Democratic Republic of the Congo - Energy Outlook

Kungliga Tekniska Höskolan

Energy and Environment

MJ2413

Democratic Republic of Congo - Energy Outlook

Authors:Rachit KansalSunay GuptaHammad FarrukhTimothy 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 DemocraticRepublic of the Congo. Two supplementary energy scenarios are developed namelyNew Policies and Green HouseGas Mitigation along with the baseline scenario. The different scenarios take into account various developmentsand their impact on the energy balance of country. New Policies Scenario takes into account the planned energyrelated projects in the country whereas the GHGmitigation scenario focus of GHG emissions reduction based onvarious 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 thelargest consumer of energy. Hydropower, with the potential to meet the total energy demand only contributesto 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 aregenerated, 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.

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Contents

1 Introduction 61.1 Scope and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Background 92.1 Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.1 Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1.2 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.4 Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.5 Energy Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Energy Transformation and Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.3 Energy Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Growth Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3 Methodology 173.1 General Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Scenario Development 224.1 Baseline and Reference Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.1.1 General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.1.2 Key Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.1.3 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.1.3.1 Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.1.3.2 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.1.3.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.1.3.4 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1.4 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.1.4.1 Transmission and Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.1.4.2 Electricity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.1.4.3 Charcoal Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.1.4.4 Micro Scale Biogas Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1.5 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2 New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2.1 Power Generation Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2.2 Electrification of DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.3 Green House Gas Mitigation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3.1 Non-Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.2 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.3 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.4 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.4 A Brighter DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5 Results 345.1 Reference Energy System Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.2 Baseline Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.2.1.1 Household Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.2.1.2 Industrial Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2.2 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.3 Commercial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

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5.2.4 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3 New Policies Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.3.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3.2 Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.4 Greenhouse Gas Mitigation Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.5 Brighter DRC Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485.6 Overall Scenario Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

6 Uncertainties & Sensitivity Analysis 536.1 General Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6.2.1 Population & GDP Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.2.2 Dispatch Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7 Lessons Learned 57

8 Conclusion 58

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List of Figures

1 GDP Breakdown for the DRC by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Total Energy Supply of DRC in 2013 (Excluding Electricity Trading) . . . . . . . . . . . . . . . . . . 63 Total 2013 Electricity Production in DRC by Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Electrification of Various Countries in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Energy Consumption Per Capita - Comparison Between DRC and Industralized Countries in 2012 86 EnergyConsumptionPerCapita - ComparisonBetweenDRCandOther Sub-SaharanAfricanCoun-

tries in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 The Inga Dam Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 DRC 2012 Electricity Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 DRC 2002-2012 Consumption Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1110 DRC 2012 Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 DRC 2012 Urban and Rural Household Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . 1212 Percentages of Electrified Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213 Transport Sector Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1414 Electricity Consumption Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 DRC Mineral Production in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1516 Population of the DRC vs.Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1617 Urban and Rural Populations vs.Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1718 GDP Predictions for the DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1719 General Methodology for Developing an Energy Model . . . . . . . . . . . . . . . . . . . . . . . . . 1820 Model Structure of Energy Demand in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1921 Model Structure of Energy Transformation in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2022 Primary and Secondary Resources in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2123 GHG Emissions by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2924 Historical Non-LULUCF Emissions for the DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2925 Historical LULUCF Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3026 Projected Emissions by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3027 Energy System Diagram of DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3528 Energy Demand Across All Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3629 Household Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3730 Household Cooking Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3731 Household Electricity Consumption Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3832 Industrial Electricity Demand in Thousand kTOE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3833 Transport Energy Demand 2012 and 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3934 Transport Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4035 Commercial Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4036 Commercial Energy Demand by Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4137 Electricity Generation by Technology - Reference Scenario . . . . . . . . . . . . . . . . . . . . . . . 4138 DRC Electricity Demand - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4239 DRC Household Electricity Demand - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . 4240 Electricity Generation from Hydro - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . 4341 Electricity Imports - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4342 2012 Power Production in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4443 Projected 2040 Power Production in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4444 Current and Projected Cooking Emissions in DRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4545 Current and Projected Emissions due to Lighting in DRC . . . . . . . . . . . . . . . . . . . . . . . . 4546 Emissions Due to Deforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4647 Emissions Due to Fossil Fuel Based Power Plants - GHG Scenario . . . . . . . . . . . . . . . . . . . 4648 Emissions Due to Charcoal Production- GHG Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 4749 Carbon Mitigation Potential in 2040 - GHG Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 4750 Cumulative Carbon Mitigation Potential from 2012 to 2040 - GHG Scenario . . . . . . . . . . . . . 48

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51 Electricity Production in 2040 - New Policies Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 4952 Electricity Production in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 4953 Urban Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . 5054 Rural Non-Electrified Lighting Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . 5055 Rural Cooking Sources in 2040 - Brighter DRC Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 5156 Projected Emissions Across All Sectors from 2012 to 2040 . . . . . . . . . . . . . . . . . . . . . . . . 5157 Energy Trading Perspective Across All Sectors from 2012 to 2040 . . . . . . . . . . . . . . . . . . . . 5258 Population Projection Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5459 GDP Projection Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5460 Fuel Mix with High and Low Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5561 Difference in Primary Requirement of Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

List of Tables

1 DRC 2012 Electricity Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 DRC 2012 Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 DRC 2012 Household Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . 124 DRC 2012 Industrial Energy Consumption by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 DRC 2012 Energy Consumption by Transport Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 References and Assumptions for Household Demand . . . . . . . . . . . . . . . . . . . . . . . . . . 227 References and Assumptions for Industrial Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 References and Assumptions for Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Passenger Transport Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3910 Freight Transport Energy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3911 References and Assumptions for Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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1 Introduction

The Democratic Republic of the Congo (DRC), located in central Africa, has a total land area of 2,267,048 km2 andan 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 GDPof $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 factsthat the DRC has over 80 million hectacres of fertile land and over 1,100 different types of precious metals andminerals - It can be seen that The DRC is poised to go through an enormous amount of growth, and have thepotential 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, and3.5% hydro [6]:

Figure 2: Total Energy Supply of DRC in 2013 (Excluding Electricity Trading) [6]

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In this same year, 16.4% of the population had access to electricity, and of this 1.4% comes from fossil fuel basedsources 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 regionslike the Middle East boasting rates of 92% and the less developed ones such as Sub-Saharan Africa having anaverage rate of 32%. As can be seen, even when compared to the Sub-Saharan African region, the electrificationrate 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 3Basse Chute (BC) and Mid-size Hydropower Development Technical Assistance Project - a hydroelectric projectthat will be developed outside of Kinshasha, along the Inga river. This project is slated to provide electricity to

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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 itsneed [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 belowcompares the DRC’s consumption to more industrialized and developed countries. As can be seen, the DRC’sconsumption 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 economicstatuses. However, as can be seen, the country still has a low energy consumption and is well below the averagefor that region.

Figure 6: Energy Consumption Per Capita - Comparison Between DRC and Other Sub-Saharan AfricanCountries in 2012 [23]

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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 officiallyended in 2013. However, there have been fears of a revival in violence, as protests mount against President JosephKabila’s desired to rewrite the nation?s constitution and remain in office past 2 terms [12, 13]. Considering thestate of affairs in the country, and the poverty the majority of Congolese people are living under, it is clear that thefocus of the DRC will be one towards stability and general development, rather than with an intended focus onenvironmental stewardship or emission control. Luckily renewable resources are vast in the country, and progresson 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 projectwas to successfully analyze the current energy systems in place in TheDemocratic Republicof the Congo and to attempt to project possible scenarios for the nation’s future growth. The scenarios used aredefined below:

• Reference Scenario: The state of affairs in the DRC based on current data that is available for the chosenbase year.

• New Policies Scenario: The possible development of the country based on different policies and projectsthat have been proposed.

• GHGMitigation 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 growthconditions. All these parameters are modeled under the current conditions of DRC.

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2.1 Energy Demand

The report describes the energydemand for residential, industrial, and transport sector alongwith a brief overviewof commercial electricity demand. The report attempts to examine all the major energy and electricity demandgroupings in the country despite of limited data.

Most of the electricity in DRC is produced from hydroelectric sources with a very small portion coming fromnatural gas and oil, as shown in Figure 8. The total electricity consumption in the last decade has increased by57%, mainly due to increased percentage of population with access to electricity [53]. Currently only 16.4% of thepopulation 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 DemandIndustrial 4646 65.3Residential 2262 31.8Commercial 208 2.9

Total 7116 100

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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 DemandHousehold 21248.62 98.1%Industrial 418.42 1.931%Transport 0.00394 0.00002%

Commercial 0.01996 0.00009%Total 23319.568 100%

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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 intoUrban and Rural in the following manner.

Table 3: DRC 2012 Household Energy Consumption by Sector [53]

Sector. Electricity Demand (GWh) Pecentage of Total DemandUrban 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 totalelectricity 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 populationhas 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.

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2.1.2 Industry

The industrial sector in DRC, is the largest consumer of energy after households. The total energy demand by theindustrial sector was 418.41ktoe. The table ?? shows the demand split into various subsectors.

Table 4: DRC 2012 Industrial Energy Consumption by Sector [53]

Sector. Electricity Demand (GWh) Pecentage of Total DemandAgriculture 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 GDPrespectively. 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% ofthe total energy demand in DRC. The electricity demand in the commercial sector was just 2.9% of the total whichaccounted for a mere 208GWh. The electricity demand was primarily for the lighting purposes in the urbancommercial 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 isprimarily 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 andpassenger transport, water transport consumes the most amount of energy followed by road transport.

Table 5: DRC 2012 Energy Consumption by Transport Type

Sector. Electricity Demand (GWh) Pecentage of Total DemandFreight 3.895 99.00%

Passenger 0.0389 1.00%Total 3.93504 100%

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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 Miningindustry is a major player in the industrial sector of the country, the energy consumed by the mining sector isindirectly energy exported. This is because the lack of refining facilities in the country lead to all the mineralsbeing exported to other countries.

The mining industry comprises of most the country?s exports. 99% [66] of the total exports of DRC were of theextractive sort. The mining industry consumed energy equivalent to 65.65181 thousand tons of oil equivalentwhich is 77% of the total electricity demand in DRC. Moreover, all of this energy is being supplied by electricity.

Figure 14: Electricity Consumption Breakdown

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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 theepicenter for dramatic political revolutions and military conflicts. Besides having vast reserves of minerals, thecountry also has sizable crude oil potential. With a 187 million barrels of crude oil [14], the DRC sits at #62 in theworld, in terms of verified reserves [15].

The country does not have significant natural gas reserves though. At 991.1 million cubic meters [16], the DRCranks near the bottom of countries with gas potential. Understandably, it has zero production of this particularfossil fuel.

The country also has relatively negligible coal reserves, topping 97.7 million tons, as of 2011 [17]. The exploitationof 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 mostheavily 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 6000Watts per square meter. However, none of this potential has been exploited, either through solar photovoltaics orsolar 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 5kmh or

less. Hence, despite the immense natural resources available to it, the country has very low installed capacity, at2505 MW [19]. What is even more pitiful is the woeful lack of transformation of this energy production, as thenational utility sold only 64% of total electricity produced in 2004. For example, only 276,431 customers in thecountry’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 mainenergy exports in crude petroleum and one of its imports is refined oil [20].

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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 powera 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 DRCeconomy, 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 projectedto 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 inthe urban areas of the country (as seen in the figure below). This is a trend echoed across many other countries aspeople migrate to cities for better opportunities. In terms of increasing the electrification rate of the country, thisis an important trend, as it significantly increases the ease of connecting people to the grid.

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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 findsmore 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 DRCgovernment agencies, as well as international institutions like The World Bank, The International Energy Agency

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(IEA), and The International Renewable Energy Agency (IRENA). Specific details regarding the distribution ofenergy demand across sectors, the energy consumption of appliances, and the cost of electricity generation werederived from sources mentioned in the References section. In some cases, the information was missing specificallyfor the DRC and reasonable assumptions were formulated using neighboring countries as reference or commonobservations 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) andthroughout various future scenarios ending at a predetermined year (2040 was chosen here). To compare theenergy supply, demand, and cost in the future, different scenarios were developed. Each scenario had a distinctfeature to assess the impacts of variations in the energy supply, demand, and efficiency - as well as other nonenergy 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 andCommercial. These categories include both energy requirements and consumption by different utilities, andthe data is fed into LEAP at different activity levels (such as household, passenger-km,metric tons of produc-

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tion or kW-hr). Typical information needed for Demand includes: population, electrification rates, house-hold utilities, types of industries, transportationmodes and fuel economy, space heating (if any), and energyneeds 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. Thisincludes the use of resources such as wood, biomass, hydro, and oil (in DRC) for generating electricity orconversion 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 demandis 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 (ifavailable), and variable and fixed costs. Figure 21 illustrates the transformation tree structure for the DRC.

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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 ofthese resources and allocates them to different activities inDemand andTransformation. If any resource re-quirement is unmet then LEAP, by default, imports it (or exports a resource in case it is in surplus). Figure 22below illustrates the resources available or needed for energy supply in the DRC.

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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 environmentalloading (emissions), as well as the burning of agricultural waste and forest fires.

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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 projectionsin 2040 based on energy policies which are already implemented - a "Business as Usual" case. Developing thesescenarios provides a useful glance over future energy expectations in the country and can lead to possible actionplans to improve the economy. All of the assumptions used to fill in the missing data points for DRC in thisscenario 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 projectionsaccording 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 householdsin theDRCwere calculated. The activities in this branch are taken at a household level. All of the households in theDRCwere divided into urban and rural population based on a distribution obtained fromMcKinsey&Company’sreport on sub-Saharan Africa [29]. Within each subsection of urban and rural, households were divided intoelectrified and non-electrified for 2012 [27]. The householdswere further subcategorized based on final use of energywithin the household: Cooking, Lighting, Refrigeration andOther 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 20401 Electrified and

Non ElectrifiedHouseholds

Reference [27] Linearly projected usingdata available from 1990 to

20122 Refrigeration

% share ofpopulation

Same as in India [30]

Energy Intensity Reference [26] 20% increment with agefrom 2012, Reference [26]

3 Lighting% share of electricity

by householdsSame as electrified

householdsContinued on next page

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Table 6 – continued from previous pageS.No. Category Sub category (if any) 2012 2040

% share of kerosene,candles, Biogas and

Firewood byhouseholds

Using 2009 data fromReference [31] and %increase in electricityconsumption [26]

ElectricityConsumption

Reference [26] 20% increment with agefrom 2012, Reference [26]

Keroseneconsumption

Using 2009 data fromReference [31] and %change in share of

Kerosene for lighting in2012

Candlesconsumption

Taking Ethiopia’sconsumption in 2009

from Reference [32] withmonthly householdincome 4700 birr andextrapolating to 2012

Biogas consumption Reference [33]Firewood

consumptionTaking Ethiopia’s

consumption in 2009from Reference [32] with

monthly householdincome 4700 birr andextrapolating to 2012

4 CookingFuel 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]

Consumption Reference [34]LPG % share Reference [26] Reduction by 20% due to


Consumption Reference [35]Kerosene % share Reference [26] Reduction by 20% due to


5 Other UsesTV % of electrified

householdsReference [42]



Fan % of electrifiedhouseholds

Reference [42]



Continued on next page

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Table 6 – continued from previous pageS.No. Category Sub category (if any) 2012 2040

Radio % of electrifiedhouseholds

Reference [42]


40 W radio used 3 hoursa day

20% increment with agefrom 2012, Reference [26]

Cell Phone % of electrifiedhouseholds

Reference [42]


5W phone batterycharged for 5 hours a

day

20% increment with agefrom 2012, Reference [26]

4.1.3.2 Industry

DRC has five major types of industry which consume the majority of the industrial electricity demand. Theirelectricity consumption is divided based on their contribution to the total GDP [1]. The assumptions that weremade 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 & 20401 Agriculture &

Forestry% share of GDP Reference [36] and [1], extrapolation using GDP share

across yearsElectricity

Consumption% GDP share of Total Electricity Consumption of the

country [27]2 Mining &

Quarrying% share of GDP Reference [36] and [1], extrapolation using GDP share

across yearsElectricity Con-sumption

% GDP share of Total Electricity Consumption of thecountry [27]

3 Energy andwater

% share of GDP Reference [36] and [1], extrapolation using GDP shareacross years



4 Manufacturing % share of GDP Reference [36] and [1], extrapolation using GDP shareacross years



5 Construction % share of GDP Reference [36] and [1], extrapolation using GDP shareacross years



4.1.3.3 Commercial

In DRC, commercial electricity demand is primarily for lighting and there is no space heating or cooling dataavailable. It is found that 30% of the urban lighting and 20% of the rural lighting is used for commercial purposes

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[31]. In urban, 54% of the lighting demand is met by Kerosene and in Rural, 100% of the lighting demand is metby 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 intosub sectors such as Road, Rail, Air and Water.

Table 8: References and Assumptions for Transport

S.No. Category Sub category (if any) 2012 & 2040Passenger Transport

1 Road - Car Passenger km &Vehicle km

Reference [45], Assumed the average km driven dailyto be 20km and average km of weekly trip to be 40kmand using the average driven km along with the totalnumber of cars to calculate passenger/vehicle km.Extrapolated for 2040, assuming the growth in the

number of vehicles to be proportional to the growth inincome per capita.

Fuel Economy Assumed the fuel economy of the cars to be 10km/litersince the cars are supposed to be old. And 13km/liter

for 2040 since the vehicles will be replaced by thenewer ones.

2 Road - Bus Passenger km &Vehicle km

Reference [14], Using the vehicle-km and the loadfactor to calculate the total passenger-km. Load factoris 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 compartmentsper train and 80 people per compartment to calculateload factor and calculate the vehicle km. Extrapolatedit 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 thegrowth 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 for2040.

5 Water Passenger km & Ve-hicle km

Assuming the passenger km for water transport to beof the same ratio for total population as that of

passenger km (bus) in the urban population. Andassuming the load factor to be 50.

Fuel Economy Reference [51] Assumed the average of diesel ferriesand using the 1.4% Reference [52] efficiency

improvement to project for 2040.Freight Transport

Continued on next page

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Table 8 – continued from previous pageS.No. Category Sub category (if any) 2012 & 20406 Freight

Transport -Road

Energy Intensity Reference [43], assumed the higher end of the range onfigure 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 averagedistance for the movement to be 987.5 km to find themillion tons km and calculating the share of road.

7 FreightTransport -

Rail

Energy Intensity Reference [43], assumed the higher end of the range onfigure 2.8 of reference [43] for 2012 and lower end for

the 2040.% share Reference [27], used the tons-km for rail and calculated

the % share out of the total tons-km.8 Freight

Transport - AirEnergy Intensity Reference [43], assumed the higher end of the range on

figure 2.12 of reference [43] for 2012 and lower end forthe 2040.

% share Reference [27], used the tons-km for air and calculatedthe % share out of the total tons-km.

9 FreightTransport -

Water

Energy Intensity Reference [43], assumed the higher end of the range forsmall freighter on figure 2.14 of reference [43] for 2012and lower end for the 2040. Assumed small freighterbecause the ports of DRC cannot handle large freightersbecause of the infrastructural restraints, Reference [44]

% share Reference [27], used the tons-km for water andcalculated 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 forDRC [27]. It is assumed to be same for 2040 as the electrification rate is low in DRC even with plenty hydropowercapacity, hence major emphasis is expected to be on providing electricity to non electrified areas than reducinglosses 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 hydropower plants based on their installed capacity (large hydro is considered as plants with installed capacity higherthan 5 MW). Besides hydro, DRC has oil and gas power plants which uses Residual Fuel Oil and Natural Gas asfeedstock fuels respectively. Data points needed in LEAP includes:

• Exogenous Capacity: Installed capacity of hydro and thermal power plants was available on SNEL, one ofthe energy producing organization in DRC [37]

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• Historical Production: Historical production of total hydro, oil and gas was available on IEA [s]

• Dispatch Rule: In baseline scenario, the dispatch rule ofMerit Order is used. All the electricity produced byHydro andGaswill be supplied to the demand as base and peak load capacity, hence themerit order of eachplant is set as 1. As crude oil is always imported then the consumption of crude oil should be proportionalto the capacity.

• Process Efficiency: Typical efficiencies of large and small hydro are common across the globe [38] and thatof 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 ofOil and Gas were assumed to be of Heavy Fuel Oil and Open Cycle Gas Turbine [39]

• Cost: Cost includes Investment, FixedO&M,Variable O&M. For hydro, these costs were assumed to be sameas 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 largeforest cover and all thewood required in this process is assumed to be producedwithinDRC. The process followedin 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 isproduced in DRC itself using small bio gas plants in a decentralized manner. The process efficiency of biomass isfound 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, Hydroand Natural Gas. The yield and additions to reserves of these resources were adjusted so that there is no netimport of these resources. There is no input for Secondary resources and LEAP, by default, imports a resource ifit 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 otherorganizations and measures its impact on energy dynamics of the country.

4.2.1 Power Generation Projects

• Inga IIIIn order to tap one of the largest hydro resource potential in Africa, the government of DRC along with thefinancial support of other international organisations plans to build the 4800MW Inga III Dam on the Congoriver. The project is expected to start by the end of 201 after renewed support from African DevelopmentBank and theWorld Bank. Inga III is expected to cost $12 billion. Inga III is a part of the Grand Inga complexplanned to generate a 40,000 MW of power. Inga III is due to come online by 2020 [55, 56].

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• Zongo IIZongo II is a hydro project funded by the China as a part of understanding between the two countries. Theproject is expected to generate to 150MWand lead to increased employment in the Bas-Congo province. Theproject 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 theelectrification to carry on with the same rate, starting from 2012, the expected population with electricity accesscomes 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 policiesscenario, 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 governmentpolicy is to mitigate greenhouse gas emissions as much as possible, while encouraging economic growth anddevelopment. The Democratic Republic of Congo has the second largest rainforest area in the world, after theAmazon. 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 acountry 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 forLand 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 arevery important. Figure 23 below shows the main non-LULUCF emissions in the country, categorized by differentsectors 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 includedhere and were actually -11.5 million tons for that year. Hence, LULUCF contributed positively to greenhouse gasmitigation and has done so in every year prior.

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Figure 23: GHG Emissions by Sector [59]

Figure 24 shows the historical trend in the information fromFigure 23, from2000 to 2010. As can be seen, emissionsfrom the agriculture sector have been fairly constantwhile there has been a steady increase in emissions associatedwith 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, associatedremovals have been on a decline for the last decade, leading to a steady increase in net emissions. A large part of

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this trend can be attributed to deforestation in the country, along with the slash-and-burn technique used to clearforest 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, thetrends shown in this figure are remarkably different from those in Figure 24. While agriculture will still be a bigcontributor to emissions, energy demand is poised to grow dramatically and increase emissions significantly as aresult.

Figure 26: Projected Emissions by Sector [59]

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The following steps are planned to be taken to implement the GHG scenario.

4.3.1 Non-Energy

• Deforestation MitigationAs has been mentioned, deforestation is a big contributor to emissions within the DRC. The World Bankhas 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 awarenessand incentivizing more sustainable alternatives for the logging and agriculture industries. Among all theirprojects, the one focused solely on deforestation mitigation was chosen and scaled up in the simulation topredict the carbon savings for the country in the decades to come.

4.3.2 Demand

• Household

– Efficient CookstovesAs has beenmentioned 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 involvesreplacing old, inefficient models with newer, cleaner-burning ones. Data from this project was also fedinto the simulation to demonstrate the potential of this option.

– Efficient LightingThe 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 solarlighting. Data from it was chosen and extrapolated to provide an estimate for the proliferation of solarlighting in the DRC by 2040.

– Efficient appliancesWhile there is no specific policy push for this area in the DRC, natural technological advancementswere taken into account and as residents in the DRCwill buy appliances from other countries, this willallow for higher efficiencies in the country in the coming years.


• Distribution LossesIn this case, the model takes into account that the power grid in the DRC is currently in an abysmal stateand that its transmission and distribution losses will decrease in the coming decade.

• Power Generation EfficiencyWhile most of the DRC’s power generation comes from hydro power, there is generation from fuel oil andnatural gas as well. The efficiencies of these power plants also increase in the coming decades, and that isfed into the model also.

• Charcoal ProductionCharcoal forms a significant part of the DRC?s energy needs and its urban demand is projected to increasesignificantly, as the fuel is more condensed in energy. Studies have shown that the efficiency of those kilnscan be increased from 25 to 30%, if proper management practices are taught and enough awareness is cre-ated.

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4.3.4 Assumptions

• Deforestation Mitigation

– The first assumption for this initiative was that the deforestation rates projected in the IFLMP projectcan 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 valueof 2.9 tonnes per hectare per year [60].

– The last assumption was that even though the IFLMP project formally ends in 2029, projects with asimilar effect will continue on until 2040.

• Efficient Lighting

– "Lighting Africa" is a continent-wide initiative and hence, includes the total number of solar lampsdistributed for Africa. The first assumption made was that the ratio of distributed solar lamps to totalAfrican population is the same as that of distributed lamps in the DRC to its population. This wasnecessary 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 thatthe 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 cookingefficiency 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 withsimilarly 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 efficientappliances 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 efficiencyof 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, sothat this efficiency becomes ubiquitous among all the kilns in the country [63].

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4.4 A Brighter DRC

Thus far, we have seen how the country fares in a business-as-usual scenario, aNewPolicies one and aGreenhouseGas Mitigation scenario. This scenario aims to be the most optimistic yet realistic one of them all. The priorityof this scenario is electrification and economic development, but in a sustainable and environmentally friendlymanner. The former is usually a priority for most governments but the latter has been included, as following thepath 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 higherpercentage of the population, would be low. Hence, off-grid generation was selected.

• Off-Grid ElectrificationGiven the natural resources detailed earlier in the report, solar energy was the obvious candidate for thiscase. 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 electrifyother parts of the home in the aim of improving quality of life and efficiency. The first, obvious avenue topursue was lighting.

• LightingKerosene met 81% of total lighting needs in non-electrified urban households and 54% in non-electrifiedrural households in the New Policies Scenario. It is associated with a myriad of health problems includingrespiratory irritation, acute dermatitis, skin and eye irritation[68]. It is also known to offer poor lightingwhile using a large amount of fuel, giving it an abysmal efficiency overall. Hence, electrifying it will help tosignificantly improve the local quality of life.

• CookingAs 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 charcoaland fuelwood, which are highly polluting when burnt and are major drivers for deforestation within thecountry. 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. Thistarget 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 is45.4%. Hence, 34.6% of the population still needs to be electrified in 2040 and all of this will be doneusing Solar PV

– Solar lamp distribution - Solar lamps had been proposed in the GHG scenario to a small extent, basedon 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 lightingwithin the DRC.

– Solar cookers - Since the DRC receives a fair amount of direct, normal irradiation, solar cookers werechosen as a possibility for the cooking area. The cooking space is predominantly powered by fuelwoodand charcoal, which are highly polluting and destructive to the environment. Hence, any improvementin this area would take the country in a positive direction

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– 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 notbeen fixed due to either financial troubles or government bureaucracy. Hence, increasing the technicalavailability of these projects would be very beneficial to the country as it would provide a lot moreelectricity with minimal capital cost investment.

• Assumptions

– Solar PV DistributionThe distribution of PV panels is an expensive and complicated affair. To simplify this and meet the80% electrification goal, the non-electrified urban population is targeted first. This means that thissub-group is a 100% electrified by 2040, while total rural electrification reaches 58.2%.

– Solar Lamp DistributionThere will only be one solar kit per household. This assumption was done based on the luminosityof the solar kit, which was 910 lumens to the average luminosity offered by kerosene lamps, whichwas 45 lumens. Also, as the solar kit comes with a battery that lasts approximately 9 hours, one kitwas assumed to be sufficient to meet a household?s lighting needs The distribution of solar lamps willfollow the distribution of solar panels, as is makes the most sense to pair the two together. In the caseof rural distribution of these solar lamp kits, it would be comprehensive and able to reach remote partsof the country with relative ease and reasonable costs

– Solar CookersOne important assumption in this area is that residents in urban areas can, in the future, have easieraccess to modern fuels such as LPG, to a greater extent, than rural residents would. Moreover, thesecookers can take between 2-4 hours for cooking a regular meal, which is less likely to be appealing forurban 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 thehydro-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 anyenergy flow in case of any anomaly. Figure ?? shows the energy system diagram of DRC and some of the keyinsights that can be inferred are:

• Hydro and Natural Gas are the primary sources of electricity generation and fuel oil is not shown in thisdiagram 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

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• Solar resource is implemented in the GreenHouse GasMitigation scenario for direct consumption by house-holds primarily for lighting

• The link betweenDemand andTransmission andDistribution is legitimate but LEAPdid not showany in thediagram. However, results from energy balance shows that LEAP considers this connection in calculationsof electricity supply and demand. LEAP software team confirmed about this error in LEAP diagram butassured 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 wouldbe 27.1 MTOE. Figure 28 shows that major energy demand in DRC comes from household because of massiveconsumption of wood for cooking purposes. Industrial demand share of electricity increases from 5.2% to 7.2% in28 years which could be due to increase in production capacities. The demand by transportation and commercialsector is very low because of the limited transport facilities and commercial centers in the country.

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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 fuelshare even in 2040. Due to poor electrification rate of 18% in 2012 and expected 31% in 2040, the share of electricityremains low and is primarily used for refrigeration, lighting and watching TV or using fan. The contribution ofcharcoal 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 electrifiedhouseholds. It can be concluded that there exists a tremendous potential to change the cooking fuel demand inthe entire country and move towards more efficient cooking appliances.

Household consumption of electricity in Figure 31 reveals that share of electricity consumption by electric stoveis going to increase which justifies argument presented in previous paragraph for Figure 30. Moving towardselectric stoves will reduce the pressure on wood demand resulting in lower greenhouse gas emissions (due todecreased 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 withefficient devices with lower energy intensity.

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Figure 29: Household Energy Demand by Fuel

Figure 30: Household Cooking Demand by Fuel

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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 contributorto 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. Theincrease attributed to the expected increase in the number of vehicles in the country. The passenger transportenergy demand in 2040 was 7.31.

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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 demandfor 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 Total2012 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 sectoras the second biggest consumer of energy.

Table 10: Freight Transport Energy Demand

Freight Transport Energy Demand (toe) Road Rail Air Water Total2012 0.00609 0.00203 0.00006 0.0312 0.03932040 - Baseline Scenario 0.01044 0.00418 0.00011 0.0669 0.0816

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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 Equivalentin 2040. All of the energy demand for commercial sector is attributed to lighting because of the unavailability ofrelevant data.

Figure 35: Commercial Energy Demand

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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 showsthat hydro power is themajor electricity generation technologywithmore than 97% contribution in both 2012 and2040. Despite of being rich in hydro resources, DRC still has a low electrification rate of 18% in 2012 and needsa sincere improvement in its grid infrastructure. Natural gas increases its contribution from 0.4% to 1.3% in 2040and oil remains low because the dispatch rule has been set to ’proportion to capacity’ since oil is being importedwhich 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 thebaseline scenario for 2040.

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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 thousandtonnes of oil equivalent in 2040 for new policies scenario. The increase is reflected in household demand, becausethe 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 35.1thousand GWh in 2040 for new policies scenario.

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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, thecountry will have to import 19.77 thousand Gigawatt Hour of electricity in 2040, since the electricity demand willincrease and the current power generation capacity will not be enough. With the two new dams in new policiesscenario, 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 42and 43, which show the power production breakdown in the DRC in years 2012 and 2040 respectively. As can beclearly seen, hydroelectric power is projected to remain as a dominant player in power production well into thefuture with the expectation that the mammoth Inga III project will be bringing large amounts of electricity to thecountry.

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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 continueto 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 tosave around 7.5 million metric tonnes of carbon dioxide in the year 2040 itself, as is seen in the figure.

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Figure 44: Current and Projected Cooking Emissions in DRC

Then, Figure 45 shows the emissions associated with household lighting. Firstly, one significant difference can beseen 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 usagewhile 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 emissionsin 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 ofthe World Bank?s IFLMP project. The avoided emissions can be clearly seen.

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Figure 46: Emissions Due to Deforestation

Figure 47 shows the current andprojected emissions fromoil and natural-gas plants in the country. One significantdifference from other plots is that the scale of the emissions here is much lower, due to the low proliferation offossil-based plants in the country?s energy mix. However, this proliferation is poised to grow in the decades tocome, leading to increased emissions from both sources. Avoided emissions are 77,000 metric tonnes of carbondioxide in the year 2040. One important observation here is that these avoided emissions are not unique to theGHG 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 toshow 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, theGHG scenario assumes that increased awareness of proper management techniques of charcoal kilns will lead to

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an increase in efficiency from 25 to 30%. As can be seen, this leads to a significant decrease in emissions (over35.45 million metric tonnes). Hence, it is clear that charcoal production is very carbon-intensive and that growthin 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 arebased 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 year2040. 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

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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 emissionswhencomparing it to the New Policies Scenario (NPS), in 2040. As this scenario was built on the New Policies one, itseems fair for that to be the reference in this case. Figures 51 and 52 compare and contrast the breakdowns ofelectricity production in the year 2040 for the NPS and the Brighter DRC Scenario (BDRCS). As the figures clearlyshow, the portion of total electricity produced by solar PV is substantially higher in BDRCS, due to the distributionof those panels. This directly supports the hypothesis that increased electrification can be achieved in a country,without necessarily investing in costly grid infrastructure.

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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 fromkerosene and the associated health risks of it. As electrifiedhouseholds had electric lighting already, non-electrifiedones 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 tosolar lamps was a right and important decision to make.

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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 alsomade changes (to some extent) for cooking, wherein some sources were replacedwith 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 ofemissions are occurring due to fuelwood.

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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 scenariosbetween 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 obtainingtheir 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 theReference case.

Figure 56: Projected Emissions Across All Sectors from 2012 to 2040

Energy Trading Perspective

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Figure 57 compares the electricity imports of the country for the three different scenarios between the year 2012and 2040. The figure shows a considerable decrease in the electricity import of the country with the two newscenarios. It can be observed that for the GHG scenario, the amount of electricity to be imported is least. This isprimarily because of the new dams which are to be built and the increased efficiencies of electrical appliances in2040 for the GHG scenario.

Figure 57: Energy Trading Perspective Across All Sectors from 2012 to 2040

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6 Uncertainties & Sensitivity Analysis

6.1 General Uncertainties

• During extraction of information for DRC from different sources, some of the references were inconsistentwith each other or the information was missing wherein reasonable assumptions were considered. Thisincreases the uncertainty in accuracy of the data.

• For some parameters fed into LEAP, the informationwas available only for few specific years and projectionscan deviate from the linear forecasting.

• The planned new policies includes installation of new hydropower dams in DRC. The information fed intoLEAP is based on the official reports in DRC and the expected and actual first operational year of new damscould be different.

• The capacity and load factors of power plants have been assumed to be constant due to limited informationand 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 byWorld 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 forestresidue 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 parameterswhich 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 thecountry would change. In order to measure the change, two scenarios, namely New Policy High demand andNew 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 generationplans and electrification projections. This would enable to review the differences in amore representativemanner.

Table 11: References and Assumptions for Transport

Scenario Population (Million) GDP (billion $USD) Variation in Energy DemandLow Demand 127.44 [64] 80.17 [65] 0.2868New Policies 166.06 94.19 -High Demand 213.02 [7] 97.91 [29] 27.10%

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Figure 58: Population Projection Uncertainties

Figure 59: GDP Projection Uncertainties

The variation in the energy demand for LowDemand andHigh Demand from the baseline scenario, which in thiscase is the New Policies scenarios, is considerable. There is a 27.1% increase in the energy demand in case of highdemand 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.

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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 frombiomass at a smaller scale. Fuel Oil is imported in DRC and thus, there is an additional cost incurred. A defaultsystem 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 capacitiesare fully utilized.

2. New Policy Scenario covers only government declared power plants expansion policies and hence, it will bemore realistic for year 2040.

3. Green House Gas Mitigation scenario can have uncertainties in 2040 due to assumptions considered in thedata.

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 increasedfrom 1 to 2 meaning that Oil and Natural Gas are now used mainly for peak load following while hydro for baseload following, the share of hydro increases while that of Oil and Natural Gas decreases. For Case B, i.e. when theprocesses are utilized based on their running costs, the difference in primary requirements is zero which could

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be because the running cost of hydro is the lowest and that of Natural Gas is the highest. Hence, the merit ordervalues in Rule 1.b has same effect as that in Rule 2, therefore the difference is zero and it is not visible in theFigure 61.

Figure 61: Difference in Primary Requirement of Fuel

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7 Lessons Learned

• Developing an energy model on LEAP needs structured approach and data management which makes itmore convenient to calculate dependent parameters and feed data into LEAP. This is really helpful becausethe amount of data is huge and information flow can get really complex.

• DRCas a country is very rich in natural resourceswhich can be exploited judiciously to improve the economyand living standards of its citizens. Currently, hydro and wood are the two primary natural resources usedwhere wood energy demand for cooking surpasses any other energy demand in the entire country. Thisshows that there exists a vast scope in improving the utilization efficiency of wood as a resource and shiftingto other sources which supplements wood demand with lower emissions.

• Hydropower is used extensively with appropriate capacity to generate electricity for supplying based onexisting capabilities. Hydropower potential is still underutilized due to poor grid connectivity and withbetter grid infrastructure, this renewable resource can replace all other power producing technologies inDRC.

• DRC uses several lighting sources including electricity, candles, kerosene, biogas and fuel wood. This is atrend observed in both urban and rural non electrified households and shows that market for lighting isvery 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 whichdiscourages the growth of transportation industry. The number of passenger vehicles were found to be203,170 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 ofwood and it is leading to high amountof green house gases in the atmosphere. DRC has a good amount of solar irradiation and it can reduce itswood demand by shifting to other renewable sources like solar for cooking or lighting.

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8 Conclusion

The Democratic Republic of Congo is a very unique country to study. As has been mentioned, the country isblessed with extraordinary natural resources that are largely unexploited. It includes a large part of the CongoBasin, 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 firsttwo, 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 9.2millionmetrictonnes of carbon dioxide in the year 2040 alone. The scenario also includes projects proposed by the World Bankto curtail deforestation, saving 3.12 million tonnes of greenhouse gases. What was surprising though, was how anincrease in charcoal-producing kilns caused a dramatic mitigation of carbon dioxide, contributing to 73.9% of thetotal amount mitigated. Overall, the scenario slashed emissions by a third and can be described as a resoundingsuccess.

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 increaseincreases the number of households with access to electricity and also decreases the emissions slightly because ofreduced 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 curtailsgreenhouse gases. This report, thus, fully recommends the country to adopt said scenario.

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