Sierra Leone Integrated Resource Plan and Tariff Study PPA Energy

Integrated Resource Plan and Tariff Study

Submitted to: Ministry of Energy

PPA Energy 1 Frederick Sanger Road Surrey Research Park Date: January 2015 Guildford, Surrey GU2 7YD, UK www.ppaenergy.co.uk Tel: +44 (0)1483 544944

CONTENTS

EXECUTIVE SUMMARY ...... 10 BACKGROUND ...... 10 DEMAND FORECAST ...... 10 DEVELOPMENT OF NEW GENERATION ...... 15 TRANSMISSION AND DISTRIBUTION DEVELOPMENT ...... 16 1 INTRODUCTION ...... 24 1.1 SCOPE OF WORK ...... 24 1.2 CONTRACT VARIATIONS ...... 24 1.3 DELIVERABLES SUBMITTED ...... 25 2 PRESENT SITUATION ...... 26 2.1 INTRODUCTION ...... 26 2.2 MACROECONOMY AND EBOLA VIRUS IMPACTS ...... 26 2.3 SECTOR-WIDE ASPECTS ...... 27 2.4 GENERATION ...... 33 2.5 TRANSMISSION ...... 35 2.6 DISTRIBUTION AND SUPPLY ...... 36 2.7 RURAL ELECTRIFICATION AND ACCESS ...... 37 3 DEMAND FORECAST ...... 38 3.1 INTRODUCTION ...... 38 3.2 BASE CASE TOTAL DEMAND ...... 39 3.3 HIGH CASE TOTAL DEMAND ...... 40 3.4 LOW CASE TOTAL DEMAND...... 41 4 IMPROVING RURAL ACCESS TO ELECTRICITY ...... 44 4.1 OVERVIEW ...... 44 4.2 CURRENT STATUS OF SYSTEMS OUTSIDE FREETOWN ...... 45 4.3 RENEWABLE ENERGY AND DIESEL GENERATION ...... 45 4.4 BARRIERS TO RURAL ELECTRIFICATION ...... 49 5 INDICATIVE GUIDELINES FOR THE EXPANSION OF TRANSMISSION AND DISTRIBUTION NETWORKS ...... 55 6 CANDIDATE TECHNOLOGY FOR GENERATION DEVELOPMENT ...... 43 6.1 COAL FIRED THERMAL PLANTS [1] [2] ...... 43 6.2 DIESEL PLANTS [3] [4] ...... 45 6.3 GAS TURBINE BASED PLANTS [5] [6] [7] ...... 46 6.4 HYDROELECTRIC PLANTS ...... 47 6.5 SOLAR PLANTS [8] [9] [10] ...... 48 7 PRELIMINARY SCREENING OF GENERATION OPTIONS...... 53 7.1 FUEL AVAILABILITY AND PRICING ...... 53 7.2 COSTS OF ELECTRICITY GENERATION ...... 60 7.2.1 OBJECTIVES ...... 60 7.2.2 SCOPE ...... 60 7.2.3 METHODOLOGY...... 62 7.2.4 ASSUMPTIONS ...... 62 8 DERIVATION OF LEAST COST EXPANSION DEVELOPMENT ...... 72 8.1 INTRODUCTION ...... 72

Ministry of Energy 1 January 2015 20414.

8.2 GENERATION DISPATCHING SIMULATIONS ...... 73 8.3 REVIEW OF SHORT-TERM GENERATION OPTIONS ...... 88 8.4 REVIEW OF MEDIUM-TERM GENERATION EXPANSION OPTIONS ...... 102 9 ENVIRONMENTAL AND SOCIAL IMPACT ...... 118 9.1 OBJECTIVES ...... 118 9.2 MODEL STRUCTURE ...... 118 9.3 BUILDING COSTS ...... 119 9.4 OPERATING COSTS ...... 125 9.5 DISMANTLING COSTS ...... 135 9.6 USING THE MODEL ...... 135 10 IMPLEMENTATION PROGRAMME AND OVERALL RECOMMENDATIONS ...... 136 10.1 IMPLEMENTATION ...... 136 10.2 REHABILITATION OF THE DISTRIBUTION NETWORK IN FREETOWN ...... 136 10.3 INSTALLATION OF ADDITIONAL DIESEL GENERATION IN FREETOWN ...... 137 10.4 CONSTRUCTION OF NEW DOUBLE CIRCUIT 225KV TRANSMISSION LINE BETWEEN BUMBUNA AND FREETOWN ...... 137 10.5 ESTABLISHMENT OF PLANNING UNIT WITHIN MOE ...... 138 10.6 HYDROLOGY IN SIERRA LEONE ...... 138 11 BIBLIOGRAPHY ...... 140 A APPENDIX A – DEMAND FORECAST ...... 144 A.1 INTRODUCTION ...... 144 A.2 INSTALLED CAPACITY IN THE WESTERN AREA ...... 147 A.3 FREETOWN DEMAND FORECAST ...... 155 A.4 RURAL DEMAND FORECAST ...... 189 A.5 MINING DEMAND ...... 216 A.6 CONCLUSION ...... 239 B APPENDIX B: SOCIAL TARIFF DESIGN AND WILLINGNESS-TO-PAY FOR CUSTOMERS OF REMOTE RURAL AREAS ...... 244 B.1 INTRODUCTION ...... 244 B.2 HOUSEHOLD EXPENDITURE ...... 244 B.3 WILLINGNESS TO PAY FOR ELECTRICITY ...... 249 C APPENDIX C: REVIEW OF WIND POWER GENERATION TECHNOLOGIES [11] [12] [13] [14] ...... 254 D APPENDIX D: MODEL FOR THE CALCULATION OF THE INDICATIVE QUANTITATIVE ENVIRONMENTAL AND SOCIAL COSTS OF POWER GENERATION - USER MANUAL ...... 257

Ministry of Energy 2 January 2015 20414

TABLES

Table 0-1: Base Case Total Demand Forecast ...... 11 Table 0-2: High Case Total Demand Forecast ...... 12 Table 0-3: Low Case Total Demand Forecast ...... 14 Table 3-1: Base Case Total Demand Forecast ...... 39 Table 3-2: High Case Total Demand Forecast ...... 40 Table 3-3: Low Case Total Demand Forecast ...... 41 Table 5-1: Sequence of transmission assets commissioning and specifications (Options A, B and C) ...... 60 Table 5-2: Expansion of Sierra Leone’s Distribution Network / Sequence of MV assets commissioning and specifications ...... 67 Table 5-3: Reference costs for high and medium-voltage assets ...... 41 Table 5-4: Indicative costs estimates for transmission and distribution expansion scenarios . 41 Table 6-1: Indicative costs for solar technologies [10] ...... 52 Table 7-1 Historic Prices of Petroleum Products – Regression parameters ...... 54 Table 7-2 Price Projections for HFO and Diesel – scenario 1 ...... 56 Table 7-3: Price Projections for HFO and Diesel – scenario 2 ...... 56 Table 7-4: Australian Coal Price Projections – scenario 1 ...... 57 Table 7-5: Australian Coal Price Projections – scenario 2 ...... 57 Table 7-6: Estimated Coal Freight Rates (Newcastle to Freetown) ...... 58 Table 7-7 Freetown Price of Fuel – scenario 1 ...... 59 Table 7-8 Freetown Price of Fuel – scenario 2 ...... 60 Table 7-9: Capital Cost Assumptions for a Coal Based Power Plant (125 MW) ...... 62 Table 7-10: Interest During Construction Calculations for a ...... 63 Table 7-11: Annual Fixed Cost ...... 63 Table 7-12: Variable Fuel Cost ...... 64 Table 7-13: Capital Cost Assumption for a Low-speed HFO Based Power Plant (40 MW) .. 64 Table 7-14: Interest During Construction Calculations ...... 65 Table 7-15: Annual Fixed Cost ...... 65 Table 7-16: Variable Cost for Scenarios 1 and 2 ...... 65 Table 7-17: Capital Cost Assumption for a medium-speed HFO Based Power Plant (10 MW) ...... 66 Table 7-18: Interest During Construction Calculations ...... 66 Table 7-19: Annual Fixed Cost ...... 67 Table 7-20: Variable Cost for Scenarios 1 and 2 ...... 67 Table 8-1: Short Term Interconnected Demand Forecast (2015-2019) ...... 81 Table 8-2: Long Term Interconnected Demand Forecast (2019-2024) – base case and high case scenarios ...... 81 Table 8-3: Fuel Price Forecast – Scenario 1 ...... 82 Table 8-4: Fuel Price Forecast – Scenario 2 ...... 83 Table 8-5: List of existing and committed plants ...... 85 Table 8-6: Overall Availability of the Plants ...... 86 Table 8-7: Seasonal Maximum Energy Sent Out Estimates ...... 86 Table 8-8: OPEX estimates per type of plant ...... 87 Table 8-9: CAPEX estimates for committed plants ...... 88 Ministry of Energy 3 January 2015 20414

Table 8-10: Assumptions for base units of options A, B, C ...... 89 Table 8-11 - Proposed LOLE criteria ...... 90 Table 8-12: Selected Configuration and Sequencing for each short-term option ...... 91 Table 8-13: LOLE results under each short-term option ...... 91 Table 8-14: Proposed sequencing for the CEC Plant ...... 93 Table 8-15: LOLE results under the proposed sequencing for the CEC Plant ...... 93 Table 8-16: Proposed sequencing for CEC and ENDEAVOUR ...... 94 Table 8-17: LOLE estimates for the combination of CEC and ENDEAVOUR Plants ...... 94 Table 8-18: Annual Energy Dispatching simulation results – none of the options implemented ...... 95 Table 8-19: Annual Energy Dispatching simulation results – option A implemented ...... 96 Table 8-20: Annual Energy Dispatching simulation results – option B implemented ...... 97 Table 8-21: Annual Energy Dispatching simulation results – option C implemented ...... 98 Table 8-22: Annual Energy Dispatching simulation results – option “CEC+ENDEAVOUR” implemented ...... 99 Table 8-23: NPV Calculation – Option A (coal) ...... 100 Table 8-24: NPV Calculation – Option B (low-speed HFO) ...... 100 Table 8-25: NPV Calculation – Option C (medium-speed HFO) ...... 101 Table 8-26: NPV Calculation – Option CEC+ENDEAVOUR...... 101 Table 8-27: Results of the short-term NPV Analysis ...... 102 Table 8-28: LOLE estimates from 2020 to 2024 under base case demand scenario and with committed plants only ...... 103 Table 8-29: Cost and hydrology assumptions for base hydro units ...... 104 Table 8-30: Review of medium-term options under base case demand scenario ...... 106 Table 8-31: LOLE estimates under each medium-term option and under the base case demand scenario ...... 107 Table 8-32: Review of medium-term options under high case demand scenario to supplement Bumbuna II ...... 112 Table 8-33: LOLE estimates for medium-term options under high case demand scenario ... 114 Table 9-1: Indicative costs for Forest valuation (in $/ha unless otherwise stated) [21] ...... 122 Table 9-2: Effects of SOx, NOx and Mercury [27] ...... 127 Table 9-3: Average emission rates per type of plant ...... 129 Table 9-4: Indicative damage costs for several pollutants ...... 130 Table A-1 – Annual Energy Supply from BHP to Freetown ...... 148 Table A-2: Bumbuna Monthly Energy Production (MW daily equivalent) ...... 149 Table A-3: BHP’s Seasonal Characteristics ...... 149 Table A-4: Transmission Line Loss Calculations ...... 150 Table A-5: BHP’s Seasonal Characteristics, Net of Losses ...... 151 Table A-6: Sent Out Electricity Generation in Sierra Leone (MWh) ...... 153 Table A-7 - Captive generators registered at NPA ...... 154 Table A-8 - Assumptions made for captive plants ...... 155 Table A-9- NPA generation characteristics ...... 159 Table A-10: Historical Electricity Sales by Customer Category (MWh) ...... 159 Table A-11 - Some industrial captive power plants in Freetown ...... 162 Table A-12 - Unconstrained industrial demand in Freetown area ...... 162 Table A-13 - Unconstrained industrial demand (2008) ...... 164 Table A-14: Unconstrained Industrial Energy Demand for 2013 ...... 165 Ministry of Energy 4 January 2015 20414

Table A-15: Industrial Demand Forecast ...... 166 Table A-16 - Unconstrained commercial demand in Freetown area ...... 169 Table A-17 - Unconstrained commercial demand ...... 170 Table A-18: Unconstrained Commercial Energy Demand for 2013 ...... 172 Table A-19: Econometric Assumptions for Commercial Demand ...... 172 Table A-20: Commercial Demand Forecast ...... 172 Table A-21 - Unconstrained residential demand in Freetown area ...... 176 Table A-22 - Unconstrained residential demand ...... 177 Table A-23: Suppressed Energy Demand (2008) – Domestic Customers ...... 178 Table A-24: Domestic Specific Demands – Base Case ...... 179 Table A-25: Econometric Assumptions for Domestic Demand ...... 180 Table A-26: Energy Demand in 2013 ...... 180 Table A-27: Domestic Demand Forecast ...... 180 Table A-28: Government Sales in 2013...... 181 Table A-29: Forecast Government Sales ...... 182 Table A-30: Distribution Losses Forecast ...... 185 Table A-31: Unsuppressed Maximum Demand Estimate (2013) ...... 185 Table A-32: Estimate of Losses at time of System Peak (2013) ...... 186 Table A-33: Estimate of sent out Maximum Demand (2013) ...... 186 Table A-34: Overall Projections of Future Demand in Freetown ...... 188 Table A-35: Electrification Candidate Areas ...... 190 Table A-36: Area Electrification Assumptions – Northern Province ...... 193 Table A-37: Maximum Demand Forecast in kW – Kambia Town ...... 194 Table A-38: Maximum Demand Forecast in kW – Port Loko ...... 195 Table A-39: Maximum Demand Forecast in kW – Lungi ...... 195 Table A-40: Maximum Demand Forecast in kW – Makeni ...... 196 Table A-41: Maximum Demand Forecast in kW – Lunsar ...... 196 Table A-42: Maximum Demand Forecast in kW – Magburaka ...... 196 Table A-43: Maximum Demand Forecast in kW – Kabala ...... 197 Table A-44: Other Northern Province Maximum Demand Forecasts ...... 197 Table A-45: Overall Projection of Maximum Demand in kW across Northern Province ..... 198 Table A-46: Area Electrification Assumptions – Eastern Province ...... 199 Table A-47: Maximum Demands in kW – Koidu - Sedafu ...... 200 Table A-48: Maximum Demands in kW – Kailahun ...... 200 Table A-49: Maximum Demands in kW – Kenema ...... 201 Table A-50: Other Eastern Province Maximum Demand Forecasts ...... 201 Table A-51: Overall Projection of Maximum Demand in kW across Eastern Province ...... 202 Table A-52: Area Electrification Assumptions – Southern Province ...... 203 Table A-53: Maximum Demands in kW – Bo ...... 204 Table A-54: Maximum Demands in kW – Moyamba ...... 204 Table A-55: Maximum Demands in kW – Punjehun ...... 205 Table A-56: Maximum Demands in kW – Bonthe ...... 205 Table A-57: Other Southern Province Maximum Demand Forecasts ...... 205 Table A-58: Overall Projection of Maximum Demand in kW across Southern Province ..... 206 Table A-59: Total Suppressed Demand Forecast in Urban Rural Areas (MW) ...... 207 Table A-60: Unsuppressed Power and Energy Requirements Forecast for Rural Areas ...... 209 Table A-61: Rural Base Case, High Case and Low Case Power and Energy Requirements 210 Ministry of Energy 5 January 2015 20414

Table A-62: Unsuppressed Additional Power and Energy Requirements ...... 212 Table A-63: Total Rural Energy and Power Requirements ...... 213 Table A-64: Sierra Leone’s Mining Potential – IMF 2010 ...... 217 Table A-65 Mineral Layers in Tonkolili Site ...... 220 Table A-66: African Minerals Electricity Requirements ...... 222 Table A-67: Mining Demand Forecast in Sierra Leone 2013-2020 ...... 237 Table A-68: Demand from the other large self-generating industrial stakeholders in Sierra Leone 2013-2020 ...... 238 Table A-69: Total demand from mining and other large self-generating industrial stakeholders ...... 239 Table A-70: Mining Demand after 2020 ...... 242 Table B-1: Average Expenditure on Electricity ...... 249 Table C-1: Indicative costs for wind generation [10] ...... 255

Ministry of Energy 6 January 2015 20414

FIGURES

Figure 0-1: Base Case Total Demand (MW) ...... 11 Figure 0-2: Energy Profiles in 2013 and 2030 (Base Case) ...... 12 Figure 0-3: High Case Total Demand Forecast (MW) ...... 13 Figure 0-4: Energy Profiles in 2013 and 2030 (High Case) ...... 13 Figure 0-5: Low Case Total Demand Forecast (MW) ...... 14 Figure 0-6: Energy Profiles in 2013 and 2030 (Low Case) ...... 15 Figure 1-1 – Transmission in Sierra Leone...... 29 Figure 3-1: Base Case Total Demand (MW) ...... 39 Figure 3-2: Energy Profiles in 2013 and 2030 (Base Case) ...... 40 Figure 3-3: High Case Total Demand Forecast (MW) ...... 41 Figure 3-4: Energy Profiles in 2013 and 2030 (High Case) ...... 41 Figure 3-5: Low Case Total Demand Forecast (MW) ...... 42 Figure 3-6: Energy Profiles in 2013 and 2030 (Low Case) ...... 43 Figure 4-1 – Solar Radiation Levels in Sierra Leone ...... 47 Figure 5-1: 2014 Transmission network in Sierra Leone – Map View (Nations Online Project) ...... 57 Figure 5-2: 2014 Transmission Network in Sierra Leone – Diagram View ...... 58 Figure 5-3: Transmission Network Diagram – Horizon 2030 ...... 59 Figure 5-4: Layout at transmission level for Lunsar (before the commissioning of the 225 kV line) ...... 64 Figure 5-5: Layout at transmission level for Lunsar (after the commissioning of the 225 kV line) ...... 65 Figure 6-1: Coal Fired Thermal Plant Diagram [3] ...... 43 Figure 6-2: How temperature collected increase with concentration [46] ...... 49 Figure 6-3: Parabolic concentrator in Odeillo (France) – 52kW (ConstruireSolaire.Com) .... 49 Figure 6-4: Parabolic through concentrator on Solar de Almeria platform (Spain) (DLR/Markus Steur)...... 50 Figure 6-5: Solar tower concentrator at Solar Two Power Plant in Mojave Desert, California (USA) (Global-Greenhouse-Warning.com) ...... 50 Figure 6-6: Linear Fresnel Solar Concentrator designed by Heliodynamics Ltd. (Heliodynamics Ltd.) ...... 51 Figure 6-7: 80 MW Photovoltaic Plant in Nevada (USA) (Inhabitat.com) ...... 51 Figure 7-1 : Historic Prices of Petroleum Products ...... 54 Figure 7-2 Petroleum Products Correlations with Crude Oil Prices ...... 55 Figure 7-3 : EIA Projections for Crude Oil Prices ...... 55 Figure 7-4: 2016 Screening curves for Coal and HFO – scenario 1 ...... 67 Figure 7-5: 2020 Screening curves for Coal and HFO – scenario 1 ...... 68 Figure 7-6: 2016 Screening curves for Coal and HFO – scenario 2 ...... 69 Figure 7-7: 2020 Screening curves for Coal and HFO – scenario 2 ...... 70 Figure 8-1: Illustration of stacking ...... 73 Figure 8-2: Peak and base load ...... 74 Figure 8-3: Reversed load duration curve with axis in pu ...... 75 Figure 8-4: Step by step methodology ...... 76 Figure 8-5: Illustration of the Hydro Stacking Process ...... 78 Ministry of Energy 7 January 2015 20414

Figure 8-6: Base Stacking ...... 79 Figure 8-7: Peak Stacking ...... 79 Figure 8-8: Unserved energy and unserved demand ...... 80 Figure 8-9: Least cost medium-term programmes after short-term option “CEC+ENDEAVOUR” and under base-case demand scenario for variable hydro CAPEX 109 Figure 8-10: Least Cost Medium-Term Programmes after short-term option “CEC+ENDEAVOUR” and under base case demand scenario for variable hydro CAPEX and gas prices ...... 110 Figure 8-11: Least cost medium-term programmes under high case demand scenario ...... 116 Figure 8-12: Least Cost Medium-Term Programmes after short-term option “CEC+ENDEAVOUR” and under high case demand scenario for variable hydro CAPEX and gas prices ...... 117 Figure 9-1: Thermal plants air emissions [27] ...... 126 Figure 9-2: Once-through cooling system [37] ...... 132 Figure 9-3: Wet closed-cycle cooling system [37] ...... 132 Figure A-1: Sierra Leone Map Showing Major Town Centres ...... 144 Figure A-2 – Monthly Energy Supply from BHP to Freetown ...... 148 Figure A-3 – Electricity Supplied to Freetown since 2008 (GWh sent out) ...... 152 Figure A-4: Hourly generation profile of the IPPs in Freetown ...... 158 Figure A-5: Historical Industrial Sales (GWh) ...... 160 Figure A-6: Hourly Profile: unconstrained industrial demand - weekday ...... 163 Figure A-7: Hourly Profile: unconstrained industrial demand - weekend ...... 163 Figure A-8: Historical Commercial Sales in GWh ...... 167 Figure A-9: Hourly Profile: unconstrained commercial demand - weekday ...... 169 Figure A-10: Hourly Profile: unconstrained commercial demand - weekend ...... 170 Figure A-11: Historical Domestic Sales in GWh ...... 173 Figure A-12: Hourly Profile: unconstrained residential demand – weekday ...... 176 Figure A-13: Hourly Profile: unconstrained residential demand - weekend ...... 177 Figure A-14: Historic Government sales (GWh) ...... 181 Figure A-15: Total System losses as % of sent out energy ...... 184 Figure A-16: Demand forecast – base case ...... 187 Figure A-17: Demand forecast – high case ...... 187 Figure A-18: Demand forecast – low case ...... 188 Figure A-19: Population Projections by District ...... 191 Figure A-20: Northern Province Towns ...... 193 Figure A-21: Eastern Province Towns ...... 199 Figure A-22: Southern Province Towns ...... 203 Figure A-23: Total Rural Unsuppressed Demand Projections in MW ...... 206 Figure A-24: Total Suppressed Demand and Total Unsuppressed Demand Forecast ...... 208 Figure A-25: Rural demand in MW for base case, high case and low case ...... 211 Figure A-26: Total Rural Demand – Base Case ...... 214 Figure A-27: Total Rural Demand – High Case ...... 214 Figure A-28: Total Rural Demand – Low Case ...... 215 Figure A-29: Sierra Leone’s Mineral Resources ...... 217 Figure A-30 Main Mining Stakeholders in Sierra Leone in 2013 ...... 219 Figure A-31: Tonkolili Site – African Minerals ...... 220 Figure A-32: Mineral Layers and Resources in Tonkolili site – African Minerals ...... 221 Ministry of Energy 8 January 2015 20414

Figure A-33: Sierra Rutile’s dry and dredge mining processes in Lanti ...... 223 Figure A-34: Sierra Rutile’s operations in Sierra Leone ...... 225 Figure A-35: London Mining’s Marampa Site – London Mining ...... 226 Figure A-36: Koidu Holding’s Kimberlite Project – Koidu Holdings ...... 228 Figure A-37: Cape Lambert’s Marampa Project ...... 229 Figure A-38: Amara Gold Mining Project: Breakdown by layer ...... 231 Figure A-39: Present Diamond Mining Companies in Sierra Leone ...... 232 Figure A-40: Other Gold Mining Companies in Sierra Leone ...... 233 Figure A-41: Demand Forecast for Low Case, Base Case and High Case ...... 239 Figure A-42: Yearly energy consumption forecast for Low Case, Base Case and High Case ...... 240 Figure A-43: Mining and Other Base Case Demand ...... 240 Figure A-44: Mining and Other High Case Demand ...... 241 Figure A-45: Mining and Other Low Case Demand ...... 241 Figure A-46: Mining Demand Forecast 2013-2030...... 243 Figure B-1: Average Household Expenditure by District ...... 245 Figure B-2: Total Household Expenditure over the Survey Population ...... 246 Figure B-3: Total Household Expenditure over the Survey Population in Urban and Rural Areas ...... 246 Figure B-4: Household Expenditure in Eastern Region ...... 247 Figure B-5 : Household Expenditure in Northern Region ...... 247 Figure B-6: Household Expenditure in Southern Region ...... 248 Figure B-7: Household Expenditure in Western Region ...... 248 Figure B-8: Projected Energy Requirements Eastern Province Remote Rural Areas (Base Case) ...... 251 Figure B-9 : Projected Energy Requirements Northern Province Remote Rural Areas (Base Case) ...... 252 Figure B-10: Projected Energy Requirements Southern Province Remote Rural Areas (Base Case) ...... 252 Figure C-1: Wake effect at the Horns Rev1 offshore wind farm, on 12/02/2008 (Vattenfall Wind Power). Photographer is Christian Steiness. [15] ...... 255

Ministry of Energy 9 January 2015 20414

Executive Summary

Background

PPA Energy was awarded a contract to produce an Integrated Resource Plan (IRP) and Tariff Study by the Ministry of Energy in Sierra Leone in June 2013, following a process of international competitive bidding. Funding for the work was provide by the UK Government under the Department for International Development (DfID), administered by the World Bank. The original duration of the contract was 11 months to end May 2014. This was subsequently extended to run to end January 2015

Over the course of this assignment a number of changes occurred to the key staff and contacts in Sierra Leone. In addition, the outbreak of the Ebola virus meant that some attention was diverted from the problems of the power sector, and it was no longer possible for consultancy staff to visit Sierra Leone.

Demand Forecast

It should be noted that the overall envelope of projected demand is broadly consistent with the forecast presented in the Ministry of Energy Paper entitled “Energy Sector Review 2014- 2017”, prepared in September 2014.

The forecasts have not been adjusted to reflect the impact of the present medical crisis in Sierra Leone, resulting from the outbreak of the Ebola virus. The rationale for this assumption is that it is considered any short term diminution of economic growth will be recovered over the period of the forecast. At the time of submission of this report there are also some uncertainties associated with mining demands. Whilst the price of iron ore remains at low levels, and economic growth in China (which drives many raw materials prices) has declined in recent times, the extent of the resources available in Sierra Leone, coupled with its possible petrochemical potential, is such that the overall projections for the mining sector demands in the longer term continue to be considered valid.

In deriving the overall forecasts, the estimated power and energy requirements for the Freetown area were added to the projections for the rural areas and the mining sector to assess the total estimated power and energy requirements on the system from 2013 to 2030. The forecasts which have been produced represent values which are assumed not to be restricted by the present supply side constraints. The determination of the levels of suppressed demand cannot be considered an exact science, and therefore it has been necessary to make a number of assumptions. Recognising this difficulty, the datum 2014 forecasts vary between the base, upper and lower cases, and there is a relatively wide range of disparity between future values.

Base Case Total Demand

Results for the base case scenario are presented below.

Ministry of Energy 10 January 2015 20414

Table 0-1: Base Case Total Demand Forecast

Year Freetown Rural Mining Total GWh MW GWh MW GWh MW GWh MW 2013 499 GWh 96 MW 14 GWh 13 MW 347 GWh 53 MW 861 GWh 161 MW 2014 527 GWh 101 MW 21 GWh 17 MW 788 GWh 120 MW 1335 GWh 238 MW 2015 550 GWh 105 MW 31 GWh 23 MW 1229 GWh 187 MW 1810 GWh 315 MW 2020 714 GWh 135 MW 81 GWh 65 MW 3434 GWh 523 MW 4228 GWh 723 MW 2025 924 GWh 172 MW 205 GWh 179 MW 5638 GWh 858 MW 6767 GWh 1209 MW 2030 1274 GWh 235 MW 370 GWh 328 MW 7842 GWh 1194 MW 9486 GWh 1757 MW

Figure 0-1: Base Case Total Demand (MW)

2000 MW 1800 MW 1600 MW 1400 MW 1200 MW Mining 1000 MW Rural 800 MW 600 MW Freetown 400 MW 200 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

The following figures show the evolution of the energy profile for this base case scenario. In 2030, the unsuppressed rural demand exceeds that of Freetown (19% against 13%), while mining represents represents 68% of the total demand.

Ministry of Energy 11 January 2015 20414

Figure 0-2: Energy Profiles in 2013 and 2030 (Base Case)

13%

Freetown 19% Rural Mining

33% 68%

59% 8%

Total Demand in 2013 | 161 MW Total Demand in 2030 | 1757 MW

High Case Total Demand

Results for the high case scenario are presented below.

Table 0-2: High Case Total Demand Forecast

Year Freetown Rural Mining Total GWh MW GWh MW GWh MW GWh MW 2013 687 GWh 126 MW 21 GWh 19 MW 347 GWh 53 MW 1056 GWh 198 MW 2014 753 GWh 138 MW 31 GWh 25 MW 1109 GWh 169 MW 1893 GWh 332 MW 2015 816 GWh 148 MW 46 GWh 35 MW 1871 GWh 285 MW 2733 GWh 468 MW 2020 1288 GWh 225 MW 122 GWh 98 MW 5681 GWh 865 MW 7090 GWh 1188 MW 11843 2025 2045 GWh 339 MW 308 GWh 269 MW 9491 GWh 1445 MW 2052 MW GWh 13301 17358 2030 3503 GWh 550 MW 554 GWh 492 MW 2024 MW 3066 MW GWh GWh

Ministry of Energy 12 January 2015 20414

Figure 0-3: High Case Total Demand Forecast (MW)

3500 MW

3000 MW

2500 MW

2000 MW Mining 1500 MW Rural Freetown 1000 MW

500 MW

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2013

The following figures show the evolution of the energy profile for this base case scenario. In 2030, mining represents 66% of the total demand, while rural demand and Freetown demand are very close (18% for Freetown demand, 16% for rural unsuppressed demand)

Figure 0-4: Energy Profiles in 2013 and 2030 (High Case)

18%

Freetown Rural 16% 27% Mining 66%

10% 64%

Total Demand in 2013 | 198 MW Total Demand in 2030 | 3066 MW

Ministry of Energy 13 January 2015 20414

Low Case Total Demand

Results for the low case scenario are presented below.

Table 0-3: Low Case Total Demand Forecast

Year Freetown Rural Mining Total GWh MW GWh MW GWh MW GWh MW 2013 374 GWh 67 MW 7 GWh 6 MW 347 GWh 53 MW 729 GWh 126 MW 2014 389 GWh 69 MW 10 GWh 8 MW 604 GWh 92 MW 1003 GWh 170 MW 2015 399 GWh 71 MW 15 GWh 12 MW 861 GWh 131 MW 1275 GWh 213 MW 2020 476 GWh 82 MW 41 GWh 33 MW 2143 GWh 326 MW 2659 GWh 441 MW 2025 561 GWh 94 MW 103 GWh 90 MW 3426 GWh 521 MW 4089 GWh 705 MW 2030 701 GWh 115 MW 185 GWh 164 MW 4708 GWh 717 MW 5594 GWh 996 MW

Figure 0-5: Low Case Total Demand Forecast (MW)

1200 MW

1000 MW

800 MW Mining 600 MW Rural 400 MW Freetown

200 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

The following figures show the evolution of the energy profile for this base case scenario. In 2030, mining represents 72% of the total unsuppressed demand, rural demand 16% and Freetown demand 12%.

Ministry of Energy 14 January 2015 20414

Figure 0-6: Energy Profiles in 2013 and 2030 (Low Case)

12%

16% Freetown Rural 42% Mining 53% 72%

Total Demand in 2013 | 126 MW Total Demand in 2030 | 996 MW

Development of New Generation

Consideration as to the requirements for new generation were split into two time windows, namely the period to 2020 and that from 2020 onwards. This split was based on the assumption that the earliest probable date for the commissioning of major new large scale generation plant (hydro or thermal) would be 2020. This recognises the likely delays in reaching Financial Close on the proposed extension to the Bunbuna 2 / Yiben complex as well the lead time associated with other major privately funded generation in country.

Under the base case demand scenario, there is a requirement for up to 200 MW of new diesel generation capacity over the period to 2020. Of this, 128 MW will be provided through the contract which it is understood has been agreed with CEC.

It has been assumed in this report that the first phase of this capacity, representing 56 MW, will be available by 2016. It is to be expected that the Power Purchase Agreement with CEC will provide details as to the anticipated programme from the commissioning of this plant, but it is to be expected that EPC and O&M contacts will not be placed in advance of Financial Close. It is understood that CEC are seeking a Partial Risk Guarantee from the World Bank and it is likely that Financial Close will follow a specified period after this. The construction period for Phase 1 will be dependant upon the lead time and availability of the diesel generators and programme for

Ministry of Energy 15 January 2015 20414

commissioning on site. It will also require completion of the connection to the EDSA system (assuming that the connection will be at 33 kV).

Completion of this work by 2016 is now considered a very ambitious goal, and it is possible that 2017 might be a more realistic target. It is therefore very important that emphasis is placed on the achievement of Financial Close as soon as possible, such as to minimise the period for substantive load shedding.

It is recommended if possible that the CEC plant should be slow speed diesel units capable of burning Heavy Fuel Oil (HFO) and, in the future, gas should it become freely available in Sierra Leone. Ideally unit sizes should be greater than are currently proposed by CEC, allowing for economies of scale.

Over the time period to 2020 there is a requirement for additional capacity which it is assumed could be provided by Joule Africa / Endevour. Under the base case demand scenario it is anticipated that an additional 50 MW of diesel generation is required for commissioning in 2019. Again, ideally this plant should be slow speed units capable of burning HFO and gas.

The above plants might be complimented by solar PV units where the total cost of generation from the PV plants is less than the short run marginal cost of diesel units. Since PV plants will not be capable of generation at the time of the evening peak, after sunset, they cannot be considered to have a capacity credit and therefore should not displace the commissioning of conventional thermal plant.

Post 2020, uncertainties associated with the energy yields and seasonality of hydro plants make the determination of a least cost development programme difficult to assess. It is recommended that, as a matter of urgency, gauging stations should be re- established adjacent to the sites of potential hydro power stations, in order to assess in the inter and intra annual variation in flows.

Under the high demand growth scenario, substantially more generation capacity will be required in line with the projections in Energy Sector Review.

Transmission and Distribution Development

1 “Rehabilition and Reinforcement of the Western Area Sub-Transmission and Distristribution”, Power Planning Associates Ltd (PPA Energy) , April 2004 for NPA

Ministry of Energy 16 January 2015 20414

the completion of the 33 kV sub-transmission network, and this was seen to be a key factor in the reduction of the demand of the 11 kV system.

In parallel with this, it is also necessary to continue to address the level of non technical losses. The continued installation of pre-payment meters, replacing credit meters, is an initial step, but it is recommended that additional measures, backed by the Ministry of Energy be implemented. These could include the following:

 Appointment of a high level official within EDSA with responsibility for reduction in non technical losses;

 Enactment of legislation if necessary to empower EDSA to adopt legal measures for recovery of debts / criminal prosecutions;

 Disconnection of all customers with illegal connection / in arrears;

 Prosecution of worst cases of non payment;

 Creation of “task force” backed by police to enforce disconnection of illegal customers in areas where particular problems are experienced;

 Installation of tamper proof smart meters with remote reading for largest customers

A recommended programme for the development of the transmission network in Sierrra Leoen is summarised below

Ministry of Energy 17 January 2015 20414

Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

2016 161/132/66kV substation in Lunsar 1 x 5 MVA 161/132/66 kV transformers in Lunsar

2018 The existing single circuit 161 kV line will not be sufficient to transmit the existing and committed generation to Freetown (without compensation the voltage drop means the line rating is approximately 50 MW).

The proposed solution is to construct at 225 kV double circuit line between Bunbuna and Freetown to accommodate the generation at Bumbuna, and Makeni in the short term and in preparation for a further 200 MW of hydro generation in Bumbuna and/or Yiben by 2021.

Once the 225 kV line is operational it will be desirable to operate the 161 kV line between Bumbana and Freetown open. Thus the existing 161 kV system at Bumbuna will have to be connected with the new 225 kV system. Detailed system studies should be undertaken to determine the best location for this open point in terms of system operation and losses.

225/161 kV substation in Bumbuna

WAPP Double 225 kV Circuit 2 x 225/161 kV (150 MVA) transformers in Bumbuna 225/66 kV substations in Kamakwie, connecting Kamakwie, Yiben, Yiben, Bekongor Bumbuna, Bekongor and 2 x 225/33 kV transformers in Kamakwie (5 MVA), Kenema Yiben (5 MVA), Bekongor (25 MVA) 225/66 kV substation in Kenema (WAPP MasterPlan)

2 x 225/66kV 10 MVA transformers in Kenema (the above are part of the WAPP Double 225kV (2 wires per MasterPlan) phase, 400mm²) circuit (the above are part of the WAPP MasterPlan)

Ministry of Energy 18 January 2015 20414.

Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

connecting Bumbuna and 225/MV substation in Freetown 2 x 150 MVA 225/MV transformers in Freetown Freetown

2019 Demand at Port Loko, north of Lunsar and Magburaka, south of Makeni is to be supplied by the transmission system in 2019.

An additional 225/66 kV transformer should be commissioned in Lunsar to supply Port Loko, and the initial 161/132/66 kV transformer would be then dedicated to supplying the Southern ring.

Whilst there is a small amount of generation in Port Loko presently supplying the demand looking forwards the demand in the Port Loko and Lungi are expected to increase in period to 2030 and this has been taken into consideration in the proposed transmission system.

Double 66 kV (1 wire per 1 x 15 MVA 225/66 kV transformers in Lunsar phase, 250mm²) line connecting Port Loko to 1 x 161 kV/MV transformer in Makeni and 2 x Lunsar Upgrading the existing 161 kV/MV MV/33 kV transformers (exact design of Makeni substation in Makeni to supply dependent on existing voltage and substation Single 33 kV (1 wire per Magburaka arrangements at biomass power station and phase, 150mm²) line requirement for 33 kV transmission at Magburaka) connecting Makeni to Magburaka

Ministry of Energy 19 January 2015 20414

Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

2020 Demand at Lungi and Kambia is to be supplied by extending the 66 kV system from Port Loko. To provide N-1 security of supply to Lungi a double circuit transmission line is proposed. The line to Kambia could be single circuit, anticipating it being also connected to a network in the north to achieve N-1 security.

In anticipation of the demand at Bô increasing beyond the ability of the existing 66 kV line from Kenema around 2025 and increasing to 38 MW by 2030, the reinforcement of the supply to Bô is needed. There is a small amount of demand at Moyamba to supply (with the possibility of significant mining demand). The proposed option to provide a flexible transmission network in the south is to create a 132 kV ring between Lunsar, Moyamba, Bô and Kenema. It is likely that this ring could be initially energised at 66 kV until the demand is such that 132 kV is necessary. Detailed studies should be undertaken to determine the level of demand at Moyamba and Bô for which 66 kV is adequate and at what point it is necessary to use 132 kV. Use of the ring provides N-1 security to the substations, but studies would be necessary to determine if an open point is required to prevent unwanted paralleling issues with the 225 kV system and ensure satisfactory system operation and losses.

Single Circuit 132 kV (1 wire per phase, 400mm²) line connecting Lunsar, Moyamba and Bô, energized at 66kV

Double Circuit 66 kV (1 wire per phase, 250mm²) line connecting Port Loko and Lungi

Ministry of Energy 20 January 2015 20414

Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

Single 66 kV (1 wire per phase, 250mm²) line connecting Port Loko and Kambia

Post 2020 As discussed above it is expected that as the demand in the south grows it will be necessary to move from 66 kV to 132 kV on the Lunsar – Moyamba – Bô – Kenema ring. The old 66 kV line between Kenema and Bô will need to be replaced with a 132 kV line. Under Option A the 132 kV ring can be single circuit as reliability is obtained from the ability to reconfigure the ring should there be an outage on one section of line.

Upgrading Kenema’s substation 1 x 225/132 kV 40 MVA transformer in Kenema 66 kV line between Bô and Kenema decommissioned Upgrading Bô’s substation 2 x 40 MVA 132/66 kV transformers in Bô 132 kV (1 wire per phase, Upgrading Moyamba’s substation 2 x 132/66kV 5 MVA transformers in Moyamba 400mm²) line between Bô and Kenema

Ministry of Energy 21 January 2015 20414

Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

Line Lunsar-Moyamba-Bô energized at 132 kV

Ministry of Energy 22 January 2015 20414

Such development would result in the creation of a network illustrated below by an assumed horizon year of 2030:

Ministry of Energy 23 January 2015 20414.

1 Introduction

1.1 Scope of Work

The IRP component of the assignment required completion of the following tasks:

 Definition of Policy Priorities;

 Sector Organisation and Reform Roadmap;

 Analysis of Historical Energy Demand and Forecast of Future Demand;

 Evaluation of Options for Increasing Rural Access;

 Assessment of Potential for DSM;

 Development of Systems Planning Analysis;

 Financial and Policy Analysis;

 Scenario Analysis to investigate impact of changes of assumption to basic parameters;

 Completion of Multi-Attribute Trade Off Analysis; and

 Dissemination of Results and Capacity Building

1.2 Contract Variations

Work commenced under the contract in June 2013 with an initial visit to Freetown by the designated Project Director, Neil Pinto. This was followed by further visits by other members of the nominated team including Don Webster (Team Leader, Tariff Study), Maite Pina (Financial Expert) and Trevor Fry (Rural Electrification Expert).

Ministry of Energy 24 January 2015 20414

An extension to the contract was formally approved which extended the contract duration to end January 2015, with a change in work scope to reflect the situation in Sierra Leone.

1.3 Deliverables Submitted

A Draft Inception Report was submitted in July 2013, followed by a Final Inception Report in September 2013. This report covered the first two topics under the Terms of Reference, namely the definition of policy priorities and sector organisation and reform roadmap.

Although not formally required as a contract deliverable, a stand alone Rural Electrification report was submitted in November 2013 in response to a request from Sierra Leone that this element of the work be fast tracked. A summary of this report in included within this submission.

A standalone demand forecast report was submitted in February 2014, and this was updated in October 2014 following the extension to the contract duration. This forms the basis of the demand forecast section of this report.

In addition, working papers have been submitted as follows:

 Preliminary analsysis into optimal generation development programme – December 2014

 Proposed contracting arrangements with Joule Africa / Endevour in the event of delays to the commissioning to the Bumbuna 2 / Yiben hydropower complex.

It is proposed that this IRP report should be presented to key staff at a workshop scheduled in Ivory Coast in February 2015, in parallel with a presentation on the tariff study and training in the model developed under this assignment.

Ministry of Energy 25 January 2015 20414

2 Present Situation

2.1 Introduction

Whilst it is the nature of an IRP to take a medium- and long-term perspective, it is essential that that all analysis and projections are founded on a clear understanding of the current status of the sector. At the time that this report is prepared – in January 2015 – the IRP study has been running for more than 18 months, i.e. since June 2013, and there has been a number of changes over this period.

The Government of Sierra Leone (GoSL) has been actively encouraging private sector participation (PSP) in the power sector for several years and, in response, private sector developers have shown considerable interest in partnering with GoSL to develop the power sector, and particularly the generation sub-sector. In addition, the donor community has continued to provide support to GoSL, across the power sector, to help meet the nation’s development objectives. As a consequence, the ‘current status’ has evolved appreciably since the start of the study. Although the situation as at January 2015 is of primary interest, it is also important to note some of the key changes over the period of the study.

Within the past 18 months, Sierra Leone has been beset by two major issues that inevitably impact on the development of the power sector. First and foremost, in the middle of 2014 the ebola virus spread from Guinea to Sierra Leone and very quickly affected communities across the nation. The fight against this fatal and highly virulent disease, together with the fear of the contagion spreading, have had immediate impacts on economy activity and plans for expanding the economy and national infrastructure. Even without the ebola virus, Sierra Leone has been affected by a collapse in global commodity prices, particularly the iron ore that has been of strategic importance to plans for economic growth in the country.

This section of the report is structured as follows, in order to capture all the important elements of the present situation in the power sector:

 Macroeconomy and ebola virus impacts

 Sector-wide aspects;

 Generation;

 Transmission;

 Distribution and supply; and

 Rural electrification and access.

2.2 Macroeconomy and Ebola virus impacts

2.2.1 Macroeconomy

Ministry of Energy 26 January 2015 20414

In June 2014, before the ebola outbreak took hold, the IMF reported that GDP growth in Sierra Leone was very strong. Iron ore exports had pushed GDP growth to around 20% in 2013. By June 2014, IMF and GoSL were projecting growth of 11.3% for the year. In addition, inflation was steadily reducing to relatively modest levels. Within the past 6 months however, the ebola virus and depressed commodity prices have surfaced, thus prompting fears of a temporary brake on the previous high rates of economic growth. The IMF has not reported on the country since June 2014.

Economic growth in Sierra Leone has generally been strong since the end of the civil war. Several sectors have contributed towards this expansion: large-scale agriculture, agro-processing, and mining in particular. Mining operations that were active before the civil war have re-opened, but the main boost has come from the development of two large iron-ore mines in the past 3 or 4 years. Iron ore extraction, primary-processing, rail shipment and bulk carrier loading facilities have been developed on a large scale. Unfortunately, over the past two years, global demand for iron ore has declined, especially from Sierra Leone’s main market: China. The market price of iron ore fines is currently only around 50% of the price just two years ago. The Consultant understands that both iron ore miners in Sierra Leone have scaled-back production appreciably in recent months.

Apart from these two major factors, however, the fundamentals in Sierra Leone appear to be robust. It is therefore widely expected that any decline in economic activity will be both short-lived and immediately followed by an economic re-bound.

The Ebola outbreak reached Sierra Leone in May 2014 and by January 2014 there had been over 3000 fatalities in the country. Pressures on the healthcare sector from ebola are also likely to have increased death rates from other health issues such as malaria, diarrhoea, and pneumonia. By extrapolating from previous outbreaks of the disease, the rate of new infections is expected to start declining within the next couple of months. In the meantime, there has inevitably been considerable disruption to economic activity.

2.3 Sector-wide aspects

2.3.1 Infrastructure Overview

The Bumbuna I power station is connected to Freetown by a single circuit 161kV transmission line, which passes through Makeni and Lunsar. Since April 2013 Makeni has been supplied at 34.5kV from the shield wire on this line.

The towns of Bo and Kenema also have a small system supplied by a combination of hydro and thermal plants. In addition EDSA is redeveloping isolated diesel-based systems in a number of other towns, such as Lungi.

Demand is projected to grow substantially in the near future, in part driven by the significant demands of the mining sector. Two companies are currently mining iron ore for export, and these were projected to have a combined indicative demand of around 200 MW by 2018, though it is noted that at least one of the companies has recently announced that it is scaling back its expansion plans. Other mining operations are also planning an extension of their operations. Ministry of Energy 27 January 2015 20414

There is also a high level of suppressed demand in the country, with many potential customers operating stand-alone diesel plants in order to ensure a continuous electricity supply.

Sierra Leone has substantial hydroelectric potential, the development of which will be an important element in the future supply of electricity in the country. Potential sites for hydropower stations are shown in Figure 1.1. A number of other supply-side opportunities are also under consideration, including power stations supplied by LNG and coal, as well as additional diesel plants.

The West African Power Pool (WAPP) also plans an extension of the international interconnections, and has developed proposals for the construction of new transmission lines between Sierra Leone and the Republic of Guinea to the north, and the Republic of Liberia to the south. These are shown as dotted red lines in Figure 1.1.

Ministry of Energy 28 January 2015 20414

Figure 2-1Transmission in Sierra Leone

Key:  Potential hydro power station Projected WAPP transmission lines

Over the past 5 years or so, the generation situation has improved significantly over the situation a decade ago. The Bumbuna I hydropower project commenced operations in 2011 and new diesels at Kingtom and Blackhall replaced older and less reliable units at Kingtom. However, economic growth over the period has been very strong and supply has failed to keep pace with demand.

2.3.2 Demand for electricity

Demand for electricity across the country as a whole has been growing rapidly in recent years, due to large mining developments in particular and strong economic growth generally. As will be demonstrated in the following section, this strong growth in demand is expected to continue for many years. At the present time, however, EDSA is currently only able to satisfy part of the ‘unconstrained’ demand, due to severe constraints in generation and transmission infrastructure. Consequently, demand from mining operations and from urban towns outside the Western Area is either being met by diesel-based self-generation, other alternatives energy sources. In many instances the demand is not being met, with a consequential impact on economic output and efforts to reduce poverty.

Ministry of Energy 29 January 2015 20414

GoSL has stated ambitions to achieve 1000MW of installed capacity in the country by 2018. In the following section, Base, High and Low demand forecasts are presented. The High forecast is broadly consistent with the GoSL ambitions. In view of the facts that in January 2015 there is little evidence of either the ebola virus infection rates declining, or of iron ore and other mineral prices rising, the Consultant’s Base forecast adopts demand figures in the medium-term that are better aligned with the current situation in the country.

2.3.3 Tariffs

The IRP is being undertaken in parallel with a Tariff Study for the sector. The most recent tariff review was in 2008 and despite being amongst the highest in the region, gradual erosion of the value of the Leone relative to hard currencies such as the US dollar, they are currently set below cost-recovery levels. High technical and technical losses, together with low collection rates, are partly responsible for this situation.

Since 2008, NPA tariffs have remained constant in nominal terms but declined considerably in real terms.

Bumbuna I has been commissioned since the 2008 review and has helped to depress the average cost of generation. Similarly, medium-speed diesels fuelled on HFO, which have a lower operating cost than the emergency diesels, have also been commissioned since 2008. However, there can be little doubt that tariffs are now well below cost- recovery levels and thus all customer categories are effectively being subsidised by GoSL to varying degrees. Nevertheless, tariffs in Sierra Leone remain very high by international comparisons, and particularly with countries in West Africa that have significant indigenous hydropower or oil resources.

Changes to the generation mix, general price escalation, depreciation of the Leone against the US$ and other hard currencies, together with changes in global fuel prices, combine to affirm that a review of tariffs in Sierra Leone is long overdue. However, tariffs need to consider numerous other issues beyond the cost base of the industry as it stands at present. These other issues include the following:

 In the interests of sustainability, tariffs need to be broadly cost-reflective but with an optimal deployment of cross-subsidies and/or direct government subsidies in order to meet broader development objectives such as poverty alleviation. Some account may need to be taken of future costs, in order to avoid major changes in the tariff at some point in the near future, e.g. due to significant changes in the generation mix.

 The need to attract the required investment. GoSL has a stated policy (in its Agenda for Prosperity) to expand the installed capacity from around 75MW in 2013 to 1000MW by 2018.2 Major participation from the private sector will be required if this goal is to be met. However, private investors will find it difficult

2 The 75MW figure does not include capacity, such as at Bo Kenema, which is serving locations outside Freetown.

Ministry of Energy 30 January 2015 20414

to secure financial backing unless the offtaker is financially sound and tariffs broadly cost-reflective. A tariff review is therefore extremely important if GoSL’s expansionary plans are to be realised.

 To avoid the situation that has arisen over the past 6 years, since the 2008 tariff review, a system of automatic tariff adjustments is required to take account of volatile factors in the cost base, such as fuel prices and exchange rates.

 Currently, the NPA system is effectively limited to the Western Area, with a transmission line feeding power from Bumbuna I. The network is therefore extremely limited at present. However, by 2017 it is anticipated that not only will a more extensive transmission network be established in Sierra Leone, the country will also be interconnected regionally, through the West Africa Power Pool (WAPP). WAPP opens up the potential for exports to and imports from neighbouring countries.

The tariff review also needs to take account of the current state of the power sector infrastructure, systems, institutions, skills, etc. In each of these areas the current position is sub-optimal, as shall be elaborated elsewhere in this report. The implication of these shortcomings is that they inevitably impose constraints on the tariff methodology. As a consequence, GoSL needs to ensure that any proposed tariff methodology does not impose institutional and HR requirements that are well beyond the level available. This aspect, together with the issues raised above, suggests that a phased approach to the tariff setting methodology is probably appropriate.

2.3.4 Sector Restructuring

Plans for restructuring the power sector were in place at the start of the IRP study and over the intervening period these plans have largely been implemented. Until this restructuring was implemented, NPA was the vertically-integrated electricity utility with a network that extended over Freetown and parts of the Western Area. NPA owned and operated all the generation and distribution infrastructure, and was also responsible for metering, billing and collection activities. NPA also nominally had isolated systems in some of the regional centres. In addition, there was the semi-autonomous subsidiary covering the towns of Bo and Kenema. Since November 2011, when the Bumbuna I hydropower project entered service, the structure of the sector changed. The hydro plant and the 161kV transmission line that delivered its output to Freetown, were neither owned nor operated by NPA. The Consultant understands that both are owned by the Ministry of Energy and operated by Salini. NPA purchases Bumbuna I power through a PPA, the signatories to which are NPA and the Ministry of Energy. Since late-2011, therefore, NPA has been purchasing wholesale electricity from the Ministry of Energy and the cost of this has co-mingled with the cost of its own generation from Blackhall Road and Kingtom, together with the cost of NPA’s distribution and supply activities.

However, there are more radical measures in the pipeline that will soon impact on ownership structures and the flow of money from end users to infrastructure owners. The new National Electricity Act 2011 (the Act) heralds the restructuring of the power sector, with the creation of the following entities: Ministry of Energy 31 January 2015 20414

 Electricity Generation and Transmission Company (EGTC). EGTC has taken over responsibility of the existing power stations at Kingtom, Blackhall Road, Bo and Kenema. It will be empowered to construct new power stations, including both thermal and hydro plants, and develop the transmission system in the country. It is noteworthy that there is no defined voltage demarcation between transmission and distribution in the Act. It is assumed that EGTC will take over the 161kV transmission between Freetown and Bumbuna I.

 Electricity Distribution and Supply Authority (EDSA). EDSA has effectively taken over the role historically played by NPA in respect of the distribution networks and retail of electricity to end customers. EDSA will be the counter- signatory to Power Purchase Agreements (PPAs) with existing and new power stations. The Act appears to grant monopoly rights to EDSA for distribution and retail, though there is some uncertainty over this. It is important that this issue is clarified, as it could provide an impediment to the establishment of off grid networks under PPP structures. Unusually, the Act confers upon EDSA the responsibility to act as the Single Buyer within Sierra Leone. Such responsibility is normally separated or placed in a ring-fenced entity within the transmission company, and there are few instances of the function being allocated to the distribution company.

In addition to the above, it is envisaged that there will be a number of new IPPs, again including hydro plants. These IPPs may also have responsibility for transmission connection to the grid. The Act is not clear as to the exact allocation of responsibilities in this area between the IPP developer and EGTC.

An Energy Asset Unit is also proposed, within the Ministry of Energy. The responsibilities of this Unit are defined as follows within the Act:

 Ensuring the transfer of assets currently held by the Ministry, NPA and the Bo- Kenema Power System to EGTC and EDSA respectively

 Management of all Government energy facilities

 Any future development of any energy in Sierra Leone.

One interpretation of this is that the Unit has only a transitional phase, pending finalisation of the unbundling process. An alternative interpretation is that it will endure as the owner of Bumbuna I, though this assumption is challenged by the nature of the Bumbuna II development, which may see the existing power station falling under the responsibility of Joule Africa for the duration of their PPA.

This issue was considered in a preliminary tariff report written by Meier3, which goes on to note that, in order to ensure the future integrity and viability of the power sector,

3 Power Tariff Methodology for Sierra Leone, Peter Meier, June 2011

Ministry of Energy 32 January 2015 20414

it will be necessary to remove distressed assets and unfunded liabilities from NPA. These will need to be held in a Special Purpose Vehicle (SPV).

The power sector also anticipates the establishment of an operational Regulating Commission, as stated in the Electricity and Water Regulatory Commission Act 2011.

The restructuring envisaged under the National Electricity Act 2011 needs to be considered in conjunction with broader objectives of GoSL. The Agenda for Prosperity paper written by GoSL, covering the period for 2013 to 2018, recognises the current problems affecting the power sector, and proposes the following goals:

 To increase installed capacity within Sierra Leone from the current level of 90 MW to 1000 MW by 2018, of which 600 MW will be supplied from base load thermal plant;

 Redevelopment of the National Transmission and Distribution Network, including reinforcement of the single circuit overhead transmission line from Bumbuna I to Freetown;

 Development of the WAPP interconnections, including negotiation of a Power Purchase Agreement (PPA) with Côte D’Ivoire and identification of power purchase opportunities from the Mano River Union countries;

 Unbundle and restructure the electricity sector; and

 Ensure all District Headquarter towns have electricity.

The Consultant is charged with developing methodologies for setting wholesale and retail tariffs that are consistent with both the emerging industry structure and the broader national agenda.

2.4 Generation

2.4.1 Overview

The power sector in Sierra Leone suffers from severe supply-side constraints. These constraints threaten the sustainability of the current strong economic growth that largely derives from mining, from more intensive agriculture, and expansion of agro-industries. GoSL is highly focused on this situation and has prioritised the removal of these constraints and the rapid expansion of capacity in the power sector.

The scale of investment required to satisfy medium- and long-term demand for generation capacity is, realistically, well beyond the resources of GoSL and the donor community through conventional public financing arrangements. Consequently, GoSL has placed great emphasis on private sector participation to provide the necessary capacity.

Ministry of Energy 33 January 2015 20414

2.4.2 Current Infrastructure

Freetown is supplied by three power stations, a hydro plant at Bumbuna (Bumbuna I) with an installed capacity of 50 MW, and thermal installations at Blackhall Road (16 MW) and Kingtom (10 MW). The towns of Bo and Kenema also have a small system supplied by a combination of hydro and thermal plants. In addition NPA is redeveloping isolated diesel-based systems in a number of other towns, such as Lungi.

It is noteworthy that whilst the ownership of most of this generation capacity has passed to EGTC, Bumbuna I is owned by MoE.

Bumbuna I was commissioned in November 2011. Although studies for this project put the average annual energy at 316 GWh, in each of the years to 2014 the annual generation did not exceed 161 GWh. Initial reliability issues are understood to account for much of this difference, although the subsequent correction of technical problems is unlikely to lead to generation as high as was initially expected.

2.4.3 On-going developments

Over the past 18 months, the portfolio of candidate generation projects has changed on a number of occasions, which is generally to be expected when the emphasis is placed on private sector participation; some projects move smoothly from conception to financial close, whilst others are delayed, cancelled, up-scaled or down-scaled as circumstances in the external and internal environments change.

The Addax Bioenergy project is probably a good example of a project that has developed quite smoothly. To some extent, this may be due to it being largely an ethanol production facility, with the sale of surplus electricity as a secondary output. There is a PPA with EDSA that should see the production of up to 72 GWh annually, from April 2015.

There is considerable undeveloped hydropower capacity in Sierra Leone, most notably in the Bumbuna/Yiben cascade. The existing Bumbuna I scheme has only seasonal storage, which entails that the firm output in the dry season is only around 18MW, compared with the 50MW installed capacity. For many years there has been plans to provide greater and firmer capacity from the catchment by developing over-year storage at Yiben, upstream of Bumbuna, together with additional generation units at both Yiben and Bumbuna, in combination raising the firm capacity from 18MW to 78MW. For the past two years, a developer has held a concession to develop Bumbuna II. Although the original plan was for a dam at Yiben and a new power station at Bumbuna, there is now a degree of uncertainty as to the timing of the Yiben dam, which is a crucial element if the investment is to be cost-effective. In view of this uncertainty over Yiben, the developer – Endeavour – has outlined a commitment to build HFO-fired diesel generation in Freetown, in order to help bridge the medium-term capacity deficit.

Other projections presented by the Minister of Energy to the Mining, Energy / Oil and Gas Indaba in July 2013 included more hydro projects (Bekongor II, 39 MW in 2016 and Betmal II, 60MW in 2016). It is the Consultant’s understanding, however, that these projects are unlikely to be commissioned before 2022 and 2023, respectively. Ministry of Energy 34 January 2015 20414

GoSL has been in negotiation with CEC Africa for around two years, also. The scope of this project has evolved over time and currently centres on the development of HFO- fired diesel generation in Freetown, with related transmission, to be commissioned in phases between 2016 and 2017.

At one stage during the course of this IRP study there was a proposal from a developer for a 2x175MW coal fired thermal plant, based on imported coal from Southern Africa. It is the Consultant’s understanding that the original sponsor behind this proposal is no- longer actively pursuing this option.

At various stages during recent years, developers have apparently considered generation developments in the neighbouring countries (e.g. hydropower in Guinea and coal in Liberia, with a view to exporting part of the output to Sierra Leone through the WAPP interconnector. The Consultant is not aware that any of these outline proposals have advanced materially.

2.5 Transmission

To-date, NPA has not had a transmission network. Distribution around Freetown and the Western Area is currently at 11kV and 400V, and a 33kV network is gradually being rolled-out. However, these are distribution voltages, as opposed to transmission voltages. The only transmission line in the country is the 161kV line between Bumbuna I and Freetown, which is used solely for the purpose of evacuating power from Bumbuna I.4 Within the next 4 to 10 years, leading up to and beyond the realisation of GoSL’s objectives in the Agenda for Prosperity, an extensive transmission network will be created with distinct elements as follows:

 Transmission lines that constitute part of the WAPP regional interconnection;

 Transmission lines that exist principally to evacuate power from IPPs;

 Transmission lines that exist principally to deliver power to major industrial operations, such as the mines; and

 Transmission lines to deliver power to the main regional population centres.

The cost structure relating to each type of transmission line needs to be defined, together with how these costs feed into the wholesale and retail tariff structures. Similarly, the cost of using the transmission networks will also need to be allocated to power imports and exports.

It is understood that EGTC is to be given responsibility to develop the transmission system in the country, and will also take over the 161kV transmission line between Freetown and Bumbuna I. It is therefore anticipated that EGTC will require tariffs that will provide them with a reasonable return on capital, after transmission-related O&M

4 Since April 2013 Makeni has been supplied at 34.5kV from the shield wire on this line.

Ministry of Energy 35 January 2015 20414

costs have been met. However, since EGTC is expected to own generation assets in addition to transmission infrastructure, the tariff setting methodology will need to undertake a virtual ring-fencing of the EGTC business, into transmission and generation entities, with suitable allocation of administrative costs.

EDSA as single-buyer: As noted above, the Act confers upon EDSA the responsibility to act as the Single Buyer within Sierra Leone, which is unusual in the industry.

Long lead times on most IPPs, and the need for capacity in the interim: A number of potential IPPs have been brought to the attention of the Consultant. However, none of these has currently reached financial close or commenced construction.5 The lead-time for most of these projects may be in the order of 5 or 6 years. In the interim, however, demand is likely to grow enormously. This demand may only be met by generation plant with short lead times, which are likely to have a high marginal cost. There is also the risk that there will be little demand from this capacity when generation with appreciably lower marginal cost (e.g. coal and hydro) becomes available. The developers of any such interim capacity would need assurance that they would be able to fully recover their costs, even if they are not required to despatch, otherwise they may be disinclined to invest in the first place. The design of the wholesale tariff structure needs to take account of this consideration.

Sierra Leone is a member of WAPP and, for several years, the organisation has had plans to develop a 250kV transmission through the centre of Sierra Leone and linking the country with Guinea and with Liberia. The line will form the spine of the transmission system in Sierra Leone, together with the transmission lines linking the Bumbuna/Yiben hydropower cascade with the main demand centre in Freetown and the Western Area. Crucially, the WAPP interconnector will facilitate electricity imports and exports with Sierra Leone. Since before the start of this IRP study in 2013, the commissioning date has remained at 2017. However, since the three countries involved with the interconnector are the also at the heart of the current ebola epidemic, it is likely that there may be delays to the commissioning of this line.

2.6 Distribution and Supply

EDSA is the entity for distribution and supply of electricity across the country. It also holds the role of Single Buyer. Under the current regulatory frameworks, generators are unable to supply power directly to major consumers such as the mining companies.

It is only in the past 3 or 4 years that, with the support of the donor community, that decline in the Western Area distribution network has started to be reversed. By any measure, e.g. losses, collections, quality, reliability, service, etc.) NPA’s performance was extremely poor. Although the decline may have been halted, considerable investment is required to raise performance to even tolerable levels. These investments are required in virtually all areas of EDSA’s business, from network infrastructure through to metering, billing and collection systems. This IRP sudy and the parallel

5 One possible exception is the sale of surplus power by the Addax biofuels plant.

Ministry of Energy 36 January 2015 20414

tariff study have suffered greatly from extremely poor data management by the utility, and example of which is that it is unable to provide reliable information on fundamentals such as customer numbers and sales.

2.7 Rural electrification and access

Electricity supply is currently limited to a very small proportion of households in the country, estimated at less than 10%. GoSL has, nevertheless, made commitments to radically improve access to electricity in the country. The Tariff Study undertaken in parallel with this IRP has addressed access and affordability issues.

Radical increase in access is an ambition that has to confront a number of major challenges in the medium-term. Outside the major towns, incomes are very low and subsistence farming is the norm. Major projects that would lower the average cost of generation, together with transmission networks that would deliver power to rural areas, are probably at least 5 years distant. Isolated generation that depends on imported petrochemical fuels are generally very expensive, on a per kWh basis. Consequently, there is currently a mis-match between supply and demand realkities, in this context.

However, with continued economic growth, household incomes will gradually close the affordability gap. In addition, in December 2014 the Consultant and MoE reached broad agreement on mechanisms for improving access and affordability in Sierra Leone, and with a wholesale shift of emphasis away from subsidising consumption and towards subsidising new connections. Briefly:

 Outside the urban centres a lower specification for distribution networks will be adopted and all but major customers will be provided with load limiters.

 The responsibility of revenue collection should be a collective one, imposed on local councils.

 These consumers would be expected to pay for their load limiter at the time of connection.

Ministry of Energy 37 January 2015 20414

3 Demand Forecast

3.1 Introduction

The demand forecast summary in this section of the report is based on the revised version of the demand forecast report that was originally submitted in February 2014 and which was produced following meetings with Ministry of Energy and other key staff in London in October 2014. A full version of the demand forecasts and the methodology adopted is presented in Appendix A to this report.

The forecasts have not been adjusted to reflect the impact of the present medical crisis in Sierra Leone, resulting from the outbreak of the Ebola virus. The rationale for this assumption is that it is considered any short term diminution of economic growth will be recovered over the period of the forecast. At the time of submission of this report there are also some uncertainties associated with mining demands, and in particular the future of London Mining is in doubt following an inability to secure funding. Whilst the price of iron ore remains at low levels, and economic growth in China (which drives many raw materials prices) has declined in recent times, the extent of the resources available in Sierra Leone, coupled with its possible petrochemical potential, is such that the overall projections for the mining sector demands in the longer term continue to be considered valid.

In deriving the overall forecasts, the estimated power and energy requirements for the Freetown area were added to the projections for the rural areas and the mining sector to assess the total estimated power and energy requirements on the system from 2013 to 2030. The forecasts that have been produced represent values which are assumed not to be restricted by the present supply side constraints, and hence are considered to be “unsuppressed”. The determination of the levels of suppressed demand cannot be considered an exact science, and therefore it has been necessary to make a number of assumptions. Recognising this difficulty, the datum 2014 forecasts vary between the base, upper and lower cases, and there is a relatively wide range of disparity between future values.

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3.2 Base Case Total Demand

Results for the base case scenario are presented below. The demand figures below are net of auxiliary consumption, while installed capacity figures are not.

Table 3-1: Base Case Total Demand Forecast

Figure 3-1: Base Case Total Demand (MW)

2000 MW 1800 MW 1600 MW 1400 MW 1200 MW Mining 1000 MW Rural 800 MW 600 MW Freetown 400 MW 200 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ministry of Energy 39 January 2015 20414

Figure 3-2: Energy Profiles in 2013 and 2030 (Base Case)

13%

Freetown 33% 19% Rural Mining 59% 8% 68%

Total Demand in 2013 | 161 MW Total Demand in 2030 | 1757 MW

3.3 High Case Total Demand

Results for the high case scenario are presented below.

Table 3-2: High Case Total Demand Forecast Year Freetown Rural Mining Total GWh MW GWh MW GWh MW GWh MW 2013 687 GWh 126 MW 21 GWh 19 MW 347 GWh 53 MW 1056 GWh 198 MW 2014 753 GWh 138 MW 31 GWh 25 MW 1109 GWh 169 MW 1893 GWh 332 MW 2015 816 GWh 148 MW 46 GWh 35 MW 1871 GWh 285 MW 2733 GWh 468 MW 2020 1288 GWh 225 MW 122 GWh 98 MW 5681 GWh 865 MW 7090 GWh 1188 MW 11843 2025 2045 GWh 339 MW 308 GWh 269 MW 9491 GWh 1445 MW 2052 MW GWh 13301 17358 2030 3503 GWh 550 MW 554 GWh 492 MW 2024 MW 3066 MW GWh GWh

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Figure 3-3: High Case Total Demand Forecast (MW)

3500 MW

3000 MW

2500 MW

2000 MW Mining 1500 MW Rural Freetown 1000 MW

500 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Figure 3-4: Energy Profiles in 2013 and 2030 (High Case)

18%

27% Freetown

Rural 16% 10% 64% Mining

66%

Total Demand in 2013 | 198 MW Total Demand in 2030 | 3066 MW

3.4 Low Case Total Demand

Results for the low case scenario are presented below.

Table 3-3: Low Case Total Demand Forecast

Ministry of Energy 41 January 2015 20414

The following figures show the evolution of the energy profile for this base case scenario. In 2030, mining represents 72% of the total unsuppressed demand, rural demand 16% and Freetown demand 12%.

Figure 3-5: Low Case Total Demand Forecast (MW)

1200 MW

1000 MW

800 MW Mining 600 MW Rural 400 MW Freetown

200 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ministry of Energy 42 January 2015 20414

Figure 3-6: Energy Profiles in 2013 and 2030 (Low Case)

12%

16% Freetown Rural 42% Mining 53% 72%

Total Demand in 2013 | 126 MW Total Demand in 2030 | 996 MW

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4 Improving Rural Access to Electricity

4.1 Overview

The provincial capital towns in Sierra Leone that previously were electrified in most cases had been relatively well developed, with many medium sized buildings, large houses and significant commercial activity. However, during the period of civil war, the population of these areas reduced significantly and many buildings were abandoned. Those towns that lost their electricity supplies have been slow to redevelop and need extensive construction of networks, power generation and electrification facilities.

To construct new networks where there is no existing nearby source of electricity, such as hydropower or transmission, the installation of diesel generators is a well tried and realistic option for the provision of power. However, due to the need to transport fuel over large distances from Freetown, and due to the sometimes poor road access during the wet season, the cost of fuel and fuel transportation will be high. In addition, it is likely that the predominant demand will be from residential consumers, contributing to a low load factor and creating a situation of relatively low energy consumption in relation to peak load.

If there is sufficient demand, fuel efficiency and fuel cost can be optimised by using medium speed or low speed diesel generators, fuelled by heavy fuel oil (HFO). If demand is low, it is necessary to use smaller, high speed diesels, more closely matched to the load using lighter and more expensive diesel oil. The relationship between engine size and loads is important, as the operation of diesel engines at low load, e.g. 30% or less over extended periods leads to mechanical problems and high maintenance costs. In addition, diesel engine fuel efficiencies are poor when loads are lower, particularly if they are lower than around 50% of engine rating.

Usually, diesel engines of greater than 1MW rating are available for HFO operation, but the costs of fuel handling / treatment can be excessive for small installations. For low speed operation, engine ratings normally need to be higher - near to 20 MW or greater.

With the exception of the areas which currently have an electricity supply, as discussed in Section 4.2, newly electrified towns are likely to initially have a peak load of less than 1 MW. Consequently, to achieve realistic configurations and reliable arrangements for diesel generators, small high speed generators of a few hundred kW each or less would be required for town electrification. One exception to this is Lungi, where there are large intermittent loads from the hotel and the airport.

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4.2 Current Status of Systems Outside Freetown

There are a number of systems which operate outside Freetown as noted below:

 Koidu 2 x 3 MW diesel

 Pujehun 77 kVA diesel

 Makeni 1 x 1.28 MW diesel + 1 x 0.8 MW diesel. Makeni is also supplied at 34.5 kV from the earth wire on the 161 kV transmission line between Freetown and Bumbuna.

 Bo / Kenema These two towns are interconnected by a 33 kV line. A small run of river hydro project exists at Bo, which has an installed capacity of 6 MW.

 Lungi 3 x 2 MW diesel newly installed

 Lunsar Lunsar is planned to be eventually supplied at 34.5 kV from the earth wire on the 161 kV transmission line between Freetown and Bumbuna.

 Moyamba 160 kVA diesel

4.3 Renewable Energy and Diesel Generation

4.3.1 Renewable Energy Sources

4.3.1.1 Small Scale Solar Photovoltaic (PV)

Solar PV systems are well established in developing countries and remote locations throughout Africa and in other parts of the world.

There is a range of possibilities for the configuration and application of solar PV systems in Sierra Leone where a network connection or other sources of electricity such as small diesels are not applicable or not affordable. This would particularly apply to very small isolated villages or clusters of houses where the use of small diesels might not be suitable for reasons such as difficulty in obtaining fuel and consumables or lack of knowledge of how to maintain the engines.

Possibilities with domestic solar PV systems include:

1. Small, low voltage solar PV system for one small dwelling, with small PV panel, regulator and battery, low voltage connection for low voltage lights, e.g. LED, or low voltage TV set etc.

2. Low voltage solar PV system, similar to option 1, but to supply more than one dwelling or a large single dwelling. The system may have more than one PV panel and a larger capacity battery. Ministry of Energy 45 January 2015 20414

3. Solar PV system with alternating current output, via an inverter, usually for larger houses and increased light output via energy saving 230 / 240V lamps. These systems can also be used for larger facilities, such as community halls.

4.3.1.2 Biofuels

Although there is a high potential for agricultural development in Sierra Leone, there are a relatively small number of new major plantation developments. Environmental, land ownership and leases, usage of family land, payments to land owners, local consultation, participation and commercial arrangements are topics that are currently the subject of much discussion.

Ethanol production from various sources, including municipal waste and numerous types of biomass, is possible and in many cases can be cost effective in comparison to petroleum based fuels. Ethanol can be used as a fuel either directly in combustion engines or in boilers, so could be used in electricity production. In Sierra Leone, Addax has built a development to produce ethanol on a commercial basis. GoSL has entered into an agreement with the Addax Company to produce up to 30 MW from sugar cane processing residue, bagasse. While bagasse is a useful source of energy, the sugar cane processing is seasonal and cannot provide a continuous year-round supply of fuel. However, the bagasse fuelled generation will be complementary to some of the hydropower projects that are based on run-of-the-river schemes and will have reduced output during the dry season.

It is known that oil producing palm trees can be highly productive in Sierra Leone, but currently there are only a limited number of commercial plantations and the development of more plantations is at an early stage. Currently there are no known plans for biofuel production from palm oil. Experience in other countries, particularly around South East Asia, has shown that correctly refined palm oil can be used in small diesel engines, either as a diesel mix or on its own, without major problems.

Wood gasification is an established method of fuel production and can be successfully used to fuel small spark ignition engines, e.g. 50 kW. Wood receiving, handling and processing facilities are required and the processes are generally labour intensive. A high level of maintenance is required and waste products need to be carefully handled and disposed of. Due to the production of a mixture of gases, including carbon monoxide, safety issues need to be addressed in detail during design, construction and operation. Specialist support will be needed during the life of the gasifier plant.

The Consultant visited a small power generation plant in Kychom, Kambia District, powered from a wood gasification plant. The plant had been supplied and installed by an Indian company. Although this plant was not operating at the time of the visit, due to problems with a wood cutting machine and external overhead cabling, it is relatively low technology, simple to operate and is capable of providing reliable electricity to the surrounding area. Its current configuration does have some limitations, as it will run for about 2 hours on one load of fuel wood before it needs emptying and refilling. (Refer to Appendix A for further information). Although attractive and suitable for small communities, sustained operation of wood gasifiers and equipment may be difficult in

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remote areas of Sierra Leone unless technical support and spare parts are readily available, as is the case for Kychom.

4.3.2 Application of Renewables

4.3.2.1 Hybrid Systems

As Sierra Leone is in the tropics, it does receive a high level of solar irradiance throughout the year, although, particularly during the rainy season, cloud cover has to be taken into account. Typical solar radiation levels in Sierra Leone, based on 10 year averages for Freetown, are shown in Figure 4-1, with an annual average of 4.8 kWh/m2 per day. Assuming an average efficiency level over the life of the array of 15%, in order to produce an output of 100 kW designed to supply a small community, an array would need to have an area of approximately 1,500 square meters. The possibility of using large solar photovoltaic (PV) systems to contribute to Sierra Leone’s energy supply is technically realistic.

Figure 4-1 – Solar Radiation Levels in Sierra Leone

Source: Bandasolar.com

With solar panel prices for large quantities of panels (e.g. 10MWp) having dropped to a level of around 1 US dollar per peak watt, PV can be seriously considered as part of any proposed new electrification scheme, although the PV panel unit cost for smaller schemes will be higher per unit than for large schemes.

To ensure continuity of supply outside the hours of daylight, the array would need to be supplemented by an additional power source, which could be a battery array, an alternative renewable source, or a diesel generator. A battery would add substantially to capital costs and would reduce the useful output of the PV array, allowing for the trickle charge battery current. In the case of some of the towns to be electrified, it would be possible to supplement diesel generation with solar PV to produce a “hybrid” power system. Benefits to be gained from a hybrid system can include:

Ministry of Energy 47 January 2015 20414

 Reduced diesel consumption

 Significantly lower energy production cost

 Reduced diesel engine maintenance

However, the combination of the two sources of electricity does require more effort for operational management and control, as under fluctuating load, or rapidly changing cloud cover conditions, load sharing between diesels and the PV system will change. In addition, the larger PV systems with a three phase output, require relatively complex inverter systems, with the consequent need for “high-tech” maintenance and fault finding support.

Compared to a conventional small diesel generating station, a hybrid system will have a significantly higher capital cost, as the diesel capacity to be installed will need to have full load capability to cover use during darkness and the cost of the solar PV system will be additional to the basic power station cost. Thus, the reduction and cost saving in diesel consumption has to be defined carefully to ensure that the additional capital and operational management costs required for the solar installation can be economically justified.

Power generation using a combination of diesel generators and solar PV systems (hybrid systems) have been successfully implemented in India and some tropical countries. The larger sized solar PV systems that normally include multiple inverters can be used to offset diesel consumption during times of high insolation. It is not common to use battery storage in combination with these systems, but it is technically possible. Selection of optimum solar PV system size in relation to diesel generating set size has to be carefully performed to avoid diesel engines running on very low load and also to ensure stable operation under changing insolation and/or load conditions. Hybrid system operation and management is more complex than for either stand-alone diesels or PV systems.

In the Sierra Leone context, hybrid systems could be best applied to some of the larger diesel generating stations that have 24 hour operation. As solar panels normally have a guaranteed life of at least 20 years, in the event of a transmission system interconnection being available at a later date, the PV systems could be dismantled and relocated.

In most of the towns to be electrified, NPA has retained the previously operated power station sites on a “caretaker” basis and it is likely that these sites will be able to be redeveloped for new diesel generating facilities. However, the construction of a solar PV system in conjunction with a diesel based power station will require an unobstructed site for the installation of solar panels and control facilities. This site would preferably be adjacent to the power station, but this is not essential. A solar PV site capable of producing 100kWp would require an unobstructed site of approximately 2,000 square metres for the panel installation and control facilities, which could be within the limits of land that NPA or the GoSL already occupies in the districts. Higher capacity installations would require proportionately larger sites which may raise issues for land acquisition and usage. Sites remote from power stations will need infrastructure such Ministry of Energy 48 January 2015 20414

as access roads, water supply (for panel cleaning) and high voltage power interconnection, all of which will increase capital costs.

4.3.3 Substitution of diesel

As Sierra Leone has good potential for agricultural production, the possibility of substituting liquid biofuels for diesel is a realistic option but may take time to become established. Biofuels such as ethanol can be produced from many feedstocks, including municipal waste or forestry waste. Other locally produced liquid biofuels such as vegetable oils could also be used successfully and would need intensive farming and oil extraction facilities.

The location of liquid biofuel production in Sierra Leone will depend on what feedstocks will be readily available and whether there are sufficient incentives to attract a commercial organisation to invest in biofuel production. Biofuel cost will need to be competitive with petroleum based fuels in order to be commercially attractive to power generating organisations.

There are several issues that need to be addressed before any commitment is made to the use of liquid biofuels in power generation. These include:

 Can the fuel be produced cost effectively in sufficient volumes?

 How will the fuel be distributed to users?

 Will it be possible to locate production facilities near load centres?

 Will the source of feedstock for biofuel production be sustainable?

 Will the feedstock prices be stable?

 Will the local population support the supply of feedstock and its production?

4.4 Barriers to Rural Electrification

4.4.1 Institutional Constraints

4.4.1.1 Technical electricity regulations and standards

Without the support of technical regulations and the adoption of technical standards, electrification of rural areas of Sierra Leone could develop in a haphazard and unsafe way. This could result in unnecessarily high life-cycle costs, poor quality, dangerous and low reliability electrification. Consequently, it is regarded as essential that electricity technical regulations are adopted and applied in Sierra Leone, in conjunction with other regulatory instruments.

However, as a caveat to the previous paragraph, in early December 2014 the Minister of Energy and his senior team agreed that they would consider relaxing technical standards in instances such as connecting remote, low-income rual communities, in Ministry of Energy 49 January 2015 20414

order to reduce the unit cost of supply. This may include, for example, single-phase feeders and load-limited household connections.

4.4.1.2 Shortage of electricity related skills

The impression gained by the Consultant is that there is a shortage of electrical professional and trade skills within NPA and in Sierra Leone generally and that there is a lack of modern training facilities and trainers. A number of NPA staff commented that they would welcome training programmes similar to one that was funded by an international aid agency in the late 1980s / early 1990s.

Due to the low level of electrification, it is expected that most of the electrical transmission and distribution skills within the country are employed by NPA either in Freetown or in the few centres that do have 11 kV distribution. From observation of the quality of electrical installations in parts of Freetown, it has also been assumed that there are a large number of self-taught and untrained electricity workers operating privately as electricians and that there is insufficient oversight capacity to monitor and ensure that installations and safety considerations are to a high standard.

It is known that some external contractors have been involved in recent 11 kV construction in headquarter towns and that some skills also reside with mining companies.

4.4.1.3 Shortage of solar PV skills

There are a small number of private solar panel importers and installers based in Freetown, but the penetration of PV solar throughout Sierra Leone is very limited.

The Barefoot College initiative was established in India several years ago and has been introduced into many developing countries. It has been successfully introduced to the Port Loko district on a relatively small scale. This initiative involves local women who have received dedicated training in India and have then returned home to Sierra Leone to produce, install and maintain low cost solar home systems and to train other women. The local users pay a monthly fee, related to what they would pay for other fuels and lighting facilities.

In addition, some NGOs are assisting with solar installations in villages, particularly for remote health clinics and community facilities.

The Consultant considers that all these activities should be encouraged, as it will be difficult to provide network or generator supplied electricity to small villages, particularly those that are remote, in the foreseeable future.

4.4.1.4 Diesel generator skills

The large mining sites in Sierra Leone use diesel generators, often with a combined capacity of more than 10MW. For larger generating set support, one international supplier of diesel generators has a local agent with a base, trained staff and full workshop facilities in Freetown. An expatriate supervisor is employed to manage local Ministry of Energy 50 January 2015 20414

staff and assist with technical issues. Other international diesel generator suppliers have agents and service bases in nearby countries, such as Ghana. Mining companies also normally have large diesel powered vehicles, and therefore in order to cover power generation and vehicle needs they usually employ diesel mechanics on site, assisted by local staff.

Other diesel engine skills reside with truck repair workshops in Freetown, but it is understood that workshop facilities are limited and the number of fully trained mechanics is also limited.

4.4.2 Prerequisites for the Expansion of Rural Electrification

Since the bulk of the population is unfamiliar with the use of electricity and its characteristics, it is essential that each new consumer and the public at large are made aware of electrical safety issues, hazards and emergency procedures.

As the existing low level of rural electrification means that few people in the community are accustomed to working with electricity in the home, very few people will have developed good or bad habits in the installation and use of electricity. In some countries where electricity has been available for many years, and where the general level of education of the population has been low, unskilled workers often illegally install mains voltage wiring in houses in an unsafe way. Consumers are often unaware of, or underestimate the potential dangers of incorrect wiring, exposed wires and the danger of electric shocks at mains voltage.

In Sierra Leone, the existing low level of electrification provides an opportunity for public awareness to be improved in a planned and comprehensive way in locations to be electrified and throughout the country, prior to the introduction of electricity and before poor work practices have the opportunity to become entrenched, although there could be an issue if untrained electrical workers move from Freetown to the rural areas to seek work during and after the electrification programme, without further training or certification.

It will be essential for each town to retain or have ready access to at least one electrically trained person (“Inspector”) to inspect and / or advise on electrical issues. Ideally, this person would have undertaken specialised training, be certified to an appropriate level and would be able to serve surrounding areas.

Before the “rollout” of any national electrification, a focused and comprehensive public awareness / education programme would need to be initiated and continued into the foreseeable future, so that a culture of awareness and safety is developed.

4.4.2.1 Approach to training Inspectors

Regardless of the local, institutional and commercial arrangements for electrification in rural towns and villages, Inspectors would need to be independent and not influenced by local politics or be susceptible to illegal financial incentives. The Consultant has seen many instances of publicly employed electrical staff in developing countries being influenced and diverted from their primary roles by local pressures, to the detriment of

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the wider community and the national interest. One of the ways to reduce the risk of Inspectors becoming too close to the community, would be to move the Inspectors to new locations periodically, taking into account language and cultural factors.

To be fully effective, an education programme to develop clear understandings of technical, electrical and safety issues would require a duration of up to two years. Also trainees would need secondary level education as a prerequisite for the training course. The technical and electrical education would need to be classroom based and could be run concurrently with training in trade skills and installation practices.

4.4.2.2 Approach to Public Awareness Programme

A public awareness programme to educate the public at large about electricity would need to address, as a minimum, the following issues:

 The need for correct installation;

 Avoiding poor quality and substandard/dangerous equipment;

 The need to always treat electricity and its use with caution; and

 What to do in emergencies.

In the case of those prospective rural consumers likely to receive solar photovoltaic installations, even if without inverters, it is recommended that similar approaches to safety and awareness are adopted, as eventually these consumers will probably receive a mains electricity supply.

The approach being adopted by UNDP in its recent Popularisation of the Land Policy initiative, based on extensive local knowledge and experience, is to engage the assistance of NGOs to undertake consultation in all Chiefdoms and to use a range of communication methods to ensure wide coverage. This approach would have some merits for electricity awareness, but would need to be supplemented by longer-term activities to ensure continuing public awareness.

4.4.2.3 Impacts of skills shortages on electrification

Discussions with Chief District Administrators and Chairmen of district councils, as well as UNDP, UNFPA and some NPA district personnel highlighted the shortage of trained electrical personnel in the rural areas.

The current shortage of electrical skills, at a professional level and at technician or trades level within Sierra Leone will provide a constraint on development of electrification throughout the country. Considering the GoSL objective as stated in the Agenda for Prosperity to provide electrification for headquarter and other towns in a relatively short time frame, a large number of experienced technicians, trades staff and professional engineers would be needed in the near future to become involved in planning and construction activities.

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It is considered that to avoid being constrained by skills shortages, and to achieve a rapid rate of electrification, foreign resources will be needed for the electrification construction programme and for limited post-construction support. These resources (possibly foreign Engineer, Procure, Construct (EPC) contractors) could be contractually required to use local unskilled labour and local skilled resources, if available.

4.4.3 Possible GEF / Carbon Financing Options

With the realisation that man-made CO2 emissions are the main contributor to global warming and climate change, various multilateral and bilateral organisations are providing financial assistance towards renewable energy projects. To the extent that renewable energy is used as the prime mover in off-grid rural electrification schemes, this funding may be available. In addition, and in instances where it can be established that the renewable energy is displacing non-renewable carbon-based (i.e. fossil) fuel, it may be possible to bundle the CO2 savings and to sell the carbon credits through Clean Development Mechanism (CDM) carbon markets.

The Global Environment Facility (GEF) is particularly active in providing support to the field of “climate change mitigation”, which includes reducing or avoiding greenhouse gas emissions in the areas of renewable energy. GEF provides grants and concessional funding to cover the "incremental" or additional costs associated with transforming a project with national benefits into one with global environmental benefits. Sierra Leone has been a member of GEF since 1994 and to-date has received over US$21 million in GEF grants for a total of 12 national projects. Only three of these projects have been in the field of climate change, however. Generally, GEF provides co-financing for projects, together with the host government and/or donor agencies. However, it may fully-finance preliminary investigations for qualifying projects, e.g. geothermal exploration.

The GEF Small Grants Programme (SGP) for Sierra Leone was launched in June 2013 and is therefore very new. The maximum grant available from the SGP for a single project is just US$50,000, which perhaps restricts its relevance to village scale off-grid electrification projects.

As noted above, quite apart from the GEF there are numerous multilateral and bilateral organisations that will invest in rural electrification projects, particularly where renewable energy is the main source of generation. However, in view of the large scale of the demand for rural electrification investment relative to the small scale of the available funds from these organisations, priority is generally given to projects that a) can demonstrate high levels of sustainability, and b) where funding is also available from the private sector or from the customers themselves.

Sierra Leone has already made successful use of carbon finance in the development of the Bumbuna I hydropower plant, and the carbon credits from the displacement of HFO-fuelled generation were brokered through the World Bank Carbon Finance Unit (CFU). The challenge with accessing carbon finance for rural electrification projects is that the scale of the projects tend to be small in relation to the administrative costs associated with securing and verifying the credits. As a consequence, successful

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examples are few. However, the Consultant was involved with the West Nile Electrification Project in Uganda, which obtained carbon credits through the Prototype Carbon Fund.

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5 Indicative guidelines for the expansion of transmission and distribution networks

5.1 Overview

In this section, high-level consideration is given to the expansion of the transmission network. Distribution expansion requirements are also derived, based upon the assumed phasing of the transmission network, and the costs associated with each option considered are evaluated.

5.2 Sierra Leone Transmission Network

Existing transmission lines represented in Error! Reference source not found. and Figure 5-2 below are:

 A 400 mm2, single circuit, 161 kV line connecting Freetown and Bumbuna I, passing through Lunsar and Makeni, of approximately 120km total length; and

 A 66 kV single circuit line connecting Bô and Kenema (operating as an isolated network), of approximately 60km total length.

5.1.1 Main assumptions

In addition it has been assumed that:

 There is a 11 kV/161 kV substation in Makeni to inject power from the ADDAX plant into the grid, which could also be used to supply surrounding villages;

 Whilst there is a commitment to install an HV substation in Lunsar, there might be some flexibility on the voltage adopted (allowing for a standardisation of voltage levels in the country);

 There is a strategic and political will to ensure an electricity supply to all of the regional headquater towns, though supply from the network many not be possible in the short to medium term;

 There are commitments to construct the following new power plants:

o An HFO fired diesel plant of 120 MW capacity, to be located in Freetown, owned and operated by CEC Africa;

o Small hydro projects conservatively including a 2 MW plant in Port Loko, a 2 MW plant (“Charlotte”) in Freetown, and a 5MW plant in Moyamba.

 Delays have been experienced in the finalisation of the Bumbuna II / Yiben hydropower complex, and as a consequence an additional thermal plant may be

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developed by Joule Africa / Endevour to meet capacity requirements prior to the commissioning of major new hydro / thermal capacity; and

 Regional imports or exports with the WAPP system have not been included due to the uncertainties associated with the power sector developments in neighbouring countries.

The analysis has been undertaken exclusive of any mining load due to the present uncertainty of some of these projects in Sierra Leone.

The analysis is based on a “N-1” reliability criterion, meaning that any load centre would remain electrified with the loss of one transmission asset. Voltages and conductor configuration (diameter and number of wires per phase) have been selected such that voltage drops would not exceed 10% between points of voltage transformation and load centres in the “N-1” configuration.

The exisiting voltage level of 161 kV is close to the proposed voltage for the new WAPP line of 225 kV, but it is understood that there is no aspiration to commission further lines at this voltage. Standardised voltage levels of 33 kV, 66 kV, 132 kV and 225 kV have therefore been selected for adoption in Sierra Leone.

Several network configuration options have been considered in order to determine the optimal arrangement as presented in this report meeting technical, cost and reliability requirements.

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Figure 5-1: 2014 Transmission network in Sierra Leone – Map View (Nations Online Project)

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Figure 5-2: 2014 Transmission Network in Sierra Leone – Diagram View

5.1.2 Transmission expansion proposal overview

Figure 5-3 illustrates the proposed configuration of the transmission system by 2030. Details as to the evolution proposed, with an indicative schedule for the commissioning of lines and the full list of assets, is presented in Table 5-1.

This arrangement connects Freetown directly to Bumbuna via a double 225 kV circuit and creates a ring encompassing Lunsar – Kenema – Bumbuna in 2020 to guarantee the “N-1” reliability of the network in Southern parts of Sierra Leone.

It is proposed to build a transmission line between Lunsar, Moyamna and Bô suitable for 132 kV but initially operating at 66 kV. Based on the projections in the demand forecast, from about 2025 the existing 66 kV line between Bô and Kenema is no longer sufficient to supply Bô, at which point the whole southern ring would be upgraded to 132 kV.

It is proposed that a 161/132/66 kV substation in Lunsar is commissioned to supply Moyamba and Port Loko via a transmission line energised at 66 kV. Once the double Ministry of Energy 58 January 2015 20414

circuit 225 kV line from Bumbuna to Freetown is commissioned, an additional 225/66 kV transformer should be commissioned in Lunsar to supply Port Loko, while the initial 161/132/66 kV transformer would then be dedicated to supplying the Southern ring. This transformer can support the 66 kV Moyamba and Port Loko supply in the event of an outage of the 225/66 kV transformer at Lunsar (the southern ring may have to be reconfigured to feed Moyamba from Kenema).

The small 3 MW hydro project at Port Loko, and the 6 MW of diesel plant at Lungi will support the system in the north of the country, aided by the double circuit between Lunsar and Lungi allowing adherence to the “N-1” reliability criterion, while the reliability in Kambia is guaranteed by the interconnection to Guinea.

Figure 5-3: Transmission Network Diagram – Horizon 2030

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Table 5-1: Sequence of transmission assets commissioning and specifications (Options A, B and C) Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

2016 161/132/66kV substation in Lunsar 1 x 5 MVA 161/132/66 kV transformers in Lunsar

225/161 kV substation in Bumbuna WAPP Double 225 kV Circuit connecting Kamakwie, Yiben, 225/66 kV substations in Kamakwie, 2 x 225/161 kV (150 MVA) transformers in Bumbuna Bumbuna, Bekongor and Yiben, Bekongor Kenema 2 x 225/33 kV transformers in Kamakwie (5 MVA), (WAPP MasterPlan) 225/66 kV substation in Kenema Yiben (5 MVA), Bekongor (25 MVA)

Double 225kV (2 wires per (the above are part of the WAPP 2 x 225/66kV 10 MVA transformers in Kenema phase, 400mm²) circuit MasterPlan) connecting Bumbuna and (the above are part of the WAPP MasterPlan) Freetown 225/MV substation in Freetown

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Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

2 x 150 MVA 225/MV transformers in Freetown

2019 Demand at Port Loko, north of Lunsar and Magburaka, south of Makeni is to be supplied by the transmission system in 2019.

An additional 225/66 kV transformer should be commissioned in Lunsar to supply Port Loko, and the initial 161/132/66 kV transformer would be then dedicated to supplying the Southern ring.

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Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

Single Circuit 132 kV (1 wire per phase, 400mm²) line connecting Lunsar, Moyamba and Bô, energized at 66kV

Double Circuit 66 kV (1 wire per phase, 250mm²) line connecting Port Loko and Lungi

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Year Transformers commissioned Transmission substations to be Lines commissioned (to meet “N-1” reliability criteria) commissioned/upgraded

Single 66 kV (1 wire per phase, 250mm²) line connecting Port Loko and Kambia

66 kV line between Bô and 1 x 225/132 kV 40 MVA transformer in Kenema Kenema decommissioned Upgrading Kenema’s substation 2 x 40 MVA 132/66 kV transformers in Bô 132 kV (1 wire per phase, Upgrading Bô’s substation 400mm²) line between Bô and 2 x 132/66kV 5 MVA transformers in Moyamba Kenema Upgrading Moyamba’s substation

Line Lunsar-Moyamba-Bô energized at 132 kV

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5.1.3 Substation overview at Lunsar

Figure 5-4 provides an indicative layout at transmission level for Lunsar before the commissioning of the double-circuit 225 kV line between Bumbuna and Freetown, showing that Moyamba and Port Loko are supplied at 66 kV from Lunsar using a triple- winding 161/132/66 kV transformer. The “N-1” criterion would still be guaranteed by running existing diesel units and small hydro plants in case of the loss of the 161/132/66 kV transformer.

Figure 5-4: Layout at transmission level for Lunsar (before the commissioning of the 225 kV line)

Figure 5-5 provides an indicative layout at transmission level for Lunsar after the commissioning of the double-circuit 225 kV line between Bumbuna and Freetown, showing that Moyamba is supplied at 132 kV, while Port Loko is still supplied at 66 kV. An additional 225/66 kV transformer is commissioned here and supplies Port Loko on a normal basis. This could be considered as a transformer tee with a circuit breaker at 66 kV, however the security of the connection could be improved if 225 kV circuit breakers were used with the flexibility to connect to either of the 225 kV circuits. The 132 kV winding of the existing 161/132/66 kV is now used to supply Moyamba, while the 66 kV winding is normally open but could be used in case of the loss of the 225/66 kV transformer.

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Figure 5-5: Layout at transmission level for Lunsar (after the commissioning of the 225 kV line)

5.3 Distribution Networks

Indicative distribution expansion have been identified for the small and medium-sized distribution load centres in the vicinity of the proposed bulk supply points. The following analysis is common to all three transmission expansion options.

Table 5-2 below details the assumed bulk supply point for each distribution system, including the assumption with regard to the supply voltage, based on the demand forecast as previously described. Specifications of the lines have been selected such that the voltage drop between two successive voltage transformation nodes does not exceed 10% under “N-1” system configuration.

The overall assumptions can be summarised as follows:

 Matoka, Masingbi, Koidu-Sedafu, Yengema, Bumpe and Njaiama can be supplied from Bekongor transmission node by creating a loop, but because these load centres are quite distant from each other, this network needs to be electrified at 66 kV. Transformation substations will then be required at each distribution node to lower the voltage to 11 kV and 400 V.  To supply Kambia, a 66/11 kV substation should be commissioned in Kambia.  To supply Kamakwie and Kamaku from Kamakwie transmission substation, a 33/11 kV substation should be commissioned in Kamakwie.  Blama, Lago, Panguma, Boajibu can be supplied from Kenema transmission node by creating a loop, but because these load centres are quite distant from each other, this network needs to be electrified at 66 kV. Transformation substations will then be required at each distribution node to lower the voltage to 11 kV and 400 V. Ministry of Energy 65 January 2015 20414

 A 66/11 kV substation should be commissioned in Lungi and Lunsar to lower the voltage to a distribution level.  A 66/11 kV substation should be commissioned in Moyamba to supply Moyamba and Njala at 11 kV.  Port Loko, Kychom, Kassiri and Mange can be supplied from Port Loko transmission node by creating a loop, but because these load centres are quite distant from each other, this network needs to be electrified at 66 kV. Transformation substations will then be required at each distribution node to lower the voltage to 11 kV and 400 V.

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Table 5-2: Expansion of Sierra Leone’s Distribution Network / Sequence of MV assets commissioning and specifications

Ministry of Energy 67 January 2015 20414.

5.4 Cost Estimates and Conclusions

5.2.1 Assumptions and reference costs

Reference unit costs for high and medium-voltage assets assumed in this study, based on recent projects in the region, are listed in Table 5-3 below:

Table 5-3: Reference costs for high and medium-voltage assets

Cost estimates for the electrification of distribution load centres at voltages below 33 kV are based on the PPA Energy “Sierra Leone Rural Electrification Report” submitted in November 2013.

5.2.2 Results

The overall estimate for the total capital cost of the proposed transmission and distribution requirements in Sierra Leone, in constant 2014 US Dollars, is approximately USD million 382, as shown in Table 5-4.

Table 5-4: Indicative costs estimates for transmission and distribution expansion scenarios

Ministry of Energy 41 January2015 20414.

5.2.3 Conclusion

The option presented above provides some flexibility as to the further extension of the distribution network (and especially in the area between Moyamba and Bô), and to the possible connection (and/or injection) near Moyamba of large industrial customers such as mining companies.

Further development of the proposed options could include a loop in the Northern part of the country, connecting for instance Kambia and Kamakwie. There would also be an opportunity for the extension of the 66/132 kV network further south and west of Kenema (including a possible additional interconnection with Liberia) to supply additional load centres of Bomi, Pujehun, Bomi, Mattru Jong, Bonthe and Bendu.

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6 Candidate Technology for Generation Development

This section of the report explores the relative merits of the various candidate technologies for generation development in Sierra Leone.

References to sources are indicated between brackets. For instance, [1] relates to the source referred as [1] in the bibliography page 140.

6.1 Coal Fired Thermal Plants [1] [2]

Coal-fired plants convert the thermal energy extracted from burning coal into mechanical energy, which then operates an electrical generator to turn it into electricity.

Figure 6-1: Coal Fired Thermal Plant Diagram [3]

Coal is shipped to the power station, and is then arranged into storage piles. A series of conveyors transport the coal into the plant, which then passes through mills which crush the coal into a fine powder prior to burning. The pulverised coal is fed into a large industrial furnace surrounded by boiler tubes filled with water. The intense heat from the burning coal heats the water in the tubes and turns it into steam. The steam is transferred under pressure at high speed through large pipes to turbines. This pressure and flow pushes the blades of the turbines causing them to spin. The turbine shaft is connected to the shaft of a generator, converting mechanical energy into electricity. The steam is then cooled by a condenser which typically uses water drawn in from a nearby lake, river, estuary, sea or a cooling tower.

Large coal-fired generating units are usually designed to operate with a minimum of modification for at least 25 years [2]. It is standard procedure to extend the life of a power plant to 40 years, and some units have operated for more than 50 years. This is achieved by “refurbishing boiler parts, upgrading

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the turbines, and adding flue gas cleaning to meet new emission regulations”. Life extension is often possible due to “the conservative nature of the original plant design and the fact that only a relatively small number of the components have a limited life” [2].

Indicative overnight capital and O&M costs are provided in section 7.2.4.

Coal fired plants can be a source of significant environmental pollution. The major emissions are as follows:

 Gases from the combustion process (e.g. CO2, nitrogen oxides);

 Sulphur oxides (SO2 / SO3), depending on the sulphur content of the coal; and

 Ash in the form of solid waste and slurry;

In view of its potential role in global warming, many countries have targets for the reduction of carbon dioxide. Innovative technologies, including capture and storage, are being explored in developed countries, but are not yet sufficiently cost-effective to be considered appropriate for countries such as Sierra Leone. Reduction in nitrogen oxide emissions can be achieved through the adoption of specially designed burners.

Sulphur dioxide and sulphur trioxide both dissolve in rainwater to create mild acids. They are emitted though the combustion of high sulphur coal, creating a requirement for the power station to incorporate technology to reduce sulphur oxide emissions. This technology adds to the capital cost of the power station, increases operating costs and reduces plant efficiency.

An alternative approach to the reduction of emissions is the adoption of coal gasification technology, in combination with combined cycle gas turbines. Commercial development of this concept, however, remains limited.

The overall efficiency of coal fired plants has increased with technological development, but is limited by thermodynamics. Generally, such developments have been associated with increases in the temperature and pressure of the steam produced in the boiler, with state-of-the-art plant operating at temperatures of 600°C and pressures of 300 bar with potential efficiencies of up to the low 40s percentage. Such plants, however, are not yet appropriate for adoption in developing countries.

In the context of Sierra Leone it is important to note there is no indigenous coal in the country or in the immediate region. Coal would probably have to come from Southern Africa, or even further afield, and fuel transport costs would be significant, as would fuel handling facilities. In part, this explains why there are few, if any, coal-fired plants in the region.

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Another important consideration with coal-fired plant, as with many industrial processes, is the principle of economies of scale. The unit cost (US$/kW installed) of large plants (e.g. 800MW units) is typically much less than small plants (e.g. 150MW). In the short- to medium-term, the system in Sierra Leone could only absorb a relatively small unit size, and the 125MW unit size adopted for this analysis is the smallest size for which published data is available. Even smaller units may be available, but these will almost certainly have appreciably higher unit cost.

6.2 Diesel Plants [3] [4]

Similarly to coal-fired plants, diesel plants have diesel engines converting the thermal energy of combustion into mechanical energy, which then drives an electrical generator.

Diesel engines are internal combustion engines that converts chemical energy in fuel to mechanical energy. Energy is released in “a series of small explosions (combustion) as fuel reacts chemically with the oxygen from the air”. This moves pistons up and down, turning the shaft connected to the generator.

Diesel engines differ from gas or petrol engines in the way the explosions occur: in gas and petrol engines “the gas/air mixture in the prechamber is ignited by a spark plug, whereas in diesel engines, fuel ignites on its own”.

Diesel fuel is named after diesel engines, and not vice-versa; diesel engines strictly are compression-ignition engines, and can operate on a variety of different fuels, depending on configuration and location. Therefore diesel oil- fired thermal plants can be converted to work with the full spectrum of crude oil distillates, from natural gas, alcohols, gasoline, wood, gas to the fuel oils from diesel oil to cheaper residual fuels.

Diesel engines require regular maintenance that involves changing the lubricating oil to keep the engine parts running smoothly.

A diesel generator is the association of a diesel engine and a generator.

Indicative overnight capital and O&M costs are provided in chapter 7.2.4.

Diesel units are often categorised by their speed of operation: high-, medium- or low-speed. With a degree of generalisation, the following principles apply:

 The unit cost (US$/kW installed) of high-speed diesels is lower than for medium-speed, and medium-speed unit costs are lower than for low- speed units.

 There are limits on the size (in MW) for each technology; high-speed units are rarely larger than 2MW; low-speed units are rarely smaller than

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around 40MW, and medium-speed engines are typically in the range 1MW to 20MW.

 The efficiency of the units increases with size and low-speed units’ efficiency do not degrade as rapidly as do high-speed units. Also, the economic life of low-speed units is appreciably longer than for high- speed units.

 Whereas high-speed units are restricted to relatively expensive diesel fuel, medium- and low-speed units will operate on lower cost HFO. All units can, if suitably designed, operate on natural gas.

 Starting and stopping low-speed units is more complex than it is for high-speed units. Consequently, low-speed units are unsuited for stop- start peaking duty. Low-speed and, to a slightly lesser degree, medium- speed units are best suited to base load duty in a system, i.e. with very intermittent stops and starts.

 Where diesel units undertake base load duty, it may be cost-effective to improve their overall efficiency by adding a steam-cycle driven by waste heat from the diesel exhaust. However, this adds to the capital cost and to the O&M complexity. In the Consultant’s experience it is rarely shown to be cost-effective in developing countries.

6.3 Gas Turbine Based Plants [5] [6] [7]

In gas turbine based plants, natural gas is pumped into the gas turbine, where it is mixed with air and burned, converting its chemical energy into heat energy. As well as heat, burning natural gas produces a mixture of gases called combustion gas. The heat makes the combustion gas expand. In the enclosed gas turbine, this causes a build-up of pressure. The pressure drives the combustion gas over the blades of the gas turbine, causing it to spin, converting some of the heat energy into mechanical energy.

Gas engines, like diesel engines, are internal combustion engines. The basic operation of the gas turbine is similar to that of the steam power plant except that air is used instead of water. Fresh atmospheric air flows through a compressor that brings it to higher pressure. Energy is then added by spraying fuel into the air and igniting it so the combustion generates a high-temperature flow. The high-temperature high-pressure gas expands in the turbine, producing output through the shaft. The turbine shaft work is used to drive the compressor and other devices such as an electric generator that may be coupled to the shaft. The energy that is not used for shaft work comes out in the exhaust gases, so these have either a high temperature or a high velocity. The purpose of the gas turbine determines the design so that the most desirable energy form is maximized.

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Simple-cycle or open cycle gas turbine plants have a lower thermal efficiency than combined cycle power plants, using a heat recovery steam generator (HRSG) in conjunction with a gas turbine. It is referred to as a combined cycle power plant because “it uses both a Brayton cycle for the topping cycle and steam Rankine cycle for the bottoming cycle HRSG”. The turbines are fuelled “either with natural gas, syngas or fuel oil”.

The economics and key application of OCGT and CCGT plants are typically quite different, even though both types use similar gas turbine technology and fuel types. The steam cycle of a CCGT plant adds overall fuel efficiency at the expense of additional capital cost. It also involves greater O&M complexity if the availability is to be maintained at a high level, which is essential if it is to sustain the ideal operational duty. Unlike OCGT units, CCGT units are not suited to frequent stopping and starting. Due to their high efficiency, however, they are well-suited to base load duty. OCGT units, on the other hand, are well- suited to peaking duty but, due to their relative inefficiency, they are expensive to operate at high capacity factors.

The O&M skills set required for OCGT and CCGT units is a step up from diesel technology, but a number of utilities in Sub-Saharan Africa have made suitable provisions and managed the introduction of these technologies adequately.

Perhaps the most important factor for Sierra Leone is that CCGT and, in particular, OCGT technologies can be expensive to operate on liquid fuels and, since the currently currently has neither indigenous gas nor import facilities, gas-firing of these technologies is not a practicable possibility in the medium- term.

6.4 Hydroelectric Plants

Most hydroelectric stations use water diverted around either the natural drop of river, (such as a waterfall, or rapids) or a dam built across a river to raise the water level and provide the drop needed to create a driving force.

Water at the higher level is collected and flows into the plant’s intake into a pipe, which carries it down to a turbine. The water pushes the blades of the turbine, causing it to spin. The turbine is connected to a generator, converting mechanical energy into electricity. In Run-of-river plants, the falling water having served its purpose exits the generating station to the “tail race” where it rejoins the mainstream of the river.

Regulated hydroelectric plants have the capacity to control the upstream water level (and therefore available capacity and effective generation) by pumping water from or down to a downstream reservoir.

The characteristics and costs of hydroelectric projects are very site specific, being dependent upon the following variables:

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 River flow (and its variability on a daily, monthly, seasonal and annual basis);

 Head;

 Geology;

 Project layout (need for tunnels, underground powerhouse, dam etc);

 Installed generation capacity; and

 Environmental impact and costs of mitigation

The issue of hydrological seasonality is particularly relevant in the Sierra leone context. Run-of-river (ROR) has pronounced advantages in terms of reduced social and environmental impacts but, where there is high seasonality of rainfall – as there is in Sierra Leone - the ‘firm’ capacity is typically well below the installed capacity, thus limiting the plant’s utility in satisfying peak system demand reliably. To emphasise this point, even though Bumbuna I has a large impounding dam, the scheme is reduced to ROR duty for a large part of the year, with an output of only around 18MW compared with the installed capacity of 50MW. Large impounding dams such as Yiben may provide over-year storage that is more advantageous in contributing towards satisfying peak system demand, but such schemes potentially have greater social and environmental impact. Consequently, extensive time-consuming studies and consultations are required for such projects, especially if they are to be funded with the assistance of donors.

Due largely to this high seasonality in Sierra Leone, the unit cost (US$/kW) is considerably less favourable than in many other countries.

6.5 Solar Plants [8] [9] [10]

Two types of technologies allow to capture and to convert the energy contained in the sun’s emissions:

Solar thermal technologies capture the thermal energy contained into the sun’s radiance using solar collectors (or “absorber tubes”). This heat is used to turn water into saturated steam. This steam in turn powers a generator.

But as for most techniques for generating electricity from heat, “high temperatures are required to achieve reasonable efficiencies. The output temperatures of non-concentrating solar collectors are limited to temperatures below 200°C. Therefore, concentrating systems must be used to produce higher temperatures. Due to their high costs, lenses and burning glasses are not usually used for large-scale power plants, and more cost-effective alternatives are used, including reflecting concentrators.” [8]

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Figure 6-2: How temperature collected increase with concentration [46]

There are four main existing technologies for reflecting concentrators:

 Parabolic concentrators

Parabolic concentrators are a class of concentrators that consist of rotated parabolic sections which have a concentration factor for planar receivers equal is the thermodynamic limit, therefore optimal. But due to the shape of the parabolic mirror, manufacturing costs remain very high.

Figure 6-3: Parabolic concentrator in Odeillo (France) – 52kW (ConstruireSolaire.Com)

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 Parabolic through concentrators

This is a variant to the above, but “parabolic through” mirrors only have a single curve direction (instead of two for a parabola). This tends to cut down the costs of manufacturing. Figure 6-4: Parabolic through concentrator on Solar de Almeria platform (Spain) (DLR/Markus Steur).

 Solar tower concentrators

A power tower is a large tower surrounded by tracking mirrors called heliostats. These mirrors align themselves and focus sunlight on the receiver at the top of tower, collected heat is transferred to a power station below. While the efficiency is reduced compared to previous technologies, a larger area can be covered.

Figure 6-5: Solar tower concentrator at Solar Two Power Plant in Mojave Desert, California (USA) (Global-Greenhouse-Warning.com)

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 Fresnel’s mirrors concentrators

A linear solar Fresnel solar plant uses “an array of single axis, linear solar mirrors that each rotate to follow the sunlight and concentrate it towards a receiver tube. In that way it is similar to a solar parabolic trough system, whilst requiring flat mirrors substantially cheaper to produce”. This type of system also allows “the mirrors to remain near the ground, avoiding wind loads”. [9]

Figure 6-6: Linear Fresnel Solar Concentrator designed by Heliodynamics Ltd. (Heliodynamics Ltd.)

Solar panel electricity systems, also known as solar photovoltaics (PV), capture and convert the sun's radiance into electricity using photovoltaic cells. A photovoltaic solar plant is a group of solar photovoltaic panels connected in parallel and/or derivation.

Figure 6-7: 80 MW Photovoltaic Plant in Nevada (USA) (Inhabitat.com)

Solar thermal and photovoltaic productions are function of the intensity of the sun’s emissions as seen from the plant, which varies along the day and with the shape of surrounding shapes including clouds, trees, pylons… These parameters are extremely difficult to anticipate, which brings new challenges for the supply- demand balancing.

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The EIA (US Department of Energy, Energy Information Administration) has published indicative costs in April 2013 for utility scale electricity plants, and more specifically on solar technologies. However these costs (see Table 6-1) are valid in the USA, while actual costs in Sierra Leone can be expected to be appreciably higher.

Table 6-1: Indicative costs for solar technologies [10]

Technology Overnight Capital Fixed O&M Variable Cost6 O&M ($/kWh) ($/kW) ($/MWh) Large scale solar thermal 5,067 67.26 0.00 (above 20MW) Large scale solar 3,873 24.69 0.00 photovoltaic (above 20MW) Small scale solar photovoltaic 4,183 27.75 0.00 (up to 20MW)

The economics of solar PV technology has improved considerably in recent years and is starting to become competitive with conventional generation technologies in certain instances. The challenges for solar technology in Sierra Leone, however, are as follows:

 Until such time as large mining and industrial loads can be connected, the system peak will remain during the early evening, when the sun doesn’t shine and when solar plants therefore cannot generate. Consequently, solar PV would – at best – be limited to fuel cost saving duty during daylight periods. It could not contribute capacity towards meeting system peak demand.

 Since the short-run marginal cost of solar is currently higher than the cost of diesel generation, it has a limited role in the development of an economically optimal plant mix, at present.

A presentation on wind power generation technologies is provided in Appendix C.

6 Exclusive of any mark-up

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7 Preliminary Screening of Generation Options

7.1 Fuel Availability and Pricing

7.1.1 Introduction

This section presents the assumptions made in this report with regard to the availability and pricing of fossil fuels in Sierra Leone. The analysis presented in this report, consistent with that throughout the IRP, is presented in economic terms. Costs for tradable items are therefore presented at border prices, excluding all taxes, levies and subsidies which might be imposed in Sierra Leone. It has been assumed that the prices of fuels can be calculated from internationally recognised benchmark values, adjusted for the transport costs to Sierra Leone. It is noted that these values might diverge from financial prices which might be offered in the market place or by private sector developers.

7.1.2 Availability

At present Sierra Leone imports all fuel requirements for electricity generation, namely Heavy Fuel Oil (HFO) and Diesel. The former is used in the reciprocating diesel plants in Freetown, with the latter used in small high speed diesels. Diesel fuel is also used for start up and shut down in the Freetown units, and has also been used when HFO was not available.

Proposals have been made for the construction of coal fired thermal plant in Sierra Leone. Such plants, the location of which is understood to be still under discussion, would require the construction of coal handing terminals. It is possible that such facilities could be constructed at Pepel, adjacent to the existing iron ore terminals. However, a number of other potential locations are also believed to be under consideration, though these would involve more complex connections to the transmission system. Jindal, an Indian based company, is one proponent of such a project. Jindal owns a number of coal mines in Southern Africa, namely in Mozambique and Botswana, and it is understood that they have proposed the importation of fuel from these sources to Sierra Leone.

At present, commercially exploitable quantities of petrochemicals have yet to be found within Sierra Leone. There are, however, encouraging signs as to the possible opportunities of offshore findings, based on discoveries in other West African jurisdictions. It is, therefore, feasible that gas from local sources might be a possible future power generation fuel.

7.1.3 Petroleum Products

7.1.3.1 Historic Prices

Crude oil prices are continuously changing with market conditions, production levels, consumption and inventory levels. In addition to this the expectations of

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future economic growth is often considered as the main driver behind international crude oil prices. Figure 7-1 shows the historic prices of petroleum products. The price changes of petroleum products such as HFO and Diesel are closely correlated with the change in crude oil prices.

Figure 7-1 : Historic Prices of Petroleum Products

1400 160 1200 140 1000 120 100 800 80 600 60 400 40 200 20

Prices in US dollars per MT per dollars US in Prices 0 0

Prices in US dollars per barrel per dollars US in Prices

Oct-90 Oct-91 Oct-92 Oct-93 Oct-94 Oct-95 Oct-96 Oct-97 Oct-98 Oct-99 Oct-00 Oct-01 Oct-02 Oct-03 Oct-04 Oct-05 Oct-06 Oct-07 Oct-08 Oct-09 Oct-10 Oct-11 Oct-12 Oct-13 Oct-14

Crude Oil HFO Diesel

Crude Oil: Brent Crude Oil (London) Price History (US Dollars per Barrel) HFO: Rotterdam Heavy Fuel Oil Price History (US Dollars per tonne) Diesel: Rotterdam Diesel Domestic Heating Oil (US Dollars per tonne) Naptha: European Northwest Price History (US Dollars per tonne) Source: National Institute of Statistics and Economic Studies (France) URL: http://www.insee.fr

Table 7-1 and Figure 7-2 summarise the correlation results that were found between crude oil and other petroleum product prices.

Table 7-1 Historic Prices of Petroleum Products – Regression parameters

Correlation with Petroleum Crude Oil Prices R2 Product Equation for Regression Line

HFO y = 5.6914x - 14.351 97.56%

Diesel y = 8.4833x + 17.792 99.19%

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Figure 7-2 Petroleum Products Correlations with Crude Oil Prices

HFO Vs Crude Oil Diesel Vs Crude Oil 800 1400 700 1200 600 500 1000 400 800

HFO HFO (USD/MT) 300 600

200 400 Diesel(USD/MT) 100 200 0 0 0 50 100 150 0 50 100 150 Crude Oil (USD/MT) Crude Oil (USD/MT)

7.1.3.2 Price Projections

The Annual Energy Outlook 2014, produced by Energy Information Administration (EIA) provides three scenarios for future crude oil prices. EIA cases were developed by varying assumptions about: (a) Investment and production decisions that would be taken by Organization of the Petroleum Exporting Countries (OPEC); (b) Development of tight oil and bitumen resources, and (c) Demand growth in China, Middle East and other countries outside (OEPC). Figure 7-3 illustrates the three scenarios that were developed by EIA.

Figure 7-3 : EIA Projections for Crude Oil Prices

Source: U.S Energy Information Administration; URL: http://www.eia.gov/forecasts/aeo/MT_intl.cfm#oilprice

North Sea Brent Crude Oil Spot Price 250 200 150 100 50

2012dollarsper barrel

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 Year

Reference High Oil Price Low Oil Price

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The crude oil price projections developed by EIA were based on 2012 US Dollar Prices. For analysis purposes this was adjusted to 2014 dollar price levels. This correction was done based on an assumption that US inflation for the period between 2012 and 2014 was 3% per annum. Projections for HFO and Diesel prices were based on their historic mathematical relationship to the price of crude oil.

Table 7-2 Price Projections for HFO and Diesel – scenario 1

US Inflation Crude Oil Prices (2012 Adjusted 2014 HFO Diesel dollars per barrel) dollars per barrel

Based on EIA 2014 Brent Brent Spot Brent Spot USD/Tonne USD/Tonne Projections 2014 99 105 583 908 2015 97 103 572 892 2020 97 102 569 887 2025 109 116 644 999 2030 119 126 704 1089 2035 130 138 769 1186

The international crude oil market has seen high volatility in recent times. Since the datum price in 2014 in the EIA forecast [15] is substantially higher than current values, an additional scenario has been considered based on projections available from Sproule. Sproule is an international consultancy firm with more than 60 years of experience in the energy sector. Table 7-3 illustrates the price projections that were made based on Sproule’s November 2014 forecast [16].

Table 7-3: Price Projections for HFO and Diesel – scenario 2

Sproule Forecast (2014 HFO Diesel dollars per barrel) Based on EIA 2014 Brent Spot USD/Tonne USD/Tonne Brent Projections 2014 83 458 722 2015 89 490 770 2020 95 526 824 2025 95 526 824 2030 95 526 824 2035 95 526 824

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7.1.3.3 Oil Freight Rates

The average freight rate for 130 000 Te shipments from ports between Bilbao and Hamburg, plus southern Sweden and western Norway (defined in shipping terms as UKC) and ports from Gibraltar to Istanbul (referred as Med) to West Africa was around 11.6 USD/Tonne in 2011 prices. In 2014 prices this is estimated to be around 12.4 USD/Tonne (assuming annual inflation of 3%).

7.1.4 Coal

7.1.4.1 Price Projections

The World Bank Commodities Price Forecast that was released in January 2014 includes a projection for Australian Coal Prices. These projections were for coal from Newcastle/Port Kembla, with a calorific values of 6,700 kcal per kg. The World Bank projections were presented on a real 2010 USD basis and these were adjusted to 2014 dollar price levels assuming an annual inflation of 3%. Table 7-4 shows the details of Australian coal price projections.

Table 7-4: Australian Coal Price Projections – scenario 1

Coal Australian Price $/mt Real 2010 USD Real 2014 USD 2014 80.8 90.9 2015 82.6 93.0 2020 80.9 91.1 2025 78.6 88.5

Since the projections only extend to 2025, a constant coal price of 88 USD/Tonne was assumed for the period between 2025 and 2035.

Again, similar to crude oil prices, international coal prices have also witnessed volatility and a revised World Bank Commodities Price Forecast was released in October 2014, as shown in Table 7-5. A constant coal price of 90.5 USD/Tonne was assumed for the period between 2025 and 2035.

Table 7-5: Australian Coal Price Projections – scenario 2

Coal Australian Price $/mt Real 2010 USD Real 2014 USD 2014 66.8 75.2 2015 70.3 79.1 2020 75.5 85.0 2025 80.4 90.5

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7.1.4.2 Freight Charges:

We used Australian coal prices for our analysis, as this was the only available and trustworthy source at the time where we prepared the projections. Therefore to ensure consistency of the calculation of border prices, and whilst coal would be in fact more likely supplied from South Africa, freight charges below are calculated on the basis of transporting coal from Australia to Sierra Leone.

The average cost of transporting coal from Newcastle (Australia) to Freetown was estimated by the Consultant to be at 18.40 USD/Tonne. The coal handing charges (including building coal handling facilities) were estimated to be at 4.7 USD/Tonne and total freight cost of coal was estimated to be 23.09 USD/Tonne. The coal handling charges were estimated based on capex estimates of a new coal terminal at Beira Port [17] in Mozambique. It is estimated that the new coal terminal at Beira Port would have a capex of $ 600 million for a total capacity of 15 Million tonnes per annum.

Table 7-6: Estimated Coal Freight Rates (Newcastle to Freetown)

Shipment From Destination Nautical miles Size USD/Tonne (Tonne) Newcastle Freetown 9515 60000 16.99 Newcastle Freetown 9515 70000 19.80 Average Shipment Cost 18.40 Coal Handling Charges 4.70 Total Freight Cost 23.09

7.1.4 Gas

Unlike oil and coal, there is no internationally recognised benchmark price for gas. Where gas is discovered in very large quantities it can be liquefied and exported, thereby setting a datum value. In many cases, however, and in the case of Sierra Leone, gas volumes are not likely to be such as to render the economics of liquefaction economic.

One approach to the calculation of the economic price can be to assess its netback value – ie the value inferred through the displacement of other fuels [18]. Such a calculation, however, will be dependent on the volumes of gas available. Gas is not yet a candidate fuel, and is unlikely to be so before 2020. Consideration of gas in this report is therefore limited to discussion as to the implications of its future availability on generation development decisions which need to be taken in the short to medium term.

There is optimism that offshore natural gas may be discovered in Sierra Leone’s home waters. However, as Ghana’s experience has demonstrated, it can take a great many years between discovery and commercial development. Pipelines

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from gas-rich nations has been tried in West Africa with the West Africa Gas Pipeline (WAGP), but the volumes available for export have been considerably less than orginal expectations and the pipeline operates at well below the design capacity. Importing natural gas as LNG is a further option but scale effects are a major consideration. Until recently, trade in LNG was restricted to frequent and regular, large scale LNG shipments in dedicated LNG tankers. The volumes involved are considerably greater than may possibly be required by the Sierra Leone power sector in the medium-term.

However, technology advances in very recent years mean that large-scale dedicated LNG tankers are no longer de-rigour, and smaller LNG shipments are possible, albeit at greater unit cost. Expensive receiving infrastructure would be required, and this can now be provided by floating regasification and storage units. Nevertheless, the Consultant considers that LNG is not a practicable option for Sierra Leone in the medium-term.

7.1.5 Assumed Delivered Sierra Leone Prices

The estimated freight charges estimated as shown above were then added to fuel price projections presented in Table 7-2 in order to estimate the delivered price of fuel in Freetown, as shown in Table 7-7 and

Table 7-8 for scenarios 1 and 2 respectively.

Table 7-7 Freetown Price of Fuel – scenario 1

Freetown Price of Fuel

HFO Diesel Coal

Based on EIA 2014 USD/Tonne USD/Tonne USD/Tonne Brent Projections 2014 595 920 114 2015 585 904 116 2020 581 899 114 2025 656 1011 112 2030 717 1101 112 2035 782 1198 112

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Table 7-8 Freetown Price of Fuel – scenario 2

Freetown Price of Fuel HFO Diesel Coal

Based on EIA 2014 USD/Tonne USD/Tonne USD/Tonne Brent Projections 2014 470 734 113 2015 503 782 98 2020 539 836 107 2025 539 836 112 2030 539 836 112 2035 539 836 112

7.2 Costs of Electricity Generation

7.2.1 Objectives

This chapter of the report estimates and compares the cost of electricity generation in Sierra Leone for different technologies, in order to give conservative and preliminary recommendations as to which technology and configuration to prioritize, in order to balance forecast demand and supply in the horizon to 2020. This is not a standalone study as this will further be completed by more exhaustive least cost development analysis, including NPV analysis based on seasonal dispatching simulations and demand forecast.

7.2.2 Scope

We have not included in our analysis the following technologies:

 Gas-turbines based plants, due to the lack of availability of gas in Sierra Leone. As previously noted, diesel units could be converted to burn gas, should gas be available and affordable for Sierra Leone in the future.

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 Solar Plants, because although Sierra Leone has important solar radiation potential (estimated in [19] between 1400 and 1860 kWh/m² 7, with peak radiation in March), solar may well form part of the future energy mix despite of significant investment costs. Nevertheless as solar generation does not contribute to supplying peak demand, we are not considering this technology in the following analysis.

 Wind farms. The existing data on wind velocities, although unverified and probably out of date, indicate “an average speed of 3-5m/s onshore”, and “up to 12m/s” in specific locations [20]8. Further studies, including wind data sets covering several locations over a long period of time, need to be conducted in order to properly assess Sierra Leone wind potential.

 Major hydropower projects are not included in the following comparison as the energy potential and installed capacity have yet to be determined, prior to feasibility studies and preliminary design / costing. Lahmyer International prepared a Masterplan in 1994 [21] covering hydroelectric opportunities and related costs, but the study is now over 20 years outdated: potential energy yields need to be revised, and CAPEX estimates need to be updated as technologies have moved a long way over the last two decades.

 Biomass projects, as they are generally initiated by industrial stakeholders from the agriculture sector to rather evacuate excess power than specifically to fill a gap in the electricity sector, resulting (like ADDAX) in very limited amount of power evacuated to the interconnected grid.

Therefore we are limiting our comparison to:

 Coal plants (calculations per MW based on a standard size of 125 MW)

 Low-speed Diesel HFO-fired plants (calculations per MW based on a standard size of 40 MW)

 Medium-speed Diesel HFO-fired plants (calculations per MW based on a standard size of 10 MW, representative of the anticipated size of the CEC plant’s units)

7 Radiations in the world range from below 500kWh/m² in East China, to 1000kWh/m² in London, to over 2500kWh/m² in Sahara Desert. [18]

8 Wind speed in Europe range from 3.5m/s in North Italy to over 6 m/s in Scotland. [20]

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7.2.3 Methodology

This screening curve analysis is a preliminary activity that is undertaken in order to compare the cost of coal against HFO generation, and is based on the following principles and definitions:

 A plant’s load factor is the ratio between the yearly total energy generated by the plant divided by the maximum energy output if the plant’s power output was equal to installed capacity all year long.  A screening curve is representing a cost per kW installed (Y-axis) as a function of the plant’s load factor.  This cost per kW should be inclusive of CAPEX costs, OPEX costs and fuel costs including handling and transport. As fuel costs vary every year, a screening curve is therefore representative of a specific year.  A screening curve analysis includes drawing a screening curve for each of the different generation technologies, for a specific year, and understanding in which load factor boundaries a technology or configuration is cheaper than the others. Boundaries are defined by the intersections of curves.  The analysis is based on assumed 2016 fuel prices, as this is the earliest year where it was assumed that new generation could be commissioned.

7.2.4 Assumptions

7.2.4.1 Cost of coal generation

This section of the report estimates the cost of generation from coal power plant and is based on investment data that was used in the West African Power Pool master plan [22]. In 2011, the overnight capital cost for a 125 MW coal based power plant was estimated to be 2 512 USD/kW, equivalent to 2 827 USD/kW in 2014 USD assuming annual inflation of 3%. An additional mark-up of 10% was assumed, recognising the risk of constructing the first coal based power plant in the country, increasing the overnight capital cost for a 125 MW plant to around 3 110 USD/kW. The cost estimations were made based on a total plant efficiency of 32.7% (11 000 kJ/kWh). More details on capital cost assumptions are given in Table 7-9:

Table 7-9: Capital Cost Assumptions for a Coal Based Power Plant (125 MW)

Power Generation Technology Thermal Coal Capacity, rated gross MW 125 Capacity, rated net MW 116 Source Document WAPP Master Plan Number of Units Number 1 Plant Technology Circulating Fluidised Bed Life of programme Years 30 Lead Time Years 3 to 4

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Equivalent Availability (%) % 82% Discount Rate % 10% Quoted Overnight Capex USD/kW 2 827 (2014 USD) Net Heat Rate kJ/kWh 11 000 Efficiency % 32.7% Sierra Leone Mark-up % 10% Overnight Capital Costs for USD/kW 3 110 Sierra Leone

Table 7-10 shows the Interest During Construction (IDC) calculation details. For a 125 MW Coal plant, a construction schedule and a capital requirement schedule of 3 to 4 years was assumed. The interest during construction was computed based on an assumption that the interest on the opening balance would be capitalised in the same year.

Based on above mentioned assumptions, the Overnight Capital Cost for a 125 MW coal plant after capitalisation of IDC was computed as 3,602 USD/kW.

Table 7-10: Interest During Construction Calculations for a Coal Based Power Plant (125 MW)

Capital Cost Calculations Without IDC With IDC Capital Cost USD/kW 3 110 3 602 Capita Cost USD 388,750,743 450,231,673 Annuity Factor 0.106

Construction Schedule Capital Cost (USD/kW) Year % Open Addition Close 1 15% 0 467 467 2 35% 467 1 089 1 602 3 35% 1602 1 089 2 850 4 15% 2850 467 3 602

In 2011, the WAPP Master Plan assumed a fixed O & M cost of 75 USD/kW. In 2014 terms this equates to 84 USD/kW (assuming 3% year on year inflation). The annuitized capital cost after adjustment of IDC is 412 USD/kW per annum. Annual fixed cost details are summarised in Table 7-11.

Table 7-11: Annual Fixed Cost

Fixed O&M (2011 USD) USD/kW - Year 75 Fixed O&M (2014 USD) USD/kW – Year 84

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Capital (2014 USD) USD/kW – Year 412 Total Fixed Cost USD/kW - Year 496

Table 7-12 shows the details on variable fuel cost.

Table 7-12: Variable Fuel Cost

Fuel Cost Calculations Heat Rate kJ/kWh 11 000 Lower Calorific Value kJ/kg 28 006 FUEL Price of Fuel in 2016 USD/Tonne 115.7 PRICE SCENARIO 1 Variable Fuel Cost USD/MWh 45.5 FUEL Price of Fuel in 2016 USD/Tonne 102.2 PRICE SCENARIO 2 Variable Fuel Cost USD/MWh 40.1

7.2.4.2 Cost of low-speed HFO-fired generation

This section of the report estimates the cost of generation from low-speed HFO fuelled power plant, and this is based on investment data that was used in the WAPP MasterPlan. The generation cost was computed with an assumption that HFO plant would have an efficiency of 44.1% (8 172 kJ/kWh) [23]. More details on capital cost assumptions are given in Table 7-13:

Table 7-13: Capital Cost Assumption for a Low-speed HFO Based Power Plant (40 MW)

Power Generation Technology Thermal HFO Capacity, rated gross MW 40 Capacity, rated net MW 38.5 Source Document WAPP MasterPlan Number of Units Number 1 Plant Technology Diesel Year of Fuel Price 2016 Life of programme Years 30 Lead Time Years 2 Equivalent Availability % 86% Discount Rate % 10% Overnight Capex USD/kW 1450 (2014 USD) Net Heat Rate kJ/kWh 8 172 Efficiency % 44.1%

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Table 7-14 shows the Interest During Construction (IDC) calculation details. For a 40 MW unit, a capital requirement schedule of 2 years was assumed. The interest during construction was computed based on an assumption that the interest on the opening balance would be capitalised in the same year.

Table 7-14: Interest During Construction Calculations

Capital Cost Calculations Without IDC With IDC Capital Cost USD/kW 1 450 1 523 Capital Cost USD 58,000,000 60,900,000 Annuity Factor 0.106 Capital Cost USD/Year 6,152,596 6,460,226

Construction Schedule Year % Open Addition Close 1 50% 0 725 725 2 50% 725 725 1 523

Table 7-15 and Table 7-16 show the details of annual fixed and variable cost respectively.

Table 7-15: Annual Fixed Cost

Fixed O&M USD/kW - Year 52.5 (2012 USD) Fixed O&M USD/kW - Year 56 (2014 USD) 168 Capital USD/kW - Year Total Fixed Cost USD/kW - Year 223

Table 7-16: Variable Cost for Scenarios 1 and 2

Fuel Cost Calculations Heat Rate kJ/kWh 8 172 Lower Calorific Value kJ/kg 40 916 FUEL Price of Fuel in 2016 USD/Tonne 562.22 PRICE SCENARIO 1 Variable Fuel Cost USD/MWh 112.3 FUEL Price of Fuel in 2016 USD/Tonne 495.23 PRICE SCENARIO 2 Variable Fuel Cost USD/MWh 98.9

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7.2.4.3 Cost of medium-speed HFO-fired generation

This section of the report estimates the cost of generation from medium-speed HFO fuelled power plant and this is based on investment data that was used in the Barbados IRP [23]. The generation cost was computed with an assumption that HFO plant would have an efficiency of 41.4% (8 687 kJ/kWh) [23]. More details on capital cost assumptions are given in Table 7-17:

Table 7-17: Capital Cost Assumption for a medium-speed HFO Based Power Plant (10 MW)

Power Generation Technology Thermal HFO Capacity, rated gross MW 10.4 Capacity, rated net MW 10 Source Document Barbados IRP Number of Units Number 1 Plant Technology Diesel Year of Fuel Price 2016 Life of programme Years 25 Lead Time Years 2 Equivalent Availability % 84% Discount Rate % 10% Overnight Capex USD/kW 1 250 (2014 USD) Net Heat Rate kJ/kWh 8 687 Efficiency % 41.4%

Table 7-18 shows the Interest During Construction (IDC) calculation details. For a 40 MW plant (4 x 10MW), a capital requirement schedule of 2 years was assumed. The interest during construction was computed based on an assumption that the interest on the opening balance would be capitalised in the same year.

Table 7-18: Interest During Construction Calculations

Capital Cost Calculations Without IDC With IDC Capital Cost USD/kW 1 250 1 325 Capital Cost USD 13,000,000 13,780,000 Annuity Factor 0.106 Capital Cost USD/Year 1,432,185 1,518,116

Construction Schedule Year % Open Addition Close 1 60% 0 750 750 2 40% 750 500 1325

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Table 7-19 and Table 7-20 show the details of annual fixed and variable cost respectively.

Table 7-19: Annual Fixed Cost Fixed O&M USD/kW - Year 88 (2014 USD) 152 Capital USD/kW - Year Total Fixed Cost USD/kW - Year 239

Table 7-20: Variable Cost for Scenarios 1 and 2

Fuel Cost Calculations Heat Rate kJ/kWh 8 687 Lower Calorific Value kJ/kg 40 916 FUEL Price of Fuel in 2016 USD/Tonne 562.22 PRICE SCENARIO 1 Variable Fuel Cost USD/MWh 119.4 FUEL Price of Fuel in 2016 USD/Tonne 495.23 PRICE SCENARIO 2 Variable Fuel Cost USD/MWh 105.1

7.1.5 Results and conclusions

7.2.4.4 Results under fuel price scenario 1 (2016)

The following chart shows the 2016 screening curves for coal-fired, medium (MSD) and low-speed (HFO) HFO-fired generation under fuel price scenario 1.

Figure 7-4: 2016 Screening curves for Coal and HFO – scenario 1

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The intersection of the screening curves indicates that:

 If the load factor is below 45.1%, low-speed HFO is more economical than Coal

 If the load factor is above 45.1%, Coal is more economical than low- speed HFO

7.2.4.5 Results under fuel price scenario 1 (2020)

The following chart shows the 2020 screening curves for coal-fired, medium (MSD) and low-speed (HFO) HFO-fired generation under fuel price scenario 1.

Figure 7-5: 2020 Screening curves for Coal and HFO – scenario 1

The intersection of the screening curves indicates that:

 If the load factor is below 42.4%, low-speed HFO is more economical than Coal

 If the load factor is above 42.4%, Coal is more economical than low- speed HFO

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7.2.4.6 Results under fuel price scenario 2 (2016)

The following chart shows the 2016 screening curves for coal-fired, medium (MSD) and low-speed (HFO) HFO-fired generation under fuel price scenario 2.

Figure 7-6: 2016 Screening curves for Coal and HFO – scenario 2

The intersection of the screening curves indicates that:

 If the load factor is below 51.0%, HFO is more economical than Coal

 If the load factor is above 51.0%, Coal is more economical than HFO

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7.2.4.7 Results under fuel price scenario 2 (2020)

The following chart shows the 2020 screening curves for coal-fired, medium (MSD) and low-speed (HFO) HFO-fired generation under fuel price scenario 2.

Figure 7-7: 2020 Screening curves for Coal and HFO – scenario 2

The intersection of the screening curves indicates that:

 If the load factor is below 48.0%, low-speed HFO is more economical than Coal

 If the load factor is above 48.0%, Coal is more economical than low- speed HFO

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7.2.4.8 Conclusions

Firstly we observe that for each of the previous screening curves analysis at no point on the range of load factors is medium-speed diesel more economical than both coal and low-speed diesel.

Further generation dispatching simulations are required at this stage to estimate under which load factor the candidate plants would operate, taking into account forecast energy requirements, and the generation of existing plants. Simulation results presented in chapter 8 show that the candidate plants would operate with load factors ranging from 14.5% to 47.9%. Chapter 8 details a more extensive NPV analysis to compare the three options.

Other points are to be considered, including but not limited to:

 As explained before, a suitably specified diesel unit running on HFO can be converted to gas firing in the event that gas is available and cheaper than HFO; and

 As detailed in the chapter 9.4, atmospheric emissions of SOx, Mercury and CO2 associated with coal generation are substantially higher than with HFO.

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8 Derivation of Least Cost Expansion Development

8.1 Introduction

Alternative generation development scenarios are compared in this section based on their total capital and operating costs. A discounted cash flow (DCF) analysis estimates and compares the net present value of the cash flow and the levelised cost of energy sent out under each of the options considered and over the same period of time (traditionally until cash flow simulations become no longer reliable). The option with the lowest levelised cost of energy sent is the most financially appealing.

In order to estimate the cash flow generated by each of the options, we need to simulate what would be the actual dispatching of the new plants over time, as we have demonstrated in our “Screening Curve Analysis Report” that the energy cost (and therefore the appeal) of a generation option is dependent upon the load factor of the new units commissioned. The calculation of cash flows has been limited to generation CAPEX and OPEX costs. Economic benefits of reducing unserved energy, and economic environmental costs such as carbon costs have not been included in the following analysis.

We used a simple generation dispatching simulation model, run on a seasonal basis, whose aim is to determine how the plants of the generation fleet in Sierra Leone contribute to meeting the system peak demand, and the system’s energy requirements, in accordance with our “Demand Forecast Report”. The relationship between demand (MW) and energy requirements (GWh) is governed by the load duration curve of the system.

The first part of our analysis will consider potential generation expansion at a short term horizon, over the period running from 2015 to 2019. In addition to the existing firmly committed projects, we will consider three potential options for an additional plant in Freetown: in option A, this plant would be made of coal-fired units, of slow-speed HFO-fired units in option B, and of medium- speed HFO-fired units in option C. We assume that no mining demand is connected over this period of time, but that rural demand would appear as the load centres are connected to the interconnected grid according to the sequencing defined in our transmission and distribution planning analysis (chapter 5).

The second part of our analysis will explore options at a longer term horizon, over the period running from 2020 to 2024. We will consider several options to supplement the Bumbuna II project: additional hydro units, additional coal- fired units, and a mix of both hydro and coal units. We will consider a base- case interconnected demand forecast scenario excluding any mining demand, and a high case interconnected demand forecast scenario including the progressive supply of up to 50% of the Mining Demand by the interconnected grid by 2024.

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8.2 Generation Dispatching Simulations

8.2.1 Methodology

The aim of the model is to determine which interval on the Y-axis of the load duration curve (see Figure 8-1) can and should each plant fill in in order to meet both demand and energy requirements of the system. For each plant the following constraints apply:

 The width of the interval considered (Y-axis) (퐷푝 in the example below) cannot exceed the available capacity of the plant.

 The area of the interval considered (퐸푝 in the example below) cannot exceed the maximum generation output of the plant for the period of time considered (one year in the example below).

Figure 8-1: Illustration of stacking

The order in which each plant is “fitted” under the load duration curve is defined by increasing marginal cost of each plant.

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The load duration curve traditionally has three characteristic zones:

 Base

 Transition

 Peak

Figure 8-2: Peak and base load

The shape of the load duration curve varies from one country to another and is very sensitive to the system load factor, related to the customer mix. More industrial customers will result in a higher load factor and a wider base in the load duration curve, while more residential customers will result in a lower system load factor and a wider peak.

While a constant energy output is expected throughout the day from “base” plants, the energy output of plants in the “transition” or “peak” has to be regulated. Therefore plants with no or very limited regulation capability (such as run-of-river hydroelectric plants for instance) can only be “fit” in the base of the load duration curve.

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In our model, the energy requirements and the maximum demand are changing for each year and each season. We therefore used a seasonal load duration curve 퐷′: 푥′ → 퐷′(푥′) rather than an annual load duration curve. In order to compare all results on the same basis, we worked on a per unit basis:

′ 퐷  퐷′ is in per unit and 퐷 = (퐷 = 1 for 퐷 = 퐷푚푎푥) 퐷푚푎푥

 푥′ is in pu (푥′ = 100% for 푥’ = the total number of hours in the season considered)

Figure 8-3: Reversed load duration curve with axis in pu

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Step by step methodology

The following simplified flow-chart presents the structure of the generation planning model:

Figure 8-4: Step by step methodology

Inputs

A number of inputs are required, including but not limited to:

- Commissioning and de-commissioning years for each plant

- Available capacity for each plant, for each season

- Maximum energy output for hydroelectric plants, if available

- Availability factors

- Demand and energy forecasts

- CAPEX for new plants

- Fixed and variable OPEX for new plants

- Fuel forecasts, thermodynamic parameters, and efficiencies

- Sequencing of new plants commissioning

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Stacking process

The rationale for using a stacking process based on the estimated load duration curve has been explained earlier in this section.

This stacking process goes through the following steps:

1. Hydro generation stacking

Earlier in this section we had mentioned that the merit order was the increasing marginal cost order. The marginal cost of hydroelectric generation being negligible, the first step of the process is to fit as much of the hydro generation available as possible.

The hydro stacking operates semi-iteratively. The number of iterations is set to 100 and aims at finding the optimal interval (in MW terms, on the Y-axis) for the hydro generation such that the effective hydro generation is as close as possible from the maximum expected hydro generation, while taking into account the total hydroelectric available capacity on the system.

In this simplified approach contrasting with more advanced models such as WASP, all of the hydro fleet is grouped together, independently of the regulation capability of each plant.

For each iteration, the top boundary of the interval is a function of the iteration number, and the distance between top and bottom is a constant, equal to the total available hydro capacity in pu (퐻 in the example below). The first stack is between the top (1pu) and “top - hydro capacity”, the 100th is between 0pu and “0 + hydro capacity”.

For each interval the model calculates the total energy generated by hydro plants (area under the curve, between top and bottom boundaries). The selected interval is the one where the difference between calculated energy generated (퐸ℎ푐 in example below) and forecast (maximum) energy to (퐸ℎ푚푎푥 in the example below) generate is the minimum.

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Figure 8-5: Illustration of the Hydro Stacking Process

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The following steps consider the other sources of generation.

2. Base stacking

This step fills in the area of the load duration curve below the area selected for hydro generation (if any place left). The considered area is filled in from bottom to top.

Figure 8-6: Base Stacking

The plants are considered in increasing merit order. For each plant, the algorithm fits as much capacity as available on the Y-axis, and calculates the corresponding energy (area under the selected part of the curve), providing that this energy does not exceed the expected maximum output.

3. Peak stacking

The remaining capacity is similarly fit in the area above the one selected for the hydro generation.

Figure 8-7: Peak Stacking

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4. Calculation of unserved energy and unserved demand

The sum of the areas of the curve which have not been covered by any of the plants due to a lack of capacity corresponds to the unserved energy.

Figure 8-8: Unserved energy and unserved demand

Served Demand =

5. Adjustments

For clarity purposes, we are initially using the same shape for the load duration curve for all years and seasons. This results in small discrepancies between the energy required forecast and the energy calculated from the load duration curve. This final step adjusts results from steps 2 to 4 in order to make sure that the sum of the energy generated and unserved equals the energy forecast, whilst taking into account generation constraints of each plant and their position on the load duration curve.

8.2.2 Main assumptions for generation dispatching simulations

8.2.2.1 Demand and Energy Requirements Forecast

We have considered the following demand growth scenarios based on our Demand Forecast Report (Table 8-1 and Table 8-2).

The interconnected demand growth scenarios are made of:

- Freetown Demand Forecast (Demand Forecast Report).

- Additional demand derived from the progressive connection of load centres outside Freetown (as justified in our transmission and distribution planning analysis presented in the Screening Curve Analysis Report and in chapter 5 of this report).

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- In addition, at a longer term horizon (2020-2024) we are considering two demand scenarios: one excluding mining demand, and another one including 50% of our base case mining demand forecast (as estimated in our Demand Forecast Report and in chapter 3 of this report) by 2024.

Table 8-1: Short Term Interconnected Demand Forecast (2015-2019)

Demand Forecast Energy requirements (MW) (GWh)

2015 109 MW 557 GWh

2016 117 MW 590 GWh

2017 127 MW 627 GWh

2018 155 MW 692 GWh

2019 176 MW 750 GWh

Table 8-2: Long Term Interconnected Demand Forecast (2019-2024) – base case and high case scenarios

BASE CASE HIGH CASE (excludes mining demand) (includes 50% of base case mining demand by 2024)

Demand Energy Demand Energy Forecast Requirements Forecast Requirements (GWh) (GWh) (MW) (MW)

2020 209 MW 829 GWh 223 MW 1119 GWh

2021 232 MW 889 GWh 300 MW 1595 GWh

2022 276 MW 988 GWh 404 MW 2187 GWh

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BASE CASE HIGH CASE (excludes mining demand) (includes 50% of base case mining demand by 2024)

Demand Energy Demand Energy Forecast Requirements Forecast Requirements (GWh) (GWh) (MW) (MW)

2023 306 MW 1060 GWh 512 MW 2849 GWh

2024 337 MW 1136 GWh 635 MW 3602 GWh

8.2.2.2 Fuel Price Forecasts

We have considered the following fuel price scenarios from our Screening Curve Analysis Report. The following are inclusive of transport and handling charges and are presented in constant 2014 prices.

Table 8-3: Fuel Price Forecast – Scenario 1

Fuel Price HFO (USD/T) LFO (USD/T Coal (USD/T) Scenario 1

2014 595.2 920.2 114.0

2015 584.6 904.5 116.1

2016 562.2 871.1 115.7

2017 552.5 856.7 115.5

2018 556.5 862.6 115.0

2019 567.9 879.6 114.7

2020 581.1 899.3 114.1

2021 596.1 921.6 113.7

Ministry of Energy 82 January 2015 20414

Fuel Price HFO (USD/T) LFO (USD/T Coal (USD/T) Scenario 1

2022 611.3 944.3 113.1

Table 8-4: Fuel Price Forecast – Scenario 2

Fuel Price HFO (USD/T) LFO (USD/T Coal (USD/T) Scenario 2

2014 470.4 734.3 114.0

2015 502.7 782.4 116.1

2016 495.2 771.3 115.7

2017 515.1 800.9 115.5

2018 538.7 836.1 115.0

2019 538.7 836.1 114.7

2020 538.7 836.1 114.1

2021 538.7 836.1 113.7

2022 538.7 836.1 113.1

8.2.2.3 Assumptions for existing and committed plants

List of plants

We keep existing thermal plants (Blackhall and Kingstom) running until 2025, since they were commissioned in 2010 and the average life for thermal plants is generally around 20/25 years. Similarly we assume that ADDAX biomass plant will also be running until 2025.

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We have limited the list of additional plants commissioned by 2019 to the small- scale hydro plants of Port Loko and Charlotte.

While discussions on a 128 MW diesel plant in Freetown to be commissioned by the company CEC Africa are at a very advanced stage, the financial close has not yet been achieved. Therefore we have not included this project in the following list of committed projects. Nevertheless we observe that this project presents size and cost similarities with the generation expansion options reviewed later in this report. If the project is eventually confirmed by the World Bank, the following consideration and comparison of different options should guide the Government producing specifications for the plant (see Table 8-14) and defining its optimal sequencing. We also considered that an additional 50 MW of capacity could be supplied by 2019 by Endeavour / Joule Africa. It is assumed and recommended that both of these plants would be located in the Freetown area, close to the major load centre.

We also considered the possible commissioning of HFO-fired units as part of the ENDEAVOUR Project to supplement the CEC units (see Table 8-16)

If the CEC project is not confirmed, the following analysis can be the basis for the development of an alternative project (see

Table 8-12).

At a longer term horizon, we understand that Bumbuna I hydro plant will be upgraded, including the extension of the existing of Bumbuna I plant and the commissioning of new turbines in Yiben although the exact specifications and configuration of are not fixed yet. We assume that the following phases will form the upgrade of the plant:

1. Commissioning of 4x55 MW additional turbines at Bumbuna (BUMBUNA II in Table 8-5). During this first phase, the additional turbines will deliver up to 220 MW power output during the wet season, and up to 30 MW during the dry season, generating on average 727 GWh per year. This would also decrease the power output of the existing 2x25 MW turbines (down to zero in all months except September and October), resulting in substantially lower annual generation of 35 GWh per year.

2. Partial Redesign of Yiben Dam (upstream of Bumbuna) and commissioning of 2x33 MW turbines in Yiben (YIBEN in Table 8-5). The Yiben turbines would generate approximately 315 GWh / annum, with up to 65 MW available during the wet season, and up to 35 MW available during the dry season. The redesign of Yiben Dam would also result in increased generation (up to 1089 GWh per year) and power available (up to 120 MW

Ministry of Energy 84 January 2015 20414

available during the dry season) for the additional 4x55 MW turbines at Bumbuna’s plant.

We propose to conservatively assume that the first phase of this upgrade will be commissioned in 2020, and the second phase in 2021.

Table 8-5: List of existing and committed plants

Plant Name Commissioning Decommissioning Generation Installed Date Date Type Capacity (MW)

BUMBUNA 1 2000 2021 Hydro 50

BUMBUNA 2 2020 after 2022 Hydro 220

YIBEN 2021 After 2022 Hydro 66

BLACKHALL 2010 after 2022 HFO Fired 16

KINGSTOM 2010 after 2022 HFO Fired 10

after the end of the 159 ADDAX 2013 period Biomass

after the end of the 2 PORT LOKO 2019 period Hydro

after the end of the 2 CHARLOTTE 2018 period Hydro

Availability of the plants

We use the following values to quantify the availability of each of the plant’s units, based on the WAPP MasterPlan reference values and adjusted from historical dispatching.

9 Share of the installed capacity dedicated to the interconnected network

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Table 8-6: Overall Availability of the Plants

Plant Name Generation Type Overall Availability

BUMBUNA 1 Hydro 85%

BUMBUNA 2 Hydro 85%

YIBEN Hydro 85%

BLACKHALL HFO Fired 80%

KINGTOM HFO Fired 80%

ADDAX Biomass 80%

PORT LOKO Hydro 85%

CHARLOTTE Hydro 85%

Seasonal Maximum Energy Sent Out Estimates

We make the following assumptions as to the maximum energy sent out by each plant after their commissioning on a seasonal basis. In the following analysis we call “Wet Season” the season running from June to December.

Table 8-7: Seasonal Maximum Energy Sent Out Estimates

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Plant Name Commissioning Maximum Maximum Date Energy Sent Out Energy Sent Out (GWh) (GWh) WET SEASON DRY SEASON

BUMBUNA 1 2000 146.8 GWh 49.7 GWh (27 GWh from 2020) (0 GWh from 2020) 620.1 GWh in 2020 106.9 GWh in 2020 BUMBUNA 2 2020 (720.7 GWh from (368.3 GWh from 2021) 2021)

YIBEN 2021 195.7 GWh 119.3 GWh

BLACKHALL 2010 34.4 GWh 24.2 GWh

KINGSTOM 2010 15.9 GWh 11.2 GWh

ADDAX 2013 24.5 GWh 29.7 GWh

PORT LOKO 2019 8.7 GWh 2.5 GWh

CHARLOTTE 2018 8.7 GWh 2.5 GWh

OPEX estimates for existing and committed plants

We use the following estimates on fixed and variable OPEX, based on reference values from the WAPP MasterPlan [22]:

Table 8-8: OPEX estimates per type of plant

Generation Fixed OPEX Variable OPEX Efficiency Type (USD/kW/annum) (USD/MWh) (%)

HYDRO PLANTS 2% of CAPEX10 Included in fixed OPEX N/A

THERMAL PLANTS 56 6 44.1%

10 Conservative assumption

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Generation Fixed OPEX Variable OPEX Efficiency Type (USD/kW/annum) (USD/MWh) (%)

BIOMASS PLANTS 136 Included in fixed OPEX N/A

CAPEX estimates for committed plants

We use the following estimates on fixed and variable OPEX, based on reference values from the WAPP MasterPlan [22] and our experience on similarly-sized projects.

Table 8-9: CAPEX estimates for committed plants

Plant Name CAPEX (USD/kW)

BUMBUNA II (extension) 1422.5

YIBEN (dam and turbines) 4716.0

CHARLOTTE 6709.0

PORT LOKO 6709.0

8.3 Review of short-term generation options

8.3.1 Assumptions and sequencing

The first part of our analysis considers potential generation expansion at a short term horizon, over the period running from 2015 to 2019. These options are common to both base case and high case demand scenario as the high case only differs from the base case from 2020 onwards.

In addition to the existing firmly committed projects listed in Table 8-5, we will consider three potential options for an additional plant in Freetown:

- OPTION A: commissioning of 40 MW HFO-fired diesel units

Ministry of Energy 88 January 2015 20414

- OPTION B: commissioning of 125 MW coal-fired unit

- OPTION C: commissioning of 10.4 MW medium-speed HFO-fired diesel units

Assumptions for the units of options A, B and C

The following table from the Screening Curves Analysis Report presents the assumptions for the base units of each option.

Table 8-10: Assumptions for base units of options A, B, C

Large Slow Smaller Medium Steam Plant Speed diesel Speed diesel Coal fired boiler Low Speed Medium Speed Power Generation Thermal and steam Reciprocating Reciprocating Technology turbine engine engine Capacity, rated gross MW 125 40 10.4 Capacity, rated net MW 116 38.5 10 Plant Technology Coal HFO HFO Life of programme Years 30 30 25 Equivalent Availability % 82% 86% 84% Maximum Load Factor % 82% 86% 84% Net Heat Rate kJ/kWh 11000 8172 8687 Efficiency % 32.7% 44.1% 41.4% Overnight Capex (2014 USD/kW 3110 1450 1150 USD) inst USD/kW Fixed O&M so 412 56 88 Variable O&M USD/MWh 4 6 10

System Reliability criterion

The reliability of a system is traditionally quantified by a “Loss-Of-Load Expectation” criteria, defined as the number of hours a year where available capacity cannot meet part or all of the system demand, resulting in balancing load shedding.

In the PPA Energy report of 200911, estimates were derived as to the appropriate generation reliability criterion which should be adopted in Sierra Leone.

The final proposal in the report was that a LOLE value of 480 hours, reducing to 240 hours from 2010 to 2018, and reducing further to 94 hours from 2019

11 Power Sector Study for the National Commission for Privatisation, September 2009

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onwards.12 In practice we are looking for further improvements to system reliability, but due to the uncertainty of the assumptions of this analysis we have used a conservative target.

The implication of using an LOLE standard that is lower (i.e. less onerous) than most other low-income economies is that it is cheaper for the economy of Sierra Leone to endure some loss of load, than it is to invest in generation capacity that would reduce this loss of load.

Considering that the targets over the period 2009-2013 have not been met, it is proposed to increase revise the timing as shown in Table 8-11.

Table 8-11 - Proposed LOLE criteria

Year LOLE criterion (hours pa)

2014-2016 480 2017-2018 240 From 2019 94

Number of units commissioned and sequencing

We have reviewed a number of possible phasing scenarios for options A, B and C in order to select for each option the phasing scenario:

- Involving commissioning a minimum number of base units (to keep energy costs at their minimum over the period considered)

- Commissioning each unit the latest possible (to keep energy costs at their minimum over the period considered)

- Commissioning the first unit in 2016 or after (as an earlier commissioning would not be feasible)

- While meeting the reliability criterion as defined in Table 8-11

The following

12 94 hours is the standard in Botswana, and 240 hours has been used in Ethiopia

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Table 8-12 presents the proposed sequencing for each option, while Table 8-13 indicates estimated LOLE under each option, and verifies that each option meets the LOLE criteria every year of the period considered.

Due to a longer construction period, a coal-fired plant could not practically be commissioned by the end of 2015. But in order to undertake a fair comparison between coal and HFO-fired technologies we assumed that the first coal-fired units would be commissioned the same year the first HFO-fired units would (2016).

Table 8-12: Selected Configuration and Sequencing for each short-term option

Additional units 2016 2017 2018 2019 TOTAL commissioned (MW)

2x116 MW - 1x116 MW - 348 MW Option A (Coal)

192.5 Option B 2x38.5 MW 1x38.5 MW 1x38.5 MW 1x38.5 MW MW (Slow Speed HFO)

Option C 8x10 MW 2x10 MW 3x10 MW 4x10 MW 170 MW (Medium Speed HFO)

Table 8-13: LOLE results under each short-term option

LOLE estimates 2016 2017 2018 2019 (h)

480 240 240 94 LOLE criterion

188.6 217.54 51.46 78.31 Option A (Coal)

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Option B 468.88 168.28 102.24 59.82 (Slow Speed HFO)

Option C 282.23 164.14 174.08 58.71 (Medium Speed HFO)

Case where the CEC Power Plant is commissioned instead of options A, B or C

In the case where the HFO-Fired Plant developed by CEC is commissioned instead of options A, B or C, we suggest the following phasing assuming that:

- The first unit could be commissioned in 2016 the earliest - In Phase 1, 7 units with a net capacity of approximately 8 MW each will be commissioned - In Phase 2, 2 largers units with a net capacity of approximately 18 MW each will be commissioned - In Phase 3, 2 units with a net capacity of approximately 18 MW each will be commissioned - We expect at least one year between Phase 1 and Phase 2 - All units have the same overall availability of 84%

Besides we commissioned each phase the latest possible while trying to meet reliability criterion the earliest possible, for reasons similar to before.

Table 8-14 shows the proposed sequencing, and

Table 8-15 presents the related LOLE estimates. We observe that as only Phase 1 (56 MW in total) could be commissioned in 2016, the reliability criterion could not be met in 2016. To meet this criterion in 2017, both Phase 2 and Phase 3 need to be commissioned in 2017. Under this option, the reliability criterion is not met in 2019.

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Table 8-14: Proposed sequencing for the CEC Plant

Additional units 2016 2017 2018 2019 TOTAL commissioned (MW)

7x8 MW 2x18 MW (PHASE 2) - - 128 MW CEC (PHASE 1)

2x18 MW (PHASE 3)

Table 8-15: LOLE results under the proposed sequencing for the CEC Plant

LOLE estimates 2016 2017 2018 2019 (h)

480 240 240 240 LOLE criterion

1392.93 29.28 219.31 562.9 CEC

The above analysis establishes that additional generation is required in 2019. We understand that this could be supplied by a HFO-fired Endeavour/Joule Africa power plant. Total capacity could reach 100 MW with flexibility of a phased commissioning.

For the purpose of the following analysis, we assume that this plant will comprise of 25 MW slow-speed HFO-fired units, with costs and thermodynamic assumptions similar to the ones used under Option B (see Table 8-10).

Our analysis shows that only 2x25 MW units would be required to meet the reliability criterion in 2019 (Table 8-16 and Table 8-17). We therefore suggest the following sequencing for the ENDEAVOUR Plant:

Ministry of Energy 93 January 2015 20414

- 50 MW commissioned in 2019

- 50 MW commissioned between 2021 and 2024, or as early as 2020 if the Bumbuna project is delayed (proposed sequencing under two demand growth scenarios are detailed in chapters 8.4.1 and 0)

Table 8-16: Proposed sequencing for CEC and ENDEAVOUR

Additional units 2016 2017 2018 2019 TOTAL commissioned (MW)

7x8 MW 2x18 MW (PHASE 2) - - 128 MW CEC (PHASE 1)

2x18 MW (PHASE 3)

- - - 2x25 MW 50 MW ENDEAVOUR

Table 8-17: LOLE estimates for the combination of CEC and ENDEAVOUR Plants

LOLE estimates 2016 2017 2018 2019 (h)

480 240 240 240 LOLE criterion

CEC + 1392.93 29.28 219.31 56.29 ENDEAVOUR

Results of generation dispatching simulations

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The following tables (Table 8-18, Table 8-19, Table 8-20, Table 8-21 and

) present the results from generation dispatching simulations under options A, B, C, “CEC+ENDEAVOUR” and firstly with none of the options implemented.

Table 8-18: Annual Energy Dispatching simulation results – none of the options implemented

Energy Sent 2015 2016 2017 2018 2019 Out (GWh)

Unserved 220.1 253.1 290.3 366.0 428.4 Energy Bumbuna I 196.6 196.6 196.6 184.5 175.9

Blackhall 58.6 58.6 58.6 55.0 52.5

Kingtom 27.1 27.1 27.1 25.4 24.2

Addax 54.2 54.2 54.2 50.9 48.5

Port Loko 0.0 0.0 0.0 0.0 10.0

Charlotte 0.0 0.0 0.0 10.5 10.0

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Table 8-19: Annual Energy Dispatching simulation results – option A implemented

Energy Sent Out 2015 2016 2017 2018 2019 (GWh)

Unserved Energy 220.1 0.0 0.0 0.0 0.0

Bumbuna I 196.6 196.6 196.6 184.5 175.9

Blackhall 58.6 0.0 0.0 0.0 0.0

Kingtom 27.1 0.0 0.0 0.0 0.0

Addax 54.2 54.2 54.2 54.2 53.9

Port Loko 0.0 0.0 0.0 0.0 10.0

Charlotte 0.0 0.0 0.0 10.5 10.0

Candidate Plant 0.0 338.8 376.0 443.2 499.7

MW installed for candidate plant 0.0 232.0 232.0 348.0 348.0 (MW) Load Factor for Candidate Plant #N/A 16.7% 18.5% 14.5% 16.4% (%)

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Table 8-20: Annual Energy Dispatching simulation results – option B implemented

Energy Sent Out 2015 2016 2017 2018 2019 (GWh)

Unserved Energy 220.1 5.7 0.0 0.0 0.9

Bumbuna I 196.6 196.6 196.6 184.5 175.9

Blackhall 58.6 7.5 0.6 0.0 2.5

Kingtom 27.1 2.8 0.0 0.0 0.7

Addax 54.2 54.2 54.2 54.2 53.9

Port Loko 0.0 0.0 0.0 0.0 10.0

Charlotte 0.0 0.0 0.0 10.5 10.0

Candidate Plant 0.0 322.8 375.4 443.2 495.6

MW installed for candidate plant (MW) 0.0 77.0 115.5 154.0 192.5

Load Factor for Candidate Plant (%) #N/A 47.9% 37.1% 32.8% 29.4%

Ministry of Energy 97 January 2015 20414

Table 8-21: Annual Energy Dispatching simulation results – option C implemented

Energy Sent Out 2015 2016 2017 2018 2019 (GWh)

Unserved Energy 220.1 5.0 1.7 1.7 0.0

Bumbuna I 196.6 196.6 196.6 184.5 175.9

Blackhall 58.6 7.2 4.5 3.8 0.8

Kingtom 27.1 2.6 1.2 1.1 0.1

Addax 54.2 54.2 54.2 54.2 53.9

Port Loko 0.0 0.0 0.0 0.0 10.0

Charlotte 0.0 0.0 0.0 10.5 10.0

Candidate Plant 0.0 323.9 368.6 436.6 498.8

MW installed for candidate plant (MW) 0.0 80.0 100.0 130.0 170.0

Load Factor for Candidate Plant (%) #N/A 46.2% 42.1% 38.3% 33.5%

Ministry of Energy 98 January 2015 20414

Table 8-22: Annual Energy Dispatching simulation results – option “CEC+ENDEAVOUR” implemented

Energy Sent Out 2015 2016 2017 2018 2019 (GWh)

Unserved Energy 220.1 26.1 0.0 2.2 0.0

Bumbuna I 196.6 196.6 196.6 184.5 175.9

Blackhall 58.6 13.4 0.0 3.9 0.0

Kingtom 27.1 5.4 0.0 1.3 0.0

Addax 54.2 54.2 54.2 52.6 49.8

Port Loko 0.0 0.0 0.0 0.0 10.0

Charlotte 0.0 0.0 0.0 10.5 10.0

CEC Plant 0.0 293.8 376.0 437.4 480.6

MW installed for CEC Plant (MW) 0.0 56.0 128.0 128.0 128.0

Load Factor for CEC (%) #N/A 59.9% 33.5% 39.0% 42.9%

ENDEAVOUR 0.0 0.0 0.0 0.0 23.1

MW installed for ENDEAVOUR (MW) 0.0 0.0 0.0 0.0 50.0

Load Factor for ENDEAVOUR (%) #N/A #N/A #N/A #N/A 5.3%

Ministry of Energy 99 January 2015 20414

8.3.2 Costs comparisons

To compare options A, B, C and CEC, we used a Net Present Value Analysis. With this approach, we estimated yearly generation costs under each option (including CAPEX, fixed and variable operation & maintenance costs) for each year of the period of simulation (from 2015 to 2019) and adjusted last year’s figure with the residual value of the additional units commissioned over the remaining of their life to finally calculate the net present value associated to each option using a 10% discount rate. Detailed calculation for each option are provided in Table 8-23, Table 8-24, Table 8-25 and Table 8-26.

Table 8-23: NPV Calculation – Option A (coal)

Total CAPEX Total Fixed Total Variable Total Cost Of Year OPEX OPEX Service (mUSD) (mUSD) (mUSD) (mUSD) 2015 360.8 7.0 10.5 378.3 2016 254.5 26.5 16.6 297.6 2017 343.6 26.5 18.4 388.5 2018 278.2 36.5 21.6 336.3 2019 246.7 36.8 24.3 307.8 Residual Value 1,095.8 - - 1,095.8 Net Present Value 459.4 95.9 66.9 622.2

Table 8-24: NPV Calculation – Option B (low-speed HFO)

Total CAPEX Total Fixed Total Variable Total Cost Of Year OPEX OPEX Service (mUSD) (mUSD) (mUSD) (mUSD) 2015 55.8 7.0 10.5 73.3 2016 85.8 11.3 39.4 136.4 2017 110.8 13.5 43.7 168.0 2018 207.8 15.9 51.9 275.6 2019 220.5 18.3 59.6 298.5 Residual Value 287.5 - - 287.5 Net Present Value 305.2 48.0 147.4 500.6

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Table 8-25: NPV Calculation – Option C (medium-speed HFO)

Total CAPEX Total Fixed Total Variable Total Cost Of Year OPEX OPEX Service (mUSD) (mUSD) (mUSD) (mUSD) 2015 60.0 7.0 10.5 77.5 2016 57.0 14.0 43.1 114.2 2017 87.5 15.8 47.6 150.9 2018 197.0 18.7 56.6 272.3 2019 212.6 22.5 65.3 300.4 Residual Value 218.0 - - 218.0 Net Present Value 298.6 56.6 160.1 515.3

Table 8-26: NPV Calculation – Option CEC+ENDEAVOUR

Total CAPEX Total Fixed Total Variable Total Cost Of Year OPEX OPEX Service (mUSD) (mUSD) (mUSD) (mUSD) 2015 42.0 7.0 10.5 59.5 2016 84.0 11.9 40.3 136.2 2017 91.0 18.3 47.9 157.2 2018 188.2 18.5 56.7 263.5 2019 228.9 21.6 65.6 316.0 Residual Value 238.5 - - 238.5 Net Present Value 298.5 56.0 158.3 512.8

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Table 8-27 shows the total net present values for each option:

Table 8-27: Results of the short-term NPV Analysis

Option NPV Residual NPV without Value with residual residual value value

(mUSD) (mUSD) (mUSD)

Option A (coal) 1302.6 1095.8 622.2

Option B (HFO) 679.1 287.5 500.6

Option C (MSD) 650.7 218.0 515.3

Option “CEC+ENDEAVOUR”13 660.9 238.5 512.8

The most economically appealing options are Options B (medium-speed diesel units), Option C (slow-speed diesel units), and Option “CEC+ENDEAVOUR” with similar NPV results, while Option A (coal) is nearly 25% more costly than Option B (HFO).

8.4 Review of medium-term generation expansion options

This second part of our analysis considers potential generation expansion at a medium term horizon, over the period from 2020 to 2024 (5 years).

8.4.1 Under the base case demand scenario

8.4.1.1 Assumptions and sequencing

In the base case demand scenario we assume that no mining customers are connected over that period of time.

The reliability LOLE criterion over the period from 2020 to 2024 is 94h (see Table 8-11) but a tolerance of 10% can be permitted as approximately 60% of the forecast sales are residential or commercial.

13 Only including the first 50 MW of the ENDEAVOUR Project, commissioned in 2019

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Table 8-28 shows our LOLE estimates for the period 2020 to 2024, under the base-case demand scenario. They all assume that the Bumbuna extension is commissioned in 2020, and that a redesigned dam and powerhouse at Yiben is commissioned in 2021.

LOLE estimates are dependent upon the generation option selected for the shorter term, as defined in chapter 8.3.1. We observe that:

- For Option A, the reliability criterion is met every year without further additions

- For Option B, the reliability criterion is met every year without further additions

- Under Option C, the reliability criterion is not met in 2024

- Under Option “CEC+ENDEAVOUR” and with only 50 MW commissioned in the ENDEAVOUR Plant, the reliability criterion is not met in 2020, 2022, 2023 and 2024.

Table 8-28: LOLE estimates from 2020 to 2024 under base case demand scenario and with committed plants only

LOLE estimates 2020 2021 2022 2023 2024 (h)

94 94 94 94 94 LOLE criterion

Option 2020 2021 2022 2023 2024 Implemented in 2015-2020

Option A (Coal) 50.67 4.88 13.32 27.73 50.84 commissioned in 2015-2020 Option B (Slow Speed HFO) 35.29 1.9 12.29 33.6 80.55 commissioned in 2015-2020 Option C 87.52 1.04 18.76 77.84 227.46 (Medium Speed HFO)

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LOLE estimates 2020 2021 2022 2023 2024 (h)

commissioned in 2015-2020

CEC Plant + ENDEAVOUR14 391.97 13.47 110.53 284.58 563.4 commissioned in 2015-2020

To meet the reliability criterion under option C, and in addition to Bumbuna II and Yiben, we consider the following medium-term options:

- OPTIONS D-1: addition of hydro turbines only

- OPTION E-1: addition of coal units only

We assume identical parameters for the base coal unit of medium-term option E-1 as for the base coal unit of short-term option A (see Table 8-10), while we make the following assumptions for the base hydro unit (Table 8-29), based on data available for existing and committed hydro plants in Sierra Leone, on reference values from the WAPP MasterPlan [22] and on our recent experience on hydroelectric projects. A conservative assumption has been taken for O&M costs (equal to 2% of CAPEX).

Table 8-29: Cost and hydrology assumptions for base hydro units

Hydro Plant Power Generation Hydro Turbine Technology Capacity, rated net MW 33 Maximum Power Output – MW 33 WET Season Maximum Power Output – MW 11 DRY Season Average Monthly Power MW 21 Output – WET Season Average Monthly Power MW 11 Output – DRY Season Life of programme Years 50 Equivalent Availability % 85%

14 Including only 50 MW commissioned at the ENDEAVOUR Plant

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Hydro Plant Average Wet Season Load % 81% Factor Average Dry Season Load % 25% Factor Average Annual Energy GWh 167.0 USD/kW Fixed O&M so 2% of CAPEX Included in Variable O&M USD/MWh Fixed O&M

Under option “CEC+ENDEAVOUR” we consider commissioning additional 25 MW units as part of the ENDEAVOUR Power Project (Option G-1).

All options D-1, E-1 and G-1 described above assume that the Bumbuna extension is commissioned in 2020, and that Yiben dam is redesigned and new turbines commissioned in 2021.

Option G-2 considers a medium-term scenario where neither Bumbuna II or Yiben is commissioned, and where the reliability criteria is met by commissioning coal units instead.

To meet the LOLE criterion, we assume the following sequencing for options D-1, E-1, G-1 and G-2.

Table 8-31 shows that the LOLE criterion is met every year (or within the range of tolerance) in each combination of short-term and medium-term options, except in 2020 under Option “CEC+ENDEAVOUR”. We would not recommend commissioning additional coal or hydro units in 2020 here as the LOLE drops in 2021 with the redesign of Yiben dam and the commissioning of additional turbines.

Ministry of Energy 105 January 2015 20414

Table 8-30: Review of medium-term options under base case demand scenario

Short- Medium- BUMB TOTA TOTA -UNA L L term term II option option commis 2020 2021 2022 2023 2024 (2020- (2015- s-ioned 2024) 2024) ?

348 Option A Yes ------0 MW MW (Coal) + BUMBUNA II + BUMBUNA II

192.5 Option B Yes 0 MW ------+ BUMBUNA II MW (HFO) + BUMBUNA II

Option 4x33 Option C Yes 132 302 D-1 - - - - MW MW MW (MSD) (HYDRO) (Hydro) + BUMBUNA II + BUMBUNA II

Option 1x116 116 286 Option C Yes E-1 - - - - MW MW MW (MSD) + BUMBUNA II + BUMBUNA II (Coal) (COAL)

Option Option 1x25 1x25 228 CEC 50 MW Yes MW MW + G-1 - - - + BUMBUNA II MW ENDEAVO ENDEAVO ENDEAVO + BUMBUNA II ENDEAVO UR UR UR UR

Option 2x25 Option 1x116 1x116 282 460 CEC NO MW MW MW - - + G-2 ENDEAVO MW MW UR (COAL) (COAL) ENDEAVO ENDEAVO UR UR+COAL

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Table 8-31: LOLE estimates under each medium-term option and under the base case demand scenario Short-term option Medium-term option 2020 2021 2022 2023 2024

LOLE Criterion 94h 94h 94h 94h 94h (tolerance = 10%)

Option A 50.67 h 4.88 h 13.32 h 27.73 h 50.84 h - (Coal)

Option B 35.29 h 1.9 h 12.29 h 33.6 h 80.55 h - (HFO)

Option C Option D-1 87.52 h 1.04 h 18.76 h 77.84 h 56.85 h (MSD) (Hydro)

Option C Option E-1 87.52 h 1.04 h 18.76 h 77.84 h 40.52 h (MSD) (Coal)

Option CEC Option G-1 116.64 h 2.64 h 31.66 h 46.43 h 73.58 h (MSD) ENDEAVOUR

Option Option CEC 42.92 h 26.17 h 23.58 h 39.24 h 67.48 h + ENDEAVOUR G-2 ENDEAVOUR+COAL

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8.4.1.2 Cost comparisons

Short-term options A and B, after the commissioning of Bumbuna II and Yiben, are sufficient to meet the reliability criteria until 2024.

We have considered medium-term options D-1 (hydro) and E-1 (coal) to supplement short-term option C (medium-speed diesel units), Bumbuna II and Yiben. The volumes of capacity commissioned are similar in D-1 (132 MW) and E-1 (116 MW), such that on this basis only option D-1 (hydro) is more attractive. But both options are commissioning units in 2024, and therefore running the simulations beyond the 2024 horizon would be required to produce firm recommendations, which is impracticable to do at this stage considering the high uncertaintly on both demand growth and generation expansion.

We have considered medium-term options G-1 (further ENDEAVOUR units in addition Bumbuna II and Yiben), and G-2 (further ENDEAVOUR units and coal units instead of Bumbuna II and Yiben) to supplement short-term option “CEC+ENDEAVOUR” (128 MW CEC Plant and 50 MW commissioned at the ENDEAVOUR Plant). Due to the long-term uncertainty on most of the assumptions that the previous analysis is based on, we cannot undertake such a detailed analysis as the NPV analysis presented for the short-term options in paragraph 8.3.2.

In place of a NPV analysis, we have used a more “high-level” approach assuming a capital cost for coal units and estimating the maximum average capital cost for hydro units commissioned in the medium-term that would make hydro units more financially appealing than coal units over the 2015- 2024 period and after accounting for their residual value.

In this paragraph, the hydro units commissioned in the medium-term are limited to 220 MW in 2020 (Bumbuna II) and 66 MW in 2021 (Yiben).

Based on our analysis, the maximum average capital cost for these units is limited to USD/kW 3,041 (in 2014 USD). In other words we conservatively estimate that if the “Bumbuna II+Yiben” total overall capital cost is below 2014 USD/kW 3,041 this project is indeed the least cost solution compared to a coal- based alternative replacing the Bumbuna extension and Yiben (Figure 8-9 below). This assumes that all other assumptions, including demand and fuel price forecasts, OPEX assumptions, and coal CAPEX assumptions are still valid by 2024.

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Figure 8-9: Least cost medium-term programmes after short-term option “CEC+ENDEAVOUR” and under base-case demand scenario for variable hydro CAPEX

8.4.1.3 Opportunity for gas-fired generation

In the medium-term, there would be an opportunity to convert diesel HFO-fired units to gas-fired turbines, if the gas prices including handling and transport are kept below a certain threshold. Converting diesel unit to gas-firing would allow increased plant efficiency and creates the opportunity for lower fuel costs.

Figure 8-10 below shows the least cost medium-term development programme under the base case demand forecast and with the option “CEC+ENDEAVOUR” implemented in the short term, leaving the opportunity for all the units of the CEC Plant (128 MW) to be converted to gas-fired engines operating in combined cycle by 2020. The X-axis shows the maximum average CAPEX for hydro units in USD/kW and the Y-axis shows the maximum average gas price in USD/1000m3 inclusive of handling, transport and potential conversion costs.

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Figure 8-10: Least Cost Medium-Term Programmes after short-term option “CEC+ENDEAVOUR” and under base case demand scenario for variable hydro CAPEX and gas prices

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8.4.2 Under high-case demand scenario.

In the High Case Demand Forecast, 50% of the mining demand is connected by 2024. This results in higher capacity requirements and stricter reliability criteria.

The reliability LOLE criterion over the period from 2020 to 2024 is 94h (see Table 8-11). The high-case demand is driven by the mining sector (70% of the forecast sales), where key stakeholders will only request connection to the grid if the electricity supply is reliable. Therefore in the following analysis we are allowing only 5% tolerance on the reliability criterion.

Under this demand scenario and independently from the short-term option selected, additional capacity is required as early as 2020 to meet both demand and the reliability criterion.

We will be considering several medium-term options for additional capacity over the period from 2020 to 2024, each of them supplementing Bumbuna II and Yiben commissioning.

- OPTION D-3, D-4, D-5: addition of hydro turbines after respectively short- term options A, B, and C

- OPTION E-3, E-4, E-5: addition of coal units after respectively short-term options A, B, and C

- OPTION F-3, F-4, F-5: addition of hydro and coal units after respectively short-term options A, B, and C

- OPTION D-6: addition of ENDEAVOUR’s HFO units and hydro turbines after short-term option “CEC+ENDEAVOUR”

- OPTION E-6: addition of ENDEAVOUR’s HFO units and coal units after short-term option “CEC+ENDEAVOUR”

- OPTION F-6: addition of ENDEAVOUR’s HFO units, hydro and coal units after short-term option “CEC+ENDEAVOUR”

We assume identical parameters for the base coal unit of medium-term options E-x and F-x as for the base coal unit of short-term option A (see Table 8-10), while we use the assumptions of Table 8-29 for hydro units.

We propose the following sequencing for option D-x, E-x and F-x in order to meet the reliability criterion:

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Table 8-32: Review of medium-term options under high case demand scenario to supplement Bumbuna II

Short- Medium- TOTA TOTA term term 2020 2021 2022 2023 2024 L L option option (2020- (2015- 2024) 2024)

Option 1404 Option A 8x33 12x33 12x33 1056 D-3 - - MW (Coal) MW MW MW MW (Hydro)

Option E- 1x116 2x116 1x116 464 812 Option A - - 3 MW MW MW MW MW (Coal) (Coal)

1x116 1x116 Option F- 1x116 MW MW MW [coal] [coal] 546 894 Option A 3 - - [coal] MW MW (Coal) (Hydro & 4x33 2x33 Coal) MW MW [hydro] [hydro]

Option 6x33 10x33 12x33 12x33 1320 1512.5 Option B - D-4 MW MW MW MW MW MW (HFO) (Hydro)

Option E- 1x116 1x116 2x116 1x116 Option B - 580 772.5 4 MW MW MW MW (HFO) MW MW (Coal)

1x116 1x116 Option F- MW MW 1x116 1x116 [coal] [coal] 728 920.5 Option B 4 MW MW [coal] [coal] MW MW (HFO) (Hydro & 4x33 4x33 Coal) MW MW [hydro] [hydro]

Option 8x33 8x33 16x33 14x33 1518 1688 Option C - D-5 MW MW MW MW MW MW (MSD) (Hydro)

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Short- Medium- TOTA TOTA term term 2020 2021 2022 2023 2024 L L option option (2020- (2015- 2024) 2024)

Option E- 1x116 2x116 1x116 1x116 580 750 Option C - 5 MW MW MW MW MW MW (MSD) (Coal)

1x116 1x116 1x116 MW MW MW Option F- [coal] 1x116 [coal] [coal] 794 964 Option C 5 MW - MW MW (MSD) (Hydro & [coal] 4x33 2x33 4x33 Coal) MW MW MW [hydro] [hydro] [hydro]

2x25 Option MW Option 10x33 14x33 14x33 D-6 [ENDEAVO 1370 1548 CEC+ UR] - MW MW MW (Hydro & [hydro] [hydro] [hydro] MW MW ENDEAVO 2x33 ENDEAVO UR MW UR) [hydro]

2x25 Option E- MW Option [ENDEAVO 1x116 1x116 2x116 UR] 630 808 6 - MW MW MW CEC+ MW MW ENDEAVO (Coal & 1x116 [coal] [coal] [coal] UR ENDEAVO MW UR) [coal]

1x116 1x116 1x116 Option E- 2x25 MW MW MW MW [coal] [coal] [coal] Option 6 [ENDEAVO 728 906 UR] - CEC+ (Hydro, MW MW ENDEAVO Coal & 2x33 2x33 2x33 4x33 MW MW MW UR ENDEAVO MW [hydro] [hydro] [hydro] UR) [hydro]

Table 8-33 shows the LOLE estimates for each of the combinations listed above and verifies that they are within the range of tolerance.

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Table 8-33: LOLE estimates for medium-term options under high case demand scenario

Short-term option Medium-term option 2020 2021 2022 2023 2024

LOLE Criterion 94h 94h 94h 94h 94h (tolerance = 10%)

79.23 49.97 78.71 72.74 83.4 h Option A Option D-3 h h h h (Coal) (Hydro)

79.23 49.97 89.86 38.51 68.48 Option A Option E-3 h h h h h (Coal) (Coal)

79.23 49.97 77.75 73.61 34.6 h Option A Option F-3 h h h h (Coal) (Hydro & Coal)

58.89 35.71 46.15 79.92 85.84 Option B Option D-4 h h h h h (HFO) (Hydro)

63.39 89.86 38.51 68.48 40.3 h Option B Option E-4 h h h h (HFO) (Coal)

63.39 89.86 77.24 77.57 40.3 h Option B Option F-4 h h h h (HFO) (Hydro & Coal)

65.63 16.66 94.4 h 36.3 h 57.44h Option C Option D-5 h h (MSD) (Hydro)

93.33 59.46 33.41 49.08 96.1 h Option C Option E-5 h h h h (MSD) (Coal)

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93.33 59.46 65.99 77.29 82.05 Option C Option F-5 h h h h h (MSD) (Hydro & Coal)

57.75 29.25 67.84 14.42 Option D-6 49.8 h Option CEC+ h h h h ENDEAVOUR (Hydro & ENDEAVOUR)

13.49 44.03 84.94 41.59 23.5 h Option CEC+ Option E-6 h h h h ENDEAVOUR (Coal & ENDEAVOUR)

Option E-6 57.75 29.25 102.57 90.23 83.1 h Option CEC+ (Hydro, h h h h ENDEAVOUR Coal & ENDEAVOUR)

8.4.2.4 Cost comparisons

Similarly to paragraph 8.4.1.2, we estimate for each short-term option A, B, C and CEC the maximum average capital cost for hydro units commissioned in the medium-term that would make a long-term option more financially appealing than another one over the 2015-2024 period and after accounting for their residual value.

In this paragraph, the hydro units commissioned in the medium-term include 220 MW in 2020 (Bumbuna II), 66 MW in 2021 (Yiben) and the sequence of additional hydro units presented in Table 8-32 above.

Using this approach and for each short-term option, the Figure 8-11 below shows the maximum average CAPEX for hydro plants commissioned after 2020 such that the option “Hydro only” (Options D-x) remains more attractive than “Hydro + Coal” (Options F-x), and the maximum average CAPEX for hydro plants such that the option “Hydro + Coal” remains more attractive than the “Coal only” option. The Figure 8-11 shows the least cost programme for a given CAPEX for hydro plants commissioned after 2020. The values estimated are conservative and assume that all other assumptions, including demand and fuel price forecasts, OPEX assumptions, and coal CAPEX assumptions are still valid by 2024.

Specifically under this high case demand forecast and if the short-term option “CEC+ENDEAVOUR” is implemented, the “Hydro only” programme is the least cost programme for hydro CAPEX values up to 2,382 USD/kW, above which the “Hydro+Coal” mix is to be preferred. Finally, the “Coal only” option becomes more attractive than the “Hydro+Coal” mix for hydro CAPEX values above 8,423 USD/ kW.

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Figure 8-11: Least cost medium-term programmes under high case demand scenario

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8.4.2.5 Opportunity for gas-fired generation

In the medium-term there would be an opportunity to convert diesel HFO-fired units to gas-fired turbines, if the gas prices including handling and transport are kept below a certain threshold. Converting diesel units to gas-firing would allow increased plant efficiency and creates the opportunity for lower fuel costs.

The Figure 8-12 below shows the least cost medium-term development programme under the high case demand forecast and with the option “CEC+ENDEAVOUR” implemented in the short term, leaving the opportunity for all the units of the CEC Plant (128 MW) to be converted to gas-fired engines operating in combined cycle by 2020. The X-axis shows the maximum average CAPEX for hydro units in USD/kW and the Y-axis shows the maximum average gas price in USD/1000m3 inclusive of handling, transport and potential conversion costs.

Figure 8-12: Least Cost Medium-Term Programmes after short-term option “CEC+ENDEAVOUR” and under high case demand scenario for variable hydro CAPEX and gas prices

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9 Environmental and Social Impact

9.1 Objectives

The objective of this assignment was, as per the Terms of Reference for our study, not to conduct “a formal environmental and social assessment”, but rather to develop a methodology for quantification of the environmental and social costs associated with power generation. This was to include the “review of all available reports” and design a “generic model” that could be used for the preliminary valuation of a variety of environmental and social factors, and fed by “a set of baseline assumptions” to be justified.

The following sub-sections address these objectives, recognizing the lack of environmental and social impact data in Sierra Leone. The baseline assumptions provided have been extracted from studies undertaken in various locations in the world and a mark-up would need to be estimated and applied (or alternatively similarly formal assessments) in order for these assumptions to truly reflect the environmental and social costs in Sierra Leone.

9.2 Model Structure

The model aims at estimating global environmental and social costs associated with the building, the operation and the dismantling of a power plant. They are limited to the impact of the power plant and are exclusive of costs associated with the building and the operation of transmission and distribution infrastructures required to evacuate the energy generated by the plant.

Damage costs represent the indirect cost of social or environmental damage (for example, studies have proved that SOx emissions worsen public health, and therefore increase the annual health expenses, while deforestation impacts biodiversity, timber processing and tourism). Mitigation costs represent the estimated costs of the mitigation plan that would be required to neutralize the adverse impact of the power plant. Whenever possible and for primary order impacts (such as deforestation or water pollution), the model will prefer damage costs to mitigation costs, as they better reflect the real impact of the industrial activity. For secondary order impacts (e.g. climate change due to CO2 emissions) mitigation costs are more appropriate.

We have divided these costs into three chronological categories:

i. Costs of damages related to the building of the power plant

ii. Incremental cost of damages related to the operation of the power plant

iii. Costs of damages related to the dismantling or decommissioning of the power plant

Costs falling under other categories have been excluded from our analysis.

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All costs depend on the type of plant considered. The scope of the model is limited to existing sources of generation in Sierra Leone and plausible alternatives:

- HFO plants

- LFO plants

- Coal plants

- Gas plants

- Small hydropower plants

- Large hydropower plants

Although the user is at liberty to enter any input value into the model, a number of indicative values based on recent studies are provided for guidance purposes. References are indicated throughout the document (e.g. [24]) and relate to the bibliography at the end of the report.

9.3 Building costs

A number of subcategories are considered in the building costs:

- Cost of wildlife and ecosystems destruction in the zone of immediate impact (building site)

- Cost of resettling populations living inside or near the building site

- Cost of resettling livelihood

- Other impacts

9.3.1 Cost of wildife and ecosystems destruction in the zone of immediate impact

9.3.1.1 Building area

The building area (in ha) refers to the building site surface (i.e. land requirements).

Indicative values

In [25] indicative values for different types of power plants are reported:

- 0.1-4.7 ha/MW for coal-fired plants

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- 0.26 ha/MW for gas-fired plants

- 6.6 ha/MW for hydroelectric plants

All values above are purely indicative and are associated with a wide margin of error.

9.3.1.2 Type of building area

Different types of building areas are considered:

- Forest (damage cost relates to deforestation)

- Agricultural (damage cost relates to the commercial value of land)

- Aquatic (damage cost relates to commercial and recreational fishing activities)

- Other (damage cost is considered to be negligible)

9.3.1.3 Deforestation costs

[26] provides guidelines to estimate the cost of deforestation in tropical and non- tropical forests. Specifically damage costs are divided into three categories:

- Direct use (cost of “depriving the industry from primary resources” such as timber of fuel-wood)

- Non-extractive (cost of “depriving the ecosystem from biodiversity and genetic information”, cost of “depriving the service sector from tourism and recreational values”)

- Indirect cost (cost of “depriving the surrounding ecosystems from some environmental benefits provided by forests” such as watershed, carbon storage and sequestration).

Direct use costs include:

- Conventional/sustainable logging costs: the value of the timber is given by the maximum that a concessionaire (buyer) should be willing to pay for the concession (land). Logging is either conventional (more profitable, but less eco-efficient) or sustainable (less profitable, but more eco-efficient)

- Fuel-wood costs: All sources agree that fuel-wood is of major importance for poorer countries and for the poor within those countries. In Sierra Leone, fuel-wood still represents the main energy source in the country.

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- Other non-timber forest products (NTFPs): Other extractive uses include: hunting wild animals for food, taking animals, fish, crustaceans and birds for local or international trade or for subsistence use, and taking tree products such as latex, wild cocoa, honey, gums, nuts, fruits, etc…

Non-extractive costs include:

- Damage cost to genetic information: Species richness “increases from the poles to the equator”. The economic value of this diversity is very challenging to evaluate.

- Damage to the service sector (Recreation and tourism cost): Ecotourism is a growing activity and constitutes “a potentially valuable nonextractive use of tropical forests”.

Indirect costs include:

- Cost of depriving the ecosystem from watershed benefits: Watershed protection functions include: “soil conservation, water flow regulation, water supply, water quality regulation”.

- Cost of depriving the ecosystem from carbon storage and sequestration

- Cost to biodiversity (other than genetics)

The following table, extracted from [26] and based on an analysis made for forests in the USA, reports indicative costs for all of these subcategories:

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Table 9-1: Indicative costs for Forest valuation (in $/ha unless otherwise stated) [21]

9.3.1.4 Agricultural land destruction

Land that is sterile deprives the agricultural industry from part of its revenues. The cost of damage can be defined as the maximum price that a concessionaire (buyer) should be willing to pay for the concession (land). The value of land per surface unit depends on the country’s climate, the share of the agricultural sector in GDP, and the climatic conditions in the specific region have to be considered.

Indicative value:

The value of land in Sierra Leone (independently from the location within the country) has been fixed to 2014 USD 20.2/ha in Sierra Leone 2009 Land Act [27]. But this value is to be compared with some values adopted in Europe, above USD 10,000/ha in Scotland, Wales, England, Italy, Belgium, Northern Ireland, Spain, Denmark and Netherlands, and above USD 1,000/ha for all European countries [28]. A number of articles such as [24] suggest that the value of land in Africa is often underestimated.

9.3.1.5 Destruction of aquatic life

The destruction of aquatic life is quantified in terms of suppressed revenues from commercial and recreational fishing activities, resulting in an “economic

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and environmental damage” Although many journalists and scientist write on the adverse impact of the power industry on aquatic life, there is very little literature on the quantification of the economic and environmental impact associated to it, which is nevertheless a non-negligible share of the environmental impact of building hydroelectric power plants with no mitigation plan to preserve aquatic ecosystems. A number of assumptions need to be made to quantify the cost of destruction of aquatic life (calculated on the whole life plant basis), including:

- The mean market price of fish15

- The average weight of fish sold in markets

- The number of adult fish killed during construction of the power plant

- The natural death derating factor (percentage of fish killed that would have died from natural death anyway)

- The catching rate factor (percentage of fish killed that would have been caught alive by fishermen).

In recent studies such as [29], commercial and recreational benefits are demonstrated to be of the same magnitude. But this is dependent on the country’s tourism activity and the variety of fishes in this particular region. We therefore cannot provide any indicative value for the ratio [recreational costs / commercial costs].

9.3.2 Cost of resettling population living in or near the building site

Displacing populations is sometimes necessary for the construction of a power plant, due to pollution, risk of flood, safety issues or road works requirements. [30] lists the most common social risks related to population resettlement:

- Landlessness: the expropriation of land removes the main foundation upon which people’s productive systems, commercial activities and livelihoods are constructed. This is the principal form of de- capitalization and pauperization of displaced people, as they lose both natural and man-made capital.

- Joblessness: the risk of losing wage employment is very high both in urban and rural displacements for those employed in enterprises, services or agriculture. Yet, creating new jobs is difficult and requires substantial investment. Unemployment or underemployment among

15 This value is estimated to 2014 USD 1.38 from [4]

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resettlers often endures long after physical relocation has been completed.

- Homelessness: loss of shelter tends to be only temporary for many resettlers; but, for some homelessness or a worsening in their housing standards remains a lingering condition.

- Marginalization: marginalization occurs when families lose economic power and spiral on a “downward mobility” path.

- Food insecurity: forced uprooting increases the risk that people will fall into temporary or chronic undernourishment, defined as calorie-protein intake levels below the minimum necessary for normal growth and work.

- Increased morbidity: massive population displacement threatens to cause serious declines in health levels. Displacement-induced social stress and psychological trauma are sometimes accompanied by the outbreak of relocation-related illnesses. Unsafe water supply and improvised sewage systems increase vulnerability to epidemics and chronic diarrhea, dysentery, etc… The weakest segments of the demographic spectrum-infants, children and the elderly- are affected most strongly.

- Loss of access to common property resources: For poor people, loss of access to the common property assets that belonged to relocated communities results in significant deterioration in income and livelihood levels.

- Community disarticulation: Forced displacement tears apart the existing social fabric. The social capital lost through social disarticulation is typically unperceived and uncompensated, and this real loss has long-term consequences.

The cost of resettlement is expressed in terms of subsidy awarded per household resettled, which should take as many of these risks into account.

9.3.3 Cost of relocating fauna and flora

In order to avoid or limit destruction of terrestrial or aquatic fauna and/or flora, plans to relocate some of the most valuable fauna and flora within the “zone of impact” of the power plant have been designed and implemented in the past.

One of the most famous example is the “Operation Noah”, which was a wildlife rescue operation in Rhodesia, lasting from 1958 to 1964, caused by the creation of Lake Kariba by the Kariba Dam. The dedicated website [31] reports that “wildlife was move from the rising waters and largely relocated to Matusadona National Park and around Lake Kariba”. In total, “over 6,000 animals

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(elephant, antelope, rhino, lion, leopard, zebra, warthog, small birds and even snakes) were rescued”.

No indicative cost can be provided for such category as the cost should be estimated on a case by case basis.

9.3.4 Other costs

At the building stage, other impacts include other social and environmental costs specific to the project, including but not limited to:

 The pollution cost associated with the manufacturing of the plant’s components,

 The social cost of using foreign workforce,

 The impact of project on staff’s health condition

 etc…

9.4 Operating costs

A number of subcategories are considered in the operating costs:

- Air pollution costs including:

o Cost of PM2.5, SOx, NOx and Hg emissions (impact on public health and ecosystems)

o Cost of CO2 emission (greenhouse gas impacting climate change)

- Destruction of aquatic ecosystems (Water Pollution)

- Soil pollution

9.4.1 Power Plant Pollution

9.4.1.1 Environmental impact of PM2.5, SOx, NOx and Hg emissions

Indicatively, it is reminded in [32] that thermal Power Plants are the single largest source of carbon dioxide (CO2, 40%), sulphur dioxide (SO2, 73%) and mercury emissions (Hg, 49%) in the USA. They are also the second largest source of nitrogen oxide emissions (NOx, 24%).

[32] reports that in 2005 in the USA, “fine particulate matter, largely from SO2 and NOx emissions, were attributed to between 130,000 and 320,000 premature deaths, 180,000 non-fatal heart attacks, 200,000 hospital and emergency room

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visits, 2.5 million of asthma exacerbations, and 18 million lost days of work, and other public health effects (…).”

Besides, “in addition to health effects, elevated ozone can cause crop and forest damage (…), contaminates wildlife and fish that people catch and consume, (…); and elevated atmospheric nitrogen can alter the structure and function of terrestrial and aquatic ecosystem.”

The schematic drawing below illustrates that “once emitted from power plants, SO2 and NOx react in the atmosphere to form sulphuric acid, nitric acid, and several secondary pollutants that have a cascade of health and environmental effects” (including fine particulates PM2.516). Similarly “mercury, after it is released in the atmosphere, can change chemical form and depending on its form be deposited in rain, snow, gaseous particles within kilometres from the source or circulate globally.”

Figure 9-1: Thermal plants air emissions [27]

16 Particulate matter, or PM, is the term for particles found in the air, including dust, dirt, soot, smoke, and liquid droplets. Particles can be suspended in the air for long periods of time. (…) Particles less than 10 micrometers in diameter (PM10) pose a health concern because they can be inhaled into and accumulate in the respiratory system. Particles less than 2.5 micrometers in diameter (PM2.5) are referred to as "fine" particles and are believed to pose the greatest health risks. [57]

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The following table from [32] summarises the adverse effects of SO2, NOx and Mercury emissions.

Table 9-2: Effects of SOx, NOx and Mercury [27]

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9.4.1.2 Environmental impact of CO2 emissions

Carbon dioxide (CO2) is the primary greenhouse gas emitted through human activities. In 2012, CO2 accounted for about 82% of all U.S. greenhouse gas emissions from human activities [33].

A finite amount of carbon is stored in fossil fuels, the sea, living matter and the atmosphere.

Without human influence, transfers between these stores roughly balance each other – for example, plants absorb carbon as they grow, but release it as they decay [34].

But when humans cut down trees or burn fossil fuels, they release extra carbon into the atmosphere, increasing the greenhouse effect (the natural process by which the atmosphere traps some of the Sun's energy, warming the Earth enough to support life).

The combustion of fossil fuels to generate electricity is the largest single source of CO2 emissions in the nation, accounting for about 38% of total U.S. CO2 emissions and 31% of total U.S. greenhouse gas emissions in 2012 [33].

9.4.1.3 Air Pollution: Average emission rates per type of plant

The following table shows median emission rates for different types of fossil- fired plants in the US (based on statistics in [35]) and for hydro plants (based on [36] and [37]). These are reference values, but they are a function of the efficiency of the plant. A mark-up should be applied for African power plants recognizing that their efficiency is generally lower than in the USA.

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Table 9-3: Average emission rates per type of plant Average Average Average Average Average Primary SOx NOx Hg CO2 PM2.5 Source emissions emissions emissions emissions emissions (kg/MWh) (kg/MWh) (kg/GWh) (kg/MWh) (kg/MWh)

4.03 1.73 0.010 1002.91 0.15 Coal Based on 608 Based on 608 Based on 462 Based on 609 Based on 416 plants plants plants plants plants

0.010 0.45 0.00 555.92 0.040 Natural Gas Based on Based on Based on 165 Based on Based on 660 1415 plants 1447 plants plants 1453 plants plants

0.93 8.99 0.00 793.99 0.26 Oil Based on 780 Based on 779 Based on 104 Based on 779 Based on 243 plants plants plants plants plants

Small-scale negligible negligible negligible 8 [37] negligible Hydroelectric

Large-scale negligible negligible negligible 20 [36] negligible Hydroelectric

9.4.1.4 Damage costs

The following table produces estimates for the damage costs associated with PM2.5, SOx, NOx and Hg emissions. These estimates have been produced for and by the European Union and therefore reflect the damage costs in Europe. Extensive studies outside the scope of our present work should be conducted in Sierra Leone to refine these costs and make them reflective of the fundamentally different situation in the country, including differences in wages, health quality and cost, standard of living, and morbidity.

PM2.5 cost estimates from [38] take into account a number of factors including:

- Chronic effects on mortality (adults and infants), morbidity (bronchitis)

- Acute effects on morbidity (including respiratory hospital admissions, cardiac hospital admissions, consultations with primary care physicians, restricted activity days, use of respiratory medication, symptom days)

SOx and NOx cost estimates from [38] take into account a number of factors including:

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- Acute effects on mortality and morbidity (including respiratory hospital admissions, minor restricted activity days, use of respiratory medication, symptom days)

- Exposure of crops to ozone

Hg cost estimates from [39] take into account a number of factors including:

- Atmospheric transport, emission deposition and potential risks for ecosystems

- Human health impacts

- Cost of damages to individuals’ IQ

Table 9-4: Indicative damage costs for several pollutants SOx17 NOx Hg18 PM2.5

Median Damage cost $ $ 12,642 $ 8,127 $ 61,920 (USD / T) 186,310,000 Base case scenario

9.4.1.5 Social Cost of carbon

In [40] the authors define the social cost of carbon the measure of the “full global cost today of an incremental unit of carbon emitted now, summing the full global cost of the damage it imposes over the whole of its time in the atmosphere. The SCC (…) signals what society should, in theory, be willing to pay now to avoid the future damage caused by incremental carbon emissions”.

The social cost of carbon is generally “associated with (…) a stabilisation goal” quantified in ppm (parts per million: atmospheric concentration of all

17 Results for SOx, NOx, Hg and PM2.5 are based on a European study and for a specific set of assumptions. These values would need to be refined for Sierra Leone specifically.

18 While the damage cost for mercury is substantially higher than for the other pollutants, the emission rates of mercury as per “Table 9-3: Average emission rates per type of plant” are low relatively to CO2, SOx, NOx and PM2.5.

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greenhouse gases recalculated into CO2 equivalents). There is a broad agreement on fixing this goal for the world between 450 and 550pm CO2e.

The Stern Review [41] calculates that the 550pm target implies a social cost of carbon of $30/tCO2e in 2000, equivalent to $41.4 in 2014 real terms.

9.4.2 Destruction of aquatic ecosystems

In thermoelectric plants, water is brought to boiling point to produce high- pressure steam that turns turbines, producing electricity. The steam is then cooled by a condenser which typically uses water drawn in from a nearby lake, river, estuary, sea or a cooling tower.

There are three major types of cooling systems [42]:

- The oldest one is called “once-through cooling system” (see Figure 9-2): in a once-through cooling system the cooling water is not reused, instead it is discharged at a higher temperature back into the water body from which it was withdrawn. To continue cooling steam, the power plant must constantly withdraw enormous amounts of new water, injuring and killing fish and other aquatic organisms in the process.

- Wet-closed cycle cooling system (see Figure 9-3), where water is first circulated through the plant to absorb heat, and then moved through the cooling towers to release heat to the atmosphere, primarily through evaporation. The condensed water is then recirculated through the plant. This reduces power plant water intake by approximately 95 to 98 percent, thereby reducing the destruction of aquatic life by 95 to 98 percent.

- Dry-closed cycle cooling system, using air flow, rather than the evaporation of water, to transfer heat from the power plant.

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Figure 9-2: Once-through cooling system [37]

Figure 9-3: Wet closed-cycle cooling system [37]

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While the social and environmental cost of closed cycle systems is negligible, the damage can be substantial with once-through systems.

The article listed as [43] in the Bibliography focuses on the major thermoelectric plants in the USA using once-through cooling systems. The intake capacity reported ranges from 2 to 8 million of L/day/MW installed. Depending on the intake capacity, the number of eggs and larvae killed every year ranges from 30 million (1135 MW Oak Creek Coal-fired Power plant on Lake Michigan) to 12 billion (499MW Bayshore coal-fired plant in Ohio). This figure depends on the cooling system configuration and aquatic life density and variety.

We propose the same approach as in paragraph 9.3.1.5 to estimate the associated cost of destructing aquatic life during the cooling process. Consequently, a number of assumptions need to be made, including:

- The mean market price of fish19

- The average weight of fish sold in markets

- The number of eggs and larvae killed during the power plant building

- The natural death derating factor (percentage of fish killed that would have died from natural death anyway before growing to adult size)

- The catching rate factor (percentage of fish killed that would have been caught alive by fishermen when grown to adult size).

We also need to make an assumption on the ratio the ratio [recreational costs / commercial costs].

9.4.3 Soil pollution

Soil pollution results from the build-up of contaminants, ash, toxic compounds, radioactive materials, salts, chemicals and cancer-causing agents. The most common soil pollutants are hydrocarbons, heavy metals, herbicides, pesticides, oils, tars, and dioxins.

Some power plants store their refuse underground, releasing heavy metals into the soil of the power plant building site, and surrounding villages. These tend to include, according to [44] and [45] the following:

- Lead - Chromium

19 This value is estimated to 2014 USD 1.38 from [4]

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- Cadmium - Iron - Copper - Zink

The article [45] focuses on the impact of the 850MW Jamshoro Thermal Power Plant in Pakistan on the concentration of heavy metals in soil in the vicinity of the plant at the depth of 9 and 18 inches. After the plant’s commissioning, it is estimated that Cu and Fe concentrations have been multiplied by 10. In [44], it is observed that concentration of Zn, Cu and Cd reach a critical level around the 100MW Turceni’s Thermal Station in Romania.

When the capacity of the soil to retain heavy metals is reduced due to repeated use of wastewater, soil can release heavy metals into groundwater or soil solution available for plant uptake. [46]

Once transferred to the water supply these metals, in different proportions, can be poisoning and harmful for the weakest segments of the demographic spectrum (infants, children and the elderly) [47] [48].

Heavy metals can also contaminate the food chain and reduce crop yields. [46]

The damage cost is highly dependent on the heavy metals released in the soil, the soil’s initial composition, the type of land impacted (infertile land, forest, agricultural land), population density and age composition, and conditions of water contamination.

For this model we propose a generic approach, where depending on the type of land surrounding the power plant we have to assume:

- The type of land above soil contaminated - The area of land above soil contaminated - The value of land / ha (see value of Forest or Agricultural land in previous chapters) - The annual derating factor (how much the value of land decreases every year compared to its initial value) - A lump sum to cover the cost of the damage of water contamination on public health

These have been kept as manual inputs, and we cannot provide a set of baseline assumptions here as geology studies would need to be conducted in order to assess these, and literature suggests that these parameters differ greatly from one project to the other.

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9.5 Dismantling costs

Dismantling costs are often neglected or forgotten because the industry has very little feedback on this matter and because this is often considered to be “out of the scope” of the project. Nevertheless, provisional social and environmental costs should be allocated for:

- Waste Disposal (mitigation and/or damage costs of additional soil pollution)

- Recycling of plants (pollution associated with the recycling of the plant’s components, if not re-used)

Indicatively a recent EU-funded study [49] has been conducted to support and provide cost estimates for the dismantling of the 345MW Kosovo-A Thermal Plant, due to poor condition and alarming air pollution levels. The plant dismantling and site clearing is spread over 10 years, including 5 years between the decommissioning of the last unit and the end of the clearing process. The total cost of decommissioning and dismantling the plant in order to reach an acceptable level of decontamination has been estimated at “roughly” 2010 EUR 28,400,000 (appx. 2014 USD 38,764). But the total cost, including social impact, environmental impact and handwork reaches 2010 EUR 437.7 million (appx. 2014 USD 600 million).

9.6 Using the model

Guidelines on how to use the model are provided in Appendix D.

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10 Implementation Programme and Overall Recommendations

10.1 Implementation

In the preparation of this report it has been necessary to make a number of assumptions which will have a material impact on the planning of the development of the power sector in Sierra Leone. It is, nonetheless, clear that there are a number of issues which require action in the short term. This section of the report summarises these issues.

10.2 Rehabilitation of the distribution network in Freetown

The distribution network in Freetown is in urgent need of rehabilitation in order to address the current overloading of transformers and distribution lines, and therefore reduce technical losses. The PPA Energy report of April 200420 detailed measures which could be adopted in this regard. At this time, as now, work was outstanding on the completion of the 33 kV sub-transmission network, and this was seen to be a key factor in the reduction of the demand of the 11 kV system.

On the basis of a limited survey of Freetown undertaken at the time, the low voltage network was considered to be characterised by long feeders, unbalanced loads, sub-optimal conductor selection, inadequate protection, high losses and low voltages. An optimal maximum length for LV feeders is generally considered to be 400m, while a feeder of 1.7km was encountered during the survey.

 Appointment of a high level official within EDSA with responsibility for reduction in non technical losses;

 Enactment of legislation if necessary to empower EDSA to adopt legal measures for recovery of debts / criminal prosecutions;

 Disconnection of all customers with illegal connection / in arrears;

 Prosecution of worst cases of non payment;

20 “Rehabilition and Reinforcement of the Western Area Sub-Transmission and Distristribution”, Power Planning Associates Ltd (PPA Energy) , April 2004 for NPA

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 Creation of “task force” backed by police to enforce disconnection of illegal customers in areas where particular problems are experienced;

 Installation of tamper proof smart meters with remote reading for largest customers

10.3 Installation of additional diesel generation in Freetown

As indicated in Section 8 of this report, there is a requirement for up to 200 MW of new diesel generation capacity over the period to 2020. Of this, 128 MW will be provided through the contract which it is understood has been agreed with CEC.

10.4 Construction of new double circuit 225kV transmission line between Bumbuna and Freetown

In order to ensure that optimal use is made of the WAPP interconnector and in particular its role in the development of a national transmission grid in Sierra Leone, it is important that the electrical connection between the Bumbuna substation and Freetown is replaced. In this report it has been recommended that a double circuit 225 kV line be built, which can then allow the existing 161 kV line to be dedicated to the supply of electricity to intermediate areas of the country.

It is important that the construction of this new transmission line should be decoupled from the proposed extension to the Bumbuna hydro power station, the commissioning date for which currently remains uncertain.

It is therefore recommended that discussions are held as soon as possible with potential aid donors who might fund the costs associated with the construction of the transmission line and associated substations at Bumbuna and in Freetown.

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10.5 Establishment of Planning Unit within MoE

It is suggested that a planning unit should be established in Sierra Leone to take forward the future development of the power sector in the country. This unit would be responsible for regular updates of the IRP to reflect increased visibility of, and accuracy as to, future electricity demand by both the public and private sectors. Updates should also reflect progress in achieving loss reduction targets and realistic commissioning programmes for committed generation and transmission projects.

Until the new regulatory body is fully operational, it is proposed that the planning unit should be located within the Ministry of Energy. The unit should have responsibility for the following tasks:

 Updating projections for electricity demand across the country based on actual sales and estimated levels of unserved energy;

 Evalating requirements for new generation capacity (both in respect of timing and capacity), including recommendation as to the proposed technology;

 Evaluating the timing for the extension of the transmission network, including the connection of provincial headquarter towns;

 Technical studies on the transmission system to evaluate the optimal configuration and parameters for expansion;

The unit should have the ability to second technical resource from the Electricity Generation and Transmission Company (EGTC) as well as from the Electricity Distribution and Supply Authority (EDSA).

10.6 Hydrology in Sierra Leone

The Ministry of Water Resources is understood to have launched a programme for the installation of of river gauging stations in Sierra Leone, as shown in Figure 10-1. The rationale for the selection is twofold. A number of geographical locations have been selected on the basis of a study undertaken by HYDROCHINA. Other locations have been selected based on historical sites of an Onchocerciasis (river blindness) programme that was implemented by World Health Organisation (WHO). It is understood that none of the original stations are remaining and that data records are very limited.

In order to assess the energy generation from hydro power stations it is essential that such hydrological data is available, ideally over a long term period to ensure that the variation of flow, both inter as well as intra annually are understood. Such data allows an assessment to be made of the firm flow in the river (normally that which is observed with a 95% reliability), which in turn can be used to estimate the firm energy which could be generated.

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Hydrological data is also necessary to assess maximum flows which might occur, and therefore the design for the spillway.

Figure 10-1 Location of Gauging Stations

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11 Bibliography

[1] Tennesse Valley Authority, “Coal Fired Plant Diagram - Media Downloads”.

[2] IEA coal, “Life extension of coal-fired plants,” IEA CLEAN COAL CENTRE, 2005.

[3] US Department of Energy, “Diesel Engine,” 2003.

[4] Wartsila, “Combustion Engines,” [Online].

[5] EDF Energy, “How electricity is generated through gas,” [Online]. Available: http://www.edfenergy.com/energyfuture/.

[6] R. E. Sonntag, Introduction to Engineering Thermodynamics, 2007.

[7] AD.D. RAO, University of California, USA, Natural gas-fired combined cycle systems, 2012.

[8] Renewable Energy World, “Solar Thermal Power Plants - Technology Fundamentals,” 2003.

[9] Harlan Bengston, “Concentrating Solar Power Technologies,” Bright Hub.

[10] US Energy Information Administration (EIA), “Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants,” 2013.

[11] Generator Technologies, “Benefits and challenges of a grid coupled wound rotor synchronous generator in a wind turbine application”.

[12] Philip L., Induction Machines - Alternating-Current Generators and Motors, 1949.

[13] Vattenfall Wind Power, Denmark. Photography from Christian Steiness., [Online].

[14] European Topic Centre, “Wind-energy potential in Europe 2020-2030,” 2008.

[15] US Energy Information Administration, “Annual Energy Outlook 2014 - April 2014 release,” 2014.

[16] Sproule - Worldwide Petroleum Consultants, “Constant Forecast - November 2014 Release”.

Ministry of Energy 140 January 2015 20414

[17] PortAndCorridor.org, “Development of Mozambique Railways and Port System,” February 2013. [Online]. Available: http://portandcorridor.org/wp- content/uploads/2013/04/Development-of-Mozambique-Railways-and-Port- Systems_-February-2013.pdf.

[18] J. B.-J. Neil Pinto, “Demand and Netback Values for Gas in Electricity,” World Bank Technical Paper 106, 1989.

[19] Energy Policy Adviser - Ministry of Energy and Water Resources of Sierra Leone, “Potential Solar Energy Applications in Sierra Leone”.

[20] GIZ (Germany), “Country Chapter: Sierra Leone”.

[21] Lahmeyer International, “Power Sector Masterplan Sierra Leone,” 1994.

[22] TRACTEBEL Engineering, “Update of the ECOWAS Revised Masterplan for the Generation and Transmission of Electrical Energy - VOL. 2,” 2011.

[23] The Barbados Light & Power Company Limited, “2012 Integrated Resource Plan,” 2013.

[24] The Guardian, “The land rush doesn't have to end in a poor deal for Africans,” 16/08/2010.

[25] W.K.POKALE, “EFFECTS OF THERMAL POWER PLANT ON ENVIRONMENT,” Shri Saraswati College of Social Work, India, 2012.

[26] D. W. Pearce, “The Economic Value of Forest Ecosystems,” CSERGE-Economics, University College London, 2001.

[27] GoSL, “The Provinces Land Act Cap 122,” 2009.

[28] S. KEITH, “Land Markets & land structures across Europe,” 5th International LANDNET Workshop, 2013.

[29] ASA Analysis & Communications Inc., “Estimation of potential economic benefits of cooling tower installation at the Diablo Canyon Power Plant,” Prepared for Pacific Gas & Electric Company, USA, 2003.

[30] M. M. Cernea, “Impoverishment Risks, Risk Management, and Reconstruction,” George Washington University, USA, 2000.

[31] “Operation Noah Website,” [Online]. Available: http://operationnoah.blogspot.co.uk/.

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[32] Harvard School of Public Health, “Co-benefits of Carbon Standards,” 2014.

[33] United States Environmental Protection Agency (EPA), “Overview of Greenhouse Gases,” 2014. [Online]. Available: http://www.epa.gov/climatechange/ghgemissions/gases/co2.html.

[34] BBC News, “Guide to Climate Change,” [Online]. Available: http://news.bbc.co.uk/1/shared/spl/hi/sci_nat/04/climate_change/html/greenhouse.stm.

[35] Commission for Environmental Cooperation of North America, “North American Power Plants Air Emissions,” 2004.

[36] EDF Energy, “Does Hydropower have a carbon footprint ?,” EDF Energy, 2014. [Online]. Available: http://www.edfenergy.com/energyfuture/energy-gap-climatechange/hydro-marine-and-the-energy-gap-climate-change.

[37] IPCC, “IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation,” 2011.

[38] AEA Technology Environment, “Damages per tonne emission of PM2.5, NH3, SO2, NOx and VOCs from each EU25 Member State and surrounding seas,” 2005.

[39] Norwegian Institute for Air Research, Norway, “Socio-economic costs of continuing the status-quo of mercury pollution,” 2008.

[40] DEFRA, “The Social Cost of Carbon and the Shadow Price of Carbon,” 2007.

[41] N. Stern, “Stern Review,” Grantham Research Institute on Climate Change and the Environment, 2006.

[42] GRACE Communications Foundation, “Power Plants Kill Fish,” [Online]. Available: http://gracelinks.org/211/power-plants-kill-fish-an-introduction.

[43] Sierra Club, “Giant Fish Blenders: How Power Plants Kill Fish & Damage Our Waterways,” 2011.

[44] G. Lazar, “Evaluation of the Heavy Metals Content in Soil Around a Thermal Station,” Environmental Protection Agency, Romania.

[45] A. Rind, “Impacts of Jamshoro Thermal Power Station on Soil of the surrounding area,” Center for Environmental Sciences, University of Sindh, Pakistan, 2008.

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[46] A. Chopra, “Scenario of heavy metal contamination in agricultural soil and its management,” Department of Zoology and Environmental Science, India, 2009.

[47] Philip J. Landrigan, “Environmental Pollutants and Disease in American Children,” The Center for Children's Health and the Environment, 2002.

[48] United States Environmental Protection Agency (EPA), “Lead in Soil: Why it it a problem ?”.

[49] EVONIK Industries, “Study for Decommissioning of Kosovo-A Power Plant,” EU- funded project, 2010.

[50] Duke Energy, “Generating Electricity,” [Online]. Available: http://www.duke- energy.com.

[51] Solar Server, [Online]. Available: http://www.solarserver.com/.

[52] Meteotest, “Meteonorm.com,” [Online].

[53] Wind Atlas, “Windatlas.dk,” [Online].

[54] K. Sundseth, “Economic benefits from decreased mercury emissions,” Norwegian Institute for Air Research, 2009.

[55] S. Sankoh, “Fish markets in Sierra Leone: Size, structure, distribution networks and opportunities for aquaculture development,” Institute of Aquaculture, University of Stirling. Stirling, Scotland, 2007.

[56] Union of Concerned Scientists, “Environmental Impacts of Hydroelectric Power,” 2014. [Online]. Available: http://www.ucsusa.org/clean_energy/our-energy- choices/renewable-energy/environmental-impacts-hydroelectric-power.html#bf-toc-0.

[57] United States Environmental Protection Agency, “Fine Particle Designations,” [Online].

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A APPENDIX A – DEMAND FORECAST

A.1 Introduction

A.1.1 General

The majority of electricity demand in Sierra Lone is in the Western Metropolitan region of Freetown. The rural electricity demand mainly comes from some district towns and newly developed load centres. The majority of industrial demand comes from the mining load located in different parts of the country.

The map in Figure A-1 shows the major town centres of the country:

Figure A-1: Sierra Leone Map Showing Major Town Centres

For the purpose of analysis this demand forecast report, the total demand of the country is divided between that in Freetown, and other areas. The other areas, which include the Provincial Headquarter towns, have been termed rural in the context of this report. In view of the importance of the Mining sector, this demand is considered separately. The second chapter of this report provides the details of the Freetown electricity demand, the third chapter provides details of the rural electricity demand and the fourth chapter provides the details of the mining demand in the country. The final chapter then provides the overall demand of the country.

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Appendix B summarises the details of the social tariff design and willingness to pay survey results. The outcomes of this study are used in the rural demand forecast.

Electricity demand forecasting is the process of determining the future peak demand (MW) and energy requirements (GWh) in the future. Forecasting enables decision makers to answer the question “How much electricity will be required in Sierra Leone and when?” Demand forecasting is thus the starting point of the electricity utility’s planning cycle.

Demands forecasts are required over different time horizons. A forecast covering the next 24 hour period is used to determine the anticipated hourly operating regime of the different generating plants on the system. Forecasting over a period of one week may be used to consider the operation of hydro plant with limited storage, such as to minimise water spill and thermal supplementation. Forecasts covering a period of months are used to optimise the timing of generation plant maintenance, and the output of hydro plant with seasonal storage capabilities. The forecast presented in this report covers the long term, years and decades, and is used for the future planning of new generation and transmission projects. Inevitably, as the timescale for forecasts increase, the level of uncertainties also rises. A forecast used for generation and transmission planning, therefore, is subject to a high level of uncertainty. This is normally addressed by the consideration of a range of scenarios, each used to develop a separate forecast, thereby producing an envelope within which future demand might be expected to lie.

For many years electricity demand in Sierra Leone has been severely restricted by supply side constraints, and it is difficult to accurately assess the true level of actual demand in the country. The situation is made more complex by the high level of losses and ambiguities in data. The initial challenge, therefore, was to determine the electricity requirements which would have been present given a more reliable power system.

Electricity demand can be measured at a number of different points on the system. The output of generating plants is metered, but some of this is used to supply auxiliary equipment necessary for the operation of the plant, and therefore energy sent out is also metered at the outgoing substation.

System losses will be incurred on the transmission and distribution network. These losses are made up of technical and non-technical elements. Technical loss can be defined as the energy consumed by all of the transmission and distribution hardware necessary to transmit power from the power station to the consumer’s premises. Non-technical losses result from faulty or inaccurate metering and from theft or collusion between customers and utility staff. It should be noted that financial losses, resulting from the non-collection of bills, are excluded from this consideration.

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Sales to end customers, therefore, are equal to energy sent out from the power stations less total system losses.

The traditional approach to demand forecasting is to consider future sales to each separate tariff category. The sum of these values, plus losses, equates to the required energy to be sent out by the generating plants. Maximum demand projections are developed by consideration of the demand patterns for the different customer groups, again adjusted for losses.

As specified in the Terms of Reference, this report draws upon other demand forecasts which were developed in the past. A power sector study was undertaken by PPA Energy in 2009 for the National Commission for Privatisation, which included a detailed review of demand projections for the Western Area. The report, which has been termed the PPA Energy 2009 report, was used to assist in the assessment of supressed demand.

A.1.1 Demand Forecast Methods

The demand forecasting usually adopts two alternative methodologies: (a) Econometric analysis and (b) End user approach.

A.1.1.1 Econometric Analysis

Econometric analysis is application of statistical methods on historical data to determine the expected response from electricity consumers to changes in major economic indicators such as GDP, inflation, imports, exports, employment, population, weather, retail sales, agriculture activities, production of energy intensive industries.

Such an approach required accurate historic data, and assumes that any econometric relationship which was valid in the past will continue to be appropriate in the future, and there is thus no structural change to the economy.

A.1.1.2 End User Analysis

The End-Use method predicts electricity demand by using extensive information about end users. In this method detailed information is collected about the location of current customers, projected location of future customers, customer occupation, age, size of houses, size of factories, production targets, expansion plans and so on.

A.1.1.3 Use of Different Demand Forecast Methods

The demand forecast for Freetown was based on econometric analysis, whilst that for other areas was developed from consideration of the potential consumption of end users, number of households, willingness to pay for energy and possible electrification rates.

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In view of the magnitude of the mining loads, separate projections of demand were developed for each mining company.

A.2 Installed Capacity in the Western Area

A.2.1 Background

As already noted, electricity sales in Sierra Leone are constrained by supply side limitations. At present three separate power stations supply the Western Area as follows:

A.2.2 Bumbuna Hydro Power Station

The Bumbuna Hydroelectric Project (BHP) is located on the river Seli about 200 km northwest of Freetown. It consists of an asphalt-faced rock-fill dam 88 metres high, two multipurpose tunnels and a powerhouse at the toe of the dam with a design capacity of 50 MW. The BHP is the first stage of a five-stage programme of 275 MW ultimate capacity. A single-circuit 161 kV transmission line transfers power from Bumbuna to Freetown and intermediate towns. A 161 kV/33 kV substation in Freetown links Bumbuna with the 33 kV sub- transmission system, which is owned and operated by NPA. The project was substantially complete (85%), when work was suspended in May 1997 due to the conflict situation in Sierra Leone. The Government of Sierra Leone (GoSL), through the BHP Project Implementation Unit (PIU) of the Ministry of Energy and Power planned that work would recommence on the Project at the beginning of the dry season commencing 1st December 2004, with commissioning expected approximately 20 months later. Power first started to flow from the power station in December 2009.

Figure A-2 below shows the monthly variation in energy supplied to Freetown from BHP. A marked seasonality in output is clearly evident, though some distortion is present due to operational problems at the power station.

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Figure A-2 – Monthly Energy Supply from BHP to Freetown

20 18 16 14 12 10 8 6 4

Bumbuna Power Station (GWh) Station Power Bumbuna 2 Energy Supplied to Freetown from from Freetown to Supplied Energy

Dec Dec Dec Dec Dec

Sept Sept Sept Sept

June June June June

March March March March 2009 2010 2011 2012 2013

Table A-1 gives details of the annual supply of energy from BHP since commissioning.

Table A-1 – Annual Energy Supply from BHP to Freetown

Year Energy supplied to Freetown in GWh 2010 136.24 2011 156.57 2012 120.20

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2013 102.08

The April 1994 Project Summary Report by Studio Pietrangeli presents the energy production characteristics used to design the BHP project. Studio Pietrangeli defined the dry season as January to May, inclusive, though it is noted that this is not observed from the actual outputs of the station.

The following table (Table A-2) reproduces the average and firm energy outputs, on a monthly basis, from the April 1994 Studio Pietrangeli report. We refer to “firm capacity” as the amount of energy which can be guaranteed to be available at a given time.

Table A-2: Bumbuna Monthly Energy Production (MW daily equivalent)

Month J F M A M J J A S O N D

Average 21 18 18 18 18 41 47 52 53 53 52 42

Firm 18 18 18 18 18 28 34 49 53 53 52 30

The values in Table A-12 show the equivalent constant power output (in megawatts) for each month, with the exception of April, when 3 days of scheduled outage is assumed at the end of the month. The power and energy values, tabulated in Table A-2, were obtained by Studio Pietrangeli using the Sequential Streamflow Routing Method, based on the reconstruction of 58 years of monthly streamflow. A routing time interval of 1 day was adopted and the daily flows were obtained by uniform subdivision of the monthly flows. The “firm” values are at the 95% exceedance level.

Using the above data, it was possible to develop overall design assumptions for the power station as shown in Table A-3.

Table A-3: BHP’s Seasonal Characteristics

Dry Season Wet Season Total Average Energy (GWh) 66.1 249.5 315.6 Firm Energy (GWh) 64.7 220.3

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Dry Season Wet Season Total Dependable Capacity 43.3 49.8 (MW)21

The average and firm values in Table A-3 were developed simply by multiplying the corresponding values in Table A-2 by the number of hours in each month.

The “dependable capacity” values are also required for simulation modelling purposes. The value of installed capacity of the scheme (nominally 50 MW) is of limited use for purposes of simulation modelling, since during large parts of the year the reservoir will be drawn-down and unable to generate at the rated output. The values indicated on the table are based on a rating curve produced for the Consultant by Studio Pietrangeli, together with average reservoir levels from the simulation studies undertaken by Pietrangeli as part of the April 1994 report. The average reservoir level of 225.8 metres in May defines the dry season minimum output and the level of 232.2 metres, the average level in June/July defines the wet season.

The data in Table A-4 needs to be modified to take account of own consumption and transmission losses. Ordinarily, transmission losses would be incorporated into the demand forecast. However, since the 161 kV transmission line will, for several years, be dedicated to the evacuation of power from the Bumbuna project, we allocate all losses on this line to this plant.

Computations of transmission losses, for each of the hydrological seasons, are documented in Table A-4, below.

Table A-4: Transmission Line Loss Calculations

Wet Season Dry Season Characteristics of the Length: 203 km transmission line Impedance: 0.1825 Ohms/km Power: 49.8 MW Power: 43.1 MW Average power Power Factor: 0.90 Power Factor: 0.90 characteristics at output Voltage: 161 kV Voltage: 161 kV of the power station Current per phase: 198 A Current per phase: 172 A Losses at peak power 4.37 MW 3.28 MW Proportional losses (compared to power 8.8% 7.6% output)

21 Dependable Capacity takes account of seasonal fluctuations in reservoir level, together with the fact that station output decreases with reservoir level.

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Duration of season 7 months 5 months Energy output (whole 248 GWh 68 GWh season) Load Factor 98% 43% Loss Load Factor 93% 19% Energy Losses 21.3 GWh 2.2 GWh Losses (% of total energy 8.6% 3.3% output)

BHP’s seasonal characteristics in Table A-2 can be modified using the loss factors in Table A-4, to take account of the own consumption and transmission losses, and the resulting output is summarised in the following table (Table A-5).

Table A-5: BHP’s Seasonal Characteristics, Net of Losses

Dry Season Wet Season Total Average Energy (GWh) 63.7 227.0 290.7 Firm Energy (GWh) 62.3 197.9 Dependable Capacity (MW)22 39.8 45.2

It is noteworthy that the actual annual energy delivered to Freetown has varied between 41% and 54% of the value shown in Table A-5. Part of the difference is due to energy supplied to Makeni, through energisation of the 34.5kV shield wire, though data on these values is uncertain. However, it is likely that the most significant factor in the difference between the values in Table A-5 and the actual energy delivered to Freetown is due to outages and operational problems at BHP.

A.2.3 Blackhall Road (BHR) Power Station

The BHR Power Station contains two Wartsila V32 diesel HFO fired units rated at 8.28 MW. The units are capable of conversion to natural gas firing. At the time of the initial site visit in June 2013, one of the units was undergoing

22 Dependable Capacity takes account of seasonal fluctuations in reservoir, together with the fact that station output decreases with reservoir level.

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maintenance. The governor of this unit was non-operational and was awaiting repair. In practice the units can produce a maximum output of approximately 7.5 MW. Warranty has now expired, and the units are understood to have registered 5100 operating hours and 5600 operating hours respectively

The units were funded by the Arab Bank for Economic Development in Africa (BADEA) and were commissioned in March 2011. At the time of the 2009 PPA Energy report it was noted that the development was originally to comprise three units with a total capacity of 22.68 MW, but during tender negotiations this was revised. The contract was awarded to Jacobsen Elektro of Norway.

Prior to this, output from BHR has predominantly been from an IPP owned by Income Electrix, who had been awarded a contract for the supply of 25 MW by the then Ministry of Energy and Water Resources.

Although BHR is interconnected to the Kingtom Power Station, the Freetown system had been split into two islands for approximately 9 months due to problems with the 33kV line connecting the BHR / Falconbridge / Kingtom substations.

A.2.4 Kingtom Power Station

The Kingtom Power Station used to house a number of Mirrlees, Sulzer. Mitsubishi and Caterpillar units, but these have now been decommissioned / relocated. A new power house has been built on the same site, funded by JICA, containing two 5 MW Niigata HFO fired diesel units. JICA continue to support this power station, and at the time of the initial site visit one of the units was undergoing scheduled maintenance with support from JICA counterpart staff.

Prior to the commissioning of this power station an IPP operated by Global Trading Group (GTG) was located at the Kingtom site. GTG has been awarded a contract initially funded by the World Bank for the provision of 15 MW firm of emergency generation. The plant comprised a number of skid mounted Caterpillar diesel units operating on light fuel oil, operated by expatriate contractors.

A.2.5 Summary of NPA supply

The overall pattern of electricity of electricity generation since 2008 is shown in Figure A-3 below.

Figure A-3 – Electricity Supplied to Freetown since 2008 (GWh sent out)

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25 Global Trading Group (Kingtom) Other 20 Bumbuna Hydro - NPA Purchases NPA Kingtom (JICA) BHR (BADEA) 15 Income Electrix (BHR)

10 Energy Generation in GWh in Generation Energy 5

Oct Oct Oct Oct Oct Oct

July July July July July July

April April April April April April

Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-08

Annual totals of sent out generation are shown in Table A-6 below.

Table A-6: Sent Out Electricity Generation in Sierra Leone (MWh)

2008 2009* 2010 2011 2012 2013 Kingtom 117064 52428 0 13263 31605 28315 BHR 20975 10769 0 9352 40386 34747 BHP 0 15490 136235 156569 120199 104408 NPA 915 101 38140 0 0 0 Other 0 0 26 0 0 0 Total 138954 78788 174401 179184 192190 167470 * Detailed breakdown of generation by plant only available for 7 months. Total actual energy sent out for whole year was 130,637 MWh.

A.2.6 Captive Generation

In the PPA Energy 2009 report an assessment was made of captive plants operating in Freetown. At this time details of all of the autogeneration plants, irrespective of capacity, had to be declared to NPA, which in turn was responsible for issuing generation licences. These licences had a duration of one year only. It was expected that the total capacity of the autogenerators

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licenced by NPA would increase in line with the decline of NPA’s power outputs, but data collected as shown in Table A-7 does not reflect this, and therefore there is concern as to the veracity of the information. This concern was backed up by reports from companies and individuals encountered during the execution of the report. NPA staff also acknowledged that it was difficult to log all the captive plants and it was assumed that at least 15% by capacity were not registered. This brings the total assumed capacity of the captive plants up to 58.8 MW. Additional assumptions were made concerning the load factor of these captive plants and the split between customer categories (see Table A-8).

Table A-7 - Captive generators registered at NPA

2005 2006 2007 2008 2009 (Q1)

Total captive power capacity - 61.7 35.72 40.67 12.2(**) MW (*)

Residential capacity - MW 2.12 1.47

Commercial / industrial 33.6 39.20 capacity - MW

(*) Data was provided in MVA and a power factor of 0.8 has been assumed to convert this to MW.

(**) This value refers to the renewal of generator licences for Q1 2009 and not to the installed capacity. It was assumed that 85% of the value represents new licences. As the captive plants have to renew their licence every year, it could be assumed that in 2009, about 51.4 MW captive plants should have obtained a generation licence from NPA.

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Table A-8 - Assumptions made for captive plants

% of total Load Available Installed Coincidence installed factor capacity - capacity - factors capacity MW MW

Industrial 65% 0.8 30.59 38.24 0.9

Commercial 30% 0.95 16.76 17.65 1

Residential 5% 0.8 2.35 2.94 0.8

Total 49.7 58.82

A.3 Freetown Demand Forecast

A.3.1 General

This section of the report explains the demand forecast analysis for the Freetown Metropolitan area over the period of 18 years between 2013 and 2030. As noted earlier, PPA Energy previously undertook a demand forecast for the western region in September 2009 and data that was collected during that study has been taken into account in this analysis. This forecast has therefore been built on a review of the assumptions made in previous reports, which have been amended as appropriate to reflect the current situation and future projections for the overall economic development of the country.

In addition, new data has been collected during visits to Freetown associated with this study in order to produce updated demand forecasts reflecting changes which have occurred since 2009.

The demand forecast was prepared based on the following approach:

 Projections of unsuppressed sales by tariff category;

 Projections of future losses, split between technical and non-technical elements. Future projections were developed assuming loss reduction measures are implemented.

 Estimates of peak demand, based on assumed coincidental factors and the expected time of system peak.

This calculation of unconstrained demand is based on sales figures (including non-technical losses which are assumed to be converted into sales) and should not be confused with power station sent out figures which NPA/ IPPs will be required to generate.

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Unconstrained energy demand is difficult to determine and in view of the uncertainties associated with the projections, three different projections of demand were developed. Different assumptions were made to produce base, high and low case scenarios. Unusually these were based on three varying estimates of unsuppressed demand in 2013.

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A.3.2 PPA Energy 2009 Report Assumptions

In the 2009 PPA Energy report, projections of demand were formulated on two alternative bases as follows:

A.3.2.1 Captive power plant capacities

The demand forecast was based on the current unconstrained demand level in the Western area. The total unconstrained demand was assumed to be made up of the following elements:

 The demand served by NPA;

 The demand served by autogenerators;

 Demand which could have been served if the system had been more robust, including energy sales lost due to system outages;

 Unmetered consumption, usually classified as non-technical losses (due to theft and faulty meters) of which it is assumed that a proportion would have been converted to sales; and

 Outstanding applications for supply.

A.3.2.2 Generation and Demand Profiles

Due to the lack of data available, and the resultant high number of assumptions, it was decided to assess the unconstrained demand in 2008 based on a second method to validate the previous results, and to estimate peak demand figures. The analysis used actual data supplied by NPA where possible (total generation, sales by customer category etc.). In this methodology, the unconstrained load per customer category was estimated from demand and generation hourly profiles. Daily generation profiles for Kingtom and Blackhall Road power station were estimated based on annual generation figures published by NPA and an understanding of their current operational regimes. The daily demand profiles of the industrial and commercial customers reflected typical values. The level of supply by each plant to the different end-users were based on geographic location of NPA customers.

The assumptions concerning the captive plants are presented in Section A.2.6. The daily generation profiles for Kingtom and Blackhall Road power station were assumed to remain constant all year long, while the energy demand profiles per customer category may vary between week days and weekend days.

The analysis considered separate demand profiles for each of the tariff categories during the week and at weekends.

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Figure A-4 illustrates the generation profiles of Kingtom and Blackhall Road power stations. During the mission to Sierra Leone, it had not been possible to obtain the hourly information relating to the electricity sent out from the NPA’s power plants. It was therefore necessary to create the generation profiles displayed below based on best understanding of the plant operation, generation capacity and 2008 data related to generation and sales by customer group.

The annual electricity output from the two IPPs was 138.53 GWh, representing a daily generation of 368.8 MWh. The system peak generation capacity of about 21.4 MW is reached in the evening, when both plants are running at full capacity (see

Table A-9). Assumed IPP Operation - Hourly Generation Profile Figure A-4: Hourly generation profile of the IPPs in Freetown 16

Kingtom 14 Blackhall Road 12

8 MW

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time

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Table A-9- NPA generation characteristics

Kingtom Blackhall Road NPA's system

Load Factor Day 99% 80%

Night 73% 50%

Overall 86% 45%

Peak MW Day 15 3.2 18.20

Night 15 6.4 21.40

Overall 15 6.4 21.40

Energy GWh (Annual) Day 70.45 13.55 83.99

Night 42.55 11.68 54.23

Overall 112.99 25.23 138.22

A.3.3 Review of the recent historic sales data

A summary of estimated electricity sales per annum across both credit and prepayment customers, split by tariff category, is shown in Table A-10 below. The table illustrates that the total electricity sales decreased in the period between 2003 and 2007 due to a deterioration in generation capability and increased from then on with the commissioning of emergency plant. Industrial and residential customers are the main users of electricity.

Table A-10: Historical Electricity Sales by Customer Category (MWh)

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Customer 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013* Segment Industrial 27,802 23,061 16,326 6,561 5,952 20,111 22,947 26,448 35,597 38,513

Gov't 4,903 4,405 11,873 10,035 12,642 9,992 12,053

Residential 30,363 23,001 13,557 4,712 3,142 19,538 31,503 52,269 50,588 48,752 Commercial 10,772 7,129 3,469 4,712 3,840 24,871 6,059 8,158 9,646 12,205

Other 37,561 60,427 60,234 60,957

Total 68,937 53,191 33,352 20,888 17,339 76,393 108,105 159,943 166,057 172,480 * Data on credit sales split between consumer groups still awaited from NPA

National GDP has sharply increased since the end of the Civil War (1991 to 2002), which has increased the underlying demand for electricity. This increase, coupled with NPA’s increasing inability to supply electricity, has driven customers who are reliant upon a secure supply to install their own in-house capacity. Hence, it could be said that the underlying demand for electricity is much higher than that indicated by NPA sales.

A.3.4 Industrial Demand

A.3.5.1 Introduction

Figure A-5 below shows the historical sales to industrial customers. From discussions with industrial customers, and observations made during missions to Sierra Leone, it is clear that many industrial customers supplement supply from NPA from in house diesel generators. During previous visits some customers had advised that they took no supply from NPA due to the poor levels of reliability.

Figure A-5: Historical Industrial Sales (GWh) 35

0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

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In the PPA Energy 2009 report, unconstrained sales to the industrial sector in 2008 were estimated to be 126.8 GWh, compared with actual sales in the year of 20.1 GWh. The approach taken to determine this is detailed below:

A.3.5 PPA Energy 2009 Forecast

A.3.5.2 Calculation based on capacity of the captive power plants and the level of electricity sold

 Assessment of the supply by NPA from Blackhall Road and Kingtom power stations:

o Blackhall Road power station: during the day (12 hours) Income Electrix operated 2 x 1.6 MW at a 80% load factor almost exclusively to serve industrial customers. Therefore it is assumed that the station supplied an industrial annual demand of 11,213 MWh before consideration of losses. After allowing for losses this was equivalent to sales of 6,198 MWh.

o Kingtom power station: it was calculated that 13,912 MWh were sold to industrial customers from this station, as the total electricity sales to industrial customers reached 20,110 MWh in 2008. Industrial customers are assumed to be served approximately 70% of the time, and have a load factor of 50%.

o Total NPA’s supply: from the above calculation, NPA was estimated to supply an industrial load of 20,111 MWh per year and a peak demand of 6.1 MW.

 Non-technical losses due to industrial customers were estimated to be about 3,833 MWh annually for a peak load of 0.88 MW, and 100% of these losses should transform into sales if proper remediation solutions are applied.

 Autogeneration when NPA was not available: industrial customers operated autogenerators when NPA failed to meet the demand, which was assumed to be 30% of the time. From the above calculation it was estimated that NPA supplied 6.1 MW of industrial demand. Assuming a load factor for industrial customers of 50%, and a random distribution of outages, this equates to unserved energy due to system failures of 8,018 MWh, which was met from autogenerators.

 Autogeneration run as an alternative to NPA supply: from the interviews with key industrial customers it was understood that some of them own and operate autogenerators which were operated in preference to NPA supply to get a more secure and reliable source of power. This peak demand needed to be added to the peak demand met by NPA, unlike the demand met by the back-up autogenerators described above.

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From Table A-7, it can be seen that the available capacity of industrial captive power plants was estimated to be about 30.59 MW, based on a coincidence factor of 0.9. Of this capacity 6.1 MW were operated only when NPA failed to supply. The remaining 24.49 MW operated at a coincidence factor of 0.9, 12 hours per day. Therefore the peak demand and annual load met by these autogenerators were estimated at 22.04 MW and 96.5 GWh/year respectively.

Data related to the operation of autogeneration plants by some of the largest industrial plants who were interviewed during the mission is shown in Table A-11. Some of these supplied all of their requirements from in-house generation whilst others took supply from NPA when it was available.

Table A-11 - Some industrial captive power plants in Freetown

Company Capacity Power sources MW

Leocem 6.8 100% captive power plant

Shankerdas 2.8 Mixed source: up to 1MW supplied by NPA

Coca Cola Factory 0.72 Mixed source: 40% NPA - 60% Captive plant

Brewery 0.72 Mixed source: 40% NPA - 60% Captive plant

Seaboard 0.52 100% captive power plant

Total industrial demand from the calculations presented above is shown in Table A-12, and was estimated at about 128.49 GWh, of which NPA is only able to supply 23,944 MWh (accounting for the industrial sales and the non-technical losses due to industrial customers).

Table A-12 - Unconstrained industrial demand in Freetown area

Sales MWh/year NPA Supply - Blackhall Road 6,198 NPA Supply - Kingtom 13,912 Total constrained NPA supply 20,111 Non-technical losses 3,833 Autogeneration - backup supply 8,018 Autogeneration - single source of power 96,524 Total unconstrained demand 128,486

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9.3.2.1 Based on the generation and demand profiles

The assumed industrial demand profiles for weekdays and weekend days are shown in Figure A-6 and Figure A-7 respectively. Table A-13 details the estimate of the unconstrained demand for industrial customers.

The unconstrained industrial demand is assessed at 125 GWh for a peak demand of 28.58 MW occurring on a week day at midday. Industrial Demand Profile - Weekday Figure A-6: Hourly Profile: unconstrained industrial demand - weekday 35 Autogen (single source) Autogen Backup 30 Non Technical Losses Total NPA

20 MW 15

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time Figure A-7: Hourly Profile: unconstrained industrial demand - weekend

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Industrial Demand Profile - Weekend

35 Autogen (single source) Autogen Backup 30 Non Technical Losses Total NPA

20 MW 15

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Table A-13 - Unconstrained Time industrial demand (2008)

Weekday Weekend Total

MW MWh pa MW MWh pa MW MWh pa

NPA Supply Blackhall Road 1.41 3,541 1.41 1,417 1.41 4,958

Kingtom 4.04 10,824 4.04 4,329 4.04 15,153

Total 5.46 14,365 5.46 5,746 5.46 20,111

Non-technical losses 0.59 2,738 0.59 1,095 0.59 3,833

Autogeneration Backup 4,310 1,724 6,033

Single Source 22.62 67,930 22.62 27,172 22.62 95,102

Total demand 28.58 89,342 28.58 35,737 28.58 125,079

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A.3.5.2 Current Forecast

The forecast of industrial demand was determined assuming that electricity sales to the industrial sector will maintain a close correlation to industrial GDP. This assumes that the general industrial customer profile does not change over time. Whilst the economy of Sierra Leone is undergoing very substantive changes are present due to the rapid growth in the mining sector, industrial demand in Freetown is not expected to exhibit marked changes and therefore the assumption is considered valid, and electricity sales in Freetown are assumed to be correlated with industrial GDP.

In the PPA Energy 2009 report the average of the two alternative approaches for the projection of industrial demand gave a total sales estimate of 126,790 MWh. According to IMF, the 2008 GDP for Sierra Leone was 6,280 Billion Le (based on 2006 prices). Freetown’s contribution to national GDP in 2008 was 3.58% of the total country’s GDP or 224,815 million Le. The ratio of electricity sales to industrial customers to the industrial sector GDP for Freetown (the electricity intensity factor) is therefore 563.9 kWh/mLe. Based on an assumed industrial sector maximum demand of 28.5 MW, the capacity intensity factor is 0.1269 kW/mLe.

Alternative estimates of electricity intensity were based on the data in the 2009 report. Under the high forecast, supressed industrial demand was estimated to be 600% of the registered industrial sales (including the non-technical losses), giving 2009 sales of 167,607 MWh, and an energy intensity factor of 746 kWh/mLe. Also the peak supressed demand is estimated to be 500 % of the total peak demand supplied by NPA (including non-technical losses). On this basis the high case peak demand for industrial customers in 2009 was computed to be 34.9 MW and this translates to a capacity intensity factor of 0.1554 kW/mLe.

In the low case, suppressed industrial demand is estimated to be 300% of the registered industrial sales (including the non-technical losses). This would give unsuppressed industrial sales in 2009 of 95,755 MWh. Based on these numbers the low case energy intensity factor is 426 kWh/mLe. Also the peak supressed demand is estimated to be 300 % of the total peak demand supplied by NPA (including non-technical losses). On this basis the low case peak demand for industrial customers in 2009 was computed to be 20.9 MW and this translates to a capacity intensity factor of 0.093 kW/mLe.

For the base case it was assumed that industrial GDP of the Freetown area would increase by 6% pa and in high case and low case of 10% pa and 4% pa respectively.

Applying these factors, the unsuppressed industrial energy sales and demand for 2013 are estimated to be as shown in Table A-14:

Table A-14: Unconstrained Industrial Energy Demand for 2013

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Energy Industrial GDP 2013- Unsuppressed Maximum Intensity GDP 2008- millions Industrial Demand Factor 2008 millions Leone Energy Sales imposed by (kWh/mLeGDP) Leone (2006 Prices) in 2013 industrial (2006 Prices) (GWh) customers (MW) Base Case 563.9 224,815 303,310 171.0 38.4 High Case 746.0 224,815 303,310 234.7 48.9 Low Case 426.0 224,815 303,310 126.8 27.7

Based on the above assumptions the three scenarios for industrial demand were created as shown in Table A-15.

Table A-15: Industrial Demand Forecast

Year Base Case High Case Low Case Sales MW Sales MW Sales MW (GWh) (GWh) (GWh 2013 171.0 38 234.7 49 126.8 28 2014 181.3 41 258.1 54 131.8 29 2015 192.2 43 283.9 59 137.1 30 2016 203.7 46 312.3 65 142.6 31 2017 215.9 49 343.6 72 148.3 32 2018 228.9 52 377.9 79 154.2 34 2019 242.6 55 415.7 87 160.4 35 2020 257.2 58 457.3 95 166.8 37 2021 272.6 61 503.0 105 173.5 38 2022 289.0 65 553.3 115 180.4 39 2023 306.3 69 608.7 127 187.7 41 2024 324.7 73 669.5 140 195.2 43 2025 344.2 77 736.5 154 203.0 44 2026 364.8 82 810.1 169 211.1 46 2027 386.7 87 891.1 186 219.5 48

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Year Base Case High Case Low Case Sales MW Sales MW Sales MW (GWh) (GWh) (GWh 2028 410.0 92 980,240 204 228.3 50 2029 434.6 98 1,078.3 225 237.4 52 2030 460.6 104 1,186.1 247 247.0 54

A.3.6 Commercial Demand

A.3.6.1 Introduction

Figure A-8 below shows the historical sales to commercial customers in Western Region. It is understood that most of the commercial customers get the power they require either from NPA and / or from auto generation - which means that when NPA supply fails, they have sufficient back-up to continue with their operations.

Figure A-8: Historical Commercial Sales in GWh

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0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

A.3.6.2 PPA Energy 2009 Report Forecast

Calculation based on capacity of the captive power plants and the level of electricity sold

The basis of the commercial sector forecast was:

 Assessment of the supply by NPA: mainly from Kingtom power station: it was calculated (from NPA statistics) that 36,745 MWh are sold to commercial customers per year. It was estimated that the commercial customers were supplied only 80% of the time; therefore the peak load met by the station is about 10.49 MW.

 Non-technical losses due to commercial customers are estimated to be about 5,749 MWh annually, from which only 75% would transform into sales (4,312 MWh) with a peak demand of 0.98 MW.

 Autogeneration when NPA is not supplying: commercial customers operate autogenerators when NPA fails to meet the demand, which was assumed to be 20% of the time. From the above calculation it has been estimated that NPA currently supplies approximately 10.49 MW of commercial demand. Again, assuming a load factor for commercial customers of 50%, and a random distribution of outages, this equates to unserved energy due to system failures of 9,186 MWh met from autogenerators.

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 Autogeneration run as an alternative to NPA supply: from the interviews with key commercial customers it was understood that some of them own and operate autogenerators which are operated in preference to NPA supply to get a more secure and reliable source of power. This peak demand needs to be added to the peak demand met by NPA, unlike the demand met by the back-up autogenerators described in the previous bullet point.

From Table A-8 it can be seen that the available capacity of commercial captive power plants is about 16.76 MW. It is assumed that these units run with a coincidence factor of 1. From these plants, 10.49 MW are operated as a backup option. The remaining 6.28 MW are estimated to be operated at a coincidence factor of 1, 12 hours per day, and five days per week. Therefore the peak demand and annual load met by these autogenerators are estimated to be 6.28 MW and 19.64 GWh/year.

Total commercial demand: from the calculations presented above is shown in Table A-16, and is estimated to be about 69,885 MWh, of which NPA is only able to supply 41,058 MWh (accounting for the commercial sales and the non- technical losses due to commercial customers).

Table A-16 - Unconstrained commercial demand in Freetown area

Sales MWh/year Total constrained NPA supply 36,745 Non-technical losses 4,312 Autogeneration - backup supply 9,186 Autogeneration - single source of power 19,641 Total unconstrained demand 69,885

Based on the generation and demand profiles

The commercial demand profiles for weekdays and weekend days are shown in Figure A-9 and Figure A-10 respectively. Table A-17 details the estimate of the unconstrained demand for commercial customers.

The unconstrained commercial demand is assessed at 171.6 GWh for a peak demand of 17.29 MW occurring on a week day at 13:00.

Figure A-9: Hourly Profile: unconstrained commercial demand – weekday

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Commerical Demand Profile - Weekday

Autogen (single source) 18 Autogen Backup Non Technical Losses 16 Total NPA

10 MW

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time Figure A-10: Hourly Profile: unconstrainedCommerical Demand commercial Profile demand - Weekend - weekend

Autogen (single source) Autogen Backup 10 Non Technical Losses Total NPA

6 MW

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time

Table A-17 - Unconstrained commercial demand

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Weekday Weekend Total

MW MWh pa MW MWh pa MW MWh pa

NPA Supply Blackhall Road 0.35 793 0.35 317 0.35 1,110

Kingtom 6.35 30,702 3.30 4,932 6.35 35,634

Total 6.41 31,495 3.51 5,249 6.41 36,744

Non-technical losses 0.67 3,080 0.67 1,232 0.67 4,312

Autogeneration Backup 6,299 1,050 7,349

Single Source 10.35 17,270 6.63 5,943 10.35 23,213

Total demand 17.29 58,144 10.67 13,473 17.29 71,618

A.3.6.3 Current Forecast

The derivation of the commercial sector demand follows a similar methodology to that adopted for industrial customers, and is based on application of the electricity intensity factors as determined in the PPA Energy 2009 report. In this the average of the two alternative approaches for the estimate of unsuppressed commercial sales in 2008 was 70,740 MWh.

According to IMF, the 2008 GDP for Sierra Leone was 6,280 Billion Le (based on 2006 prices). The Freetown’s commercial sectors’ contribution to national GDP in 2008 was 43.21 % of the total country’s GDP or 2,713,446 million Le. The electricity intensity factor for the commercial sector was estimated to be 26.07 kWh/mLe for the base case. The maximum demand for commercial customers was estimated to be 17.3 MW, equivalent to a capacity intensity factor of 0.0064 kW/mLe.

In the PPA Energy 2009 report the supressed demand in the high case for commercial customers was estimated to be 140 % of the total sales (including the non-technical losses). This computes to a supress demand of 59,492 MWh and total energy demand of 101,987 MWh, giving a high case energy intensity factor of 37.59 kWh/mLe. Also the peak supressed demand is estimated to be 200 % of the total peak demand supplied by NPA (including non-technical losses). On this basis the high case peak demand for commercial customers is computed to be 22.94 MW, equivalent to a capacity intensity factor of 0.0085 kW/mLe.

The assumed energy supressed demand in the low case for commercial customers was estimated to be 20 % of the total sales (including the non-

Ministry of Energy 171 January 2015 20414

technical losses). This computes to a supressed demand of 8,498 MWh and total energy demand of 50,993 MWh. Based on these numbers the low case energy intensity factor is 18.79 kWh/mLe. Also the peak supressed demand is estimated to be 100 % of the total peak demand supplied by NPA (including non-technical losses). On this basis the low case peak demand for commercial customers is computed to be 11.47 MW and this translates to a capacity intensity factor of 0.0042 kW/mLe.

Applying these values to the assumed commercial sector GDP for 2013 gave results as shown in Table A-18.

Table A-18: Unconstrained Commercial Energy Demand for 2013

Energy Commercial Commercial Commercial Maximum Intensity GDP 2008- GDP 2013- Energy Sales Demand 2013 Factor 2008 millions millions 2013 (GWh) (MW) (kWh/mLeG Leone Leone DP) (2006 Prices) (2006 Prices) Base Case 26.07 2,713,446 3,580,736 93.4 22.7 High Case 37.59 2,713,446 3,580,736 139.7 31.4 Low Case 18.79 2,713,446 3,580,736 66.0 14.8

The demand forecast was done based on expected GDP growth in the country. In the base case it is assumed that the country would have a GDP growth of 6% and in high case and low case of 10% and 4% respectively. The demand forecast also assumes that any increase in the GDP of the country would directly increase the commercial electricity demand in the Freetown Region.

Table A-19 shows the econometric assumptions for projecting commercial electricity demand.

Table A-19: Econometric Assumptions for Commercial Demand

Commercial Demand GDP Growth % Commercial Demand Growth % Base Case 6% 6% High Case 10% 10% Low Case 4% 4% Based on the above assumptions the three scenarios for commercial demand were created as shown in Table A-20:

Table A-20: Commercial Demand Forecast

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Year Base Case High Case Low Case Sales MW Sales MW Sales MW (GWh) (GWh) (GWh) 2013 93.4 23 139.7 31 66.0 15 2014 99.0 24 153.6 35 68.7 15 2015 104.9 26 169.0 38 71.4 16 2016 111.1 27 185.9 42 74.3 17 2017 117.9 29 204.5 46 77.2 17 2018 124.9 30 224.9 51 80.3 18 2019 132.4 32 247.4 56 83.5 19 2020 140.4 34 272.2 61 86.9 20 2021 148.8 36 299.4 67 90.4 20 2022 157.7 38 329.3 74 94.0 21 2023 167.2 41 362.3 81 97.7 22 2024 177.2 43 398.5 90 101.6 23 2025 187.8 46 438.3 99 105.7 24 2026 199.1 49 482.2 108 109.9 25 2027 211.0 51 530.4 119 114.3 26 2028 223.7 55 583.4 131 118.9 27 2029 237.1 58 641.8 144 123.7 28 2030 251.4 61 705.9 159 128.6 29

A.3.7 Domestic Demand

A.3.7.1 Introduction

It is understood that relatively few residential customers have back-up options which are used at time of NPA failure to supply. Therefore for the residential customers, a large proportion of the unconstrained demand is effectively unserved demand.

Figure A-11 below shows the historical sales to domestic customers.

Figure A-11: Historical Domestic Sales in GWh

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0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 *

*: Data on historic domestic sales still awaited from NPA

A.3.7.2 PPA Energy 2009 Report

The unconstrained energy demand is difficult to determine and different levels of unconstrained demands were assumed in the base, high and low case scenarios. Again, the approach followed that of the 2009 PPA Energy report, which is explained below.

 Assessment of the supply by NPA:

o From Blackhall Road power station: at night (during 12 hours) Income Electrix operates 4 x 1.6 MW at a 50% load factor to serve residential customers. Therefore the station supplies a domestic load of 6.4 MW and an annual demand of 14,016 MWh before losses. This is equivalent to sales of 7,746 MWh. It is also understood that the station is not generating enough power to meet the whole demand due to constraints in the supply of diesel fuel.

o From Kingtom power station: it was calculated that 11,792 MWh are sold to residential customers from this station (based on total

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NPA sales of 19,539 MWh). It was assumed that the residential customers were served only 16.67% of the time23.

 The unsuppressed residential demand is estimated to be substantially more the current level, as residential customers only receive supply for 12 hours in each three days. The supply that is made is predominantly in the evening, which corresponds to the time of the residential peak demand. Assuming that peak demand is supplied for 3.5 hours per night, and that the average demand over the remaining 20.5 hours per day is 10% of this, the unconstrained demand would be 3.1 times the current level of supply, or approximately 44,600 MWh.

 Non-technical losses due to residential customers are assessed to be about 28,747 MWh annually (75% of the total non-technical losses), from which only 50% would transform into sales (14,374 MWh) for a peak load of 3.28 MW.

 Autogeneration when NPA is not supplying: only a few residential customers have back-up captive plants. It was assessed that 5% of the 58.82 MW autogeneration capacity was operated by residential customers. This 2.35 MW of generating capacity is assumed to operate with a coincidence factor of 0.8; which means that it serves a peak demand of 1.88 MW, and generates 8,588 MWh as it is operated 83.4% of the time.

 However it is assumed that there is no autogeneration concomitant to NPA supply from residential customers, as they do not require the same quality and reliability of power as industrial and commercial customers.

 Total residential demand: from the calculations presented above it is assessed that the total residential load is about 42.5 GWh. Of which it is understood that NPA was only able to supply 19,539 MWh in 2008 (accounting for the residential sales and the non-technical losses due to industrial customers). However the level of unconstrained demand accounting for the unserved residential load is significantly higher at 58,973 MWh.

23 In most of residential areas, the customers are believed to be supplied only 12 hours every three days.

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Table A-21 - Unconstrained residential demand in Freetown area

Current demand Unconstrained MWh pa demand (including unserved load) MWh pa NPA Supply - Blackhall Road 7,747 NPA Supply - Kingtom 11,792 Total constrained NPA supply 14,374 Estimated unconstrained NPA supply 44,600 Non-technical losses 14,374 14,374 Autogeneration - backup supply(*) 8,588 Total unconstrained demand 42,500 58,973

The residential demand profiles for weekdays and weekend days are displayed in Figure A-12 and Figure A-13.

Table A-22 details the estimate of the unconstrained demand for residential customers. The unconstrained residential demand is assessed at 69.4 GWh for a peak demand of 21.03 MW occurring at 19:00 every day. Domestic Demand Profile - Weekday Figure A-12: Hourly Profile: unconstrained residential demand – weekday 25 Suppressed

Autogen (additional source) 20 Non Technical Losses

Total NPA

15 MW

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time

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Domestic Demand Profile - Weekend Figure A-13: Hourly Profile: unconstrained residential demand - weekend

25 Suppressed

Autogen Backup (additional source) 20 Non Technical Losses Total NPA

MW 10

0 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time

Table A-22 - Unconstrained residential demand

Weekday Weekend Total

MW MWh pa MW MWh pa MW MWh pa

NPA Supply Blackhall Road 3.54 5,626 3.54 2,250 3.54 7,876

Kingtom 1.46 3,084 5.46 8,583 5.46 11,667

Total 4.78 8,710 8.78 10,833 8.78 19,543

Non-technical losses 2.23 10,267 2.23 4,107 2.23 14,374

Autogeneration Backup 9,815 3,926 13,740

Single Source 12.14 18,463 8.14 3,290 12.14 21,754

Total demand 21.03 47,255 10.67 22,156 21.03 69,410

The average of these two alternative estimates gives an unconstrained demand for the residential customers of 64.2 GWh in 2008, equivalent to a supressed demand of 189%. Alternative high and low scenarios were developed assuming

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supressed demands of 220% and 160% respectively. Table A-23 shows the overall assumptions for suppressed energy and peak demands for domestic customers in 2008.

Table A-23: Suppressed Energy Demand (2008) – Domestic Customers

Suppressed Unsuppressed Unsuppressed Energy energy sales contribution Demand in 2008 to peak (GWh) demand (MW) Base Case 189% 64.2 20.98 High Case 200% 74.6 27.60 Low Case 160% 54.3 13.80

A.3.7.3 Current Forecast

The forecast for future domestic sales was based on consideration of the following three groups of domestic customers:

 Mature customers who have been connected for more than 5 years;

 Customers connected for between one and five years; and

 Newly connected customers.

In the case of the former, an econometric approach was used to project the number of customers, assuming the following formula:

nt = nt-1 * (yt * (xt – xt-1)/(xt-1)))

Where:

yt elasticity in year t

xt GDP at constant prices in year t (million Leones)

The elasticity is given by:

yt = 1.85 – xt /20,000

This approach was originally developed by Lahmayer who produced a Power Masterplan in 1995. This report had the benefit of the use of sales data which was not supply side constrained. Since it is not now feasible to develop a new formula, the structure of the Lahmayer formula was adopted, with changes as noted below to some of the values.

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The elasticity value for the period 1970 to 1994 was 2.06, i.e. connected household numbers grew at twice the real GDP growth rate. However, in the same way that Lahmeyer considered this value to be unrealistically high; the value has been halved to just 1.03 in this analysis.

The datum specific unsuppressed consumption in 2008 for established customers was determined to be 1,807 kWh pa. This specific consumption was assumed to be linked by income elasticity, using an assumed factor of 0.6 times the GDP growth rate.

The specific consumption of customers connected over the previous one to five years was assumed to be at a lower level of 1,000 kWh/year, which was again assumed to grow at 0.6 times the GDP growth rate.

Newly electrified customers in each year were assumed to have a constant specific consumption of 300 kWh pa.

The domestic specific demands calculations for domestic customers in 2008 for the base case are shown in Table A-24.

Table A-24: Domestic Specific Demands – Base Case

Domestic Demand Customers Specific Annual Total Demand Base Numbers Demand Base Case (kWh) (2008) Case (kWh) Domestic Customers (Old) 33,148 1,807 59,884,798 Domestic Customers 1,470 1,000 1,470,000 Connected Recently (More than 5 years ago) New Domestic Customers 9,451 300 2,835,202 (Connected less than 5 years ago)

Total 64,190,000

In the case of the high and low scenarios, the specific consumptions for mature customers were estimated to be 2,104 kWh pa and 1,470 kWh pa, giving total consumptions for the sector of 74.6 GWh and 54.3 GWh respectively.

The GDP assumptions used to develop the forecasts of domestic customer numbers are shown in Table A-25

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Table A-25: Econometric Assumptions for Domestic Demand

Domestic Demand GDP Growth % Increase in Domestic Customer Numbers % Base Case 6% 6.18% High Case 10% 10.30% Low Case 4% 4.12%

Table A-26 shows the unconstrained energy demand in 2013 for the base, high and low case scenarios.

Table A-26: Energy Demand in 2013

Domestic Demand Energy Demand for 2013 Peak Demand for 2013 (GWh) (MW) Base Case 89.1 30.8 High Case 116.9 37.9 Low Case 75.2 17.3

Based on the above assumptions the three scenarios for domestic demand were created as shown in Table A-27:

Table A-27: Domestic Demand Forecast

Year Base Case High case Low Case Sales MW Sales MW Sales MW (GWh) (GWh) (GWh) 2013 89.1 31 116.9 38 75.2 17 2014 95.5 33 130.7 41 79.6 18 2015 102.5 36 146.9 45 84.1 19 2016 110.2 39 165.6 49 88.9 20 2017 118.6 43 187.6 53 93.9 20 2018 127.8 47 212.4 58 99.1 21 2019 137,9 51 240.9 64 104.7 22 2020 149.0 56 273.8 70 110.6 23 2021 161.0 61 311.7 76 116.9 24 2022 174.1 67 355.4 84 123.6 25 2023 188.4 74 406.0 92 130.7 26 2024 204.0 81 464.5 102 138.2 27 2025 221.0 89 532.2 112 146.2 28 2026 239.6 98 610.6 123 154.7 29 2027 260.0 108 701.4 136 163.7 30

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2028 282.2 118 806.8 151 173.3 31 2029 306.5 130 929.2 167 183.5 33 2030 333.1 144 1,071.2 184 194.3 34

A.3.7 Government Customers

Government Ministries and Departments have their own tariff group, T-4. Historic sales to this group over the period since 2006 are shown in Figure A-14. Figure A-14: Historic Government sales (GWh)

0 2006 2007 2008 2009 2010 2011 2012 2013

Separate consideration was not made as to the level of suppressed demand in the PPA Energy 2009 report, but it has been assumed that it is similar to that of the commercial sector. The datum 2013 estimates for the base, high and low scenarios are therefore as shown in Table A-28.

Table A-28: Government Sales in 2013

Government Sales Supressed Unsupressed Unsurpressed Energy Energy Sales contribution Sales in in 2013 to peak 2013 (GWh) demand (MW) (GWh) Base Case 10.1 22.8 5.6 High Case 18.1 31.0 7.6 Low Case 2.5 15.0 3.7

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In projecting future demand it was assumed that this tariff group would have a GDP elasticity of 0.5. The overall projection of demands over the period to 2030 are shown in Table A-29.

Table A-29: Forecast Government Sales

Year Base Case High Case Low Case Sales MW Sales MW Sales MW (GWh) (GWh) (GWh) 2013 22.8 5.6 31.0 7.6 15.0 3.7 2014 23.5 5.7 32.5 7.9 15.6 3.8 2015 24.2 5.9 34.1 8.3 16.3 4.0 2016 24.9 6.1 35.8 8.7 16.9 4.1 2017 25.6 6.3 37.6 9.2 17.6 4.3 2018 26.4 6.4 39.5 9.6 18.3 4.5 2019 27.2 6.6 41.5 10.1 19.0 4.6 2020 28.0 6.8 43.6 10.6 19.8 4.8 2021 28.9 7.0 45.7 11.2 20.6 5.0 2022 29.7 7.3 48.0 11.7 21.4 5.2 2023 30.6 7.5 50.4 12.3 22.3 5.4 2024 31.5 7.7 53.0 12.9 23.1 5.6 2025 32.5 7.9 55.6 13.6 24.1 5.9 2026 33.4 8.2 58.4 14.2 25.0 6.1 2027 34.5 8.4 61.3 15.0 26.0 6.4 2028 35.5 8.7 64.4 15.7 27.1 6.6 2029 36.6 8.9 67.6 16.5 28.2 6.9 2030 37.6 9.2 71.0 17.3 29.3 7.1

Sales to welders have been included within this group.

A.3.8 Distribution Losses Forecast

Losses in the Freetown system are only from the 33kV and the 11kV network, as purchases from BHP are metered at the 161kV substation in Freetown. PPA Energy have undertaken a number of studies into loss reduction measures in Freetown since 2004. This work, funded by the World Bank, included the metering of typical feeders and load flow modelling in order to make a reasonable estimate as to the level of technical losses on the system due to the resistance of the conductors (I2R losses) and hysteresis losses in transformers. It is not possible to directly measure the level of non-technical losses, which can only be inferred from the difference between total losses and technical losses.

In Section 5.1.1 of the 2004 report it was noted that:

Data on losses have been collected during the first visit and overall levels appear to have been at approximately 40% of total generation for many years. There is some debate as to the division between technical and

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commercial losses, however NPA estimates indicated the following breakdown.

 Generation auxiliaries 5%  Distribution technical losses 20%  Commercial losses 18% Two sample loss audits were performed during the first visit on distribution transformer service areas covering predominantly residential and commercial consumers respectively.

Analysis undertaken using 2002 data indicated the following split of losses:

Excluding auxiliaries (since more recent data on generation has been on the basis of sent out values – ie after auxiliary consumption), losses were estimated to total 35.5%, split 38% technical to 62% non-technical. In the absence of other data, this assumption is assumed to remain valid.

Recent trends in total technical and non-technical losses (excluding auxiliaries) are shown in Figure A-15.

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Figure A-15: Total System losses as % of sent out energy

50.0%

45.0%

40.0%

35.0%

30.0%

25.0%

20.0%

15.0%

10.0%

5.0%

0.0% 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

This data would seem to indicate that losses remain at almost the same levels as those observed in 2004.

In preparing this forecast, the following assumptions have been made with regard to losses:

 75% of non-technical losses are due to domestic customers. It is further assumed that 50% of these losses could be converted to sales if rigorous measures were implemented and therefore should be included within the unsuppressed forecast;  15% of non-technical losses are due to commercial customers, and 75% of these could be converted to sales if rigorous measures were implemented and therefore should be included within the unsuppressed forecast;  10% of non-technical losses are due to industrial customers, and 100% of these could be converted to sales if rigorous measures were implemented and therefore should be included within the unsuppressed forecast; and  All Government customers are legally connected to the system and therefore do not contribute to non-technical losses. Using an assumed total level of technical and non-technical losses of 44.7%, it is assumed losses can be split 17.0% technical, 27.7% non-technical. The application of the above principles for the conversion of non-technical losses to sales would convert these values to 17.0% technical, 11.4% non-technical, equivalent to a total loss figure of 28.4%.

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Table A-30 shows that total system losses are assumed to reduce from current levels to 16% by the end of 2030, through the commissioning and extension of the 33kV system. The implementation of technical measures (replacing overloaded transformers, balancing loads on the different phases of distribution lines, maximising the length of feeders and restringing / reconfiguring where appropriate) is assumed to reduce technical losses to 10%, whilst an on-going campaign against non-technical losses will effect a reduction from 11.4% to 6%.

Table A-30: Distribution Losses Forecast

Year Total Technical Non-Technical Losses Losses Losses 2013 28.4% 17.0% 11.4% 2014 26.2% 15.8% 10.4% 2015 24.1% 14.6% 9.5% 2016 22.2% 13.5% 8.7% 2017 20.5% 12.6% 7.9% 2018 18.8% 11.6% 7.2% 2019 17.4% 10.8% 6.6% 2020 to 2030 16.0% 10.0% 6.0%

A.3.9 Forecast of System Maximum Demand

It has been assessed with the generation and demand profile analysis that the peak demand occurs at midday on a weekday. At system peak time, the industrial demand corresponds to 100% of the maximal industrial peak, the commercial demand and government demand to 90% of the maximal commercial peak, and the residential load to 20% of the maximal residential peak. Table A-31 shows the 2013 peak sale value which was assessed at 70.15 MW.

Table A-31: Unsuppressed Maximum Demand Estimate (2013)

Customer Category Peak Demand Coincidence Contribution to in 2013 (MW) Factors System Peak

PEAK- Industry 38 100 % 38.5 PEAK - Commercial 23 90 % 20.5 PEAK - Domestic 31 20 % 6.2 PEAK- Government 6 90 % 5.0

Total Peak Demand 70.15 MW

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Demand losses as time of system peak were estimated by determination of the loss load factor as follows:

Loss load factor = 0.3 (LF) + 0.7 (LF)2

Based on the above loss load factor the average peak system loss is computed using the equation below:

Average peak loss (MW) = (System losses in MWh)/ (Loss load factor * 8760)

Table A-32 below shows the computation of average peak losses for the year 2013

Table A-32: Estimate of Losses at time of System Peak (2013)

Parameter For 2013 System Load Factor (LF) 70 % Loss Load Factor Calculation (LLF) 0.553 Total Demand from Customers 376,319 MWh System Losses % 24.6 % (Excluding Recoverable Technical Losses) System Losses MWh 122,778 MWh (Excluding Recoverable Technical Losses) Average Peak Loss (MW) 25 MW

Table A-33 shows the overall estimate of sent out maximum demand for 2013.

Table A-33: Estimate of sent out Maximum Demand (2013)

Parameter For 2013 Total Peak Demand (MW) 70 MW Average Peak Loss (MW) 25 MW Total System Demand (MW) 95 MW

Peak demand forecasts were thus projected over the period to 2030.

A.3.10 Conclusion

In the base case of the demand forecast analysis, the current maximum demand (2013) of the western region is projected to be at 95 MW and the equivalent energy demand is projected to be 499 GWh. The base case projected demand for 2030 is 235 MW and equivalent energy demand is projected to be 1274 GWh.

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Figure A-16 shows the details of the base case forecast:

Figure A-16: Demand forecast – base case

1,400 250

1,200 200 1,000

800 150 MW GWh 600 100 400 50 200

- -

Industry Commercial Domestic Gov't Total Losses Maximum Demand (MW)

In the high case of the demand forecast analysis, the current maximum demand (2013) of the western region is projected to be at 126 MW and the equivalent energy demand is projected to be 687 GWh. The base case projected demand for 2030 is 550 MW and equivalent energy demand is projected to be 3503 GWh. Figure A-17 shows the details of the high case forecast:

Figure A-17: Demand forecast – high case

4,000 600

3,500 500 3,000 400 2,500

2,000 300

MW GWh 1,500 200 1,000 100 500

- -

Industry Commercial Domestic Gov't Total Losses Maximum Demand (MW)

In the high case of the demand forecast analysis, the current maximum demand (2013) of the western region is projected to be at 67 MW and the equivalent

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energy demand is projected to be 374 GWh. The base case projected demand for 2030 is 115 MW and equivalent energy demand is projected to be 701 GWh.

Figure A-18 shows the details of the low case forecast:

Figure A-18: Demand forecast – low case

800 140

700 120 600 100 500 80

400 MW GWh 60 300 40 200 100 20 - -

Industry Commercial Domestic Gov't Total Losses Maximum Demand (MW)

The overall projection of demand for the Freetown area is therefore as shown in Table A-34.

Table A-34: Overall Projections of Future Demand in Freetown

Year Base Case High Case Low Case Demand MW Demand MW Demand MW (GWh) (GWh) (GWh) 2013 499.1 95.5 687.2 126.3 374.3 66.9 2014 526.5 100.6 752.5 137.6 388.8 69.2 2015 549.6 104.8 815.8 148.2 399.4 70.6 2016 578.4 110.1 892.7 161.0 413.6 72.8 2017 609.2 115.8 978.1 175.2 428.3 75.0 2018 642.0 121.8 1071.1 190.4 443.4 77.3 2019 676.8 128.1 1173.9 207.1 459.1 79.7 2020 713.7 134.8 1287.7 225.4 475.5 82.1 2021 750.9 141.5 1409.8 244.5 491.3 84.4 2022 790.4 148.5 1545.0 265.3 507.7 86.7 2023 832.3 156.0 1694.6 287.9 524.7 89.0 2024 876.8 163.9 1860.6 312.6 542.5 91.5 2025 924.2 172.3 2044.7 339.4 561.0 94.0

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2026 984.8 183.2 2273.1 373.5 586.5 97.8 2027 1049.7 195.0 2529.3 411.2 613.1 101.9 2028 1119.3 207.5 2816.8 452.8 641.1 106.0 2029 1193.8 220.9 3139.9 498.8 670.4 110.4 2030 1273.8 235.3 3503.3 549.7 701.2 114.9

A.4 Rural Demand Forecast

A.4.1 General

This section of the report explains the demand forecast analysis for the rural (non-Freetown) cities and towns in Sierra Leone over the period of 18 years between 2013 and 2030. Chapters A.4.2 to A.4.11 detail the unsuppressed demand forecast for urban parts of rural areas, including northern, eastern and southern provinces. Additional demand is added in chapter A.4.9. Total unsuppressed rural demand is presented in chapter 0.

The values in this report differ from those presented in the Rural Access Report which was submitted in November 2013. The values in the previous report represent estimates including assumed supply-side constraints.

It should be noted that these forecasts exclude the demands of large industrial or mining customers, which are considered separately.

A.4.2 Methodology

In the absence of detailed information related to the potential demands in the rural areas, a generic approach has been adopted to the forecasting of demand. The End-Use method predicts electricity demand by projecting information about the proposed areas for electrification. This includes the following:

 Number of households;

 Electrification rate;

 Specific consumption after diversity between customers; and

 Potential commercial and small industrial demand.

In addition to the twelve district headquarters, this study has also identified a number of other areas which might be considered for electrification. The overall list of electrification candidates is shown in Table A-35 below:

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Table A-35: Electrification Candidate Areas

Province District District Other town/ village Headquarters

Northern Kambia Kambia

Port Loko Port Loko Lungi Lunsar

Bombali Makeni

Koinadugu Kabala

Tonkolili Magburaka Binkolo Kychom Town Mange Town Kassiri Town Kamakwie Town Kunshu Town Kamalu Town Batkanu Town Alikalia Town Matotaka Town Masingbi Town Rokupr Town

Eastern Kono Koidu-Sedafu

Kailahun Kailahun

Kenema Kenema Yengema Town Bumpeh Town Bendu Town Lago Town Panguma Town Boajibu Town Blama Town

Southern Bo Bo

Moyamba Moyamba

Pujehun Pujehun

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Province District District Other town/ village Headquarters

Bonthe Bonthe Bomi Town Mattru Jong Njala /university

Overall population data for the three Sierra Leone districts considered in this report is shown in Figure A-19 below:

Figure A-19: Population Projections by District

These projections were based on IMF population forecasts.

Forecasts for individual areas considered for electrification were based on the information collected, augmented by internet research and mapping. Annual growth rates were based on the average value across the province in which the area was located.

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The potential number of domestic consumers was calculated based on an assumed average household size of 6 persons. Estimates were made of the electrification rates which might be achieved at the time of the initial commissioning of the networks – in most cases a value of 30% was assumed. This value was assumed to rise with time, reaching a value of 90% by the final year of the forecast.

It was assumed that newly electrified rural residential customers would each have an average consumption of 51 kWh/year in the first year in which they take supply. This figure takes account of the diversity of demand between residential customers, and is based on the assumption of a typical initial installation of two electrical lights. After diversity energy requirements have been assumed to grow rapidly with increased prosperity, rising to a value of 1300 kWh/year within 15 years, based on observed values in areas already electrified, and assuming electricity use for fans, television, lighting, refrigeration etc. In the forecast, therefore, the numbers of new customers connected each year were estimated, and the year group was assumed to follow a linear trajectory in growth in specific energy requirements over 15 years.

Separate allowance was made in each area for commercial and small industrial energy requirements, based on observations during the field visit.

A.4.3 Northern Province

The location of the candidate areas for electrification in the Northern Province is shown in Figure A-20:

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Figure A-20: Northern Province Towns

A.4.3.1 Electrification Rate

Table A-36 below shows the assumed electrification rates for the towns in the Northern Province.

Table A-36: Area Electrification Assumptions – Northern Province

Towns Year Electrified Initial Electrification rate

Kambia 2017 30% Port Loko 2015 30% Lungi 2014 30% Lunsar 2014 30% Makeni Existing 32% Kabala 2016 30% Magburaka 2014 30% Binkolo 2016 30% Kychom Town 2017 30% Mange Town 2017 30% Kassiri Town 2015 30% Kamakwie Town 2017 30%

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Towns Year Electrified Initial Electrification rate

Kunshu Town 2017 30% Kamalu Town 2017 30% Batkanu Town 2017 30% Alikalia Town 2017 30% Matotaka Town 2015 30% Masingbi Town 2017 30% Rokupr Town 2017 30%

A.4.3.2 Kambia Town

Forecast maximum demands for Kambia town (2013 population 13,469) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-37. Kambia is located close to the border with Guinea. It has some commercial and small industrial potential demand (petrol station, shops, institutional etc). Initially Kambia is likely to be an isolated system, but there is an opportunity for a future supply via Port Loko.

Table A-37: Maximum Demand Forecast in kW – Kambia Town

2013 2015 2020 2025 2030 Domestic 0 0 249 811 1694 Commercial 0 0 177 280 445 Industrial 0 0 121 195 314 Total 0 0 547 1286 2453

A.4.3.3 Port Loko

Forecast maximum demands for Port Loko (2013 population 23,915) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-38. Port Loko is located near to the port terminal used by African Minerals, and should this facility develop further it might grow significantly. In this projection, however, a more prudent assumption has been made in this regard, and it has been assumed that other loads will also include institutional, shops and small workshops.

Again it has been assumed that demand will initially be supplied by an isolated grid, but there is potential for Port Loko to be supplied in future from Lunsar, and for a separate distribution line to run from here to Kambia and to Lungi.

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Table A-38: Maximum Demand Forecast in kW – Port Loko

2013 2015 2020 2025 2030 Domestic 0 64 720 1800 3410 Commercial 0 150 263 412 652 Industrial 0 0 633 766 955 Total 0 214 1616 2979 5017

A.4.3.4 Lungi Town

Forecast maximum demands for Lungi town (2013 population 9,143) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-39. Lungi is the location of the international airport which serves Freetown, though it is understood that there are plans for its relocation to avoid the necessity for a boat or lengthy road journey between the two. Planning is, however, at a preliminary stage. Funding for the project is understood to be from Chinese sources. Lungi has had a small isolated network in the past, and NPA are currently understood to be installing new diesel capacity totalling 6 MW. The presence of the airport and associated hotels and other facilities means that Lungi has a greater potential demand than many of the other areas under consideration.

In the longer term Lungi could be interconnected with the main grid either directly at Lunsar, or via Port Loko.

Table A-39: Maximum Demand Forecast in kW – Lungi

2013 2015 2020 2025 2030 Domestic 0 61 329 755 1378 Commercial 0 2395 3682 5705 8903 Industrial 0 220 354 571 919 Total 0 2676 4366 7031 11200

A.4.3.5 Makeni Town

Forecast maximum demands for Makeni town (2013 population 122,755) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-40. Makeni is close to the Addax bio energy project, which is scheduled to be commissioned in 2014. In the past it has been supplied by small diesel units, though now is supplied by the shield wire on the 161 kV transmission line between Bumbuna and Freetown, which is energised as far as Makeni at 34.5 kV. The area is a thriving commercial centre, with a significant potential commercial and small industrial demand.

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Table A-40: Maximum Demand Forecast in kW – Makeni

2013 2015 2020 2025 2030 Domestic 3468 3758 5725 9761 13362 Commercial 270 323 510 810 1292 Industrial 100 321 517 833 1341 Total 3838 4402 6753 11403 18994

A.4.3.6 Lunsar Town

Forecast maximum demands for Lunsar town (2013 population 37,011) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-41. Lunsar is located on the main route from Freetown to Bunbuna. It was planned to be supplied from Freetown by the 34.5kV shield wire in a similar way to the Makeni supply from Bumbuna, but this has not yet been energised. Lunsar may benefit from economic activity associated with the London Mining concession, and therefore some growth in small industrial demand has been assumed in the longer term.

Table A-41: Maximum Demand Forecast in kW – Lunsar

2013 2015 2020 2025 2030 Domestic - 247 1334 3058 5577 Commercial - 170 264 414 654 Industrial - - 500 552 609 Total - 417 2098 4024 6841

A.4.3.7 Magburaka Town

Forecast maximum demands for Magburaka town (2013 population 43,538) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-42. Magburaka is located relatively close to Makeni. It also has an active commercial sector which it is assumed would contribute to demand. It is in the process of being electrified.

Table A-42: Maximum Demand Forecast in kW – Magburaka

2013 2015 2020 2025 2030 Domestic - 291 1569 3597 6561 Commercial - 470 747 1192 1907 Industrial - 200 322 519 835 Total - 961 2638 5308 9303

Ministry of Energy 196 January 2015 20414

A.4.3.8 Kabala Town

Forecast maximum demands for Kabala town (2013 population 20,212) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-43. Kabala is located to the north of the province. Although a district headquarters town, and therefore considered a priority for electrification, it is unlikely to be an economic candidate for connection to the national grid in the short term in view of the relatively limited maximum demand and its distance from other major centres.

Table A-43: Maximum Demand Forecast in kW – Kabala

2013 2015 2020 2025 2030 Domestic - - 490 1370 2717 Commercial - - 304 484 773 Industrial - - 140 225 362 Total - - 933 2079 3849

A.4.3.9 Other Northern Province Demand Potential

The forecast maximum demand for the other smaller electrification candidates in the Northern Province is shown in Table A-44.

Table A-44: Other Northern Province Maximum Demand Forecasts

Demand (kW) 2013 2015 2020 2025 2030 Kychom Town - 150 279 510 878 Mange Town - 150 353 750 1381 Kassiri Town - 168 436 863 1509 Kamakwie Town - 150 334 690 1255 Kunshu Town - 150 279 510 878 Kamalu Town - 150 297 570 1004 Batkanu Town - 150 334 690 1255 Alikalia Town - 150 316 630 1130 Matotaka Town - 156 306 547 921 Masingbi Town - 150 297 570 1004 Rokupr Town - 150 464 1112 2136 Binkolo - - 779 1649 2997

A.4.3.10 Total Northern Province Rural Demand

The overall projection of energy requirements across the Northern Province is shown in Table A-45.

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Table A-45: Overall Projection of Maximum Demand in kW across Northern Province Maximum 2013 2015 2020 2025 2030 Demand (kW) Kambia 0 0 547 1286 2453 Port Loko 0 214 1616 2979 5017 Lungi 0 2676 4366 7031 11200 Lunsar 0 417 2098 4024 6841 Makeni 3838 4402 6753 11403 18994 Kabala 0 0 933 2079 3849 Magburaka 0 961 2638 5308 9303 Binkolo 0 0 779 1649 2997 Kychom 0 150 279 510 878 Mange 0 150 353 750 1381 Kassiri 0 168 436 863 1509 Kamakwie 0 150 334 690 1255 Kunshu 0 150 279 510 878 Kamalu 0 150 297 570 1004 Batkanu 0 150 334 690 1255 Alikalia 0 150 316 630 1130 Matotaka 0 156 306 547 921 Masingbi 0 150 297 570 1004 Rokupr 0 150 464 1112 2136 Sub Total 3838 10345 23424 43200 74004

A.4.4 Eastern Province

The location of the candidate areas for electrification in the Eastern Province is shown in Figure A-21.

Ministry of Energy 198 January 2015 20414

Figure A-21: Eastern Province Towns

A.4.4.1 Electrification Rate

Table A-46 below shows the assumed electrification rates for the towns in the Eastern Province.

Table A-46: Area Electrification Assumptions – Eastern Province

Maximum Demand Year Electrified Initial Electrification rate (kW) Koidu-Sedafu 2014 30% Kailahun 2017 30% Segbwema 2017 30% Pendembu 2017 30% Kenema Existing 20% Yengema Town 2017 30% Bumpeh Town 2017 30% Bendu Town 2017 30% Lago Town 2017 30% Panguma Town 2017 30% Boajibu Town 2017 30% Blama Town 2017 30%

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A.4.4.2 Koidu-Sedafu Town

Koidu and Sedafu are twin towns which are close to each other and are treated as a single area in this report. Forecast maximum demands for Koidu-Sedafu town (2013 population 79,909) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-47. Koidu is located relatively close to a candidate hydro project at Benkongor. The WAPP international 225 kV transmission lines are also planned to route through Benkongor, and it is assumed that it would be a logical location for a step down transformer, with the potential to supply Koidu and Sedafu. The towns are located in an area of significant diamond mining activity, and it is anticipated that economic activity associated with this will contribute to electricity demand.

Table A-47: Maximum Demands in kW – Koidu – Sedafu

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 0 533 2880 6604 12044 Commercial 0 550 886 1427 2297 Industrial 0 204 756 835 921 Total 0 1287 4522 8865 15263

A.4.4.3 Kailahun Town

Forecast maximum demands for Kailahun town (2013 population 18,871) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-48.

Table A-48: Maximum Demands in kW – Kailahun

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 0 0 349 1137 2374 Commercial 0 0 277 440 703 Industrial 0 0 122 196 315 Total 0 0 748 1773 3392

A.4.4.4 Kenema Town

Kenema is the third largest city in Sierra Leone (after Freetown and Bo) and the largest city in the Eastern Province. The city is a major centre of trade and is the capital and largest city of Kenema District. The town is currently supplied by a 33 kV line from Bo which is 70 km away, forming the Bo Kenema Power System (BKPS). Generation supplying this network is predominantly from hydro plant which at the time of installation had a capacity of 6 MW. There is, however, a marked seasonality of production, and output varies significantlybetween the wet and the dry seasons. In addition there is some limited diesel capacity. In addition to a number of schools, a polytechnic is

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located in Kenema. Forecast maximum demands for Kenema town (2013 population 207,778) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-49.

Table A-49: Maximum Demands in kW – Kenema

2013 2015 2020 2025 2030 Domestic 3731 4152 7447 14544 26397 Commercial 420 505 802 1281 2050 Industrial 100 321 517 833 1341 Total 4251 4978 8767 16658 29788

A.4.4.5 Other Eastern Province Demand

The Forecast maximum demands for the other smaller electrification candidates in the Eastern Province are shown in Table A-50.

Table A-50: Other Eastern Province Maximum Demand Forecasts

Maximum 2013 2015 2020 2025 2030 Demand (kW) Segbwema 0 0 712 1657 3150 Pendembu 0 0 787 1902 3662 Yengema 0 150 427 992 1884 Bumpeh 0 150 297 570 1004 Bendu 0 150 260 449 752 Lago 0 150 279 510 878 Panguma 0 150 279 510 878 Boajibu 0 150 427 992 1884 Blama 0 150 612 1594 3142 Njaiama 0 150 242 389 627

A.4.4.6 Total Eastern Province Demand

The overall projection of energy requirements across the Eastern Province is shown in Table A-51.

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Table A-51: Overall Projection of Maximum Demand in kW across Eastern Province

Maximum 2013 2015 2020 2025 2030 Demand (kW) Koidu- Sedafu 0 1287 4522 8865 15263 Kailahun 0 0 748 1773 3392 Segbwema 0 0 712 1657 3150 Pendembu 0 0 787 1902 3662 Kenema 4251 4978 8767 16658 29788 Yengema 0 150 427 992 1884 Bumpeh 0 150 297 570 1004 Bendu 0 150 260 449 752 Lago 0 150 279 510 878 Panguma 0 150 279 510 878 Boajibu 0 150 427 992 1884 Blama 0 150 612 1594 3142 Njaiama 0 150 612 1594 3142 Total 4251 7315 18115 36470 65679

A.4.5 Southern Province

A.4.5.1 Southern Province

The location of the candidate areas for electrification in the Southern Province is shown in Figure A-22

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Figure A-22: Southern Province Towns

A.4.5.2 Electrification Rate

Table A-52 below shows the assumed electrification rates for the towns in the Southern Province.

Table A-52: Area Electrification Assumptions – Southern Province

Maximum Demand Year Electrified Initial Electrification rate (kW) Bo Existing 18% Moyamba Existing 40% Pujehun Existing 30% Bonthe 2017 30% Bomi Town 2017 30% Mattru Jong 2017 30% Njala /university 2017 30%

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A.4.5.3 Bo Town

Bo also commonly referred to as Bo Town, is the second largest city in Sierra Leone (after Freetown) and the largest city in the Southern Province. The city serves as the capital and administrative centre of Bo District. Bo lies approximately 250 km south-east of Freetown and about 70 km from Kenema. After Freetown, Bo is the leading financial, educational, commercial and urban centre of Sierra Leone. The city is the primary home of Njala University, the second largest university in Sierra Leone, after the Fourah Bay College. Bo is also home to the Bo Government Secondary School, commonly known as Bo School, which is one of the biggest and most prominent secondary schools in West Africa. Forecast maximum demands for Bo town (2013 population 245,867) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-53.

Table A-53: Maximum Demands in kW – Bo

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 3837 4315 8196 16650 30835 Commercial 420 505 802 1281 2050 Industrial 100 321 517 833 1341 Total 4357 5140 9515 18763 34225

A.4.5.4 Moyamba Town

Moyamba is a relatively small town which has a small existing network supplied by a 1600 kVA diesel generator. There have been plans to install a 10 MW hydro plant, funded by Chinese sources. Forecast maximum demands for Moyamba town (2013 population 2,000) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-54.

Table A-54: Maximum Demands in kW – Moyamba

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 71 76 108 172 275 Commercial 30 35 50 75 115 Industrial 0 0 0 0 0 Total 101 111 159 248 390

A.4.5.5 Punjehun Town

Punjehun is a relatively small town which has a small existing network supplied by a 77 kVA diesel generator. It is understood that the system has been operated

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as a cooperative, but fuel supply limitations have severely restricted the operation of the generator. Forecast maximum demands for Punjehun town (2013 population 9,851) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-55.

Table A-55: Maximum Demands in kW – Punjehun

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 263 285 442 768 1302 Commercial 30 35 50 75 115 Industrial 0 0 0 0 0 Total 293 320 493 843 1417

A.4.5.6 Bonthe Town

Bothe is located on an island. Forecast maximum demands for Punjehun town (2013 population 13,469) by tariff groups in 2013, 2015, 2020, 2025 and 2030 are shown in Table A-56

Table A-56: Maximum Demands in kW – Bonthe

Maximum Demand (kW) 2013 2015 2020 2025 2030 Domestic 0 0 329 973 1897 Commercial 0 0 104 162 255 Industrial 0 0 0 0 0 Total 0 0 433 1135 2152

A.4.5.7 Other Southern Province Demand

Forecast maximum demands for the other smaller electrification candidates in the Eastern Province is shown in Table A-57.

Table A-57: Other Southern Province Maximum Demand Forecasts

Maximum 2013 2015 2020 2025 2030 Demand (kW) Bomi 0 0 208 560 1068 Mattru Jong 0 0 387 908 1675 Njala 0 0 369 467 598

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A.4.5.8 Total Southern Province Demand

The overall projection of energy requirements across the Southern Province is shown in Table A-58:

Table A-58: Overall Projection of Maximum Demand in kW across Southern Province Maximum Demand (kW) 2013 2015 2020 2025 2030 Bo 4357 5140 9515 18763 34225 Moyamba 101 111 159 248 390 Pujehun 293 320 493 843 1417 Bonthe 0 0 433 1135 2152 Bomi 0 0 208 560 1068 Mattru Jong 0 0 387 908 1675 Njala 0 0 369 467 598 Sub Total 4751 5571 11564 22924 41525

A.4.6 Total Demand

The total rural energy projections across all three provinces considered are shown in Figure A-23:

Figure A-23: Total Rural Unsuppressed Demand Projections in MW

200

180

160

140

120

100

0 2013 2015 2020 2025 2030

Northern Province Eastern Province Southern Province

These figures are higher than in the previous report, where demand reached 100.3MW in 2030. But as reminded in the introduction of this chapter, these new figures describe the unsuppressed demand.

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We made the following assumptions for suppressed demand in urban parts of rural areas:

Table A-59: Total Suppressed Demand Forecast in Urban Rural Areas (MW)

Total Suppressed Year Demand (MW) MW 2013 4 MW 2014 4 MW 2015 5 MW 2016 6 MW 2017 8 MW 2018 10 MW 2019 13 MW 2020 17 MW 2021 20 MW 2022 25 MW 2023 30 MW 2024 35 MW 2025 41 MW 2026 48 MW 2027 55 MW 2028 63 MW 2029 72 MW 2030 81 MW

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Figure A-24: Total Suppressed Demand and Total Unsuppressed Demand Forecast

200 MW 180 MW 160 MW 140 MW 120 MW Predicted Suppressed Demand 100 MW (MW) 80 MW Predicted Real Demand (MW) 60 MW 40 MW 20 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

A.4.7 Total Unsuppressed Energy Requirements

It might be expected that the load factors of the rural areas will be significantly lower than that for the Freetown area. Provisional estimates, based on best engineering judgement, are for constant values of 13% for domestic customers, 21% for commercial customers and 25% for industrial customers. These values are lower than the equivalent figures for each tariff group in the Freetown area, reflecting the differences between the levels of maturity of customers in the two markets. Forecasts by customer category are reported in the table below:

Ministry of Energy 208 January 2015 20414

Table A-60: Unsuppressed Power and Energy Requirements Forecast for Rural Areas

Year Domestic Commercial Industrial GWh MW GWh MW GWh MW 2013 11.6 11.4 1.9 1.2 0.6 0.3 2014 12.5 12.2 6.6 4.0 1.4 0.7 2015 14.1 13.8 11.6 7.0 5.0 2.5 2016 16.3 16.0 13.4 8.1 5.6 2.9 2017 19.6 19.2 16.6 10.1 8.0 4.1 2018 23.8 23.3 18.2 11.0 8.9 4.5 2019 28.7 28.1 19.9 12.0 10.0 5.1 2020 34.3 33.5 21.9 13.3 12.9 6.5 2021 40.6 39.7 23.9 14.5 13.9 7.1 2022 47.7 46.7 26.2 15.9 15.0 7.6 2023 55.6 54.4 28.7 17.4 16.2 8.2 2024 64.4 63.0 31.4 19.0 17.5 8.9 2025 74.1 72.5 34.4 20.8 19.0 9.7 2026 84.7 82.9 37.7 22.8 20.6 10.5 2027 96.3 94.2 41.3 25.0 22.3 11.3 2028 108.8 106.5 45.2 27.4 24.2 12.3 2029 122.5 119.9 49.6 30.1 26.3 13.4 2030 137.3 134.3 54.4 32.9 28.6 14.6

A.4.8 High case and low case

We use sensitivity factors based on best engineering judgement to produce a high case and a low case. High case demand is assumed to be 150% of base case demand, while base case demand is assumed to be 50% of base case demand, as shown in the table below.

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Table A-61: Rural Base Case, High Case and Low Case Power and Energy Requirements

Rural High Year Rural Base Case Rural Low Case Case GWh MW GWh MW GWh MW 2013 14.1 12.8 21.2 19.3 7.1 6.4 2014 20.6 17.0 30.9 25.5 10.3 8.5 2015 30.7 23.4 46.1 35.1 15.4 11.7 2016 35.4 27.0 53.1 40.4 17.7 13.5 2017 44.3 33.4 66.4 50.1 22.1 16.7 2018 50.9 38.9 76.4 58.3 25.5 19.4 2019 58.6 45.2 87.8 67.8 29.3 22.6 2020 69.0 53.3 103.5 80.0 34.5 26.7 2021 78.4 61.3 117.6 91.9 39.2 30.6 2022 88.9 70.2 133.3 105.3 44.4 35.1 2023 100.5 80.1 150.7 120.1 50.2 40.0 2024 113.3 91.0 170.0 136.5 56.7 45.5 2025 127.4 103.0 191.1 154.5 63.7 51.5 2026 142.9 116.2 214.4 174.2 71.5 58.1 2027 159.8 130.6 239.8 195.8 79.9 65.3 2028 178.3 146.3 267.5 219.4 89.2 73.1 2029 198.4 163.3 297.6 245.0 99.2 81.7 2030 220.2 181.8 330.4 272.8 110.1 90.9

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Figure A-25: Rural demand in MW for base case, high case and low case

300 MW

250 MW

200 MW

Base Case 150 MW High case 100 MW Low Case

50 MW

A.4.9 Additional rural demand

Until now, we only considered urban parts of rural areas. We used the methodology presented in Appendix B from p.244 to calculate additional energy requirements for the rest of the districts for three scenarios:

- Base Case: In 2020, 25% of the rural population will pay the higher tariff of 20c US per kWh. The remaining 75% will be charged the heavily subsidised rate of 10c US per kWh. By 2030, this distribution is inversed. - Low case: 50% of base case - High Case: 150% of base case

Total results are presented in the following table.

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Table A-62: Unsuppressed Additional Power and Energy Requirements

Year Base Case High Case Low Case GWh MW GWh MW GWh MW 2013 0.0 0.0 0.0 0.0 0.0 0.0 2014 0.0 0.0 0.0 0.0 0.0 0.0 2015 0.0 0.0 0.0 0.0 0.0 0.0 2016 0.0 0.0 0.0 0.0 0.0 0.0 2017 0.0 0.0 0.0 0.0 0.0 0.0 2018 0.0 0.0 0.0 0.0 0.0 0.0 2019 0.0 0.0 0.0 0.0 0.0 0.0 2020 12.0 11.7 18.0 17.6 6.0 5.9 2021 24.4 23.9 36.6 35.8 12.2 11.9 2022 37.2 36.4 55.8 54.6 18.6 18.2 2023 50.4 49.3 75.6 74.0 25.2 24.7 2024 63.9 62.5 95.8 93.8 31.9 31.3 2025 77.7 76.0 116.5 114.1 38.8 38.0 2026 91.8 89.8 137.6 134.7 45.9 44.9 2027 106.0 103.7 159.0 155.6 53.0 51.9 2028 120.4 117.8 180.6 176.8 60.2 58.9 2029 134.9 132.0 202.4 198.0 67.5 66.0 2030 149.4 146.2 224.1 219.3 74.7 73.1

A.4.10 Total Unsuppressed Rural Demand

The following table shows the total rural demand, including urban and rural areas.

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Table A-63: Total Rural Energy and Power Requirements

Rural High Year Rural Base Case Rural Low Case Case GWh MW GWh MW GWh MW 2013 14.1 12.8 21.2 19.3 7.1 6.4 2014 20.6 17.0 30.9 25.5 10.3 8.5 2015 30.7 23.4 46.1 35.1 15.4 11.7 2016 35.4 27.0 53.1 40.4 17.7 13.5 2017 44.3 33.4 66.4 50.1 22.1 16.7 2018 50.9 38.9 76.4 58.3 25.5 19.4 2019 58.6 45.2 87.8 67.8 29.3 22.6 2020 81.0 65.1 121.5 97.6 40.5 32.5 2021 102.8 85.2 154.2 127.8 51.4 42.6 2022 126.1 106.6 189.2 159.9 63.1 53.3 2023 150.9 129.4 226.3 194.1 75.4 64.7 2024 177.2 153.5 265.8 230.3 88.6 76.8 2025 205.1 179.0 307.7 268.5 102.6 89.5 2026 234.7 206.0 352.0 308.9 117.3 103.0 2027 265.8 234.3 398.8 351.5 132.9 117.2 2028 298.7 264.1 448.1 396.2 149.4 132.1 2029 333.3 295.4 500.0 443.0 166.7 147.7 2030 369.7 328.1 554.5 492.1 184.8 164.0

A.4.10.1 Base Case

This chart shows the contribution of towns and remote rural areas to the total rural unsuppressed demand for the base case scenario:

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Figure A-26: Total Rural Demand – Base Case

350 MW

300 MW

250 MW

200 MW Additional Demand 150 MW Urban Rural 100 MW

50 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

A.4.10.2 High Case

This chart shows the contribution of towns and more rural areas to the total rural unsuppressed demand for the high case scenario:

Figure A-27: Total Rural Demand – High Case

600 MW

500 MW

400 MW

300 MW Additional Demand Urban Rural 200 MW

100 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 A.4.10.3 Low Case

This chart shows the contribution of towns and more rural areas to the total rural unsuppressed demand for the low case scenario:

Ministry of Energy 214 January 2015 20414

Figure A-28: Total Rural Demand – Low Case

180 MW 160 MW 140 MW 120 MW 100 MW Additional Demand 80 MW Urban Rural 60 MW 40 MW 20 MW

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

A.4.11 Losses

Experience from Makeni has indicated that technical and non technical losses are very substantially lower than for the Freetown area. This is because the networks do not suffer from the same problems of congestion and it is understood that all customers have pre payment meters. New networks can be designed to ensure that the opportunities for non technical losses are minimised, for example by the use of bundled aerial conductors, and the lengths of feeders can be designed such that they do not exceed pre determined values.

Typically losses therefore might be expected to be within the range of 10 to 15% of the forecasts developed above. Since this is within the range of estimating error, losses have not been separately identified.

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A.5 Mining Demand

A.5.1 Overview

Foreign direct investment into Sierra Leone and economic growth are primarily driven by mining. After falling in 2008 and 2009 due to the financial and economic crisis, export growth resumed in 2010 by nearly 34% and further by 6.2% in 2011 on account of the expansion of mining and agricultural activities. Exports grew by a record 147.3% in 2012, reflecting the impact of iron ore production and exports, notably by African Minerals and London Mining. Exports of other minerals, including rutile and diamonds, also increased during 2012. In 2012, the mining sector contributed nearly 10% of the 15.2% real GDP growth24.

The mining sector’s contribution to GDP is also projected to increase substantially in the coming years, from 4% in 2011 to as much as 30% in 201725 due for the most part, to the continued expansion of iron ore operations in the country. With projected annual growth in production of between 4% and 10% and new investment in the mining sector, Sierra Leone’s GDP could be 6% higher in five years than it would otherwise be, and 17% higher by 2020.

Sierra Leone has very large bauxite, iron ore, rutile, diamond and gold resources (see map and table below), bestowing on the country a significant potential for future growth in electricity demand and economic activity.

24 World Bank Website, “Sierra Leone Overview”. http://data.worldbank.org/country/sierra-leone

25 African Development Group, “Sierra Leone Country Strategy Paper: 2013-2017”. August 2013.

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Figure A-29: Sierra Leone’s Mineral Resources

Table A-64: Sierra Leone’s Mining Potential – IMF 2010

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Typically energy costs are estimated to represent around 15% of the total cost of production in the mining industry in the US26 and in Australia27. Energy is required all along the mining process: for the extraction phase, to transport and handle materials and to process them. The level of energy consumption is also dependant on the process selected (dredge mining, dry mining, etc.), the performance of the equipment, the mineral under consideration and the site’s characteristics.

There are currently three types of mining operations in Sierra Leone:

 Mining of non-precious metals such as iron ore and bauxite;  Mechanised mining of precious materials such as gold and diamonds, and  Artisanal mining by individuals. At present there are 8 large scale mining companies (operating or planned), over fifteen small-scale mining companies and up to 200,000 artisanal miners in Sierra Leone. In addition, there are an estimated 180 mining companies with exploration licences covering 70% of the country28.

The following map shows the estimated mining, steel and cement demand in Sierra Leone in 2013:

26 Cleantech Magazine. 2010. http://www.cleantechinvestor.com/portal/fuel-cells/6422-mining-and- energy.html

27 Australian Government. 2010. http://eex.gov.au/industry-sectors/mining/#fn-166-3

28Government of Sierra Leone. “Agenda for Prosperity: Road to Middle Income Status – Sierra Leone’s Third Generation Poverty Reduction Strategy Paper (2013 – 2018)”. 2011.

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Figure A-30 Main Mining Stakeholders in Sierra Leone in 2013

2013 Demand (MW)

A.5.2 Largest mining companies

In this section the potential electricity demands of the major mining companies in Sierra Leone are reviewed, including both those that are operational and those that are planned.

A.5.2.1 African Minerals29

African Minerals is the largest of the current mining companies operating in Sierra Leone. Since 2011 it has been extracting ore at a rate of 20 Mtpa from one of the largest magnetite deposits in the world (12.8Bt), in Tonkolili (see Figure A-31 below). A railway line from Tonkolili to the coast, and a port at Pepel were also developed to allow the export of the ore. The predominant

29 African Minerals, “Tonkolili Phase 2 Electricity Strategy”. September 2013.

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market for the ore is understood to be China, and African Minerals has long term contracts for sales.

Figure A-31: Tonkolili Site – African Minerals

The mining site is composed of three main layers of ore and power requirements are different for each of these, as described in Table A-65below.

Table A-65 Mineral Layers in Tonkolili Site

Layer Mineral Power Beginning of Requirements operations Layer 1 126.5 Mt of Direct <1 MW / Mtpa 2011 Shipping Ore Layer 2 1.1 Bt of Saprolite 5-9 MW / Mtpa 2016 Layer 3 11.6 Bt of Magnetite 12-14 MW / Mtpa After 2018

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Figure A-32: Mineral Layers and Resources in Tonkolili site – African Minerals

The current power demand is met by a 10 MW diesel generator at the mine site and another 10 MW generator at Pepel port.

African Minerals expect to increase the production rate to 35 Mtpa by 2016 and start the extraction of saprolite (layer 2). African Minerals is exploring sourcing such power from government sources, including the possibility of obtaining offtake agreements from the neighbouring hydro plants of Bumbuna and Yiben.30 In practice, however, it is very unlikely that the grid will be adequately reliable, or the generation capability available to the public sector sufficient, to support such a demand and therefore African Minerals have been planning the installation of onsite generation, based on diesel units, to meet this requirement.

Information which has been provided by African Minerals indicates that the most likely electricity requirements in the future are as shown in Table A-66. The data in this table is based on relative energy calculations using an approximation of the possible mining expansion plans of the company. It would fair to say that the actual production of the mine would vary depending upon the international prices/demand of the ore material and this would in turn have an impact on the electricity demand in the country.

30 Mining News, “Tonkolili – 3 projects in one”. August 2013.

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Table A-66: African Minerals Electricity Requirements

Year Maximum Energy Requirement Autogeneration Demand (MW) (GWh) Capacity (MW)

2016 55 226 63

2017 55 265 63

2018 55 366 63

2019 155 695 110

2020 155 1029 110

2021 255 1453 110

2022 255 1712 110

2023 255 1710 110

2024 255 1557 110

2025 312 1706 110

A.5.2.2 Sierra Rutile 31

The mining operations in Lanti started in 2006, and are currently operating in excess of their designed rate of 3.5 million tonnes per annum. The electricity needed is supplied by a 23 MW diesel power plant and the current power demand is estimated to be less than 9 MW. Until 2013, the only process in place was a dredge mining process, using a wet concentrator plant capable of mining 1000 T of ore per hour equating to approximately 7.2 MTpa.

Sierra Rutile is upgrading the Lanti site by adding a dry plant, using earth moving equipment to mine 500 T of ore per hour equating to approximately 3.6 Mtpa. The power requirements should be met by the existing installed capacity.

31 Sierra Rutile, “Company Presentation”. February 2013.

Ministry of Energy 222 January 2015 20414

Figure A-33 shows the association of dredge and dry mining process in Sierra Rutile’s Lanti site.32

Figure A-33: Sierra Rutile’s dry and dredge mining processes in Lanti

In addition to the Lanti site, Sierra Rutile is working on two more projects in the region. The first will be located in Gangama, with 42 Mt of ore resources available. The expected rate of the production is 7 Mtpa. Electricity requirements can be met from the existing installed capacity. The second project will be located in Sembehun, with 274 Mt of ore resources available. The expected rate of production is 13Mtpa. Electricity requirements will be met by a new 18.5MW on-site power plant.

It is understood that in January 2014 Sierra Rutile signed a Memorandum of Understanding with a developer to allow the offtake of power from a proposed run-of-river hydropower plant33 located at the Singimi Falls on the Ggangba

32 Sierra Rutile Website. http://www.sierra-rutile.com/

33 Mining Weekly, “Sierra Rutile signs MoU to secure hydropower”. December 2013.

Ministry of Energy 223 January 2015 20414

River. This project has a planned capacity of between 11 and 14 MW, and is located within 20km of Sierra Rutile’s Lanti and future Gamgama sites. It will be developed as a public-private partnership with the Government of Sierra Leone and will serve the communities of Moyamba, Njala University, as well as Sierra Rutile. A feasibility study is currently being completed for the project, and financing, permitting and construction are expected to be completed by 2017.34 It is, however, noted that under the new Electricity Act EDSA will be single buyer with responsibility for the purchase of electricity from all sources in Sierra Leone, and with sole remit for sales to end customers.

The physical location of Sierra Rutile’s existing operations and planned new developments are shown in Figure A-34:

34 Ibid

Ministry of Energy 224 January 2015 20414

Figure A-34: Sierra Rutile’s operations in Sierra Leone

Singimi Falls Hydro Project

Ministry of Energy 225 January 2015 20414

A.5.2.3 London Mining

London Mining started extracting iron ore at Marampa in 2011 (see Figure A-35), where over 1BT of ore resources are available. The mine life is estimated to be 40 years35.

Figure A-35: London Mining’s Marampa Site – London Mining

In 2012, the production rate was increased from 1.6 to 5.4 Mtpa, which required upgrading the existing 12 MW power plant to 15 MW capacity (10 x 1.55 MW units), although the power consumption did not exceed 3 MW prior to the upgrade.

35 London Mining, “Supplying high quality iron ore to the global steel industry”. July 2013.

Ministry of Energy 226 January 2015 20414

The medium-term production target is 9 Mtpa, while the long term target is 16 Mtpa. The potential is assessed to be 30 Mtpa.36

Meeting the medium-term target will necessitate additional processing capacity, primary crusher and additional mills. This will require the expansion of the power facility to 50 MW, using 4 x 8.55 MW new units. A total peak demand of 50 MW is anticipated, with a steady-state demand of 31MW. 37

A.5.2.5 Sierra Minerals (Vimetco) 38

Sierra Minerals operates the only bauxite mine in Sierra Leone, at Moyamba, with 31 Mt of ore resources available. It is the second largest mining employer in the country.

The mining operations formally started in November 2005 and the mine currently produces around 1.2 MTpa. It is understood to have reserves sufficient for operation to 2033.

Power requirements are quite low, compared to other sites, and are estimated to be approximately 2 MW.

A.5.2.6 Koidu Holdings39

The Koidu Kimberlite Project is a diamond mine situated within the Tankoro Chiefdom of the Kono District in the Eastern Province of Sierra Leone, approximately 2 km south of the district capital, Koidu and approximately 360 km east of Freetown, the capital city.

36 Ibid

37 London Mining, “Analyst Workshop – Life of mine study results”. September 2013.

38 Sierra Minerals Website. http://bauxite.vimetco.com/

39 Koidu Holdings Website. http://www.koiduholdings.com/

Ministry of Energy 227 January 2015 20414

Figure A-36: Koidu Holding’s Kimberlite Project – Koidu Holdings

The present power demand is estimated to 5 MW.

Koidu is the middle of a five-year expansion plan, which includes ramping up mining from the open pits from 300,000 tonnes per month to 1,500,000 tonnes per month with the construction of a new 180 tonne per hour Dense Media Separation processing plant capable of treating 1.2 million tonnes of ore per annum.

A.5.2.7 Marampa Iron Ore (Cape Lambert)

The Marampa Iron Ore Project (“Marampa”) is an advanced hematite iron ore project at feasibility assessment and permitting stage located 90km northeast of Freetown (see Figure A-37).

Ministry of Energy 228 January 2015 20414

Figure A-37: Cape Lambert’s Marampa Project

Resources available for extraction total 681 Mt, supporting up to 15 Mtpa high quality hematite concentrate production for over 20 years. The Maramba development will use a combination of existing and new infrastructure facilities. The former includes the 73km railway going to the port at Pepel, and the latter a new a concentrate pipeline between Marampa and Tagrin Port.40

Three phases have been identified for this project41:

 Phase 1 with an extraction rate of 2.5Mtpa, starting in 2014  Phase 2 with an extraction rate of 10Mtpa  Phase 3 with an extraction rate of 15Mtpa This project will require the commissioning of a 125 MW HFO Plant (located on the plant site) and a 26 MW HFO Plant (located on the port nearby).42

40 Cape Lambert, “ASX Announcement”. May 2013.

41 Ibid

42 Vale Columbia Center, “A Framework To Approach Shared Use Of Mining-Related Infrastructure”. January 2014.

Ministry of Energy 229 January 2015 20414

A.5.2.8 China Kingho

Kingho, which is currently carrying out iron ore exploration in Sierra Leone, signed a MOU with the West African nation’s mines industry in May 2013. According to the MOU, the firm plans to spend $6 billion.43

Under the agreement, the company foresees construction of a 250km railway from the northern Tonkolili district, where it has two exploration licenses, to the coastal town of Sulima near the border with Liberia.

Other planned projects include construction of a deep-water port, a smelting facility, an industrial park and road upgrades. The smelter would be powered by a 350 MW hydro-electric station to be built on the Sewa River.

In January 2014, China Kingho revealed44 plans to mine 110MT of iron ore in the Pujehun district. It is understood that Kingho Investment are planning an underground mining operation using advanced technology for a period of 30 years. Power requirements are estimated to be approximately 15MW.

A.5.2.9 Amara Mining

Amara Mining is at the feasibility stage of the Baomahun Gold Project. Reserves are estimated to 23.3 Mt, supporting an open pit operation with average annual throughput of 2Mtpa over 11.5 years mine life45 (see Figure A-38).

43 Reuters UK, “China Kingho may spend up to $10 bln in Sierra Leone”. May 2013.

44 Awareness Times, “In Sierra Leone, Kingho to Mine 110M Tons of Iron Ore in Pujehun”. January 2014. http://news.sl/drwebsite/publish/article_200524553.shtml

45 Amara Mining, “Feasibility Study for Baomahun Gold Project”. July 2013.

Ministry of Energy 230 January 2015 20414

Figure A-38: Amara Gold Mining Project: Breakdown by layer

Although no decision has been made yet, Baomahun has the potential for operating cost savings from hydro-electric power. A study has been completed for a 24 MW run-of-river hydro-electric power generation project within 40km of Baomahun46. This is estimated to have sufficient capacity to provide the project’s power needs for 72% of the year, with a heavy fuel oil power station required for the height of the dry season.

A.5.2.10 Diamonds Mining Companies

In addition to Koidu Holdings, a number of diamond mining companies are operating (or soon-to-be) in Sierra Leone, including but not restricted to47:

 Billion Diamonds (Kono)  Flam Tap (Kenema)  M.S Ventures (Baoma)  Millenium Mining Corporetion (Tikonko)  Nour Mining  Pride Diamonds (Kenema)  Shadow Minerals (Kono)  West African Group for Mining (Port Loko) In addition to those, Oraco Resources has recently been granted a licence for three sites: Nimikoro, Pujehun and Koidu.

46 Amara Mining, “Results Of First Phase Of Baomahun Optimisation Work”. January 2014.

47 GoSL, Inventory of Licences Granted to the Mining Industry. June 2013.

Ministry of Energy 231 January 2015 20414

Demands for these companies have been included in the single “Other mining companies” category.

Figure A-39 shows the approximate location of the main diamond mining companies in Sierra Leone, coloured by size of the area of operation. Two clusters exist in the regions of Kenema and Kono.

Figure A-39: Present Diamond Mining Companies in Sierra Leone

A.5.2.11 Gold Mining Companies

A number of other smaller gold mining companies are operating in Sierra Leone, including but not restricted to48:

 AMR Gold (Kamkwie)  Golden Saint Resources (Kenema, Tongo and Moa)  Mile Stone Trading Limited (Tonkolili)  Resource Finance (Gbane)  Wara wara Gold Limited (Koinadugu)

48 GoSL, Inventory of Licences Granted to the Mining Industry. June 2013.

Ministry of Energy 232 January 2015 20414

In addition Akoda Mining has been granted licences for gold exploration in Sierra Leone.

Demands for these companies have been included in the single “Other mining companies” category.

Figure A-40 shows the approximate location of the main gold mining companies in Sierra Leone, coloured by size of the area of operation, located in two clusters, from Kono to Kenema and from Tonkolili to Koinadugu.

Figure A-40: Other Gold Mining Companies in Sierra Leone

A.5.2.12 Other Mining Companies

All other mining companies not mentioned in this chapter, such as Gondwana Investment (Rutile), Mile Stone Traing (Irone Ore) or Sierra-Geo Resources (Rutile), are included in the “Other mining companies” category.49

A.5.3 Other Large Self-Generating Industrial Stakeholders

49 GoSL, Inventory of Licences Granted to the Mining Industry. June 2013.

Ministry of Energy 233 January 2015 20414

In addition to the large mining loads, aluminium, cement, and steel factories are also planned for development in Sierra Leone. Each of these have significant energy demands.

A.5.3.1 China Kingho – Aluminium

As mentioned, Kingho’s projects include a smelting facility, which would be powered by a 350 MW hydro-electric station to be built on the Sewa River.

A.5.3.2 Sierra Leone Cement Corporation – Cement

Sierra Leone Cement Corp Ltd. operates one grinding plant in the capital Freetown. The company is a major supplier of cement in Sierra Leone. 50

The power demand currently is 4.2 MW, met by an on-site plant of 7 MW capacity (5 MW + 2 MW). Over the medium-term and long-term Sierra Leone Cement is looking at maintaining the existing 7 MW plant, or investing in a HFO power plant.51

A.5.3.3 Dangote Cement – Cement

In May 2013 Dangote Cement embarked upon a $45 million project capable of producing 1.3 metric tons of cement per year, employing 200 workers52. Due to the lack of information about this project, it has been included in the category “Other Companies” in the Demand Forecast for “other large industrial stakeholders”.

A.5.3.4 Samshi Afrika Limited – Steel

This project is planned as an integrated steel mill and captive power plant with a production capacity of 150 000tpa of reinforcing steel. The mill will be developed near Port Loko, with potential production commencing in early 201553.

In terms of power requirements, Samshi states that in the short term, power requirements for site works will be handled through “Diesel Generators of

50HeidelbergCement Website. http://www.heidelbergcement.com/africa/en/country/operations/sierra_leone.htm

51 Sierra Leone Cement, “Information Expected from Investors for Infrastructure and Energy Roundtable”. May 2012.

52 Sierra Express Media, “Dangote Group brings $45m project to Sierra Leone”. May 2013.

53 Samshi Afrika Ltd, “Information Expected from Investors for Infrastructure and Energy Roundtable”. May 2012.

Ministry of Energy 234 January 2015 20414

adequate size”. In the medium-term (from mid-2015), Samshi will install a 50 MW semi-captive power plant and anticipates it will have around 10 MW of excess power to sell, providing that “power infrastructure” are “established next to the site” by then, including an “adequate substation”. In the long-term (by 2025) Samshi anticipates increasing the installed power capacity to 100 MW.

A.5.4 Mining Demand Forecast

A.5.4.1 Assumptions

A.5.4.2 General Assumptions

 When the peak demand is unknown, it is estimated at 80% of the firm capacity  “Mining Demand” is shown separately from the demand of the largest industrial companies in other sectors, which is categorised as “Other Demand”  In 2013, the demand from Sierra Rutile, Sierra Minerals, London mining and African Minerals represents 90% of the total mining demand  In 2013, the demand from Sierra Leone Cement represents 90% of the total demand from all self-generating industrial stakeholders (other than mining)  A probability factor has been allocated to each project, based on an assessment of the actual demand (relative to that stated, timing uncertainties, and uncertainty as to actual approval and development).  For the calculation of energy requirements, the assumption has been made that the projects operate at a 75% load factor  Power and energy requirements are assumed to increase linearly between 2013 and 2020

A.5.4.3 Base Case Assumptions

 A set of probability factors define the base case  In 2020, the demand from the largest mining companies represents 90% of the total mining demand  In 2020, the demand from the largest companies from other sectors than mining represents 90% of the total demand from the other self- generating industrial stakeholders

A.5.4.4 Low Case

 A set of probability factors define the low case

Ministry of Energy 235 January 2015 20414

 In 2020, the demand from the largest mining companies represents 95% of the total mining demand  In 2020, the demand from the largest companies from other sectors than mining represents 95% of the total demand from the other self- generating industrial stakeholders

A.5.4.5 High Case

 A set of probability factors define the high case  In 2020, the demand from the largest mining companies represents 80% of the total mining demand  In 2020, the demand from the largest companies from other sectors than mining represents 80% of the total demand from the other self- generating industrial stakeholders

Ministry of Energy 236 January 2015 20414

A.5.5 Demand from Mining

Table A-67: Mining Demand Forecast in Sierra Leone 2013-2020

By 2020 2013 LOW CASE BASE CASE HIGH CASE

Estimate Estimate Estimate Estimate Estimate Estimate Estimate Firm Firm d Peak Probabilit d Firm d Peak Probabilit d Firm d Peak Probabilit d Firm d Peak Company Location capacit capacit Demand y factor capacity Demand y factor capacity Demand y factor capacity Demand y (MW) y (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW)

290 African Minerals Tonkolili 20 MW 16 MW 40% 116 MW 93 MW 60% 174 MW 139 MW 80% 232 MW 186 MW MW Lanti Sierra Rutile 23 MW 9 MW 42 MW 70% 29 MW 23 MW 90% 37 MW 30 MW 100% 42 MW 33 MW Gangama

Sembehu n London Mining Marampa 15 MW 12 MW 50 MW 60% 30 MW 24 MW 80% 40 MW 32 MW 100% 50 MW 40 MW

Sierra Minerals Moyamba 4 MW 2 MW 3 MW 80% 2 MW 2 MW 90% 3 MW 2 MW 100% 3 MW 2 MW

Koidu Holdigs Tankoro 6 MW 5 MW 20 MW 80% 16 MW 13 MW 90% 18 MW 14 MW 100% 20 MW 16 MW 151 Marampa Iron Ore Marampa 60% 91 MW 72 MW 80% 121 MW 97 MW 100% 151 MW 121 MW MW Baomahu Amara Mining 24 MW 20% 5 MW 4 MW 40% 10 MW 8 MW 80% 19 MW 15 MW n China Kingho Punjehun 20 MW 20% 4 MW 3 MW 40% 8 MW 6 MW 80% 16 MW 13 MW share of share of share of 600 "others" in "others" in "others" in Mining Sub Total 68 MW 44 MW 293 MW 234 MW 410 MW 328 MW 533 MW 426 MW MW total total total demand demand demand Other Mining 8 MW 5 MW 5% 15 MW 12 MW 10% 46 MW 36 MW 20% 133 MW 107 MW Companies 600 Mining Total 75 MW 49 MW 308 MW 247 MW 456 MW 365 MW 666 MW 533 MW MW

Ministry of Energy 237 January2015 20414

A.5.6 Demand from the other large self-generating industrial stakeholders

Table A-68: Demand from the other large self-generating industrial stakeholders in Sierra Leone 2013-2020

By 2020 2013 LOW CASE BASE CASE HIGH CASE

Firm Estimat Firm Estimate Estimate Estimate Estimate Estimate Estimate capacit ed Peak capacit Probabili d Firm d Peak Probabili d Firm d Peak Probabili d Firm d Peak Company Location y Demand y ty factor capacity Demand ty factor capacity Demand ty factor capacity Demand (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW)

Sewa 350 China Kingho 20% 70 MW 56 MW 40% 140 MW 112 MW 80% 280 MW 224 MW River MW Sierra Leone Freetown 7 MW 4 MW 15 MW 30% 5 MW 4 MW 50% 8 MW 6 MW 80% 12 MW 10 MW Cement Port Samshi Afrika 50 MW 40% 20 MW 16 MW 60% 30 MW 24 MW 80% 40 MW 32 MW Loko share of share of share of 415 "others" "others" "others" Other Sub Total 7 MW 4 MW 95 MW 76 MW 178 MW 142 MW 332 MW 266 MW MW in total in total in total demand demand demand Other Companies 1 MW .4 MW 5% 5 MW 4 MW 10% 20 MW 16 MW 20% 83 MW 66 MW 415 Other Total 8 MW 4 MW 99 MW 80 MW 197 MW 158 MW 415 MW 332 MW MW

Ministry of Energy 238 January 2015 20414

A.6 Conclusion

A.6.1 Mining Demand 2013-2020

In conclusion, maximum demand in 2020 for mining and other large industrial consumers is estimated to be:

 326MW for the low case scenario (408MW total firm capacity)  523MW for the base case scenario (653MW total firm capacity)  865MW for the high case scenario (1081MW total firm capacity)

Table A-69: Total demand from mining and other large self-generating industrial stakeholders 2013 By 2020 LOW CASE BASE CASE HIGH CASE

Estimate Estimate Estimate Estimate Estimate Estimated Estimate Firm d Peak d Firm d Peak d Firm d Peak Firm d Peak capacit Demand Capacity Demand Capacity Demand Capacity Demand y (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW)

Mining 75 MW 49 MW 308 MW 247 MW 456 MW 365 MW 666 MW 533 MW Total Others Total 7 MW 4 MW 99 MW 80 MW 197 MW 158 MW 415 MW 332 MW 82 1081 Total 53 MW 408 MW 326 MW 653 MW 523 MW 865 MW MW MW Energy Consumptio 347 GWh 2143 GWh 3434 GWh 5681 GWh n Figure A-41: Demand Forecast for Low Case, Base Case and High Case

Self-generating Mining, Cement, Steel and Aluminium Peak Demand Forecast 1000 MW

865 MW 800 MW

600 MW LOW CASE 523 MW BASE CASE 400 MW HIGH CASE 326 MW

200 MW

53 MW MW 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Ministry of Energy 239 January 2015 20414.

Figure A-42: Yearly energy consumption forecast for Low Case, Base Case and High Case

Self-generating Mining, Cement, Steel and Aluminium Energy Consumption Forecast 6000 GWh 5681 GWh

5000 GWh

4000 GWh 3434 GWh LOW CASE 3000 GWh BASE CASE HIGH CASE 2000 GWh 2143 GWh

1000 GWh

347 GWh GWh 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

The following charts show the growth of the share of each stakeholder in the mining energy mix.

Figure A-43: Mining and Other Base Case Demand

600 MW Samshi Afrika 500 MW Sierra Leone Cement China Kingho 400 MW Other Mining Companies China Kingho 300 MW Amara Mining Marampa Iron Ore 200 MW Koidu Holdigs Sierra Minerals 100 MW London Mining Sierra Rutile MW African Minerals 2013 2020

Ministry of Energy 240 January 2015 20414

Figure A-44: Mining and Other High Case Demand

900 MW Samshi Afrika 800 MW Sierra Leone Cement 700 MW China Kingho 600 MW Other Mining Companies 500 MW China Kingho 400 MW Amara Mining Marampa Iron Ore 300 MW Koidu Holdigs 200 MW Sierra Minerals 100 MW London Mining MW Sierra Rutile 2013 2020

Figure A-45: Mining and Other Low Case Demand

350 MW Samshi Afrika 300 MW Sierra Leone Cement

250 MW China Kingho Other Mining Companies 200 MW China Kingho Amara Mining 150 MW Marampa Iron Ore 100 MW Koidu Holdigs Sierra Minerals 50 MW London Mining MW Sierra Rutile 2013 2020

A.6.2 Mining Demand 2013-2020

Due to the uncertainties around the realization of the forecast mining projects, we cannot realistically project demand after 2020. However, we give indicative low case, base case and high case estimations on the period 2020-2030 using a linear extrapolation of the demand over the period 2013-2020.

Ministry of Energy 241 January 2015 20414

Table A-70: Mining Demand after 2020

Year Base Case High Case Low Case Demand Demand Demand MW MW MW (GWh) (GWh) (GWh) 2013 347 53 347 53 347 53 2014 788 120 1109 169 604 92 2015 1229 187 1871 285 861 131 2016 1670 254 2633 401 1117 170 2017 2111 321 3395 517 1374 209 2018 2552 388 4157 633 1630 248 2019 2993 456 4919 749 1887 287 2020 3434 523 5681 865 2143 326 2021 3875 590 6443 981 2400 365 2022 4315 657 7205 1097 2656 404 2023 4756 724 7967 1213 2913 443 2024 5197 791 8729 1329 3169 482 2025 5638 858 9491 1445 3426 521 2026 6079 925 10253 1561 3682 560 2027 6520 992 11015 1677 3939 599 2028 6961 1059 11777 1792 4195 639 2029 7402 1127 12539 1908 4452 678 2030 7842 1194 13301 2024 4708 717

Ministry of Energy 242 January 2015 20414

Figure A-46: Mining Demand Forecast 2013-2030 14000

12000

10000

Base Case 8000 High Case

6000 Low Case

4000

2000

0 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Ministry of Energy 243 January 2015 20414

B Appendix B: Social Tariff Design and Willingness-to-Pay for Customers of Remote Rural Areas

B.1 Introduction

The TOR require the Consultant to evaluate social protection alternatives to retail tariffs premised on cost-recovery principles. To this end, the TOR suggested a ‘small survey to estimate customers’ ability to pay for electricity’. Following discussions with senior officials at Statistics Sierra Leone (SSL) that drew the Consultant’s attention to the difficulties in realising a representative survey of household incomes in the country, an alternative but ultimately more satisfactory approach has been adopted. SSL advised the Consultant of an extensive household income and expenditure survey conducted in 2011 with support from World Bank, i.e. the Integrated Household Survey, 2011. Due to a number of difficulties, the ‘income’ element of the survey had to be abandoned, due largely to the fact that outside Freetown the overwhelming majority of households were subsistence farmers with little or no access to cash on a dependable basis. Nevertheless, a detailed ‘expenditure’ survey proved more practicable, and the database compiled by this survey is of great utility for the purpose of estimating willingness-to- pay by poor households across the country.

A synopsis of the survey was included as an appendix to the Consultant’s Draft Final Tariff Methodology Report. Some key features are summarised as follows:

 The survey covered 6727 households throughout Sierra Leone.54  The survey sampled a representative cross-section of the population, in all districts of the country.  The database from the survey can be interrogated by urban and rural divisions of each district, and several urban centres are aosl specifically identifiable.

A fundamental objective of the survey was to assess the level of poverty across the country. However, the level of detail in the questionnaire provides a clear indication on the level of expenditure on anything, from food to education and health and various forms of household energy. It is possible to use this expenditure data to gain an estimate of the amount that a household in a remote rural area may be willing and able to spend on electricity.

B.2 Household Expenditure

The survey collected detailed information on household spending. The graph below summarises the average household spending on energy for each of the districts.

54 This survey is two orders of magnitude larger than anything that could be undertaken as part of the Tariff Study.

Ministry of Energy 244 January 2015 20414

Figure B-1: Average Household Expenditure by District

14,000,000 13,120,943 13,120,943

12,000,000

10,000,000 9,524,034

8,290,878 8,290,878

8,171,297 8,171,297

7,923,018 7,923,018

7,886,829 7,825,362 7,825,362

8,000,000 7,326,642

7,043,579 7,043,579

6,945,750 6,945,750

6,754,040 6,754,040

6,616,768 6,616,768

6,147,085 5,890,435 5,890,435 6,000,000

4,000,000 Expenditure in Leones per Year per Leones in Expenditure 2,000,000

Kono

Bonthe

Kambia

Tonkilili

Kenema

Bombali

Pujehun

Kailahun

Port Loko Port

Moyamba

Koinadugu

Western other Western Western urban Western Eastern Northern Southern Western

The graph shows significantly more expenditure in the more developed Freetown (Western Urban area) that elsewhere. Spending in Moyamba and Tonkilili is particularly low.

So far we have been considering average household expenditure across each district. It is important to be aware of the profile of expenditure across the whole population. The graph below illustrates this by plotting the household expenditure of all the households surveyed. This graph shows that there is a large amount of variability in the expenditure levels over the population, with more than 30% of the population spending an average of 3,647,000 Le (about $842 US) per year, and at the other end of the scale, the top 5% an average of 25,900,000 Le (about $6,000 US) per year.

Ministry of Energy 245 January 2015 20414

Figure B-2: Total Household Expenditure over the Survey Population 8.00E+07

7.00E+07

6.00E+07

5.00E+07

4.00E+07

3.00E+07 Leones per year per Leones

2.00E+07

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

The graph below explores this variability further by separating out the data from urban and rural households. The data from Freetown is also shown as a separate line. This graphs shows that, in general, the urban population have higher levels of expenditure than that of the rural population. The population in Freetown have the highest levels of expenditure.

Figure B-3: Total Household Expenditure over the Survey Population in Urban and Rural Areas 8.00E+07 Freetown

7.00E+07 Rural Towns

Remote Rural 6.00E+07

5.00E+07

4.00E+07

3.00E+07 Leones per year per Leones

2.00E+07

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

There is also some variability across the districts. The graphs below show the expenditure over the surveyed population of each of the districts.

Ministry of Energy 246 January 2015 20414

Figure B-4: Household Expenditure in Eastern Region 5.00E+07 Kailahun Kenema Kono 4.00E+07

3.00E+07

2.00E+07 Leones per year per Leones

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure B-5 : Household Expenditure in Northern Region 5.00E+07 Bombali

Kambia

4.00E+07 Koinadugu

Port Loko

Tonkilili 3.00E+07

2.00E+07 Leones per year per Leones

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Ministry of Energy 247 January 2015 20414

Figure B-6: Household Expenditure in Southern Region 5.00E+07 Bo Bonthe Moyamba 4.00E+07 Pujehun

3.00E+07

2.00E+07 Leones per year per Leones

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure B-7: Household Expenditure in Western Region 5.00E+07 Western urban Western 4.00E+07 other

3.00E+07

2.00E+07 Leones per year per Leones

1.00E+07

0.00E+00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Ministry of Energy 248 January 2015 20414

B.3 Willingness to Pay for Electricity

Whereas the Draft Final Tariff Methodology Report drew broad conclusions on the general willingness to pay for grid electricity, from the regional distributions of energy expenditure, the Consultant now aims to undertake more detailed analysis in order to assess what households could afford to pay for in terms of grid electricity.

As noted in the introduction to this appendix, the Consultant’s premise is that it may be possible to gain an estimate of the amount that the population of Sierra Leone would be willing or able to pay for an electricity supply from the expenditure data gained from the 2011 survey.

In order to predict potential expenditure on electricity, the expenditure data of those households with electricity was studied. The table below shows the average expenditure on electricity as a proportion of household expenditure. The average value over the population with electricity is 6%.

Table B-1: Average Expenditure on Electricity Location Average Expenditure on Electricity as a proportion of Household Expenditure Freetown 6% Bo City 4% Kenema City 7% Western Area Rural 7% Average over survey 6%

The electricity expenditure data in the table above is entirely made up of urban households, and is generally regarding more affluent households. It is estimated that the proportion of household expenditure that can be spent on electricity by rural households will be lower than that of urban households. The assumption made for this study is that rural households will spend an average of 4% of the household expenditure on electricity. This aligns quite reasonably with the results of a similar household expenditure survey that the Consultant undertook in Ethiopia, several years ago, and where between 4% and 6% of household expenditure was spent on electricity.

The database from the Integrated Household Survey is representative of income and expenditure levels as they existed in 2011. With the presumption of strong economic growth and an equitable sharing of this economic growth across all social strata, it is to be anticipated that real incomes will grow steadily and that this will be reflected by the population expending more on electricity in each successive year. The Consultant’s approach for capturing this income growth is outlined as follows.

As a base case, the Consultant assumes that real GDP grows at an annual rate of 6%. To convert this to income growth we apply the ‘income elasticity of electricity growth’. The value of this elasticity coefficient is difficult to determine in countries where connectivity is extremely low and supply is severely constrained. Consequently, the Consultant must resort to generic values. In 2006, PPA Energy adopted an income elasticity coefficient of 1.0 for Uganda, when studying the Bujagali hydropower project. The Consultant considers this a reasonable value for Sierra Leone. The impact

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of this coefficient is that a 1% increase in real GDP produces a 1% growth in incomes. As mentioned, this study assumed that real GDP grows at an annual rate of 6%, and thus the average income will grow by 6%.

However, it is assumed that this growth in income is not spread equally amongst the population. This assumption implies that income distribution, as measured by the Gini coefficient, rises over time. In 2011, Sierra Leone’s Gini coefficient was assessed as 35.4, which in world comparisons is a reasonable spread of wealth. Higher values represent increasingly unequal societies, whereas lower values represent more equal societies. Recent evidence is that in countries that have grown quite rapidly due to the exploitation of mineral wealth, the Gini coefficient increases and thus the income growth is more concentrated on the wealthier households, and the incomes of the poorer households rising more slowly. For simplicity, the Consultant has assumed that the income of the poorer, rural households will rise by only 2% per year.

It should be noted that because the household incomes of rural households are assumed to increase by 2% each year, the amount assumed to be spent on electricity will also rise by 2% each year. This is because the proportion of the household expenditure spent on electricity each year will remain constant (at 4% for rural households).

Projections for the number of households in each area are made assuming an annual growth in population for each region. This information is then used to predict the potential total expenditure that is available in each location.

Within the rural areas, it is reasonable to assume that only a small proportion of this potential expenditure will translate to actual electricity purchases within the first years of electrification. This represents the amount of time taken to connect additional rural customers. It is assumed that the electrification of the rural areas will commence in 2020. In 2020 and in each year thereafter, an additional 2.5% of the potential customers receive a connection. Therefore, in 2020, 2.5% of the potential customers are connected, and correspondingly 2.5% of the potential expenditure on electricity results in actual expenditure. In 2021, a further 2.5% of the potential customer base is connected, bringing the total to 5% of rural households. By 2030, the connectivity level rises to 27.5%.

In order to determine the amount of energy, in kWh, can be consumed by the average household, a per-kWh tariff is assumed. In each district there will be a certain number of households that can afford to pay a cost-reflective tariff for electricity supplying a range of electrical appliances such as refrigerators, TV, fans, etc. in addition to electric lighting inside and outside the house. An indicative price point for such households may be US20c per kWh, which is the equivalent of $20US per month (US$240 per year) with a consumption of 100kWh/month.

At the opposite end of the scale, there will be extremely poor households that would, nevertheless, obtain an appreciable improvement in their welfare if they could simply provide electric lighting in their one or two-roomed dwellings. Assuming limited usage of an energy efficient compact fluorescent lamp (CFL) and a social tariff that is heavily subsidised, a monthly consumption of just 5kWh is possible and, at a social tariff of just 10UScents/kWh, this represents a price point of US$0.5 per month (US$6 per year).

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Therefore, this model uses two tariff levels to estimate a potential energy requirement:

 Tariff 1: Where all customers pay 20c US per kWh  Tariff 2: Where all customers pay a heavily discounted rate of 10c US per kWh In 2020, it is assumed that 25% of the rural population will pay the higher tariff of 20c US per kWh. The remaining 75% will be charged the heavily subsidised rate of 10c US per kWh. As the income of each household increases, it is assumed that a higher proportion will be moved to the higher tariff of 20c per kWh. It is assumed that this occurs at a steady rate, so that in 2030, 75% of the connected households will be paying 20c US per kWh, and the remaining 25% will be paying 10c US per kWh. The graphs below show the resulting energy requirements from these assumptions (referred as “base case” in this report).

Figure B-8: Projected Energy Requirements Eastern Province Remote Rural Areas (Base Case)

30,000,000 Kailahun Rural Kenema Rural 25,000,000 Kono Rural

20,000,000

15,000,000

10,000,000 Energy Requirements / kWh / Requirements Energy

5,000,000

- 2010 2015 2020 2025 2030 2035 2040

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Figure B-9 : Projected Energy Requirements Northern Province Remote Rural Areas (Base Case) 30,000,000 Bombali Rural Kambia Rural Koinadugu Rural 25,000,000 Port Loko Rural Tonkolili Rural

20,000,000

15,000,000

10,000,000 Energy Requirements / kWh / Requirements Energy

5,000,000

- 2010 2015 2020 2025 2030 2035 2040

Figure B-10: Projected Energy Requirements Southern Province Remote Rural Areas (Base Case)

30,000,000 Bo Rural

Bonthe Rural 25,000,000 Moyamba Rural

Pujehun Rural 20,000,000

15,000,000

10,000,000 Energy Requirements / kWh / Requirements Energy

5,000,000

- 2010 2015 2020 2025 2030 2035 2040

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These graphs show that the energy requirements of the rural populations of the Sierra Leone districts in 2020 are expected to be modest, though they will rise as a greater proportion of the customers become connected, and as the expenditure per customer rises.

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C Appendix C: Review of wind power generation technologies [11] [12] [13] [14]

A wind turbine converts kinetic energy into electricity.

The turbine’s pales convert the wind’s kinetic energy into mechanical energy. This movement turns a main shaft, connected a generator, which produces the electricity. Several types of machines can be used in wind turbines:

Synchronous machines are frequently used for wind generation as they are lighter, reliable and well proven. But they have one major disadvantage: they are by definition synchronous with the network, which means that their rotation speed is fixed and defined by the network frequency. This can generate “high mechanical loading on the structure and prevent from maintaining maximum aerodynamic efficiency”. [11]

This constraint can be mitigated by the use of a (static) multiplier between the blades and the generator and/or (dynamic) power electronics equipment.

Asynchronous machines are cheaper and give more flexibility on the rotor’s speed.

In both asynchronous and synchronous motors, the AC power supplied to the motor's stator creates a magnetic field that rotates in time with the AC oscillations.

Whereas a synchronous motor's rotor turns at the same speed as the stator field, an induction motor's rotor rotates at a slower speed than the stator field. The induction motor stator's magnetic field is therefore rotating relative to the rotor. This induces an opposing current in the induction motor's rotor, in effect the motor's secondary winding.

According to the Lentz law, the rotor tries to “catch up” the stator. But it can’t: otherwise the magnetic field would not be moving relative to the rotor conductors and no currents would be induced. Increasing the rotor resistance stretches the speed range and thus allows the turbine to increase the speed by about 10%. [11]

The drawback is that their allowable wind speed range is, although wider than for synchronous machines, very limited and depends on the number of pair poles. This technology also shows “poor grid performance”.

Double-fed asynchronous machines (DFAS or DFIG) have been so far the “most common solution for variable speed wind turbines”. The rotor speed can be changed by the converter by absorbing or injecting active power. The typical speed range of DFAS is “±30% around synchronous speed”. [11]

DFAS are equipped with an optimization system called “Maximum Power Point Tracking”. It adapts the rotation speed to the wind speed to maximize the power output. In addition, this technology enables to control reactive power flows.

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Wind farms are groups of wind turbines. The arrangement of turbines is determined after wind studies (to optimize power output of each turbine individually) and after considering wake modelling results (to take into account the effect that each turbine has on to the next).

Figure C-1: Wake effect at the Horns Rev1 offshore wind farm, on 12/02/2008 (Vattenfall Wind Power). Photographer is Christian Steiness. [15]

Wind farms can be “onshore” or “offshore”. While the offshore higher wind speeds allow increased output, the investments (including foundation and connection costs) and maintenance costs (including desalinating the turbines’ blades) are substantially higher. For offshore farms, costs also increase with “water depth and distance to coast”. [14]

The EIA (US Energy Information Administration) has published indicative costs in April 2013 for utility scale electricity plants, and more specifically on wind farms:

Table C-1: Indicative costs for wind generation [10]

Technology Overnight Capital Cost Fixed O&M Variable 55 O&M ($/kWh) ($/kW) ($/MWh)

Onshore wind 2,213 39.55 0.00

55 Exclusive of any mark-up

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Offshore wind 6,230 74.00 0.00

Wind power can only be generating when the wind is blowing, and variations are considerable over time, while wind speed and direction are still challenging to forecast. This brings new challenges for the supply-demand balancing. Besides the development of windfarms requires extensive wind data at numerous spots over a long period of time, not readily available in Sierra Leone.

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D Appendix D: Model for the calculation of the indicative quantitative environmental and social costs of power generation - USER MANUAL

The following instructions explain how to use the model.

1. Open the model (QESIA.xlsm)

2. The “Start” window pops up. Click on “Building”.

If no window pops up, then please make sure that macros are activated and click on “start a new valuation”.

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3. Fill in the blanks in “General Details”. All fields should be filled in. If not, the tool will automatically fill-in the blanks with “0” when submitting.

4. Repeat for all tabs of “Building”, then click on “OK”. The building social and environmental cost assessment has been completed: the “building” button turns green on the “Home” page.

5. Click on “Operating” and fill-in the blanks in each tab. Click on “OK”.

6. Click on “Dismantling” and fill-in the blanks in each tab. Click on “OK”.

7. If you want to do any change, click on any section you would like to come back to. When you are happy with the information provided click on “Calculate Total Social and Environmental Cost”.

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8. Choose “See Results Data Table” to see the detailed cost breakdown, or “See charts” to see graphic summaries.

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