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

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

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

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

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EAPP/EAC Regional
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

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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

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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

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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

Final Master Plan Report Introduction
May 2011
EAPP/EAC Regional
SECTION 1
Introduction

Final Master Plan Report 1-1 Introduction
May 2011
EAPP/EAC Regional
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)];

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• 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

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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

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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.

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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.

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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

Final Master Plan Report WBS 1100 Demand Forecast
May 2011
EAPP/EAC Regional
SECTION 2
Demand Forecast
WBS 1100

May 2011
EAPP/EAC Regional
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

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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

Final Master Plan Report 1-1 WBS 1100 Demand Forecast
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EAPP/EAC Regional
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

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EAPP/EAC Regional
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.

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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.

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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.

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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:

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• 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.

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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.

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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

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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.

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Table 3-2 LCEMP Demand Forecast (Base Case)

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Table 3-3 LCEMP Demand Forecast (High Case)

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Table 3-4 LCEMP Demand Forecast (Low Case)

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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.
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.

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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

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Table 3-6 Extended NELSAP Demand Forecast for East DRC (High Case)
(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

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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.
(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

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As the EEHC demand forecast only covered the period to 2026, we have extended the
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.

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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.

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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
the existing forecast we have used trend line analysis to identify existing trends in both the
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.

May 2011
EAPP/EAC Regional
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
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

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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.
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.

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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
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.

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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
the existing forecast we have used trend line analysis to identify existing trends in both the
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

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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.

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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)
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)

May 2011
EAPP/EAC Regional
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

May 2011
EAPP/EAC Regional
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)
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)

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