EASTERN AFRICA POWER POOL
(EAPP) AND EAST AFRICAN
COMMUNITY (EAC)
REGIONAL POWER SYSTEM MASTER PLAN
AND GRID CODE STUDY
FI...
PREFACE 
The  objective  of  the  present  study  is  to  identify  regional  power  generation  and  interconnection 
pro...
Final Master Plan Report Acronyms and Abbreviations
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Acronyms and Abbrevi...
Final Master Plan Report Acronyms and Abbreviations
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
EETC Egyptian Electr...
Final Master Plan Report Acronyms and Abbreviations
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
IFC International Fi...
Final Master Plan Report Acronyms and Abbreviations
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
N
NBI Nile Basin Ini...
Final Master Plan Report Acronyms and Abbreviations
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
S
SAPP Southern Afri...
Final Master Plan Report Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
SECTION 1
Introduction
Final Master Plan Report 1-1 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
1 INTRODUCTION
1.1 Study Objec...
Final Master Plan Report 1-2 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
• Cross-border electrification...
Final Master Plan Report 1-3 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 1-1 Ongoing Interconnect...
Final Master Plan Report 1-4 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
From To
Type /
Length
Capacity...
Final Master Plan Report 1-5 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
1.3 Content and objectives of ...
Final Master Plan Report 1-6 Introduction
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
1.4 Organization of the report...
Final Master Plan Report WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
SECTION 2
Demand Forec...
Final Master Plan Report WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
TABLE OF CONTENTS
1.  ...
Final Master Plan Report WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
LIST OF TABLES
Table 3...
Final Master Plan Report 1-1 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
1. DEMAND FORECAST...
Final Master Plan Report 1-2 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
consideration, the...
Final Master Plan Report 1-3 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Finally there may ...
Final Master Plan Report 1-4 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Econometric Analys...
Final Master Plan Report 2-1 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
2. ADOPTED APPROAC...
Final Master Plan Report 2-2 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
• Identify most re...
Final Master Plan Report 3-1 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
3. REVIEW OF EXIST...
Final Master Plan Report 3-2 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-1 Extended...
Final Master Plan Report 3-3 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
forecast developed...
Final Master Plan Report 3-4 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-2 LCEMP De...
Final Master Plan Report 3-5 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-3 LCEMP De...
Final Master Plan Report 3-6 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-4 LCEMP De...
Final Master Plan Report 3-7 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
3.3 East DRC
The l...
Final Master Plan Report 3-8 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-5 Extended...
Final Master Plan Report 3-9 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-6 Extended...
Final Master Plan Report 3-10 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-7 Extende...
Final Master Plan Report 3-11 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
As the EEHC deman...
Final Master Plan Report 3-12 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-8 Extende...
Final Master Plan Report 3-13 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Further details o...
Final Master Plan Report 3-14 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-9 Extende...
Final Master Plan Report 3-15 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
2008 LCPDP
The de...
Final Master Plan Report 3-16 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-10 Extend...
Final Master Plan Report 3-17 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-11 Extend...
Final Master Plan Report 3-18 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
analysis of the r...
Final Master Plan Report 3-19 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-12 Extend...
Final Master Plan Report 3-20 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
3.8 Sudan
In 2005...
Final Master Plan Report 3-21 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 3-13 Extend...
Final Master Plan Report 3-22 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
3.9 Tanzania
In D...
Final Master Plan Report 3-23 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
sectors to grow a...
Final Master Plan Report 3-24 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Further details o...
Final Master Plan Report 4-1 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
4. INDEPENDENT PB ...
Final Master Plan Report 4-2 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Table 4-1 PB Base,...
Final Master Plan Report 4-3 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Figure 4-1 PB Peak...
Final Master Plan Report 4-4 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
4.2 Djibouti
The m...
Final Master Plan Report 4-5 WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
PSMP & Grid Code Study
Figure 4-3 PB Peak...
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
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EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)
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EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)

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EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC)

  1. 1. EASTERN AFRICA POWER POOL (EAPP) AND EAST AFRICAN COMMUNITY (EAC) REGIONAL POWER SYSTEM MASTER PLAN AND GRID CODE STUDY FINAL MASTER PLAN REPORT VOLUME I 01 -Introduction 02 -Demand Forecast (WBS 1100) 03 -Generation Supply Study & Planning Criteria (WBS 1200) 04 -Supply-Demand Analysis & Project Identification (WBS 1300) May 2011 SNC LAVALIN INTERNATIONAL INC. in association with PARSONS BRINCKERHOFF
  2. 2. PREFACE  The  objective  of  the  present  study  is  to  identify  regional  power  generation  and  interconnection  projects  in  the  power  systems  of  EAPP  and  EAC  member  countries  in  the  short‐to‐long  term.  The  study also aims at developing a common Grid Code (Interconnection Code) in order to facilitate the  integrated development and operations of the power systems of the member countries.    The  study  further  aims  at  contributing  to  the  institutional  capacity  building  for  the  EAPP  and  EAC  through training of counterpart staff. The development of institutional capacity will enable EAPP/EAC  to  implement  the  subsequent  activities,  including  the  updating  of  both  the  Master  Plan  and  the  Interconnection Code.  This  study  covers  the  following  countries  in  alphabetical  order:  Burundi,  Djibouti,  Democratic  Republic of Congo, Egypt, Ethiopia, Kenya, Rwanda, Sudan, Tanzania and Uganda.  The Master Plan Report has been organized according to the following structure:  Volume  Section  Executive Summary    Volume I  01 – Introduction  02 ‐ Demand Forecast (wbs 1100)  03 ‐ Generation Supply Study & Planning Criteria (wbs 1200)  04 ‐ Supply‐Demand Analysis & Project Identification (wbs 1300)  Volume II  05 ‐ Transmission Network Study (wbs 1400)  06 ‐ Interconnection Studies (wbs 1500)  07 ‐  Regional Market Operator Location (wbs 2900)  Volume III  08 ‐ System Studies For Expansion Plan (wbs 2100)  Volume IV  09 ‐ Environment Impact Assessment (wbs 2200)  10 ‐ Cost Estimates And Implementation Schedules (wbs 2300)  11 – Financial & Economic Evaluation – Risk and Benefits (wbs 2400/2500)  12 ‐ Development and Investment Plan (wbs 2600)  13 ‐ Institutional and tariff aspects (wbs 2700)  14 – Project Funding (wbs 2800)  15 – Conclusions  Appendix A  TOR, Cost Estimates and Implementation Schedules for Feasibility Studies  for Projects identified in the first five years  Appendix B  Part I – WBS 1100 Demand Forecast  Part II – WBS 1200‐1300 Gen. Supply Study – Supply Demand Analysis  Part III – WBS 1400‐1500 Transm. Network – Interconnection Studies  Part IV – WBS 2600‐2700 Investment Plan – Institutional & Tariff Aspects     
  3. 3. Final Master Plan Report Acronyms and Abbreviations May 2011 EAPP/EAC Regional PSMP & Grid Code Study Acronyms and Abbreviations A AC Alternate Current AEO Annual Energy Outlook AfDB African Development Bank AICD Africa Infrastructure Country Diagnostic ARIMA Autoregressive Integrated Moving Average ARR Annual Required Revenue Avg Average B BADEA Arab Economic Development Bank in Africa bbl Oil barrel BCR Benefit/Cost Ratio BR Burundi C CAPEX Capital Expenditure CBEMA Computer and Business Equipment Manufacturers’ Association CCGT Combined Cycle Gas Turbine - Thermal Power Plant CDM Clean Development Mechanism CEO Chief Executive Officer CF Capacity Factor CIRR Commercial Interest Reference Rate CKT Circuit CO2 Carbon Dioxide COR Composite Outage Rate CPI Consumer Price Index D DB Djibouti DC Direct Current DC Democratic Republic of Congo DGHER General Directorate for Hydropower and Rural Electrification DOE Department of Energy (USA) DRC Democratic Republic of Congo DSCR Debt Service Coverage Ratio E EAC East African Community EAPMP East African Power Master Plan Study EAPP Eastern Africa Power Pool EdD Électricité de Djibouti EDF Électricité de France EEHC Egyptian Electric Holding Company EEPCo Ethiopia Electric Power Corporation
  4. 4. Final Master Plan Report Acronyms and Abbreviations May 2011 EAPP/EAC Regional PSMP & Grid Code Study EETC Egyptian Electricity Transmission Company EG Egypt EIA Energy Information Administration EIC Existing Interconnections EIJLLST Egypt, Iraq, Jordan, Libya, Lebanon, Syria and Turkey EIRR Economic Internal Rate of Return EMF Electro-Magnetic Field EMP Environmental Management Plan ENPTPS Eastern Nile Power Trade Program Study ENPV Economic Net Present Value ENTRO Eastern Nile Technical Regional Office EPC Engineering Procurement and Construction EPCM Engineering Procurement and Construction Management Esc. Escalation ESIA Environmental and Social Impact Assessment ET Ethiopia EU European Union F FC Fictitious Company FDI Foreign Direct Investment FIRR Financial Internal Rate of Return FNPV Financial Net Present Value FOR Forced Outage Rate FS Feasibility Study FttH Fibre-to-the-Home G GCI Global Competitiveness Index GDP Gross Domestic Product GHG Green House Gases GNI Gross National Income GoE Government of Ethiopia GT Gas Turbine GTP Growth and Transformation Plan H HFO Heavy Fuel Oil HPP Hydro Power Plant HVAC High Voltage Alternate Current HVDC High Voltage Direct Current I ICNIRP International Commission of Non-Ionizing Radiation Protection ICS Interconnected System (Ethiopia) ICT Information and Communication Techonology IDC Interest during Construction IDO Industrial Diesel Oil
  5. 5. Final Master Plan Report Acronyms and Abbreviations May 2011 EAPP/EAC Regional PSMP & Grid Code Study IFC International Financial Corporation IMF International Monetary Fund Inst. Cap. Installed Capacity IP Internet Protocol IPO Initial Public Offering IPP Independent Power Producer IRR Internal Rate of Return IT Information Technology J JMP Joint Multipurpose Project K KenGen Kenya Electricity Generation Company KETRACO Kenya Electricity Transmission Company Limited KPLC Kenya Power and Lighting Company Ltd KTCIP Kenya Telecommunications Infrastructure Project KY Kenya L LAP Libyan African Portfolio LCEMP Least Cost Electricity Master Plan LCPDP Least Cost Power Development Plan LD Liquidated Damage LDC Load Duration Curve LDCs Least Developed Countries Level of Prep. Level of Preparedness LFO Light Fuel Oil LNG Liquefied Natural Gas LOLE Loss of Load Expectation LOLP Loss of Load Probability LRMC Long Run Marginal Cost LRO Light Residual Oil LSD Low-Speed Diesel Engine LTPSPS Long-Term Power System Planning Study LVL Level M MAED Model for Analysis of Energy Demand Max Maximum MD Maximum Power Demand Min Minimum MINIFRA Rwanda Ministry of Infrastructure MOU Memorandum of Understanding MoWR Ministry of Water and Energy MP Master Plan MPIP Medium-term Public Investment Plan MSD Medium-Speed Diesel Engine
  6. 6. Final Master Plan Report Acronyms and Abbreviations May 2011 EAPP/EAC Regional PSMP & Grid Code Study N NBI Nile Basin Initiative NEC Sudan National Electricity Corporation NELSAP Nile Equatorial Lakes Subsidiary Action Program NG Natural Gas NGP National Generation Plan Nom. Cap. Nominal Capacity NPV Net Present Value O OCGT Open Cycle Gas Turbine - Thermal Power Plant ODA Official Development Assistance OECD Organization of Economic Cooperation and Development OLADE Organización Latinoamericana de Energía (Latin American Energy Organization) OLTC On-Load Tap Changers O&M Operation and Maintenance ONRD Office of Natural Resources Damage OPEC Organization of the Petroleum Exporting Countries OPEX Operating Expenditure OPTGEN Optimal Generation (Planning Model) P PF Plant Factor PPA Power Purchase Agreement PPE Personal Protective Equipment PSIP Power Sector Investment Plan PSMP Power System Master Plan Study pu Per Unit R RALF Regression Analysis Load Forecast RCC Regional Coordination Center RECO Rwanda Energy Corporation Ref Reference REGIDESO Régie de production Distribution d’Eau et d’Electricité RFP Request for Proposal RGP Regional Generation Plan RMO Regional Market Operator RMOC Regional Market Operation Center RoC Return on Capital RoCE Return on Capital Employed RoE Return on Equity ROR Run-Of-River RTL Rwandatel S.A. RW Rwanda RWASCO Rwanda Water Supply Corporation
  7. 7. Final Master Plan Report Acronyms and Abbreviations May 2011 EAPP/EAC Regional PSMP & Grid Code Study S SAPP Southern African Power Pool SCS Self-Contained System (Ethiopia) SD Sudan SDDP Stochastic Dual Dynamic Programming SEACOM SEEE Society of Electrical and Electronics Engineers SIL Surge Impedance Loading SINELAC Société Internationale d’Électricité des Pays des Grands Lacs SNEL Société Nationale d’Électricité – République Démocratique du Congo SPV Special Project Vehicle SRMC Short Run Marginal Cost SSEA Strategic/Sectoral, Social and Environmental Assessment of Power Development Options in the Nile Equatorial Lakes Region STPP Steam Thermal Power Plant SVC Static Var Compensator T TANESCO Tanzania Electric Supply Company Ltd TOR Terms of Reference TPP Thermal Power Plant TSO Transmission System Operator TZ Tanzania U UETCL Uganda Electricity Transmission Company UEGCL Uganda Electricity Generation Company Limited UG Uganda UIC Unlimited Interconnections UN United Nations UNCTAD United Nations Conference on Trade And Development USBR United States Bureau of Reclamation UTL Uganda Telecom Ltd W WACC Weighted Average Cost of Capital WB World Bank WBS Work Breakdown Structure WEF World Economic Forum Y yr Year
  8. 8. Final Master Plan Report Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study SECTION 1 Introduction
  9. 9. Final Master Plan Report 1-1 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study 1 INTRODUCTION 1.1 Study Objectives The objective of the study is to identify power generation and interconnection projects, at Master Plan level, to interconnect the power systems of EAPP and EAC countries in short- to-long term. The study also aims at developing common Transmission Interconnection Code in order to facilitate the integrated development and operations of the power systems of EAPP and EAC countries. The study further aims at contributing to the institutional capacity building for the EAPP and EAC staff through training of counterpart staff. The development of institutional capacity will enable EAPP / EAC to implement the subsequent activities, including the updating of both the Master Plan and the Grid Code reports. This study covers the following countries in alphabetical order: Burundi, Djibouti, Democratic Republic of Congo, Egypt, Ethiopia, Kenya, Rwanda, Sudan, Tanzania and Uganda. 1.2 Project Background On 24 February 2005, the Energy Ministers from seven (7) Eastern Africa countries, namely: Burundi, Democratic Republic of Congo (DRC), Egypt, Ethiopia, Kenya, Rwanda and Sudan signed an Inter-Governmental Memorandum of Understanding (MOU) for the establishment of the Eastern Africa Power Pool (EAPP). The signature of the MOU was followed by the signature of an Inter-Utility MOU by the Chief Executive Officers (CEOs)/Managing Directors of the countries’ nine (9) Power Utilities. This event heralded the formal launching of EAPP. The EAPP member utilities are: REGIDESO (Burundi), SNEL (DRC), EEHC (Egypt), EEPCo (Ethiopia), KenGen and KPLC (Kenya), ELECTROGAZ (Rwanda), NEC (Sudan) and SINELAC (DRC, Rwanda and Burundi). In further developments, EAPP has been adopted by the 11th Summit of the Common Market for Eastern and Southern Africa (COMESA) Authority of Heads of State and Government held in Djibouti from 15-16 November 2006 and has been considered as COMESA’s Specialized Institution for Electric Power. Given that some member countries of EAC overlap with those of EAPP, these two institutions signed an MOU on September 2009, whereby EAPP and EAC agree to jointly implement the present Power Master Plan and Grid Code Study for which EAPP is designated as the Implementation Agency. In this document when reference is made to “EAPP countries” it is understood that this designates the group of ten countries mentioned above. Countries in the region, by and large, have been planning and implementing the development of their power system in an isolated manner with a view to satisfying the national demand growth. Bilateral power exchange agreements exist between some countries in the Region. However, the volume of power exchange is not significant and exporting parties have frequently been unsuccessful in their commitments to deliver the power in accordance with their contractual obligations because of deficits in their systems. The existing power interconnection projects include: • DRC, Burundi, and Rwanda interconnected from a jointly developed hydro power station Ruzizi II, (capacity 45 MW) operated by a joint utility [SOCIETE D’ELECTRICITE DES PAYS DES GRAND LACS (SINELAC)];
  10. 10. Final Master Plan Report 1-2 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study • Cross-border electrification between Uganda and Rwanda, Tanzania and Uganda, and Kenya and Tanzania; • Kenya – Uganda interconnection; and • Egyptian power system interconnection through Libya to other Maghreb countries and Southern Europe; and through Jordan to Eastern Mediterranean. Other ongoing power interconnection systems are shown in:Table 1-1 Power trading through common planning and implementation of regional generation and interconnection projects has been identified as one important strategy for tackling the problems associated with power supply shortages, low access, high cost and poor supply reliability. However, at present, the power interconnections within the region are limited for realization of shared benefits that would be generated through integrated development of their power systems. Presently, Kenya, Tanzania and Uganda under the auspices of the East African Community (EAC), are developing plans to (i) interconnect and strengthen their power systems in order to share power supplies, and (ii) further extend the power system interconnections to countries outside EAC countries. The Master Plan which was finalized in March 2005 has identified regional generation and transmission projects for integrated development. A series of studies have been completed in the last 5 years that cover opportunities for cross-border interconnections in the region. These include the EAPMP1 , SSEA2 , ENTRO3 , Ethiopia-Djibouti Interconnection, and the 2004 World Bank Scoping Study4 . Implementation planning is going ahead for the interconnection of the national grids for the five equatorial Lakes countries (Burundi, Kenya, Uganda, DRC, and Rwanda). 1 East Africa Power System Master Plan Study (Uganda, Kenya, Tanzania) 2 Stategic/Sectoral, social and Environmental Assessment of Power Development Options (Burundi, Eastern DRC, Kenya, Rwanda, Tanzania, Uganda) 3 Eastern Nile Power Trade Study (Egypt, Sudan, Ethiopia) 4 Joint UNDP/WB Energy Sector Management Assistance Program (ESMAP), Opportunities for Power Trade in the Nile Basin, Final Scoping Study, January 2004
  11. 11. Final Master Plan Report 1-3 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 1-1 Ongoing Interconnection projects From To Type / Length Capacity (MW) Earliest Year in Operation Status Comments Tanzania Kenya 400 kV 2 circuits 260 Km 1520 2015 Ongoing FS, detailed design and tender documents preparation Bidding for line construction may start at the end of 2011. Rusumo Rwanda 220 kV 1 circuit 115 Km 320 2015 FS completed Lines associated to the Rusumo Falls HPP connecting the project with the grids of Tanzania, Rwanda and Burundi. Rusumo Burundi 220 kV 1 circuit 158 Km 280 2015 Rusumo Tanzania 220 kV 1 circuit 98 Km 350 2015 Ethiopia Kenya 500 kV-DC bipole 1120 Km 2000 2016 Design and tender document preparation study to start early 2011 New design study aims at highly optimistic completion of phase I (1000 MW) of the project by 2013 and phase II upgrade to 2000 MW by 2019. Ethiopia Sudan 500 kV 4 circuits 570 Km 3200 2016 FS completed
  12. 12. Final Master Plan Report 1-4 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study From To Type / Length Capacity (MW) Earliest Year in Operation Status Comments Egypt Sudan 600 kV-DC bipole 1665 Km 2000 2016 FS completed Uganda Kenya 220 kV 2 circuits 254 Km 300 2014 Under construction Runs from Lessos substation in Kenya to Bujagali substation passing through Tororo substation in Uganda, duplicating the existing 132kV line. Uganda Rwanda 220 kV 2 circuits 172 Km 250 2014 Detailed and Tender Documents preparation study starts in 2011 Line from Mbarara to Mirama (border Uganda) to Birembo/Kigali (Rwanda) Rwanda DRC 220 kV 1 circuit 68 Km 370 2014 Under construction Line between new substation at Kibuye Methane Gas plant in Rwanda and Goma (DRC), thus completing the loop around lake Kivu. DRC Burundi 220 kV 1 circuit 105 Km 330 Expected in 2014 FS, detailed engineering and tender documents preparation study to start early 2011 Line from future substation Kamanyola/Ruzizi III (DRC) to Bujumbura (Burundi). Study Includes 220kV line between a new substation in Bujumbura to Kiliba (DRC). Burundi Rwanda 220 kV 330 2016 FS update to start early 2011 Line Rwegura (Burundi) – Kigoma (Rwanda), previous FS recommended 110kV. Feasibility Study update to re-examine 220kV option and re-route line to feed intermediate locations.
  13. 13. Final Master Plan Report 1-5 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study 1.3 Content and objectives of the master plan report This Master Plan Report provides the findings from the Regional Power System Master Plan. The Interconnection Code (Grid Code) is part of a separate report. The Master Plan first discusses all the input data necessary for the planning exercise: Demand Forecast (WBS 1100), Generation Supply analysis, including existing and future thermal, hydro and renewable energy projects, and planning criteria (WBS 1200). The existing transmission network data and models are compiled in WBS 1400 and common planning criteria and basic unit costs are developed for the candidate interconnection projects in WBS 1500. A preliminary identification of the regional projects (generation and interconnections) is performed including a supply-demand analysis for each country and a regional interconnection plan is developed under WBS 1300. An estimation of the regional benefits of different scenarios is also performed. Detailed system studies for each country and reinforcement needs are identified in WBS 2100 while other aspects of the projects such as the environmental impacts (WBS 2200), Cost Estimates (2300), Financial-Economic Analysis and risk assessment (WBS 2500) are presented in the report. Finally an investment plan for the identified interconnection projects is developed in WBS 2600 and the analysis of institutional and tariff aspects as well as project funding requirements are included in WBS 2700 and WBS 2800 respectively. An analysis of the requirements and recommendation for the location of the Regional Market Operator (RMOC) – RCC is carried out under WBS 2900. Appendix A contains for the initial phase of development (2013-2017) the TOR, cost estimates and implementation schedules for the indentified projects. Appendix B contains specific information and tables for particular sections of the report.
  14. 14. Final Master Plan Report 1-6 Introduction May 2011 EAPP/EAC Regional PSMP & Grid Code Study 1.4 Organization of the report EXECUTIVE SUMMARY MAIN REPORT 1 INTRODUCTION 2 DEMAND FORECAST (1100) 3 GENERATION SUPPLY STUDY AND PLANNING CRITERIA (1200) 4 SUPPLY-DEMAND ANALYSIS AND PROJECT IDENTIFICATION (1300) 5 TRANSMISSION NETWORK STUDY (1400) 6 INTERCONNECTION STUDIES (1500) 7 REGIONAL MARKET OPERATIONS CENTRE LOCATION (2900) 8 SYSTEM STUDIES FOR EXPANSION PLAN (2100) 9 ENVIRONMENTAL IMPACT ASSESSMENT (2200) 10 COST ESTIMATES AND SCHEDULES (2300) 11 FINANCIAL AND ECONOMICAL EVALUATIONS – Risks and Benefits (2500) 12 DEVELOPMENT AND INVESTMENT PLAN (2600) 13 INSTITUTIONAL AND TARIFF ASPECTS (2700) 14 PROJECT FUNDING (2800) 15 CONCLUSIONS APPENDICES APPENDIX A – TOR, Cost Estimates and Implementation Schedules for Feasibility Study for Projects Identified for the Initial Phase Development (2013-2017) APPENDIX B – General Appendices
  15. 15. Final Master Plan Report WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study SECTION 2 Demand Forecast WBS 1100
  16. 16. Final Master Plan Report WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study TABLE OF CONTENTS 1.  DEMAND FORECASTING: GENERAL PRINCIPLES ..............................................1-1  1.1  The Need for Demand Forecasting ............................................................................1-1  1.2  Demand Forecasting Techniques...............................................................................1-1  2.  ADOPTED APPROACH TO DEMAND FORECASTING...........................................2-1  2.1  Data Collection ...........................................................................................................2-1  2.2  Approach to Reviewing the Existing National Demand Forecasts..............................2-1  2.3  PB Independent Demand Forecasts ..........................................................................2-2  3.  REVIEW OF EXISTING NATIONAL DEMAND FORECASTS ..................................3-1  3.1  Burundi .......................................................................................................................3-1  3.2  Djibouti........................................................................................................................3-2  3.3  East DRC....................................................................................................................3-7  3.4  Egypt ........................................................................................................................3-10  3.5  Ethiopia.....................................................................................................................3-12  3.6  Kenya .......................................................................................................................3-14  3.7  Rwanda ....................................................................................................................3-18  3.8  Sudan .......................................................................................................................3-20  3.9  Tanzania...................................................................................................................3-22  3.10  Uganda .....................................................................................................................3-23  4.  INDEPENDENT PB DEMAND FORECASTS............................................................4-1  4.1  Burundi .......................................................................................................................4-1  4.2  Djibouti........................................................................................................................4-4  4.3  DRC............................................................................................................................4-6  4.4  Egypt ..........................................................................................................................4-8  4.5  Ethiopia.....................................................................................................................4-10  4.6  Kenya .......................................................................................................................4-12  4.7  Rwanda ....................................................................................................................4-15  4.8  Sudan .......................................................................................................................4-18  4.9  Tanzania...................................................................................................................4-21  4.10  Uganda .....................................................................................................................4-23 
  17. 17. Final Master Plan Report WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study LIST OF TABLES Table 3-1  Extended NELSAP Demand Forecast for Burundi (Base Case) .....................3-2  Table 3-2  LCEMP Demand Forecast (Base Case)..........................................................3-4  Table 3-3  LCEMP Demand Forecast (High Case)...........................................................3-5  Table 3-4  LCEMP Demand Forecast (Low Case)............................................................3-6  Table 3-5  Extended NELSAP Demand Forecast for East DRC (Base Case)..................3-8  Table 3-6  Extended NELSAP Demand Forecast for East DRC (High Case)...................3-9  Table 3-7  Extended NELSAP Demand Forecast for East DRC (Low Case) .................3-10  Table 3-8  Extended EEHC Demand Forecast for Egypt (Base Case)...........................3-12  Table 3-9  Extended EEPCO Demand Forecast for Ethiopia (Base Case – Moderate I Scenario) .......................................................................................................3-14  Table 3-10  Extended 2008 LCPDP Demand Forecast for Kenya (Base Case)...............3-16  Table 3-11  Extended 2009 LCPDP Demand Forecast (Base Case) ...............................3-17  Table 3-12  Extended NELSAP Demand Forecast for Rwanda (Base Case)...................3-19  Table 3-13  Extended LTPSP Demand Forecast for Sudan (Base Case) ........................3-21  Table 3-14  Extended PSMP Demand Forecast for Tanzania (Base Case).....................3-23  Table 3-15  PSIP Demand Forecasts for Uganda (Base, High and Low Cases)..............3-24  Table 4-1  PB Base, High and Low Demand Forecast for Burundi...................................4-2  Table 4-2  PB Base, High and Low Demand Forecast for Djibouti ...................................4-4  Table 4-3  RSWI Base, High and Low Demand Forecast for East DRC...........................4-6  Table 4-4  PB Base, High and Low Demand Forecast for Egypt......................................4-8  Table 4-5  PB Base, High and Low ICS Demand Forecast for Ethiopia .........................4-10  Table 4-6  PB SCS Demand Forecast for Ethiopia.........................................................4-12  Table 4-7  PB Base, High and Low Demand Forecast for Kenya...................................4-13  Table 4-8  PB Base, High and Low Demand Forecast for Rwanda................................4-16  Table 4-9  PB Base, High and Low Demand Forecast for Sudan...................................4-19  Table 4-10  PB Base, High and Low Demand Forecast for Tanzania ..............................4-21  Table 4-11  PB Base, High and Low Demand Forecast for Uganda.................................4-23  LIST OF FIGURES Figure 4-1  PB Peak Demand Forecast for Burundi (MW).............................................4-3  Figure 4-2  PB Sent Out Generation Forecast for Burundi (GWh).................................4-3  Figure 4-3  PB Peak Demand Forecast for Djibouti (MW) .............................................4-5  Figure 4-4  PB Sent Out Generation Forecast for Djibouti (GWh) .................................4-5  Figure 4-5  RSWI Peak Demand Forecast for East DRC (MW).....................................4-7  Figure 4-6  RSWI Sent Out Generation Forecast for East DRC (GWh).........................4-7  Figure 4-7  PB Peak Demand Forecast for Egypt (MW) ................................................4-9  Figure 4-8  PB Sent Out Generation Forecast for Egypt (GWh) ....................................4-9  Figure 4-9  PB ICS Peak Demand Forecast for Ethiopia (MW) ...................................4-11  Figure 4-10  PB ICS Sent Out Generation Forecast for Ethiopia (GWh)........................4-11  Figure 4-11  PB Peak Demand Forecast for Kenya (MW)..............................................4-14  Figure 4-12  PB Sent Out Generation Forecast for Kenya (GWh)..................................4-14  Figure 4-13  PB Peak Demand Forecast for Rwanda (MW)...........................................4-17  Figure 4-14  PB Sent Out Generation Forecast for Rwanda (GWh)...............................4-17  Figure 4-15  PB Peak Demand Forecast for Sudan (MW) .............................................4-20  Figure 4-16  PB Sent Out Generation Forecast for Sudan (GWh) .................................4-20  Figure 4-17  PB Peak Demand Forecast for Tanzania (MW).........................................4-22  Figure 4-18  PB Sent Out Generation Forecast for Tanzania (GWh).............................4-22  Figure 4-19  PB Peak Demand Forecast for Uganda (MW) ...........................................4-24  Figure 4-20  PB Sent Out Generation Forecast for Uganda (GWh) ...............................4-24
  18. 18. Final Master Plan Report 1-1 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 1. DEMAND FORECASTING: GENERAL PRINCIPLES A demand forecast is the prediction of demand for power (MW) and energy (GWh) into the future. The maximum power demand (MD) in a period is known as the peak demand, and this is usually the headline figure which is quoted when developing demand forecasts. It should be noted however, that in electrical systems with predominantly thermal capacity, it is more important for planning purposes to know the peak demand rather than the amount of electrical energy required, since the peak demand often sets the capacity expansion goal. On the other hand, for systems with large amounts of hydro-electric capacity, it is equally important to know the level of energy demand, as these systems may have energy limitations. It is thus the usual practice in any detailed demand forecast to predict the level of energy demand first, and then derive the peak demand using appropriate load and coincidence factors. 1.1 The Need for Demand Forecasting A demand forecast is a primary requirement for electricity planning studies. Demand forecasts are needed for: • Generation planning, • Transmission planning, • Distribution planning, • Financial planning, • Feasibility studies, • Pricing and tariff setting, and, • Operational planning (short-term). Different demand forecasts are required for the short, medium or long term and for different levels of the system (e.g. generation, transmission substations, distribution substations and at consumer terminals). Rigorous demand forecasting may be necessary for a number of reasons, such as: • It is often essential for outside parties (e.g. bilateral and multilateral financiers, private sector investors and project shareholders) to be convinced of the reasonableness of future load growth and the corresponding investment plan before making a financial commitment. • Large consumers are often more optimistic about future growth than is justified by the prevailing economic climate. This may result in an over-estimate of load with a consequent over-investment. • In markets where demand is approaching saturation, judgements formed from buoyant market growth in the past may not be a good guide to growth in the future. • Utilities will frequently over-estimate demand allowing for the time required to secure finance and the necessary project construction approvals. 1.2 Demand Forecasting Techniques The only certainty about a demand forecast is that it will not match the out-turn. To cover this eventuality it is essential to develop a demand forecasting technique that is appropriate and suitable to the objectives of the forecast. No technique can be considered incorrect for demand forecasting. The technique adopted will depend on the time frame under
  19. 19. Final Master Plan Report 1-2 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study consideration, the size of the system, the plant available and the data available. In other words the type of demand forecast technique adopted should fall in line with the requirements of the study and based on the availability of data. There are four main demand forecasting techniques, namely: • Intuitive based demand forecasting • Extrapolation based demand forecast • End user demand forecast • Econometric demand forecasting A general overview of each of these methods is detailed in the sub-sections below. Intuitive The term intuitive forecasting can be used to describe methods which rely largely on experience and quick calculations using simple assumptions (i.e. the use of the immediate past performance and an assumption that the rates of change will continue unaltered in the near future). The intuitive load forecast should not be entirely discounted, as it is after all in the background of reviewers’ minds when they appraise other peoples’ demand forecasts. In some instances, the lack of available data may make intuitive forecasting the only possible option. The forecast may be appropriate for minor developments, isolated systems and small Island utilities. An alternative approach, but still within the intuitive forecasting framework, would be to apply a growth factor that is obtained for a country with similar economic characteristics. Indeed, it may be beneficial to compare load forecasts with the performance of a similar system in another part of the world at a comparable stage of development. This will particularly be the case where (i) there is little statistical information available on past loads, such as in new areas of supply, (ii) data errors that cannot be easily corrected, or, (iii) it becomes necessary to forecast on the results of direct enquiry and demographic and economic statistics. Such forecasting is no more than guesswork, but the results can be used to cross-check on forecasts prepared by more scientific methods. Where a new system of forecasting is to be prepared, it is often helpful to make a comparison of the intuitive forecasts prepared in the past and subsequent performance. Extrapolation Extrapolation techniques look at past trends in energy and power demand over time and, extend them into the future. Any time series may be decomposed into three elements: • Trend • Seasonal variation • Serial dependency (auto-regression). Trend is defined as “the long-term average growth and may be regarded in some way as an average increase in a time series”. Superimposed on this may be a seasonal variation. Seasonal in this sense is defined as “a cyclic variable that has roughly the same beginning and end values for a given period of time (similar to the properties of a sine wave)”. Such variations may be seen over a 24 hour period, a weekly period, an annual period, or even a longer period.
  20. 20. Final Master Plan Report 1-3 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Finally there may be a dependency between successive values. For example, if the value in the previous period was high, the value in the current period may be high. Such behaviour could relate to the random use of batch processing equipment. This interdependency is known as auto-regression. There are a wide range of techniques for analysing data on a time series basis including: • Moving Average • Exponential smoothing • Autoregressive techniques • Simple Regression • ARIMA (autoregressive integrated moving average) End User End-user demand forecast modelling draws on many utility forecasting methods. The distinguishing characteristic of end-user modelling is the detailed description of how energy is used. Such models usually begin by specifying uses for which energy is ultimately required, such as heating water, cooling buildings and cooking food. The model then describes, via mathematical equations and accounting identities, the types of energy-using equipment that businesses and households have, and how much energy is used by each type of equipment to satisfy the predetermined levels of end-use energy demanded. A large amount of survey data and statistics are needed by such a model. By summing up the units of equipment times the average energy used by each class of equipment, total energy demand by fuel type is revealed. Multiplying types of equipment by average use values is just an accounting framework, but even so, it can generate insights into the way energy is used now and in the future. Optimisation end-user models are a step beyond accounting end-user models. By specifying an objective function (such as minimising cost) and identifying both the unit costs of using energy in the given processes and the constraints to the system, the accounting end-user model can be transformed into a device that will predict how customers will act (assuming that their objective function is properly specified), given the assumptions about costs and constraints. End-user models are often linked to econometric models. End-user models are often weakest in predicting consumers' fuel-use decisions. With the available data, they can easily describe where the energy is being used and for what purposes but, without a theory to explain choices, they are limited in their ability to predict the future. The ideal end-user model (which is rarely achieved) would, for example, not only tell us the average watts of lighting energy in households, and how this amount has changed over time, but also what caused households and/or housing operators to make these changes. End-user forecasting can be highly accurate, particularly for green-field developments, and for forecasts of residential demand. An extension of end-user demand forecasting is load- density-based forecasting, in which the maximum load in any area is based upon the surface area occupied by each consumer type and a power density (i.e. watts per square meter) associated with that consumer type. This can be especially useful for distribution planning. End-user forecasts also encompass developments in sectors such as industry and agriculture where consumption patterns can be established for, say, cement production or water pumping.
  21. 21. Final Master Plan Report 1-4 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Econometric Analysis This class of model, like the time series model (extrapolation), uses historical data to predict the future. Econometric analysis however, attempts to go beyond time series models by explaining the causes of the identified trends. Econometric models postulate explicit causal relationships between the dependent variable (either energy or power) and independent variables (either economic (e.g. GDP), technological (e.g. number and type of appliances; industrial processes), demographic (e.g. population) or other variables (e.g. weather)). Assuming these relationships are true it should then be possible to determine the historical relationships between electrical demand and such parameters as GDP by sector, personal income, the price of electricity etc. Future levels of these economic variables are then forecast and used as inputs to determine future levels of consumption. One advantage of econometric forecasting is the ease with which high and low scenario load forecasts can be derived and the logical basis on which the can be formed. This merely requires changes in the forecast rate of the input variables, e.g. economic growth and electricity price. A faster economic growth will produce a higher load forecast whilst the imposition of price increases will reduce forecast levels of energy demand. Econometric modelling would be preferred to time series analysis. Even if both techniques could predict changes in demand with equal accuracy, the econometric model would be more valuable since it might help in understanding why changes in demand were occurring. Top-down and Bottom-up Approaches An additional classification of demand forecast techniques is between bottom-up and top- down approaches. Most demand forecasting methodologies utilise a bottom-up approach. A bottom-up approach concentrates on predicting demand at the consumer level (i.e. electricity sales). This sales forecast may then be converted to a system power demand forecast at different voltage levels by summation of each individual consumer level sales forecast and the use of loss estimates and load factors (see Equation 2.1). Using a top-down approach is generally not recommended. A top-down approach involves the estimate of demand at a generation level (i.e. forecasting MW sent out, GWh sent out). This technique includes implicit assumptions about the behaviour of losses in the future, and does not permit a breakdown by consumer sector.
  22. 22. Final Master Plan Report 2-1 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 2. ADOPTED APPROACH TO DEMAND FORECASTING The purpose of this report is to identify or provide an array of demand forecast scenarios (namely base, high and low scenarios) for each EAPP/EAC member country, suitable for deriving Master Plans for the EAPP/EAC member countries. In this sub-section we detail the approach adopted to achieve this objective. Our approach can be divided into three parts: • Data collection • Review of existing national demand forecast • Derivation of independent demand forecast scenarios We detail our approach to each of these parts in turn below. 2.1 Data Collection The first step in achieving the objective detailed above is to carry out an extensive data collection exercise. The data collection exercise comprised: • A short visit to each country to meet with the utility representative(s) and to initiate the data collection. The visit to each country also allowed the Consultant to see at first hand the level of development in the country. Where data relating to demand forecasting was not readily available, requests were made for: − Previous demand forecasts. − Historic electrical data (hourly load data, loss data, peak demand, generation, sales data etc). − Historic economic and demographic data (GDP, population etc). − Economic and demographic forecast data (GDP, population etc). − Any background information relating to topics such electrification, loss reduction etc. • Following the visit to each country: − A review of the data collected was undertaken. − Desktop research was carried out to expand on the data made available in country. − The Consultants (PB and SNC) databases were searched for information relating to the countries of the EAPP/EAC. − Where gaps were identified we made requests for additional data. 2.2 Approach to Reviewing the Existing National Demand Forecasts The next step in determining base, high and low demand forecast scenarios for each EAPP/EAC country member is to identify the most recent existing national demand forecast available and review the adopted methodology, key assumptions and overall results. This review will allow us to form an opinion on the suitability of the forecast for use in the EAPP/EAC study. The EAPP/EAC study horizon year is 2038 and most existing national demand forecasts do not extend this far into the future. As such, we have extended the existing national demand forecasts to cover the study horizon. The process for reviewing the existing national demand forecast (for each country) is summarised follows:
  23. 23. Final Master Plan Report 2-2 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study • Identify most recent demand forecast available for each country • Review the most recent demand forecast for: − Methodology. − Assumptions. − Level of detail1 . − Magnitude of demand growth. − Suitability for inclusion in the EAPP/EAC study, including a comparison with the current level of demand to ensure that the demand forecasted today is in line with the current level of demand. • Extend the national forecast to cover the planning horizon of the study by either using the same methodology as used to develop the original forecast (if possible) or by using trend line analysis2 or growth rate extrapolation techniques. • Offer our comments on the extended existing demand forecast, including the likelihood of this forecast being achieved and the constraints that may hinder its attainment. 2.3 PB Independent Demand Forecasts In addition to reviewing the most recent existing demand forecast for each country, we have developed independent base, high and low demand forecasts. Our independent demand forecasts are based on our own assumptions and methodologies, utilising the data collected and analysed as part of the data collection process (see sub- section 2.1). Where data availability and quality permit, the independent demand forecasts are based on our econometric based Regression Analysis Load Forecast (RALF) model. The data available for some of the EAPP/EAC countries however is of poor quality, un- reliable and contains many gaps. If the data does not permit an independent econometric demand forecast to be developed, then we use a combination of growth rate analysis, electrification assumptions, population data and specific consumption assumptions to derive suitable independent demand forecasts3 . 1 This typically includes identifying whether the forecast includes both an energy and power forecast, whether it is developed at a sales level, broken down by consumer category etc. 2 Trend Line Analysis is carried out using the Microsoft Excel trend line tool. A trend line can be added to any charted historic dataset (using a simple X Y Chart). A trend line equation and a R2 correlation statistic can also be displayed. The R2 statistic can be used to determine the reasonableness of the trend line fit to the historic data and the equation can be used to project future values. 3 A description of the methodologies used (RALF or other) are provided in the Appendices of those countries where these methodologies have been employed.
  24. 24. Final Master Plan Report 3-1 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 3. REVIEW OF EXISTING NATIONAL DEMAND FORECASTS In this section of the report we outline the existing national demand forecasts available for each member country of the EAPP/EAC. Each existing national demand forecast has been extended to cover the period to 2038. In the following sub-sections we detail the existing national demand forecast, covering the following: • Who developed the forecast, • When was the forecast developed, • What methodology was employed, • How we extended the forecast to cover the period to 2038, • The extended forecast, and, • Comments on the existing/extended demand forecast Further details of each review are provided in the respective Appendices provided with this report. 3.1 Burundi The latest national demand forecast available for Burundi was produced by Fichtner and RSWI in October 2008 as part of the Nile Basin Initiative (NBI) study entitled ‘Nile Equatorial Lakes Subsidiary Action Program (NELSAP). The Burundian NELSAP demand forecast was developed in tandem with demand forecasts for Tanzania and Rwanda. The objective of the demand forecast was to develop an end- user model, which focused on the structure of the different electricity consumer groups and their specific consumption. It should be noted, however, that some elements of trend-line and econometric techniques were also been taken into consideration. As the NELSAP demand forecast only covered the period to 2025, we have extended the current national forecast to cover the period up to the planning horizon of this study. In order to extend the existing forecast we used trend line analysis to identify existing trends in generation sent out, sales and peak demand forecasts and used the resulting mathematical trend line formulas to project the forecast for the additional 13 years required. Several demand forecast scenarios were developed as part of the study. Further details of the methodology and assumptions used in the derivation of the NELSAP demand forecast are provided in Appendix A. The extended NELSAP base case demand forecast scenario is presented in Table 3-1 below.
  25. 25. Final Master Plan Report 3-2 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-1 Extended NELSAP Demand Forecast for Burundi (Base Case) We consider the assumed growth rates in peak demand, generation and sales to be very high, with average annual growth around 11 per cent per annum. An average annual increase of this size would require a significant amount of annual investment in generation, transmission and distribution. The assumption that losses are to remain at around 26 per cent from 2015 onwards does not seem to reflect the most effective use of resources. It is also a concern to see such a large growth in demand not reflected in a change in the make-up of demand. The load factor is assumed to fall from 36 per cent to around 30 per cent. It would be reasonable to expect that the load factor would increase as more connections are made to the system and the timing and type of demand begins to reflect that of other similarly sized economies. A full description of our review of the NELSAP demand forecast is provided in Appendix A. 3.2 Djibouti The most recent Least Cost Electricity Master Plan (LCEMP) study for Djibouti was completed by PB in November 2009 and covers the period 2008 to 2038. The PB demand Sales Generation Peak  Demand Load Factor Sales Generation Peak  Demand (GWh) (GWh) (%) (GWh) (MW) (%) (%) (%) (%) 2008 63 27 30.0% 90 29 2009 86 31 26.5% 117 37 36.5% 30.0% 29.4% 2010 93 41 30.6% 134 43 35.2% 8.1% 14.5% 16.0% 2011 99 36 26.7% 135 44 34.9% 6.5% 0.7% 1.6% 2012 104 39 27.3% 143 47 34.8% 5.1% 5.9% 6.3% 2013 125 45 26.5% 170 56 34.5% 20.2% 18.9% 19.8% 2014 147 53 26.5% 200 66 34.5% 17.6% 17.6% 17.8% 2015 170 61 26.4% 231 77 34.2% 15.6% 15.5% 16.5% 2016 195 70 26.4% 265 89 34.0% 14.7% 14.7% 15.4% 2017 222 79 26.2% 301 102 33.8% 13.8% 13.6% 14.4% 2018 251 88 26.0% 339 116 33.5% 13.1% 12.6% 13.6% 2019 281 100 26.2% 381 131 33.3% 12.0% 12.4% 13.1% 2020 314 111 26.1% 425 147 33.0% 11.7% 11.5% 12.4% 2021 348 124 26.3% 472 165 32.8% 10.8% 11.1% 12.0% 2022 385 137 26.2% 522 184 32.5% 10.6% 10.6% 11.6% 2023 425 151 26.2% 576 204 32.2% 10.4% 10.3% 11.3% 2024 467 166 26.2% 633 227 31.9% 9.9% 9.9% 10.9% 2025 513 182 26.2% 695 251 31.6% 9.9% 9.8% 10.7% 2026 560 200 26.3% 760 274 31.6% 9.2% 9.3% 9.3% 2027 610 217 26.3% 827 300 31.5% 8.8% 8.9% 9.4% 2028 661 236 26.3% 898 327 31.4% 8.5% 8.5% 9.0% 2029 716 256 26.3% 972 355 31.2% 8.2% 8.2% 8.7% 2030 772 277 26.4% 1,049 385 31.1% 7.9% 7.9% 8.3% 2031 831 298 26.4% 1,129 415 31.0% 7.6% 7.7% 8.0% 2032 892 320 26.4% 1,213 448 30.9% 7.4% 7.4% 7.7% 2033 956 344 26.4% 1,299 481 30.8% 7.1% 7.2% 7.5% 2034 1,022 368 26.5% 1,389 516 30.8% 6.9% 6.9% 7.2% 2035 1,090 393 26.5% 1,482 552 30.7% 6.7% 6.7% 7.0% 2036 1,160 419 26.5% 1,579 589 30.6% 6.5% 6.5% 6.8% 2037 1,233 445 26.5% 1,679 628 30.5% 6.3% 6.3% 6.6% 2038 1,308 473 26.6% 1,781 667 30.5% 6.1% 6.1% 6.4% Assumed  Calender Year Losses
  26. 26. Final Master Plan Report 3-3 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study forecast developed for the Djibouti LCEMP is derived using PB’s econometric based RALF model. Base, high and low demand forecast scenarios were developed for this study. Further details of the methodology and assumptions used in the derivation of the LCEMP demand forecast are provided in Appendix B. The base, high and low LCEMP demand forecasts are presented in Table 3-2, Table 3-3 and Table 3-4 below.
  27. 27. Final Master Plan Report 3-4 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-2 LCEMP Demand Forecast (Base Case)
  28. 28. Final Master Plan Report 3-5 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-3 LCEMP Demand Forecast (High Case)
  29. 29. Final Master Plan Report 3-6 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-4 LCEMP Demand Forecast (Low Case)
  30. 30. Final Master Plan Report 3-7 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 3.3 East DRC The latest available demand forecast for the eastern region of the DRC is that produced by RSW International in October 2007 as part of the Nile Basin Initiative (NBI) Nile Equatorial Lakes Subsidiary Action Programme (NELSAP) feasibility study on the Interconnection of the Electricity Networks of the Nile Equatorial Lakes Countries. The NELSAP study indentifies two key variables in the derivation of future power requirements in the DRC. These are: • Consumer demand • Power losses By initially working out the level of consumer demand it is assumed that through the addition of losses and the application of a load factor, a peak demand forecast can be derived. As a consequence of the data available to RSW, the proposed consumer demand forecasting approach considers a mix of econometric and simplified analytical approaches to determining the level of consumer demand, including the introduction of key estimates based on its overall and regional experience, and also when necessary, simple common sense. As the NELSAP demand forecast only covered the period to 2020, we have extended the current national forecast to the end of the planning horizon of this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in sales, sent out generation and peak demand forecasts and used the resulting mathematical trend line formulas to project the forecast for the additional 18 years required. Details relating to the specific assumptions made for the base, high and low demand forecast scenarios are provided in Appendix C. The base, high and low NELSAP demand forecasts are presented in Table 3-5, Table 3-6 and Table 3-7 below. Projections of demand for the eastern region of DRC are very hard to develop given the lack of reliable and consistent historical data. The projected growth rates for the base, high and low scenarios are reasonable and not overly optimistic given the potential for development in the region.
  31. 31. Final Master Plan Report 3-8 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-5 Extended NELSAP Demand Forecast for East DRC (Base Case) Total Sales Losses Losses Generation Peak Demand Load Factor (GWh) (GWh) (%) (GWh) (MW) (%) 1 2005 168.0 42.0 20.0% 210.0 50.0 47.9% 2 2006 176.4 43.1 19.6% 219.5 52.2 48.0% 3 2007 185.3 44.1 19.2% 229.4 54.5 48.1% 4 2008 194.7 45.2 18.8% 239.8 56.9 48.1% 5 2009 204.5 46.1 18.4% 250.7 59.4 48.2% 6 2010 214.9 47.1 18.0% 262.0 62.0 48.2% 7 2011 227.0 48.0 17.5% 275.0 65.1 48.2% 8 2012 239.9 48.8 16.9% 288.7 68.3 48.2% 9 2013 253.6 49.5 16.3% 303.1 71.7 48.3% # 2014 268.2 49.9 15.7% 318.2 75.3 48.3% # 2015 283.8 50.2 15.0% 334.0 79.0 48.3% # 2016 300.5 51.8 14.7% 352.3 83.3 48.3% # 2017 318.3 53.3 14.3% 371.6 87.8 48.3% # 2018 337.3 54.6 13.9% 391.9 92.6 48.3% # 2019 357.6 55.8 13.5% 413.4 97.7 48.3% # 2020 379.3 56.7 13.0% 436.0 103.0 48.3% # 2021 402.1 58.6 13.0% 460.7 108.7 48.4% # 2022 426.4 60.6 12.4% 487.0 114.8 48.4% # 2023 452.1 62.8 12.2% 514.9 121.4 48.4% # 2024 479.3 65.3 12.0% 544.6 128.3 48.5% # 2025 508.1 68.1 11.8% 576.1 135.6 48.5% # 2026 538.4 71.2 11.7% 609.6 143.4 48.5% # 2027 570.3 74.7 11.6% 645.0 151.6 48.6% # 2028 604.0 78.5 11.5% 682.5 160.2 48.6% # 2029 639.3 82.8 11.5% 722.1 169.4 48.7% # 2030 676.4 87.5 11.5% 764.0 179.0 48.7% # 2031 715.4 92.7 11.5% 808.1 189.1 48.8% # 2032 756.2 98.3 11.5% 854.6 199.8 48.8% # 2033 799.0 104.5 11.6% 903.5 211.0 48.9% # 2034 843.7 111.2 11.6% 954.9 222.8 48.9% # 2035 890.5 118.5 11.7% 1,009.0 235.1 49.0% # 2036 939.3 126.3 11.9% 1,065.6 248.0 49.0% # 2037 990.3 134.7 12.0% 1,125.0 261.5 49.1% # 2038 1,043.4 143.8 12.1% 1,187.2 275.7 49.2% Assumed  Calender Year
  32. 32. Final Master Plan Report 3-9 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-6 Extended NELSAP Demand Forecast for East DRC (High Case) Total Sales Losses Losses Generation Peak Demand Load Factor (GWh) (GWh) (%) (GWh) (MW) (%) 2005 168.0 42.0 20.0% 210.0 50.0 47.9% 2006 178.1 43.5 19.6% 221.6 52.7 48.0% 2007 188.9 45.0 19.2% 233.9 55.5 48.1% 2008 200.4 46.5 18.8% 246.9 58.5 48.2% 2009 212.7 47.9 18.4% 260.6 61.7 48.2% 2010 225.8 49.2 17.9% 275.0 65.0 48.3% 2011 240.7 50.8 17.4% 291.5 68.9 48.3% 2012 256.6 52.4 16.9% 309.0 73.0 48.3% 2013 273.8 53.7 16.4% 327.5 77.4 48.3% 2014 292.3 54.9 15.8% 347.2 82.1 48.3% 2015 312.2 55.8 15.2% 368.0 87.0 48.3% 2016 333.6 58.3 14.9% 391.9 92.6 48.3% 2017 356.7 60.6 14.5% 417.3 98.6 48.3% 2018 381.6 62.8 14.1% 444.4 105.0 48.3% 2019 408.5 64.8 13.7% 473.3 111.8 48.3% 2020 437.5 66.5 13.2% 504.0 119.0 48.3% 2021 468.2 69.4 13.0% 537.6 126.8 48.4% 2022 501.1 72.3 12.6% 573.4 135.1 48.4% 2023 536.3 75.4 12.3% 611.6 144.0 48.5% 2024 573.8 78.7 12.1% 652.5 153.5 48.5% 2025 613.7 82.4 11.8% 696.1 163.6 48.6% 2026 656.1 86.4 11.6% 742.5 174.4 48.6% 2027 701.1 90.7 11.5% 791.8 185.8 48.6% 2028 748.8 95.4 11.3% 844.2 197.9 48.7% 2029 799.3 100.5 11.2% 899.8 210.7 48.8% 2030 852.7 106.0 11.1% 958.6 224.2 48.8% 2031 909.0 111.9 11.0% 1,020.9 238.5 48.9% 2032 968.4 118.2 10.9% 1,086.6 253.5 48.9% 2033 1,031.0 125.1 10.8% 1,156.0 269.4 49.0% 2034 1,096.8 132.4 10.8% 1,229.1 286.1 49.0% 2035 1,165.9 140.2 10.7% 1,306.1 303.6 49.1% 2036 1,238.5 148.5 10.7% 1,387.0 322.0 49.2% 2037 1,314.6 157.4 10.7% 1,472.1 341.4 49.2% 2038 1,394.4 166.9 10.7% 1,561.3 361.6 49.3% Assumed  Calender Year
  33. 33. Final Master Plan Report 3-10 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-7 Extended NELSAP Demand Forecast for East DRC (Low Case) 3.4 Egypt The latest national demand forecast available for Egypt was produced by EEHC in 2007 and estimates demand for electricity from 2008 to 20264 . The EEHC electricity demand forecast utilises the econometric based computer package E- views, focussing on regression analysis to determine electricity sales in each consumer category. The economic and demographic factors considered in the regression analysis are GDP/sector, electricity price/sector and population. 4 See Appendix D for a description of the transformation made to convert the financial information provided by EAPP into a calendar year format. Total Sales Losses Losses Generation Peak Demand Load Factor (GWh) (GWh) (%) (GWh) (MW) (%) 1 2005 168.0 42.0 20.0% 210.0 50.0 47.9% 2 2006 174.6 42.5 19.6% 217.1 51.7 48.0% 3 2007 181.5 43.0 19.1% 224.4 53.4 48.0% 4 2008 188.6 43.4 18.7% 232.0 55.2 48.0% 5 2009 196.1 43.8 18.3% 239.9 57.1 48.0% 6 2010 203.8 44.2 17.8% 248.0 59.0 48.0% 7 2011 213.4 44.7 17.3% 258.1 61.4 48.0% 8 2012 223.6 45.1 16.8% 268.7 63.9 48.0% 9 2013 234.3 45.4 16.2% 279.7 66.5 48.0% # 2014 245.5 45.6 15.7% 291.1 69.2 48.0% # 2015 257.4 45.6 15.0% 303.0 72.0 48.0% # 2016 270.2 46.1 14.6% 316.4 75.3 48.0% # 2017 283.8 46.5 14.1% 330.3 78.7 47.9% # 2018 298.1 46.8 13.6% 344.9 82.3 47.8% # 2019 313.3 46.8 13.0% 360.1 86.1 47.8% # 2020 329.3 46.7 12.4% 376.0 90.0 47.7% # 2021 346.5 46.5 13.0% 393.0 94.6 47.4% # 2022 364.3 46.5 11.3% 410.8 99.2 47.3% # 2023 383.0 46.4 10.8% 429.4 104.0 47.1% # 2024 402.7 46.2 10.3% 448.9 109.1 47.0% # 2025 423.3 46.0 9.8% 469.3 114.5 46.8% # 2026 444.9 45.7 9.3% 490.6 120.2 46.6% # 2027 467.4 45.4 8.8% 512.8 126.2 46.4% # 2028 491.0 45.0 8.4% 535.9 132.5 46.2% # 2029 515.6 44.5 7.9% 560.1 139.1 46.0% # 2030 541.2 44.0 7.5% 585.1 146.0 45.7% # 2031 567.9 43.3 7.1% 611.2 153.3 45.5% # 2032 595.7 42.7 6.7% 638.3 160.9 45.3% # 2033 624.6 41.9 6.3% 666.4 168.9 45.0% # 2034 654.6 41.0 5.9% 695.6 177.3 44.8% # 2035 685.7 40.1 5.5% 725.8 186.0 44.6% # 2036 718.0 39.1 5.2% 757.1 195.1 44.3% # 2037 751.5 38.0 4.8% 789.6 204.6 44.1% # 2038 786.2 36.8 4.5% 823.1 214.5 43.8% Assumed  Calender Year
  34. 34. Final Master Plan Report 3-11 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study As the EEHC demand forecast only covered the period to 2026, we have extended the current national forecast to the end of the planning horizon of this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in sales, generation sent out and peak demand forecasts and used the resulting mathematical trend line formulas to project the forecast for the additional 12 years required. Further details of the methodology and assumptions used in the derivation of the EEHC demand forecast are provided in Appendix D. The extended base case EEHC national demand forecast is presented in Table 3-8 below. The EEHC demand forecast is econometric based and utilises the well-known E-views forecasting software. The E-views software is software is an excellent demand forecasting tool and thus we concur with the methodology adopted to derive the EEHC demand forecast. The key assumptions of the EEHC demand forecast relate to the forecasts of GDP and population. The population forecast growth rate ranges from 1.8 per cent and 1.3 per cent per annum. We find this rate of growth to be reasonable and in line with the latest United Nations (UN) Population Division estimate. Of more significance to the forecast results are the sectoral GDP forecast assumptions. Total GDP is forecast to grow at a rate of 5.5 per cent per annum throughout the EEHC forecast period. At this rate of growth, GDP is expected to be around 2.6 times today’s value by 2026. We find this rate of overall growth to be plausible and not excessive given the current stature of the Egyptian economy and potential for further growth. An average annual increase in demand of around 5 per cent per annum would require a reasonable but not unsustainable amount of annual investment in generation, transmission and distribution. We find no issue with the EEHC demand forecast, although it should be noted that high and low demand forecasts were not provided.
  35. 35. Final Master Plan Report 3-12 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-8 Extended EEHC Demand Forecast for Egypt (Base Case) 3.5 Ethiopia The most recent demand forecast available for Ethiopia is presented in EEPCO’s “Highlights on Power Sector Development Program” Report dated June 2008. It is assumed for this study that the Moderate I Scenario is the current base case national forecast5 . The Moderate I forecast is based on an econometric model which presents the relationships between electricity demand growth, electricity price in each tariff category and the level of economic activity. The econometric model contains three sub-forecasts (ICS, SCS and rural forecasts). The ICS forecast utilises assumptions relating to GDP and electrification rates. The SCS forecast has been based on trend analysis while the rural electrification forecasts are treated separately based on the Government electrification target. The sales forecasts are then combined with projected loss rates to produce forecasts of energy generation and through the use of average load factors, the capacity (MW) requirement to deliver the demanded energy was estimated. 5 We make this assumption on the basis that the forecast suggested in the Target Scenario is extremely high and assumes an annual average growth rate of 15.5 per cent over 20+ years. We do not believe this to be credible without the identification of a new and vast oil or gas reserve. The Moderate I scenario provides a demand forecast which is higher than the Moderate II forecast but significantly less than Target forecast. Sales Generation Peak  Demand Load Factor Sales Generation Peak  Demand (GWh) (GWh) (%) (GWh) (MW) (%) (%) (%) (%) 2008 106,558 22,240 17.3% 128,798 21,000 70.0% 2009 117,920 19,135 14.0% 137,056 22,330 70.1% 10.7% 6.4% 6.3% 2010 125,536 20,220 13.9% 145,756 23,729 70.1% 6.5% 6.3% 6.3% 2011 133,559 21,352 13.8% 154,910 25,200 70.2% 6.4% 6.3% 6.2% 2012 142,000 22,532 13.7% 164,532 26,753 70.2% 6.3% 6.2% 6.2% 2013 150,876 23,762 13.6% 174,638 28,383 70.2% 6.3% 6.1% 6.1% 2014 160,190 25,041 13.5% 185,231 30,089 70.3% 6.2% 6.1% 6.0% 2015 169,965 26,369 13.4% 196,334 31,880 70.3% 6.1% 6.0% 6.0% 2016 180,241 27,752 13.3% 207,993 33,760 70.3% 6.0% 5.9% 5.9% 2017 191,043 29,171 13.2% 220,214 35,651 70.5% 6.0% 5.9% 5.6% 2018 202,398 30,626 13.1% 233,024 37,630 70.7% 5.9% 5.8% 5.6% 2019 214,333 32,137 13.0% 246,470 39,703 70.9% 5.9% 5.8% 5.5% 2020 226,881 33,707 12.9% 260,589 41,874 71.0% 5.9% 5.7% 5.5% 2021 240,076 35,339 12.8% 275,416 44,149 71.2% 5.8% 5.7% 5.4% 2022 253,956 37,036 12.7% 290,992 46,534 71.4% 5.8% 5.7% 5.4% 2023 268,558 38,800 12.6% 307,358 49,034 71.6% 5.7% 5.6% 5.4% 2024 283,920 40,633 12.5% 324,553 51,654 71.7% 5.7% 5.6% 5.3% 2025 300,086 42,540 12.4% 342,626 54,402 71.9% 5.7% 5.6% 5.3% 2026 317,100 44,523 12.3% 361,623 57,284 72.1% 5.7% 5.5% 5.3% 2027 335,626 45,662 12.0% 381,288 60,213 72.3% 5.8% 5.4% 5.1% 2028 354,876 47,114 11.7% 401,991 63,311 72.5% 5.7% 5.4% 5.1% 2029 375,198 48,454 11.4% 423,651 66,541 72.7% 5.7% 5.4% 5.1% 2030 396,638 49,663 11.1% 446,301 69,909 72.9% 5.7% 5.3% 5.1% 2031 419,248 50,724 10.8% 469,972 73,417 73.1% 5.7% 5.3% 5.0% 2032 443,075 51,619 10.4% 494,693 77,071 73.3% 5.7% 5.3% 5.0% 2033 468,168 52,330 10.1% 520,498 80,874 73.5% 5.7% 5.2% 4.9% 2034 494,577 52,839 9.7% 547,416 84,832 73.7% 5.6% 5.2% 4.9% 2035 522,350 53,128 9.2% 575,478 88,947 73.9% 5.6% 5.1% 4.9% 2036 551,537 53,180 8.8% 604,717 93,224 74.0% 5.6% 5.1% 4.8% 2037 582,186 52,976 8.3% 635,162 97,668 74.2% 5.6% 5.0% 4.8% 2038 614,346 52,499 7.9% 666,846 102,282 74.4% 5.5% 5.0% 4.7% LossesAssumed  Calender Year
  36. 36. Final Master Plan Report 3-13 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Further details of the methodology and assumptions used in the derivation of the EEPCO demand forecast are provided in Appendix E. As the EEPCO demand forecast only covered the period to 2030, we have extended the current national forecast to cover the whole of the planning horizon of this study. In order to extend the existing Moderate I forecast we have adopted generation and peak demand growth rate assumptions. The extended EEPCO base case demand forecast (Moderate I scenario) is presented in Table 3-9 below. We believe the econometric model used to derive the above forecast to be typical of most econometric models. Whilst we find no issue with the methodology adopted to derive the national demand forecast, it should be noted that we consider the resulting demand forecast to be high. The assumed underlying GDP growth rate would result in a level of real GDP that is 5 times its current value in 2030, but almost 10 times its current value in 2038. Even in very favourable global and local market conditions the assumed level of GDP growth would be very difficult to achieve. Peak demand is estimated to increase at an average annual rate of 10.6 per cent per annum between 2008 and 2038. An average annual increase in peak demand of this nature would require a significant amount of annual investment in generation, transmission and distribution. A full description of the EEPCO demand forecast is provided in Appendix E.
  37. 37. Final Master Plan Report 3-14 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-9 Extended EEPCO Demand Forecast for Ethiopia (Base Case – Moderate I Scenario) 3.6 Kenya In recent years the MoE in Kenya have developed annual demand forecasts as part of their Least Cost Power Development Plan (LCPDP). The two most recent forecasts are contained in the 2008 and the 2009 LCPDP. Base, high and low demand forecast scenarios were developed, but we focus our review on the base case scenario in each LCPDP study. Generation Sent Out Peak Demand Load Factor Generation Growth Rate Peak Demand Growth Rate (GWh) (MW) (%) (%) (%) 2009 4,828 1,201 45.9% 2010 5,620 1,398 45.9% 16.4% 16.4% 2011 6,325 1,573 45.9% 12.5% 12.5% 2012 7,083 1,762 45.9% 12.0% 12.0% 2013 7,897 1,964 45.9% 11.5% 11.5% 2014 8,816 2,193 45.9% 11.6% 11.6% 2015 9,823 2,443 45.9% 11.4% 11.4% 2016 10,917 2,715 45.9% 11.1% 11.1% 2017 12,038 2,994 45.9% 10.3% 10.3% 2018 13,182 3,279 45.9% 9.5% 9.5% 2019 14,374 3,575 45.9% 9.0% 9.0% 2020 15,610 3,883 45.9% 8.6% 8.6% 2021 16,888 4,201 45.9% 8.2% 8.2% 2022 18,265 4,543 45.9% 8.2% 8.2% 2023 19,750 4,912 45.9% 8.1% 8.1% 2024 21,351 5,311 45.9% 8.1% 8.1% 2025 23,079 5,741 45.9% 8.1% 8.1% 2026 24,944 6,204 45.9% 8.1% 8.1% 2027 26,958 6,705 45.9% 8.1% 8.1% 2028 29,134 7,247 45.9% 8.1% 8.1% 2029 31,486 7,832 45.9% 8.1% 8.1% 2030 34,030 8,464 45.9% 8.1% 8.1% 2031 36,787 9,150 45.9% 8.1% 8.1% 2032 39,766 9,891 45.9% 8.1% 8.1% 2033 42,987 10,692 45.9% 8.1% 8.1% 2034 46,469 11,558 45.9% 8.1% 8.1% 2035 50,233 12,495 45.9% 8.1% 8.1% 2036 54,302 13,507 45.9% 8.1% 8.1% 2037 58,701 14,601 45.9% 8.1% 8.1% 2038 63,455 15,783 45.9% 8.1% 8.1% Year Moderate I
  38. 38. Final Master Plan Report 3-15 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 2008 LCPDP The demand forecast contained within the 2008 LCPDP covers the period 2008 to 2030. The projection of power and energy demand was made through the use of the Model for Analysis of Energy Demand (MAED). The MAED model is an end-use forecast model that is designed to evaluate medium and long-term demand for energy in a country (or in a region). As the LCPDP demand forecast only covered the period to 2030, we have extended the current national forecast to the end of the planning horizon of this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in both the generation sent out and peak demand forecasts and used the resulting mathematical trend line formulae to project the forecast for the additional 8 years required. The extended 2008 LCPDP base case demand forecast is presented in Table 3-10. The MAED model used to derive the 2008 LCPDP demand forecast provides a robust end- user demand forecasting tool. We understand that the underpinning assumption behind the MAED model is the GDP growth forecasts. In the base case, an unfaltering GDP growth rate of 10 per cent per annum for the years 2013 to 2030 is assumed. Even in very favourable global and local market conditions this level of GDP growth would be very difficult to achieve. Furthermore, historical analysis of GDP growth statistics in countries worldwide indicates that this level of sustained economic growth has rarely occurred and can rarely be sustained without (i) vast, new mineral reserves being discovered or, (ii) a significant increase in Foreign Direct Investment (FDI). An average annual increase in demand of around 9 per cent per annum would also require a significant amount of annual investment in generation, transmission and distribution. A full description of the 2008 LCPDP demand forecast is provided in Appendix F.
  39. 39. Final Master Plan Report 3-16 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-10 Extended 2008 LCPDP Demand Forecast for Kenya (Base Case) 2009 LCPDP The demand forecast developed for the 2009 LCPDP covers the period 2010 to 2030. In contrast to the 2008 LCPDP, the projections of power and energy demand in the 2009 LCPDP were made through the use of the Microsoft E-views econometric software. As the LCPSP demand forecast only covered the period to 2030, we have extended the current national forecast to the end of the planning horizon of this study. In order to extend the existing forecast we have maintained a constant growth rate in sales, generation and peak demand of 14.3 per cent per annum for the remainder of the planning period. The extended demand forecast (base case only) is presented in Table 3-11 below. Generation Peak Demand Load Factor Generation Peak Demand (GWh) (MW) (%) (%) (%) 2008 7,676 1,194 73.4% 2009 8,140 1,313 70.8% 6.0% 10.0% 2010 8,954 1,445 70.8% 10.0% 10.0% 2011 9,847 1,589 70.8% 10.0% 10.0% 2012 10,830 1,747 70.8% 10.0% 10.0% 2013 12,134 1,958 70.8% 12.0% 12.0% 2014 13,739 2,193 71.5% 13.2% 12.0% 2015 15,390 2,456 71.5% 12.0% 12.0% 2016 16,743 2,672 71.5% 8.8% 8.8% 2017 17,988 2,871 71.5% 7.4% 7.4% 2018 19,327 3,085 71.5% 7.4% 7.4% 2019 20,765 3,314 71.5% 7.4% 7.4% 2020 22,310 3,561 71.5% 7.4% 7.4% 2021 24,187 3,860 71.5% 8.4% 8.4% 2022 26,222 4,185 71.5% 8.4% 8.4% 2023 28,428 4,537 71.5% 8.4% 8.4% 2024 30,723 4,919 71.3% 8.1% 8.4% 2025 33,307 5,333 71.3% 8.4% 8.4% 2026 35,936 5,753 71.3% 7.9% 7.9% 2027 38,786 6,210 71.3% 7.9% 7.9% 2028 41,831 6,697 71.3% 7.9% 7.9% 2029 45,217 7,227 71.4% 8.1% 7.9% 2030 48,775 7,795 71.4% 7.9% 7.9% 2031 52,412 8,393 71.3% 7.5% 7.7% 2032 56,402 9,037 71.2% 7.6% 7.7% 2033 60,651 9,723 71.2% 7.5% 7.6% 2034 65,170 10,453 71.2% 7.4% 7.5% 2035 69,968 11,229 71.1% 7.4% 7.4% 2036 75,058 12,053 71.1% 7.3% 7.3% 2037 80,450 12,927 71.0% 7.2% 7.2% 2038 86,154 13,852 71.0% 7.1% 7.2% Assumed  Calender Year
  40. 40. Final Master Plan Report 3-17 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-11 Extended 2009 LCPDP Demand Forecast (Base Case) As previously stated, we believe that it is unrealistic to assume a five to seven fold increase in GDP between now and 2030 unless major new mineral reserves are discovered or FDI contributions increase manifold. It should be noted that there is a considerable difference between the 2008 and the 2009 LCPDP demand forecasts. Although the key input assumptions remain largely unchanged, the 2009 LCPDP forecast is considerably higher than the 2008 LCPDP forecast. The marked difference in projected load levels can only be attributed to the change to the adopted forecasting methodology and model. Furthermore, the out-turn demand for electricity in Kenya in 2009 indicates growth at a slower rate than that projected in the 2008 LCPDP. This would seem to indicate that the 2009 LCPDP forecast should have been more conservative with the assumptions. Our Generation Peak Demand Load Factor Generation Peak Demand (GWh) (MW) (%) (%) (%) 2009 7,391 1,205 70.0% 2010 7,838 1,278 70.0% 6.0% 6.1% 2011 8,292 1,352 70.0% 5.8% 5.8% 2012 8,916 1,454 70.0% 7.5% 7.5% 2013 9,692 1,581 70.0% 8.7% 8.7% 2014 10,935 1,783 70.0% 12.8% 12.8% 2015 12,495 2,038 70.0% 14.3% 14.3% 2016 14,278 2,328 70.0% 14.3% 14.2% 2017 16,315 2,661 70.0% 14.3% 14.3% 2018 18,643 3,040 70.0% 14.3% 14.2% 2019 21,303 3,474 70.0% 14.3% 14.3% 2020 24,342 3,970 70.0% 14.3% 14.3% 2021 27,815 4,536 70.0% 14.3% 14.3% 2022 31,783 5,183 70.0% 14.3% 14.3% 2023 36,318 5,923 70.0% 14.3% 14.3% 2024 41,500 6,768 70.0% 14.3% 14.3% 2025 47,421 7,733 70.0% 14.3% 14.3% 2026 54,186 8,837 70.0% 14.3% 14.3% 2027 61,917 10,097 70.0% 14.3% 14.3% 2028 70,751 11,538 70.0% 14.3% 14.3% 2029 80,846 13,184 70.0% 14.3% 14.3% 2030 92,380 15,065 70.0% 14.3% 14.3% 2031 105,560 17,214 70.0% 14.3% 14.3% 2032 120,620 19,670 70.0% 14.3% 14.3% 2033 137,829 22,477 70.0% 14.3% 14.3% 2034 157,493 25,683 70.0% 14.3% 14.3% 2035 179,963 29,348 70.0% 14.3% 14.3% 2036 205,638 33,535 70.0% 14.3% 14.3% 2037 234,976 38,319 70.0% 14.3% 14.3% 2038 268,500 43,786 70.0% 14.3% 14.3% Assumed  Calender Year
  41. 41. Final Master Plan Report 3-18 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study analysis of the results of the two forecasts shows that this is not the case and we would question the validity of this forecast. A full description of the 2009 LCPDP demand forecast is provided in Appendix F. 3.7 Rwanda The latest national demand forecast available for Rwanda was produced by RSWI and Fichtner in October 2008 as part of the Nile Basin Initiative (NBI) Nile Equatorial Lakes Subsidiary Action Program (NELSAP) study on the Electricity Transmission Lines linked to the Rusumo Falls Hydro-Electric Generation Plant. The Rwandan NELSAP demand forecast was developed in tandem with demand forecasts for Tanzania and Burundi. The objective of the demand forecast was to develop an end-user model, which focused on the structure of the different electricity consumer groups and their specific consumption. It should be noted, however, that some elements of trend-line and econometric techniques have also been taken into consideration. As the NELSAP demand forecast only covered the period to 2025, we have extended the current national forecast to the end of the planning horizon for this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in the sent out generation and peak demand forecasts and used the resulting mathematical trend line formulas to project the forecast for the additional 13 years required. Further details of the methodology and assumptions used in the derivation of the NELSAP demand forecast are provided in Appendix G. We consider the assumed growth rates in peak demand and generation to be very high, with average annual growth around 11 per cent per annum. Growth rates of this magnitude require massive amounts of coordinated investment in infrastructure and while “technically” possible, in our view, we do not believe this is likely to be achieved under a base case scenario. Given our concerns with the base case demand forecast detailed above, we have not reviewed the other demand forecast scenarios developed as part of the 2009 LCPDP.
  42. 42. Final Master Plan Report 3-19 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-12 Extended NELSAP Demand Forecast for Rwanda (Base Case) Generation Peak Demand Load Factor (GWh) (MW) (%) 1998 186.8 37.0 57.6% 1999 189.7 37.6 57.6% 2000 192.6 38.1 57.6% 2001 195.5 38.7 57.6% 2002 198.3 39.3 57.6% 2003 201.2 39.9 57.6% 2004 204.1 40.4 57.6% 2005 207.0 41.0 57.6% 2006 231.6 45.4 58.2% 2007 256.2 49.8 58.7% 2008 280.8 54.2 59.1% 2009 305.4 58.6 59.5% 2010 330.0 63.0 59.8% 2011 386.2 73.4 60.1% 2012 442.4 83.8 60.3% 2013 498.6 94.2 60.4% 2014 554.8 104.6 60.5% 2015 611.0 115.0 60.7% 2016 697.4 131.8 60.4% 2017 783.8 148.6 60.2% 2018 870.2 165.4 60.1% 2019 956.6 182.2 59.9% 2020 1043.0 199.0 59.8% 2021 1161.8 224.8 59.0% 2022 1280.6 250.6 58.3% 2023 1399.4 276.4 57.8% 2024 1518.2 302.2 57.3% 2025 1637.0 328.0 57.0% 2026 1780.5 355.8 57.1% 2027 1922.5 385.7 56.9% 2028 2070.7 417.0 56.7% 2029 2225.0 449.7 56.5% 2030 2385.4 483.8 56.3% 2031 2552.0 519.3 56.1% 2032 2724.7 556.1 55.9% 2033 2903.6 594.3 55.8% 2034 3088.6 633.9 55.6% 2035 3279.7 674.9 55.5% 2036 3477.0 717.3 55.3% 2037 3680.4 761.0 55.2% 2038 3890.0 806.1 55.1% Year Historic Data NELSAP National Plan PB Extension
  43. 43. Final Master Plan Report 3-20 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 3.8 Sudan In 2005, PB were commissioned with the task of developing a LTPSP study for the whole of Sudan, which included an extensive end-user survey based demand forecast. A variety of methodologies have been utilised to derive the demand forecasts for the LTPSP study, primarily based around the results of the detailed market survey performed by NEC. Forecasts for the domestic and agricultural forecasts use end-use approaches. From the results of the household energy survey, average electricity consumption patterns were identified on a state by state and urban rural/basis for each of the 7 income categories identified in the survey. An end-use demand forecast model was developed to calculate changes in total domestic consumption as household income and electrification rates increase respectively. The short-term demand forecast for the large commercial and industrial sector is based upon production output forecasts from existing NEC customers. In the medium-term the load from committed large commercial and industrial projects are added to the underlying growth of existing customers and in the long-term the energy and electricity requirements to serve the growing economy in Sudan are used as the driving parameters to estimate future electricity demands. Growth in demand for the small commercial and Government sectors are based upon estimates of customer numbers and specific consumption per customer. The forecasts for each consumer category were developed on a state by state basis. The electricity forecasts for total generation (GWh) and peak demand (MW) at the sent-out generation level are derived from the application of power and energy losses to the total sector sales forecasts presented above and the application of appropriate coincident after diversity load factors. Further details of the methodology and assumptions used in the derivation of the LTPSP demand forecast are provided in Appendix H. As the LTPSP demand forecast only covered the period to 2030, we have extended the current national forecast to the end of the planning horizon for this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in both electricity sales and peak demand forecasts and used the resulting mathematical trend line formulae to project the forecast for the additional 8 years required. The extended LTPSP base case demand forecast is presented below in Table 3-13.
  44. 44. Final Master Plan Report 3-21 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 3-13 Extended LTPSP Demand Forecast for Sudan (Base Case) The demand forecast developed as part of the LTPSP study was an end-user forecast based on an extensive survey. The survey results provided an indication of the patterns, requirements and uses of electricity in Sudan at the time. The end-user methodology adopted to develop the demand forecast is reasonable for determining the future load in Sudan. A key component in determining the demand for electricity into the future however, is the electrification rate. At the time of the study, NEC declared that they would invest significantly in increasing the number of connections to the grid and this led to the assumption that 80 per cent of the country would be connected to the grid by 2025. As the LTPSP study specifically states, “Achieving the high level of demand growth is heavily reliant on the successful completion of the stated electrification projects across the whole of the country. We note that the number of connections required on an annual basis are significantly higher than have been achieved historically. NEC are confident that they will be able to achieve these electrification rates and to fulfil the Government’s policy. Failure to complete these projects and/or lower growth rates in final connection to the distribution networks by households will inevitably lead to lower outturn levels of electricity demand than shown here.” In the 5 years since the demand forecast was first developed, it is apparent that NEC have not reached the levels of electrification that were assumed in the study. While the level of growth experienced in Sudan is very high and commendable, this is significantly below the forecast figure and indicates that NEC fell short of its own targets. Sales Generation Peak Demand Load Factor Sales Generation Peak Demand (GWh) (GWh) (%) (GWh) (MW) (%) (%) (%) (%) 2006 6,371 2,067 24.5% 8,438 1,475 65.3% 2007 10,483 3,220 23.5% 13,704 2,244 69.7% 64.5% 62.4% 52.1% 2008 14,596 4,237 22.5% 18,833 3,013 71.4% 39.2% 37.4% 34.3% 2009 18,708 5,124 21.5% 23,832 3,781 71.9% 28.2% 26.5% 25.5% 2010 22,820 5,884 20.5% 28,704 4,550 72.0% 22.0% 20.4% 20.3% 2011 25,088 6,077 19.5% 31,166 4,979 71.5% 9.9% 8.6% 9.4% 2012 27,357 6,417 19.0% 33,774 5,407 71.3% 9.0% 8.4% 8.6% 2013 29,625 6,725 18.5% 36,350 5,836 71.1% 8.3% 7.6% 7.9% 2014 31,894 7,001 18.0% 38,895 6,264 70.9% 7.7% 7.0% 7.3% 2015 34,162 7,246 17.5% 41,408 6,693 70.6% 7.1% 6.5% 6.8% 2016 36,731 7,523 17.0% 44,254 7,153 70.6% 7.5% 6.9% 6.9% 2017 39,300 7,766 16.5% 47,066 7,614 70.6% 7.0% 6.4% 6.4% 2018 41,869 7,975 16.0% 49,844 8,074 70.5% 6.5% 5.9% 6.0% 2019 44,438 8,151 15.5% 52,589 8,535 70.3% 6.1% 5.5% 5.7% 2020 47,007 8,295 15.0% 55,302 8,995 70.2% 5.8% 5.2% 5.4% 2021 49,700 8,429 14.5% 58,129 9,437 70.3% 5.7% 5.1% 4.9% 2022 52,393 8,529 14.0% 60,923 9,879 70.4% 5.4% 4.8% 4.7% 2023 55,087 8,597 13.5% 63,684 10,321 70.4% 5.1% 4.5% 4.5% 2024 57,780 8,634 13.0% 66,414 10,763 70.4% 4.9% 4.3% 4.3% 2025 60,473 8,639 12.5% 69,112 11,205 70.4% 4.7% 4.1% 4.1% 2026 63,292 9,042 12.5% 72,334 11,741 70.3% 4.7% 4.7% 4.8% 2027 66,111 9,444 12.5% 75,556 12,276 70.3% 4.5% 4.5% 4.6% 2028 68,931 9,847 12.5% 78,778 12,812 70.2% 4.3% 4.3% 4.4% 2029 71,750 10,250 12.5% 82,000 13,347 70.1% 4.1% 4.1% 4.2% 2030 74,569 10,653 12.5% 85,222 13,883 70.1% 3.9% 3.9% 4.0% 2031 77,383 11,055 12.5% 88,437 14,327 70.5% 3.8% 3.8% 3.2% 2032 80,208 11,458 12.5% 91,666 14,847 70.5% 3.7% 3.7% 3.6% 2033 83,031 11,862 12.5% 94,893 15,372 70.5% 3.5% 3.5% 3.5% 2034 85,849 12,264 12.5% 98,113 15,902 70.4% 3.4% 3.4% 3.4% 2035 88,658 12,665 12.5% 101,323 16,437 70.4% 3.3% 3.3% 3.4% 2036 91,456 13,065 12.5% 104,521 16,977 70.3% 3.2% 3.2% 3.3% 2037 94,239 13,463 12.5% 107,702 17,522 70.2% 3.0% 3.0% 3.2% 2038 97,005 13,858 12.5% 110,863 18,072 70.0% 2.9% 2.9% 3.1% Assumed  Calender Year Losses
  45. 45. Final Master Plan Report 3-22 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 3.9 Tanzania In December 2007 SNC published their Power System Master Plan (PSMP) study report. This study was carried out for TANESCO on behalf of The Government of the United Republic of Tanzania. In 2009, an updated PSMP demand forecast was developed by TANESCO experts, under the supervision of SNC during an ‘on-the-job’ training course. For this study, we consider the regional extrapolation/trend line demand forecast to be the ‘official’ forecast of demand. The regional load forecast was carried out in four steps: • Derive a forecast of sales for the load centres area using a trend-line approach in which the trends in number of customers and the unit consumption in each category of load are studied and projected; • Assess the impact of the issues specific to Tanzania; • Estimate the losses and derive the energy required; • Estimate the load factors that would apply in an unconstrained system. The process to be used for the trend-line forecast will consist of the following steps: • For each category for which data are available, tabulate the number of customers, the sales and the unit consumption for the full historical period available (roughly twenty years) • Plot the above data • Review the data and the graphs derived from it to assess anomalies and trends • Either correct anomalies or obtain explanations for them • Project the number of customers for the period taking account of issues likely to have an impact on growth (e.g. rural electrification policies) • Project the unit consumption for the same period taking account of issues likely to have an impact on growth (e.g. the removal of constraints on generation) • Multiply the unit consumption in each year by the number of customers forecast for that year to obtain the estimated sales Further details of the methodology and assumptions used in the derivation of the PSMP demand forecast are provided in Appendix I. As the PSMP demand forecast only covered the period to 2033, we have extended the current national forecast to the end of the planning horizon for this study. In order to extend the existing forecast we have used trend line analysis to identify existing trends in both the generation sent out and peak demand forecasts and used the resulting mathematical trend line formulae to project the forecast for the additional 5 years required. We believe the methodology employed to determine the PSMP demand forecast is robust and in line with demand forecasting best-practice. The PSMP demand forecast projects an average annual increase in peak demand of around 7.2 per cent. An average annual growth rate of this figures results in a 7 fold increase over the 28 year period. Similar growth rates are projected for sent out generation. An average annual increase in peak demand/generation of this nature would require a significant amount of annual investment in generation, transmission and distribution. If such a large amount of investment is required to fund the new generation, transmission and distribution projects required in order to meet this demand, then less money would be available for investment in other sectors of the economy, and this in turn would cast doubts on the ability of other
  46. 46. Final Master Plan Report 3-23 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study sectors to grow at the rates required to achieve the high growth rates predicated in the demand forecast. It should be noted however, that the Government of Tanzania has identified 5 key areas of strategic importance (of which Energy Infrastructure is one) in its medium-term Public Investment Plan (MPIP) for the period 2009/10 to 2014/15. The MPIP highlights the importance of fast-tracking the flow of public investment into the energy infrastructure industry so as to stimulate increased participation of other key players in the Tanzanian economy. This suggest that Government will do all it can to ensure funds are available to allow the energy sector to develop in line with the demand forecast developed as part of the PSMP. Table 3-14 Extended PSMP Demand Forecast for Tanzania (Base Case) 3.10 Uganda The latest demand forecast available for Uganda was developed by PB as part of the on- going Power Sector Investment Plan (PSIP) study. The PSIP demand forecast projects demand for electricity over the period 2008 to 2038 for three different scenarios; base, high and low. The PSIP demand forecast is derived using PB’s econometric based RALF model. Generation Peak Demand Load Factor (GWh) (MW) (%) 2010 5,293 895 67.5% 2011 5,773 981 67.2% 2012 6,439 1,103 66.7% 2013 7,081 1,213 66.6% 2014 7,489 1,285 66.5% 2015 8,135 1,398 66.5% 2016 8,987 1,542 66.5% 2017 9,895 1,698 66.5% 2018 10,704 1,839 66.5% 2019 11,326 1,945 66.5% 2020 11,994 2,061 66.4% 2021 12,701 2,182 66.5% 2022 13,440 2,311 66.4% 2023 14,398 2,479 66.3% 2024 15,245 2,628 66.2% 2025 16,145 2,783 66.2% 2026 17,112 2,953 66.1% 2027 18,116 3,131 66.0% 2028 19,379 3,353 66.0% 2029 20,536 3,558 65.9% 2030 21,745 3,770 65.8% 2031 23,042 4,002 65.7% 2032 24,449 4,254 65.6% 2033 26,164 4,532 65.9% 2034 27,917 4,838 65.9% 2035 29,854 5,168 65.9% 2036 31,978 5,527 66.1% 2037 34,311 5,918 66.2% 2038 36,873 6,344 66.4% Assumed  Calender Year
  47. 47. Final Master Plan Report 3-24 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Further details of the methodology and assumptions used in the derivation of the PSIP demand forecast are provided in Appendix J. The base, high and low PSIP demand forecasts are presented below in Table 3-15. Table 3-15 PSIP Demand Forecasts for Uganda (Base, High and Low Cases) Generation (GWh) Peak Demand (MW) Load Factor (%) Generation (GWh) Peak Demand (MW) Load Factor (%) Generation (GWh) Peak Demand (MW) Load Factor (%) 2009 2784 541 58.7% 2877 561 58.5% 3397 597 65.0% 2010 2901 570 58.1% 3026 596 58.0% 3982 702 64.8% 2011 3012 597 57.6% 3188 633 57.5% 4564 805 64.7% 2012 3121 623 57.2% 3371 673 57.2% 5146 908 64.7% 2013 3203 643 56.9% 3560 715 56.8% 5663 998 64.8% 2014 3279 662 56.5% 3788 764 56.6% 6165 1084 64.9% 2015 3351 679 56.3% 4030 816 56.4% 6651 1167 65.1% 2016 3419 695 56.2% 4288 871 56.2% 7122 1247 65.2% 2017 3481 710 56.0% 4561 929 56.0% 7577 1324 65.3% 2018 3540 724 55.8% 4851 990 55.9% 8018 1398 65.5% 2019 3622 741 55.8% 5123 1045 56.0% 8518 1480 65.7% 2020 3676 750 56.0% 5362 1091 56.1% 8977 1551 66.1% 2021 3778 772 55.9% 5685 1158 56.0% 9537 1647 66.1% 2022 3913 800 55.8% 6056 1233 56.1% 10178 1759 66.1% 2023 4048 828 55.8% 6434 1310 56.1% 10831 1873 66.0% 2024 4184 857 55.7% 6821 1389 56.1% 11497 1990 66.0% 2025 4321 886 55.7% 7216 1470 56.0% 12175 2109 65.9% 2026 4459 915 55.6% 7620 1552 56.0% 12867 2231 65.8% 2027 4598 944 55.6% 8031 1636 56.0% 13570 2355 65.8% 2028 4738 973 55.6% 8450 1722 56.0% 14287 2482 65.7% 2029 4880 1003 55.5% 8878 1809 56.0% 15016 2612 65.6% 2030 5022 1033 55.5% 9313 1898 56.0% 15757 2744 65.6% 2031 5065 1032 56.0% 9754 1978 56.3% 16548 2883 65.5% 2032 5246 1069 56.0% 10203 2069 56.3% 17348 3025 65.5% 2033 5428 1107 56.0% 10659 2162 56.3% 18156 3168 65.4% 2034 5611 1145 55.9% 11123 2257 56.3% 18972 3312 65.4% 2035 5796 1184 55.9% 11594 2353 56.2% 19797 3459 65.3% 2036 5982 1223 55.8% 12072 2450 56.2% 20629 3607 65.3% 2037 6170 1262 55.8% 12559 2549 56.2% 21470 3757 65.2% 2038 6358 1301 55.8% 13052 2650 56.2% 22319 3908 65.2% PB High CasePB Base CasePB Low Case Year
  48. 48. Final Master Plan Report 4-1 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 4. INDEPENDENT PB DEMAND FORECASTS In this section of the report we outline the independent PB demand forecasts developed specifically using the data made available for this study. Further details of each review are provided in the respective Appendices provided with this report. 4.1 Burundi In addition to reviewing the most recent national demand forecast available for Burundi, we have produced our own base; high and low national demand forecast scenarios. These scenarios are based upon our own assumptions and methodology, utilising the data collected/made available as part of this study. Due to the lack of economic data as well as the unavailability of sales by consumer category, this forecast has been developed on the basis of the country electrification rate, an assumed level of specific consumption, an assumption relating to the number of persons per household and a population forecast provided by the UN. High and low demand forecast scenarios have also been developed. These forecasts differ from the base case demand forecast having adopted different assumptions relating to the rate of electrification and population for the derivation of total sales. Details of the methodology employed and any assumptions made are provided in Appendix A. The base, high and low independent PB demand forecasts are presented in Table 4-1 and summarised in Figure 4-1 and Figure 4-2 below.
  49. 49. Final Master Plan Report 4-2 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Table 4-1 PB Base, High and Low Demand Forecast for Burundi (GWh) (MW) LF (%) (GWh) (MW) LF (%) (GWh) (MW) LF (%) 2008 93.6 28.9 36.9% 93.6 28.9 36.9% 93.6 28.9 36.9% 2009 98.2 29.6 37.9% 98.2 29.6 37.9% 98.2 29.6 37.9% 2010 102.2 30.0 38.9% 102.2 30.0 38.9% 102.2 30.0 38.9% 2011 119.9 34.3 39.9% 124.3 35.6 39.9% 132.8 38.0 39.9% 2012 138.0 38.5 40.9% 146.9 41.0 40.9% 164.3 45.9 40.9% 2013 156.4 42.6 41.9% 170.0 46.3 41.9% 196.5 53.5 41.9% 2014 175.1 46.6 42.9% 193.6 51.5 42.9% 229.6 61.1 42.9% 2015 194.2 50.5 43.9% 217.6 56.6 43.9% 263.4 68.5 43.9% 2016 213.3 54.2 44.9% 242.2 61.6 44.9% 298.5 75.9 44.9% 2017 232.7 57.9 45.9% 267.3 66.5 45.9% 334.4 83.2 45.9% 2018 252.4 61.4 46.9% 292.8 71.3 46.9% 371.1 90.3 46.9% 2019 272.3 64.9 47.9% 318.8 76.0 47.9% 408.6 97.4 47.9% 2020 292.5 68.3 48.9% 345.2 80.6 48.9% 446.9 104.3 48.9% 2021 312.3 71.4 49.9% 371.6 85.0 49.9% 485.9 111.2 49.9% 2022 332.2 74.5 50.9% 398.3 89.3 50.9% 525.6 117.9 50.9% 2023 352.4 77.5 51.9% 425.5 93.6 51.9% 566.0 124.5 51.9% 2024 372.7 80.4 52.9% 453.0 97.7 52.9% 607.1 131.0 52.9% 2025 393.1 83.3 53.9% 480.8 101.8 53.9% 648.9 137.4 53.9% 2026 413.2 85.9 54.9% 508.4 105.7 54.9% 690.7 143.6 54.9% 2027 433.4 88.5 55.9% 536.3 109.5 55.9% 733.1 149.7 55.9% 2028 453.6 91.0 56.9% 564.4 113.2 56.9% 776.1 155.7 56.9% 2029 474.0 93.5 57.9% 592.8 116.9 57.9% 819.7 161.6 57.9% 2030 494.5 95.8 58.9% 621.5 120.5 58.9% 863.8 167.4 58.9% 2031 514.7 98.1 59.9% 650.2 123.9 59.9% 908.5 173.1 59.9% 2032 534.9 100.3 60.9% 679.0 127.3 60.9% 953.7 178.8 60.9% 2033 555.1 102.4 61.9% 708.1 130.6 61.9% 999.4 184.3 61.9% 2034 575.5 104.4 62.9% 737.5 133.8 62.9% 1,045.6 189.8 62.9% 2035 595.9 106.5 63.9% 767.0 137.0 63.9% 1,092.4 195.2 63.9% 2036 616.0 108.3 64.9% 796.9 140.2 64.9% 1,140.7 200.6 64.9% 2037 636.1 110.2 65.9% 826.9 143.2 65.9% 1,189.5 206.1 65.9% 2038 656.2 112.0 66.9% 857.1 146.3 66.9% 1,238.9 211.4 66.9% PB Low Case PB Base Case PB High Case Year
  50. 50. Final Master Plan Report 4-3 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Figure 4-1 PB Peak Demand Forecast for Burundi (MW) Figure 4-2 PB Sent Out Generation Forecast for Burundi (GWh) 0 50 100 150 200 250 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 Peak Demand (MW) PB Low Case PB Base Case PB High Case 0 200 400 600 800 1000 1200 1400 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 Generation (GWh) PB Low Case PB Base Case PB High Case
  51. 51. Final Master Plan Report 4-4 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study 4.2 Djibouti The most recent national demand forecast available for Djibouti was developed by PB in 2009 as part of the LCEMP study. The forecasts developed for the LCEMP study (as discussed in Section 5 and Appendix B of this report) are representative of PB’s independent view of electrical demand growth in Djibouti. The base, high and low LCEMP demand forecasts are presented in Table 4-2 and summarised in Figure 4-3 and Figure 4-4 below. Table 4-2 PB Base, High and Low Demand Forecast for Djibouti Generation (GWh) Peak Demand (MW) Load Factor (%) Generation (GWh) Peak Demand (MW) Load Factor (%) Generation (GWh) Peak Demand (MW) Load Factor (%) 2005 210 50 47.9% 210 50 47.9% 210 50 47.9% 2006 217 52 48.0% 220 52 48.0% 222 53 48.0% 2007 224 53 48.0% 229 54 48.1% 234 56 48.1% 2008 232 55 48.0% 240 57 48.1% 247 59 48.2% 2009 240 57 48.0% 251 59 48.2% 261 62 48.2% 2010 248 59 48.0% 262 62 48.2% 275 65 48.3% 2011 258 61 48.0% 275 65 48.2% 291 69 48.3% 2012 269 64 48.0% 289 68 48.2% 309 73 48.3% 2013 280 66 48.0% 303 72 48.3% 328 77 48.3% 2014 291 69 48.0% 318 75 48.3% 347 82 48.3% 2015 303 72 48.0% 334 79 48.3% 368 87 48.3% 2016 316 75 48.0% 352 83 48.3% 392 93 48.3% 2017 330 79 47.9% 372 88 48.3% 417 99 48.3% 2018 345 82 47.8% 392 93 48.3% 444 105 48.3% 2019 360 86 47.8% 413 98 48.3% 473 112 48.3% 2020 376 90 47.7% 436 103 48.3% 504 119 48.3% 2021 393 95 47.4% 461 109 48.4% 538 127 48.4% 2022 411 99 47.3% 487 115 48.4% 573 135 48.4% 2023 429 104 47.1% 515 121 48.4% 612 144 48.5% 2024 449 109 47.0% 545 128 48.5% 653 154 48.5% 2025 469 115 46.8% 576 136 48.5% 696 164 48.6% 2026 491 120 46.6% 610 143 48.5% 742 174 48.6% 2027 513 126 46.4% 645 152 48.6% 792 186 48.6% 2028 536 132 46.2% 682 160 48.6% 844 198 48.7% 2029 560 139 46.0% 722 169 48.7% 900 211 48.8% 2030 585 146 45.7% 764 179 48.7% 959 224 48.8% 2031 611 153 45.5% 808 189 48.8% 1021 238 48.9% 2032 638 161 45.3% 855 200 48.8% 1087 254 48.9% 2033 666 169 45.0% 904 211 48.9% 1156 269 49.0% 2034 696 177 44.8% 955 223 48.9% 1229 286 49.0% 2035 726 186 44.6% 1009 235 49.0% 1306 304 49.1% 2036 757 195 44.3% 1066 248 49.0% 1387 322 49.2% 2037 790 205 44.1% 1125 262 49.1% 1472 341 49.2% 2038 823 214 43.8% 1187 276 49.2% 1561 362 49.3% PB High CasePB Base CasePB Low Case Year
  52. 52. Final Master Plan Report 4-5 WBS 1100 Demand Forecast May 2011 EAPP/EAC Regional PSMP & Grid Code Study Figure 4-3 PB Peak Demand Forecast for Djibouti (MW) Figure 4-4 PB Sent Out Generation Forecast for Djibouti (GWh) 0 50 100 150 200 250 300 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 Peak Demand (MW) PB Low Case PB Base Case PB High Case 0 200 400 600 800 1000 1200 1400 1600 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 Generation (GWh) PB Low Case PB Base Case PB High Case

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