy is growing at a rate of 10% per annum and could be double that in developing markets [195].

Rural electrification continues to show its potential for socio-economic development for rural communities, villages and non-urban areas. The impact of rural electrification exceeds the improvement of quality of life; but it offers the opportunity for economic growth

[196].

While off-grid systems have long been a good idea, it is only in recent times that the off- grid renewable-energy sector has become both commercially viable and scalable.

Although the majority of the off-grid sector’s investment has een in solar-house systems, particularly the rooftop panels; the sector is expanding into mini-grids for local power generation [197] [198].

According to Inga Vesper [199], electricity is a means and not an end within itself. The rural communities have little to benefit directly from electricity alone, unless technological infrastructures are in place [198]. While most governments are happy to measure access to electricity with the power capacity to cook and have lights at night, the power to stimulate economic growth, lies with the capacity of the technological infrastructure, such

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that a rural community has access to the same opportunities of advancement as a person in a developed community [199] [198].

Figure 2.63 demonstrates the need to accelerate the growth of rural access to electricity in

Sub-Saharan Africa by 2030 [200], while Figure 2.64 displays the techno-economic viability of hybrid-energy systems with BESS.

Figure 2. 63 Evolution of fraction of population with electricity access in Sub Saharan Africa [201]

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Figure 2. 64 Techno-economic viability of Hybrid PV-wind-diesel-battery storage energy system [202] 2.17 Electricity Tariffs and Prices Electricity tariff is the rate and price that the National Energy Regulator of South

Africa (NERSA) sets for Eskom and the Regional Electricity Distributors (REDs) at which to sell and distribute. The tariff price is sub-divided into three main categories:

Urban, Residential and Rural. Table 2.11 displays the Net tariff prices for electricity in South Africa from April 2016, according to Eskom [26].

Table 2. 11 Eskom electricity tariffs from April 2016 [26] Net tariffs in c/kWh [ZAR]

RESIDENTIAL TARIFFS Eskom’s residential tariffs [VAT incl.] Block 1 [< 600kWh] 124,59 Block 2 [> 600kWh] 196.72 – 200.34 Network capacity charge [ZAR/POD/day] 5.34 – 20.65 Local authority’s residential tariffs [VAT incl.] Block 1 [< 600kWh] 124,59 Block 2 [> 600kWh] 196.73 – 200.36 Network capacity charge [ZAR/POD/day] 5.34 – 20.65 Eskom’s residential tariffs (bulk) [VAT incl.] Energy charge [c/kWh] 163.57 Network capacity charge [ZAR/kVA] 33.85

URBAN TARIFFS Eskom’s Urban tariffs [VAT incl.] (Business rate) Energy charge [c/kWh] 106.31 – 286.06 Ancillary service charge [c/kWh] 0.41

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Network demand charge [c/kWh] 15.01 Network capacity charge [ZAR/POD/day] 21.55 – 62.73 Service & administration charge 18.62 [ZAR/POD/day] Local authority’s urban tariffs [VAT incl.] (Business rate) Energy charge [c/kWh] 108.84 – 292.88 Ancillary service charge [c/kWh] 0.41 Network demand charge [c/kWh] 15.08 Network capacity charge [ZAR/POD/day] 21.64 – 63.04 Service & administration charge 18.53 [ZAR/POD/day]

RURAL TARIFFS Eskom’s rural tariffs [VAT incl.] (Landrate) Landrate 1 – Landrate 4 Energy charge [c/kWh] 105.78 – 228.47 Ancillary service charge [c/kWh] 0.41 Network demand charge [c/kWh] 26.43 Network capacity charge [ZAR/POD/day] 28.25 – 69.43 Service & administration charge 23.46 [ZAR/POD/day] Local authorities’ rural tariffs [VAT incl.] (Land rate) Landrate 1 – Landrate 4 Energy charge [c/kWh] 108.30 – 233.91 Ancillary service charge [c/kWh] 0.41 Network demand charge [c/kWh] 26.70 Network capacity charge [ZAR/POD/day] 28.50 – 70.08 Service & administration charge 23.35 [ZAR/POD/day]

From the rural tariffs, the energy charge lies between 105.78 -228.47 c/kWh. At this tariff, access to electricity becomes unlikely and even more so in remote communities, which adds to the difficulty of electrifying remote and rural villages.

2.17.1 South Africa’s electricity tariff from a global perspective It is significantly important for South Africa to keep up with the rest of the world in matters of energy, and more to the point, as regards the electricity tariff. NUS

Consulting [203] has conducted a study, which shows the electricity prices of selected countries in the world, in 2014, as can be seen in Figure 2.65. In 2014,

South African consumers were liable for USD 0.08 per kWh (estimated R1.00 or

R1.06). This was after Eskom had requested permission to increase its tariffs by a

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further 10 per cent on top of an already approved 12 per cent in 2014/2015. These increases in tariffs have still remained competitive; however, Eskom has the following challenges when dealing with rural and remote electrification: (i) Charging rural communities electricity at the average tariff price becomes unfeasible; and at- best the cost would break-even. (ii) Grid connection remains high and continues increasing, year-on-year [26] [204].

Figure 2. 65 Electricity prices in selected countries in 2014 ($/kWh) [203]

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More statistics show that South Africa had the biggest increase in tariffs in a two- year period, which gave it a 7.5 per cent price change during that period, as demonstrated in Figure 2.66. This can be justified by the increasing price of grid- connection. Between 1995 and 2009, the price grew from R 15 450 per household, to R17 000 in 2013; and currently, it sits at R 25 000 in 2019. The increase in the price of grid-connectivity continues to justify off-grid solutions, such as hybrid-energy systems as the most suitable solutions to extend the access of electricity to all households in South Africa [204]. When Jamal [204], from the European University in

Flensburg Germany, studied and researched the options for electricity of rural households in South Africa, he found that one source of energy would not be sufficient for targeting the 7 per cent of off-grid electrification required for rural households in South Africa.

Figure 2. 66 International Electricity Price Survey -2013/2014 South Africa with the biggest change [203]

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2.17.2 South Africa’s tariffs compared to other African countries From an African perspective, South Africa shows that it maintains a very competitive price point. The average price of electricity in South Africa is the third best behind

Egypt and Zambia from a total of 14 countries surveyed, as can be seen in Figure

2.67 [203]. The figure displays the average price of electricity in Africa based on the

USD, per country.

Figure 2. 67 African countries' prices in 2015/2016 [203]

2.18 Levellised Cost of Energy (LCOE) One of the most important design factors for developing and investing in renewable energy projects is the Levellised Cost of Energy (LCOE). The LCOE measures the lifetime costs divided by the power output in units ($/ MWh). In calculating the LCOE, the current values of the initial capital cost are combined with the annual operation cost, over an assumed lifetime, usually 20 -30 years for large power plants [205]. The LCOE measures the

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average revenue per unit of electricity generated, in order to recover implementation and operating costs for a set lifetime cycle [206].

The LCOE is the price of electricity required for a project to be implemented, factoring in all revenues, costs and the profit on investment equal to its discounted rate; it summarizes the overall competitiveness of different technologies [206] [207].

The LCOE is determined by two major components, namely the Initial capital cost and the capacity factor. The capacity factor is defined as the average power delivered, divided by the theoretical maximum power. The capacity factor is improved by the operating hours of a power plant; and a plant that operates 24 hours, 7 days a week, would give a greater return on the investment.

The Cost of Energy (COE) is the actual cost needed to purchase energy; while the

Levellised Cost of Energy (LCOE) is the cost of breaking-even to generate energy [208].

The measure of LCOE is defined as the present value of all costs divided by the present value of all energy produced over the energy project’s lifetime [209].

Where:

= lifetime of project (yrs)

= weighted average cost of capital (WACC)

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= AEP in year t

= fuel expenditure in year t

= operation and maintenance cost (OPEX)

= capital expenditure (CAPEX) in year t

From a more technical perspective, the energy production of a mini-grid must be calculated inclusive of the load factor, the demand factor and the capacity factor, as demonstrated below:

2.18.1 Load Factor The load factor is the ratio of total energy consumed in a particular period of time to the total energy capacity connected to the consumer during that time. The load factor of a system considers the consumer behaviour pattern and the average consumption of power per households [210].

2.18.2 Demand Factor The demand factor is referred to as the “ratio of the maximum demand to the connected load of the system”. The connected load is also known as the theoretical load, which gives the absolute maximum that can be ideally generated, based on its maximum capacity

[210].

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2.18.3 Capacity Factor The plant’s capacity factor is defined as the ratio of actual energy produced in a given time period to the total energy that could have been produced in the same time period [210].

2.19 Off-grid system sizing Solar PV and small-scale wind turbine hybrid power plants have been proven in grid-tiered plants; but they have not quite broken through in rural off-grids. Implementing a mini-grid that utilizes these energy sources, with battery storage, continues to have potential; but it has not yet seen widespread commercialization in South Africa. Figure 2.68 displays the flow-chart of a system consisting of a hybrid-energy system:

Total Power Generation

Solar PV System Wind energy System

Calculating daily Calculating daily load demand load demand

Calculating solar PV Calculating wind supply turbine supply

Calculating solar PV Calculating wind system cost turbine system cost

Hybrid Sizing for Solar PV and Wind turbines 118

Figure 2. 68 Flow chart of system sizing [76]

Solar PV System Calculating the solar PV part of a hybrid energy system would be no different from calculating a solar-only PV system. The panel module technology will determine the energy yield; and the number of PV panels can be calculated as follows:

Where:

= power plant design capacity

= PV module power rating

The Area covered by the PV modules can be calculated as follows, per PV module:

)

The types of hybrid-energy systems that are most commonly used in rural areas are tabulated in Table 2.12. These four types of hybrid-energy systems are considered to be the best combination for the community off-gird energy generation based on the available energy resource and the feasi ility of the project’s LCOE.

Table 2. 12 Types of Hybrids Hybrid Types Solar PV SS -Wind turbines Battery Storage + Diesel generator H 1 X X X H2 X X H 3 X X

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H 4 X X Total

2.19.1 Hybrid type 1: Solar PV (with storage) + Wind turbine system = Hybrid-power plant

In this model, the solar energy would be the primary source of power generation and the wind energy, acting as a supplementary source. The solar energy would operate by using the grid-interactive dynamic, in which the energy is stored; and it is later supplied to the grid on a consistent level, which is also known as the base load. The solar energy must supply enough energy to provide directly to the grid; and still be able to store some of its energy for later use, particularly during the peak hours.

2.19.2 Modelling the expected Solar PV power output The process of modelling a solar PV based mini-grid or off-grid system will rely on the implementation of the following items:

 Selection of the location of the hybrid system (based on the most suitable

communities)

 Modelling the temperature profile in the area (daily /monthly and seasonally)

 Modelling the electricity demand profile in the area (monthly/seasonally and

annually)

 Modelling the solar PV plant and its energy storage to meet the demand of the

entire community

2.19.3 Mini-grid sizing criteria A study done by G.I.Z on the guidelines of what should determine the sizing of mini-grids, has shed some light on the relationship between forecasting the size of the mini-grid and the power required for a typical household based on its electrical appliances [211]. The type of tier that a household or community may fall into is determined by the energy

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demand that the needs to be met. This need is driven by the type and the number of appliances in use and the duration of use on a daily basis. Figures 2.69 and 2.70 displays the multi-tier framework that G.I.Z used to determine the size of a mini-grid and the items used in households, in order to determine the energy consumption.

Figure 2. 69 mini-grid multi-tier framework for access to households [211]

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Figure 2. 70 Energy consumption The ‘cumulative energy/day’ column reflects the cumulative energy need when all appliances are being used on a given day. For example, running a desk light, phone, lights and TV for the hours shown above requires a total of 144Wh.

2.20 Summary The off-grid hybrid energy sector in the renewable energy industry continues to see growth, as the exploration for better business models of successfully implementing off- grids in rural villages continues. The banking and financing models in place today continue to demonstrate favour for larger IPP projects over smaller, mini-grid systems. This has been due to the lack of financial creditworthiness of rural villagers for long-term loans that require re-payment over time for the investments made for communal benefit, as opposed to the individual SHS system.

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However, rural payment methods have been investigated; and they still continue to grow.

This section has largely covered in detail the various contributors towards the implementation of rural off-grid systems. The research methodology used for this research will be discussed in the chapter that follows.

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CHAPTER THREE

3.0 The Research Methodology 3.1 Introduction This chapter focuses on the methods used to narrow down from national level to the community in need of electricity, based on the criteria for which would yield the greatest probability of being implemented. As part of the methodology, it is important to consider some parameter analyses. The methodology for identifying off-grid communities that can benefit from Hybrid-renewable energy systems, begins at the national level; and the criteria for determining those provinces that host the highest ratio of households without access to electricity, compared to the total number of households available.

3.2 Hypothesis 1  Investigates which provinces have the highest number of non-electrified

households

 Investigates which districts have the highest number of non-electrified households

 Investigates which municipalities have the highest number of non-electrified

households

 Define those areas with the least access to electricity, by developing a set selection

of criteria whereby you can rank the municipalities, according to the following:

1) Total need and size of community;

2) The municipalities’ readiness to accept off-grid electricity;

 Determine whether the aggregated demand of the community can justify a hybrid-

energy system;

Hypothesis 1 Determine whether there are enough municipalities or communities without electricity.

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The basis of the first hypothesis is to determine whether the number of municipalities and communities in South Africa without electricity would justify the need for off-grid solutions.

The dissertation will investigate the combination of renewable off-grid solutions available in each municipality or community.

Hybrid-energy systems are becoming the basis of the renewable energy off-grid solutions.

Figure 3.1 displays the flowchart of the off-grid system and the factors used to model a suitable hybrid-energy system for a remote community in South Africa.

Figure 3. 1 Off-grid systems flowchart

From the scope of the first hypothesis, determining whether there are enough municipalities without electricity in South Africa, the determinant is found by analysing the

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number of households without electricity in South Africa from each province; and then proceeding to highlight the provinces that demonstrate the highest number of households without any electrical connection.

The second step is the listing of the highest percentage of non-electrified households, then the categorization of provinces into two groups. The first is the highest top-three provinces that have more than 15 per cent of the province with access to electricity. The second group comprises those provinces with less than 15 per cent, but more than 12.5 per cent of households without any access to electricity. What differentiates the first and the second categories, is the increased likelihood of finding rural communities where a need for electricity exists, and thereby also increasing the feasibility of the project. This is also summarized and displayed in Figure 3. 2.

This categorization allows the focus to be on those areas that are seriously disadvantaged.

The step that follows thereafter, for proving the first hypothesis to be true, is to break down each province into Districts, Municipalities, and finally into the actual communities that are in need of electrification, further leading to the step that follows displayed on Figure 3.3.

The data used in the first hypothesis are gathered from the 2016 energy and household statistics gathered from the District and the Municipal Independent Development Plan

(IDP) 2017-2019.

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National Provincial District Criteria Criteria Criteria

•Determine each •Determine each districts •Determine each provinces' ratio of ratio of households municipality's ratio of househoulds without without access to households without access to electricity electricity, from each access to electricity, from •Select the top 3 provinces Province. each of the selected with the highest •Select the districts with districts. percentage of households househoulds with a •Rank and select the without access to greater than 14% need for municipalities with 16% electricity electricity. or more households •Proceed only with the without access to provinces which are over electricity. 50% rural. •If there are less than 3 municipalities from national level to district criteria, then the first Hypothesis 1 is disqualified

Figure 3. 2 Hypothesis 1 Criterion.

Municipality Topography Ward Criteria Critieria Criteria

• Narrow the selection •Determine the topography •Using the topography group by ranking and of the municipalities. criteria, select a selecting only the •Disqualify the community no bigger municipalities with municipalities which are than 250 households to 30% or more not in : Mountaineous access. households without areas, Remote Areas, •Use the Upper Blinkwater Located 30kms from Grid feasibility study data to access to electricity. lines, located 10 kms benchmark the number of • Determine all the from regional roads. househoulds (63) that can wards which are •Individual municipalities achieve access to without electricity, per can be chosen based on electricity. municipality. topography, but for this •Determine the LCOE of 3 research, only one communities from one province was chosen as chosen ward. the focused, based on the •Select the final topography. community based on the •Select one ward that lowest LCOE, and the fulfills the topogrphy number households being criteria. electrified.

Figure 3. 3 Hypothesis 2 Criterion

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National Criteria

The National criteria consist primarily of the selection process, according to the requirement of choosing the top-three provinces with the most households without electricity in South Africa. These would then be analysed and investigated further.

Provincial Criteria

From the National criteria, the Provincial criteria mainly focus on the provincial analysis from a district level. The provincial criteria determine the districts that have the greatest need for electricity – from the top three provinces. This is done from the analysis of the ratio and the percentage of the total number of households without any electricity.

District Criteria

The District criteria will outline the top-three districts with the highest percentage of households without access to electricity; and one would then proceed to analyse the municipalities in those districts. The District criteria must capture all the municipalities and then rank them, according to those with the highest demand of the percentage of households without any access to electricity. The set minimum for a municipality is 14 per cent, which is on a par with the national ratio of households without any access to electricity.

Municipal criteria

The Municipal criteria analyse the district criteria in a more detailed way. The analyses focus on determining those municipalities that have an adequate need for electricity by the use of the percentage ratio of households without any access to electricity. The municipal criteria will determine which municipalities have a high enough need to be considered as potential benefiters of the Hybrid-energy system, by setting a 30 per cent minimum

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requirement of households in a municipality of those without any access to electricity. This includes access to electricity for cooking and lighting.

Topographical Criteria

The topographical criteria seek to define the best-suited environment and the landscapes for a rural off-grid system. The landscape or topography is often what makes service delivery to remote and rural areas difficult. The topography of the accessed municipality must be rural, mountainous and remote.

Ward Criteria

The ward criteria look into the wards that have the greatest need, based on the number of houses in the ward without access to electricity. The ward criteria are the final criteria used; since they include the types of villages in a ward, as well as their energy demand.

3.2.1 Data Collection The first step in the process of determining which households are without electricity is the collection of the data and their analysis. The data used were collected from independent

Integrated Development Plans (IDP) for each District and Municipality. These data were again verified by more data from the Department of Energy, in addition to the other weather and climate platforms. This process is summarised and displayed in Figure 3.4 and Figure 3.5.

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• Determine the ratio of households without access to electricity Quantitative Data

• National Criteria • Provincial Critiera • District Critieria • Municipality Criteria Selection • Topographical Criteria Critieria • Ward Criteria • Community Criteria

• Identify the Village which fulfills the entire criteria Findings

Figure 3. 4 Data collection flowchart

3.2.2 Data Analysis In data analysis, the comparison between the number of households without access to electricity in Kwa-Zulu Natal and the Eastern Cape are done, in order to determine whether the first hypothesis has been satisfied. In this process, the municipalities are ranked from the least to the most households without electricity.

3.3 Hypothesis 2  Quantify the demand based on the size of the community;

 Design a supply solution;

 Develop a costing tool, or use a pre-existing industrial standardized costing tool

that can provide a highly detailed and analysed budget for an off-grid system;

 Look into the opportunity cost, the LCOE, the parity between the tariffs, Eskom vs

the projected cost over a period of 5-10 years.

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 Automatically provide feedback on whether it is better to remain on the grid, or to

go off-grid.

• Quantify the electrical demand Quantitative Data

• Determine potential resource • Profile wind & PV for the selected village Resource • Calculate the Energy demand for the village analysis

• Model the communitty according to households without access to electricity to determine its LCOE Output

Figure 3. 5 Data Analysis Flowchart Hypothesis 2 For the second hypothesis, a site identification criterion is used to determine the municipalities and communities with the greatest need for electricity. The following criteria describe the selection criteria used to determine which community best suits an off-grid system [212].

Customer

 Municipality with a need for electricity (Municipality > 30% non-electrified

households);

 Household density (General density of ≥ 50 households/ km2 in clusters);

 Community commitment (Alignment of the community’s expectations);

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Electrification

 Off-grid electrification plans (no electrification plans for the next five years)

Topography

 Isolated villages (Potential impact of revised grid-electrification plans)

 Renewable resources (Preference for wind-based or solar-based generation)

Others

 Secure generation site (Anti-theft or vandalism measures)

 Positive political landscape (Communities that are willing to permit development)

3.3.1 Criterion Categories 1. Category 1: Provinces (P > 15%)

The provinces considered under category one, are those provinces with the highest levels of households without electricity. These provinces would be the top priority of the study, to identify the reasons for their current state of affairs, regarding energy and the lack of distribution. These provinces include Gauteng, the Eastern Cape and Kwa-Zulu Natal, in order of severity. Due to the severity of rural and poverty-stricken local municipalities, which exist in the Eastern Cape and in Kwa-Zulu Natal, these two provinces became the focal point of this research.

Due to Gauteng’s lack of access to electricity, being influenced by informal settlements and the migration of people to the province in search of employment opportunities, the province will not be considered for off-grid solutions; as it also violates one of the minimum requirements of being in remote areas, village areas and low-income households. Gauteng also fails due to its two metropolitan municipalities, City of Tshwane and City of

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Johannesburg. The scope covered by these two big spending metropolitan areas eliminates the likely of communities going without electrification for over 5 years.

2. Category 2: Provinces (12.5% < P < 15%)

Those provinces considered under category two, are the provinces with-medium-to-high levels of households without electricity. These provinces also indicate that there is considerable scope for off-grid solutions. These provinces include the Free State, North

West and the Northern Cape, in order of severity. Category 2 type of households offer less concentrated areas without access to electricity, which can be assessed, whereas category 1 municipalities failed the process.

3.3.2 Calculating the Percentage ratio The percentage of households without access to electricity can be defined as the total number of households without electricity divided by the total number of households, multiplied by a hundred, to achieve the resultant percentage; where the total number of households is the sum of those households with and those without access to electricity.

The percentage of households without electricity considers the ratio of the households without electricity compared to the total number of households in that district, municipality, or ward.

The ratio is not standardised; as the output percentage changes, according to location, and the detailed analysis is not changed. This process takes place as part of hypothesis 1.

This method also aids in the implementation process of off-grid systems, when the percentage of households without electricity is considered as a key determinant; since the numbers become more significant, as a detailed study investigates the communities that are willing and ready to receive off-grid hybrid-energy solutions.

(3.1)

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This method used to calculate the number of households without access to electricity from national level to municipal level, before three communities are selected, in order to analyse and determine their LCOE, which would determine which community is best suited to become a benefiter of the hybrid-energy off-grid system.

3.3.3 Calculating the household energy consumption The calculation of the LCOE requires that the load profile of the average household energy consumption be determined. This would include items, such as light bulbs, 2- plate stoves and a refrigerator, amongst others, which are used on a daily basis. For a rural household, the energy consumption is best kept below 10 kWh per day. This process begins the detailed investigation of hypothesis 2.

3.3.4 Calculating the energy demand One of the most critical factors in the calculation of the LCOE of a mini-grid is determining the amount of power needed, as well as the energy consumption of each house. The energy consumption is determined by including a list of the used items daily and the load demand, as well as the length of time they are used. The consumption rate is measured over 24 hours; and the peak demand is calculated by analysing the peak over a 12-month period.

3.3.5 Community selection The community selection process is guided by the ward and the topographical criteria. After the municipality and the potential ward have been identified, the community analysis takes place, using two factors: the number of households and the landscape of the community. Using the co-ordinates of the communities and

Google maps to locate these, would be crucial in providing a closer view of the community and its landscape.

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3.3.6 Calculating the available renewable energy resources After the communities have been identified, the first characterisation required is the available wind and solar resources in that community. Based on the Upper

Blinkwater data of the 2009 August – 2010 August, using the co-ordinates of each community, the data during that period were extracted from www.renewables.ninja for both solar and wind. For the solar data, the Merra2 dataset was used, with panels with the capacity of 60 W, and the tilt angle of 27 degrees at the azimuth angle of

180 degrees. For the wind data, the Merra2 dataset was also used with small-scale wind turbines type BES-150 that have the generation capacity of 150 kW, standing on a tower with the height of 30 metres.

In this section, the random analysis would consist of three summer days and three winter days, one day in a month from 6 months in each season. This would be crucial in determining the consistency in the graphical expression of the obtained results, which should justify the reliability of the wind and the solar PV energy supply.

Although the graphical expression of diesel generation has not been included, it must be assumed that it operates at base load, for which every community is chosen for the off-grid deployment.

3.3.6 DREI Costing Tool The Excel De-risking Energy Investment (DREI) costing tool is used. This is a LCOE calculation model that contains subsections, in order to achieve a comprehensive measure of the cost of the deployment of the solar PV and the diesel-generator hybrid system. The following items are the key sections which are covered when using the DREI Costing Tool:

1) Summary Output,

2) Input’s Load Profile,

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3) Input’s Baseline Diesel,

4) Input’s Solar PV Battery,

5) Load Profile,

6) Generation,

7) LCOE Diesel,

8) LCOE Solar,

9) Irradiation Data,

10) Instrument Costing,

11) Sensitivity Outputs,

12) Charts,

13) Report of Summary Table.

Among the key inputs in the Summary Output, would be the number of households, and the energy demand of each household, which must be calculated before inputting the demand profile and the systems’ summary. Figure 3. 6 outlines the methodology flowchart, which displays the different criterion and how they interlink with one another.

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Figure 3. 6 Methodology flowchart

3.4 Summary The research methodology has shown the process of selecting the final community, which meets the criteria for a rural off-grid community that needs electricity and is ready to receive it. It has also shown the calculations, which precede the DREI Costing Tool. The

DREI costing tool would allow for a detailed scope of analysis, including the insurance figures, the policy de-risking cost, and many others, which do not directly influence the

LCOE

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CHAPTER 4

4.0 RESULTS AND DISCUSSION

4.1 Introduction This chapter will present the results and the discussion, attained from the methodology for identifying off-grid communities that can benefit from the Hybrid-renewable energy systems process. The selected community will be profiled and given a detailed analysis.

4.2 Hypothesis 1

4.2.1 National Criteria In 2018, the Eastern Cape and Kwa-Zulu Natal had the highest percentage of households without electricity with Gauteng following close behind. These three provinces have been regarded to be within the first category. The first category hones into the districts and municipalities that would have the highest number of households without electricity as displayed in Figure 4.1.

25%

20%

15%

10% Percentage (%) Percentage 5%

Provinces

Figure 4. 1 National analysis of households without access to electricity

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The results deduced from the Table 4.1 show that Kwa-Zulu Natal had the highest number of households without electricity, closely followed by the Eastern Cape. These two provinces will be in the first category of provinces to be analysed. Gauteng has a unique aspect to its lack of household electrification y eing the country’s economic hu ; since it attracts an influx of people with many having to live in informal settlements, which are usually not a high priority in term of connectivity. These communities often get electrified within a 5-year period; or RDP housing gets deployed in the area.

Table 4. 1 National Criteria and Provincial Criteria National criteria Provincial criteria Limpopo X Mpumalanga X Western Cape X Northern Cape X North West X Free State X Kwa-Zulu Natal Approved Eastern Cape Approved Gauteng Disqualified

4.2.2 Eastern Cape Selection criteria The Eastern Cape has six district municipalities and two metropolitan municipalities in the form of Buffalo City and Nelson Mandela Bay, which all amounted to an estimated

1 863 009 households in 2018, which is a 4.8 per cent growth from 2016’s estimate of

1 773 395 households. These districts then govern a total of forty-three local municipalities. Table 4.2 breaks down the distribution of electricial energy source types through each district.

These are the top-four districts without access to electricity, based on the statistics from

2016. The solar home system has been indicative of growth in renewable energy in

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different districts in the Eastern Cape. Buffalo city and O.R. Tambo have relatively low quanitities of solar-home system connections, which indicates the growth in these areas.

The selection criterion is the first requirement of a municipality having a need for electricity greater than thirty per cent. The following municipalities have met that requirement; and as observed, the top-three municipalities all lie within the same district of Alfred Nzo. The municipal selection criteria are suitable for six municipalities, which will be further investigated through the wards, in order to determine those communities which meet the remaining selection criteria categories.

Table 4. 2 Eastern Cape District criteria Eastern Cape Total Households No access to Percentage District (2016) electricity without electricity (%) Nelson Mandela 368 520 11 155 3,03 Bay Chris Hani 194 291 12 769 6,57 Sarah Baartman 138 182 10 344 7,49 Buffalo City 253 477 31 165 12,30 O.R Tambo 314 080 40 334 12,84 Amathole 213 763 30 874 14,44 Joe Gqabi 95 107 15 044 15,82 Alfred Nzo 195 975 54 771 27,95

The bottom-three Districts in the Eastern Cape are Amathole, Joe Gqabi and Alfred Nzo.

Based on the pure household numbers, without any access to electricity, Buffalo City and

O.R. Tambo can be considered as the specific municipalities and communities as also seen in Figure 4.2.

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30,00

25,00

20,00

15,00

10,00 Percentage (%) Percentage

5,00

0,00 Nelseon Chris Hani Sarah Buffalo O.R Amathole Joe Gqabi Alfred Nzo Mandela Baartman city Tambo Bay Eastern Cape Districts

Figure 4. 2 Eastern Cape ranking of Districts The exclusion of Buffalo city and Nelson Mandela Bay Metropolitans is due to the lack of rural demographic data that the area occupies. The focus was narrowed to remote and rural locations, far away from the city; and the Metros where the middle-class and working class tend to flock to. Therefore, although on the basis of the pure number of households without access to electricity, Buffalo City, is one of the top-three contributors, as can be seen in Figure 4.3, but it cannot be considered. Furthermore, regarding the selection criteria, the determining factor is not the number of households without access to electricity, but rather the ratio of those households without any access to electricity.

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60 000

50 000

40 000 No access to electricity

30 000

20 000

Number of households of Number 10 000

Eastern Cape Districts

Figure 4. 3 Number of households without electricity per District in Eastern Cape

4.2.3 District Criteria The following District-to-municipal breakdown tabulates the percentage of households without electricity for each municipality in each district in the Eastern Cape. Figure 4.4 captures the Integrated Development Plan (IDP) data regarding access to electricity for each district and the municipalities within it. The data are captured from 2016; and they are backed by Statistics South Africa (STATS-SA 2016). A key point to note is that the households without electricity include those households that are using candles, kerosene, wood and other non-electric powered sources of energy. This also includes those households with household electricity only for lighting, as this is only a half measure; since they still rely on other forms of energy for cooking and heating water.

Alfred Nzo District The Alfred Nzo district has four local municipalities, which are all standing with an above thirty per cent need for electricity. The Ntabankulu local municipality has the highest ratio

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of households without access to electricity – both in the district and in the Eastern Cape

Province.

70,00

60,00

50,00

40,00

30,00

Percentage (%) Percentage 20,00

10,00

0,00 Mbizana Umzimvubu Matatiele Ntabankulu Municipality

Percentage without Electricity

Figure 4. 4 Alfred Nzo District percentage without access to electricity Amathole District The district of Amathole has six local municipalities, of which only one had above thirty per cent municipality without electricity. The results demonstrate that the Mbhashe municipalities have the greatest need for electricity as seen in Figure 4.5.

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40,00

35,00

30,00

25,00

20,00

15,00 Percentage (%) Percentage

10,00

5,00

0,00 Mbhashe Mnquma Great Kei Amahlathi Ngqushwa Raymond Mhlaba Municipality

Percentage without Electricity

Figure 4. 5 Amathole Districts without access to electricity

The municipal ranking of the Eastern Cape demonstrates the municipality with the greatest need from the district being assessed. The percentages also decline in an almost linear progression, demonstrating that there is a relationship between rural development and electrification in the province.

Joe Gqabi The Joe Gqabi district has three local municipalities, of which only one has above thirty per cent need for electricity as seen in Figure 4.6. The Elundini local municipality has the highest number of households within its borders.

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40,00

35,00

30,00

25,00

20,00

15,00 Percentage (%) Percentage 10,00

5,00

0,00 Elundini Senqu Walter Sisulu

Percentage without Electricity Municipalities

Figure 4. 6 Joe Gqabi District percentage without access to electricity

O.R. Tambo local municipality The O.R. Tambo district has five local municipalities, of which only one has a municipality with an above thirty per cent need for electricity. The Ngquza Hill municipality does not have the highest number of households without access to electricity; but it has the highest percentage of households without access to electricity, as seen in Figure 4.7. This means that the total number of households without access to electricity may be higher in another municipality, but since this study is focused on the percentage without electricity, the percentage takes precedence over the actual quantity.

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40,00

35,00

30,00

25,00

20,00

15,00 Percentage (%) Percentage 10,00

5,00

0,00 Mhlotlo King Sabela Port St Johns Nyandeni Ngquza Hill Dalindyebo Municipality

Percentage without Electricity

Figure 4. 7 O.R Tambo District percentage without electricity Ranking the municipalities in the Eastern Cape

The data collection resulted in 13 municipalities being analysed. From the total number of

31 municipalities, the 13 municipalities observed in Figure 4.8 demonstrate the ranking of the municipalities with the highest ratio of households without any access to electricity in the province.

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60,00

50,00

40,00

30,00

Percentage (%) Percentage 20,00

10,00

0,00

Municipality Percentage without Electricity

Figure 4. 8 Ranking of municipalities of Eastern Cape

The last criterion, which comes from the District criteria, was to narrow the focus to those municipalities that have a ratio of household without access to electricity, which is greater or equal to 30 per cent. This resulted in the number of municipalities being reduced from

13 to 7 municipalities to be considered for the Municipality Criteria. Figure 4.9 demonstrates the municipality being analysed. From the figure, it can be observed that the top-three municipalities come from the Alfred Nzo District (Ntabankulu at 58.69 %,

Matatiele at 43.9 % and Umzimvubu at 41.25 %).

It is also worth noting that each district has contributed a minimum of one local municipality.

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60,00

50,00

40,00

30,00

Percentage (%) Percentage 20,00

10,00

0,00 Ntabankulu Matatiele Umzimvubu Elundini Mbhashe Ngquza Hill Mbizana

Municipality Percentage without Electricity

Figure 4. 9 Ranking the municipalities with greater than 30% need for energy 4.3 Summary The Eastern Cape has three districts that have been the focus of this study, based on the ratio of total households and those households without access to electricity. The three districts have a total of 18 municipalities. From the 18 municipalities, only seven qualified to be assessed by the topographical and ward criteria, respectively. These comprise those municipalities with a need for access to electricity that is greater than 30 per cent.

4.4 Kwa-Zulu Natal Selection criteria The Kwa-Zulu Natal Province has resulted in 13 municipalities, which have a minimum of

30 per cent lack of access to electricity. The table 4.3 demonstrates and highlights the different districts in Kwa-Zulu Natal and their local municipalities, together with the percentage of electrical distribution and the percentage without any access to electricity.

This will be the focus of this study, looking at municipalities with a percentage without electricity that exceed 15%.

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From the provincial criteria the Kwa- ulu Natal province’s districts are assessed; and the minimum requirement was found to be fourteen and a half per cent of households without any access to electricity.

Table 4. 3 Kwa-Zulu Natal District Criteria District Percentage with Percentage without electricity electricity uThukela 85,4 14,6 iLembe 84,9 15,1 Zululand 84,7 15,3 Harry Gwala 81 19 uMzinyathi 69,7 30,3 uMkhanyakude 52,8 47,2

4.4.1 District Criteria The following District-to-municipal breakdown tabulates the percentage of households without electricity for each municipality in each district, in Kwa-Zulu natal.

Umzinyathi District The Umzinyathi district has four municipalities, which have only one municipality with an above thirty percent need of households without any access to electricity, as displayed in

Figure 4.10.

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45,00

40,00

35,00

30,00

25,00

20,00

Percentage (%) Percentage 15,00

10,00

5,00

0,00 Endumeni Nquthu Umvoti Msinga Municipality Percentage Without Electricty (%)

Figure 4. 10 Umzinyathi district percentage without access to electricity

iLembe District The iLembe district is occupied by four municipalities, of which only two meet the 30 per cent minimum ratio of households without electricity. Maphumulo and Ndwedwe have an over 50 per cent need for electricity, as displayed in Figure 4.11.

70,00

60,00

50,00

40,00

30,00

Percentage (%) Percentage 20,00

10,00

0,00 KwaDukuza Mandeni Maphumulo Ndwedwe

Municipality Percentage without Electricity

Figure 4. 11 iLembe district percentage without electricity

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uThukela District The uThukela district has three municipalities within it; and only the Okhahlamba municipality meets the 30 per cent minimum criterion for the percentage of households without electricity, as displayed in Figure 4.12.

45,00 40,00 35,00

30,00 25,00 20,00 15,00 Percentage (%) Percentage 10,00 5,00 0,00 Inkosi Langalibalele Aldred Duma Okhahlamba

Municipality Percentage Without Electricty (%)

Figure 4. 12 uThukela district percentage without access to electricity

Zululand District The Zululand districts occupies five municipalities of which only two meet the 30 percent minimum requirement. Both the Nongoma and eDumbe municipality demonstrate a ratio greater than 35 percent need for electricity amongst its household, as displayed in Figure

4.13.

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40,00

35,00

30,00

25,00

20,00

15,00 Percentage (%) Percentage 10,00

5,00

0,00 Ulundi uPhongolo Abaqulusi Nongoma eDumbe Municiaplity

Percentage Without Electricty (%)

Figure 4. 13 Zululand district percentage without electricity

Harry Gwala District The Harry Gwala district has four municipalities within it, and all four meet the 30 percent minimum requirement. Dr Nkosazana Dlamini Zuma, Greater Kokstad and Ubuhlebezwe all have a 40 percent ratio of households without electricity within their municipalities, as displayed in Figure 4.14.

60,00

50,00

40,00

30,00

20,00 Percentage (%) Percentage 10,00

0,00 Umzimkhulu Dr Nkosazana Greater Kokstad Ubuhlebezwe Dlamini-Zuma Municiaplity Percentage Without Electricty (%)

Figure 4. 14 Harry Gwala district percentage without access to electricity

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uMkhanyakude District uMkhanyakude district has four municipalities within and one of the highest Gini coefficient in the entire province, based on its rural environment. All four municipalities meet the 30 percent minimum requirement as displayed in Figure 4.15. Umhlabuyalingana has the highest ratio of households without electricity, as it’s a mostly traditional housing and rural municipality which seeks to remain rooted to the Zulu cultural ways of living.

90 80 70 60 50

40 30 20

Percentage(%) 10 0 Jozini Big 5 Hlabisa Mtubatuba Umhlabuyalingana Municipality Percentage without access to electricity

Figure 4. 15 uMkhanyakude district percentage without access to electricity From the 6 districts in KwaZulu Natal. which have 24 municipalities that have the potential for off-grid mini-grids, Figure 4.16 displays them, according to the ratio of households without access to electricity.

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90,00 80,00 Percentage 70,00

Without 60,00 Electricty (%) 50,00 40,00 30,00 Percentage (%) Percentage 20,00 10,00 0,00

Municiaplties Figure 4. 16 Ranking of municipalities percentage without access to electricity

Figure 4.17 displays the ranking of all the municipalities within Kwa-Zulu Natal’s districts that met the 30 percent requirement. From the 24 municipalities that came from the 6 districts, only 14 of them fulfil the 30 percent ratio and qualify to be assessed by the next criteria.

90,0 80,0 70,0

60,0 50,0 40,0 30,0 Percentage (%) Percentage 20,0 Percentage 10,0 Without Electricty (%) 0,0

Municipality

Figure 4. 17 Ranking municipalities with need greater than 30%

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Table 4.4 displays the district criteria which contains all the districts of the Eastern

Cape and that of Kwa-Zulu Natal and the districts approved and those with the (X) being the disqualified districts which did not meet the minimum requirement of 14 percent of households without access to electricity.

Table 4. 4 District Criteria

Eastern Cape District criteria Kwa-Zulu Natal District criteria District District

Nelson Mandela Bay X Amajuba X

Chris Hani X Zululand Approved Sarah Baartman X uMkhanyakude Approved Buffalo city X King Cetshwayo X O.R Tambo Approved uMzinyathi Approved Amathole Approved uThukela Approved Joe Gqabi Approved uMgungundlovu X Alfred Nzo Approved iLembe Approved Ugu X Harry Gwala Approved Table 4.5 displays the municipal criteria which forms part of the final step of Hypothesis 1, and thus proving whether or not there is a sufficient number of households without access to electricity. The municipal criteria looked at the approved and disqualified municipalities on the basis of having more than a 16 per cent need of households without any access to electricity.

Table 4. 5 Municipality Criteria Eastern Cape Kwa-Zulu Natal District Municipality Municipality District Municipality Municipal Criteria Criteria Alfred Nzo Matatiele Approved iLembe KwaDukuza X Mbizana Approved Mandeni X Ntabankulu Approved Maphumulo Approved Umzimvubu Approved Ndwedwe Approved Joe Gqabi Elundini Approved uThukela Inkosi Langalibalele X Senqu X Aldred Duma X Walter Sisulu X Okhahlamba Approved Amathole Mbhashe Approved Zululand Ulundi X

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Mnquma X uPhongolo X Great Kei X Abaqulusi X Amahlathi X Nongoma Approved Ngqushwa X eDumbe Approved Raymond Mhlaba X Harry Gwala Umzimkhulu Approved O.R TamboMhlotlo X Dr Nkosazana Approved Dlamini-Zuma Ngquza Hill Approved Ubuhlebezwe Approved Port St Johns X Greater Kokstad Approved Nyandeni X Umzinyathi Endumeni X King Sabela X Nquthu X Dalindyebo Umvoti X Msinga Approved uMkhanyakude Jozini Approved Big 5 Hlabisa Approved Umhlabuyalingana Approved Mtubatuba Approved

4.4.2 Hypothesis 1 feedback. The first hypothesis was to determine whether there was a sufficient number of municipalities in South Africa, with a greater need for electricity than the national average of 13 per cent. The 7 municipalities in the Eastern Cape and the additional 14 municipalities from Kwa-Zulu Natal, all had an over 30 per cent need for electricity.

4.5 Hypothesis 2 The second hypothesis, is contingent on the first one being fulfilled, and since the first hypothesis showed that while the need for electricity at a national level may be 13 per cent, it is actually much higher in provinces, like Eastern Cape and Kwa-Zulu Natal. The municipalities investigated showed that the need within their communities is far greater than what is actually documented. These communities must further be investigated, based on their feasibility to be electrified by an off-grid system. The first criteria in hypothesis 2 are the topographical criteria.

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Table 4. 6 Topographical Criteria Topographical Criteria Eastern Cape Kwa-Zulu Natal Approved X

Table 4.6 shows that the Eastern Cape was preferred, rather than Kwa-Zulu Natal, based on the topographical criteria, which considered the general landscape and the remoteness of the municipalities under consideration. According to this standard, Kwa-Zulu Natal was shown to have more flat plains and game-farm topography – particularly in the highly non- electrified district of uMkhanyakude. Furthermore, the electrical network in the province is well-configured; and it runs through most districts – either as main-lines, or as distribution

(11 kV) lines. Also, the access to tar-roads and other available routes, which make accessibility to the communities less remote, added to why it was disqualified when compared to the Eastern Cape.

The topography of the Eastern Cape municipalities when considered, was found to be very mountainous, with sharp inclines and deep valleys, with rivers flowing at the lower ends of the mountainous hills. The province also has mainly 2 power lines, supplying power to

East London and Port Elizabeth. The Alfred Nzo district, which had the highest non- electrified ratio of households in the entire province, made Ntabankulu local municipality the highest concentration point of households without access to electricity.

Upon further investigation Ntabankulu was chosen as the municipality; while ward 1 was the most remote and fulfilling of the requirements required for the topography.

4.5.1 Ntabankulu Municipal-resource profile Ntabankulu local municipality has been selected as the municipality to be assessed in greater detail in the second hypothesis, based purely on its ratio of 49.3 per cent of

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households without access to electricity. From this municipality, 3 communities will be chosen and further profiled and investigated, in order to determine the available wind and solar resource necessary to implement an off-grid system successfully. Firstly, the

Ntabankulu municipality is profiled for its renewable energy resources. Figure 4.18 displays the boundary line of the Ntabankulu local municipality.

Figure 4. 18 Boundary line of Ntabankulu local municipality

The Ntabankulu municipality has been profiled by its renewable energy resources and their availability over a period of 10 years between 2009 and 2019. The profile outlines the following [213]:

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 Temperature profile,

 Wind speed and gust profile,

 UV index profile,

 Sun availability (hrs & days) profile.

Figure 4.18 displays the temperature profile of Ntabankulu local municipality over a ten- year period, between 2009 and 2019. The region has an average peak temperature of 28 degrees Celsius, a yearly average temperature of 25 degrees Celsius, and a minimum average temperature of 23 degrees Celsius. This temperature profile substantiates solar

PV; and it has a predictable solar profile, year-on-year. The peaks occur between

December and February, while the troughs occur between May and August, annually.

Figure 4. 19 Temperature profile of Ntabankulu [213]

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The wind profile of Ntabankulu shows the potential wind resource available throughout the local municipality. The wind profile is measured over a ten-year period between 2009 and

2019, as displayed in Figure 4.19. During that period the wind profile shows a fairly predictable and comprehensive profile, with the average peak wind gust falling between 35 and 25 kilometres per hour (kmph); while the average wind speed was between 25 kmph and 15 kmph, annually. This puts the average wind speed at 5.5 m/s, which is suitable for a cut in wind speed of 3.5-4.0 m/s for the average small-scale wind-turbine at a height of

30 metres.

Figure 4. 20 Wind speed profile of Ntabankulu municipality [213]

The UV Index describes the strength of the ultra-violet (UV) radiation exposure [214].

Looking at Figure 4.20, the UV index of Ntabankulu is substantially above the extreme

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threshold of 11 points on the scale, with the minimum being just below 15, and peaking at

21 points, during the measured ten-year period.

Figure 4. 21 UV Index profile in Ntabankulu municipality [213]

Critical to the solar profile and the modelling of a solar PV-based hybrid-energy system, is the average number of solar hours and the sun days exposed to a particular region. Figure

4.21 shows the average solar hours and the sun days graphical expression, over ten years between 2009 and 2019. The sun days are measure quarterly, year-on-year; while the sun hours measure the available sun during that quarter. The sun hours over the years, have peaked between 200 and 300 hours of sun light. This demonstrates that during a period of

92 days, 25 days of those days were sunny days without cloud coverage in that region, thus receiving sun 235.67 hours during that time, which translates to an average of 9,4 hours daily.

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This radiation could actually be spread-out over time, with some days receiving 4-5 hours, while other days, it only received 1 hour of no cloud coverage, all adding up to 25 days of sun and a total of 235.67 hours.

Figure 4. 22 Sun hours and Sun days in Ntabankulu municipality [213]

4.5.2 Upper Blinkwater During the survey, the villagers reported that their willingness to pay for electricity would range from R 50 to R 180 per month, with most participants estimating this amount at

R150 per month. The prefeasibility study showed that over 90 per cent of households would need a minimum of 50kWh of electricity per month. Furthermore, at R 150 each household could manage 160kWh/month at the electric tariff rate of 111.99 c/kWh [215]

[54]. It was also discovered that the majority (82 %) of households prefer the electric stove over the gas-fired stove; but about 60% of the households were willing to use a gas stove,

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if this meant having more electricity to use on other applications [54]. Figure 4.22 displays the table of household items and applications and their energy consumption, giving three different scenarios of what the monthly energy consumption would amount to.

Figure 4. 23 Electric energy application scenarios [54]

4.5.3 Comparing East London Load profile to Profiled villages In order to obtain the most accurate supply-demand data for the rural Eastern Cape communities, two methods were used. Through the analysis of the presentation: Upper

Blinkwater Micro-grid Integration Load Flow Studies, done by Basetsana Molefyane and

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Mpheli Ramphokanyo from the CSIR [216], the winter and summer data were sampled and extrapolated to give the most accurate collectable data of the electrical demand for the villages, short of an actual feasibility study as seen in Figure 4.24. The data provided by the study were used to find the ratio of households similar to those of the Upper Blinkwater data of 62 households.

60000

50000

40000

30000

Power Consumption (W) Consumption Power 20000

10000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time 24H00

Figure 4. 24 Upper-Blinkwater peak demand forecast - daily profile

Figure 4.24 displays the Upper Blinkwater data, which profile the available energy supply from the wind and solar resources, while also demonstrating the grey band of total energy demand during a 12-month period, between August 2009 and August 2010. The total demand of a community is largely determined by the applications and household items being used daily. The less energy consumed, the more the renewable energy resource is available for direct supply and battery recharging. In the case of peak demand, such as the mornings and the evenings, the battery can supply power, while the generator is also on standby.

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200

180

160

140

120

100

Electricity 80

Hourly timeseries

Total Wind/PV Supply kW Total Demand kW

Figure 4. 25 Upper-Blinkwater data- Power Demand profile (2009-2010) [216]

4.5.4 Electricity consumption calculation based on Upper Blinkwater The energy E in kilowatt-hours (kWh) per day is calculated as follows:

= Power (watts)

= period of operation (hrs/day)

Based on the feasibility study done in Upper Blinkwater Eastern Cape, the following estimates were as follows:

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Table 4. 7 Typical household power usage

Duration Energy Energy Energy Application Unit Power Unit per day (kWh/day) (kWh/month) (kWh/year)

Fridge 24 hr 150 W 3.6 108 1314 (medium)

Kettle 0.5 hr 2200 W 1.1 33 401.5

Microwave 0.5 hr 1200 W 0.6 18 219

TV 2 hr 120 W 0.24 7.2 87.6

Radio 24 hr 20 W 0.48 14.4 175.2

Lights 5 hr 10 W 0.05 1.5 18.25

Phone 3 hr 5 W 0.015 0.45 5.475 charger

Toaster 0.2 hr 1200 W 0.24 7.2 87.6

Electric iron 0.75 hr 1000 W 0.75 22.5 273.75

Electric stove 2 hr 1000 W 2 60 730 (2-plate)

Total 6.905 kW 9.1 272.25 3312.4

Compared to the energy consumption used in the Upper Blinkwater study, which ranged between 135-166 kWh/month, the 272.25 kWh/month for this study estimates the extreme possibility of each household using all these daily items. The extra 110 kWh/month is suitable to allow for community growth, which would be likely in that case of a single community being electrified, while nearby there are other less-qualified communities.

4.5.5 Profiling of Mdini-community The analysis and profiling of a community investigates the electrical projects for Eastern

Cape, and the Ntabankulu local municipality, in particular. According to Eskom, the existing power lines which distribute electricity through the province of Eastern Cape, focus on Port Elizabeth and East London. Figure 4.25 displays the provincial electrical network, with 400 kV and 220 kV being the major power distribution lines [21].

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Figure 4. 26 Current Eastern Cape Province network diagram [21]

Figure 4.26 displays the provincial load forecast between 2018-2027, according to Eskom

[21]. The forecast shows a steady linear incline ranging between 18-26 MW for Port

Elizabeth at Grid Peak and slightly lower for East London ranging between 9 – 21 MW at

Grid Peak. These projections may increase with the new 2019 IRP approving more renewa le energy generation for IPP’s.

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Figure 4. 27 Eastern Cape Province load forecast [21]

The villages of Mdini in Ward 1 in the Ntabankulu local Municipality and Alfred Nzo District in the Eastern Cape were selected from several villages in the municipality to be analysed.

(-31, 156791 S 29,249710 E)

Ntabankulu has a population of 128 848 people, of which only 49 per cent of them are adults and are employed. The average annual household income is R14 600. Ntabankulu municipality has 17 wards as seen in Figure 4.28; and the following wards have an immediate need for electricity: Wards (1,2,4,5,7,10,11,12,14,16).

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Figure 4. 28 Ntabankulu Municipality [217]

Figure 4.28 displays the electrical power distribution grid lines called the Kokstad-Mt Frere

132 kV line, which passes around the municipality leading to Port Elizabeth and East

London. Ntabankulu fulfils the other criteria by having no electrical distribution lines through it, as it allows for remote locations to exist 30-plus kilometres away from the grid line and 10-plus kilometres from the regional road. Both points were fulfilled, as can be seen in the yellow line displaying the road network, while the green line comprises the grid lines.

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Figure 4. 29 Electrical grid lines and Regional routes

Figure 4.29 also displays the detailed map of Ntabankulu municipality and its various villages. The municipality has no National or Regional routes passing through it. This is largely due to its mountainous topography. The map does not show the smaller, less- known villages, which are often overlooked and found in remote locations, such as Mdini village, which lies between Xopo and KuNdile.

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Figure 4. 30 Detailed villages in Ntabankulu municipality

Ward 1 of Ntabankulu has the most mountainous landscapes in the municipality and fewer roads passing through it, thereby making it the most remote of the wards that are without access to electricity in the municipality. It also has villages, which have been recorded to be without electricity, such as:

 Luthembeko village

 Zamukulungisa

 Nomaweni

 Dungu

 Sabhungeni

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 Mawonga

 Ntlangano

 Bhayi

 Buwani

 Xopo

 Mdini

The map of ward 1 of Ntabankulu municipality is demarcated; and it can be seen in Figure

4.30. The ward has a population of 6855 people with an area coverage of 110.6 m2.

Figure 4. 31 Ntabankulu Municipality, Ward 1 [217]

The Mdini village lies within ward 1 of Ntabankulu municipality, from the Alfred Nzo district of the Eastern Cape Province. The village is located with all its households, according to

֩;the aerial view of Google maps, as seen in Figure 4.31, from the coordinates (31.15679 S

29. 249710 ° E).

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Figure 4. 32 Mdini Village Aerial view

Figure 4.32 shows the demarcation and the aerial topography of the Mdini area and the village located within its borders.

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Figure 4. 33 Mdini Village demarcation and topography

4.5.6 Profiling of Buwani community The Buwani community comprises about 70 households, which are spread out over the wide region along the mountainous region. The households in the region have sizeable yards, which prevent the households from being clustered. The Buwani village is surrounded by mountainous areas and valleys, making it quite remote. The village is close to the running Tina river, which feeds the community and the surrounding communities, such as the Mandlana and Dungu village.

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Figure 4. 34 Buwani demarcation and topography

Buwani has scattered households on the inclined mountainous landscape and on the declining landscape. Buwani is close to Dungu, as can be seen in Figure 4.33, which has more backyard farmlands. It is different to the Dungu village, which has clustered households along the gravel route. Figure 4.34 also shows the demarcation of Buwani and its coordinates.

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Figure 4. 35 Buwani Ariel view and coordinates

4.5.7 Profiling of Xopo community The Xopo community is one of the largest and most widespread communities in ward 1 of the Ntabankulu local municipality. The community has better landscapes and topography than Mdini and Buwani, as seen in Figure 4.34. The community has a cluster of households in the region of 200 households located above mountainous rifts; and it has no valleys or rivers passing through it. Figure 4.36 displays the boundary around the Xopo community in the Eastern Cape’s Ntabankulu municipality.

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Figure 4. 36 Xopo demarcation and topography By virtue of its location and size, Xopo has schools; and it has the greater likelihood of being electrified within the next 5-10 years, than the Mdini and Buwani village. Xopo is located near KuMtunzana, as can be seen in Figure 4.36, together with its coordinates.

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Figure 4. 37 Xopo Arial view and coordinates

4.6 Obtained results This subsection contains the obtained results from the analysis of the Mdini, Buwani and

Xopo community. The analysis will seek to determine the energy demand and the profile, based on the households and the wind and solar resource available in the region. The results are divided into summer and winter for both solar PV and wind energy. The second category is that the 12-month period is divided into summer (October-March) and winter (April-September).

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4.6.1 Mdini Village results The Mdini village was measured on three random days, one in each month: November,

December and January, in order to determine the typical electrical generation over a 24- hour period, as displayed in Figure 4.37. The profile shows that from a typical summer day, a random December day would produce higher electrical generation than during the other two months. Secondly, the shape of the profile has been consistent throughout the summer period, with different peak generation points, showing the power generation period. There is also consistency in the non-generation period between (00:00 – 06:00) am and (19:00 -00:00) pm.

30 November December 20 January Power Generation (kW) Generation Power 10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 38 Mdini typical summer day - Solar PV The typical winter day regarding solar PV, shows that the power generation in summer and winter conform to a similar profile and peak-power generation. On a typical day in May, it can also be observed that the peak was significantly lower than that of the other months, as seen in Figure 4.38.

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25 July May 20 June

Power Generation (kW) Generation Power 15

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 39 Mdini typical winter day - Solar PV

The Mdini wind profile on a typical winter day, demonstrates 4 significant trends, as can be seen in Figure 4.39:

 Between (00:00 – 08:00) am, during this time there exists the highest wind power

generation, which is useful for battery charging and supplying one of the peak

demands, which takes places n the early morning.

 Between (08:00 – 12:00) am, there is a steady decline; since at the same time, the

atmospheric temperature the approaches peak-power required for generation.

 During (12:00 -17:00) pm, the wind power generation is low, and except for August,

this becomes the lowest wind-generation period.

 During (17:00 – 00:00) pm, the wind generation continues to descend; and it may

begin to improve, as midnight approaches.

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100 90

80 70 60 August 50 May 40 July 30

Power Generation (kW) Generation Power 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 40 Mdini typical winter day - Wind The typical day of summer‘s wind profile, shows inconsistent wind-power generation, as can be seen in Figure 4.40. Also seen in the figure, is the trend that comes from October, when the wind-power generation increases as the evening sets in; and it continues to rise until midnight. This can also be caused by the unstable wind profile, which comes from the ending of the winter the season and the beginning of summer. During these months, the wind-power generation is substantially greater during the midday, when compared to that during the winter months.

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50 October 40 December 30 January

Power generation (kW) generation Power 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 41 Mdini typical summer day - Wind Finally, the Mdini energy profile is demonstrated in Figure 4.41, which shows the total energy supply from both wind and solar, together with the total demand, which was interpolated from the data obtained from the Upper Blinkwater study, in the Eastern Cape.

The figure displays the different interception points between the demand and the supply, which shows that the combined energy supply surpasses the average energy demand, also known as the base-load.

This makes Mdini a good option for the mini-grid implementation, as it proves there is enough renewable energy to supply the community during the middle of the day, as can be seen in Figure 4.41. Another point of note is the two areas that require battery-storage energy and the diesel generator. Based on the Figure 4.41, the mornings (06:00-10:00) am, sees the total demand surpassing and overlapping the total supply, as well as during the evening period between (16:00-21:00) pm, when the energy demand peaks; and the energy supply slumps heavily.

These results are deduced from a typical day in August, as the winter months have the greatest energy demand.

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30 Electricty (kW) Electricty 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Total Wind/PV Supply kW Total demand (Altered) kW Avg Demand (Base load) kW

Figure 4. 42 Mdini typical winter day (Supply vs Demand)

Figure 4.42 displays the energy supply profile versus the energy-demand profile, during

2009-08-01 to 2010-08-01, to demonstrate the difference, firstly in the winter months on the left side, ranging from August –September, followed by the summer months when

October to March show the reduced energy demand, before this begins to increases again for the remaining four months of winter. Secondly, the difference in the supply is also evident, but also worth noting is the intensity from the last two months of winter, which are

August –September. This is due to the significant wind-power generation available during the middle of the winter.

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200

180

160

140

120

100 Mdini Energy 80 Supply Electricty (kW) Electricty Energy Demand 60

1 314 627 940 1253 1566 1879 2192 2505 2818 3131 3444 3757 4070 4383 4696 5009 5322 5635 5948 6261 6574 6887 7200 7513 7826 8139 8452 Hourly timseries (hr)

Figure 4. 43 Mdini demand profile (2009-2010) 4.6.2 Buwani Village results Buwani has two major wind-generation peaks, as can be seen in Figure 4.43, the early morning’s wind eing strong etween (00:00-09:000) am, before peaking again later in the evening between (15:00 – 19:00) am. This, combined with peak solar-power generation, takes place during the lowest points of wind generation.

During the winter season, the wind-power generation is volatile, such that the pattern seen from a random day in July, could be perceived to be that of a summer-day generation; while also the September and August days show a peak period during the day – and settling down in the evening as can be seen in Figure 4.44. This figure shows that the peak demand during the morning and the evening can be sustained by the supply; while and the battery would subsidise the supply; and the amount of energy generated from the diesel generator is reduced. For this reason, the Buwani community has an increased chance of receiving implementation, based on the balanced energy supply and the

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reduced dependence on diesel generation; and thus, less diesel is required or used, thereby reducing the operating costs.

40 January December 30 October

Power Generation (kW) Generation Power 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 44 Buwani typical summer day – Wind

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100

60 September August

40 July Power Generation (kW) Generation Power

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 45 Buwani typical winter day - Wind

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The Buwani solar profile is seen in Figure 4.45 for a typical summer day. The random pickings of these days remain consistent with the PV profile, which points to the reliability of solar-power generation between October and March. The solar generation in summer would peak higher than the days in winter, as well as lasting longer, by virtue of its earlier start and ending later in the day.

40 January 30 December

20 March

Power Generation (kW) Generation Power 10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 46 Buwani typical summer day - Solar PV The solar profile in winter shows similar solar profiles, but different power-generation peaks. On a typical day in April, the solar-power generation proves to be greater than that of June and July, as can be seen in Figure 4.46. As winter progresses, there seems to be a decline in solar-power generation, which would cause greater reliance on other resources, such as the battery – and largely the diesel generator.

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50 45

40 35 30 25 July 20 April 15 June

Power generation (kW) generation Power 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 47 Buwani typical winter day - Solar PV

Figure 4.47 displays the typical winter-day profile of the supply versus the demand, showing an overwhelming energy deficit, which is seen by most of the supply-power generation being below the base-load line. This then means that the energy demand is great compared to the supply, when the winter months would be heavily reliant on diesel- powered generation.

70,00

60,00

50,00

40,00

30,00

20,00

10,00

0,00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Total demand (Altered) kW Avg Demand (Base load) kW Supply kW

Figure 4. 48 Buwani typical winter day (Supply vs Demand)

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Finally, the Buwani energy demand and supply profile displays a higher demand than that of Mdini, which is higher than the supply of most days in winter. The increase in energy demand is directly proportional to the number of households supplied; if it is assumed that the energy demand from each household is similar. Based on these results, Buwani gets disqualified from implementation, based on its lack of an energy supply relative to its high energy demand, as displayed in Figure 4.48.

200

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140

120

100

Power (kW) Power 80

60 Buwani Energy 40 Supply kW

20 Energy Demand (kW) 0

1 294 587 880 1173 1466 1759 2052 2345 2638 2931 3224 3517 3810 4103 4396 4689 4982 5275 5568 5861 6154 6447 6740 7033 7326 7619 7912 8205 8498 Hourly timeseries (hr)

Figure 4. 49 Buwani demand profile 4.6.3 Xopo Village results Xopo is the last of three rural communities being consider from ward 1; and it is also the largest in size; as it is home to about 200 households. The solar PV profile of a typical summer day in Xopo, showed the most variation; as the summer days are usually consistent in shape, and peak points, unlike the observed results from Figure 4.49. The image shows a huge decline in peak power-generation during a random day in February; while the other random typical days are slightly different from one another; but they are both similar in shape and in their peak-generation points.

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50 45 40

35 30 25 January 20 February 15 December Power Generation (kW) Generation Power 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 50 Xopo typical summer day - Solar PV The typical winter day in Xopo, showed better similarities in both shape and peak generation, as can be seen in Figure 4.50. This then shows that, based on the profiles plotted from winter compared to those plotted in summer, the work generation from solar

PV is much more consistent in winter than in summer; and they both peak around 45 kW, power generated.

50 45 40

35 30 25 July 20 August 15 June Power generation (kW) generation Power 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

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Figure 4. 51 Xopo typical winter day - Solar PV

The wind profile of the Xopo community is interesting to observe, as it has two consistent peak-power generation points, which take place in the morning and in the evening. This could be the contribution of living on high altitudes and rather flat, yet mountainous terrain.

Figure 4.51 displays the typical winter day in Xopo; and it shows that, interestingly, the peak wind-power generation in the morning is surpassed by that of the evening.

15 August july 10 may Power generation (kW) generation Power 5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 52 Xopo typical winter day – Wind

On the other hand, the wind profile of the summer months is less consistent; yet it does have a loose pattern of flow. The typical summer day in Xopo observes a peak wind-power generation between (14:00 and 18:00) pm. This then is followed by a steady decline during the evening, as can be seen in Figure 4.52. Another variation is the peak-power generation points, and the shape of the profiles. The typical summer day wind-peak power generation point could be as high as 55 kW, or as low as 20 kW. This type of variation makes it difficult to achieve a stable energy supply, without increasingly depending on the diesel generator.

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30 January 20 December Power Generaion (kW) Generaion Power 10 November

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Figure 4. 53 Xopo typical summer day - Wind

Finally, the image displayed in Figure 4.53 shows the overwhelming energy demand of

200 households relative to the supply that is available. This demonstrates the extent to which the available wind and solar resources in Xopo fail to supply the energy demanded by the households, such that the lowest level of energy demand is higher than the highest energy supply from the available resource. Furthermore, the lack of an adequate energy supply, would lead to an over-dependence on the diesel generator, which would then render the hybrid-energy system inefficient.

200 180 160 140

120 100 80 Power (kW) Power 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly timeseries (hr)

Total electricity supply(kW) Altered demand kW Avg demand (Base load)

Figure 4. 54 Xopo typical day (Supply vs Demand)

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Figure 4.54 further exemplifies the inability of the renewable energy resources to supply the energy demand of 200 households in Xopo. The energy profile of the community shows the orange-shaded area as the demand; while the dark shadow of the profile is the energy supplied from the available renewable resources.

200

180

160

140

120

100 Supply 80 POWER 9KW) POWER Demand 60

1 294 587 880 1173 1466 1759 2052 2345 2638 2931 3224 3517 3810 4103 4396 4689 4982 5275 5568 5861 6154 6447 6740 7033 7326 7619 7912 8205 8498 HOURLY TIMSERIES

Figure 4. 55 Xopo energy profile 4.6.4 Summary of the results The following results were obtained; and those listed below were consistent from the calculations:  Theoretical Supply = 210 (kW)

 Capacity factor = 0.15

 Demand factor = Utilization factor.

Table 4.9 displays the potential resource of each of the three communities being considered, while also comparing the outputs to the results obtained from the Upper

Blinkwater study.

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Table 4. 8 Potential Resource analysis of communities Village Household Peak Avg. Max Demand Avg. Load Max Max connected Supply Demand demand factor load factor Deficit surplus kW kW kW kW kW Mdini 50 172.493 28.48 45.28 0.22 31.9 0.7 -136.4 45.28

Buwani 70 172.49 44.05 63.4 0.3 31.9 0.5 -63.39 122

Xopo 200 170.35 113.93 181.14 0.86 31.18 0.17 -54.34 127.1

Upper- 62 171.42 34.18 54.4 0.68 31.9 0.59 -129.2 54.34 Blinkwater

The community resource availability analysis provides a display of the communities noted,

as follows:

1) Mdini (50 households)

2) Buwani (70 households)

3) Xopo (200 households)

These communities each compare their availability of wind and solar energy in the area,

relative to the power generated being categorized as either surplus energy, which is used

to recharge the battery storage, or the deficiency of energy, which would require power

from the diesel generator. Figure 4.55 also displays the size of the community relative to

its energy surplus. According to the results obtained, the community with the highest

energy deficit is Mdini, and the one with the highest energy surplus is Xopo.

Therefore, according to the peak energy deficit, the obtained results suggest that Mdini

does not have enough wind and solar resources to cover the peak demand, which would

lead to the need for an off-grid system that would be heavily reliant on the diesel

generator. Furthermore, the figure shows that Buwani has the following dynamic

advantages over the other two villages:

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1) The peak surplus is greater than the peak energy deficiency.

2) The ratio of peak surplus energy to the number of households is better than the

other two villages, at 1.74 kW per household.

250

200

150

100

50 Max surplus kW 0 1 2 3 Max Deficit (kW) -50 Households per Village Deficiency/ Surplus (kW) Surplus Deficiency/ -100

-150

-200 Community

Figure 4. 56 Community resource availability analysis Figure 4.56 displays the community’s energy-demand profile, in which it can be seen why

Xopo (Village 3), is disqualified from being the location of the off-grid implementation. The

Xopo village has roughly 200 households, with an average energy demand of 113.93 kW.

This is already more than the 100-kW battery supply can handle. At its peak demand of

181.14 kW, the community would need an additional 67.21 kW of energy of available installed power.

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200 180

160 140 120 100 Avg Demand kW 80 60 Max Demand

Energy Demand (kW) Demand Energy 40 20 0 1 2 3 Commuity

Figure 4. 57 Community Energy demand profile The intersection point between the load factor and the demand factor displays the cut-off point of the number of households, which could be electrified successfully from the solar and wind resources available. Figure 4.57 shows that Buwani (village 2) is closest to the intersection point; and since both the load factor and the demand factor are based on the maximum or peak demand per community, the area below the load-factor graph indicates the areas of feasibility up until the intersection point with the demand factor graph, which acts as a cut-off line.

This then disqualifies Xopo (village 3) as being too expensive; and it also disqualifies

Buwani as being inefficient, thus leaving Mdini as the community for implementation.

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1 0,9 0,8 0,7

0,6 0,5

Factor 0,4 0,3 0,2 0,1 0 1 2 3 Community Demand factor Load factor

Figure 4. 58 Load factor vs Demand factor In summary, the chosen village, based on the matrices and results obtained, would be

Mdini, of ward 1, in the Ntabankulu local municipality, in the Alfred Nzo district of the

Eastern Cape. Figure 4.58 indicates that at peak demand, the demand from all three communities would be below 1kW per household. At peak surplus, Buwani has a greater demand than 1 kW/ household; while at peak deficit, Mdini would need the alternative supply of battery and diesel generator simultaneously. Table 4.10 displays the ward criteria, which summarise the wards considered and the ward chosen for investment.

200

150

100

0 Max demand kW Max Deficit kW Max surplus kW -50 Demand (kW) Demand

-100

-150

-200 Mdini Buwani Xopo

Figure 4. 59 Energy demand analysis

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Table 4. 9 Ward criteria Percentage Coordinates Average Wards with greatest need Ward Municipality without Income Criteria Electricity R/yr Ntabankulu 49.30 32 10 S 28 15000 Ward Approved 35' E (1,2,4,5,7,10,11,12,14,16,17) Matatiele 43.9 32 10 S 28 8571 Ward( 7,9,14,15,18,22,26) X 35' E Umzimvubu 41.25 32 10 S 28 13333 Ward (3,6,7, 8,10,12, 24,25, X 35' E 27) Elundini 37.20 31 04 40 S 28 12321 Ward (1,3,4,57,8,9,10, X 21'36 E 11,12,13,14,16,17) Mbhashe 35.22 32.1621° S, 11908 Ward (14-23,26,28,31-32) X 28.7664° E Ngquza Hill 33.89 31.2632° S, 16250 Ward (5,6, 13, 14, 15, 16, 17, X 29.6963° E 18, 28,30,32) Mbizana 32.05 31 34 S 29 6900 Ward( X 24' E 4,10,11,14,16,19,23,25,28,30)

4.7 Off-grid Costing The off-grid costing, is perhaps the most important part in determining the feasibility of an

off-grid system’s implementation, as it considers the financial feasi ility of the project; and

it models the system, according to all the elements of influence, such as the CAPEX and

OPEX of the project, among many other factors.

In order to achieve the sophisticated costing of an off-grid system, the Derisking

Renewable Energy Investment (DREI) Off-grid electrification costing tool was used [218].

The DREI tool is focused on solar mini-grids; and it aims to make it easier to analyse the

investment feasibility of such a project. The tool is widely used for its detailed

consideration, including the financial structure of the CAPEX and the cost of diesel in its

OPEX.

Table 4.11 displays the projections of a village using the estimated energy demand per

household, as eing 9.1 kWh/day from the Upper Blinkwater study’s item energy demand

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estimations. If the assumption that the rural tariff of electricity was used at R0.72 /kWh,

then the projections would be as follows:

Table 4. 10 Demand Projections based on Tariff price

Power Energy (kW) Cost of Estimated Energy Tariff Cost of demand per electricity village Energy Consumption (R Electricity (MWh/month) day - Power (R/month) households (kWh/month) (kWh/day) /kWh) (R/month) village Village (kW/year) village

50 272,25 9,1 0,72 196 13,6 19,0 6919,8 9801

75 272,25 9,1 0,72 196 20,4 28,4 10379,7 14702

100 272,25 9,1 0,72 196 27,2 37,9 13839,6 19602

125 272,25 9,1 0,72 196 34,0 47,4 17299,5 24503

150 272,25 9,1 0,72 196 40,8 56,9 20759,4 29403

200 272,25 9,1 0,72 196 54,5 75,8 27679,2 39204

225 272,25 9,1 0,72 196 61,3 85,3 31139,1 44105

250 272,25 9,1 0,72 196 68,1 94,8 34599,0 49005

300 272,25 9,1 0,72 196 81,7 113,8 41518,8 58806

350 272,25 9,1 0,72 196 95,3 132,7 48438,5 68607

400 272,25 9,1 0,72 196 108,9 151,7 55358,3 78408

500 272,25 9,1 0,72 196 136,1 189,6 69197,9 98010

4.7.1 Xopo Community’s LCOE The results obtained from the DREI off-grid solar mini-grid costing tool, showed that Xopo

would have an 0.86 LCOE (USD/kWh), before and after derisking. If it assumed that there

would be no guarantees on the loan taken, no contribution made by the community; and

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that the entire project cost, which includes the CAPEX and the OPEX, would be carried by the investor. By so doing, the investor takes the risk, with 0.86 being the maximum possible LCOE, unless there are safe-guards against the financial commitment made to implement the project, as can be seen in Figure 4.59.

LCOE 0,86

(USD/kWh) 0,86

Solar Minigrid Solar Minigrid BAU Post-derisking

Figure 4. 60 Levelized Cost of Energy of Xopo

The baseline investment in Figure 4.60 shows the increased LCOE that comes from having a diesel generator in the hybrid-energy system. According to the DREI tool, the difference of 0.38 in the LCOE is dependent on the energy demand, based on the number of households to be supplied.

0.86 0.86 LCOE 1,24 (USD/kWh)

Baseline Solar Minigrid Solar Minigrid Investment BAU Post-derisking (Diesel Minigrid)

Figure 4. 61 Levelized Cost of Energy of Xopo, including diesel generator

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Table 4.12 displays the technical summary of the Xopo community, which accommodates

200 households; and it has an average capacity factor, which ranges between 15 and 16.9 per cent.

Table 4. 11 Technical summary of Xopo analysis SOLAR PV-BATTERY TECHNOLOGY 2025 Electrification Target (number of household 200 connections)

Average Capacity Factor 16.9% (%)

Average System Size Solar PV 60 (kW) Battery 100 (kWh)

Total Annual Serviced Demand 29 532 (kWh)

Total System Size to Reach 2025 20 Target (kW)

BASELINE Baseline energy mix Diesel generator 100%

Average system size 100 (kW)

Diesel Emission Factor 0.89 (tCO2e/MWh)

GENERAL COUNTRY INPUTS Effective Corporate Tax 20% Rate (%) Public Cost of Capital 8% (%)

200

Table 4. 12 Cost summary of Xopo off-grid system Pre-Derisking Post-Derisking

FINANCING COSTS Capital Structure Grants, as a % of total investment in 0% 0% generation and distribution assets Equity/Debt structure of remaining investment 100%/0% 100%/0%

Cost of Debt Concessional N/A N/A public loan Commercial loans with N/A N/A public guarantees Commercial loans without public guarantees N/A N/A

Loan Tenor

Concessional N/A N/A years public loan Commercial loans with N/A N/A years public guarantees Commercial loans without public guarantees N/A 10 years

Cost of 19.0% 19.0% Equity

Weighted Average Cost of Capital (WACC) 19.0% 19.0% (After-tax, excl. grants)

INVESTMENT

Total Investment (USD $216 000 $216 000 million, incl. grants)

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4.7.2 Buwani Community’s LCOE Buwani is one of the communities that has a good renewable energy resource with the maximum supply energy being greater the maximum energy demand. The village has an

LCOE of 0.74 for the solar mini-grid as displayed in Figure 4.62.

LCOE 0,74

(USD/kWh) 0,74

Solar Minigrid Solar Minigrid BAU Post-derisking

Figure 4. 62 Levelized Cost of Energy of Buwani

The LCOE, including the diesel generator, is the greatest of all three communities, due to the lack of its usefulness, compared to its cost over time. This then discourages the use of a diesel generator for the number of houses being supplied, as can be seen in Figure 4.63.

LCOE 0.74 0.74

(USD/kWh) 3,01

Baseline Solar Minigrid Solar Minigrid Investment BAU Post-derisking (Diesel Minigrid)

Figure 4. 63 Levelized Cost of Energy of Buwani, including diesel generator

Table 4.14 and Table 4.15 display the technical and cost summary of the Buwani community’s off-grid system based on the DREI Costing Tool.

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Table 4. 13 Technical summary of Buwani off-grid system

SOLAR PV-BATTERY TECHNOLOGY 2025 Electrification Target (number of household 75 connections)

Average Capacity Factor 5.6% (%)

Average System Size Solar PV 60 (kW) Battery 100 (kWh)

Total Annual Serviced Demand 29 540 (kWh)

Total System Size to Reach 2025 60 Target (kW)

BASELINE Baseline energy mix Diesel generator 100%

Average system size 100 (kW)

Diesel Emission Factor 0.89 (tCO2e/MWh)

GENERAL COUNTRY INPUTS Effective Corporate Tax 20% Rate (%) Public Cost of Capital 8% (%)

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Table 4. 14 Cost summary of Buwani off-grid system

Pre-Derisking Post-Derisking FINANCING COSTS Capital Structure Grants, as a % of total investment in 0% 0% generation and distribution assets Equity/Debt structure of remaining 100%/0% 100%/0% investment

Cost of Debt Concessional N/A N/A public loan Commercial loans with N/A N/A public guarantees Commercial loans without public guarantees N/A N/A

Loan Tenor Concessional N/A N/A years public loan Commercial loans with N/A N/A years public guarantees Commercial loans without public guarantees N/A 10 years

Cost of 19.0% 19.0% Equity

Weighted Average Cost of Capital (WACC) 19.0% 19.0% (After-tax, excl. grants)

INVESTMENT Total Investment (USD $202 250 $202 250 million, incl. grants)

4.7.3 Mdini Community’s LCOE Mdini shows the lowest LCOE, as compared with the other contending communities, as can be seen in Figure 4.64. This has been enhanced by the 50 households in Mdini being

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close to the Upper Blinkwater base study of 62 households, while also having a decent resource supply of their own.

LCOE 0,45

(USD/kWh) 0,45

Solar Minigrid Solar Minigrid BAU Post-derisking

Figure 4. 64 Levelized Cost of Energy of Mdini

The Mdini community has the highest energy demand deficit; and consequently, the need and usefulness of a diesel generator is suited for the number of households being supplied. Thus, baseline LCOE, which includes the diesel generator, is the lowest of all three communities; and it has the smallest difference of 0.08 USD/kWh, as can be seen in

Figure 4.65.

LCOE (USD/kWh) 0,57 0.45 0.45

Baseline Investment Solar Minigrid Solar Minigrid (Diesel Minigrid) BAU Post-derisking

Figure 4. 65 Levelized Cost of Energy of Mdini, including diesel generator

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Table 4.16 and Table 4.17 display the technical and cost summary of the Mdini community’s off-grid system, respectively; using the DREI Costing Tool.

Table 4. 15 Technical summary of a Mdini off-grid system

SOLAR PV-BATTERY TECHNOLOGY 2025 Electrification Target (number of household connections) 50

Average Capacity Factor 226.0% (%)

Average System Size Solar PV 60 (kW) Battery 100 (kWh)

Total Annual Serviced Demand (kWh) 3 312

Total System Size to Reach 2025 Target (kW) 20

BASELINE Baseline energy mix Diesel generator 100%

Average system size 120 (kW)

Diesel-Emission Factor (tCO2e/MWh) 0.89

GENERAL COUNTRY INPUTS Effective Corporate Tax 20% Rate (%) Public Cost of Capital 8% (%)

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Table 4. 16 Cost summary of Mdini off-grid system

Cost of Debt Concessional N/A N/A public loan Commercial loans with N/A N/A public guarantees Commercial loans without public guarantees N/A N/A

Loan Tenor Concessional N/A N/A years public loan Commercial loans with N/A N/A years public guarantees Commercial loans without public guarantees N/A 10 years

Cost of 19.0% 19.0% Equity

Weighted Average Cost of Capital (WACC) 19.0% 19.0% (After-tax, excl. grants)

INVESTMENT Total Investment (USD $94 150 $94 150 million, incl. grants)

4.7.4 Summary In summary of the obtained results is the LCOE graph, detailing the relation between the solar mini-grids compared to the inclusion of the diesel generator to the mini-grid. Figure

4.66 displays the closeness between the two types of LCOE. The Mdini village stands out as the community most suitable for implementation; as its two points are closest to one another.

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3,50

3,00

2,50

2,00

1,50

LCOE (USD/kWh) LCOE 1,00 Solar mini-grid LCOE 0,50 Diesel generator mini-grid LCOE - Xopo Buwani Mdini Community

Figure 4. 66 Summary of LCOE of analysed communities Finally, the community criteria displayed in Table 4.18, show the community that has been selected, according to the results obtained and their meeting the criteria. Three communities were randomly selected from ward 1 of Ntabankulu; and from these communities, the three selected, were profiled and assessed, according to their solar and wind resources.

Table 4. 17 Community Criteria Municipality Ward Topography Community Community Criteria Ntabankulu Ward 1 Mountainous, Mdini Approved Remote, without Buwani Disqualified roads Xopo Disqualified Luthembeko X Zamukulungisa X Nomaweni X Dungu X Sabhungeni X Mawonga X Ntlangano X Bhayi X

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4.8 Discussion The results and discussion chapter is concluded with a descriptive summary of the findings and their significance to the research, according to the hypotheses made in the beginning of the dissertation.

In hypothesis 1, the aim was to investigate those provinces that have the highest number of non-electrified households. This was achieved by using the data from the latest IDP’s and housing and electrification statistics from STATS-SA. The criteria determined which one was to be prioritized when assessing those provinces with an electrification need greater, or equal to 15 per cent. These were those provinces, which had a greater electrification need than the national average of 13 per cent. This became the first category; while the second category was for provinces with an electrification need greater than 12.5 per cent and less than 15 per cent.

This category would only be considered for assessment if the first category’s provinces had failed to produce a community which could benefit from an off-grid system. It was found that the Eastern Cape, Kwa-Zulu Natal and Gauteng had the top-three highest percentages of households without access to electricity, ranging between 18 and 21 per cent. Although Gauteng had qualified for the assessment, it had to be disqualified, based on the project’s focus eing on rural off-grid systems; and due to the nature of Gauteng hosting two Metropolitan Municipalities in Tshwane and Johannesburg, the access to electrification should be achieved within the following 5-10 year period.

This then left Eastern Cape and Kwa-Zulu Natal as the remaining provinces to be assessed next.

The second criterion was to investigate, which districts have the highest number of non- electrified households from the both the Eastern Cape and Kwa-Zulu Natal. The district criteria determined that the minimum ratio for assessment was 12 per cent; this was

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regardless of the actual number of households; but it was based on the ratio of households without any access to electricity. Based on the results from IDP’s, the Eastern Cape produced 5 out of 8 districts that met the 12 per cent standard. However, Buffalo City, was disqualified from further assessment; since most of its households lived around the city of

East London, and consequently, they were not within the scope of the rural village communities. This then left only four districts from the Eastern Cape: Amathole, O.R.

Tambo, Joe Gqabi and Alfred Nzo.

The same criteria were used for Kwa-Zulu Natal, which produced 6 out of 10 districts that met the 12 per cent standard; and none were disqualified from being further assessed.

These were iLembe, uThukela, Zululand, Harry Gwala, Umzinyathi and uMkhanyakude.

The third criteria was used to further determine whether there was a sufficient number of households without access to electricity was to investigate which municipalities had the highest number of non-electrified households. The municipal criterion was determined to assess the municipalities with a non-electrified ratio greater than 30 per cent. This meant that for the Eastern Cape, out of the total of 18 municipalities available for assessment, only 7 of them met the 30 per cent requirement. These were: Matatiele, Mbizana,

Ntabankulu, Umzimvubu, Elundini, Mbhashe and Ngquza Hill local municipalities.

From these municipalities, Alfred Nzo occupies the first four, and contributed the most, while also three out of the four had an above 40 percent ratio, making the district, the most non-electrical in the entire province. As for Kwa-Zulu Natal, out of the 24 municipalities available for assessment, as many as 14 of them met the 30 per cent standard. These were Umzimkulu, Nongoma, eDumbe, Okhahlamba, Msinga, Dr Nkosazana Dlamini-

Zuma, Greater Kokstad, Jozini, Ubuhlebezwe, Maphumulo, Ndwedwe, Big 5 Hlabisa,

Mtubatuba and Umhlabuyalingana. From these municipalities, 11 out of 14 have an above

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40 per cent non-electrified ratio with uMkhanyakude being the highest contributor and having the most non-electrified municipality in the entire province.

Finally, those municipalities, which contributed over 30 per cent of the households without access to electricity were ranked by each of the two provinces. Following this, each municipality was further investigated to determine the average household income and the average income per member of a household, in order to establish whether the communities within a municipality could afford electricity at a reduced price, subsidised price, or not pay at all.

These served as the indicators of readiness to accept an off-grid system as a sustainable solution for the municipality and its communities.

From the obtained results, the first hypothesis was proven to be true, as can be seen from the 14 municipalities investigated from Kwa-Zulu Natal and the 7 from the Eastern Cape, with an over 30 per cent ratio of households without any access to electricity.

In hypothesis 2, a required in-depth look into the available renewable energy resource of the approved municipality and its communities was requuired. Hypothesis 2, began with the topographical criteria, which disqualified Kwa-Zulu Natal as a contender going forward, due to its lack of remoteness in the considered municipalities, together with an extensive grid-network, which made distribution readily available for future connections in rural communities. The mountainous landscapes of the Eastern Cape were more compelling than the range-like, game-farm landscapes that were prominent in the considered municipalities of Kwa-Zulu Natal.

In order to determine whether the aggregated demand of a community would justify a hybrid- energy system, it was critical to analyse the available renewable energy resources in each of the communities being evaluated. This required a detailed look at the solar PV

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and the wind-energy generation captured during a period of 12 months. Due to the real data extrapolated by Basetsana Molefyane of the CSIR, from East London’s distri ution grid network, the Upper Blinkwater data was interpolated from there, through a reduction method, while maintaining the profile of the energy source. The datum was then further interpolated to achieve the data for all three communities which were assessed.

From the data, the peak demand is critical for the energy modelling; since through it, the combination of the battery and the diesel generator together with the renewable energy supply, the feasibility of the plant can be determined.

The DREI costing tool incorporates all these dimensions and factors, as part of its formula to achieve the most inclusive LCOE of the community. Most of what falls under hypothesis

2, as individual objectives are achieved using the DREI tool, such as battery-energy storage systems and a diesel generator, which supports the renewable energy-resourced hybrid-energy system. Instead of supplying a constant energy output, based on the available energy, the off-grid is modelled on pre-determined closely estimated peak demand levels and from that, the projected typical household needs help to guide each household’s energy consumption. In this way, each household controls its consumption; and therefore, it has enough energy for longer in its 24-hour period.

One of the interesting points of comparative analysis would be to be able to model for losses in sending energy to the grid relative to proximity. (This falls under the comparative state of analysis, where the cost of the utility supply is compared to that of an off-grid system.)

As part of hypothesis 2, the site of implementation and generation needs to be secure against theft and or vandalism, as seen in previous off-grid systems, such as in the

Lucingweni village and Hluleka Nature Reserve. Most projects are implemented in

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community-centric points, such as schools and clinics. In those cases, where there is no local school or clinic, the local royal house receives the mandated responsibility over the project, as well as its security throughout its existence.

Also critical to the implementation of an off-grid system is an understanding of the political climate, the ward and local municipalities, which hold political sway over the people.

As can be seen in Table 4.17, Mdini has the lowest cost of deployment; but it is worth noting how close the implementation costs of Buwani and Xopo are. While Buwani has 75 households and Xopo has 200, Buwani is the least feasible of all three communities being considered, due to high total project investment, as can be seen in Table 4.15.

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CHAPTER 5

5.0 CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction The final chapter of this dissertation includes the conclusions and the recommendations for future work. The chapter includes the characterizations and the analyses of the findings regarding the methodology of determining the appropriate communities that can benefit from an off-grid hybrid-energy system. The recommendations for future work within the off- grid system sector are also presented, in order to provide a continuation in the improvement of electrifying rural and remote households with hybrid-energy micro-grids, which are suited for such communities.

5.2 Conclusions With regard to micro-grids being the critical solution to off-grid systems, this dissertation has shown the global, African and South African appeal for the cost- effective option that this provides. Micro-grids have shown that they can be designed and modelled to suit the needs of a community of people, regardless of the remoteness and the topography of the landscape surrounding the area. Using renewable energy sources as a main source for the power generation of the hybrid- energy system in rural and off-grid communities is becoming necessary. This requires a keen understanding of the available resources within and around the area of interest. For this dissertation, once the communities were identified, the online renewable-energy data source called: www.renewables.ninja provided the data specific to the co-ordinates of the community, in order to analyse the wind and the solar profiles of the communities over a period of 20 years.

With each passing year, the energy crisis in South Africa reveals the need for renewable energy for sustainable solutions in both residential and rural households.

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The energy crisis in South Africa has led to load-shedding, which has added strain to the stagnant economy. The Minerals and Energy minister, Mr Gwede Mantashe has announced that mining industries can now generate power for self-use only [219].

This was announced at the 2020 Mining Indaba, in Cape Town, which meant that off- grid renewable energy systems would probably see growth in the near future.

Furthermore, the Minister allowed municipalities to source power from Independent

Power Producers (IPPs), which means that IPPs would not have to dispatch any additional energy generated; and municipalities would have the option of avoiding the rolling nationwide load-shedding that has been set to continue until 2022 [220].

With regard to community micro-grids or traditional micro-grids, in Africa and South Africa it can be deduced that currently, the traditional micro-grid model is more feasible; and it offers opportunities for technological innovation and e-commerce to exist and grow rural communities to become self-sustaining, to improve their access to electricity and to become part of the global economy. Micro-grids are the most feasible solution to electrifying off-grid communities, while also creating isolated economically viable communities which can now trade without each other and other larger communities.

As o served from the o tained results, the Eastern Cape’s Alfred Nzo district, is the least electrified district, with Ntabankulu municipality being the hardest hit of all, housing 49.3 per cent of households that are without any access to electricity. When comparing the total scope of municipalities without access to electricity, there are several municipalities in

Kwa-Zulu Natal, which have a greater need for electricity, such as the Umhlabuyalingana municipality with over 70 per cent of households without any access to electricity. Upon further investigation, the municipality and the district as a whole would remain undeveloped, due to the tribal and traditional beliefs of the people who live there,

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preferring to live in mud huts and cook with wood. This proved hypothesis 1 to be true, which was intended to determine whether South Africa had a significant need for rural and remote communities to receive access to electricity from an off-grid system.

According to the obtained results and the need for access to electricity in South Africa, this has been evident with 14 per cent of the country still without electricity; and the majority of these being in rural and remote locations. From the study of the Eastern Cape’s districts and municipalities, it may be concluded that the Alfred Nzo district is the least- electrified district in the province, revealing the top three municipalities that lack any access to electricity from its seven municipalities with an above 30 per cent need.

When comparing the topography and the terrain of the municipalities without electrified households between Kwa-Zulu Natal and Eastern Cape, the latter proved to be situated in more remote and mountainous landscapes with deep valleys and sharp inclines, while also being better resourced with wind and solar resources. Although Kwa-Zulu Natal is mountainous as well, it lacked in remoteness, as it had access to roads throughout the districts and municipalities. This further confirmed the controlled exposure that the tribal people wanted.

Since the solar data used PV panels with the capacity of 60 W, this would allow for an improved capacity factor if better solar panels are used; should the investor deem the initial cost of more efficient and higher capacity panels more effective in the long term. By using small-scale wind turbines type BES-150, which have the generation capacity of 150 kW, standing on a tower with the height of 30 metres, the available wind resource’s capacity factor should improve with height. The combined capacity of the solar PV and the wind turbines gave the theoretical supply of 210 kW, which is

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used to determine the demand factor for each community. Both these factors being considered, the improved capacity factor would affect the average load.

The battery and diesel generator capacities were kept at 100 kW. By so doing, the generator and the battery would operate interchangeably, with the battery discharging up to 50 per cent of its capacity before charging again. The direct supply of energy from the wind and solar sources were aggregated as a singular source, supplemented by a 24-hour diesel generator and a storage battery, to ensure that the community of Mdini is supplied with 9.1 kWh of energy daily.

The 110 kWh/month difference in energy consumption, between the Upper Blinkwater study and the average household consumption used in this research, allows for economic stimulation to take place, for local businesses to operate within the extra 110 kWh/month available energy. The 110 kWh/month is a fairly conservative estimate, considering that

Mdini has 50 households instead of Upper Blinkwater’s 62 households. Modelling the energy consumption from a mini-grid system should not be so stringent as to not allow for future growth and economic stimulation within the community of deployment, particularly when the mini-grid system serves a community of people for a substantial period of time.

The communities surrounding Mdini, could become indirect beneficiaries and economic contributors to the social development, which could emanate from the electrification of a community.

In most studies in the field of hybrid-energy systems, HOMER software is used to analyse the micro-grid systems; as it simply looks at the LCOE and the capacity factor of the wind and solar resource, which would be simpler. Where one seeks a more comprehensive and detailed analysis, the DREI costing tool is a more interactive programme, which was successfully used in this research.

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With regard to the LCOE and the communities’ willingness to pay (WTP) for access to electricity by collectively subsidizing the cost of deploying an off-grid system, it was found that the WTP, according to the Upper Blinkwater study, showed households willing to pay between R50 and R180 per month for electricity, but most of these averaged R150.

Although this may e true, the judgment on a community’s WTP must be determined on a case-by-case basis.

The LCOE has been calculated without factoring in any subsidisation from neither the municipalities, nor the community and if the municipality and community cooperate, the assumed minimum rate of R50, would be feasible. The LCOE represented the truest value of deploying the off-grid system without any additional financial aid. The model being investigated in this research required the analysis of different aspects, which affect and influence the electrical tariff cost, and how that model can be translated into a business model for investment, and in turn, achieve the actual project implementation. This was achieved through the DREI Costing Tool.

5.3 Recommendations and Future work Going forward, whenever off-grid systems are being considered for rural community deployment, such as Mdini, four factors must be satisfied; and these are: Technologically capable, environmentally suitable, economically beneficial, and finally, the issue of social and human development. These four factors were satisfied, according to the selection criteria and the DREI-costing tool. Technologically capable refers to the technology being used and its ability to fulfil the needs of the community in the long term. The environmentally suitable solution in this case would be the available wind and solar PV resources.

This directly affects the capacity factor of the hybrid-energy system and the efficiency of the system. The economic benefits have been well detailed in the DREI costing tool; and

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this outlines the costs and the possible beneficiation of the community. Finally, the social and human development, which would arise from the access to electricity in a rural community, goes further than being able to cook over a stove in the evening; it has the potential of influencing the education of a community dweller.

The future of rural electrification lies in off-grid solutions, such as hybrid-energy systems, mini and micro-grids, amongst others that serve an entire community and set the foundations for technological innovations that improve the quality of life of the community and stimulate economic growth.

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236

APPENDIX A: RESULTS

Electrification Statistics for April 2018 (DoE, 2018)

Province Total Projected Total Houses House without Percentage Household Households Electrified electricity without (March 2018) Connected (March 2018) (March 2018) electricity (March 2018) Eastern Cape 1 863 009 66 243 1 539 598 323 411 17.36 % Western Cape 1 804 068 10 527 1 618 674 185 394 10.85 % Gauteng 4 315 876 11 876 3 538 879 776 997 18 % Kwa-Zulu Natal 2 803 735 70765 2 318 263 485 472 17.32 %

Free State 909 007 4 586 785 418 123 589 13.6% Mpumalanga 1 187 426 33 496 1 099 106 88 320 7.44% Northern Cape 332 775 3 400 288 579 44 196 13.28% Limpopo 1 565 699 58 666 1 542 976 22 723 1.5% North West 1 172 550 16271 1 013 755 158 795 13.54%

Eastern Cape Results Households Households Percentage With without Total without District Municipality Electricity electricity Households Electricity

Alfred Nzo Mbizana 33500 15800 49300 32,05 Umzimvubu 26490 18600 45090 41,25 Matatiele 27070 21200 48270 43,9 Ntabankulu 9220 13100 22320 58,69

District Municipality Households Households Total Percentage with Electricity without Households without electricity Electricity Amathole Mbhashe 40100 21800 61900 35,22 Mnquma 51370 17100 68470 24,97

Great Kei 6935 1380 8315 16,60 Amahlwthi 25470 2990 28460 10,51 Ngqushwa 18740 919 19659 4,67 Raymond 42200 2510 44710 5,61 Mhlaba

237

District Municipality Households Households Total Percentage with Electricity without Households without electricity Electricity Joe Elundini 24310 14400 38710 37,20 Gqabi Senqu 37800 4360 42160 10,34

Walter Sisulu 23277 2630 25907 10,15

District Municipality Households Households Total Households Percentage With Electricity without without electricity Electricity O.R Mhlotlo 35200 8660 43860 19,74 Tambo Ngquza Hill 39400 20200 59600 33,89

Port St Johns 24100 9010 33110 27,21

Nyandeni 47200 18000 65200 27,61 King Sabela 91000 24300 115300 21,08 Dalindyebo

E.C Municipalitiy > 16% need for Electricity 70,0 60,0 50,0 40,0 30,0 20,0 10,0 Percentage without Electricity

0,0

…

Mhlotlo Elundini Mbizana Mnquma Great Kei Great Mbhashe Nyandeni Matatiele Ngquza Hill Ngquza King Sabela Sabela King Ntabankulu Umzimvubu Port St St Johns Port Alfred Nzo Joe Gqabi Amathole O.R Tambo

238

District Municipality Households Households Total Percentage with without Households without Electricity electricity Electricity Alfred Nzo Ntabankulu 9220 13100 22320 58,69 Alfred Nzo Matatiele 27070 21200 48270 43,9 Alfred Nzo Umzimvubu 26490 18600 45090 41,25 Joe Gqabi Elundini 24310 14400 38710 37,20 Amathole Mbhashe 40100 21800 61900 35,22 O.R Tambo Ngquza Hill 39400 20200 59600 33,89

Percentage without Electricity 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0 Percentage without Electricity

Senqu Mhlotlo Elundini Mbizana Mnquma Great Kei Great Mbhashe Nyandeni Matatiele Amahlathi Ngqushwa Ngquza Hill Ngquza Ntabankulu Umzimvubu Port St St Johns Port Walter Sisulu Walter Raymond Mhlaba Raymond King Sabela Dalindyebo Sabela King Alfred Nzo Joe Gqabi Amathole O.R Tambo

Kwa Zulu Natal Results

District Municipality Percentage of Percentage without distribution electricity Amajuba Newcastle Dannhauser eMadlangeni

92 8 Zululand Ulundi Nongoma Abaqulusi

uPhongolo

eDumbe 84,7 15,3

239

uMkhanyakude Jozini Big 5 Hlabisa Umhlabuyalingana

Mtubatuba

52,8 47,2 King Cetshwayo City of uMhlathuze Umlalazi Nkandla

uMfolozi

Mthonjaneni 91,6 8,4 uMzinyathi Msinga Nquthu Umovti

Endumeni

69,7 30,3 uThukela Alfred Duma Okhahlamba Inkosi Langalibalele

85,4 14,6 uMgungundlovu Msunduzi uMshwathi uMngeni

Richmond

Mkhambathini Mpofana Impendle 92,4 7,6 iLembe KwaDukuza Ndwedwe Mandeni

Maphumulo

84,9 15,1 Ugu Ray Nkonyeni Umzumbe uMuziwabantu

Umdoni

84,1 15,9 Harry Gwala Dr Nkosazana Dlamini- Zuma Ubuhlebezwe Greater Kokstad Umzimkhulu 81 19

240

Kwa-Zulu Natal District Municipality Households Total Other Percentage with households sources Without Electricity + None Electricity (%) Umzinyathi Endumeni 18257 21133 2876 13,61 Nquthu 25594 32622 7028 21,54

Umvoti 24179 31825 7646 24,03 Msinga 23186 40491 17305 42,74

District Municipality Households with Households Total Percentage Electricity without Households without electricity Electricity iLembe KwaDukuza 82934 8100 91034 8,90 Mandeni 39739 5846 45585 12,82

Maphumulo 8696 11829 20525 57,63 Ndwedwe 16709 25193 41902 60,12

241

District Municipality Households Households Total Percentage with without households Without Electricity Electricity Electricity (%) uThukela Inkosi Langalibalele 35342 11611 46953 24,73 Alfred Duma 60249 25152 85401 29,45

Okhahlamba 17417 12093 29510 40,98

District Municipality Households Households Total Percentage with without households Without Electricity Electricity Electricity (%) Zululand Ulundi 25850 9348 35198 26,56 uPhongolo 21004 7768 28772 27,00

Abaqulusi 31223 12076 43299 27,89

Nongoma 21851 12490 34341 36,37 eDumbe 10127 6011 16138 37,25

District Municipality Households Households Total number of Percentage with without households Without Electricity Electricity Electricity (%) Harry Umzimkhulu 22476 12041 34517 34,88 Gwala Dr Nkosazana 12182 10988 23170 47,42 Dlamini-Zuma Greater Kokstad 1827 1800 3627 49,63

Ubuhlebezwe 9454 10139 19593 51,75

District Municipality Percentage without access to electricity uMkhanyakude Jozini 51 Big 5 Hlabisa 60,3

Mtubatuba 60,5

Umhlabuyalingana 76,4

242

Percentage Without Electricty (%) 90,00 80,00 Percentage Without Electricty (%) 70,00 60,00 50,00 40,00 30,00 20,00 10,00 0,00

Jozini Umvoti Nongoma Endumeni uPhongolo KwaDukuza Maphumulo Umzimkhulu Okhahlamba Ubuhlebezwe Umhlabuyalingana Inkosi Langalibalele Inkosi iLembe uThukela Zululand Harry GwalaUmzinyathi uMkhanyakude

District Municipality Households Households Total Percentage with without households Without Electricity Electricity Electricity (%) Harry Gwala Umzimkhulu 22476 12041 34517 34,9 Zululand Nongoma 21851 12490 34341 36,4 Zululand eDumbe 10127 6011 16138 37,2 uThukela Okhahlamba 17417 12093 29510 41,0 Umzinyathi Msinga 23186 17305 40491 42,7 Harry Gwala Dr Nkosazana Dlamini- 12182 10988 23170 47,4 Zuma Harry Gwala Greater Kokstad 1827 1800 3627 49,6 uMkhanyakude Jozini 8954 30660 39614 51 iLembe Ubuhlebezwe 9454 10139 19593 51,75 iLembe Maphumulo 8696 11829 20525 57,63 iLembe Ndwedwe 16709 25193 41902 60,12 uMkhanyakude Big 5 Hlabisa 15938 28646 44584 60,3 uMkhanyakude Mtubatuba 9976 15279 25255 60,5 uMkhanyakude Umhlabuyalingana 9863 31929 41792 76,4

243

APPENDIX B: PROVINCIAL ADDITONAL DATA

Smart meter pilot projects [178].

Project Location Duration Outputs Revenue Nala, Naledi, Govan October 2014 Over 2000 smart meters installed in 5 Enhancement Mbeki, Thabazimbi, – pilot municipalities. Project (SANEDI) Mogale City March 2016 Introduction of data analytics Improved visibility and control of municipality network via energy management. City Power Smart Johannesburg Ongoing Over 65000 meters installed by May Metering Project (2016) 2015. (City of Jo’ urg) Improved operational efficiency, more accurate in billing information. Soweto Split Soweto, Ongoing Payment levels increased to 90% = Metering Project Johannesburg (2016) increased revenue. (Eskom) Reduced outages Community engagement improves project successful implementation. No “ghost Credit Dispensing Units” (CDUs) in the project area. Active Network Durban Ongoing Implementing embedded generation. Management and (2016) Advanced grid with SCADA, GIS, ADMS (eThekwini Outage control and other systems Municipality) fully integrated.

Existing Projects in Eastern Cape Table: Projects in progress and are planned for completion [21] Province Project Name Completion date Eastern Cape  Dedisa OCGT Financial Year: 2017/2018  Poseidon 500 MVA (400/132 kV) transformer

 Vuyani Substation

 Eros-Vuyani 400 kV line

 Neptune- Vuyani 400 kV line

 Southern Grid TX Transformer Normalisation: Financial Year: Buffalo & Pembroke substations 2022/2023

 Grassridge – Dedisa 132 kV line

 PE Strengthening Phase 3: Poseidon; Delphi; Grassridge; Dedisa Shunt capacitors

Substations (132 kV line) Reactive power compensation:Financial Year:  Grassridge substation 2018-2027

244

 Dedisa substation

 Poseidon substation

 Delphi substation

Province Household Households Percentage Household Households Percentage (2011 at without without (2015 at without without 2% electricity electricity 2% electricity electricity growth) (2011) (2011) growth) (2015) (2015)

Eastern 1 721 133 352 876 20.5% 1 755 555 349 478 19% Cape Free State 839 782 83 832 9.8% 856 578 85 774 10%

Gauteng 3 987 202 507 857 12.7% 4 066 946 565 564 14%

Kwa-Zulu 2 590 218 497 003 19.2% 2 642 022 509 186 19.2% Natal Mpumalanga 1 096 998 92 262 8.4 % 1 118 938 85 618 7.4%

Northern 307 433 28 081 9.1% 313 582 28 605 9.2% Cape Limpopo 1 466 464 105 166 7.2% 1 475 398 93 992 6.4 %

North West 1 083 255 147 654 13.6% 1 104 920 143 347 13%

Western 1 666 680 99 579 6% 1 700 014 114 074 6.7% Cape

245

Eastern Cape:Tariff Price (NERSA)

Table: Projects in progress and are planned for completion [21] Province Project Name Completion date

Kwa-Zulu  Avon 3rd transformer Financial year : 2017/2018 Natal  Mersey 3rd transformer

 Incandu 3rd transformer

 KZN 756 kV Pinetown Strengthening: Ariadne substation Financial Year: 2022/2023  Mbewu Substation

 Umfolozi Mbewu 765 kV line

 Loop in/out Athene- Umfolozi & Umfolozi – Imvubu 400kV line

New substations: (2 × 500MVA) 400/ 132kV Financial Year :2018-2027  Iphiva Substation in Northern KZN

 St. Faith Substation in South Coast KZN

 Shongweni Substation around Hillcrest in Western eThekweni Municipality

 Inyaninga Substation in the Northern eThekwini Municipality

246

Projects according to Eskom, to be established in the East London Area.  None of the projects are in Ntabankulu or its surrounding municipalities.

247