analysis of uganda's electricity access situation
DESCRIPTION
An analysis of the problems accounting for Uganda's low electricity diffusion rate and a systems dynamics model showing how Uganda’s power sector is expected to evolve over a period of 80 years in terms of power supply and demand given the existing market structure and prevailing local conditions.TRANSCRIPT
2009
MSc. Thesis Author:
Donna Namujju (1385925)
Thesis submitted in partial fulfillment of the
requirements for the degree of Masters in
Engineering & Policy Analysis
Delft University of Technology
August 2009
Scaling up Uganda’s Electricity Access
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Title : Scaling Up Uganda’s Electricity Access
Author(s) : Donna Namujju
Date : August 2008
Professor(s) : Prof.dr.ir. W.A.H. Thissen
Supervisor(s) : Dr. E. Pruyt, Dr. L.J. de Vries, Gonenc Yucel
Section : Faculty of Technology, Policy and Management
Section for Engineering & Policy Analysis
Delft University of Technology
Copyright ©2009 Section for Engineering and Policy Analysis
All rights reserved.
No parts of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the author or the section for engineering and policy analysis
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Abstract
Access to power is tied to any country’s development. It provides opportunities for increased social welfare, education, health and income generating opportunities all of which Uganda needs. Uganda’s economic development is being stifled by power inaccessibility. Electricity access levels are as low as 9% nationally. The study was aimed at building a working theory on the internal setup and inner workings of Uganda’s power sector, using this theory to facilitate a better understanding of how elements of the power system contribute to the problem and the formulation of effective policies that take into account prevailing local conditions to remedy the situation. System dynamics methodology was applied to build a model showing how Uganda’s power sector is expected to evolve over a period of 80 years in terms of power supply and demand given the existing market structure and prevailing local conditions. Findings from the study show that while physical access to power is a big problem, major problems regarding the nature of power accessed exist for those consumers within the grid covered area: Insufficient power supply to meet an existing and growing power demand, an unreliable power supply and high power service costs. On top of the obvious reasons of Uganda’s lack of cheap high value primary energy resources, poor investment climate so few suppliers and limited negotiating power for the regulator, the study finds the biggest cause to be the nature of the existing capacity planning process in terms of how future capacity requirements are determined and the agreements made with generators as to how and when they fulfill their investment obligations. Policies to do with gradual targeted reduction of Uganda’s extremely high power losses, obligatory upfront capacity investment as opposed to spreading the investment over the period of the awarded concession, among others, are explored to determine their impact on system performance. The investigated policies highlight how slight changes to the capacity planning process requiring little or no investment could yield significant gains on the problems identified.
Keywords: Energy policy, electricity access, power supply, power demand, System dynamics, continuous systems modeling, Uganda
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Acknowledgements
Sincere and heartfelt thanks to the following:
My family – for loving me, looking up to me, always cheering me on.
Nancy – for your unwavering faith; many times over the past two years it was all I had.
My thesis supervisors: Dr. Erik Pruyt – for his idea that I do this project, always pushing me to go a step further, explore a bit deeper; Gonenc Yucel – he listened, advised and reviewed all the draft reports and models from the not very good to this final product; Dr. Laurens de Vries – for his invaluable knowledge on the working of electricity markets in developing countries; and Prof.dr.ir. Thissen for his constructive review of the project from its inception to this final product.
Kenneth, Ally, Modest – the loving brothers I got in Holland; despite coming from different countries you managed to create for me here a bit of home.
Martin – for love, for support and for waiting
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Table of Contents
1. Introduction .......................................................................................................................................... 1
1.1 Background ................................................................................................................................... 1
1.2 Significance of the Problem .......................................................................................................... 5
1.3 Research Objectives ...................................................................................................................... 6
1.4 Research Methodology ................................................................................................................. 7
1.5 Structure of the Report ................................................................................................................. 8
2. The Situation in Uganda’s Power Sector ............................................................................................... 9
2.1 Market Situation ........................................................................................................................... 9
2.2 Power Sector Problem Summary ................................................................................................ 13
3. Model Description............................................................................................................................... 15
3.1 System Dynamics – Background and Application ....................................................................... 15
3.2 Qualitative Model Analysis ......................................................................................................... 16
3.3 Quantitative Model Analysis ....................................................................................................... 21
3.4 Model Validity ............................................................................................................................. 28
4. Model Behavior Analysis ..................................................................................................................... 31
4.1 The Base Case ............................................................................................................................. 31
4.2 Model Sensitivity ......................................................................................................................... 38
4.3 Scenario Analysis ......................................................................................................................... 41
5. Policy Design and Analysis .................................................................................................................. 47
5.1 Impact of Power Losses on System Performance ....................................................................... 47
5.2 Impact of Upfront Capacity Investment versus Spread Investment ........................................... 50
5.3 Impact of Grid Development Policy on Unmet Demand ............................................................ 51
5.4 Combination of Policies 5.1 – 5.3 ................................................................................................ 53
5.5 Impact of Upfront Investment Obligation .................................................................................. 54
5.6 Combination of Policies 5.4 & 5.5 ............................................................................................... 55
5.7 Insights on the Devised Model Based Policies ............................................................................ 56
5.8 Policies beyond the Model .......................................................................................................... 57
6. Conclusions ......................................................................................................................................... 61
6.1 Research Findings ....................................................................................................................... 61
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6.2 Reflection beyond the Model ..................................................................................................... 62
6.3 Limitations of this study .............................................................................................................. 64
6.4 Areas of Further Research .......................................................................................................... 64
7. References ............................................................................................................................................. i
Appendix A.1: Model Structure .................................................................................................................... A
Appendix A.2: The Structure as Constructed ................................................................................................. I
Appendix B: Sensitivity Analysis .................................................................................................................... J
Appendix C: GDP per Capita Versus Electricity Consumption per Capita .................................................... L
Appendix D: Model Structural Validation ................................................................................................... M
Table of Figures
Figure 1: Market structure before and after reforms ................................................................................... 2 Figure 2: Uganda Electricity Sector Structure ............................................................................................... 3 Figure 3: Power access exponential growth hypothesis ............................................................................... 5 Figure 4: Uganda electricity generation mix 2003 ‐ 2007. Sourced from (Electricity Regulatory Authority, 2008c, p. 7) .................................................................................................................................................. 10 Figure 5: Demand distribution using population & schools as proxy. Sourced from (Kaijuka, 2007) ........ 12 Figure 6: Per capita GDP growth versus population growth ...................................................................... 12 Figure 7: Uganda electricity tariff growth trend. Sourced from (Uganda Investment Authority, 2005, p. 5) .................................................................................................................................................................... 13 Figure 8: On‐grid power demand ‐ Driving relationships ........................................................................... 17 Figure 9: Power demand ‐ reference mode ................................................................................................ 17 Figure 10: Power supply – Driving relationships ......................................................................................... 19 Figure 11: Total power supply ‐ reference modes ...................................................................................... 19 Figure 12: Economic status of power sector ‐ Driving relationships .......................................................... 20 Figure 13: Population growth scenarios for Uganda .................................................................................. 22 Figure 14: Uganda GDP per capita projections ........................................................................................... 23 Figure 15: GDP per capita vs Consumption per capita for comparable countries. .................................... 24 Figure 16: Thermal fuel cost scenarios ....................................................................................................... 26 Figure 17: Power demand ‐ Model vs actual & forecast results ................................................................. 30 Figure 18: Evolution of generation capacity ‐ Base case ............................................................................ 31 Figure 19: Expected demand growth vs actual demand growth ................................................................ 32 Figure 20: Demand vs Supply ‐ Base Case ................................................................................................... 32 Figure 21: Unmet demand ‐ Base case ....................................................................................................... 32 Figure 22: Domestic Power Demand ‐ Base Case ....................................................................................... 33 Figure 23: Consuming population ‐ Base Case ............................................................................................ 33 Figure 24: On‐grid connected vs unconnected population ........................................................................ 33
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Figure 25: Electricity price ‐ Base Case ....................................................................................................... 34 Figure 26: Generation mix ‐ Base case ........................................................................................................ 34 Figure 27: Thermal fuel costs ‐ Base Case vs oscillating costs .................................................................... 34 Figure 28: Electricity price ‐ Base Case vs oscillating costs ......................................................................... 34 Figure 30: Electricity price ‐ Base Case ....................................................................................................... 35 Figure 31: On‐grid vs Overall electricity access ‐ Base case ........................................................................ 35 Figure 32: Connections growth rate vs Unconnected population – Base case .......................................... 35 Figure 29: Effect of power expenses on demand ....................................................................................... 35 Figure 33: Sensitivity analysis results – Lower bound thermal generation efficiency of 50% .................... 39 Figure 34: Sensitivity of electricity price to changes in generation efficiency ............................................ 40 Figure 35: Domestic power demand ‐ Uncertainty bounds ........................................................................ 40 Figure 36: Unmet power demand ‐ Uncertainty bounds ............................................................................ 41 Figure 37: Nominal consumption per capita ‐ Base case vs High economic scenario ................................ 43 Figure 38: Domestic power demand ‐ Base case vs High economic scenario ............................................ 43 Figure 39: Effect of power expenses on demand – Base case vs High economic growth scenario ............ 43 Figure 40: Ratio power expenses to income ‐ Base case vs High economic scenario ................................ 43 Figure 41: Consumer electricity prices ‐ Base case vs High economic scenario ......................................... 44 Figure 42: Unmet demand ‐ Base case vs High economic scenario ............................................................ 44 Figure 43: Electricity price ‐ Low economic growth scenario ..................................................................... 44 Figure 44: Domestic demand ‐ Base case vs Low economic scenario ........................................................ 44 Figure 45: Unmet power demand – Base case vs Low economic growth scenario .................................... 45 Figure 46: Unmet demand ‐ Unfulfilled contracted capacity and longer lead times scenario ................... 45 Figure 47: Unmet power demand – Base case vs Low economic growth scenario .................................... 45 Figure 48: Phased targeted power loss reduction ...................................................................................... 47 Figure 49: Effect of loss reduction on unmet demand ............................................................................... 47 Figure 50: Electricity price – Base case vs lower power losses ................................................................... 48 Figure 51: Effect of loss reduction on power demand ................................................................................ 48 Figure 52: Effect of power loss policy on the generation mix .................................................................... 48 Figure 53: Effect of loss policy in base case scenario ................................................................................. 49 Figure 54: Effect of loss policy in high economic development scenario ................................................... 49 Figure 55: Effect of loss policy in low economic development scenario .................................................... 49 Figure 56: Effect of loss policy in unfulfilled capacity and longer lead times scenario .............................. 49 Figure 57: Effect of front end investment on unmet demand .................................................................... 50 Figure 58: Effect of upfront capacity investment on electricity price ........................................................ 51 Figure 59: Effect of upfront capacity investment on installed capacity ..................................................... 51 Figure 60: Effect of upfront capacity investment on the generation mix .................................................. 51 Figure 61: Unmet demand – Base case vs flexible connection rate ........................................................... 52 Figure 62: Electricity access – Base case vs flexible connection rate ......................................................... 52 Figure 63: Effect of grid connection policy on electricity price .................................................................. 53 Figure 64: Effect of grid connection policy on consuming population ....................................................... 53 Figure 65: Unmet demand – Base case vs combination of corrective policies ........................................... 53
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Figure 66: Effect of policies combination on electricity price ..................................................................... 54 Figure 67: Effect of policies combination on thermal capacity .................................................................. 54 Figure 68: Unmet demand – Base case vs upfront investment (2003‐2083) ............................................. 54 Figure 69: Unmet demand – Base case vs upfront investment (2010‐2083) ............................................. 54 Figure 70: Effect of policy combination on generation mix ........................................................................ 55 Figure 71: Effect of policy combination on electricity price ....................................................................... 55 Figure 72: Effect of policy mix design on power system ............................................................................. 55 Figure 73: Effect of policy combination on power service costs ................................................................ 56 Figure 74: Model structure ‐ Consuming population .................................................................................... A Figure 75: Model structure ‐ Consumption per capita ................................................................................. B Figure 76: Model structure ‐ Forecast of power deficit ................................................................................ C Figure 77: Model structure ‐ Contracting for capacity ................................................................................. D Figure 78: Model structure ‐ Capacity life cycle ........................................................................................... E Figure 79: Model structure ‐ Generator pricing ............................................................................................ F
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1. Introduction
1.1 Background Uganda has a population of 31.4 million (Index Mundi, 2009), approximately 87% of it rural; moderate economic growth averaging 6% per annum and an annual increase in per capita income of 3.7% (Ministry of Energy and Mineral Development, 2002, p. 9). Poverty remains widespread however, with about 35% of the population still living below the poverty line. The current energy demand is largely met by biomass (used mostly in its traditional form, largely as firewood and crop residues) which accounts for about 93% of the total primary energy supply. The rest is met by electricity at 6% and oil products at 1% which are all imported (Renewable Energy and Energy Efficiency Partnership, 2009).
Uganda is endowed with a variety of renewable energy resources including plentiful woody and non‐woody biomass, solar, wind, geothermal and hydrological resources. Presently, only large hydro resources along the Nile have been developed to provide electricity through a national grid utilizing less than 20% of the available hydro electric potential. The others have remained largely untapped contributing less than 2% of Uganda’s total energy consumption. Table 1 shows Uganda’s renewable energy resource potential
Table 1: Renewable energy potential. Sourced from (Ministry of Energy and Mineral Development, 2007, p. 33)
Energy Source Estimated Electrical Potential (MW) Hydro 2000 Minihydro 200 Solar 200 Biomass 1650 Geothermal 450 Peat 800 Wind ‐ Total 5300
Total installed capacity is about 380 MW from large hydropower plants, 17MW from mini & micro hydropower plants and 15 MW from co‐generation (bio‐mass based).
Uganda’s electricity sector has problems. Although 40% of the country's population lives in the area covered by the grid, the electrification rate is quite low with a national grid access of 9% with about 3% in rural areas (Ministry of Energy and Mineral Development, 2007, p. 12). Only about 1% of the population provides itself with electricity using diesel and petrol gensets, car batteries and solar PV systems; bringing total electricity access to around 10%.
The reason for these low electricity access figures may be attributed to the fact that prior to the late 1990s Uganda’s energy sector lacked a comprehensive, integrated policy framework. Rather, it was driven by annual ministerial policy statements accompanying the budget. The power sector under the control of the government owned monopoly – Uganda Electricity Board (UEB) was riddled with severe
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operational and management problems; problems which seemed to be prevalent throughout East Africa. According to (Karekezi & Kimani, 2004, p. 12), same as all other power sector institutions in the region, it was characterized by unreliability of power supply, low capacity utilization and availability factor, deficient maintenance, poor procurement of spare parts, and high transmission and distribution losses. It especially posted poor efficiency figures with high levels of system losses up to 40%. These and other reasons including: attraction of private capital, very low coverage and access to the grid, UEB’s inability to service its debts and unfulfilled export potential made the case for sector reform.
With the ultimate goal of country‐wide electricity access translating into the need for increased private investments in the sector, higher efficiency and better management, the government of Uganda developed and adopted the power sector restructuring and privatization strategy. The main corner points of the strategy were the privatization of the existing power infrastructure and provision of an enabling environment for additional private sector investments. The restructuring was achieved via the Electricity Act of 1999 that liberalized the electricity sector breaking up the monopoly of UEB and creating a regulatory body – Electricity Regulatory Authority. UEB was unbundled into three limited liability companies, namely, the Uganda Electricity Generation Company (UEGCL), the Uganda Electricity Transmission Company (UETCL) and the Uganda Electricity Distribution Company (UEDCL) responsible for generation, transmission and distribution, respectively. The generation and distribution assets of UEGCL and UEDCL respectively were privatized. Figure 1 shows the market structure before and after the sector reforms. All the power service functions previously controlled by the monopoly were separated. Metering and sales functions remained with distribution.
Figure 1: Market structure before and after reforms
While generation and distribution businesses were leased out to private operators, transmission remained a public function. The current structure of the market is that of a “single buyer model” with Uganda Electricity Transmission Company Limited (also doubling as System Operator) being the sole bulk buyer and seller of all the power generated in Uganda. The market for generation is characterized by multiple players consisting mainly of the following grid‐connected entities: Eskom Uganda Ltd, Aggreko Uganda Ltd, Kasese Cobalt Company Ltd and Kilembe Mines Ltd and imports from Tanzania and Rwanda. The markets for distribution are Umeme Uganda Ltd (domestic demand) and export markets
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including Kenya, Rwanda and Tanzania. Figure 2 shows Uganda’s electricity sector structure after the reforms.
Figure 2: Uganda Electricity Sector Structure
On top of the restructuring the government of Uganda put in place a comprehensive complementary policy framework aimed specifically at increasing the levels of electricity access in the country which included:
The Rural Electrification Strategy and Plan, 2001 which spelt out mechanisms to reduce inequalities in access to electricity and set targets for rural electrification i.e. rural electrification of 10% by 2012 (Ministry of Energy and Mineral Development, 2001, p. n/a);
The Energy Policy for Uganda, 2002 whose main goal was to meet the energy needs of Uganda’s population for social and economic development in an environmentally sustainable manner (Ministry of Energy and Mineral Development, 2002, p. 5);
The Renewable Energy Policy for Uganda, 2007 whose overall goal is to increase the use of modern renewable energy, from the current 4% to 61% of the total energy consumption by the year 2017 (Ministry of Energy and Mineral Development, 2007, p. 1).
To ensure success of these policies with a population that is 87% rural and 35% living below the poverty line (Ministry of Energy and Mineral Development, 2007, p. 9), they had to be integrated with a poverty eradication program: The Poverty Eradication Action Plan, 1998‐2000 whose main objective is to raise incomes of the poor through provision of infrastructure, credit, education, etc., to improve quality of life (Ministry of Energy and Mineral Development, 2004/5‐2007/8).
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The policy portfolio above is quite comprehensive and covers the important issues: stimulation of investment in the sector, formation of an electricity regulatory authority, targeted policy strategy for rural electrification, diversification of energy portfolio with renewable energies and all this in conjunction with focused poverty eradication programs. The problem is that there is a mismatch between the expected and the actual pace of growth in electricity access. Far from the government’s goal of accelerated growth in access with the ultimate goal of universal coverage, actual growth has been stagnant. In 2001 prior to the ‘Rural Electrification Strategy and Plan’ rural access was estimated at less than 2%; 7 years later it is estimated at only 3%. National grid access in 2002 prior to the ‘Energy Policy for Uganda’ was estimated at about 5% (Ministry of Energy and Mineral Development, 2002, p. 14); optimistic estimates now have it at just 9% (Ministry of Energy and Mineral Development, 2007, p. 27). An argument could be made that this is to be expected given the many other problems still plaguing Uganda’s electricity sector among which are lack of sufficient investor capital, huge operational inefficiencies, poor and inadequate basic infrastructure, etc. But a look at other African countries with situations comparable to Uganda could put some dents in this argument. Karekezi & Kimani (2004) compare Uganda’s rural electrification targets set by the government of 10% rural electrification by 2012 (Ministry of Energy and Mineral Development, 2001, p. n/a) with the pace of electrification in comparable African countries. Results show that in the same period of time (or even shorter) it is possible to achieve much higher electrification levels. Table 2 shows successful electrification initiatives from the selected African countries.
Table 2: Successful electrification initiatives in selected African countries. Sourced from (Karekezi & Kimani, 2004, p. 22)
Uganda’s comparatively stagnant growth could be due to many reasons:
• Insufficient extent of the transmission network coverage so that this constrains and makes it impossible to set and achieve reasonable grid connection targets
• Insufficient power supply/generation capacity so that even though grid connection rate may be high there is not enough power to meet all the connected demand
• Both the grid network coverage and generation capacity supply side could be sufficient and Uganda is simply in the initial/startup phase of an exponential access growth curve so that a ramp up phase is expected later (See Figure 3)
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Figure 3: Power access exponential growth hypothesis
Whatever the case, before the situation can be improved the factual reasons behind the problem must be identified. Given that although 40% of Uganda’s area is covered by the grid, only 9% within the grid covered area and 3% nationally are accessing power, it can be safe to say that the extent of the grid network is not the immediate pressing problem. This study has two important aims: (1) an investigation of the last two points i.e. possible generation side capacity constraints and the possibility that power access is in a temporary startup phase, the key question being, how much these two factors actually contribute to Uganda’s comparatively stagnant power access levels (2) identification of any potential problems arising from the current policy framework and market design
‘Access’ in this case is defined as a physical feed‐connection to a central or decentralized power grid as well as sufficient electricity supply from that grid. The research is aimed at surfacing and facilitating understanding of the fundamental reasons underlying the low growth in Uganda’s power access and subsequently the design and selection of effective policies to solve Uganda’s problem.
1.2 Significance of the Problem Access to power is tied to any country’s development. It provides opportunities for increased social welfare, education, health and income generating opportunities all of which Uganda needs. Uganda’s small but significant economic development of 6% p.a. (Ministry of Energy and Mineral Development, 2002, p. 9) is being stifled by power inaccessibility. An estimated 400 million U.S. dollars, 5 percent of Uganda's gross domestic product (GDP), is to be lost in 2009 due to fuel and power shortages (View, 2009). The fact that development should have been much higher than this is also supported by the relatively high power load growth of about 7% p.a. which has created an unprecedented electricity supply deficit on the national grid a maximum of about 165 MW (Ministry of Energy and Mineral
Electricity access
Exponentialgrowth phase
Start‐up phase
Time
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Development, 2007, p. 27). The result of this is massive load shedding everyday as a way to ensure that all the different grid covered areas get electricity at least part of the day. This means grid access in itself is not sufficient to guarantee round‐the‐clock electricity services.
The lack of access to electricity is most felt by Uganda’s substantial rural population. While Uganda’s aggregate development figures look very promising; development in rural areas has lagged well behind that of urban areas. Poverty remains pervasive and extensive and much of Uganda's rural population remains isolated with limited access to basic modern goods and services among which is electricity; with the most common barrier to rural electrification being the high cost of grid extension. The result is a vicious cycle whereby the potential for rapid and broad‐based economic growth is severely constrained by the lack of access to electricity while on the other hand the consequent poverty levels inhibit infrastructure investment potential. Government consensus is that if Uganda is to continue growing economically, increasing its overall net productivity, the level of electricity access must be ramped up (made to grow much more rapidly in contrast to the current slow pace/stagnant growth) to keep pace and even better begin to drive the development and key to this is obtaining a good understanding of the factors limiting the needed growth in power access.
1.3 Research Objectives Uganda’s electricity sector is complex ‐ it is tightly coupled to other sectors i.e. commercial, industrial etc. and governed by feedback between itself and these sectors. Take the case of how increased power access fuels development of the coupled sectors, which development in turn contributes to growing the power sector through increased demand and improved ability to pay for power. The problem of electricity access by extension is similarly quite complex. It involves many relevant and interconnected sub‐systems – highly technical functions (power generation, transmission, and distribution). Because of the physical nature of electricity, the entities performing these functions are not isolated but interconnected meaning that at one time or the other they make simultaneous and dependent decisions which have widespread effects on all consumers. Consequently, a good understanding of the respective sub‐system interactions reduces the complexity and is critical to obtaining good solutions to the sector’s problems.
The research was aimed at the following:
1. Building a working theory on the internal setup and inner workings of the market and institutional aspects of Uganda’s power sector
2. Using the working theory to surface the influential behavior modes generated by Uganda’s market structure (institutional set‐up) as they pertain to power generation and supply and how these ultimately affect electricity access
The end goal was to facilitate a better understanding of how elements of the power system contribute to the problem and once this primary objective was met, then the secondary research objective was to devise effective policies that took into account prevailing local conditions to remedy the situation.
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1.3.1 Research Questions The main research question was formulated as follows:
"How is Uganda’s power sector expected to evolve in terms of growth rate of electricity access and supply given the existing market structure and prevailing local conditions in Uganda?"
This question was broken up into the sub‐questions below:
• How is Uganda’s on‐grid power access expected to evolve with the current market design and local conditions in Uganda? Is the expected generation capacity development as determined by the existing capacity planning process sufficient to meet Uganda’s power demand?
If it is insufficient,
• What reasons within the market or institutional structure account for any shortfalls?
• What would therefore be needed to ensure that growth in generation capacity is sufficient to meet Uganda’s growing electricity demand?
1.4 Research Methodology Objective 1: Clarify knowledge and understanding of the behavior modes and internal dynamics of Uganda’s power sector pertaining to power access levels.
The complexity of the problem described in section 1.3 means that if we were to rely on human mental models to effectively study a problem of this magnitude, the only feasible way would be to break it up into smaller manageable pieces (sub‐systems), studying each one by one. However such an approach fails to take into account the feedback effects between the different sub‐systems. For this, there is need for a holistic system approach which allows for the study not only of individual variable interactions but also sub‐system interactions. System dynamics modeling was used to facilitate the modeling and analysis of specific problem areas within the sector and identification of the underlying structural flaws responsible for the problem. The reasoning behind the choice of this methodology is discussed in more detail in section 3.1.
Objective 2: Devise effective policies to remedy the situation
This was done using a combination of research methods including desk research based on existing literature – reports from relevant ministries, research reports on similar problems, theoretical literature etc to compare the various points of view taken by the authors and interpret these on the basis of existing interest groups; as well as a literature survey using the formed opinions of experts who have already worked on this issue in different countries on possible solutions and their advantages and disadvantages. Proposed alternative policies were tested on the simulation model to judge their effects on the set sector performance indicators.
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1.5 Structure of the Report The rest of the report is organized as follows:
Chapter 2 gives an overview of Uganda’s power sector in terms of the market structure, existing power demand supply dynamics and specific problems plaguing different parts of the sector as they pertain to electricity access
Chapter 3 presents the system dynamics model – a qualitative and quantitative analysis of power demand versus supply aspects of Uganda’s power sector, the assumptions made in the development of the model as well as the model validity
Chapter 4 gives the results obtained from the model as they pertain to power access, highlights the problems shown up by the results, and given these problems, explores the development of Uganda’s power sector under different possible futures
Chapter 5 presents an analysis of the impact of Uganda’s existing power sector policies on power access as well as corrective policies to the problems identified in Chapter 4
Chapter 6 gives the conclusions, the limitations of the study and areas of further research
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2. The Situation in Uganda’s Power Sector
In this section an indication of the current situation in Uganda’s power sector is presented. Understanding the current situation is useful input for a qualitative analysis of the sector’s problems.
2.1 Market Situation Uganda’s electricity market is small. The majority of the population is rural based, 87% (Ministry of Energy and Mineral Development, 2007, p. 9) and poor. Karekezi and Kimani (2004) present an indicative hypothesis of how the rural‐urban split in Uganda can be used as a proxy for the poor and non‐poor with the poor being defined as those people on average living below $2/day. Going by this proxy the majority of the population cannot afford to pay for electricity. The principle of the consumer covering all the costs of a kWh is quite unrealistic in Uganda given expected rising oil prices as well as the prices of imported equipment. Without appropriate government support to finance the operating losses incurred by private investors, there is limited private incentive to grow the market in terms of investing in new capacity and new connections. Given that the government of a developing country like Uganda has a limited budget and many pressing and competing priorities; the option of government financing is limited.
The financing situation in the electricity sector is a deterrent for private investment. Attraction of private capital into the generation and distribution functions has been limited. The lending capacity of the few available development banks is very highly constrained by their limited net‐worth. On the other hand, commercial banks prefer lending on the short term and avoid such long term returns on investment projects as electricity projects. Adding to the inaccessibility of financing options for projects, the interest rates in Uganda’s financial sector are regarded as the highest in the world in real terms (Electricity Regulatory Authority, 2008b, p. iii). The rates are very high owing to the high risk. Especially for the electricity sector this risk is driven by many factors including market volatility (volatility of oil prices), institutional stability and rule of law guaranteeing that agreements will be honored by the involved parties (uncertain), nature of power demand (fluctuating), level of inflation, etc. For these reasons the development and expansion of the sector is hampered by the inability to mobilize sufficient investment capital. The small percentage of people constituting viable demand as well as the limited viable options for financing in the electricity sector without government intervention means that it is nearly impossible to build a really competitive market for generation or retail in the short term. The only realistic form of competition is ‘competition for the market’.
2.1.1 Power Supply Total installed capacity in Uganda is about 380 MW from the large hydropower plants Nalubaale and Kiira, 17MW from mini & micro hydropower plants including Kilembe Mines Ltd., Kasese Cobalt Company Ltd and others, and 15 MW from co‐generation from Kakira Sugar Works (Bundesanstalt fur Geowissenschaften und Rohstoffe, 2009). The nature of the generation mix mainly includes hydro and thermal, with thermal coming to greater prominence as hydro resources continue to diminish with longer droughts. Figure 4 shows Uganda’s generation mix from 2003 ‐2007
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Figure 4: Uganda electricity generation mix 2003 ‐ 2007. Sourced from (Electricity Regulatory Authority, 2008c, p. 7)
The fall in production associated with the fall in Lake Victoria elevation levels; coupled with a relatively high load growth of about 7‐8% p.a. (Bundesanstalt fur Geowissenschaften und Rohstoffe, 2009) has created an unprecedented electricity supply deficit on the national grid a maximum of about 165 MW (Ministry of Energy and Mineral Development, 2007, p. 27). This is the equivalent of 50% of unmet power demand going by an average daily power demand of 315MW (Uganda Investment Authority, 2005, p. 3). The result is massive load shedding everyday as a way to ensure that all the different parts of the country get electricity at least part of the day. In a bid to address the power shortage in the short term, the Government of Uganda boosted the energy generation by installing at least 100 MW of additional thermal power plants (Uganda Investment Authority, 2005, p. 3) while it awaits the construction and commissioning of additional hydropower capacity. The thermal capacity is running on expensive heavy fuel diesel generators.
While demand for electricity exceeds supply, the efficiency with which new generation capacity develops from planning to commission is constrained by long lead times. The lead times in Uganda are significant; many times they tend to be indefinite with about 1 in 3 projects eventually getting cancelled owing to the high levels of risk that come with investing in Uganda including financing risk, currency risk (owing to high levels of inflation), political risk (owing to government interference in the process of setting tariffs), hydrology risk (owing to uncertainty concerning water levels), etc. The process by which existing generators can expand their existing capacity is also worth noting. The single buyer’s Power Purchase Agreement with generators guarantees payment for pre‐defined capacity. As a result, new capacity can only be contracted through the single buyer. The implication is that any capacity planning bottlenecks and inadequacies on the part of the single buyer are effectively propagated throughout the sector and affect the capacity planning processes of the generators.
Of major concern with Uganda’s power supply are the high levels of losses within the transmission and distribution networks. While transmission losses which are effectively ‘technical’ in nature are high – they stood at 4.8% in 2005, rising to 5.5% in 2006 and 2007 (Electricity Regulatory Authority, 2008c, p. 7), it is the distribution losses mostly ‘commercial’ in nature that pose the biggest problem. The biggest commercial losses are "theft losses" including end user illegal connections, meter by‐passing and
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collusion. These fell from 43% in 2005 to a still high 37% in 2006, averaging about 35% in 2007 (Electricity Regulatory Authority, 2007, p. 8). They lose the private provider sizeable revenue and increase the financing risk perceived by would be investors.
The distribution of Uganda’s power demand and its effect on the extent and nature of the transmission network is important. Kaijuka (2005) analyzes the geographic distribution of Uganda’s electricity demand using Geographic Information Systems (GIS). She uses the population distribution, health centers, schools and village trading centers as proxies for demand. Figure 5 shows an indicative demand distribution using population and schools as a proxy. Note how the demand pattern is mostly centered along the electricity transmission grid.
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Figure 5: Demand distribution using population & schools as proxy. Sourced from (Kaijuka, 2007)
The map shows concentrated pockets of electricity demand; but the pockets are widely dispersed. In her analysis, Kaijuka determines that village trading centers (proxies for electricity demand in rural areas) are usually found along the main roads and are often distributed sparsely and randomly, many miles apart from village to village. The natural terrain is also sometimes mountainous and less penetrable. To meet such dispersed demand is very costly and has made grid supplies rather impractical. This accounts for the small growth rate in electricity grid access currently at only 40%.
2.1.2 Power Demand Uganda’s electricity demand is growing at a very fast rate of 7% p.a. (African Development Bank Group, 2008). The growth in demand is majorly driven by the country’s immense population growth rate averaging 3.4% p.a. (World Bank, 2006). What would have been an explosion of power demand growth is seriously limited by the fact that the population growth has broadly kept ahead of the growth of GDP per capita (see Figure 6). So while, there are more potential consumers from the population growth, it is ill‐matched with their ability to afford the service.
Figure 6: Per capita GDP growth versus population growth
The affordability of power is further limited by the high electricity prices. See Figure 7 below for the growth trend in tariff rates in dollars.
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0.00
0.05
0.10
0.15
0.20
2002 2003 2004 2005 2006
Tariff ra
tes ($)
Tariff Rates (2002 ‐ 2006)
Domestic
Commercial
Medium Industrial
Large Industrial
Figure 7: Uganda electricity tariff growth trend. Sourced from (Uganda Investment Authority, 2005, p. 5)
Prices are negotiated and set on a ‘cost plus’ basis so that high costs of service delivery imply high electricity tariffs. From Figure 7 average electricity price for 2006 was at $0.16/kWh. Even with government subsidies as high as 30%, for a country whose GNI per capita is only $340, this makes electricity too expensive for the majority of the population. The situation can only get worse. Without hydro capacity whose maximum electrical potential is 2000MW, Uganda lacks cheap high value output primary energy resources e.g. coal, gas, etc. The alternative resources it possesses – solar (200MW), geothermal (450MW), biomass (1650MW) combined have a low output (see table 1) and by their nature can only produce intermittently. This combined with their high capital costs is the reason they are likely to remain insufficiently exploited to the level where they can make a significant contribution. This leaves Uganda relying on expensive fuel thermal generation to back up hydro power; currently it is with diesel run thermal generators. The generation costs with this option are quite high and a cost‐plus based system means electricity prices will grow with the fuel prices. Figure 7 shows tariff growth rates of about 50% between 2005 and 2006. The growth in electricity tariffs is outpacing the growth in average income per capita averaging 12% p.a (World Bank, 2006) which implies a highly volatile level of power demand – as electricity prices grow, more consumers will not be able to afford it anymore.
2.2 Power Sector Problem Summary The challenges to accelerating growth in electricity access as discussed in section 2.1 are great. The market is small and financing conditions are not attractive to private investment; the country is not endowed with sufficient high value cheap primary energy resources e.g. coal and gas making future large scale power generation prospects very costly; electricity prices are too high for the majority of the population to afford; highly dispersed power demand making centralized grid access impractical and costly. While little can be done about these without significant government financial resources, there are some problems which may lend themselves to cheaper solutions. It is these that are a focus of this study.
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The most significant of said problems is the fact that existing power supply capacity is insufficient to meet existing viable demand. Viable demand defined as that for which power providers are certain to recover their investment. The open question is why isn’t this demand being met? The study is aimed at answering this and other existing information gaps related to questions of – just how insufficient is the power supply? Is the problem temporary or permanent? Which aspects of the power sector are responsible for this behavior?
Chapter 3 gives both a qualitative and a quantitative analysis of the demand versus supply dynamics of the power sector.
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3. Model Description
3.1 System Dynamics – Background and Application Section 1.3 gives an indication of the complexity of Uganda’s power sector. To be able to understand the nature of problems we are solving en‐route to solving them, we need a method that can highlight what kind of behavior patterns the power system generates over time; under what conditions is it stable or unstable, oscillating, growing, etc. Feedback in the system calls for a method that facilitates the study of specific aspects of the sector but from a view of the system as a whole as opposed to piece by piece (1 subsystem at a time) thereby taking into account the impact of feedback. The multiplicity of relationships and interactions as well as their non‐linearity calls for a computer model which can carry out the complex and simultaneous calculations needed to generate and therefore facilitate understanding of different power sector behaviors.
System dynamics methodology which originates from the work of J.W. Forrester at M.I.T combines ideas from control engineering (concepts of feedback and system self regulation), cybernetics (nature of information and its role in control systems), organisational theory (organisational structure and mechanisms of decision making) and information technology (computer simulation) to simulate complex, non‐linear, multi‐loop feedback systems. Sterman (2000, p. 22) defines system complexity not simply as a matter of details, but rather resulting from the fact that said systems are dynamic, tightly coupled, governed by feedback, nonlinear, history dependent, policy resistant and characterized by trade‐offs. With system dynamics we are able to tell what kind of behavior patterns the power system generates over time and as discussed in section 1.4, it demands the use computer simulation modeling to numerically solve this complex power system by mimicking its fundamental structure in terms of the actual (but simplified) forces that are believed to make the system work.
System dynamics is used to address the main research question:
• How is Uganda’s power sector expected to evolve in terms of growth rate of electricity access and supply given the existing market structure and prevailing local conditions in Uganda? A simulation model of Uganda’s current power supply situation is used to arrive at an understanding of how power generation capacity will evolve within this market model and institutional set up vis‐à‐vis the growth in electricity demand. The model provides for a way to analyze and test out possible futures for the power system under different systems of rules and external conditions/scenarios.
The insights obtained from above are expected to form the foundation and inform the discussion for the sub questions:
• What reasons within the market or institutional structure account for any shortfalls?
• What would therefore be needed to ensure that growth in generation capacity is sufficient to meet Uganda’s growing electricity demand
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It is important to note that Uganda’s power sector reforms are recent changes with the first private concessions in generation and distribution awarded in 2003 and 2004 respectively. This implies that the sector data available on performance in this new setup is too few and appears too singular in nature to allow for credible time series data trends much less substantive conclusions from historical data. For this reason, the proposed model and study is designed for general understanding, not for capacity or power demand forecasting. More specifically, it is being applied in the exploration of possible futures for Uganda’s power sector given the newly implemented market design – Single buyer model as well as existing market conditions and investor behavior.
3.2 Qualitative Model Analysis Within this section is a definition of the system model boundaries and identification of the most important variables in the problem structure that is Uganda’s electricity sector. A conceptual dynamic hypothesis of expected system behavior is presented with the mechanisms and feedback loops1 believed to be generating the behavior. The dynamic hypothesis is a statement of system structure that appears to have the potential to generate the problem behavior (Richardson and Pugh 1981c, p55); a working theory on how the model generates its behavior. Because the purpose of the model is to facilitate understanding of specific problems within Uganda’s electricity sector, the dynamic hypothesis is a necessary theory on the probable causes of the problems and serves as a general guide in the modeling process.
The key points of interest in the sector that best summarize and influence the general behavior of the power system are: power demand, power supply and overall power sector profitability. The determinants or drivers of their expected behavior (reference modes) are qualitatively analyzed and explaine . d
3.2.1 OnGrid Power demand Uganda’s on‐grid power demand is driven primarily by two factors: the economic welfare of the population and the rate of physical connections to the power supply system. In this study, GDP per capita is used as a proxy for measuring the purchasing power of the population; the higher it gets, the higher the ability of the population to pay for power services. The limited physical extent of the grid (only 40% of the country covered) is the major factor in determining the number of possible grid connections. Figure 8 shows the key relationships driving the evolution of on‐grid power demand
1 A ’+’ sign at the arrowhead indicates that if the influencing variable increases (decreases), all things being equal, the influenced variable increases (decreases) too above (under) what would have been the case otherwise. A ’‐’ sign at the arrowhead indicates that if the influencing variable increases (decreases), all things being equal, the influenced variable decreases (increases) under (above) what would have been the case otherwise (Daalen, Thissen, Pruyt, & Phaff, 2007, p. 45). A feedback loop occurs when, say, a change in variable A directly causes a change in variable B which directly causes a change in variable C which in turn directly causes a change of the initial variable A. In essence, variable A feeds back after some time to itself, which makes that its current behaviour is (partly) shaped by its past behaviour.
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Domestic power demand grows due to growth in two variables: consuming population and power consumption per capita. In Figure 8, GDP per capita influences power consumption per capita both directly and indirectly. Directly – in that the purchasing power determines the approximate amount of power one can afford – the nominal consumption per capita. Purchasing power however keeps changing relative to how costly power actually is so that the more expensive, the more share of income gets spent on power (this is the indirect influence). Uganda is a developing country with low per capita inc me levels. Electricity is more of a luxury than a basic good and as
such the dynamics of increasing power prices versus those of per capita income are very significant. When prices increase
faster relative to income, power becomes too expensive; people start rationing it reducing their individual demand and slowing growth of overall power demand. This effect is especially likely to be strong in Uganda whose increasing reliance on expensive thermal generation implies that the growth rate of electricity prices will likely rival the growth rate of income per capita. The driving feedback loop in this case ‘Income – Expense Loop (‐)’ has a delayed effect on power consumption because people will mostly adjust their demand
o
after getting a hefty bill and not before.
Figure 9 shows different ways power demand may grow over time based on the main interactions given in Figure 8. Case A and C would be the ideal with power demand growing and naturally peaking at 100% of its full potential value – level equivalent to the desired universal coverage. Undesirable is Case B where growth in demand stabilizes at levels below its maximum potential maybe due to too high
power prices.
Figure 9: Power demand ‐ reference mode
Figure 8: On‐grid power demand ‐ Driving relationships
Time
Power Demand
Case C (Peak at 100%)
Case A (Peak at 100%)
Case B (Peak at <100%)
Total systemcosts
Consumerelectricity price
+
Domestic powerdemand
Actual powerconsumption per
capita
<GDP percapita>
+
Govt subsidy-
Income sharespent on power
+
+ -
-
rate of newconnections
Power Demand Sub-model
Nominal powerconsumption per
capita
Income -ExpenseLoop (-)
++
Consumingpopulation
++
Populationgrowth rate
+
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Exponential growth in all the cases is fueled by the combined growth trends in the consuming population and power consumption per capita. The stabilization in the trends for cases A and C at 100% would be due to the natural peaking in Uganda’s population growth and/or growth in power consumption per capita. The stabilization in the trend for case B at less than 100% would be due to the ‘Income – Expense Loop (‐)’ gaining strength and becoming dominant – in essence too high electricity prices make power too expensive reducing overall power demand to anywhere between the minimum (i.e. per capita demand equal to the minimum power consumption per capita) and the maximum (i.e. per capita demand equal to the nominal power consumption per capita) level. The oscillations in case A would arise due to varying strengths of the ‘Income – Expense Loop (‐)’ over time – the loop strength dependent on the relative dynamics between electricity price and GDP per capita. An increase in price relative to GDP per capita would reduce the amount of per capita power demanded and the reverse would cause the opposite effect increasing per capita power demanded.
3.2.2 OnGrid Power Supply The power supply and the choices that go into it are ideally driven by three factors; the amount of power demand, supply capacity investment constraints and the generation capacity planning process. While supply should ideally meet demand, the nature of demand in Uganda in terms of its limited ability to pay itself back enough to ensure optimal return on investment is an important factor contributing to the supply capacity investment constraints.
Figure 10 shows the important variables and relationships influencing power supply in Uganda. The projected power supply deficit in 20 years determines how much additional capacity the power regulator needs to contract for from the generators within their 20 year concession agreements. Via the ‘Deficit – Investment Loop (‐)’, the deficit drives the amount of contracted, planned and installed capacity but with two important caveats – the guaranteed Return on Investment which the regulator offers the investors and the maximum generation potential for the respective generation technologies. Because of Uganda’s unreliable power demand in terms of its price elasticity, the guaranteed return on capacity investments is an important variable which determines how much market risk investors are exposed to and therefore how much they are willing to invest to reduce the power supply deficit. The maximum generation potential for the different technologies determines how much more capacity of each technology‐type can be contracted.
Note that the direct link between domestic power demand and domestic power supply is an instantaneous link owing to the nature of electricity whereby power supply can never exceed power demand – the power generated can only be less or equal to the total power demanded
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Planned capacity
Installedcapacity
Retired capacity
-
Domesticpower supply
Total on-gridpower generated
Loss rate
Power exportrate
<DomesticPower demand>
++-
-+
+
Max generationpotential
Power Generation Sub-model
Contracted capinvestment
+
+
+
Power supplydeficit
-
+
Required capinvestment
Generator ROI
+
+
+
<Projectedpower deficit>
+
+ Deficit -Investment
Loop (-)
+
Figure 10: Power supply – Driving relationships
While investment constraints may make the expected behavior of the power supply variable over time hard to predict, the structure of the capacity planning process as well as the nature of Uganda’s power demand on which it depends can give an approximate indication. As discussed in section 2.1.1, Uganda’s power supply has seemed to lag power demand indicating that one possible trend in the power supply could be similar to that of demand (see Figure 9) but simply displaced in time. Other possible trends drawn from the capacity planning process could conform to any of the following system archetypes (Figure 11):
• ‘Limits to growth’ archetype whereby power supply grows quickly due to availability of cheap hydro resources until over‐exploitation – over drafting of water sources causes environmental degradation so that capacity levels are permanently and severely reduced (Case A). In the presence of sufficient capital investment, this case would be offset by the application of more expensive generation resources.
• ‘Balancing process with delay’ archetype whereby power sector players over react to
Figure 11: Total power supply ‐ reference modes
Case B
Case A
Power Supply
Time
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the size of the power deficit and invest in additional capacity all at once creating over capacity followed by underinvestment in the next cycle creating shortage (Case B)
3.2.3 Economy of the Power Sector There are three important factors determining the economic status of the power sector: the costs of power supply, the guaranteed return on investment for investors and the consumer electricity price. The electricity price is ideally set depending on the total costs of the power service and the available power demand utilizing the service. This means service costs are fully reflected in the electricity price. When service costs increase, electricity price increases. The price elasticity of Uganda’s power demand dictates that the resulting fall in demand should push the prices even higher. Subsidies offset the cost of power service to consumers and contribute to stabilizing demand. By varying the level of the subsidy, a form of price cap is formed which should serve to dampen any significant oscillations in power demand due to changes in electricity price. Figure 12 shows the important variables and relationships influencing the economic aspect of the power sector.
Guaranteed ROIProjected
power deficit
Averagegenerator costs
Generatorcapacity price
+
<Market riskpremium>
<Risk free rate>
+ +
Capacity Investment & Pricing Logic
Economic Sub-model
<Contracted capinvestment
+
Tot installed cap
Generator costpayments
+
+
<Transmissioncosts>
+
<Distributioncosts>
Tot powerservice costs
+
+ +
Consumer tariff
<Domesticpower supply>
+ -
Subsidisedconsumer tariff
+
+
+
<Govt subsidy>
-
Consumer Tariff setting Logic
+
Figure 12: Economic status of power sector ‐ Driving relationships
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3.3 Quantitative Model Analysis
3.3.1 Model Assumptions This section gives a description of the market structure as modeled and the assumptions made in the development and translation of the conceptual model into a quantitative model. It further highlights for the scenario variables the assumed prospective time series trends whose effects on the power sector are to be investigated in this study. The power sector is influenced by these highly dynamic variables (fuel costs, GDP per capita, population growth) whose future values cannot be predicted with much degree of accuracy. For this reason they are represented as exogenous time series trends.
Power Demand The study focuses only on the development of on‐grid demand. The transmission/grid network is assumed to be constant. Reason is that with 40% of Uganda’s area covered by the grid, only 9% within the grid covered area and 3% nationally are accessing power. With conditions as they are, it doesn’t make business sense to invest further scarce resources in the grid until that which is in existence has been fully utilized. This situation is borne out in Uganda today where the grid has been extended by only 1.1% (Electricity Regulatory Authority, 2008d, Transmission Route Length) since the introduction of private players in the sector. This marginal extension comes from the necessary connection of new generation sites to the main grid. The following assumptions are employed in the specification of Uganda’s power demand:
a. On‐grid power demand grows due to increase in the consuming population and increase in per capita income which translates to higher consumption per capita. The consuming population grows due to increase in the number of distribution connections within the grid covered area and due to natural population growth within the grid covered area.
Population Growth
Population growth within the grid covered area is assumed to be the same as the national growth rate. In section 4.3 the effect of different population growth scenarios on Uganda’s power sector is explored. The scenarios are based on different variants of expected population growth trends derived by the Population Division, United Nations Secretariat (Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, 2008). In their predictive forecasts, they have Uganda’s population at 100million, 85million and 112million for reference case, the low and high variants respectively in the year 2050. Tacking to the conservative side, these target figures are used in this study for the year 2083. Figure 13 shows the scenarios employed in this study. The medium variant is applied as the reference case.
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020406080
100120140
Popu
lation
(Millions)
Population Scenarios
Medium Variant Low Variant High Variant
Figure 13: Population growth scenarios for Uganda
Connections Growth
Uganda’s distribution concession agreement specifies an arbitrary investment obligation for the investor over the length of the agreement. A copy of the concession agreement for the distributor signed in 2004 could not be obtained to ascertain the agreed upon target number of new connections per year. For purposes of this study the connection growth rate is assumed to be 5.9% derived from a set target which assumes a programme of 12,000 new connections per annum rising to 20,000 per annum by 2010 (ECON Centre for Economic Analysis, 2001a, p. 57). The assumption is supported by the government stated annual growth rate in connections estimated between 5.5 and 7.5% (Ministry of Energy and Mineral Development, 2003, p. 9)
An average of 5 people per household and subsequently per connection are assumed (Uganda Bureau of Statistics, 2002, p. 21)
b. Power demand is assumed to be price elastic due to the low levels of income per capita which makes electricity more of a ‘luxury good’ rather than a necessity. In this study power consumption per capita has been split up into a minimum ‘Basic consumption per capita’ which is price in‐elastic and an ‘Optional consumption per capita’ which is price elastic and varies depending on the ratio of power expenses to income levels.
Basic power consumption per capita per year is assumed to be 288kWh derived from a minimum average monthly household power consumption of 120kWh (daSilva & Baringanire, 2007) and an average number of 5 people per household (Uganda Bureau of Statistics, 2002, p. 21)
The relationship between the share of per capita income spent on electricity and electricity consumption per capita is derived from two study findings: (1) African households on average
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spend 5.3% of their income on fuel (Case, 1998, p. 19) (2) a UN compilation on consumption by expenditure which finds that the average Ugandan household spends 14% on Rent, Fuel & Power (UNCTAD & ICC, 2001, p. 8). The explicit model assumption is that with an income share spent on power of less or equal to 5.3%, the power demanded per capita equals the full nominal consumption per capita and with the income share greater than 10% power demanded per capita is only equal to the basic consumption per capita.
c. GDP per capita may be the single most influential variable in this study determining the population’s ability to pay for the power service therefore influencing level of power demand and the prices set for power. Section 4.3 analyzes the effect of different scenarios of GDP per capita growth on Uganda’s power sector. Scenarios are constructed based on Uganda’s economic growth trend so far as well as a comparison with the more developed African countries including Botswana, Gabon, Libya, Tunisia, Egypt, Algeria, and South Africa. Figure 14 below shows the respective scenario projections
0100020003000400050006000
GDP pe
r Ca
pita ($
)
GDP per Capita Scenarios
Reference High Low
Figure 14: Uganda GDP per capita projections
d. The relationship between GDP per capita and Uganda’s average consumption per capita is
assumed from a comparison of different country GDP per capita figures against their respective power consumption per capita. Figure 15 shows said comparison for a selection of comparable African and Asian countries2. Assumption is that Uganda will develop along the same path. See Appendix C for list of comparable countries and their respective GDP and consumption per capita values.
2 Sourced from http://earthtrends.wri.org/text/energy‐resources/variable‐574.html //(International Energy Agency (IEA) Statistics Division, 2007) for Consumption per capita //(Development Data Group, 2008) for GDP per capita
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y = 796.27ln(x) ‐ 4553.2R² = 0.4514
‐2000
0
2000
4000
6000
0 2000 4000 6000 8000
Consum
ption pe
r Ca
pita
(kWh)
GDP per Capita ($)
Consumption per capita ‐ Comparable Countries
Consumption per capita Log. (Consumption per capita)
Figure 15: GDP per capita vs Consumption per capita for comparable countries.
A logarithmic trend is selected given that Uganda is a tropical country and therefore its household levels of power consumption are most likely to stabilize at much lower levels than higher consumption countries experiencing colder climates e.g. South Africa which accounts for the outlier above 4000kWh.
Power Supply The study is focused on the development and evolution of only the two major generation technologies in Uganda – hydro generation and diesel fuel thermal generation. The other technology which is biomass based cogeneration accounts for only 2.9% of Uganda’s power supply and in its present state is too unreliable to be considered a major power producer over the 80 year period of this study.
a. Generation Capacity Guaranteed capacity The generators are assumed to be working at a generation efficiency of 70%. For hydro generation 70% is an average efficiency value taking into account periods of drought and low water levels when it falls to as low as 60% and periods of sufficient water levels when it can be as high as 80% and above. For the study their availability is assumed at 100% all year through i.e. 24*365 hours per year. This is obviously contrary to reality where they have down time scheduled or otherwise for routine or emergency maintenance. The availability assumption means that all the time at nominal levels of energy input, any generator guarantees availability of 70% of their installed capacity. Capacity lead times and lifetimes
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The effect of plant ageing is explicitly taken into account with hydro capacity being retired after 40 years and thermal after 30 years. While normal expected plant lifetimes are on average 30 years for hydro and 20 years for thermal, in Africa retirement is put off as long as possible due to financial constraints. It is assumed that once the decision has been made to invest in new or replacement capacity, hydro capacity takes 6 years to commissioning while thermal capacity takes 3 years. Capacity Planning Process The current system in Uganda manages contracts with generators through a concession arrangement. This effectively leases existing assets to the concessionaire, and imposes an investment obligation on the concessionaire (ECON Centre for Economic Analysis, 2001b, p. 26). The concessioning process requires potential concessionaires to bid their required return on investment together with a commitment to a target or minimum level of investment. The result of this process is that generation concessions with fixed investment obligations for the generator at pre‐negotiated and therefore fixed Return on Investment for a particular pre‐negotiated length of time are awarded to successful investors in a bidding process. In planning for new capacity, the single buyer therefore uses a planning horizon equal to the length of the concession agreements. Any capacity that they will need over this length of time needs to be negotiated within the concession contract. For the sake of simplicity this study assumes the following:
• A capacity planning horizon of 20 years which is the standard length of current concession contracts in Uganda (D’Ujanga, 2004).
• Renewable contracts every 20 years at which time new capacity requirements are re‐negotiated
• A constant after‐tax rate of Return on Investment on all concession agreements of 20% ‐ Uganda’s minimum rate is around this figure (ECON Centre for Economic Analysis, 2001c, p. 12).
• Generators’ total obligated investment over the length of the concession is made or spread out equally over the 20 years (base case).
b. Power System Costs
Capacity Costs
It is assumed that unit capacity plant costs i.e. fixed costs, maintenance costs and fuel costs are the same for all plants of similar technology irrespective of size. All hydro plants are assumed to have a capacity cost of 0.0949 $/kWh derived from the levelized capacity cost of a generic hydro
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plant of 200MW capacity (Ocampo, 2009). Thermal plants are assumed to have a capacity cost of 0.1397 $/kWh derived from the levelized capacity cost of a conventional oil thermal plant of 300MW capacity (Ocampo, 2009). Note that capacity cost includes capital, operation and maintenance as well as fuel costs.
Unit capacity Operation and maintenance costs for both hydro and thermal technologies are assumed to be constant. Thermal fuel costs are assumed to vary depending on world oil prices. The effect of different scenarios of world oil prices on Uganda’s power system is analyzed in section 4.3. The scenarios are derived from world oil price projections in Annual Energy Outlook 2009 (Energy Information Administration (EIA), 2009), defined in terms of the average price of imported light crude oil to U.S. refiners. Figure 20 below shows the respective scenario projections used in this study. The low thermal fuel costs is based on a maximum annual average price per barrel projection of $130; Reference thermal fuel costs based on a maximum projection of $200 per barrel and high thermal fuel costs on a maximum projection of $280. Also investigated is the possibility of oscillations in fuel costs ‐ Oscillating thermal fuel costs. Note that $1 is equivalent to Sh 1,800.
Thermal Fuel Costs
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
100,000
200,000
300,000
400,000
500,000
600,000
700,000Sh/MW
Ref thermal fuel costs
Osc thermal fuel costs
High thermal fuel costs
Low thermal fuel costs
Non-commercial use only!
Figure 16: Thermal fuel cost scenarios
Transmission Costs
As previously highlighted in this section, the transmission network is assumed to be constant. The rationale for more investment is not supported by the low power access statistics within the grid covered area. For this reason the fixed costs of the network are assumed to be constant. Transmission charges are also assumed constant per unit of power transmitted. Domestic charges are set at 29.2754 Sh/kWh3 while export charges are at 159.984 Sh/kWh derived from bulk supply tariffs of 146.377 Sh/kWh and 799.92 Sh/kWh respectively (Electricity Regulatory Authority, 2008a) with the assumption that the average transmission price is approximately 25 per cent of the bulk supply tariff (ECON Centre for Economic Analysis, 2001a, p. 48).
3 assumed exchange rate of Sh 1,800 per US$ 1
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Distribution Costs
An average cost of approximately US$700 is assumed per connection in this study. This is derived from feasibility study estimates done on the cost and tariff structure in Uganda. The study assumes a programme of 12,000 new connections per annum rising to 20,000 per annum by 2010. It compares Uganda to South Africa’s electrification programme which has had investment costs of approximately US$600 in rural areas and concludes that the large scale of South Africa’s electrification programme (450,000 per annum) helped to bring costs down, and so the figure used of US$700 per connection can be viewed as a reasonable comparison (ECON Centre for Economic Analysis, 2001a, p. 57). It is assumed that capital costs are completely recovered within the concession period of 20 years at an after‐tax return level of 15 per cent. This appears to be the minimum allowed return by investors for the power distribution function (ECON Centre for Economic Analysis, 2001a, p. 75). Power loss is assumed to be 34% in this study. The figure is an average value of power losses mostly commercial in nature which were at 43% in 2005, 37% in 2006(Electricity Regulatory Authority, 2007, p. 12), 35% in 2007 (Electricity Regulatory Authority, 2007, p. 8). Over the past 4 years they have been ranging between 32% and 37%.
c. Power Exports ‐ Export Volumes
Power export volumes vary considerably from month to month. This is because during periods of peak demand when supply is less than unconstrained demand, contractual obligations imply that the single buyer is not obliged to export and exports are constrained i.e. exports are expected to be non‐coincident with Uganda peak demand (ECON Centre for Economic Analysis, 2001a, p. 21). Nevertheless, UEB statistics show cases where Ugandan demand is load shed while exports continue (ECON Centre for Economic Analysis, 2001a, p. 22). For this study the nominal percentage of power exported is assumed to be constant at 3% of total generation capacity derived from yearly export statistics (Electricity Regulatory Authority, 2008d, Bulk Energy Sales (MWh)) shown in Table 3 below
Table 3: Fraction of power exported
Year 2005 2006 2007
Exports/Total Energy Sales (%) 2.889798 2.835559 2.989872
Also, in addition to the nominal level of power exports, all surplus power generated is assumed to be exported.
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a. Market Set up
The study assumes one player – the single buyer controlling investment and power pricing decisions. The single buyer in Uganda would be the transmission operator but all veto power on market decisions rests on the regulatory authority so one could assume the regulatory authority is effectively the single buyer. Single buyer controls investment via the 20 year master capacity plan which is negotiated in the concession agreement. Also, while prices between generators and the Transmission Company will be negotiated between these parties, they will be subject to oversight and approval by the ERA (ECON Centre for Economic Analysis, 2001c, p. n/a).
The only form of competition in Uganda’s power sector is at the concession bidding stage. For this study the competition has not been explicitly modeled but rather represented by a preferential structure which assumes that cheaper hydro capacity is the preferred option to thermal capacity in the award of concessions and it’s only when available hydro capacity is not sufficient that thermal capacity is put in place.
Power Pricing
The current generator price structure is essentially a take‐or‐pay arrangement, independent of actual volume of power or energy delivered (ECON Centre for Economic Analysis, 2001b, p. 4). A Capacity Price based on the revenue requirement for the generating company and the maximum available capacity is calculated. The generating company charges the transmission company a monthly sum based on this Capacity Price and the capacity made available for dispatch (ECON Centre for Economic Analysis, 2001c, p. n/a).
Volume risks associated with variations in Uganda’s power demand or transmission network outages are transferred to end‐users in the consumer electricity price. Payments to generators are based on capacity made available and not power actually dispatched (ECON Centre for Economic Analysis, 2001b, p. 22). So regardless of demand growth, generators get paid their contracted revenue requirement as long as target availability levels are met (ECON Centre for Economic Analysis, 2001b, p. 28).
All power service delivery costs are transferred to the consumer in the consumer electricity price and subsidized to make power more affordable. Reference subsidy rate is assumed at 30% of the full cost‐plus market rate.
See Appendix A for the detailed specification and formulation of the system dynamics model.
3.4 Model Validity Before the model results, a brief indication of the model validity is presented to build confidence in the model results. The primary purpose of the model is to provide a working theory on the inner workings of Uganda’s power market and institutional setup in how they pertain to power supply and demand. The
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model structure as built gives this working theory. Model validity is examined on the purpose for which the model is intended.
The model is specifically intended to highlight how power access is expected to evolve over a period of 80 years, clearly indicate expected problems regarding access and facilitate understanding of the source or inherent causes of problem behavior. The model constructed shows the evolution of power supply and demand over a period of 80 years. It relies on the development of the following criteria/output variables to show the status of the power sector: domestic power demand, unmet power demand and electricity price. These three variables highlight how power demand is expected to evolve; whether power supply is sufficient to meet Uganda’s growing demand and the evolving cost of power service respectively. The behavior, problematic or otherwise, of these output variables is explained based on the status/behavior of the underlying contributing endogenous variables including size of grid connected population, capacity lead times, cost of power generation, transmission and distribution as well as exogenous factors including GDP per capita, size of overall population, fuel prices and most importantly the institutional framework governing investment and pricing decisions. In this way described, the model is valid for the function for which it was intended.
The model was subjected to a series of verification and validation tests to determine its validity. Verification was done during the course of building the model to ensure correct coding of the model, dimensional consistency and a model free of numerical errors. For validation both direct structure tests (involving examination of the structure without running the model) and structure‐oriented behavior tests (involving running the model and analyzing its output) were done. Direct structure tests included:
• Direct extreme conditions test – model equations were evaluated under extreme conditions to ensure the results from each model equation corresponded to reality in the same situation. All model equations passed. Appendix D shows a sample showing the application of this test on a small section of the model
• Empirical parameter confirmation test – model parameters were compared with knowledge from the real situation in the power sector. All variables were found to correspond to elements in the real system both conceptually and numerically
Two structure‐oriented behavior tests were done: Extreme conditions test (similar to direct extreme test except that the model is run) described below and sensitivity analysis described in section 4.2
• Extreme conditions test –model equations were compared with real system relationships; testing plausibility of equation results with extreme values and comparing the results of model equations to the expected system results. The results of extreme condition tests obtained showed that the model‘s response to extreme inputs corresponds to the anticipated behavior of the real system. See Appendix D for a sample showing application of this test on a small section of the model
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The findings from all these tests showed that the model as constructed is a valid representation of Uganda’s existing power sector structure as it pertains to power supply and demand.
To increase confidence in the model on top of the tests done above ‐ the need to determine the extent to which the model can reproduce behavior patterns that exist in the real system. As pointed out in section 3.1 Uganda’s power sector reforms are recent changes with the first private concessions in generation and distribution awarded in 2003 and 2004 respectively. This implies that the sector data available on performance in this new setup is too few and too singular in nature to allow for credible time series data trends from historical data. Behavioral validation of this model based on historical data was therefore not possible for this study. In its stead the feasibility of key model outputs was investigated. The model’s evolution of power demand was compared to electricity demand projections based in the East African Power Master Plan (Uganda Investment Authority, 2005, p. 9). See Figure 17. Model results are shown to closely correspond to actual demand values up to 2005 and forecast values from 2010 – 2025. The numerical discrepancy may be attributed to the fact that the forecast was based among others on a rural electrification target of 10% coverage by 2010 which will not be realized seeing as it is currently at only 3%. Note that because system dynamics only seeks to predict dynamic implications of policy, not forecast the values of quantities at a given time in the future ("System Dynamics Methodology," 2009), data accuracy and precision are not the end goal but rather a well founded indication of the general expected behavior of the system.
Figure 17: Power demand ‐ Model vs actual & forecast results
From the findings of the validation tests performed on the model it can be concluded that the model results presented henceforth can be trusted for purposes of this study.
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4. Model Behavior Analysis
In this section, model results are analyzed to obtain answers to the following sub‐questions:
• How is Uganda’s on‐grid power access expected to evolve with the current market design and local conditions in Uganda?
• Is generation capacity development sufficient to meet Uganda’s power demand?
• What reasons within the model structure account for any shortfalls?
Results show the expected evolution of Uganda’s power system over a period of about 80 years. The results are determined primarily by the system structure in terms of the defined power sector policies on issues such as pricing, investment, etc.; the scenarios ‐ unpredictable exogenous factors such as fuel prices, population growth, economic development (GDP), etc.; and the defined system starting conditions.
4.1 The Base Case
4.1.1 Power Demand versus Supply Dynamics a. Power Supply
Error! Reference source not found. shows the simulated evolution of generation capacity over 80 years given Uganda’s capacity planning process. General trend shows approximate 20 year cycles. Contracted capacity is that amount that generators are obligated to invest in but has not yet been planned or installed. It is the additional capacity required to fulfill power demand over the period of the generator concession of 20 years. Planned capacity is that capacity under construction while installed capacity is the commissioned/online capacity.
Contracted capacity is mostly greater than installed capacity meaning that to meet power demand in the next 20 years requires an additional amount of capacity greater than existing installed capacity. The implication, at least in the first 40 years of the study and allowing for retiring capacity that keeps reducing installed capacity, is that power demand more than doubles every 20 years.
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Figure 18: Evolution of generation capacity ‐ Base case
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The big dip in contracted capacity after 2043 is attributed to a sharp decrease in the growth rate of power demand (Figure 19). Much less additional capacity on top of that already installed is needed to meet power demand over the next 20 years. The uncharacteristic break in the trend for the growth rate of power demand actually results in a period of surplus capacity. While the single buyer may have planned for new capacity expecting demand to grow along the dotted line, the subsequent reduction in demand instead means more capacity will have been contracted for than will be needed.
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Power supply mostly lags power demand (Figure 20). This is due to two factors: (1) The price elastic nature of demand coupled with uncertainty in generation costs (thermal fuel costs) makes accurate forecasting of demand 20 years in the future an impossible fit (2) Forecasts are made in such a way that errors are on the side of caution because the costs of surplus capacity are borne by the consumers in form of higher prices.
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Figure 20: Demand vs Supply ‐ Base Case Figure 21: Unmet demand ‐ Base case
Figure 21 shows the simulated development of unmet demand over a period of 80 years. Note that Unmet demand = Power deficit/Power demand. It is expected to get as high as 30% within the initial 10 years of the study. This is viable demand for which consumers are willing to pay the asking price but with no power available. The initial high levels of unmet demand are due to absence of capacity. In this period, the power system has only just changed from the previous government run monopoly and its
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associated shortages described in section 1.2 to the liberalized power sector. Contracted capacity arising from the concessioning agreements has not yet been planned or commissioned and yet demand is increasing. About 2010, the effect of new capacity starts to kick in accounting for the decline in unmet demand. Subsequent oscillations are the result of the 20 year generation capacity cycles.
b. Power demand
Results in the base case show initial exponential growth in power demand (Figure 22) which eventually tapers off increasing at a much slower pace after about 2043. The trend in power demand is influenced by the growth rate in the size of the consuming population as is evidenced in Figure 23 below whereby consuming population follows a trend similar to that of power demand.
Figure 22: Domestic Power Demand ‐ Base Case Figure 23: Consuming population ‐ Base Case
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The break in the trend of the consuming population about 2043 occurs because the unconnected population becomes less than the nominal target connection rate so that the unconnected population begins to be connected in real time. See Figure 24
On-Grid Connected vs Unconnected Population
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Figure 24: On‐grid connected vs unconnected population
c. Power service costs
Figure 25 shows the simulated evolution of the electricity price. Price initially constant shoots up around 2013. The sharp increase can be attributed to the fact that the more expensive thermal generation starts to become a more prominent means of generation. Hydro capacity can no longer satisfy demand at this point. Figure 26 shows the evolution of the generation mix over time with the thermal contribution beginning to increase sharply about the same time of 2013.
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Figure 25: Electricity price ‐ Base Case Figure 26: Generation mix ‐ Base case
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The correlation of electricity price with the generation mix i.e. spiking with the prominence of thermal generation suggests that the electricity price is highly dependent and vulnerable to changes in fuel price. Further exploration of this hypothesis by applying a scenario of oscillating fuel prices reveals that the electricity price indeed shows the same manner of oscillations as the fuel price (See Figure 27 and Figure 28).
Figure 27: Thermal fuel costs ‐ Base Case vs oscillating costs
Figure 28: Electricity price ‐ Base Case vs oscillating costs
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There is an additional upward trend in the oscillations of electricity price that is absent in the trend for fuel price. This can be attributed to the price elasticity of power demand so that as electricity price increases due to increase in fuel costs, its share in consumers’ expenses increases, causing a decline in the per capita demand. The decline in per capita demand shown in Figure 29 implies that all the power service costs transferred to the consumer are now spread over a lower amount of power units increasing the price per kWh of electricity. The two processes feedback onto each other ‐ as per capita demand decreases, electricity price increases but the vice versa is also true – as price increases above a threshold determined by the share of power expenses of consumers’ budgets, demand decreases. This accounts for why all inflection points in Figure 29 are matched by similar inflection points in the curve of electricity price ‐ Figure 30.
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Consumer Electricity Price
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Figure 30: Electricity price ‐ Base Case Figure 30: Electricity price ‐ Base Case Figure 29: Effect of power expenses on demand
4.1.2 Power access 4.1.2 Power access Section 1.1 defines power access as a physical feed‐connection to a central or decentralized power grid as well as sufficient electricity supply from that grid. The variables of interest in the analysis of how on‐grid power access is expected to evolve are: (1) Growth trend in the size of consumer population (2) the level of unmet demand
Section 1.1 defines power access as a physical feed‐connection to a central or decentralized power grid as well as sufficient electricity supply from that grid. The variables of interest in the analysis of how on‐grid power access is expected to evolve are: (1) Growth trend in the size of consumer population (2) the level of unmet demand
a. Growth trend in the size of consumer population a. Growth trend in the size of consumer population Figure 31 shows the simulated development of on‐grid power access as well as overall power access in Uganda. On‐grid power access is that percentage of the population within the grid covered area with a physical connection to the grid while overall power access is that percentage of the whole population with physical access to the grid. With a constant growth rate of 5.9% physical grid access is expected to peak around 2040 when all unconnected people in the grid covered area should have a physical grid connection. Figure 32 shows the rate of grid connection versus the unconnected population within the grid covered area.
Figure 31 shows the simulated development of on‐grid power access as well as overall power access in Uganda. On‐grid power access is that percentage of the population within the grid covered area with a physical connection to the grid while overall power access is that percentage of the whole population with physical access to the grid. With a constant growth rate of 5.9% physical grid access is expected to peak around 2040 when all unconnected people in the grid covered area should have a physical grid connection. Figure 32 shows the rate of grid connection versus the unconnected population within the grid covered area.
On-grid Vs Overall Electricity Access Connections Growth rate vs Unconnected Population
Figure 31: On‐grid vs Overall electricity access ‐ Base case Figure 31: On‐grid vs Overall electricity access ‐ Base case Figure 32: Connections growth rate vs Unconnected population – Base case
Figure 32: Connections growth rate vs Unconnected population – Base case
While 100% within the grid covered area should be physically connected, the quality of power service that they receive is important. Sections b below further explores this aspect of power access While 100% within the grid covered area should be physically connected, the quality of power service that they receive is important. Sections b below further explores this aspect of power access
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b. The level of existing but unmet power demand
Figure 21 shows the unmet demand going as high as 30% of total power demanded. This means consumers are only receiving 70% of their power needs. So while consumers may have grid access, it does not necessarily equate to full power access. This translates into power that is unreliable and of poor quality. High power demand combined with low available supply means the system is overloaded and such a system is more prone to unscheduled blackouts. From Figure 21 the problem is most severe in the initial 10 years and is not expected to go away soon. Power demand is growing much faster than the rate of capacity addition. This can be attributed to a very high population growth rate coupled with a constant connections growth rate that means new connections are continuously added irrespective of shortages in power supply.
One key question is whether this very high and sustained level of unmet demand is a feasible model outcome when compared to the reality. The situation as simulated is quite feasible given that already in 2007, there was an unprecedented electricity supply deficit on the national grid a maximum of about 165 MW (Ministry of Energy and Mineral Development, 2007, p. 27) which is the equivalent of 50% of unmet power demand going by an average daily power demand of 315MW (Uganda Investment Authority, 2005, p. 3). It was the expectation that a long awaited 250 MW hydro project at Bujagali would solve this problem. That project has been plagued by many problems however ‐ approved in 1999, construction was due to begin in 2003 but was delayed by protests by environmentalists and financial problems eventually getting a government go ahead in February 2005. For this study, that 250MW of hydro capacity is modeled as planned capacity in the initial year 2003 so that it fully goes online around 2010 accounting for the sharp decline in unmet demand at that time.
4.1.3 Critical Insights from the Model Results This section lists the most important issues to come out of the model analysis in the base case.
1. The power supply as simulated is insufficient to meet Uganda’s growing power demand in both the near and the midterm. From Figure 21 it is clear that simulated power supply does not catch up to power demand until about 40 years into the future. A key question is whether this is the reality or the result of the model structure. Note that this has to do with the nature of the capacity planning process – (1) the estimates made of future capacity requirements are conservative (linear as opposed to exponential forecasts) to reduce the risk of expensive surplus capacity (2) Uganda’s price elasticity of demand makes demand that much more unpredictable coupled with the uncertainty of oil prices which means price changes over 20 years are also unpredictable (3) Even if the estimates of power demand over the next 20 years were accurate, the freedom for generators to spread their investment over this period means capacity budgeted to meet demand in a specific period is still coming online at the end of it. The reality in Uganda is that generators like all investors invest for existing demand rather than the anticipated demand due to high financial risks of broken contracts, political instability, etc.
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2. There are undesirable oscillations in unmet power demand which indicate an unreliable power supply. The oscillations are arising from the cycles in power supply and since supply is lagging demand it is reflected as oscillating unmet demand. This is a serious problem in the quality of supply ‐ it may be more desirable for consumers to have a lower but stable power supply than higher but unstable/oscillating supply
3. The results highlight 2 major problems associated with power service costs: (1) In the event that electricity price is increasing while consumer incomes are stagnant and in the absence of a suitable price cap mechanism, the feedback between electricity price and power demand has the potential to drive price to unsustainably high levels (2) even if power supply is sufficient to meet power demand, high costs of power service will serve to drive per capita demand artificially low so that the real power demand will still not be fully satisfied.
4. Figure 26 shows the expected evolution of Uganda’s generation mix barring the introduction of viable new technologies in the mix. Oil (diesel) based thermal generation is generating about 80% of Uganda’s supply by 2043. The effect of thermal generation has a significant impact on power system costs, the resulting electricity prices and the evolution of Uganda’s power demand. As thermal generation becomes more dominant, the power sector is held hostage to the dynamics of the oil market which is not a safe place to be
4.1.4 Context of Access Results Access results of section 4.1.1 represent only that area of Uganda that is currently covered by the main power grid. They do not take into account a possible extension of the main grid and are only limited to extension via distribution connections. The reasons have been discussed in section 3.3.1. This means results exclude whatever impact a main grid extension would have had on power access levels over the 80 years under consideration.
Results represent a best case scenario given the existing power sector setup as modeled that assumes that investors will always be available and willing to invest every time the single buyer puts out bids for new generation or distribution concessions. Reality may obviously be different.
The effect of poor quality service in terms of high levels of unmet demand is not taken into account. In reality poor quality power service will drive consumers to alternative energy sources as is the case now in Uganda where most businesses relying on power have to run their own generators. For some the poor quality service may be too disruptive to business that the own generators are not temporary but permanent.
The effect of learning that would otherwise facilitate power service cost reductions over time has not been taken into account. In reality this effect should cause reductions in the electricity price which are important to the development of power demand. The study also does not take into account the emergence of cheaper and more efficient technologies over time.
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GDP per capita has been used in this study not only as an indicator for consumers’ purchasing power but also as a proxy for consumer economic welfare. It should be noted however that GDP per capita is not a direct measure of personal income and also, it does not take disparity in incomes between the rich and poor into account. This means GDP may increase while incomes for the majority of a country's citizens may even decrease or change disproportionally.
4.2 Model Sensitivity As an extension of model validity, model results are examined on the robustness of the assumptions made in setting up the model structure. This section explores the robustness of the model results when key assumptions are varied over a feasible range of values.
There are some relationships and parameters within the model that are both highly uncertain and likely to be influential. Sensitivity analysis is applied to determine how sensitive the model results are to changes in the values of these parameters. As mentioned in section 3.4, system dynamics rather than forecasting only seeks to predict dynamic implications of policy. This means data accuracy and precision are not the end goal but rather a well founded indication of the general expected behavior of the system. Interest is not so much on numerical sensitivity but rather on behavioral sensitivity of the model results. For the tests, uncertain parameters were varied evenly across an entire specified range of possible values over 60 simulation runs. The uncertain and influential parameters tested include:
• Market risk premium value used in the calculation of generator return on investment – The exact value of this parameter is unknown for existing concessions. Parameter was varied between 10% and 30% (modeled as 15% in the base case).
• Distribution Return on Investment used to calculate profit return for the distribution company – Same as for the market risk premium; varied between 10% and 30% (modeled as 15% in the base case)
• Generation efficiency – This value varies/fluctuates depending on many factors e.g. water levels for hydro generation, ageing of generation plants, amount of resource inputs, etc. It directly affects how much power is obtainable from a plant of fixed capacity. The parameter was varied between 50% and 90% for both hydro and thermal generation (modeled as 70% in the base case for both technologies).
• Capacity online lead time – This parameter represents the time it takes between planning a power plant in Uganda and commissioning it. As stated in section 2.1.1, the lead times in Uganda are significant and many times they tend to be infinite with about 1 in 3 projects eventually getting cancelled owing to the high levels of risk. Thermal capacity online lead time was varied between 1 and 6 years (base case is at 3 years) while hydro capacity online lead time was varied between 3 years and 10 years (base case is at 6 years)
• Basic consumption per capita – This is the minimum price inelastic amount of electricity assumed to be demanded by Uganda’s power consumers. The value used in this study is only an estimate value derived from average household consumption per month. Because of high
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income inequality in Uganda an average value for household consumption is far from representative of the population consumption patterns. The value of this parameter is varied between 50kWh and 500kWh (base case is at 288kWh) to take into account this uncertainty
• Power loss constant – This value has been consistently varying between 30 and 40% over the last 8‐10 years. It is varied across this range (base case is at 34%)
An examination of the effect of variations in these parameters over the specified range of values on two key variables – domestic power demand and unmet power demand shows that as expected changing the numerical value of the uncertain parameters makes a numerical difference in the model results observed (See Appendix C) with some variations resulting in greater or more significant numerical deviation than others. Generation efficiency turns out to be the most sensitive variable of those tested resulting in behavioral sensitivity in domestic power demand but not in unmet power demand. Figure 33 shows the total potential numerical and behavior range for these two variables when generation efficiency is varied across the specified range (50% ‐ 90%) over 60 simulation runs. Note that the red line represents the average, the upper boundary the maximum and the lower boundary the minimum value obtained from all the simulation runs. While the distribution of values for unmet demand is more uniform about the average, the variation yields a minimum value of 10TWh (~30TWh below the average) for domestic power demand.
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Figure 33: Sensitivity analysis results – Lower bound thermal generation efficiency of 50%
Given a constant energy input, a higher efficiency yields higher power output than a lower one. 90% efficiency yields substantially higher levels of power output than the base case which has efficiency of 70%. After 2043 when the growth in power demand is significantly reduced, this considerably higher output and its associated costs are being spread over a small power demand base increasing the unit price of power (Figure 34). Price elastic power demand shown in Figure 33 falls sharply as a result.
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Electricity Price
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Figure 34: Sensitivity of electricity price to changes in generation efficiency
The significance of the behavior sensitivity exhibited for generation efficiency on the model results is important. Generation efficiency has significant implications on the cost of power service and consequently the development of power demand. The uncertainty within the model results as a consequence of sensitivity to this and other parameters is discussed in section 4.2.1.
For the other uncertain variables tested apart from generation efficiency, although they induce substantial numerical sensitivity in the two criteria variables, when the uncertain parameters are altered individually they have only a negligible influence on their overall behavior (see Appendix C). Because the model purpose is not for prediction but more for explanation of system behavior, the model assumptions used can be regarded as fairly robust.
4.2.1 Model Uncertainty Of the two criteria variables under observation (power demand and unmet demand), only power demand shows behavioral sensitivity. Error! Reference source not found. shows the uncertainty bounds for power demand when all the uncertain parameters described in section 4.3 are varied together across their respective specified ranges over 60 simulation runs. Note that p percentile is that value of the variable below which p% of all possible variable values generated by the study will fall.
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Figure 35: Domestic power demand ‐ Uncertainty bounds
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Error! Reference source not found. shows the different ranges of uncertainty bounds for power demand. While the range of possible values between 25 – 75th percentile is only about 15 TWh, that between 0 ‐ 100th percentile is more than 3 times wider (about 50 TWh) indicating a very high level of both numerical and behavior uncertainty.
Figure 36 shows the uncertainty bounds for unmet power demand. The uncertainty in the results is wholly numerical. Behavior for all percentile ranges remains relatively consistent with the average trend.
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Figure 36: Unmet power demand ‐ Uncertainty bounds
As indicated in section 4.2, behavioral uncertainty is what is of significance in this study. There is substantial behavior uncertainty in power demand in the latter 40 years of the study. The study later presents an analysis of the robustness of designed corrective policies under different extreme scenarios that should take into account this high level of behavioral uncertainty.
4.3 Scenario Analysis In this section the access results are examined under different possible futures for Uganda and its power sector. Contextual scenarios on economy and demography as well as investment behavior are constructed and explored. Table 4 below highlights the contextual scenarios
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Table 4: Contextual scenarios
Context
Scenario Population GDP per Capita Fuel price
High economic development – Uganda experiences higher than expected economic development.
Low population growth
High GDP per capita
Low fuel prices
Low economic development –Uganda experiences lower than expected economic development.
High population growth
Low GDP per capita
High fuel prices
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Also, given Uganda’s high financing risk, the following investment scenario is explored which takes into account unfulfilled contracted capacity and longer than planned lead times to capacity commissioning.
Unfulfilled contracted capacity ‐ Uganda’s high financing risk means that almost 1 in 3 generation projects get cancelled in the planning phase so that they don’t get completed.
Longer than planned lead times – Generation projects in Uganda will in all likelihood take longer than planned. An example is the Bujagali project originally planned for commission in 2006 is still under construction expected completion 2010.
A combined scenario whereby only 70% of all contracted capacity gets to be commissioned and construction lead times are one and a half times longer than planned is analyzed to determine its effect on the system
4.3.1 High Economic Development Scenario As can be expected, the higher GDP per capita results in a higher power consumption per capita than the base case. See Figure 37. This increase is reflected in the increase in power demand shown in Figure 38.
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Domestic Power Demand
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Figure 37: Nominal consumption per capita ‐ Base case vs High economic scenario
Figure 38: Domestic power demand ‐ Base case vs High economic scenario
Note that the consuming population is lower in this scenario than in the base case. This means the increase in power demanded comes from the higher GDP per capita reflecting an increase in purchasing power of the population. A comparison of the base case and the scenario on the effect of power expenses on amount of power demanded shows that power demand is more inelastic in this scenario than in the base case (Figure 39). Changes in electricity price have a much lower effect on the amount of power demanded. This is because the increased income means that power expenses are now a smaller fraction of the consumers’ income per capita (Figure 40)
Figure 39: Effect of power expenses on demand – Base case vs High economic growth scenario
Figure 40: Ratio power expenses to income ‐ Base case vs High economic scenario
Multiplicative Effect of Power Expenses on Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
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Also, contributing to the inelasticity of demand in this scenario is the low cost of fuel which reflects in a lower electricity price. See Figure 41.
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Consumer Electricity Price
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
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Figure 41: Consumer electricity prices ‐ Base case vs High economic scenario
A look at unmet demand in this scenario shows that it is not significantly altered from the base case. See Figure 42. This means that the level of unmet demand has little to do with the level of power demand but rather a lot to do with the process of power supply – capacity planning.
Unmet Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.00
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Figure 42: Unmet demand ‐ Base case vs High economic scenario
4.3.2 Low Economic Development Scenario The higher fuel costs in this scenario contribute to the high electricity prices shown in Figure 43. The shock price rise and drop is due to dynamics in power demand. See Figure 44
Figure 43: Electricity price ‐ Low economic growth scenario Figure 44: Domestic demand ‐ Base case vs Low economic scenario
Consumer Electricity Price
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The dip in GDP per capita in this scenario coupled with increasing electricity prices due to high fuel costs make power demand much more price elastic than the base case. Consumer purchasing power is seriously constrained reducing power demand significantly from the base case (Figure 44). When power demand falls that sharply, power service costs are now being spread over a minimal amount of consumed power units making each power unit much more expensive. When demand begins to increase again, unit price begins to decrease.
As in the scenario for high economic growth, Figure 45 shows similar behavior for the unmet demand especially in the initial years when GDP per capita is still growing for the scenario and the base case.
Unmet Demand
Jan 01, 2003 Jan 01, 2023 Jan 01, 2043 Jan 01, 2063 Jan 01, 20830.00
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Figure 45: Unmet power demand – Base case vs Low economic growth scenario
4.3.3 Unfulfilled contracted capacity and longer lead times As can be expected from the high unmet demand in the base case, a scenario where only 70% of contracted capacity gets commissioned results in even higher levels of unmet power demand (Figure 46). Construction is taking longer than in the base case meaning that generation capacity in this scenario comes online later than in the base case ‐ the magnitude of the available power supply is displaced in time relative to that of the base case. The time displacement is clearly illustrated in Figure 47.
Figure 46: Unmet demand ‐ Unfulfilled contracted capacity and longer lead times scenario
Figure 47: Unmet power demand – Base case vs Low economic growth scenario
Unmet Demand
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Jan 01, 2003 Jan 01, 2063
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4.3.4 Summary of Scenario Analysis In this section key findings from the scenario analysis are highlighted:
• Power access levels are the same in all three contextual scenarios because of the policy of arbitrary target connection rates irrespective of generation side conditions i.e. whether there is available capacity to meet the demand from new connections. If policy was responsive to conditions in the power sector we should expect to see a faster connection rate and grid access in the high economic development scenario than in the base case and low economic development scenarios
• The simulated development of power supply is insufficient to meet Uganda’s power demand in the first 40 years of the study in all investigated scenarios
• The level of unmet demand in the initial 40 years is mostly consistent with the base case for the scenarios investigated. This is because behavior trend of power demand over these years within all these scenarios is similarly consistent. The divergence in the behavior of power demand from the base case occurs in the latter 40 years of the study for the low and high economic development scenarios. Over this period the increase in demand in the high economic development scenario is met by the surplus capacity that existed in the base case due to the sharp decline in power demand growth. The sharp drop in demand in the low economic development scenario results in even higher amounts of surplus capacity for this scenario than the base case.
The scenarios provide different contexts in which designed policies can be plugged and their effect within these different situations analyzed to determine whether they are robust (effective in a variety of possible scenarios) or not.
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5. Policy Design and Analysis
There are three major problems that have come out of this study: (1) a persistent unmet power demand. This problem is the most pressing in the near term considering that the simulated level of unmet demand gets to as high as 40% in the coming 3‐5 years. (2) an oscillating and unreliable power supply (3) high power service costs. This chapter presents options for the solution of the first two within the existing policy framework and solutions to the latter from outside the existing policy framework
5.1 Impact of Power Losses on System Performance The model assumes a base case loss of 34% of generated power all of which is commercial in nature. The losses mostly occur as a result of theft or illegal connections which go unchecked owing to the high levels of corruption by the service employees. Note that because these are almost wholly commercial losses, they are in principle, the easiest type of losses to reduce, since reduction can be achieved with relatively low capital investment. A power tariff study done for Uganda finds that in developing countries, including well managed utilities, non technical losses tend to be in the minimum range of 2‐3 per cent (ECON Centre for Economic Analysis, 2001a, p. 18). This means that much lower power loss levels are achievable.
The effect of an incremental targeted loss reduction schedule over time is investigated. Figure 48 shows a possible targeted loss reduction schedule from 34% to 4% over 10 years. It results in a substantial reduction in unmet demand cutting the simulated peak in unmet demand in the initial years from 30% to about 25% (Figure 49).
Figure 48: Phased targeted power loss reduction Figure 49: Effect of loss reduction on unmet demand
Phased Targeted Loss Constant
Jan 01, 2003 Jan 01, 2023 Jan 01, 2043 Jan 01, 2063 Jan 01, 2083
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This policy option does nothing for the problem of oscillations in unmet power demand. It has a contribution to the problem of high power service costs however as costs previously expended on power losses are now spent on meeting power demand. With consumers covering a much lower cost of power losses, they pay much less for power compared to the base case. Figure 50 shows the electricity
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price in the two cases (Reference – Base case, Current – Lower power losses). The lower prices result in increase in power demand (Figure 51).
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Figure 50: Electricity price – Base case vs lower power losses
Figure 51: Effect of loss reduction on power demand
The dip in electricity price in the initial 20 years of the study is attributed to the dominance of cheap hydro generation for a comparatively longer time than in the base case. See Figure 52 (* represents the base case). With cheap hydro whose costs are more or less constant, the unit cost of power reduces as power demand increases. Price only begins to increase when thermal capacity becomes the dominant technology
Hydro Vs Thermal fraction of Gen Mix
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.0
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Figure 52: Effect of power loss policy on the generation mix
The higher electricity price with higher losses is because of Uganda’s power sector policy which stipulates that most of the cost of losses is transferred to consumers. Even with set loss targets for the distributor so that they are forced to absorb some of the loss costs, the targets are not set high enough due to lack of negotiating power on the part of the regulator. A case in point is the fact that the agreed upon loss target for the distributor for 2001 was set at 35 per cent of energy generated which fraction was considerably higher than losses in 2000 (ECON Centre for Economic Analysis, 2001a, p. 18).
Important to keep in mind in the design of the power loss policy: (1) There is an important trade‐off for the government and regulator – in Uganda there are huge commercial losses arising from illegal
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connections to domestic consumers but they are only a fraction of the losses from industrial consumers whose share of the power demand is about 2/3 and increasing (Electricity Regulatory Authority, 2007, p. 13). Industrial power theft is mostly achieved through by‐passing the utility’s meters. Although tariffs for this sector are subsidized, they are still very high cutting into big parts of businesses’ profits so a power loss policy is bound to have some implication on Uganda’s economy. (2) Any measures to reduce commercial losses in Uganda will likely require substantial investment in more modern metering equipment as existing ones are too rudimentary for Uganda’s generally poor but street‐smart population.
5.1.1 Robustness of Power Loss Policy The robustness of this policy is analyzed to determine its effectiveness under the three different possible futures described in section 4.3 i.e. high economic development scenario, low economic development scenario and unfulfilled contracted capacity and longer lead times scenario. Figures below show the effect of the designed power loss policy on unmet power demand on these respective scenarios. The designed policy can be regarded as robust ‐ effectively reduces the level of unmet demand in all the analyzed possible futures.
Figure 53: Effect of loss policy in base case scenario
Figure 54: Effect of loss policy in high economic development scenario
Figure 55: Effect of loss policy in low economic development scenario
Figure 56: Effect of loss policy in unfulfilled capacity and longer lead times scenario
Unmet Demand
Jan 01, 2003 Jan 01, 2023 Jan 01, 2043 Jan 01, 2063 Jan 01, 20830.00
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5.2 Impact of Upfront Capacity Investment versus Spread Investment One of the reasons for the persistent power deficit is the fact that planned capacity in the base case is always lagging power demand. While the regulator plans for capacity over a 20 year period, investors mostly have the freedom to put off meeting their investment obligations as long as possible within these 20 years. The study explores impact of spreading the investment obligation of the generators over 20 years versus a more upfront/front‐end investment obligation. A comparison is made between the base case where the investment obligation is spread over 20 years (Case A) to one where it is front‐ended in the first 10 years of the concession period (Case B).
Figure 57 shows the level of unmet power demand in the two cases. Case B (Current run) has lower levels of unmet power demand than the reference case. This can be attributed to the fact that obligatory investment in all contracted capacity in the first 10 years means that existing power demand is met sooner than in the base case which by comparison has investors still fulfilling residual contractual obligations at the end of the 20 years. In essence the policy reduces the time lag between power supply and power demand.
Unmet Demand
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Figure 57: Effect of front end investment on unmet demand
While the policy reduces unmet demand it does not solve the problem of oscillating unmet demand. It however has a positive impact on reducing power service costs resulting in generally lower electricity prices than in the base case (Figure 58). It results in availability of needed capacity sooner rather than later accounting for the higher levels of installed capacity in the initial 40 years for the policy as compared to the base case (Figure 59).
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Figure 58: Effect of upfront capacity investment on electricity price
Figure 59: Effect of upfront capacity investment on installed capacity
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What is specifically important for costs is that hydro capacity rather than being contracted and unused for extended periods of time, the policy ensures that it gets constructed faster than in the base case, put in use and allowed to retire freeing up that hydro potential for re‐use. The implication is that more of the cheap hydro potential is utilized for the policy than in the base case. The base case ends up relying on more expensive thermal capacity because for a long time a lot of hydro potential is tied up (contracted) but not in use. See Figure 60 (* refers to the base case).
Hydro Vs Thermal fraction of Gen Mix
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Figure 60: Effect of upfront capacity investment on the generation mix
An examination of the effect of this policy in all the possible futures described in section 4.3 shows that as is the case for the loss policy in section 5.1.1 it is robust reducing the level of unmet demand in all the analyzed scenarios.
5.3 Impact of Grid Development Policy on Unmet Demand The nature of power access in Uganda where unmet demand levels are consistently high seems to be a result of competing interests on the part of the regulator. On the one hand there is the primary interest of supplying sufficient power to meet consumers’ total demand and on the other, another key interest in ramping up the speed of power access in terms of grid connections. In terms of the policy framework however, there is no feedback between the level of unmet demand and the speed of connections. The
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latter interest accounts for the policy of arbitrary target connection rates irrespective of generation side conditions resulting in high unreliability of power supply for connected consumers.
Key question then becomes how to balance out these competing interests in such a way that ensures that grid development (focus on distribution connections) and the corresponding growth in grid access is in synch with growing electricity demand and the required growth in electricity supply. The obvious solution here is to incorporate the set grid access targets into the capacity planning process as is already being done but given the uncertainties in demand forecasts associated with the thermal fuel oil costs and the price elasticity of demand arising as well as the fact that forecasts are done over a period of 20 years, under/over demand estimates are still highly bound to occur. The question is: Can the grid connections policy be flexible enough to allow for adjustments in face of different conditions allowing for a lower connection rate in case of higher than expected demand and consequently deficits or a higher rate in case of lower than expected demand and consequently surplus capacity? This would most especially be useful in the initial 40 years where the levels of unmet demand are unusually high
An option is presented here which allows for a certain level of flexibility in the connections growth rate. The policy utilizes a minimum acceptable level of power service quality e.g. 20% unmet demand and in order not to exceed this minimum, no additional connections are made to the grid if the unmet demand is projected to exceed 20%. If the projected unmet demand is less than 20%, then the nominal target connection rate applies. Note that because the capacity addition process is demand driven and this policy would limit demand growth, the incentive to add new capacity would now be limited by the artificially low demand to values just enough to keep deficits below 20%. The policy must keep track of what the unconstrained demand would have been and this is the value to be used in planning for future required capacity.
Figure 61: Unmet demand – Base case vs flexible connection rate
Figure 62: Electricity access – Base case vs flexible connection rate
Unmet Demand
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The impact of this policy on the level of unmet demand is shown in Figure 61. The initial high unmet demand of up to 30% is now capped at 20%. The policy also deals with the oscillations in unmet demand reducing their frequency and amplitude which means higher reliability of service for the consuming population. Figure 62 compares the growth in power access levels for the two cases i.e. the base case (Reference) versus case of flexible connection growth rate (Current). As expected, growth in electricity access is much lower in this case but this trade off has to be weighed against the higher reliability in power service for the existing consumers
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On the issue of power service costs the policy results has a negligible impact on electricity prices (Figure 63) for the lower number of connected consumers (Figure 64). This is because the drop in consuming population is small and power supply is maintained at the same levels as the base case. This means that with this policy the spike in unmet demand of 30% in the initial 10 years of the study can be avoided providing consumers with more reliable power supply at approximately similar cost.
Figure 63: Effect of grid connection policy on electricity price
Figure 64: Effect of grid connection policy on consuming population
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An examination of the effect of this policy in all the possible futures described in section 4.3 shows that as in the base case, it limits the level of unmet demand to 20% and below and reduces the frequency and amplitude of oscillations in unmet demand making power supply much more reliable for consumers. It is therefore a robust policy.
5.4 Combination of Policies 5.1 – 5.3 A combination of the three corrective policies above i.e. targeted power loss reduction, obligated front‐end investment in the first 10 years of the concession and employing a connection growth rate that is responsive to the generation side is investigated to determine their combined impact on unmet power demand. Figure 65 shows that the combination yields significantly lower levels of unmet demand while the oscillations in unmet power demand persist
Unmet Demand
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Figure 65: Unmet demand – Base case vs combination of corrective policies
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The combination of policies results in substantial savings in power service costs. The electricity price is reduced by more than 30% compared the base case (Figure 66). Especially in the first 20 years of the study electricity price decreases over time. This is attributed to comparably lower levels of expensive thermal capacity than in the base case (Figure 67). The costs of the dominant hydro capacity in those 20 years keep on decreasing as consumers and power demand increases so that cost is spread over more power units. After 20 years thermal capacity begins to increase sharply accounting for the subsequent price increase
Figure 66: Effect of policies combination on electricity price
Figure 67: Effect of policies combination on thermal capacity
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5.5 Impact of Upfront Investment Obligation To address the cycles in power supply there is need to eliminate the time between contracting for the capacity and the start of construction of that capacity so that successful bidders for generation concessions fulfill all their obligated investment for the 20 years upfront – not over 20 years as in the base case and not over the first 10 years as in the policy of section 5.1.1. This means required capacity is constructed as soon as it is contracted for with minimal delay. Figure 68 shows the behavior of unmet power demand with this policy applied from 2003 to 2083. It is substantially reduced from the base case and the pronounced cycles in the base case are replaced by slight ripples implying a more reliable power supply. Figure 69 shows the simulated path of unmet demand when the policy is applied starting in 2010
Figure 68: Unmet demand – Base case vs upfront investment (2003‐2083)
Figure 69: Unmet demand – Base case vs upfront investment (2010‐2083)
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The constraint with this policy is to do with financial resources and financial risk. For the regulator to convince private investors to invest in big chunks of generation capacity at one go is a hard sale. This is because of the high financial risk in Uganda having to do with market volatility (volatility of oil prices), institutional stability and rule of law guaranteeing that agreements will be honored (uncertain), nature of power demand (fluctuating), etc. To get willing investors would require even higher rates of return on investment which consumers will be hard pressed to afford.
The effect of the policy on unmet power demand is uniform in all the scenarios described in section 4.4 – the level of unmet demand is reduced and the oscillations in unmet demand reduced to slight ripples. The policy is shown to reduce power service costs by reducing the amount of expensive thermal generation compared to the base case (‘*Thermal fraction’ in Figure 70). This reduction in thermal generation yields a substantial decrease in consumer electricity price (Figure 71)
Figure 70: Effect of policy combination on generation mix
Figure 71: Effect of policy combination on electricity price
Hydro Vs Thermal fraction of Gen Mix
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5.6 Combination of Policies 5.4 & 5.5 When the policy of upfront investment obligation n the first year of the concession is combined with the designed policies of section 5.4, the result is a system with the lowest levels of unmet demand compared to the results from the individual policy designs presented in the study and more reliable power supply. See Figure 72. This combo of policy revisions presents a robust solution to the main problems identified in this study ‐ the very high levels of unmet demand in the initial 40 years of the study as well as the unreliability in demand brought about by the cycles in power supply. This solution is good for the short, mid and long term of Uganda’s power sector.
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Figure 72: Effect of policy mix design on power system
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The effect of the policy combination on power service costs is similar to that of the policies combination discussed in section 5.4 Electricity price is lower than in the base case by more than 30% due to comparatively lower levels of expensive thermal capacity than in the base case. Note that both power demand and supply are higher with the policy combination than the base case. Figure 73 below shows these results
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Figure 73: Effect of policy combination on power service costs
5.7 Insights on the Devised Model Based Policies The following highlights are derived from the devised model based policies:
• The policies discussed in 5.1 – 5.4 of targeted power loss reduction, upfront capacity investment in the first 10 years of the concession, a grid development policy responsive to the power deficit and taking expected losses into account in the capacity planning process all individually yield significant reductions in the level of unmet demand but they do nothing to solve oscillations in unmet power demand.
• The policy described in section 5.6 of upfront obligated investment in the first year of the concession not only reduces the level of unmet power demand but also removes oscillations in unmet demand making power supply much more reliable.
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• The combination of these policies is a multi‐pronged approach that yields the best results with the lowest levels of unmet demand and a substantial reduction in the amplitude of oscillations in unmet demand.
• For all the policies presented, there is still a residual level of unmet demand and cases of surplus capacity (assumed to be exported). Surplus capacity is unplanned excess generation capacity; in the event that there are fixed export agreements that do not allow for export of this capacity, it becomes a big problem. Persistent unmet demand and unusually long periods of surplus capacity are the direct result of a capacity planning process that responds too slowly to changes in power demand. None of the designed policies have been able to fix this problem. To a big extent this problem is associated with the very nature of Uganda’s electricity market which is very highly regulated (regulator approves prices, contracts for required capacity, etc.). While a more perfect market with more competition is more likely to respond faster to changes in power demand, the present structure which involves contracts, guarantees against losses for investors etc. cannot be as flexible. Note that this is a necessary evil since the market is not developed enough to attract serious investors without external government incentives. As the market evolves and matures enough to reduce the need for government incentives and interventions, a gradual evolution to competition within the sector is bound to solve this problem i.e. in a perfect market the surplus capacity would not be the problem of the consumer and viable unmet demand presents opportunities for higher profits
5.8 Policies beyond the Model Uganda’s main problems lie with a generally poor population which therefore requires low cost power service in order to increase affordability. Delivering low cost power service is especially a challenge in Uganda given the high market risks facing investors described in section 2.1 as well as Uganda’s current lack of cheap primary energy resources in form of coal, gas, etc as well as cheap hydro potential once that potential gets used up.
The measures are presented as solutions to two dilemmas: (1) how to ensure growth in low cost generation capacity sufficient to meet demand (2) how to ensure a ramp up of electricity access at minimum cost
Uganda’s power service costs according to the model are shown to increase over time as thermal generation gains prominence as a generation technology. This is one area where Uganda can cut costs – finding cheaper alternative generation technology options which in combination or standalone are big enough to take the place of oil fuel based thermal generation and could allow for significant cost reductions facilitating lower consumer prices and faster growth in electricity access.
Another source of high power service costs lies with the extremely high rates of return on investment required to attract private investors into Uganda’s power sector. This section presents options for cheaper alternative generation technologies and possibilities to attract private investors to invest at
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much cheaper rates of return that may help to alleviate Uganda’s current cost related problems and offset expected future problems. Note that because of the limited time available for this study the necessary research – data gathering, feasibility analysis and modeling for these policies is left out of the scope of this study and is recommended as future research
5.8.1 Options for Alternative Energy Technologies Uganda does not possess primary high value natural energy resources. The limited resources it has have been described in section 1.1. A least cost strategy begins with full utilization of what is available. Apart from hydro, Uganda’s next high value energy resources are biomass, geothermal and solar energy with estimated electrical potential of 14,454GWh/yr, 3,942GWh/yr and 1,750GWh/yr respectively. Combined these should offset the need for expensive thermal generation except as a backup option. This section expounds on possible use of one of these alternative energy resources ‐ cheap solar energy
Solar Energy Option
In exploring the solar energy option it is important to understand why it hasn’t taken off in Uganda. Many pilot projects have started and eventually fizzled out in an effort to encourage the use of solar generation on a small and individual consumer scale. Push has been for application of solar generation at consumer premises. It has failed because of the high initial installation costs that make it unaffordable to the majority of the population but also because of the low value output that precludes the use of higher wattage consuming applications.
Solar energy is cheap and although Uganda’s electrical potential is only 1750GWh/yr, one could argue that this potential is only increasing with Uganda’s changing weather patterns of longer and longer droughts. If solar generation was developed on a larger scale in Uganda it has the potential to become a viable generation technology. Right now the government is effectively owner and financier of the existing thermal generation installations on top of subsidizing electricity for all consumer sectors in Uganda to the tune of billions of shillings a year. Going by the long term view, these resources may be more effectively utilized towards the use of Uganda’s solar energy in terms of large scale, high capacity solar generation output plants.
By using Concentrated Solar Power Technologies (CSP), the government can harness a cheap energy resource and provide cheap electricity to its citizens. Concentrating solar power (CSP) technologies use mirrors to reflect and concentrate sunlight onto receivers that collect the solar energy and convert it to heat. This thermal energy can then be used to produce electricity via a steam turbine or heat engine driving a generator (U.S. Department of Energy, 2008). Operational details and costs related to CSP are out of the scope of this study but many advantages make it a viable option for further investigation and research including: (1) Guaranteed low fuel costs definitely cheaper than for diesel thermal generation (2) Power service costs decrease with time as opposed to the case of diesel thermal generation where they can only increase (3) The technology provides a way to provide much needed distributed generation for those areas with plenty of solar resource but no grid access especially in Northern
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Uganda (see Figure 5 for an indication of extent of Uganda’s power grid) (4) It is a cost‐competitive option for providing clean, renewable energy
The last advantage becomes very important in the attraction private investors especially from emission credits seeking investors as further discussed below
5.8.2 Attraction of Private Investors In this section a brief look at the Clean Development Mechanism (CDM) and the development options it may present to a developing country like Uganda.
CDM is a Kyoto mechanism for the reduction of emissions of carbon dioxide and other greenhouse gases. The underlying principle is based on the concept that a ton of carbon dioxide produced anywhere on earth has the same degradation impact on the environment but because the cost of mitigating the production of these emissions varies depending on which part of the world you are in, countries can collaborate so that those unable to achieve their emission targets at home could go abroad and earn the shortfalls in their targets (Sebitosi & Pillay, 2005, p. 273). Foreign investors can earn certifiable emission credits (CERS) by investing in projects that mitigate the mentioned emissions. This way CDM presents an opportunity for an African country like Uganda to attract new financing from those countries seeking to meet their emission targets for development of its power sector.
The constraints:
The CDM market so far has developed towards a focus on only a few project types and in a limited number of host countries one of which Uganda is currently not – findings show that the general attractiveness of African host countries is quite low with the exception of South Africa (Jung, 2005, p. 1).
How does Uganda salvage the situation?
Uganda can only benefit from this program if its government can argue its case before the UN as they only recognize sovereign states as legitimate entities. Serious lobbying efforts on the part of the government with other sub‐Saharan governments for more ranking points for their respective countries in the CDM program may yield substantial benefits for these countries in terms of financing for a renewable energy based power industry.
5.8.3 A regional based solution From Table 1 Uganda has a total primary electrical potential of only 5300MW. Given the pace of population growth and the resulting speed in power demand growth, it is clear Uganda cannot sustainably rely solely on its energy resources. It must turn to its rather richly endowed neighbors.
The Democratic Republic of Congo to the west has enough energy potential to support the whole of East and Southern Africa. See Table 6 below for an indication of Congo’s energy potential.
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Table 5: Summary of the exploitable energy potential of the DRC. Sourced from (Perez, Nkanka, Ngulumingi, Gimeno, & Kazadi, 2005, p. 279)
RESOURCES EXPLOITABLE POTENTIAL
Hydro-electrics 774.000 GWh/year (±100.000 MW)
Forestry 122 million hectare, 8’3 thousand million tep
Oil (oilfield, Atlantic coast) 230 million barrels
Coal (Katanga) 720 million tons, of which 88 million recoverable
Methane gas (Lago Kivu) 50 thousand million m3
Uranium (Katanga) Not estimated
Tanzania to the south of Uganda though not so similarly endowed has coal deposits at Mchuchuma with potential to generate 400MW for up to 40 years as well as natural gas estimated at about 30 billion cubic meters at Songo Songo (Otieno & Awange, 2006, p. 78)
One option for Uganda is to import primary high value natural resources e.g. coal. A cost benefit analysis of power generation using imported coal versus using imported diesel needs to be done to determine the least cost option in the long term.
The other option is to consider a regional based solution. All African countries in this region (East Africa) face the same power issues as Uganda the most important being lack of the necessary financial resources to exploit their natural resources. Especially considering the immense hydro potential of Congo, a regional solution where the different countries pool their financial resources and invest in one or two large regional plants could supply power more cheaply and efficiently than dozens of smaller ones. Uganda especially without significant natural resources should be more highly invested in such a solution. Essentially different countries in the region co‐invest and share the power output through a regulated exchange similar to the Southern Africa Power Pool (SAPP) and the West Africa Power Pool (WAPP). This should create more robust regional power grids to Uganda’s benefit, lowering capital investment requirements per country and reducing system operational costs (World Bank Energy Transport and Water Department, 2005, p. 2).
There are constraints however: Political instability in the region makes pooling resources for an investment in another country an unattractive option because it presents a financial risk as well as a security risk whereby a country’s power system is under foreign control. Still, there are different extents to which countries can participate in these power pools. Uganda could for example join the SAPP as a buyer rather than a co‐investor or seller. This is a more immediate viable option considering her neighbors Tanzania and Congo are also part of this same power pool.
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6. Conclusions
6.1 Research Findings This study has been focused on obtaining answers to the following research questions:
• How is Uganda’s on‐grid power access expected to evolve with the current market design and local conditions in Uganda? Is the expected generation capacity development as determined by the existing capacity planning process sufficient to meet Uganda’s power demand?
• What reasons within the market or institutional structure account for any shortfalls?
• What would therefore be needed to ensure that growth in generation capacity is sufficient to meet Uganda’s growing electricity demand?
The study has found that the simulated development of power supply as determined by the existing market design and capacity planning process is insufficient to meet Uganda’s growing power demand in the initial 40 years of the study in all the investigated scenarios low, medium and high economic development. The simulated unmet power demand gets to as high as 30% within the next 5‐10 years. The power supply is found to be not only insufficient but also unreliable characterized by big oscillations in the level of unmet power demand. The low development scenario especially presents other challenges apart from high levels of unmet demand – stagnation or declining GDP per capita result in corresponding changes in per capita power demand. Declining power demand exacerbated by high fuel prices is shown in the model to lead to big spikes in consumers’ electricity price.
The problems of unmet demand and high power service costs have been found to be the direct result of the capacity planning process which is mostly lagging power demand to some extent by design (deliberately conservative projections of required future capacity to reduce the chance of expensive surplus capacity) and also due to a lack of sufficient negotiating power on the part of the regulator/single buyer to enforce timely capacity investments at reasonable rates of return on investment.
The study suggests an array of power sector policy improvements to solve these problems including:
• A gradual targeted reduction in power sector losses especially focused on commercial losses whose reduction can be achieved with relatively low capital investment
• A mandatory obligation for potential generators to invest in contracted capacity upfront at the beginning of the concession period
• A coupling of the power generation side and the distribution side in the planning process so that the consumer connection rate takes into account the level of the power deficit
Individually each policy revision has been shown to contribute to a certain extent to the solution of unmet power demand, oscillations in unmet demand and high power service costs but it’s the combination of all the suggested policy revisions that yields the most impact. It results in the lowest
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level of unmet power demand compared to the individual policy revisions and it also manages to achieve the lowest power service costs. What all the designed policy revisions fail to do is to alter the capacity planning process enough to allow for a much faster response to changes in power demand – all policies still leave a residual level of unmet demand and also have instances of unusually long periods of surplus capacity. This problem has been attributed to the absence of perfect market and competition conditions in Uganda’s power sector. As the market evolves and matures enough to reduce the need for government incentives and interventions, it is expected that a gradual evolution to competition within the sector is bound to solve this problem
6.1.1 Lessons/Insights on System Dynamics Methodology Over the course of this study the following insights have been obtained in the application of system dynamics methodology:
• It is a fine balancing act that emphasizes the need to keep the model small so that it is manageable and understandable to the target audience but also with the needed detail to be able to properly capture the dynamics of the system at the level of detail required by the model function.
• In the application of system dynamics to develop a theory, the mathematical underpinning needed for computer simulation requires that the theory be precise ("System Dynamics Methodology," 2009). Reality though is not that precise. There are significant limits to the certainty that can be applied to certain soft variables, effects e.g. ‘Effect of power expenses on power demand’ and to some important causal relationships e.g. to what extent GDP per capita influences electricity consumption per capita.
• System dynamics methodology requires extensive factual data (historical) on the system under investigation and in the absence of such data as was the case for this study the modeler and/or the client has to exercise their personal judgment in estimating what the missing values might be. This leaves the outcomes susceptible to the modeler’s bias. Two different modelers are quite likely to come up with completely different model outcomes of the same system.
The insights above ought to be kept in mind in the interpretation and use of the results from this study.
6.2 Reflection beyond the Model The reality that is Uganda’s power sector is very complex. The system dynamics model developed presents a highly reduced and simplified reality; just enough to provide a working theory on the most crucial mechanisms driving the power sector. Exogenous variables in the study are assumed either to be constant and not impacting the system’s dynamic behavior or not significantly affecting a particular policy being investigated. In this section a reflection of some of the complex realities simplified in this study:
The scope of the study excludes a detailed analysis of the evolution and development of Uganda’s GDP over time. Rather GDP is treated as an exogenous variable impacting the power system (influencing the
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development of power demand) but itself developing independent of the power system. There is a reality not explicitly addressed on the influence of the power sector on development of GDP. Power availability fuels a country’s productivity and consequently its development and that of its citizens. This poses an open question therefore about how much GDP per capita is expected to grow and the consequent growth in power demand given Uganda’s high power deficits as demonstrated in the study. In a way a question of chicken and egg – with such high levels of projected power deficits the chances that Uganda’s economy continues to grow are slim; on the other hand, the increasing power deficit could as well be the bi‐product of accelerated economic growth with a non energy intensive based economy as Uganda’s is (agricultural based). The base case is based on the latter assumption and the former is explored in the ‘Low economic growth’ scenario of section 4.3.2 which explores the development of the power sector in the event of a stagnant and even falling level of GDP.
For this study the power losses are modeled as exogenous to the power system influencing but not influenced by other variables in the system. An average value of power loss is applied for the sole purpose of determining its impact on the performance of the system. The complex reality is that Uganda’s commercial power losses are influenced by variables within the power system. Electricity price increments tend to be followed by a drop in the power demand especially for the domestic and commercial consumers whose demand is more price elastic and an approximate proportional increase in commercial power losses. One could also argue to some extent that the more grid connections made the less will be the illegal connections; although the fact that most of those illegal connections are made to temporary structures which would never have qualified for a legal connection anyway may negate this argument. These endogenous relationships should alter the projected development of power losses and their impact on the system although the extent of the change is uncertain. For the purpose of clarifying and providing understanding of the basic mechanisms driving Uganda’s power sector, this study assumes these changes are not significant enough to substantially alter the resulting power system trajectory.
The study assumes a basic minimum electricity consumption per capita for all grid connected consumers that is price inelastic so connected consumers are always demanding some level of power however expensive power gets. The assumption is an abstraction of a complex reality of income inequality whereby even though power may be too expensive for some to afford completely, there is always a part of the population that can afford it albeit at lower levels of consumption. Because the study relies on a uniform average GDP per capita for the whole population this is expressed as a minimum level of power demand for all the connected population.
The factors discussed highlight discrepancies between the complex reality that is Uganda’s power sector and a reduced simplified representation of it that is the developed system dynamics model. The abstraction was made with the purpose of this overall study in mind. Another study purpose may require a different level of abstraction and simplification
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6.3 Limitations of this study A comprehensive study of Uganda’s entire power sector requires broader scope than has been explored here. Due to time constraints, this study has been limited in scope focusing on power supply versus demand dynamics for only that part of Uganda covered by the transmission grid
• The study does not deal explicitly with the link between technical system performance in terms of level of power output and the economic performance in terms of single buyer and investor profits/losses and its resulting impact on future investment decisions. It relies rather on the guaranteed return on investment to signal a level of profitability high enough for the investors to re‐invest. The study has similarly not explored or taken into account the impact of external capital sources e.g. World Bank funding and financial guarantees, development partnerships with developed countries, etc on the development of the power sector. These might have the potential effect of lowering the return on investment demanded by foreign investors, extending coverage through low interest rate government grants, etc.
• The model assumes a uniform market structure for the 80 years under study – a highly regulated market where the regulators approves electricity prices, contracts for new capacity from investors, determines an appropriate connection rate, etc. This is the current setup owing to the yet undeveloped power market in Uganda; the market cannot realistically exist without government incentives and interventions. But over time this should change as the economy grows, power demand increases, and the market matures. Then there is potential for other kinds of markets e.g. perfect competition and the benefits that come with these which would have a huge influence on the overall dynamics of the power sector.
• The effect of learning that would otherwise facilitate power service cost reductions over time has not been taken into account. In reality this effect should cause reductions in the electricity price which are important to the development of power demand. The study also does not take into account the emergence of cheaper and more efficient technologies over time. These emerging technologies have the potential to significantly alter the projected trajectories of power service costs, level of power supply and ultimately electricity access
6.4 Areas of Further Research The system dynamics model developed in this study forms a potential starting point for a more detailed generic model of key mechanisms within Uganda’s power sector than has previously been possible. An important advantage of system dynamics models is that they can easily be extended or revised to address additional questions as they arise ("System Dynamics Methodology," 2009). By addition of more detail to the model, it can be used to contribute to and inform the planning process for generation,
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distribution and transmission capacity. The steps to be taken in further refinement and use of the system dynamics model include:
• Extending the model to take into account evolution and development of power service beyond the existing grid covered area
• Extending the model functionality to present a black box model that allows for adjustment of policy variables e.g. the level of government subsidy in a form of decision aid tool for Uganda’s power sector planners to improve their decision making
Apart from the system dynamics methodology, further research is needed in the following areas:
• The determination of the fastest and least cost way to develop the transmission network in order to ramp up Uganda’s electricity access; probably employing optimization techniques
• The feasibility or cost benefit analysis of the proposed solutions given in chapter 5. Detailed feasibility and cost benefit analyses are a necessary step to determine the fit of those solutions to local conditions in Uganda
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7. References
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Development Data Group (2008). 2008 World Development Indicators Online. Washington, DC: The World Bank.
ECON Centre for Economic Analysis (2001a). Cost structure and tariff study for Uganda: Electricity Regulatory Authority.
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Electricity Regulatory Authority (2007). ELECTRICITY SECTOR PERFORMANCE REPORT: Electricity Regulatory Authority, Uganda.
Electricity Regulatory Authority (2008a). Bulk Supply Tariffs (BST) Retrieved June 18, 2009, 2009, from http://www.era.or.ug/TransmissionTariffs.php
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Karekezi, S., & Kimani, J. (2004). Have power sector reforms increased access to electricity among the poor in East Africa? Energy for Sustainable Development, 8(4), 10‐25.
Ministry of Energy and Mineral Development (2001). Rural Electrification Strategy and Plan Covering the Period 2001 to 2010. from www.energyinstug.org/component/option,com_vfm/Itemid,71/do,download/file,Others%7CRural+E+Plan.pdf/.
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Ministry of Energy and Mineral Development (2004/5‐2007/8). Poverty Eradication Action Plan from http://www.finance.go.ug/docs/PEAP%202005%20Apr.pdf.
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Otieno, H. O., & Awange, J. L. (2006). Energy resources in East Africa: Opportunities and Challenges (illustrated ed.): Springer, 2006.
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Appendix A.1: Model Structure
This section describes the formulation of the most critical aspects of the model structure.
Power demand a. Consuming population
Figure 74 shows the model structure contributing to the consuming population.
Consuming popn
Connections growthrate
Unconnected popnwith grid access
Unconnected gridpopn growth rate
Natural growth rateof consuming popn
Overall Popngrowth rate
Nominal conngrowth const
Figure 74: Model structure ‐ Consuming population
As described in section 3.3.1 the consuming population is increased by the grid connections growth rate as well as its own natural population growth rate.
Natural growth rate of consuming popn = 'Consuming popn'*'Overall Popn growth rate' The connections growth rate is dependent on the number of unconnected population within the grid covered area as well as a constant target connection rate ‘Nominal conn growth const’. Note that a minimum function is used to ensure no more people are connected beyond zero unconnected population with grid access.
IF ('Unconnected popn with grid access' > 0, MIN (('Consuming popn'*'Nominal conn growth const'), ('Unconnected popn with grid access'/1<<yr>>)), 0<<yr^‐1>>)
The unconnected population within the grid covered area increases due its own natural population growth rate
Unconnected grid popn growth rate = 'Overall Popn growth rate'*'Unconnected popn with grid access'
b. Consumption per capita
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Figure 75 shows the model structure contributing to the total actual power consumption per capita.
Nominal Consn percapita
Optional incomespent on power
Eff of expenses ondemand
Basic consumptionper capita
Delayed powerexpenses info
Pot opt consn percapitaSubsidised
consumer tariff
GDP per Capita
GDP per Capita
GDP per capitagrowth rate
Actual opt consnper capita
GDP growth rate
Tot actual consn percapita
Figure 75: Model structure ‐ Consumption per capita
The potential optional consumption per capita is the difference between the nominal consumption per capita and the basic consumption per capita
Pot opt consn per capita = 'Nominal Consn per capita' ‐ 'Basic consumption per capita'
Optional income that would ideally be spent on power is dependent on the current power tariff and the potential optional consumption per capita. After a delay of 1 year, the optional consumption per capita is adjusted according to how much can be afforded depending on the ratio of power expenses to GDP per capita
Optional income spent on power = 'Pot opt consn per capita'*'Subsidised consumer tariff'
Eff of expenses on demand = GRAPH('Delayed power expenses info'/('GDP per Capita'*1800<<Sh/$>>),0,0.05,{1,1,0.594,0,0,0,0,0,0,0,0//Min:0;Max:1//})
Actual opt consn per capita = 'Eff of expenses on demand'*'Pot opt consn per capita'
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Power Supply a. Determination of future required capacity
Future required capacity is determined from a forecast of the power deficit in 20 years. Figure 76 shows the relevant model structure
Power supply deficit
20yr deficit
Avg deficit
Change in deficit
Trend in deficit
Capacity adj time
Past time
Figure 76: Model structure ‐ Forecast of power deficit
The deficit forecast is obtained from a calculation of its trend over the more recent past i.e. ‘Past time’ of 5 years and extrapolating this trend 20 years (Capacity adjustment time) into the future.
'Change in deficit' = ('Power supply deficit' ‐ 'Avg deficit')/'Past time'
Ideally the trend in input would be obtained as follows: 'Trend in Input' = 'Change in deficit' / 'Avg deficit' but the power deficit goes to zero sometimes which would make this function go to infinity. The function is corrected for this as below:
IF ('Avg deficit' = 0<<kWh>>,'Change in deficit'/1000<<kWh>>,'Change in deficit'/'Avg deficit')
'20yr deficit' = 'Power supply deficit' + 'Power supply deficit'*'Trend in deficit'*'Capacity adj time'
The ’20yr deficit’ is the capacity that will be required within the next 20 years. The forecast uses a linear extrapolation of the deficit.
b. Contracting for required capacity
Figure 77 shows the model structure determining how much capacity the single buyer contracts for.
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Exp hydro cap invrate
hydro installedcapacity
Maximum hydropotential
Avail hydro cap
Planned hydrocapacity
Used up hydrocapacity
Exp thermal cap invrate
Tot obligated capinv
20yr deficit
Contracted hydro cap
Figure 77: Model structure ‐ Contracting for capacity
The required capacity encapsulated in the ’20yr deficit’ is used to determine how much total capacity to contract for from the generators. This is how much they will be obligated to invest in over the period of their concession.
Tot obligated cap inv = 1.34*MAX (0<<MW/yr>>, ('20yr deficit'/(24*365*1000<<kWh/MW>>*1<<yr>>))/0.7)
1<<yr>> means the required capacity is contracted for over the present year; 1.34 is the allowance for 34% power loss and 0.7 is the allowance for a generation efficiency of 70% for both technologies. Preference is given for the cheaper hydro capacity as long as there is available hydro potential
Exp hydro capacity inv rate = IF ('Tot obligated cap inv' < 'Avail hydro cap', 'Tot obligated cap inv', 'Avail hydro cap')
Where available hydro capacity, Avail hydro cap = ('Maximum hydro potential' ‐ 'Used up hydro capacity')/1<<yr>> and Used up hydro capacity = 'hydro installed capacity' + 'Planned hydro capacity' + ‘Contracted hydro cap’
Thermal capacity is contracted for after the hydro obligation has been allocated
Exp thermal cap inv rate = 'Tot obligated cap inv' ‐ 'Exp hydro cap inv rate'
c. Generation capacity lifecycle
Figure 78 shows the capacity lifecycle through the contracted, planned, installed and retired stages. The investment obligation determined in b) above is the input rate to the contracted capacity. Actual investments are allocated to the contracted capacity over the concession period of 20 years (‘Contracted inv time’) transforming contracted into planned capacity. Planned capacity goes into construction over a period equal to each respective generation technology online lead time creating installed capacity. This is the capacity capable of power generation.
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Installed capacity ages and is retired over a period equal to the respective generation plant lifetimes
Thermal installedcapacityThermal initiation
rate
hydro installedcapacity
hydro initiation rate
hydro retirementrate
Thermal retirementrate
Planned Thermalcapacity
Thermal online rate
Planned hydrocapacity
hydro online rate
Thermal online leadtime
Hydro online leadtime
Thermal lifetime
Hydro plant lifetime
Contracted thermalcap
Contracted hydro cap
Hydro contr rate
Thermal contr rate
Exp hydro cap invrate
Exp thermal cap invrate
Contracted inv time
Contracted inv time
Figure 78: Model structure ‐ Capacity life cycle
Hydro contr rate = 'Exp hydro cap inv rate'
Thermal contr rate = 'Exp thermal cap inv rate'
hydro initiation rate = 'Contracted hydro cap'/'Contracted inv time'
Thermal initiation rate = 'Contracted thermal cap'/'Contracted inv time'
hydro online rate = 'Planned hydro capacity'/'Hydro online lead time'
Thermal online rate = 'Planned Thermal capacity'/'Thermal online lead time'
hydro retirement rate = 'hydro installed capacity'/'Hydro plant lifetime'
Thermal retirement rate = 'Thermal installed capacity'/'Thermal lifetime'
Power Service Pricing a. Generator capacity pricing
The pricing derives from the generator costs. Costs comprise of fixed costs and variable costs consisting of operation & maintenance costs and fuel costs. Fixed costs are derived from the capacity capital investment and the guaranteed return on investment. See Figure 79 below
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Generation mix
Market risk premium
risk free rate
Gen ROI
Current generatorprice
Average fixed gencap costs
hydro unit capacitycost
O&M fraction oftotal costs
thermal cap fixedcosts
Avg gen var costs
Figure 79: Model structure ‐ Generator pricing
The return on investment is a combination of a risk free interest rate (5%) and a market risk premium (15%) reflective of the level of investment risk in Uganda.
Gen ROI = 'risk free rate' + 'Market risk premium'
Average fixed and variable costs are a function of the technology generation mix since the different technologies come at different costs
Average fixed gen cap costs = ('Generation mix'[1]*((1‐'O&M fraction of total costs')*'hydro unit capacity cost') + 'Generation mix'[2]*'thermal cap fixed costs')*(1+'Gen ROI')
Avg gen var costs = 'Generation mix'[1]*'Hydro variable costs' + 'Generation mix'[2]*'Thermal variable costs'
The generator price is the sum of the variable and fixed costs
Current generator price = 'Average fixed gen cap costs' + 'Avg gen var costs'
b. Consumer Electricity pricing
Consumer electricity price is a combination of the revenues owed to the generators, transmission and distribution companies respectively. These companies estimate the costs that they expect to incur in the next year and their estimates are used to adjust the electricity price for the coming year.
Generator estimate
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Generator determines how much capacity they will have available in the coming year charged at the set generator price and this is their cost estimate input into the electricity price for next year.
Expected gen cap nxt yr = ('Exp hydro cap nxt yr '+ 'Exp thermal cap nxt yr')*'Operational fraction of inst cap'
Exp gen payments = 'Expected gen price'*'Expected gen cap nxt yr'*24*365
24*365 accounts for the total number of hours in a year assuming 100% availability for the generator. The expected generator payments is what will be owed to the generator in the coming year as long as they meet their capacity availability obligations
Transmission operator estimate
The transmission operator estimates how much power will be handled in the coming year charged at the current transmission price and this is their operation cost estimate input into the electricity price for next year. For this estimate the operator takes into account the expected domestic power supply, expected power exports and expected power losses. There are also the fixed costs of the system including cost of capital, asset depreciation, etc.
Exp Transn payments = 'Transn annual fixed asset costs' + 'Domestic Transn price'*('Exp domestic supply cap' + 'Exp loss cap')*24*365 + 'Export transn price'*'Exp export cap'*24*365
Distribution operator estimate
The distribution operator estimates their costs for the coming year from the number of new connections they expect to make in the coming year as well as the existing connections charged at an annual cost per connection that is a combination of fixed costs and operation & maintenance costs spread over the life time of the concession.
Exp Distrn payments = 'Annual cost per connection'*'1yr forecast of connected popn'/'Number per household'*(1+'Distrn ROR')
The lifetime cost per connection is assumed at $700 spread over the 20 years of the concession and it yields an annual cost per connection of $35
Export cost estimates
The cost estimates enumerated above also include the costs for the power exported. These costs are removed from the costs passed to the domestic consumers. Export costs include the generation costs as well as the transmission costs for the power exported.
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Exp export payments = 'Fraction exported'*'Exp gen payments' + ('Export transn price'*'Exp export cap'*24*365)+('Exp export cap'/('Exp export cap' + 'Exp loss cap' + 'Exp domestic supply cap'))*'Transn annual fixed asset costs'
The export transmission costs include the operation costs calculated from the actual power capacity exported times the export transmission price but also costs include the share of fixed network costs allotted to exports. Note that, the share of fixed network costs allotted to power losses and domestic power supply are all passed to the domestic consumers.
Consumer electricity price
The expected full electricity price to be passed through to the consumer in the coming year is given below:
Exp consumer tariff = 'Tot single buyer domestic payments'/('Exp domestic supply cap'*24*365*1000<<kWh/MW>>)
Where,
'Tot single buyer domestic payments' = 'Tot power service payments'‐'Exp export payments'
'Tot power service payments' = 'Exp gen payments' + 'Exp Transn payments' + 'Exp Distrn payments'
The current electricity price paid by the consumer is the ‘Exp consumer tariff’ delayed by 1 year and subsidized by the government
Subsidized consumer tariff = DELAYINF ('Exp consumer tariff', 1<<yr>>, 1, 380<<Sh/kWh>>) * (1 ‐ 'Govt tariff subsidy')
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Appendix A.2: The Structure as Constructed
Power Demand Sub-model
Single buyer Decisions - Investment Decisions, RoR, Current generator prices
Generation Capacity Commissioning and Power Generation
Domestic Power Service Costs
Generation Costs I yr capacity Calculation
Thermal installedcapacityThermal initiation
rate
hydro installedcapacity
hydro initiation rate
hydro retirementrate
Thermal retirementrate
Planned Thermalcapacity
Thermal online rate
Planned hydrocapacity
hydro online rate
Eff domestic powersupply
Power Loss rate
Thermal online leadtime
Power Lossconstant
Hydro online leadtime
Actual on-gridpower generated
Thermal Genefficiency
Thermal generationrate
hydro generationrate
Hydro Gen efficiency
Power supply deficit
Domestic powerdemand
Nominal Consn percapita
Govt tariff subsidy
Optional incomespent on power
Total installedcapacity
Thermal installedcapacity
hydro installedcapacity
Thermal lifetime
Hydro plant lifetime
Generation mix
thermal fraction
Power exported
Fraction exported
Unmet demand
Basic consumptionper capita
hydro unit capacitycost
Market risk premium
thermal unit capcost
Domestic powerdemand
Power supply deficit
risk free rate
Gen ROR
Exp hydro cap invrate
hydro installedcapacity
Maximum hydropotential
Avail hydro cap
Delayed powerexpenses info
Pot opt consn percapita
Actual consumptionper capita
Tot single buyerdomestic payments
Planned hydrocapacity
Used up hydrocapacity
Exp thermal cap invrate
Current generatorprice
Average fixed gencap costs
Max possible ongridgen output
Exp consumer tariff
Consuming popn
Connections growthrate
Unconnected popnwith grid access
Unconnected gridpopn growth rate
Number perhousehold
On-grid ElectricityAccess
Exp Distrnpayments
Number perhousehold
Annual cost perconnection
Exp Transnpayments
Popn
Popn growth rate
Overall electricityaccess
Consuming popn
Fraction exported
Exp exportpayments
Exp gen payments
thermal cap fixedcosts
Subsidisedconsumer tariff
Distrn ROR
Transn annual fixedasset costs
Hydro variable costs
O&M fraction oftotal costsThermal variable
costs
Thermal O&M costs
Avg gen var costs
Generation mix
hydro unit capacitycost
Power Lossconstant
Exp loss cap
Exp export cap
Exp domestic supplycap
Expected gen capnxt yr
Operational fractionof inst cap
Hydro online leadtime
Thermal lifetime
Thermal online leadtime
Hydro plant lifetime
Planned hydrocapacity
Planned Thermalcapacity
Thermal installedcapacity
Exp hydro cap nxtyr
Exp thermal cap nxtyr
hydro installedcapacity
1 yr delay
Exp thermal cap nxtyr
Exp hydro cap nxtyr
1 yr demandforecast
1 yr demandforecast
Exp domestic supplycap
Tot obligated capinv
1yr forecast ofconnected popn
1yr forecast ofconnected popn
O&M fraction oftotal costs
thermal cap fixedcosts
Avg gen var costs
Domestic Transnprice
Export transn price
Export transn price
Transn annual fixedasset costs
Exp loss cap
Exp domestic supplycap
Expected gen price
Expected gen price
Ratio actual topotential consn per
capita
Nominal Consn percapita
Contracted thermalcap
Contracted hydro cap
Hydro contr rate
Thermal contr rate
Exp hydro cap invrate
Exp thermal cap invrate
Contracted inv time
Contracted inv time
Natural growth rateof consuming popn
Overall Popngrowth rate
Overall Popngrowth rate
On-grid popn
Hydro fraction
GDP per Capita
GDP per Capita
GDP per capitagrowth rate
Ratio Powerexpenses to Income
Tot power servicepayments
Nominal conngrowth const
Ref thermal fuel costs
20yr deficit
20yr deficit
Actual opt consnper capita
Avg deficit
Change in deficit
Trend in deficit
Capacity adj time
Past time
Reference GDPgrowth rate
Eff of expenses ondemand
Contracted hydro cap
1yr deficit forecast
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Appendix B: Sensitivity Analysis
The effect of variations in the uncertain and influential parameters identified in section 4.2 on the two variables – domestic power demand and unmet power demand are shown below:
Parameter (Range) Effect on Domestic Power Demand Effect on Unmet Power Demand
Market risk premium (10%‐30%)
Distribution Return on Investment (10%‐30%)
Hydro generation efficiency (50%‐90%)
Thermal generation efficiency (50%‐90%)
Hydro online lead time (3‐10yrs)
Thermal online lead time (1‐6yrs)
Basic consumption per capita (50kWh‐500kWh)
Domestic Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000kWh
Non-commercial use only!
Unmet Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.000.050.100.150.200.250.30
Non-commercial use only!
Domestic Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000
kWh
Non-commercial use only!
Unmet Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.0
0.1
0.2
0.3
Non-commercial use only!
Domestic power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000kWh
Non-commercial use only!
Unmet Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.0
0.1
0.2
0.3
Non-commercial use only!
Domestic Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000
50,000,000,000
kWh
Non-commercial use only!
Unmet Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 20830.00
0.05
0.10
0.15
0.20
0.25
0.30
Non-commercial use only!
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Power loss constant (30% ‐ 40%)
Domestic Power Demand
Jan 01, 2003 Jan 01, 2043 Jan 01, 2083
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000
50,000,000,000kWh
Non-commercial use only!
Unmet Demand
Jan 01, 2003 Jan 01, 2023 Jan 01, 2043 Jan 01, 2063 Jan 01, 20830.0
0.1
0.2
0.3
Non-commercial use only!
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P a g e | L
Appendix C: GDP per Capita Versus Electricity Consumption per Capita
The table below shows GDP per capita versus electricity consumption per capita for select African and Asian countries whose development path Uganda is likely to emulate. The figures are used to plot a plausible path for the development of Uganda’s electricity consumption per capita
Table 6: GDP per Capita versus Electricity Consumption per Capita
Country GDP per capita, constant US dollars Consumption per capita (kWh)
Algeria 2121 898.6
Angola 928 142.3
Benin 321 69.8
Botswana 4382 1462.3
Cameroon 678 213.8
Côte d'Ivoire 560 174.3
China 1451 1780.5
Congo 1101 144
Egypt 1643 1225.5
Gabon 4279 932.1
Georgia 974 1671.7
Ghana 282 270.8
India 588 480.5
Indonesia 942 509.3
Kenya 426 143.9
Morocco 1562 643
Mozambique 312 467
Namibia 2133 1420
Nigeria 428 136.1
Senegal 501 152.4
South Africa 3429 4847.6
Tunisia 2412 1193.9
Zambia 356 709.5
Zimbabwe 428 961.1
Libya 6828 3336.2
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Appendix D: Model Structural Validation The results of ‘Extreme condition tests’ for a small sub‐set of the model are presented in this section. The model rate equations listed in the table below were the subject of extreme value tests.
Equation Name Definition
1 Thermal online rate ('Planned Thermal capacity')/'Thermal online lead time'
2 hydro online rate ('Planned hydro capacity')/'Hydro online lead time'
3 Thermal retirement rate 'Thermal installed capacity'/'Thermal lifetime'
4 hydro retirement rate 'hydro installed capacity'/'Hydro plant lifetime'
Table 2 shows a combination of direct extreme tests and the corresponding structural oriented behavior tests
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Eqn Parameter Reference
value Test Value
Expected Response (Empirical Validation)
Expected Response (Theoretical Validation)
Results Corresponding model behavior graphs
1 Planned Thermal capacity
Initial value ‐ 0MW
1.00E+60 Thermal online rate equals infinity and Thermal installed capacity grows to infinity
Thermal installed capacity grows to infinity and then gradually reduces due to retiring capacity unti such a time when new additional capacity will be put in plan
Passed
Thermal online lead time
3 yr 1.00E+60 Thermal installed capacity should reduce gradually to 0
Thermal installed capacity should reduce gradually to 0 since no additional capacity will be added
Passed Reference run
Current run
Jan 01, 2003 Jan 01, 21030
2e66
4e66
6e66
MW
Current
Reference
Th
erm
al in
sta
lle
dca
pa
cit
y
Non-commercial use only!
Jan 01, 2003 Jan 01, 21030
100,000,000
200,000,000
300,000,000
400,000,000MW
*Therm
al in
sta
lled
capacity
Non-commercial use only!
Jan 01, 2003 Jan 01, 2053 Jan 01, 21030.0
0.5
1.0
1.5
2.0MW
Therm
al in
sta
lled
i
Non-commercial use only!
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2 Planned hydro capacity
Initial value ‐ 0MW
1.00E+60 Hydro online rate equals infinity and Hydro installed capacity grows to infinity
Hydro installed capacity grows to infinity and then gradually reduces due to retiring capacity until such a time when new additional capacity will be put in plan
Passed Same as Eqn 1 above
Hydro online lead time
6 yr 1.00E+60 Hydro installed capacity should reduce gradually to 0
Hydro installed capacity should reduce gradually to 0 since no additional capacity will be added
Passed Same as Eqn 1 above
3 Thermal installed capacity
2MW 1.00E+75 Thermal retirement rate equals infinity
Thermal retirement rate equals infinity
Passed
Thermal lifetime
30 yr 1.00E+75 Thermal retirement rate equals 0
Thermal retirement rate equals 0
Passed
4 hydro installed capacity
303MW 1.00E+75 Hydro retirement rate equals infinity
Hydro retirement rate equals infinity
Passed
Hydro plant lifetime
40 yr 1.00E+75 Hydro retirement rate equals 0
Hydro retirement rate equals 0
Passed