DRC Energy Outlook to 2050

Kungliga Tekniska Höskolan
Energy and Environment
MJ2413
Democratic Republic of Congo - Energy Outlook
Authors:
Rachit Kansal
Sunay Gupta
Hammad Farrukh
Timothy Mulé
Supervisor:
Professor Mark Howells
Shahid Hussain Siyal
[54]
December 7, 2015

Abstract
This report examines the current energy situation and the possible future energy scenarios of The Democratic
Republic of the Congo. Two supplementary energy scenarios are developed namely New Policies and Green House
Gas Mitigation along with the baseline scenario. The different scenarios take into account various developments
and their impact on the energy balance of country. New Policies Scenario takes into account the planned energy
related projects in the country whereas the GHG mitigation scenario focus of GHG emissions reduction based on
various expected improvements. The study is conducted using the simulation software LEAP.
The energy demand of DRC is met largely by Wood and Charcoal and Hydropower, with households being the
largest consumer of energy. Hydropower, with the potential to meet the total energy demand only contributes
to less than 2% of the total demand. Most of the industrial activity in DRC is based on agriculture and mining.
Infrastructure for transport is insignificant and the commercial sector is almost non-existent. All the results are
generated, presented and analyzed in the light of various policies and expected developments in the country.
Keywords: DRC, Democratic Republic of Congo, Energy Modelling, Energy Projection, Energy Scenario, Africa,
Electrification.
1

Contents
1 Introduction 6
1.1 Scope and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Background 9
2.1 Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.1 Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.2 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.4 Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.5 Energy Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Energy Transformation and Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Energy Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Growth Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Methodology 17
3.1 General Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Scenario Development 22
4.1 Baseline and Reference Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.1 General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.2 Key Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3.1 Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3.2 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.3.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.3.4 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.4 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.4.1 Transmission and Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.4.2 Electricity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.4.3 Charcoal Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.4.4 Micro Scale Biogas Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.5 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.1 Power Generation Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.2 Electriﬁcation of DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Green House Gas Mitigation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3.1 Non-Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.2 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.3 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.4 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 A Brighter DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Results 34
5.1 Reference Energy System Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2 Baseline Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2.1.1 Household Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2.1.2 Industrial Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.2 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2

5.2.4 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3 New Policies Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3.2 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.4 Greenhouse Gas Mitigation Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.5 Brighter DRC Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.6 Overall Scenario Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6 Uncertainties & Sensitivity Analysis 53
6.1 General Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.2.1 Population & GDP Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.2.2 Dispatch Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
7 Lessons Learned 57
8 Conclusion 58
3

List of Figures
1 GDP Breakdown for the DRC by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Total Energy Supply of DRC in 2013 (Excluding Electricity Trading) . . . . . . . . . . . . . . . . . . 6
3 Total 2013 Electricity Production in DRC by Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 Electriﬁcation of Various Countries in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5 Energy Consumption Per Capita - Comparison Between DRC and Industralized Countries in 2012 8
6 Energy Consumption Per Capita - Comparison Between DRC and Other Sub-Saharan African Coun-
tries in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
7 The Inga Dam Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
8 DRC 2012 Electricity Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
9 DRC 2002-2012 Consumption Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
10 DRC 2012 Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
11 DRC 2012 Urban and Rural Household Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . 12
12 Percentages of Electriﬁed Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
13 Transport Sector Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
14 Electricity Consumption Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
15 DRC Mineral Production in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
16 Population of the DRC vs.Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
17 Urban and Rural Populations vs.Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
18 GDP Predictions for the DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
19 General Methodology for Developing an Energy Model . . . . . . . . . . . . . . . . . . . . . . . . . 18
20 Model Structure of Energy Demand in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
21 Model Structure of Energy Transformation in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
22 Primary and Secondary Resources in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
23 GHG Emissions by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
24 Historical Non-LULUCF Emissions for the DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
25 Historical LULUCF Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
26 Projected Emissions by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
27 Energy System Diagram of DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
28 Energy Demand Across All Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
29 Household Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
30 Household Cooking Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
31 Household Electricity Consumption Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
32 Industrial Electricity Demand in Thousand kTOE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
33 Transport Energy Demand 2012 and 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
34 Transport Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
35 Commercial Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
36 Commercial Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
37 Electricity Generation by Technology - Reference Scenario . . . . . . . . . . . . . . . . . . . . . . . 41
38 DRC Electricity Demand - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
39 DRC Household Electricity Demand - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . 42
40 Electricity Generation from Hydro - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . 43
41 Electricity Imports - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
42 2012 Power Production in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
43 Projected 2040 Power Production in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
44 Current and Projected Cooking Emissions in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
45 Current and Projected Emissions due to Lighting in DRC . . . . . . . . . . . . . . . . . . . . . . . . 45
46 Emissions Due to Deforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
47 Emissions Due to Fossil Fuel Based Power Plants - GHG Scenario . . . . . . . . . . . . . . . . . . . 46
48 Emissions Due to Charcoal Production- GHG Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 47
49 Carbon Mitigation Potential in 2040 - GHG Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
50 Cumulative Carbon Mitigation Potential from 2012 to 2040 - GHG Scenario . . . . . . . . . . . . . 48
4

51 Electricity Production in 2040 - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 49
52 Electricity Production in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 49
53 Urban Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . 50
54 Rural Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . 50
55 Rural Cooking Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 51
56 Projected Emissions Across All Sectors from 2012 to 2040 . . . . . . . . . . . . . . . . . . . . . . . . 51
57 Energy Trading Perspective Across All Sectors from 2012 to 2040 . . . . . . . . . . . . . . . . . . . . 52
58 Population Projection Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
59 GDP Projection Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
60 Fuel Mix with High and Low Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
61 Difference in Primary Requirement of Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
List of Tables
1 DRC 2012 Electricity Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 DRC 2012 Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 DRC 2012 Household Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 DRC 2012 Industrial Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 DRC 2012 Energy Consumption by Transport Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6 References and Assumptions for Household Demand . . . . . . . . . . . . . . . . . . . . . . . . . . 22
7 References and Assumptions for Industrial Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8 References and Assumptions for Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9 Passenger Transport Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10 Freight Transport Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
11 References and Assumptions for Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5

1 Introduction
The Democratic Republic of the Congo (DRC), located in central Africa, has a total land area of 2,267,048 km2 and
an estimated population of 79,375,136 [1]. This ﬁnds the DRC as the 2nd largest African nation in terms of area,
and the 4th largest by population [2, 3]. Equal to roughly 2
3 the size of the European Union, the DRC had a GDP
of $32.96 billion in 2014, or $700 per capita [4, 5, 1]. This GDP was composed of 40.4% agriculture, 23% industry,
and 36.6% services, as can be seen in Figure 1 below [1]:
Figure 1: GDP Breakdown for the DRC by Sector [1]
Although its largest city is the capital, Kinshasha, with a population 11.587 million, less than 40% of the popula-
tion live in urban areas [1, 4]. Considering this population distribution, its current economic state, and the facts
that the DRC has over 80 million hectacres of fertile land and over 1,100 diﬀerent types of precious metals and
minerals - It can be seen that The DRC is poised to go through an enormous amount of growth, and have the
potential to become one the richest and most prosperous countries in the African continent [4].
As of 2013, the total energy supply (excluding electricity trade) was split as: 92.2% biofuels/waste, 4.3% oil, and
3.5% hydro [6]:
Figure 2: Total Energy Supply of DRC in 2013 (Excluding Electricity Trading) [6]
6

In this same year, 16.4% of the population had access to electricity, and of this 1.4% comes from fossil fuel based
sources while the remaining 98.6% is supplied from hydroelectric power plants [7, 1].
Figure 3: Total 2013 Electricity Production in DRC by Source [1]
Figure 4 below shows the electrification rates of various countries across the world, with more developed regions
like the Middle East boasting rates of 92% and the less developed ones such as Sub-Saharan Africa having an
average rate of 32%. As can be seen, even when compared to the Sub-Saharan African region, the electrification
rate of the DRC is abysmal, sitting at a little over 10% in 2012 [22].
Figure 4: Electrification of Various Countries in 2012 [22]
The DRC has been estimated to possess the third largest potential of hydroelectric power, with roughly 100 gi-
gawatts (GW) available in the country. As of March 2014, approval has been set for development of the Inga 3
Basse Chute (BC) and Mid-size Hydropower Development Technical Assistance Project - a hydroelectric project
that will be developed outside of Kinshasha, along the Inga river. This project is slated to provide electricity to
7

an additional 9 million Congolese people with its projected completion in 2019 [8, 9]. This 4,800 megawatt (MW)
facility is the ﬁrst in a seven phase grand project planning to bring in 40,000 MW to the region [10].
As of 2012, the DRC did not import any resources to meet its energy requirements. In fact, it exported 1.9% of its
need [7]. This is due in large part to the relatively low energy use of DRC (292.4 kilograms of oil equivalent (kgoe)
per capita in 2012, as well as the large amount of available resources the country contains [23]. Figure 5 below
compares the DRC’s consumption to more industrialized and developed countries. As can be seen, the DRC’s
consumption is miniscule in comparison:
Figure 5: Energy Consumption Per Capita - Comparison Between DRC and Industralized Countries in 2012 [23]
Figure 6 goes on to compare the DRC’s consumption to other Sub-Saharan nations. This is a more relevant com-
parison as the countries are in the same region and barring South Africa and Botswana, have similar economic
statuses. However, as can be seen, the country still has a low energy consumption and is well below the average
for that region.
Figure 6: Energy Consumption Per Capita - Comparison Between DRC and Other Sub-Saharan African
Countries in 2012 [23]
8

This relatively low energy consumption resulted in a total of 2.481 million metric tons of CO2 released to the at-
mosphere [1].
The DRC has had its share of political strife and turmoil throughout its history. The latest conflicts were officially
ended in 2013. However, there have been fears of a revival in violence, as protests mount against President Joseph
Kabila’s desired to rewrite the nation?s constitution and remain in office past 2 terms [12, 13]. Considering the
state of affairs in the country, and the poverty the majority of Congolese people are living under, it is clear that the
focus of the DRC will be one towards stability and general development, rather than with an intended focus on
environmental stewardship or emission control. Luckily renewable resources are vast in the country, and progress
on projects such as the Inga Dam shown below, will help the DRC both develop the social welfare of its citizens,
while also giving it the potential to become a leader in renewable energy in the African continent.
Figure 7: The Inga Dam Project [24]
1.1 Scope and Objectives
The goal of this project was to successfully analyze the current energy systems in place in The Democratic Republic
of the Congo and to attempt to project possible scenarios for the nation’s future growth. The scenarios used are
defined below:
• Reference Scenario: The state of affairs in the DRC based on current data that is available for the chosen
base year.
• New Policies Scenario: The possible development of the country based on different policies and projects
that have been proposed.
• GHG Mitigation Scenario: The DRC’s possible outlook if a greenhouse gas mitigation stance is taken.
2 Background
This section describes the energy demand, transformation and production along with energy trading and growth
conditions. All these parameters are modeled under the current conditions of DRC.
9

2.1 Energy Demand
The report describes the energy demand for residential, industrial, and transport sector along with a brief overview
of commercial electricity demand. The report attempts to examine all the major energy and electricity demand
groupings in the country despite of limited data.
Most of the electricity in DRC is produced from hydroelectric sources with a very small portion coming from
natural gas and oil, as shown in Figure 8. The total electricity consumption in the last decade has increased by
57%, mainly due to increased percentage of population with access to electricity [53]. Currently only 16.4% of the
population has access to electricity in DRC, which hints at increased electricity consumption in the coming years
[4].
Figure 8: DRC 2012 Electricity Production [53]
The major consumer of electricity in the country is the industrial sector, followed by the residential, and commer-
cial sectors as show in Table 1. The sector wise growth in the electricity consumption is shown in Figure 9.
Table 1: DRC 2012 Electricity Consumption by Sector[53]
Sector. Electricity Consumption (GWh) Pecentage of Total Demand
Industrial 4646 65.3
Residential 2262 31.8
Commercial 208 2.9
Total 7116 100
10

Figure 9: DRC 2002-2012 Consumption Breakdown [53]
The total energy demand in DRC in 2012 was met by a mix of various resources including: wood, charcoal, elec-
tricity, kerosene, and naphtha.
Figure 10: DRC 2012 Energy Demand by Fuel
The energy consumption in DRC can be split in the following manner in diﬀerent sectors:
Table 2: DRC 2012 Energy Consumption by Sector [53]
Sector. Electricity Demand (GWh) Pecentage of Total Demand
Household 21248.62 98.1%
Industrial 418.42 1.931%
Transport 0.00394 0.00002%
Commercial 0.01996 0.00009%
Total 23319.568 100%
11

The energy demand is discussed separately in the following categories.
2.1.1 Households
The total energy demand in the households in DRC in 2012 was 21.248 Mtoe. This demand was further split into
Urban and Rural in the following manner.
Table 3: DRC 2012 Household Energy Consumption by Sector [53]
Urban 6.125 28.8%
Rural 15.123 71.2%
Total 22.9113 100%
Figure 11: DRC 2012 Urban and Rural Household Energy Demand by Fuel [53]
The household electricity demand of DRC in 2012 was 2,262GWh [53], which corresponds to 31.8% of the total
electricity demand in 2012. The household electricity demand can also further be divided into Urban and Rural.
Out of the total 16.4% electriﬁed population - 5.57% of the rural population and 36.31% of the urban population
has access to electricity, as can be seen in Figure 12.
Figure 12: Percentages of Electriﬁed Households [53]
The energy demand in the households has been categorized into cooking, refrigeration, lighting, and other uses.
12

2.1.2 Industry
The industrial sector in DRC, is the largest consumer of energy after households. The total energy demand by the
industrial sector was 418.41ktoe. The table ?? shows the demand split into various subsectors.
Table 4: DRC 2012 Industrial Energy Consumption by Sector [53]
Agriculture and Forestry 280.846 67.12%
Mining and Quarrying 61.65181 14.73%
Energy and Water 16.81413 4.02%
Manufacturing 28.02355 6.70%
Construction 31.08067 7.43%
Total 418.41617 100%
The industrial electricity demand in 2012 accounted for 65.38% of the total demand. The major economic activ-
ity in the country was fueled by agriculture and mining industry, accounting for 39.4% and 12.1% of the GDP
respectively. Construction, manufacturing and trading contributed to the rest of energy demand [53].
2.1.3 Commercial
The commercial sector had an energy consumption of only 19.961Toe in 2012, which corresponds to 0.00009% of
the total energy demand in DRC. The electricity demand in the commercial sector was just 2.9% of the total which
accounted for a mere 208GWh. The electricity demand was primarily for the lighting purposes in the urban
commercial areas. On the other hand, rural commercial lighting needs were wholly met by kerosene oil.
2.1.4 Transportation
Similar to the commercial sector, the transport sector in the DRC also has a very low energy demand. This is
primarily because of under developed transportation infrastructure and lack of economic activity in the region.
Out of the total energy demand, almost 99% of the consumption is by the passenger transport. In both freight and
passenger transport, water transport consumes the most amount of energy followed by road transport.
Table 5: DRC 2012 Energy Consumption by Transport Type
Freight 3.895 99.00%
Passenger 0.0389 1.00%
Total 3.93504 100%
13

Figure 13: Transport Sector Energy Demand
2.1.5 Energy Trading
DRC is not directly involved in the energy trading with its neighbors except for the oil imports. But since Mining
industry is a major player in the industrial sector of the country, the energy consumed by the mining sector is
indirectly energy exported. This is because the lack of reﬁning facilities in the country lead to all the minerals
being exported to other countries.
The mining industry comprises of most the country?s exports. 99% [66] of the total exports of DRC were of the
extractive sort. The mining industry consumed energy equivalent to 65.65181 thousand tons of oil equivalent
which is 77% of the total electricity demand in DRC. Moreover, all of this energy is being supplied by electricity.
Figure 14: Electricity Consumption Breakdown
14

Figure 15: DRC Mineral Production in 2012 [67]
2.2 Energy Transformation and Production
The Democratic Republic of Congo is one of the most resource-rich countries in the world, leading it to being the
epicenter for dramatic political revolutions and military conflicts. Besides having vast reserves of minerals, the
country also has sizable crude oil potential. With a 187 million barrels of crude oil [14], the DRC sits at #62 in the
world, in terms of verified reserves [15].
The country does not have significant natural gas reserves though. At 991.1 million cubic meters [16], the DRC
ranks near the bottom of countries with gas potential. Understandably, it has zero production of this particular
fossil fuel.
The country also has relatively negligible coal reserves, topping 97.7 million tons, as of 2011 [17]. The exploitation
of this reserve has thus, been correspondingly insignificant (it was around 145,000 tons in 2012 [18].
What the country lacks in conventional resources, it compensates for in renewable ones. The DRC is the most
heavily endowed in hydroelectric resources in the world, with over a 100 GW in potential production capacity
[14].
The country has also been blessed with reasonable solar potential, with intensities ranging from 3250 to 6000
Watts per square meter. However, none of this potential has been exploited, either through solar photovoltaics or
solar thermal technology. Both small-scale, individual systems and centralized plants are non-existent in this area.
Lastly, the country’s wind resources are not significant enough to exploit, with average wind speeds of 5km
h or
less. Hence, despite the immense natural resources available to it, the country has very low installed capacity, at
2505 MW [19]. What is even more pitiful is the woeful lack of transformation of this energy production, as the
national utility sold only 64% of total electricity produced in 2004. For example, only 276,431 customers in the
country’s capital city, Kinshasa, have access to electricity, despite the city having over 8 million inhabitants [19].
2.3 Energy Trading
Despite having sizable oil extraction operations, the DRC has no refineries of its own. Indeed, one of its main
energy exports in crude petroleum and one of its imports is refined oil [20].
15

In the future though, the DRC is poised to be a large net exporter of electricity, as it further develops the hydro-
electric Inga projects. The mammoth potential of those projects means that the country has the potential to power
a majority of sub-Saharan Africa on its own.
Hence, if the potential of these resources is exploited, energy trading could become an integral part of the DRC
economy, bringing in substantial revenues that could boost financial conditions inside the country.
2.4 Growth Predictions
The Democratic Republic of the Congo is currently the 20th most populous country in the world, and is projected
to grow substantially in the coming decades, as shown by the figure below:
Figure 16: Population of the DRC vs.Time [21]
An interesting feature of this growth, though, is that increasing proportions of the population will be found in
the urban areas of the country (as seen in the figure below). This is a trend echoed across many other countries as
people migrate to cities for better opportunities. In terms of increasing the electrification rate of the country, this
is an important trend, as it significantly increases the ease of connecting people to the grid.
16

Figure 17: Urban and Rural Populations vs.Time [21]
The Gross Domestic Product of the country is also set to increase substantially as the population grows and ﬁnds
more economic opportunities.
Figure 18: GDP Predictions for the DRC [25]
3 Methodology
3.1 General Methodology
To develop the energy model the DRC, most of the information was gathered from websites of diﬀerent DRC
government agencies, as well as international institutions like The World Bank, The International Energy Agency
17

(IEA), and The International Renewable Energy Agency (IRENA). Specific details regarding the distribution of
energy demand across sectors, the energy consumption of appliances, and the cost of electricity generation were
derived from sources mentioned in the References section. In some cases, the information was missing specifically
for the DRC and reasonable assumptions were formulated using neighboring countries as reference or common
observations across the globe. All the assumptions are discussed and clarified throughout the report.
All of the information was consolidated in The Long-Range Energy Alternative Planning System, or LEAP software,
which uses a bottom-top approach to model the energy scenario in a baseline year (2012 in this report) and
throughout various future scenarios ending at a predetermined year (2040 was chosen here). To compare the
energy supply, demand, and cost in the future, different scenarios were developed. Each scenario had a distinct
feature to assess the impacts of variations in the energy supply, demand, and efficiency - as well as other non
energy related activities. Figure 19 below gives an outline of the methodology:
Figure 19: General Methodology for Developing an Energy Model
3.2 Model Structure
For developing the DRC’s energy scenario, the LEAP model was divided into four primary categories: Demand,
Transformation, Resources, and Non Energy. Each category was necessary to distinguish different levels of activ-
ities involved in the bottom-up modeling approach. A description of each category is provided below:
1. Demand: The demand is split across four different sub-categories: Household, Industry, texititTransport and
Commercial. These categories include both energy requirements and consumption by different utilities, and
the data is fed into LEAP at different activity levels (such as household, passenger-km, metric tons of produc-
18

tion or kW-hr). Typical information needed for Demand includes: population, electrification rates, house-
hold utilities, types of industries, transportation modes and fuel economy, space heating (if any), and energy
needs for commercial use. Figure 20 below illustrates the demand tree structure for the DRC.
Figure 20: Model Structure of Energy Demand in DRC
2. Transformation: All of the energy needs in Demand are met by the outputs from Transformation. This
includes the use of resources such as wood, biomass, hydro, and oil (in DRC) for generating electricity or
conversion to a secondary fuel. The last step in Transformation is Transmission and Distribution of the gen-
erated electricity to meet Demand. LEAP, by default, imports the necessary resources if a specific demand
is unmet. Typical information needed for Transformation in LEAP includes transmission losses, differ-
ent kinds of power plants and their feedstock fuel, capacity, maximum availability, historical production (if
available), and variable and fixed costs. Figure 21 illustrates the transformation tree structure for the DRC.
19

Figure 21: Model Structure of Energy Transformation in DRC
3. Resources: All of the activities in Demand and Transformation need resources (primary or secondary)
which can either be found or produced in the DRC. LEAP takes inputs of the available amount or yield of
these resources and allocates them to diﬀerent activities in Demand and Transformation. If any resource re-
quirement is unmet then LEAP, by default, imports it (or exports a resource in case it is in surplus). Figure 22
below illustrates the resources available or needed for energy supply in the DRC.
20

Figure 22: Primary and Secondary Resources in DRC
4. Non Energy: This category includes all activities which do not fall under the previous three primary cate-
gories. Some of the examples speciﬁc to the DRC are deforestation activities and subsequent environmental
loading (emissions), as well as the burning of agricultural waste and forest ﬁres.
21

4 Scenario Development
4.1 Baseline and Reference Scenarios
4.1.1 General Description
The Baseline Scenario refers to the current energy outlook in 2012 and the Reference Scenario refers to the projections
in 2040 based on energy policies which are already implemented - a "Business as Usual" case. Developing these
scenarios provides a useful glance over future energy expectations in the country and can lead to possible action
plans to improve the economy. All of the assumptions used to fill in the missing data points for DRC in this
scenario are mentioned in the appropriate sections of the report.
4.1.2 Key Assumptions
Some key assumptions were included in the LEAP model for the DRC’s demographic and economic projections
according to following data:
1. Population trends and projections from Africapedia [21]
2. GDP projection based on IEA’s cumulative growth rate of GDP [26]
3. Income per capita based on The World Bank Development Indicators [27]
4. Household size based on The World Bank’s health report on DRC [28]
4.1.3 Demand
4.1.3.1 Households
Using the population projections and average household size from Key Assumptions, the number of households
in the DRC were calculated. The activities in this branch are taken at a household level. All of the households in the
DRC were divided into urban and rural population based on a distribution obtained from McKinsey & Company’s
report on sub-Saharan Africa [29]. Within each subsection of urban and rural, households were divided into
electrified and non-electrified for 2012 [27]. The households were further subcategorized based on final use of energy
within the household: Cooking, Lighting, Refrigeration and Other uses (such as: TV, Fans, Radio and Cell Phone).
Table 6 below shows the assumptions made during when analyzing the base and end years.
Table 6: References and Assumptions for Household Demand
S.No. Category Sub category (if any) 2012 2040
1 Electrified and
Non Electrified
Households
Reference [27] Linearly projected using
data available from 1990 to
2012
2 Refrigeration
% share of
population
Same as in India [30]
Energy Intensity Reference [26] 20% increment with age
from 2012, Reference [26]
3 Lighting
% share of electricity
by households
Same as electrified
households
Continued on next page
22

Table 6 – continued from previous page
% share of kerosene,
candles, Biogas and
Firewood by
households
Using 2009 data from
Reference [31] and %
increase in electricity
consumption [26]
Electricity
Consumption
Reference [26] 20% increment with age
Kerosene
consumption
Using 2009 data from
Reference [31] and %
change in share of
Kerosene for lighting in
2012
Candles
consumption
Taking Ethiopia’s
consumption in 2009
from Reference [32] with
monthly household
income 4700 birr and
extrapolating to 2012
Biogas consumption Reference [33]
Firewood
consumption
Taking Ethiopia’s
consumption in 2009
from Reference [32] with
monthly household
income 4700 birr and
extrapolating to 2012
4 Cooking
Fuel Wood % share Reference [26] Reduction by 20% due to
increase in electricity usage,
Reference [26]
Consumption Reference [34]
Charcoal % share Reference [26] Reduction by 20% due to in-
crease in electricity usage,
Reference [26]
LPG % share Reference [26] Reduction by 20% due to
Reference [26]
Kerosene % share Reference [26] Reduction by 20% due to
Reference [26]
5 Other Uses
TV % of electriﬁed
households
Reference [42]
Electricity
Consumption
Fan % of electriﬁed
households
Reference [42]
Electricity
Consumption
23

Radio % of electrified
households
Reference [42]
Electricity
Consumption
40 W radio used 3 hours
a day
20% increment with age
Cell Phone % of electrified
households
Reference [42]
Electricity
Consumption
5W phone battery
charged for 5 hours a
day
20% increment with age
4.1.3.2 Industry
DRC has five major types of industry which consume the majority of the industrial electricity demand. Their
electricity consumption is divided based on their contribution to the total GDP [1]. The assumptions that were
made when analyzing the Industry of the DRC are shown below in Table 7.
Table 7: References and Assumptions for Industrial Demand
S.No. Category Sub category (if any) 2012 & 2040
1 Agriculture &
Forestry
% share of GDP Reference [36] and [1], extrapolation using GDP share
across years
Electricity
Consumption
% GDP share of Total Electricity Consumption of the
country [27]
2 Mining &
Quarrying
across years
Electricity Con-
sumption
country [27]
3 Energy and
water
across years
Electricity
Consumption
country [27]
4 Manufacturing % share of GDP Reference [36] and [1], extrapolation using GDP share
across years
Electricity
Consumption
country [27]
5 Construction % share of GDP Reference [36] and [1], extrapolation using GDP share
across years
Electricity
Consumption
country [27]
4.1.3.3 Commercial
In DRC, commercial electricity demand is primarily for lighting and there is no space heating or cooling data
available. It is found that 30% of the urban lighting and 20% of the rural lighting is used for commercial purposes
24

[31]. In urban, 54% of the lighting demand is met by Kerosene and in Rural, 100% of the lighting demand is met
by Kerosene [31].
4.1.3.4 Transport
The transport sector of DRC is divided into Passenger and Freight Transport. Each of these is further divided into
sub sectors such as Road, Rail, Air and Water.
Table 8: References and Assumptions for Transport
Passenger Transport
1 Road - Car Passenger km &
Vehicle km
Reference [45], Assumed the average km driven daily
to be 20km and average km of weekly trip to be 40km
and using the average driven km along with the total
number of cars to calculate passenger/vehicle km.
Extrapolated for 2040, assuming the growth in the
number of vehicles to be proportional to the growth in
income per capita.
Fuel Economy Assumed the fuel economy of the cars to be 10km/liter
since the cars are supposed to be old. And 13km/liter
for 2040 since the vehicles will be replaced by the
newer ones.
2 Road - Bus Passenger km &
Vehicle km
Reference [14], Using the vehicle-km and the load
factor to calculate the total passenger-km. Load factor
is assumed to be 42.3 as an average of the capacities
between buses and mini buses, reference [14].
Extrapolating it for 2040 proportionally to the growth
in income per capita.
Fuel Economy Reference [46], Reference [26]
3 Rail Passenger km & Ve-
hicle km
Reference [48], Assumed an average of 6 compartments
per train and 80 people per compartment to calculate
load factor and calculate the vehicle km. Extrapolated
it for 2040 proportionally to the growth in income per
capita.
Fuel Economy Reference [47],Reference [26]
4 Air Passenger km &
Vehicle km
Reference [14], Extrapolating from 2008 using the
growth in population until 2012. Extrapolated it for
2040 proportionally to the growth in income per capita.
Fuel Economy Reference [49],Reference [50], Using the eﬃciency
improvement per annum mentioned to extrapolate for
2040.
5 Water Passenger km & Ve-
hicle km
Assuming the passenger km for water transport to be
of the same ratio for total population as that of
passenger km (bus) in the urban population. And
assuming the load factor to be 50.
Fuel Economy Reference [51] Assumed the average of diesel ferries
and using the 1.4% Reference [52] eﬃciency
improvement to project for 2040.
Freight Transport
25

6 Freight
Transport -
Road
Energy Intensity Reference [43], assumed the higher end of the range on
figure 2.5 of reference [43] for 2012 and lower end for
the 2040.
% share Reference [26], used the total number of vehicles in
commercial vehicles in DRC and assuming the average
distance for the movement to be 987.5 km to find the
million tons km and calculating the share of road.
7 Freight
Transport -
Rail
the 2040.
% share Reference [27], used the tons-km for rail and calculated
the % share out of the total tons-km.
8 Freight
Transport - Air
the 2040.
% share Reference [27], used the tons-km for air and calculated
the % share out of the total tons-km.
9 Freight
Transport -
Water
Energy Intensity Reference [43], assumed the higher end of the range for
small freighter on figure 2.14 of reference [43] for 2012
and lower end for the 2040. Assumed small freighter
because the ports of DRC cannot handle large freighters
because of the infrastructural restraints, Reference [44]
% share Reference [27], used the tons-km for water and
calculated the % share out of the total tons-km.
4.1.4 Transformation
Transformation section for DRC is divided into four sections which includes Transmission and Distribution, Elec-
tric Generation, Charcoal Production and Micro Scale Biogas Production.
4.1.4.1 Transmission and Distribution
For the base year 2012, Transmission and Distribution (T&D) losses were found from World Bank Indicators for
DRC [27]. It is assumed to be same for 2040 as the electrification rate is low in DRC even with plenty hydropower
capacity, hence major emphasis is expected to be on providing electricity to non electrified areas than reducing
losses on T&D.
4.1.4.2 Electricity Generation
DRC has several hydro power plants and they are grouped together in LEAP as Large and Small scale hydro
power plants based on their installed capacity (large hydro is considered as plants with installed capacity higher
than 5 MW). Besides hydro, DRC has oil and gas power plants which uses Residual Fuel Oil and Natural Gas as
feedstock fuels respectively. Data points needed in LEAP includes:
• Exogenous Capacity: Installed capacity of hydro and thermal power plants was available on SNEL, one of
the energy producing organization in DRC [37]
26

• Historical Production: Historical production of total hydro, oil and gas was available on IEA [s]
• Dispatch Rule: In baseline scenario, the dispatch rule of Merit Order is used. All the electricity produced by
Hydro and Gas will be supplied to the demand as base and peak load capacity, hence the merit order of each
plant is set as 1. As crude oil is always imported then the consumption of crude oil should be proportional
to the capacity.
• Process Efficiency: Typical efficiencies of large and small hydro are common across the globe [38] and that
of Oil and Gas were assumed to be of Heavy Fuel Oil and Open Cycle Gas Turbine [39]
• Maximum Availability: Available capacity of hydropower plants was available on SNEL [37] and that of
Oil and Gas were assumed to be of Heavy Fuel Oil and Open Cycle Gas Turbine [39]
• Cost: Cost includes Investment, Fixed O&M, Variable O&M. For hydro, these costs were assumed to be same
as in US [40] and that of Oil and Gas were assumed to be of Heavy Fuel Oil and Open Cycle Gas Turbine
[39]
4.1.4.3 Charcoal Production
Charcoal production uses Wood as a feedstock fuel and DRC has a humungous availability of wood due to large
forest cover and all the wood required in this process is assumed to be produced within DRC. The process followed
in DRC is traditional earth mounds and its efficiency is found to be 25% [41].
4.1.4.4 Micro Scale Biogas Production
DRC has surplus of biomass and it is assumed here that for the small share of biogas demand for lighting, it is
produced in DRC itself using small bio gas plants in a decentralized manner. The process efficiency of biomass is
found to be 30% [39].
4.1.5 Resources
DRC is rich in natural resources and the primary resources needed for energy needs are Wood, Biomass, Hydro
and Natural Gas. The yield and additions to reserves of these resources were adjusted so that there is no net
import of these resources. There is no input for Secondary resources and LEAP, by default, imports a resource if
it is not available.
4.2 New Policies Scenario
The new policy scenario takes into account the planned projects by the government of the DRC along with other
organizations and measures its impact on energy dynamics of the country.
4.2.1 Power Generation Projects
• Inga III
In order to tap one of the largest hydro resource potential in Africa, the government of DRC along with the
financial support of other international organisations plans to build the 4800MW Inga III Dam on the Congo
river. The project is expected to start by the end of 201 after renewed support from African Development
Bank and the World Bank. Inga III is expected to cost $12 billion. Inga III is a part of the Grand Inga complex
planned to generate a 40,000 MW of power. Inga III is due to come online by 2020 [55, 56].
27

• Zongo II
Zongo II is a hydro project funded by the China as a part of understanding between the two countries. The
project is expected to generate to 150MW and lead to increased employment in the Bas-Congo province. The
project is expected to add to the national grid by the end of 2016 [57, 58].
4.2.2 Electrification of DRC
According to the African Energy Outlook 2014, 26% electrification is planned until the year 2020. Assuming the
electrification to carry on with the same rate, starting from 2012, the expected population with electricity access
comes out to be 45.35% versus the 31.18% for the business as usual scenario.
The electrification policy will directly impact the electricity demand in 2040. As per the business as usual sce-
nario, the electricity demand in 2040 is expected to be 30.538 Billion kWh. Whereas according to the new policies
scenario, the demand in 2040 will be 33.313 Billion kWh.
4.3 Green House Gas Mitigation Scenario
The greenhouse gas mitigation scenario depicts a future in which the highest priority of nationwide government
policy is to mitigate greenhouse gas emissions as much as possible, while encouraging economic growth and
development. The Democratic Republic of Congo has the second largest rainforest area in the world, after the
Amazon. It is also a large country with incredible renewable resources and mitigation potential. Hence, the im-
portance of this scenario for this country cannot be overstated. It has the potential to catapult the DRC into a
country with clean and sustainable energy production, that affords economic opportunities to all its citizens.
A major factor in determining the net emissions of the country are LULUCF-related. LULUCF, which stands for
Land Use, Land Use Change and Forestry, is a big factor in the country?s emissions as it is directly related to de-
forestation, plantations and afforestation. Due to the country?s vast forest resources, these three components are
very important. Figure 23 below shows the main non-LULUCF emissions in the country, categorized by different
sectors in the country. As can be seen, agriculture and energy supply are the two biggest culprits with approxi-
mately 16 million tons of carbon dioxide emitted annually. Emissions associated with LULUCF are not included
here and were actually -11.5 million tons for that year. Hence, LULUCF contributed positively to greenhouse gas
mitigation and has done so in every year prior.
28

Figure 23: GHG Emissions by Sector [59]
Figure 24 shows the historical trend in the information from Figure 23, from 2000 to 2010. As can be seen, emissions
from the agriculture sector have been fairly constant while there has been a steady increase in emissions associated
with energy demand.
Figure 24: Historical Non-LULUCF Emissions for the DRC [59]
Figure 25 below builds on what was said earlier about the importance of LULUCF. As can be seen, associated
removals have been on a decline for the last decade, leading to a steady increase in net emissions. A large part of
29

this trend can be attributed to deforestation in the country, along with the slash-and-burn technique used to clear
forest area and transform it into agricultural land.
Figure 25: Historical LULUCF Emissions [59]
Figure 26 below shows the projections for non-LULUCF emissions for the coming decades. As can be seen, the
trends shown in this figure are remarkably different from those in Figure 24. While agriculture will still be a big
contributor to emissions, energy demand is poised to grow dramatically and increase emissions significantly as a
result.
Figure 26: Projected Emissions by Sector [59]
30

The following steps are planned to be taken to implement the GHG scenario.
4.3.1 Non-Energy
• Deforestation Mitigation
As has been mentioned, deforestation is a big contributor to emissions within the DRC. The World Bank
has created an umbrella initiative called the Improved Forested Landscape Management Project (IFLMP),
which tackles this problem through a combination of educating the local population, raising awareness
and incentivizing more sustainable alternatives for the logging and agriculture industries. Among all their
projects, the one focused solely on deforestation mitigation was chosen and scaled up in the simulation to
predict the carbon savings for the country in the decades to come.
4.3.2 Demand
• Household
– Efficient Cookstoves
As has been mentioned previously, 94.83% of a DRC household?s energy demand comes from cooking.
The UN has a small-scale CDM project called "The Improved Cookstoves" program which involves
replacing old, inefficient models with newer, cleaner-burning ones. Data from this project was also fed
into the simulation to demonstrate the potential of this option.
– Efficient Lighting
The World Bank has a deep interest in the DRC and has made it a part of its continent-wide program
"Lighting Africa". This initiative aims to develop a sustainable distribution system for low-cost solar
lighting. Data from it was chosen and extrapolated to provide an estimate for the proliferation of solar
lighting in the DRC by 2040.
– Efficient appliances
While there is no specific policy push for this area in the DRC, natural technological advancements
were taken into account and as residents in the DRC will buy appliances from other countries, this will
allow for higher efficiencies in the country in the coming years.
• Distribution Losses
In this case, the model takes into account that the power grid in the DRC is currently in an abysmal state
and that its transmission and distribution losses will decrease in the coming decade.
• Power Generation Efficiency
While most of the DRC’s power generation comes from hydro power, there is generation from fuel oil and
natural gas as well. The efficiencies of these power plants also increase in the coming decades, and that is
fed into the model also.
• Charcoal Production
Charcoal forms a significant part of the DRC?s energy needs and its urban demand is projected to increase
significantly, as the fuel is more condensed in energy. Studies have shown that the efficiency of those kilns
can be increased from 25 to 30%, if proper management practices are taught and enough awareness is cre-
ated.
31

4.3.4 Assumptions
• Deforestation Mitigation
– The first assumption for this initiative was that the deforestation rates projected in the IFLMP project
can be carried over and applied to the rates for the entire country.
– The second assumption was that the rainforest in the DRC’s Congo Basin can be classified as ?selec-
tively logged rainforest? as per the IPCC categories and hence, will have a carbon sequestration value
of 2.9 tonnes per hectare per year [60].
– The last assumption was that even though the IFLMP project formally ends in 2029, projects with a
similar effect will continue on until 2040.
• Efficient Lighting
– "Lighting Africa" is a continent-wide initiative and hence, includes the total number of solar lamps
distributed for Africa. The first assumption made was that the ratio of distributed solar lamps to total
African population is the same as that of distributed lamps in the DRC to its population. This was
necessary as there was not enough data available for the country.
– The second assumption made was in the projection of the proliferation of these lamps and it was that
the number of lamps distributed increases linearly from year-to-year, all the way to 2040.
– The last assumption was that the "Lighting Africa" initiative is able to continue on until 2040.
• Improved Cookstoves
– The main assumption for this program was that the specific cookstoves that the UN is distributing,
will be the able to reach the entire country and hence, the efficiency of those stoves will be the cooking
efficiency for the entire country by 2040.
– Another assumption was that all the stoves in the DRC will remain to be either charcoal or fuelwood-
based.
– The last assumption was that even though the CDM project formally ends in 2019, other projects with
similarly efficient cookstoves will continue on until 2040 [61].
• Improved Appliances
– As there was no available data on DRC-specific appliance policies for the future, assumptions on effi-
ciency had to be made based on trends in other countries. In this case, projections on energy efficient
appliances in the US were used for modeling the DRC [62].
• Charcoal Production
– The first assumption in this higher efficiency figure is that the practices which improved the efficiency
of the kiln in the study, can be effectively transferred over to kilns in the DRC.
– The second assumption is that awareness within the country will reach enough of a critical mass, so
that this efficiency becomes ubiquitous among all the kilns in the country [63].
32

4.4 A Brighter DRC
Thus far, we have seen how the country fares in a business-as-usual scenario, a New Policies one and a Greenhouse
Gas Mitigation scenario. This scenario aims to be the most optimistic yet realistic one of them all. The priority
of this scenario is electrification and economic development, but in a sustainable and environmentally friendly
manner. The former is usually a priority for most governments but the latter has been included, as following the
path of environmental destruction has led many other countries astray.
This scenario builds on the New Policies scenario, and thus assumes that the Inga III hydroelectric project is suc-
cessfully built and that a projected 26% of the total population is electrified by 2020.
As has been previously mentioned, the transmission grid infrastructure for the country is in a destitute state.
Hence, if this scenario is to achieve a large degree of electrification, simply increasing electrical production capac-
ity would not be enough. This is because that the probability that that increased capacity would reach a higher
percentage of the population, would be low. Hence, off-grid generation was selected.
• Off-Grid Electrification
Given the natural resources detailed earlier in the report, solar energy was the obvious candidate for this
case. The technology is well-developed, relatively economical and the resource is abundant in the DRC.
Now that the option for electrifying homes was technically viable, there was an opportunity to electrify
other parts of the home in the aim of improving quality of life and efficiency. The first, obvious avenue to
pursue was lighting.
• Lighting
Kerosene met 81% of total lighting needs in non-electrified urban households and 54% in non-electrified
rural households in the New Policies Scenario. It is associated with a myriad of health problems including
respiratory irritation, acute dermatitis, skin and eye irritation[68]. It is also known to offer poor lighting
while using a large amount of fuel, giving it an abysmal efficiency overall. Hence, electrifying it will help to
significantly improve the local quality of life.
• Cooking
As has been previously mentioned, cooking is a huge part of the energy expense for residents of the DRC,
especially those in non-electrified homes. A majority of the energy for cooking is derived from charcoal
and fuelwood, which are highly polluting when burnt and are major drivers for deforestation within the
country. Hence, cooking is another area this scenario hopes to improve.
• Solutions
– Solar PV distribution - This scenario aims to reach an overall electrification rate of 80% by 2040. This
target is based on previous experience of other countries which went from 20 to 80% in 25 years[29].
Since this scenario builds on the New Policies scenario, projected electrification for the latter in 2040 is
45.4%. Hence, 34.6% of the population still needs to be electrified in 2040 and all of this will be done
using Solar PV
– Solar lamp distribution - Solar lamps had been proposed in the GHG scenario to a small extent, based
on pilot projects elsewhere in Africa. Since the technology is well-developed and has an existing distri-
bution infrastructure within Africa, it was chosen as a viable alternative to all kerosene-based lighting
within the DRC.
– Solar cookers - Since the DRC receives a fair amount of direct, normal irradiation, solar cookers were
chosen as a possibility for the cooking area. The cooking space is predominantly powered by fuelwood
and charcoal, which are highly polluting and destructive to the environment. Hence, any improvement
in this area would take the country in a positive direction
33

– Increased hydropower availability - Currently, the average technical availability of the DRC?s hydro-
electric plants is 61%. The reason it is this low is the broken state of many turbines, which have not
been fixed due to either financial troubles or government bureaucracy. Hence, increasing the technical
availability of these projects would be very beneficial to the country as it would provide a lot more
electricity with minimal capital cost investment.
• Assumptions
– Solar PV Distribution
The distribution of PV panels is an expensive and complicated affair. To simplify this and meet the
80% electrification goal, the non-electrified urban population is targeted first. This means that this
sub-group is a 100% electrified by 2040, while total rural electrification reaches 58.2%.
– Solar Lamp Distribution
There will only be one solar kit per household. This assumption was done based on the luminosity
of the solar kit, which was 910 lumens to the average luminosity offered by kerosene lamps, which
was 45 lumens. Also, as the solar kit comes with a battery that lasts approximately 9 hours, one kit
was assumed to be sufficient to meet a household?s lighting needs The distribution of solar lamps will
follow the distribution of solar panels, as is makes the most sense to pair the two together. In the case
of rural distribution of these solar lamp kits, it would be comprehensive and able to reach remote parts
of the country with relative ease and reasonable costs
– Solar Cookers
One important assumption in this area is that residents in urban areas can, in the future, have easier
access to modern fuels such as LPG, to a greater extent, than rural residents would. Moreover, these
cookers can take between 2-4 hours for cooking a regular meal, which is less likely to be appealing for
urban residents Hence, the solar cooker distribution has been focused on rural households, both elec-
trified and non-electrified. Another assumption is that for the households where this is implemented,
there is only one solar cooker per household.
– Increased Hydropower Availability An important assumption here is that the technical availability of the
hydro-electric dams will be increased to the worldwide average of 90% for such scale of projects.
5 Results
LEAP provides four different tools to analyze the energy outlook in different scenarios:
• Results - Shows results of calculations as charts, tables and maps
• Reference Energy System Diagram - Show energy flows in the model
• Energy Balance - Summarizes energy consumption, conversion and production in the model
• Summaries - Customizable multi-variable reports including a cost-benefit summary of scenarios
5.1 Reference Energy System Diagram
This diagram is a very useful tool to realize the connection between energy supply and demand and revise any
energy flow in case of any anomaly. Figure ?? shows the energy system diagram of DRC and some of the key
insights that can be inferred are:
• Hydro and Natural Gas are the primary sources of electricity generation and fuel oil is not shown in this
diagram as it is a secondary resource.
• Biomass as a primary resource is directly used in conversion to biogas for lighting demand in households
• Wood is used for both charcoal production and direct consumption by households primarily for cooking
34

• Solar resource is implemented in the Green House Gas Mitigation scenario for direct consumption by house-
holds primarily for lighting
• The link between Demand and Transmission and Distribution is legitimate but LEAP did not show any in the
diagram. However, results from energy balance shows that LEAP considers this connection in calculations
of electricity supply and demand. LEAP software team conﬁrmed about this error in LEAP diagram but
assured that LEAP does the calculation correctly.
Figure 27: Energy System Diagram of DRC
5.2 Baseline Scenario Results
5.2.1 Demand
The total energy demand in diﬀerent sectors of the economy in 2012 was 16.5 MTOE and that in 2040 would
be 27.1 MTOE. Figure 28 shows that major energy demand in DRC comes from household because of massive
consumption of wood for cooking purposes. Industrial demand share of electricity increases from 5.2% to 7.2% in
28 years which could be due to increase in production capacities. The demand by transportation and commercial
sector is very low because of the limited transport facilities and commercial centers in the country.
35

Figure 28: Energy Demand Across All Sectors
5.2.1.1 Household Demand
The fuel demand distribution of household in Figure 29 clearly shows that wood takes more than 80% of the fuel
share even in 2040. Due to poor electrification rate of 18% in 2012 and expected 31% in 2040, the share of electricity
remains low and is primarily used for refrigeration, lighting and watching TV or using fan. The contribution of
charcoal is significant compared to other fuels except wood because it is widely used for cooking. Figure 30 illus-
trates a fuel demand distribution specific to cooking where charcoal and wood combined takes more than 95%
of the fuel share. LPG is common in urban households and electric stoves are a minor portion even in electrified
households. It can be concluded that there exists a tremendous potential to change the cooking fuel demand in
the entire country and move towards more efficient cooking appliances.
Household consumption of electricity in Figure 31 reveals that share of electricity consumption by electric stove
is going to increase which justifies argument presented in previous paragraph for Figure 30. Moving towards
electric stoves will reduce the pressure on wood demand resulting in lower greenhouse gas emissions (due to
decreased deforestation) and better health in general (due to improvement in air quality within the household).
Electricity consumption for refrigerator, lighting and other uses like TV, fan is going to decrease in the future with
efficient devices with lower energy intensity.
36

Figure 29: Household Energy Demand by Fuel
Figure 30: Household Cooking Demand by Fuel
37

Figure 31: Household Electricity Consumption Distribution
5.2.1.2 Industrial Demand
Industrial demand of electricity in Figure 32 shows that Agriculture and Forestry, being the leading contributor
to GDP, is the major consumer of electricity. Mining and Quarrying industry takes the next spot followed by con-
struction, manufacturing, energy and water industry.
Figure 32: Industrial Electricity Demand in Thousand kTOE
5.2.2 Transport
In the reference scenario, the total energy demand for the transport sector increases from 3.93 toe to 7.39326. The
increase attributed to the expected increase in the number of vehicles in the country. The passenger transport
energy demand in 2040 was 7.31.
38

Figure 33: Transport Energy Demand 2012 and 2040
In the passenger transport sector, majority of the energy demand was by the water transport. The energy demand
for the water transport increased from 3.68 toe to 6.96 toe in 2040.
Table 9: Passenger Transport Energy Demand
Passenger Transport Energy Demand (toe) Road Rail Air Water Total
2012 0.15092 0.00075 0.0555 3.688 3.8956
2040 - Baseline Scenario 0.22375 0.00171 0.11829 6.967 7.3116
In the freight transport sector, the largest consumer of energy was again the water sector followed by road sector
as the second biggest consumer of energy.
Table 10: Freight Transport Energy Demand
Freight Transport Energy Demand (toe) Road Rail Air Water Total
2012 0.00609 0.00203 0.00006 0.0312 0.0393
2040 - Baseline Scenario 0.01044 0.00418 0.00011 0.0669 0.0816
39

Figure 34: Transport Energy Demand by Fuel
5.2.3 Commercial
The total energy demand for the commercial sector in the country is expected to be 59.168 Tonnes of Oil Equivalent
in 2040. All of the energy demand for commercial sector is attributed to lighting because of the unavailability of
relevant data.
Figure 35: Commercial Energy Demand
40

Figure 36: Commercial Energy Demand by Fuel
The total electricity generation in DRC increased from 684 kTOE in 2012 to 1136 kTOE in 2040. Figure 4.7 shows
that hydro power is the major electricity generation technology with more than 97% contribution in both 2012 and
2040. Despite of being rich in hydro resources, DRC still has a low electriﬁcation rate of 18% in 2012 and needs
a sincere improvement in its grid infrastructure. Natural gas increases its contribution from 0.4% to 1.3% in 2040
and oil remains low because the dispatch rule has been set to ’proportion to capacity’ since oil is being imported
which incurs more cost.
Figure 37: Electricity Generation by Technology - Reference Scenario
5.3 New Policies Scenario Results
5.3.1 Demand
Increased electriﬁcation in new policies scenario will result in increased electricity demand as compared to the
baseline scenario for 2040.
41

Figure 38: DRC Electricity Demand - New Policies Scenario
The total electricity demand increases from 2625.87 thousand tonnes of oil equivalent in 2040 to 2864.43 thousand
tonnes of oil equivalent in 2040 for new policies scenario. The increase is reﬂected in household demand, because
the electriﬁcation is targeted at households and not the commercial or industrial sector.
Figure 39: DRC Household Electricity Demand - New Policies Scenario
The addition of hydro dams will change the amount of electricity generated in DRC. The new dams (Inga III
& Zongo II) will increase the electricity generation from 13 thousand GWh in 2040 for baseline scenario to 35.1
thousand GWh in 2040 for new policies scenario.
42

Figure 40: Electricity Generation from Hydro - New Policies Scenario
The addition of dams will also impact the electricity imports in the country. In the business as usual case, the
country will have to import 19.77 thousand Gigawatt Hour of electricity in 2040, since the electricity demand will
increase and the current power generation capacity will not be enough. With the two new dams in new policies
scenario, the country will have to import 0.707 thousand Gigawatt Hour of electricity in 2040.
Figure 41: Electricity Imports - New Policies Scenario
5.4 Greenhouse Gas Mitigation Scenario Results
The LEAP simulation was then run and the required results generated. First, is a comparison between Figures 42
and 43, which show the power production breakdown in the DRC in years 2012 and 2040 respectively. As can be
clearly seen, hydroelectric power is projected to remain as a dominant player in power production well into the
future with the expectation that the mammoth Inga III project will be bringing large amounts of electricity to the
country.
43

Figure 42: 2012 Power Production in DRC
Figure 43: Projected 2040 Power Production in DRC
These two ﬁgures are important as they highlight the inherent potential in greenhouse gas mitigation in the coun-
try, thanks to the type of source producing the bulk of the energy. Oil and natural gas will grow, but will continue
to remain small parts of the overall mix.
Figure 44 shows the current and projected carbon-dioxide emissions due to household cooking. As can be seen,
all fuel consumption is set to increase, with substantial rises in fuelwood and kerosene use. For the GHG sce-
nario, a program distributing improved cookstoves was implemented throughout the country and this helps to
save around 7.5 million metric tonnes of carbon dioxide in the year 2040 itself, as is seen in the ﬁgure.
44

Figure 44: Current and Projected Cooking Emissions in DRC
Then, Figure 45 shows the emissions associated with household lighting. Firstly, one significant difference can be
seen between this figure and Figure 44: emissions due to lighting are much lower than those related to cooking.
In both cases however, overall emissions almost double. Figure 45 shows a substantial increase in kerosene usage
while other fuel types increase steadily. However, in the GHG scenario, a project to distribute low-cost solar light-
ing is implemented and as can be seen, this helps to avoid 1.79 million metric tonnes of carbon dioxide emissions
in 2040 itself.
Figure 45: Current and Projected Emissions due to Lighting in DRC
Figure 46 now shows the amount of carbon dioxide that was not absorbed, due to deforestation. As can be seen,
in the GHG scenario, this value steadily decreases due to strong awareness campaigns and a scaled-up version of
the World Bank?s IFLMP project. The avoided emissions can be clearly seen.
45

Figure 46: Emissions Due to Deforestation
Figure 47 shows the current and projected emissions from oil and natural-gas plants in the country. One signiﬁcant
diﬀerence from other plots is that the scale of the emissions here is much lower, due to the low proliferation of
fossil-based plants in the country?s energy mix. However, this proliferation is poised to grow in the decades to
come, leading to increased emissions from both sources. Avoided emissions are 77,000 metric tonnes of carbon
dioxide in the year 2040. One important observation here is that these avoided emissions are not unique to the
GHG scenario, as they are based on natural technological advancements in the plants over the coming decades.
Hence, these emissions would have been mitigated in the Reference scenario but they have been included here to
show a comprehensive picture of the GHG scenario.
Figure 47: Emissions Due to Fossil Fuel Based Power Plants - GHG Scenario
Figure 48 shows the current and projected emissions due to charcoal production. As has been mentioned, the
GHG scenario assumes that increased awareness of proper management techniques of charcoal kilns will lead to
46

an increase in efficiency from 25 to 30%. As can be seen, this leads to a significant decrease in emissions (over
35.45 million metric tonnes). Hence, it is clear that charcoal production is very carbon-intensive and that growth
in this sector is bound to happen.
Figure 48: Emissions Due to Charcoal Production- GHG Scenario
Lastly, efficiencies in appliances and electric grid transmission do improve. However, as both of these factors are
based on electricity and given that almost all of the electricity is generated by a carbon-neutral source of energy,
the emission mitigation potential of both options was concluded as being negligible.
Finally, Figure 49 compares the carbon dioxide mitigation potential of various sources to each other in the year
2040. As can be seen, charcoal production has the largest potential followed by improved cooking and deforesta-
tion mitigation.
Figure 49: Carbon Mitigation Potential in 2040 - GHG Scenario
47

This trend is echoed in Figure 50 as well, which shows the cumulative potential of each of these sources.
Figure 50: Cumulative Carbon Mitigation Potential from 2012 to 2040 - GHG Scenario
5.5 Brighter DRC Results
The steps taken above lead to a significant difference in energy production, energy use and related emissions when
comparing it to the New Policies Scenario (NPS), in 2040. As this scenario was built on the New Policies one, it
seems fair for that to be the reference in this case. Figures 51 and 52 compare and contrast the breakdowns of
electricity production in the year 2040 for the NPS and the Brighter DRC Scenario (BDRCS). As the figures clearly
show, the portion of total electricity produced by solar PV is substantially higher in BDRCS, due to the distribution
of those panels. This directly supports the hypothesis that increased electrification can be achieved in a country,
without necessarily investing in costly grid infrastructure.
48

Figure 51: Electricity Production in 2040 - New Policies Scenario
Figure 52: Electricity Production in 2040 - Brighter DRC Scenario
A large part of the BDRCS is an electrification of and improvement in lighting. This means moving away from
kerosene and the associated health risks of it. As electrified households had electric lighting already, non-electrified
ones were targeted. Figure 53 shows the associated emissions, by lighting source, for non-electrified urban house-
holds. As can be seen, 596 thousand metric tonnes of carbon dioxide were not emitted in the Brighter DRC Sce-
nario, compared to the New Policies one. A similar trend can be seen for non-electrified rural households as well
(Figure 54), where 641 thousand metric tonnes were avoided. Hence, this shows that switching from kerosene to
solar lamps was a right and important decision to make.
49

Figure 53: Urban Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario
Figure 54: Rural Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario
The BDRCS also made changes (to some extent) for cooking, wherein some sources were replaced with solar cook-
ers. This change was primarily focused on the rural sector and the associated emissions are shown in Figure 55.
One stark difference between the previous two figures and this next one is that while solar cookers had a no-
ticeable difference in mitigating emissions, the difference was not significant enough and that a huge amount of
emissions are occurring due to fuelwood.
50

Figure 55: Rural Cooking Sources in 2040 - Brighter DRC Scenario
5.6 Overall Scenario Comparison
Emissions Perspective
Figure 56 compares the cumulative, projected emissions across all sectors in the country, across all three scenarios
between 2012 and 2040. As can be seen, the New Policies scenario performs slightly better than the Reference one.
The reason behind this is that electriﬁcation goal is implemented, which means that more people are obtaining
their energy from electricity instead of carbon-producing fuels. Moreover, the addition of the large Inga III hy-
droelectric project means that the emissions associated with total electricity production are even lower than the
Reference case.
Figure 56: Projected Emissions Across All Sectors from 2012 to 2040
Energy Trading Perspective
51

Figure 57 compares the electricity imports of the country for the three different scenarios between the year 2012
and 2040. The figure shows a considerable decrease in the electricity import of the country with the two new
scenarios. It can be observed that for the GHG scenario, the amount of electricity to be imported is least. This is
primarily because of the new dams which are to be built and the increased efficiencies of electrical appliances in
2040 for the GHG scenario.
Figure 57: Energy Trading Perspective Across All Sectors from 2012 to 2040
52

6 Uncertainties & Sensitivity Analysis
6.1 General Uncertainties
• During extraction of information for DRC from different sources, some of the references were inconsistent
with each other or the information was missing wherein reasonable assumptions were considered. This
increases the uncertainty in accuracy of the data.
• For some parameters fed into LEAP, the information was available only for few specific years and projections
can deviate from the linear forecasting.
• The planned new policies includes installation of new hydropower dams in DRC. The information fed into
LEAP is based on the official reports in DRC and the expected and actual first operational year of new dams
could be different.
• The capacity and load factors of power plants have been assumed to be constant due to limited information
and efficiency data for hydropower plants specific to DRC was unavailable.
• Suggestions in green house gas mitigation for reducing deforestation were based on a initiative launched by
World Bank which might not be scalable or suitable for the entire country.
• This model doesn’t consider the non-energy emissions associated with burning of agricultural and forest
residue and hence, the LEAP model does not illustrate the actual energy outlook of the country.
6.2 Sensitivity Analysis
6.2.1 Population & GDP Predictions
A sensitivity analysis was performed to take into account the effects of the variations of the uncertain parameters
which were the projections of population and GDP for 2040.
Various reports had different projections for population and GDP, because of which the energy demand of the
country would change. In order to measure the change, two scenarios, namely New Policy High demand and
New Policy Low demand were created and compared with the New Policy scenario.
For the sensitivity analysis, the New Policy scenario was selected because of the more realistic power generation
plans and electrification projections. This would enable to review the differences in a more representative manner.
Table 11: References and Assumptions for Transport
Scenario Population (Million) GDP (billion $USD) Variation in Energy Demand
Low Demand 127.44 [64] 80.17 [65] 0.2868
New Policies 166.06 94.19 -
High Demand 213.02 [7] 97.91 [29] 27.10%
53

Figure 58: Population Projection Uncertainties
Figure 59: GDP Projection Uncertainties
The variation in the energy demand for Low Demand and High Demand from the baseline scenario, which in this
case is the New Policies scenarios, is considerable. There is a 27.1% increase in the energy demand in case of high
demand scenario and 28.68% decrease in the energy demand in case of low demand scenario.
There is also considerable change in GHG emissions in both the cases. There is a 28.28% increase in the GHG emis-
sions in case of high demand scenario and 30.31% decrease in the GHG emissions in case of low demand scenario.
The uncertainties impact the consumption of all the fuels corresponding to the energy demand including wood,
charcoal and electricity.
54

Figure 60: Fuel Mix with High and Low Demand
6.2.2 Dispatch Rule
For electricity generation in DRC, there are two primary resources Hydro and Natural Gas and a secondary re-
source Fuel Oil. Hydro as a resource is naturally available and Natural Gas is produced by conversion from
biomass at a smaller scale. Fuel Oil is imported in DRC and thus, there is an additional cost incurred. A default
system load duration curve in LEAP is used for this analysis due to lack of data availability specific to DRC.
For sensitivity analysis of dispatch rule in year 2040, New Policy Scenario was considered for three reasons:
1. In Reference Scenario, electricity is imported in 2040, hence all the domestic electricity generation capacities
are fully utilized.
2. New Policy Scenario covers only government declared power plants expansion policies and hence, it will be
more realistic for year 2040.
3. Green House Gas Mitigation scenario can have uncertainties in 2040 due to assumptions considered in the
data.
Three different dispatch rules were tested in LEAP for sensitivity analysis:
1. Dispatch Rule - Merit Order
• Merit Order values - Hydro = 1, Oil = 1, Natural Gas = 1
• Merit Order values - Hydro = 1, Oil = 2, Natural Gas = 2
2. Dispatch Rule - Running Cost
Hence, two different cases were designed to analyze Primary Requirements of fuel in 2040 (in kTOE):
1. Case A = Difference between Rule 1.b and 1.a
2. Case B = Difference between Rule 2 and 1.b
It is observed from Figure 61 that for Case A, i.e. when Merit Order value for Oil and Natural Gas is increased
from 1 to 2 meaning that Oil and Natural Gas are now used mainly for peak load following while hydro for base
load following, the share of hydro increases while that of Oil and Natural Gas decreases. For Case B, i.e. when the
processes are utilized based on their running costs, the difference in primary requirements is zero which could
55

be because the running cost of hydro is the lowest and that of Natural Gas is the highest. Hence, the merit order
values in Rule 1.b has same effect as that in Rule 2, therefore the difference is zero and it is not visible in the
Figure 61.
Figure 61: Difference in Primary Requirement of Fuel
56

7 Lessons Learned
• Developing an energy model on LEAP needs structured approach and data management which makes it
more convenient to calculate dependent parameters and feed data into LEAP. This is really helpful because
the amount of data is huge and information flow can get really complex.
• DRC as a country is very rich in natural resources which can be exploited judiciously to improve the economy
and living standards of its citizens. Currently, hydro and wood are the two primary natural resources used
where wood energy demand for cooking surpasses any other energy demand in the entire country. This
shows that there exists a vast scope in improving the utilization efficiency of wood as a resource and shifting
to other sources which supplements wood demand with lower emissions.
• Hydropower is used extensively with appropriate capacity to generate electricity for supplying based on
existing capabilities. Hydropower potential is still underutilized due to poor grid connectivity and with
better grid infrastructure, this renewable resource can replace all other power producing technologies in
DRC.
• DRC uses several lighting sources including electricity, candles, kerosene, biogas and fuel wood. This is a
trend observed in both urban and rural non electrified households and shows that market for lighting is
very distributed and thus, there is a scope for improvement.
• The transportation in DRC needs a sincere improvement since majority of the roads are not paved which
discourages the growth of transportation industry. The number of passenger vehicles were found to be
203,170 in 2012 which means that only 0.2% of the total population of DRC has personal vehicles.
• DRC is suffering from severe deforestation due to extensive demand of wood and it is leading to high amount
of green house gases in the atmosphere. DRC has a good amount of solar irradiation and it can reduce its
wood demand by shifting to other renewable sources like solar for cooking or lighting.
57

8 Conclusion
The Democratic Republic of Congo is a very unique country to study. As has been mentioned, the country is
blessed with extraordinary natural resources that are largely unexploited. It includes a large part of the Congo
Basin, the second-largest forested area in the world and has immense biodiversity in flora and fauna.
Due to prolonged civil unrest in the ’90s, the country did not experience significant economic growth and hence,
had minimal deforestation and abuse of resources. However, with recent economic stability, the country is pro-
jected to have higher rates of deforestation coupled with increased emissions in the coming years.
Besides deforestation, agriculture and energy use are the biggest sources of emissions. In comparison to the first
two, energy use is projected to grow the fastest and hence, is important to curtail.
That is the reason the GHG scenario targets improved cookstoves and solar lighting, saving over 9.2 million metric
tonnes of carbon dioxide in the year 2040 alone. The scenario also includes projects proposed by the World Bank
to curtail deforestation, saving 3.12 million tonnes of greenhouse gases. What was surprising though, was how an
increase in charcoal-producing kilns caused a dramatic mitigation of carbon dioxide, contributing to 73.9% of the
total amount mitigated. Overall, the scenario slashed emissions by a third and can be described as a resounding
success.
The new policies scenario looks develops the energy scenario based on increased electrification and planned hy-
dropower projects. These hydro projects leads the country towards energy sufficiency. The electrification increase
increases the number of households with access to electricity and also decreases the emissions slightly because of
reduced wood based consumption.
Hence, the DRC has several paths that it can take. With little over 16% of its population currently electrified,
the country is definitely striving to substantially boost its economy but the path it chooses will define its fate.
The GHG scenario has shown that economic growth is possible in a manner that is both sustainable and curtails
greenhouse gases. This report, thus, fully recommends the country to adopt said scenario.
58

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The Democratic Republic of the Congo
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Share of Total Primary Energy Supply in 2013 - Democratic Republic of Congo
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World Data Bank - The Democratic Republic of the Congo
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Date Published: March 20, 2014
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DRC Inga 3 and Mid-Size Hydropower Development TA
59

http://www.worldbank.org/projects/P131027/inga-3-development-ta?lang=en
[10] International Rivers
The Inga 3 Hydropower Project
https://www.internationalrivers.org/campaigns/the-inga-3-hydropower-project
Sweden
https://www.cia.gov/library/publications/the-world-factbook/geos/sw.html
[12] BBC
Democratic Republic of Congo country proﬁle - Overview
http://www.bbc.com/news/world-africa-13283212
Date Published: August 4, 2015
[13] IPI Global Observatory
Could the DRC Be Africa?s Next Third Term Battleground?
Author: Alex Fielding
http://theglobalobservatory.org/2015/10/democratic-republic-congo-kabila-nkurunziza/
Date Published: October 27, 2015
[14] ACP-MEA & United Nations Framework Convention on Climate Change
Emissions Reduction Proﬁle: Democratic Republic of Congo
http://www.acp-cd4cdm.org/media/366216/emissions-reduction-profile-dr_congo.pdf
Date Published: June 2013
Global Crude Oil Reserves
https://www.cia.gov/library/publications/the-world-factbook/rankorder/2244rank.html
[16] Index Mundi
Democratic Republic of the Congo Natural Gas - Proved Reserves
http://www.indexmundi.com/democratic_republic_of_the_congo/natural_gas_proved_reserves.html
[17] Open Data for Africa
Democratic Republic of the Congo Coal Reserves
http://drcongo.opendataforafrica.org/gkocbhe/democratic-republic-of-the-congo-coal-reserves
60

[18] Index Mundi
The Democratic Republic of the Congo - Coal Production by Year
http://www.indexmundi.com/energy.aspx?country=cd&product=coal&graph=production
[19] Journal of Energy in Southern Africa - Vol 17 No 3
The Electricity Supply Industry in the Democratic Republic of the Congo
Authors: J M Lukamba-Muhiya and E Uken Both of the Energy Technology Unit, Cape Peninsula University of
Technology, Cape Town, South Africa
http://www.erc.uct.ac.za/jesa/volume17/17-3jesa-lukamba.pdf
Date Published: August 2006
[20] The Observatory of Economic Complexity
What Does the Democratic Republic of the Congo Export?
http://atlas.media.mit.edu/en/visualize/tree_map/hs92/export/cod/all/show/2013/
[21] Africapedia
Democratic Republic of Congo: Population Trends
http://www.africapedia.com/DEMOCRATIC-REPUBLIC-OF-CONGO:-POPULATION-TRENDS
[22] Africa Progress Panel
Power People Planet: Seizing Africa’s Energy and Climate Opportunities (Africa Progress Report 2015)
http://app-cdn.acwupload.co.uk/wp-content/uploads/2015/06/APP_REPORT_2015_FINAL_low1.pdf
Date Published: June 2015 Date Accessed: November 28, 2015
[23] The World Bank
Energy Use (kg of Oil Equivalent Per Capita
http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE/countries/1W?display=default
[24] BBC
DRC’s Inga Dam Project
http://ichef.bbci.co.uk/news/624/media/images/71120000/jpg/_71120913_5.jpg
[25] The World Bank
GDP (Current USD)
http://data.worldbank.org/indicator/NY.GDP.MKTP.CD
[26] International Energy Agency
Africa Energy Outlook: A Focus on Energy Prospects in Sub-Saharan Africa
https://www.iea.org/publications/freepublications/publication/WEO2014_AfricaEnergyOutlook.
pdf
Date Published: 2014 Date Accessed: November 17, 2015
61

[27] The World Bank
The World Bank: World Development Indicators
http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators#
[28] The World Bank
Reproductive Health at a Glance: Democratic Republic of Congo
http://siteresources.worldbank.org/INTPRH/Resources/376374-1303736328719/DRC41411web.pdf
Date Published: April 2011 Date Accessed: November 20, 2015
[29] McKinsey & Company
Brighter Africa: The Growth Potential of the Sub-Saharan Electricity Sector
Authors: Antonio Castellano, Adam Kendall, Mikhail Nikomarov, Tarryn Swemmer
p. 12, 15
http://www.mckinsey.com/~/media/mckinsey/dotcom/insights/energy%20resources%20materials/
powering%20africa/brighter_africa_the_growth_potential_of_the_sub-saharan_electricity_
sector.ashx
Date Published: February 2011 Date Accessed: November 12, 2015
[30] TheEconomicTimes
90% Village Households Don’t Have Refrigerators
http://articles.economictimes.indiatimes.com/2014-07-11/news/51354835_1_rural-households-
rice-consumption-villages
Date Published: July 11, 2014 Date Accessed: November 15, 2015
[31] Lighting Africa: An Innovation of IFC
Policy Report Note: Democratic Republic of Congo
https://www.lightingafrica.org/wp-content/uploads/bsk-pdf-manager/27_DRC-FINAL-August-2012-
x_LM.pdf
Date Published: August, 2012 Date Accessed: November 19, 2015
[32] UN-DESA
Sustainable Energy Consumption in Africa
http://www.un.org/esa/sustdev/marrakech/EnergyConsumption.pdf
Date Published: May 14, 2004 Date Accessed: November 19, 2015
[33] Sustainable Sanitation and Water Management
Direct Use of Biogas
Complied by:Eawag (Swiss Federal Institute of Aquatic Science and Technology), Niels Sacher (Xavier
University), Maria Isabel R. Dumlao (Xavier University), Robert Gensch (Xavier University)
Adapted from: Tilley, E.; Ulrich, L.; Luethi, C.; Reymond, P.; Zurbruegg, C. (2014)
http://www.sswm.info/content/direct-use-biogas
[34] Tropicultura 1996 Vol. 14 No. 2 pp. 59-66 via: CabDirect
Results of a Fuelwood Consumption Survey in Kinshasa, Zaire
Author: Tshibangu, K. w. T
http://www.cabdirect.org/abstracts/19980603030.html;jsessionid=FDA0B05739C77F95A89DD63C88F5EC58;
jsessionid=61229930099411BE8F8906CC0BCFDC3D
62

[35] International Energy Initiative
Report on the Use of LPG as a Domestic Cooking Fuel Option in India
Authors: Antonette D’Sa and K.V.Narasimha Murthy
http://www.bioenergylists.org/stovesdoc/Iei/IEIBLR-LPG-IndianhomesReport.pdf
Date Published: June 2004
[36] Africa Economic Outlook
Congo, Democratic Rep.
http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/Congo%20Democratic%
20Republic%20Full%20PDF%20Country%20Note.pdf
Date Published: 2012
[37] Société Nationale d’Electricité (SNEL)
SNEL Webpage
http://www.snel.cd/
[38] Electropaedia
Battery and Energy Technologies - Hydroelectric Power
http://www.mpoweruk.com/hydro_power.htm
[39] OSeMOSYS
The Electricity Model Base for Africa (TEMBA)
Authors: Taliotis, C., Shivakumar, A., Ramos, E., Howells, M., Mentis, D., Sridharan
http://www.osemosys.org/temba-the-electricity-model-base-for-africa.html
[40] National Renewable Energy Laboratory (NREL)
Cost and Performance Assumptions for Modeling Electricity Generation Technologies
Authors: Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke
http://www.nrel.gov/docs/fy11osti/48595.pdf
[41] FAO
Charcoal Production and Use in Africa: What Future?
Author: P. Girard
http://www.fao.org/3/a-y4450e/y4450e05.pdf
[42] US AID, UK AID, CDC, Unicef, et. al.
Democratic Republic of Congo - Demographic and Health Survey 2013-2014
http://dhsprogram.com/pubs/pdf/SR218/SR218.e.pdf
63

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