rural electrification in uganda

Master thesis

Rural electrification in Uganda Powering the Masindi district Elektrifisering av landsbyer i Uganda: Masindi District

Stud. techn. Britt-Mari Langåsen Master Program Energy and the Environment Faculty of Information Technology, Mathematics and Electrical Engineering Department of Energy and Process Engineering Spring 2004

I

Rapportnummer EPT-H-2004-34

The Norwegian University of Science and Technology

Gradering

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Rapportens tittel Rural electrification in Uganda: Powering the Masindi District

Dato 23.06.04

Elektrifisering av landsbyer i Uganda: Masindi District

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Saksbehandler / forfatter Britt-Mari Langåsen

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This master thesis is about electrification of a rural district in Uganda. The aim of the thesis is to:

investigate the electricity needs and power characteristics in a rural district in Uganda to identify and compare different technology options to recommend how electrification should proceed

Three different types of trading centres are investigated, and they differ in the number of inhabitants and load characteristics. Load profiles for the trading centres is made and put into a simulations program called HOMER. This is an optimization program that in this study use solar systems, diesel generation and grid extension as means of supplying a rural area with power. Several situations are simulated; how low the price of solar systems must go to be competitive or how high the diesel price must be to make solar systems able to compete with diesel generation. Different loads are also simulated to show how this affects the technology options. A general discussion about the applicability of grid extension and protection schemes for the network is given. It is found that for a large trading centre, grid extension is the best alternative. For a small and medium community, diesel generation or solar systems are best, depending on diesel price and distance from the main grid.

Stikkord på norsk Indexing Terms English

Gruppe 1 Elektrifisering av landsbygd

Rural electrification

Gruppe 2

U-land

Developing countries

Egenvalgte stikkord

Teknologi løsninger

Technology options

II

Norges teknisk- Institutt for energi- og naturvitenskapelige universitet processteknikk NTNU H-2004- HOVEDOPPGAVE

for

Stud.techn. Britt Mari Langåsen

Våren 2004 Elektrifisering av landsbyer i Uganda: Masindi District Rural Electrification in Uganda: Powering the Masindi District Background

Like many developing countries especially in Africa, few people in rural areas and towns of Uganda enjoy access to electricity. Since electricity is seen as an important prerequisite for development, Uganda initiated the “Energy for Rural Transformation” program with the support of the World Bank and a number of donor countries. This program aims at increasing the rural electrification rate from 1% today to 10% by 2010. Different options exist to meet this aim. Grid extension, the building of mini-grids based on local generation, and off-grid generation (e.g. PV solar systems) are potential alternative, whose attractiveness depends on various different factors. Aim The aim of this thesis is investigate rural electrification of the Masindi district in Uganda, to identify and compare different options and to recommend how electrification could proceed. The analysis should focus on so-called “trading centers”, small towns, as opposed to the evenly spread farming population. The characteristics of the electric loads should be taken into account, as well as the investment costs and operating expenses of different technical solutions. The analysis should include following elements:

♦ Description of the situation in the trading centers, their electricity needs, current supply, and power characteristics.

• Selection and description of a proper analysis framework and modeling tool. • Description of options for rural electrification. • Description/analysis of the local grid and its ability to deal with local power

supply. Specification of technical upgrades needed. • Analysis of technical and cost elements of different, “typical” situations and

recommendation of specific solutions. Specification of investment needs and power characteristics.

• Discussion of the usefulness, applicability and limits of grid extensions and different types of distributed generation.

III

Senest 14 dager etter utlevering av oppgaven skal kandidaten levere/sende instituttet en detaljert fremdrift- og evt. forsøksplan for oppgaven til evaluering og evt. diskusjon med faglig ansvarlig/ veiledere. Detaljer ved evt. utførelse av dataprogrammer skal avtales nærmere i samråd med faglig ansvarlig. Besvarelsen redigeres mest mulig som en forskningsrapport med et sammendrag både på norsk og engelsk, konklusjon, litteraturliste, innholdsfortegnelse etc. Ved utarbeidelsen av teksten skal kandidaten legge vekt på å gjøre teksten oversiktlig og velskrevet. Med henblikk på lesning av besvarelsen er det viktig at de nødvendige henvisninger for korresponderende steder i tekst, tabeller og figurer anføres på begge steder. Ved bedømmelsen legges det stor vekt på at resultatene er grundig bearbeidet, at de oppstilles tabellarisk og/eller grafisk på en oversiktlig måte, og at de er diskutert utførlig. Alle benyttede kilder, også muntlige opplysninger, skal oppgis på fullstendig måte. (For tidsskrifter og bøker oppgis forfatter, tittel, årgang, sidetall og evt. figurnummer.)

Kandidaten skal rette seg etter de reglementer og retningslinjer som gjelder ved alle (andre) fagmiljøer som kandidaten har kontakt med gjennom sin utførelse av oppgaven, samt etter eventuelle pålegg fra Institutt for energi- og prosessteknikk.

I henhold til Reglement for sivilarkitekt- og sivilingeniøreksamen ved NTNU § 8, forbeholder Instituttet seg retten til å benytte alle resultater i undervisnings- og forskningsformål, samt til publikasjoner.

Ett -1 komplett eksemplar av originalbesvarelsen av oppgaven skal innleveres til samme adressat som den ble utlevert. (Det skal medfølge et konsentrert sammendrag på maks. en maskinskrevet side med dobbel linjeavstand med forfatternavn og oppgavetittel for evt. referering i tidsskrifter). Til Instituttet innleveres to - 2 komplette, kopier av besvarelsen. Ytterligere kopier til evt. medveiledere/oppgavegivere skal avtales med, og evt. leveres direkte til, de respektive. Til instituttet innleveres også en komplett kopi (inkl. konsentrerte sammendrag) på CD-ROM i Word-format eller tilsvarende. Institutt for energi og prosessteknikk, xx.xx.2004 _________________ ______________________ Ingvald Strømmen Edgar Hertwich Instituttleder Faglig ansvarlig/veileder Kontaktperson(er)/medveileder(e): Olav Bjarte Fosso, Elkraft

IV

Preface This master thesis is written at the Department of Energy and Process Engineering,

which is a part of the Faculty of Engineering, Science and Technology at the Norwegian

University of Science and Technology, NTNU. It concludes my Master of Science degree

in Energy and the Environment and was written in the period January 2004 to July 2004.

I would like to thank Professor Edgar Hertwich and Professor Olav B. Fosso for their

guidance during the process of writing this report. I would also like to thank Professor Da

Silva at Makerere University and all the people at UEDCL that helped me in finding

relevant information. I would also like to thank Rachel Arinda and Richard Okou that

helped me during my stay in Uganda and took me into their homes and also thanks to

Lars-Petter Bingh for his support and friendly company during our stay in Kampala.

Finally I would like to thank Ingvill Horgøien, Idun Skorpa Melvær and Tore Bjølseth for

their support and inspiration during this work.

Trondheim, June 23, 2004 Britt-Mari Langåsen

Summary V

Summary

In Uganda, the electrification rate is very low and rural electrification is a part of the

government’s energy policy, introduced in the “New Electricity Act” from 1999 and

continued in the “Rural Electrification Strategy and Plan” from 2001.

The aim of the thesis is to:

investigate the electricity needs and power characteristics in a rural district in

Uganda

to identify and compare different technology options

to recommend how electrification should proceed

This has been done by choosing the modelling tool HOMER which is an optimization

program for rural electrification with both on-grid and off-grid technologies.

Three different “types” of communities have been investigated in this study, three trading

centres that have been divided into small, medium and large. These centres represent

different sizes (about 2000, 10’000 and 20’000 inhabitants) and thereby different power

characteristics. “Typical” load profiles for these centres have been used in the

simulations tool with the technology options photovoltaic, diesel generation and grid

extension to supply power. Different parameters have been changed during the

simulations to see what influences the choice of technology for supplying a rural area

with power. Parameters that have been changed are the price of solar systems and

diesel prices. Different loads have also been simulated.

The main conclusions that can be drawn from these simulations are:

Grid extension is most preferable when there is a relatively large load with a high load

factor or if the trading centre is situated close to the existing main grid. For the large

trading centre, this is the best option.

Diesel generation is the cheapest way of supplying power, but as the diesel prices rise,

then photovoltaic power becomes competing.

Photovoltaic power systems are best suited for small, dispersed loads in trading centres

far away from the main grid. Solar systems are within the energy policy for rural

electrification by renewable energy sources promoted by the government. It can also be

a supplement in medium and large trading centres.

The cost for supplying a rural trading centre is according to this study between

US$40’000 and US$7’400’000.

Britt-Mari Langåsen Spring 2004

Summary VI

Further studies that are recommended is to investigate a given trading centre and look at

the loads on a household level and se what loads should be covered by for example

solar home systems and which should be connected to a grid. Another option for further

studies is to se what financing mechanisms are best to encourage rural electrification.


Sammandrag VII

Sammandrag

I Uganda har en väldigt liten del av befolkningen tillgång på electricitet och elektrifiseing

av landsbygden är en del a regeringens energi policy och introducerades i samband

med the New Electricity Act 1999 och vidareutvecklades i the Rural Electrification Study

and Plan 2001.

Målet med den här studien är att:

undersöka elektrisitets behoven och last karakteristiken i ett landbygdsområde i

Uganda

att identifiera och jämföra olika teknologi alternativ

att rekommendera hur elektrifiering ska fortskrida

Detta har gjorts genom att välja modelleringprogrammet HOMER som är ett optimiserigs

program for elektrifisering av landsbygden med både nät tillknutna och icke nät tillknutna

teknologier.

Tre olika typer av samhällen har undersökts i denna studie, tre handels center som har

indelats i litet, medium och stort. Dessa center representerar olika storlekar (runt 2000,

10’000 och 20’000 invånare) och därmed olika effekt och energi behov. ”Typiska” last

profiler för dessa center har använts i simuleringsverktyget tillsammans med olika

tekniska løsningar, solceller, diesel kraftproduktion och nätburen electricitet, för att

leverera energi. Olika variabler har ändrats i samband med simuleringarna för att se vad

som påverkar valet av teknologi för att förse ett landsbygdsområde med energi.

Variabler som har ändrats är pris på solceller och diesel. Det har ockå gjorts simuleingar

med olika laster.

De huvudsakliga slutledningarna som kan dras från dessa simuleringar är:

Utbyggnad av kraftnätet är att föredra när det finns en relativt stor last med hög last

faktor eller om handels centrat ligger nära det existerande kraftnätet. For det stora

handels centret är detta den bästa lösningen.

Kraftproduktion från diesel generatorer är det billigaste sättet att producera energi, men

om diesel priserna stiger så blir solsystem konkurrens kraftiga.

Solceller är bäst lämpade för små, utspridda laster i handels centra som ligger långt från

kraftnätet. Solsystem är en del av regeringens energi policy for elektrifiering av


Sammandrag VIII

landsbygden genom förnybara energi källor. De kan också vara ett supplement i medium

och stora handels center.

Kostnaden for att förse ett handels senter på landsbygden med energi kostar enligt den

här studien mellan US$40’000 och US$7’400’000.

Vidare studier som är rekommenderade är att undersöka ett givet handels center och se

på de olika lasterna och se vilka som borde täckas av till exempel solceller och vilka som

borde vara tilknutna kraftnätet. Ett annat fält att undersöka vidare är vilka finansierings

mekanismer som verkar bäst för att främja elektrifiering av landsbygden.


Abbreviations IX

Abbreviations DG Distributed Generation

UETCL Uganda Electricity Transmission Company Limited

UEDCL Uganda Electricity Distribution Company Limited

kV kilo Volt

kW kilo Watt

kVA Apparent power

MW Mega Watt

DC Direct Current

AC Alternating Current

PV Photovoltaic

SHS Solar Home System

GTZ Deutsche Gesellschaft für Technische Zusammenarbiet

MUK Makerere University Kampala

Exchange rate: US$1 = 2000 Ush


Table of contents X

Table of contents

1 Introduction _________________________________________________ 1 1.1 Structure of the report __________________________________________2

2 Background _________________________________________________ 3 3 Methodology ________________________________________________ 6

3.1 Preparations and progression of the work _________________________6 3.2 Field studies __________________________________________________7 3.3 HOMER ______________________________________________________9 3.4 Sensitivity analysis ___________________________________________13

4 Power generation and technology options ______________________ 14 4.1 Power generation _____________________________________________14 4.2 Alternative energy sources _____________________________________16 4.3 Rural electrification technology options __________________________17 4.4 Conclusion __________________________________________________22

5 Analysis of trading centres ___________________________________ 23 5.1 Masindi district _______________________________________________23 5.2 Socio-economic characteristics of Masindi________________________24 5.3 Load assessment _____________________________________________25

6 Basis and optimal solutions __________________________________ 34 6.1 Simulation basis______________________________________________34 6.2 Small trading centre___________________________________________37 6.3 Medium trading centre_________________________________________48 6.4 Large trading centre___________________________________________56

7 Grid extension ______________________________________________ 65 7.1 Applicability _________________________________________________65 7.2 Protection schemes ___________________________________________67

8 Discussion _________________________________________________ 70 9 Conclusion_________________________________________________ 73 10 Literature references_______________________________________ 76 11 Appendix ________________________________________________ 78

11.1 Interviews ___________________________________________________78 11.2 HOMER input variables ________________________________________87 11.3 Attached CD with HOMER files and data _________________________110


Table of contents XI

List of tables Table 4-1 Suggested places construction of new hydro power generation .................................. 15 Table 4-2 Summary of the technology option................................................................................ 22 Table 5-1 Energy consumption for rural households in Uganda ................................................... 26 Table 5-2 Time usage ( in hours) of electrical appliances in rural households ............................. 26 Table 5-3 Present power need in the Masindi region. Based on data from UEDCL..................... 27 Table 6-1 Optimal solutions for Biizi trading centre, constrained demand.................................... 38 Table 6-2 Optimal solutions for Biizi trading centre with an unconstrained demand .................... 38 Table 6-3 Optimal results for variations in diesel price.................................................................. 43 Table 6-4 Optimal results for variations in diesel price.................................................................. 44 Table 6-5 Solutions when supplying Biizi with PV or diesel .......................................................... 45 Table 6-6 Solutions when powering with PV or diesel .................................................................. 46 Table 6-7 Optimal solutions for Mutunda with a constrained demand .......................................... 48 Table 6-8 Optimal solutions for Mutunda with an unconstrained demand .................................... 49 Table 6-9 Optimal results for variations in diesel price.................................................................. 52 Table 6-10 Optimal results for variations in diesel price ............................................................... 53 Table 6-11 Solutions when powering with PV and diesel ............................................................. 54 Table 6-12 Solutions when powering with PV and diesel ............................................................. 54 Table 6-13 Optimal solutions for Kiryandongo, constrained demand ........................................... 56 Table 6-14 Optimal solutions for Kiryandongo, unconstrained demand ....................................... 57 Table 6-15 Optimal results for variations in diesel price ............................................................... 60 Table 6-16 Optimal results for variations in diesel price ............................................................... 61 Table 6-17 Solutions when powering with PV and diesel ............................................................. 61 Table 6-18 Solutions when powering with PV and diesel ............................................................. 62


1 Introduction 1

1 Introduction In Uganda and the Masindi district, like in most developing countries, there is an existing

shortage of power. This prohibits economical development. Uganda has in its program

Energy for Rural Transformation (ERT) [19] and through the Rural Electrification

Strategy and Plan from 2001 [18] stated that by the year 2010, 10% of the population

should have access to electricity to enhance their standard of living, as compared to 1%

as of now. After the restructuring of the energy sector, independent power producers

and distributed generation is being supported and encouraged. In the Rural

Electrification Strategy and Plan it is said that “The primary objective of the RF

Strategy is to reduce inequalities in national access to electricity and the associated

opportunities for increased social welfare, education, health and income generating

opportunites.” [18] This is an enormous challenge which Uganda and its government

have to face. The solution to this problem is complex where several technology

alternatives are available and different technologies may be optimal for different areas.

An essential question is what electrification of rural areas will cost and where the

financing will come from. There is also a lack of awareness of renewable energy

technologies that prohibits a wider use of for example solar systems. In light of this aim,

this study might be of help to find out what could be the optimal way of powering a rural

community.

This project about rural electrification was initiated owing to collaboration between the

Norwegian University of Science and Technology and Makerere University. This

collaboration started in 2002. The aim of the thesis is to:

investigate the electricity needs and power characteristics in a rural district in

Uganda

to identify and compare different technology options

to recommend how electrification should proceed

The rural area that has been the main object of this study is Masindi district which lies in

the north-west of Uganda. The study will try to find out what is the optimal way to supply

an area with electricity, through photovoltaic, diesel generation, grid extension or a

combination of these.


1 Introduction 2

The study included six weeks of research in Kampala, Uganda. There I cooperated

mainly with Makerere University, GTZ and UEDCL, but I was also in contact with smaller

firms and employees of MEMD.

With help from the district population office in Masindi, 44 major communities, trading

centres, that are presently without access to grid electricity and that are important for the

development of the district was found. Using UEDCL standards for calculating loads and

an assessment of economical development in rural areas, the respective expected loads

and power characteristics for the trading centres were found. The loads were then,

together with data concerning prices etc, used in a simulation program called HOMER to

do an optimisation and a sensitivity analysis by changing different variables such as

price, distance and load.

1.1 Structure of the report In the second chapter there will be given a background to the problem that has been

investigated. A methodology chapter about field studies and the chosen simulation tools

will be given in chapter four. A description of the present power situation in the country

and the region together with different technology options for rural electrification will be

presented in chapter five and also how the government in Uganda plans the future

expansion of their power generation. Following in chapter six, the chosen trading centres

will be analysed and a load assessment for them will be performed. Different power

producing options for the district will be explored using an optimization program called

HOMER and a sensitivity analysis will be done to se what impacts changing different

variables may have on the final result. These results are presented in chapter seven. A

general discussion about grid extension will be given in chapter eight.

Finally, in chapter nine and ten, a discussion and conclusion will be drawn from the

results presented earlier. In the appendix interviews and meetings have been included

and more simulation results are presented. A CD with the program HOMER and

simulation files are attached in the end of this report.


2 Background 3

2 Background

Picture 2-1 Map over Masindi district

Getting access to electricity is an important part of enhancing the living standard for the

rural population in Uganda. Presently only a few percent of the people in rural areas

have access to electricity. Since these areas usually have low economic activity as well,

they are not high prioritised for extension of the main grid. It has to be taken into account

that not all areas are favourable for grid extension because of a scattered population

with a low load and people might be mitigating from rural area into towns. Still, several

trading centres might benefit from electrification since they are likely to stay inhabited.

Different factors that affect the electrification rate are a low population density, high

connection cost, power quality and –security. Shortage of electricity aggravates poverty

because it excludes most industrial activities that might develop a rural area by giving

more jobs and interest new investors.

During a demographic transition the response in fertility is not as rapid as the response

in mortality and the population will rapidly grow due to a longer lifespan and still a high

fertility. [20] Because of this, population as an external factor is influencing the energy


2 Background 4

consumption. When the population grow, there is an increase in the demand for energy

services. But this connection could also be seen the other way, that energy patterns

could change the population by supplying better and more accessible energy. This

would relieve women and children from the work of gathering fuel wood when they get

access to other means of energy than traditional biomass. Giving people access to

electricity will also give them energy for lighting purposes. These factors together will

give people in developing areas more time to spend on education and income

generating activities, which can also be performed during evening time due to better

lightning. It is a known fact that women with education and those that has an occupation

gives birth later in life and also have fewer children compared to those without any

education and profession. When moving up the energy ladder and using for example

electricity, then this will relieve the adverse health effects caused by smoke from the

burning fuel wood. The fuel wood is burned for cooking purposes and this means that

mostly women and children are affected who stay indoor. [20]

All in all, getting access to electricity would raise the standard of living and help

development in rural areas of the third world.

The main factors that play a role in the transition to more modern energy usage are

accessibility, the relative price which decides if people can afford the new services and

cultural preferences. [13] An interesting fact is that when people move up the energy

ladder they usually do not complete the transition between the energy sources, but they

tend to hang on the old ones still and using new ones, hence they use more energy than

would have been done if fully transitioned. An example of this is the mother of Rachel

Arinda, whom I worked with in my field studies in Uganda, that explained to me that she

use an electrical stove, a gas stove and a three stone cooker when preparing food. This

is because they al have different positive sides and some foods “have” to be prepared in

a certain way.

In this study I have concentrated on a rural district, Masindi, in the north west of Uganda.

This area was chosen because of; interest from GTZ who where starting a project on

rural electrification of a small trading centre in Masindi together with students from

Makerere University Kampala (MUK), interest from my supervisor at MUK, and also

because it is an underdeveloped part of the country relying mainly on agriculture and it

has few larger industries. Within the area I have looked at 20 trading centres that are


2 Background 5

presently not connected to the main grid and that is of economic and social importance

for the development for the Masindi district. A deeper study have been dperformed on

three different types of trading centres differing in number of inhabitants, distance from

the grid and load profile. This has been done to give an understanding on how different

variables influence the optimization result for a set of different energy sources.


3 Methodology 6

3 Methodology This chapter is about the methodology used in this study. It includes a description of field

studies in Uganda, but first is a presentation of the progression of the work of the study

and an explanation of why the used simulation programs have been chosen. It will also

give a description of the simulation program used in this study.

3.1 Preparations and progression of the work The work on this study has continued for 22 weeks. Within this time, 6 weeks where

spent in Uganda for field studies. The purpose of the trip was to find data and

information about distributed generation in Kampala, and how it could be used to

improve the grid stability and to help avoid power shortages. This was the original aim of

the master thesis. This problem definition included the use of a load flow analysis

program called SIMPOW by ABB. The reason for doing a load flow analysis was to se

the load structure, how the power flowed and where rehabilitation of the grid and

generation input was needed to help improve the power quality. After coming to Uganda

and meeting with the supervisors there, it became obvious that information about this

“problem” was hard to achieve. Instead the supervisor at Makerere had a project going

about rural electrification that he was interested that I take part in. This project was

based on finding the optimal way of supplying a rural area with power and was an

optimization problem. The project included the use of an optimization program called

HOMER by NREL. It was also intended to do a load flow analysis here as well, to see

how the grid was affected when new loads from the investigated district was added.

Before travelling to Uganda, some preparations where done in the form of collecting

literature about rural electrification and to get an idea of the program SIMPOW. During

the field studies, information was gathered, some visits to factories where done, but the

main activity where to interview people and to collect data and reports that where not

available in Trondheim. One important aspect was also for me to actually se the energy

situation and to get an understanding of the problems encountered. Back in Trondheim

the simulation work began and also some reading of literature about rural electrification.

After a while, it became obvious that there weren’t enough time, and to little data to do a

load flow analysis over Masindi district.


3 Methodology 7

3.2 Field studies This thesis included a six week stay in Uganda and these weeks where spent on field

work. The aim of the fieldwork was to receive information by interviewing people and to

assess information that is no available on the internet.

Because of cultural differences between Scandinavia and Uganda, in some situations

there might arise some confusion and misunderstanding in the meeting between the two

parts of the world.

There are several things that differ when searching for information in Uganda, compared

to Norway. One of the most obvious things is that the time perspective is different. When

in Norway one is expected to arrive to a meeting in time, Ugandans seem to have a

wider perception of the concept punctual and it’s not strange to arrive half an hour late

(according to Norwegian standards). This imprecise understanding of time reflects also

in other things, like when sending e-mails of fax. As an example it can be mentioned a

fax that I expected. It was meant to come “in a couple of days” but it arrived several

weeks later. This may also have its base in the fact that if people don’t feel part or

benefit from the result you expect from the data received, then they don’t engage such

amount of time in the matter.

Another difference is the technical resources available. Internet connection is mainly

slow and this results in that most information is gathered by meeting people face to face,

instead of looking it up on the web or contacting people through e-mail. In Masindi

district where I was to search for information, they had no access to internet, but where

reliant on fax machines. This was not available at the district population office, but was a

service that could be bought in the trading centre.

Because computer resources are scarce, most documents are in hard copy, and not in

pdf forms. This means that if a copy is wanted, some hours in front of a copying machine

is to expect. It may also take some days to get access to the reports, since clearances

have to be sent to the appropriate persons in a letter form instead of via e-mail. Because

of this scarcity of internet access and intranets, it’s likely to believe that information

sharing between instances is hard and time consuming as well. Even so, people are

very willing to help, even if they do not know about the problem them self. They are most

times happy to refer to other persons that may be of interest.


3 Methodology 8

Most people have a cell phone so this makes it reasonable easy to get in contact, but

there is a problem that the phones are often shut off, the phone numbers received are

not working or it’s hard to get a reply if an SMS has been sent or if a voice message is

left. This is probably due to difficulties in charging the phones or a shortage in money on

the pre paid cards. As an example here, it can be mentioned that people often “beeped”

me, so I could call them up instead.

One of the most time consuming activities during the day, is trying to get from one point

to another by public transport. Traffic jams are frequent and public transportation operate

on the basis that the bus (or taxi as they call them) leave when full. This can take

anything from no-time up to half an hour. In rural areas, it can take up to two hours

before the bus leave.

All in all, everything usually turns out well in the end, but there might be some frustration

before getting there…

Results achieved from the field studies

♦ Solar data from the Meteorological Institute in Kampala. To get the data, we just went to

the meteorological institute and met a person which a student I was working with had

gone to for data earlier. The data was not up to date because there where no weather

station in Masindi presently, but we got what was available. For the data I had to pay a

10 000 Ush which is almost US$ 5.

♦ SingleLineDiagram of the grid from UEDCL. This data where supposed to be used in a

load flow analysis. Because of a lack of data and time, this part of the thesis has been

changed.

♦ Meeting with local and governmental people and understanding of the way of work in

Uganda

♦ Cost of products available locally, for example PV, generators etc. These data where

gathered by going to different suppliers and asking for prices. Some where willing to give

them to us, others where worried that we would use their retail prices and then give

people an incorrect picture of the actual cost for the products. Some price data in this

report are from reports from UEDCL.


3 Methodology 9

♦ Trading centre data. This was names and population data for the communities that are

important for Masindi district development and that are without electricity. The simulations

re built upon these data. These figures and names took six weeks to receive. I was

promised to have them faxed to Kampala within a week, but after 2 weeks they still hadn’t

come. I called and the lady who where looking for the data promised to have the faxed

within the same week. After 2 more weeks they still hadn’t arrived and I went up to

Masindi town again. I asked for them at the district development office and she said she

had given them to the man, Rashid Yawiya, which was going to find the population data

for the trading centres. I went up to he district population office and found the Mr Yawiya

that where finding the figures and he had received the centre names the day before so he

wasn’t finished. This was now four weeks after they had agreed I should have gotten the

data. I had to return to Kampala and about a week after I had returned to Trondheim, I

got a fax with the data requested.

To think about

To get the most out of the field studies, there are some things that are more important

than others to think about. Some of these things I did, some I figured out during my stay,

and some again, I realized when I got home that I should have done.

♦ Try to get to know some of the cultural differences before going to a foreign country, like

what is the norm for meeting in time and dress codes.

♦ Don’t be in a hurry

♦ Be well prepared and ask specific questions

♦ Demand results and interest for what you are doing.

♦ If possible, wait for the data you requested for at the person you where asking. Otherwise

my Masindi experience can be experienced. See above.

3.3 HOMER HOMER is a program developed by NREL with the start in 1993 and is an optimization

model for distributed power. It was initiated to address the potential electricity

opportunities in rural villages and to investigate the technical and financial performance

of hybrids given a village load and availability of wind and solar resources etc. [12] It is

offered for free by NREL and can be downloaded at www.nrel.gov/homer. HOMER


http://www.nrel.gov/homer

3 Methodology 10

stands for Hybrid Optimization Model for Electric Renewables. It can design power

systems for both off-grid and grid-connection, with a variety of applications. A decision

that has to be made is what kind of components should be in the system, and what size

and quantities they should have. The program has a database with different technology

options as a base and does an optimization and sensitivity analysis to make an

evaluation of the large number of system configuration options easier. The deciding

factor for the optimal system is based on total net present cost. Input parameters that

describe the technology options like costs, loads and resource availability are put into

the program

In Figure 3-1 the user interface for HOMER can be seen. It’s a windows based program

and very user friendly. The energy sources are added and can then be modified by

clicking on the buttons and new windows will appear. After all the input parameters are

ready, then the simulation can start and finally the result will appear in the result window.

The result can be seen either in tabular or graphic form.

Figure 3-1 User interface of HOMER


3 Methodology 11

Different objects that where used for simulations in this study are: generators, grid, PV,

batteries, converters and loads. To be able to really compare PV and diesel generators

with grid extension, all simulations are done with AC loads, meaning that extra

components such as inverters are needed for PV systems. There are also applications

that run on DC power, but it would be too complicated to compare all these alternatives.

To do a simulation that will be viable for the Masindi area different factors had to be

taken in to account, among them the solar radiation, diesel costs and grid price, several

economic parameters and limitations such as the maximum allowed shortage per year

and minimum part of renewable energy.

Three different solutions for powering the trading centres have been used for the

simulations, namely solar systems, diesel generators and grid extension. Following in

chapter 4.3.1 to 4.3.3 is a description of the systems and advantages and disadvantages

of them.

3.3.1 Critique of HOMER HOMER was developed in 1997, and is still under the progress of development. The

program has large umber of users, for both school and professional use. It seems to be

widely used in university environments, but also among engineers. On HOMERs

webpage [24] it can be seen that by now, HOMER have been downloaded by 4405

persons from 157 different countries.

HOMER has a wide variety of input variables to make the simulations as precise as

possible, but this also makes the results very sensible for the accuracy of the data that is

found and put in to the program. As I have found, there is no possibility to put in a

development of the prices for example, for a period of time. It is however a possibility to

have several sensitivity variables, so in a way it’s possible to se what system

configurations are optimal over a larger time period, given that one has an idea about

the progression of prices. This is something that is to be upgraded for grid electricity

prices in the next version of HOMER.

The results that come from the simulations in HOMER will not be better than the

accuracy on the input variables. This means that if there are some uncertainties in for


3 Methodology 12

example price, there will be uncertainties in the simulation result as well. The already

existing components can be modified, but when extracting the price curves for the

components, it has to be taken into account that it isn’t certain that a larger component

may follow the same price curvature as a smaller one. This is especially relevant for

solar systems and diesel generators.

A problem with HOMER is weighting environmental concerns. Carbon tax as US$/t and

a minimum of renewable energy output can be put in as parameters. Most third world

countries have no carbon tax though, but a western standard could be put into

simulations or it has to be calculated for qualitative when analysing the results. No

carbon tax has been used in the simulations in this study. Neither have any minimum of

renewable energy been set. This was to find the absolute minimum cost for systems to

supply communities with electricity. Neither is it possible to put in a parameter for an

increase in grid extension costs in those cases that there is a rough landscape making

grid extension more expensive. This can though be done by putting a sensitivity variable

on the cost for grid extension. This has not been done in these simulations.

There have been some problems in finding accurate prices, in particular for the PV

panels. The prices that are used in the program are calculated from prices available on

the market in Uganda today. These prices are for panels of the size from 50W to 75W.

The solar panels that are used in HOMER are in the range from 500W to several

hundred kW and the price has been calculated by just multiplying the numbers of panels

by the price for a small panel. This calculation may be correct if the panels are used as

solar home systems (SHS) with some PV panels at each house. If instead a solution

where solar panels are built to a larger unit in one part of a trading centre, then the

prices will probably be different, i.e. less. This latter solution have not been investigated

and used in this study. The solar data used in these simulations are from the 1960’s, but

they where the only one available for Masindi district at the Ministry of Water, Lands and

Environment, Department of Meteorology, in Kampala. Assuming that the solar radiation

in the district has not changed remarkably the last 20 years, then this should not cause a

problem. To see the solar data for Masindi, see the attached CD.

The load profiles used are made by data from UEDCL and assessments done on during

what hours different household applications are used. There is some risk that these


3 Methodology 13

assumptions may be somewhat wrong, but the same base for the load profiles are used

for the different simulations making them as similar as possible.

3.4 Sensitivity analysis Here the concept of sensitivity analysis will be explained and why it is important. There

are different questions that need to be answered before anyone is willing to invest in a

new system. One of the most critical ones is what the risk for the investor is. Sensitivity

analysis is used in the simulation program HOMER. The sensitivity variable used is the

PV capital cost multiplier and diesel cost multiplier.

There are several reasons for doing a sensitivity analysis. It is a very good tool when

there are doubts about different variables. These might be uncertainties in expected

load, diesel price etc. By doing this type of analysis it is possible to determine how

important one variable is for the outcome of the simulation and how the result vary with

the value of the uncertain variable, i.e. you determine the sensitivity. It is also possible to

do a sensitivity analysis to determine what the price on for example PV systems have to

be, to be economically compatible with diesel generators and grid extension. This means

that by doing a single analysis, it is possible to simulate several different situations with

different variables.

Questions that can be answered by doing a sensitivity analysis in HOMER:

How low must the price of solar systems be to be competitive with diesel and grid

extension?

How close to the grid must a certain load be for it to be economically viable with

grid extension?

How high must the diesel price be to make PV systems competitive?

What happens if the load differs from the assumed one?


4 Power generation and technology options 14

4 Power generation and technology options This chapter will try to give an overview of the power generation in Uganda and Masindi

district. Hydro power is the major energy source for electricity production, and potential

hydro sites that are under planning for future expansion are given in 4.1.3. This chapter

will also deal with the power producers that exist in the area and alternative energy

resources. Finally the electrification systems used for simulations in HOMER will be

discussed.

4.1 Power generation

4.1.1 Present power generation in Uganda Presently the only major power generation is situated

around Jinja, consisting of three power stations;

Nalubaale 180 MW, Kiira 120 MW and Mubuku 14

MW. The latter one is privately owned and supplies

KCCL industries [1]. In the northern regions of Uganda,

which are more isolated due to both long distances and

because they are unsafe areas, the access to

electricity is mainly through diesel generation.

4.1.2 Power producers in Masindi district There are no major power producers within Masindi d

except for Kinyara sugar works Ltd. Kinyara Sugar Work

self sufficient regarding electricity due to own p

production. This production is based on the burnin

bagasse and they also use diesel generators as a supplem

Since there is a substation just outside the facto

connection to the main grid could easily be established. D

low energy prices it is not economically viable for Kin

sugar works (according to their own study) to produce

Britt-Mari Langåsen

Picture 4-1 The dam at thepower station in Jinja

istrict

s are

ower

g of

ent.

ry a

ue to

yara

more
Picture 4-2 Kinyara Sugar Works Ltd
Spring 2004


energy and sell the excess to the utility. It is though an alternative if the energy prices

rise. They have recently (March 2004) built a new bagasse furnace that can be

transformed to generate power. Kinyara Sugar Works are planning to expand their sugar

production and thereby burn more bagasse. [30]

There are some sawmills in the district, but these are very small and consume their

power from the grid. They are not of such a size that makes them suitable for electricity

generation, and especially not to sell electricity to the grid. [32]

4.1.3 Future power generation There are several potential generation sites within a reasonable distance from Masindi

district. These are both hydro and geothermal. The possibilities for wind power are rather

scarce as the average wind speed in Uganda is 3m/s [14].

Presently only 380 MW is built at Jinja hydro station but there are plans of building

power stations along the Nile. One problem with this is that several of the suggested

places for building power stations are situated within national parks. This raises

environmental issues and makes financing difficult. Financing are mostly from the World

Bank and foreign governments. There have been assessments made for the power that

can be recovered by hydropower from the Nile along the stretch between Lake Victoria

and Lake Albert and this has come to about 3000 MW. [1]

Some 22 places have also been identified for small hydro power generation and some of

these far away from the existing grid, making them potential energy sources for mini-

grids in rural areas. Today the installed capacity from small hydro is 13 MW. [14]

Places that are suggested for large scale hydro power generation are:

Table 4-1 Suggested places construction of new hydro power generation

Place Year of construction Kakira 2005

Bujagali 2006-2007

Karuma 2008-2009

Kalagala 2010-2015

Murchison falls 2015



4.2 Alternative energy sources An alternative type of power generation is by geothermal energy. Some research has

been done in this area in three places in Uganda, whereby one of he places is not far

from the Masindi district. This is Kisoro, close to Lake Albert in Hoima district. The other

two are in Katwe and Buranga. [14]

Presently the research is still concentrating on localising where the geothermal spots are

and doing feasibility studies on these. It is not yet decided what year a geothermal plant

may be up and running. No figure for the assumed power produced can be given at this

point either. [33] The expected output is somewhere around 450 W for Uganda. [14] This

power is meant to supply the surrounding area and the national grid [23]. More detailed

investigations are needed to give confidence to the private sector to make them willing to

invest in these projects.

Wind power is not a preferable alternative in Uganda, due to relatively low wind speeds.

This is valid also in the costal areas. The average wind speed in Uganda is 3 m/s [14].

There are some types of wind generators that work on such low wind speeds, but they

are not studied in this thesis. There are some places around Lake Victoria and on hills

that has a higher average wind speeds, but the high initial costs involved in wind power

systems are also to high for most communities in rural areas.



4.3 Rural electrification technology options Following are the electrification systems that are used as alternatives in HOMER. They

will here be presented with their benefits and disadvantages to give a better

understanding of the conclusions and recommendations later in this study.

4.3.1 Solar power systems Solar energy is a resource that is readily available

in Uganda, but due to high investment cost, it’s

not a very widespread technology in rural parts.

As with all technologies, solar energy has both

advantages and disadvantages and some of then

are put up here.

Benefits of solar energy: ♦ Solar modules convert freely available sunlight

directly into electricity.

♦ Solar systems generate no pollutants or

exhaust gases when producing energy.

♦ Solar energy systems require little maintenance

20 years.

♦ The price of solar panels have fallen over the p

making the use of solar systems an economic via

♦ Solar systems are modular and can easily be exp

♦ Because off low system voltage the risk of ele

homes and schools are also smaller when using

kerosene lanterns.

Disadvantages of solar energy: ♦ The initial cost for a solar system is high by r

people to buy the systems, even though the lif

generators or kerosene.

♦ The performance of a solar system is depend

market.

♦ Appliances that run on 12 V (PV system voltage

yet.


Picture 4-3 Example of a Solar Home System

and have an estimated lifetime of over

ast years while the oil price have risen,

ble solution for powering households.

anded when the load increase.

ctric chocks are small. The fire risk in

solar systems compared to those lit by

ural standards which make it hard for

etime cost is lower compared to diesel

ent on the batteries available on the

) is not readily available on the market

Spring 2004


♦ Solar systems require frequent maintenance.

♦ There are few trained technicians to design, install and maintain solar energy systems.

♦ Solar systems are usually of a very small scale meaning that if there is a relatively high

energy demand the investment cost will be very high.

Besides from PV having a very high cost when the systems become very large, they

also occupy a large area for the solar panels. This means that if solar systems are to

supply a large load, then an option might be to build all the panels as one unit and the

customers might come there and get their batteries charged and taken home to the

respective homes. A positive thing with this system is that cleaning and maintenance will

probably be easier if all the panels are situated at one location. A larger solar system

with a mini-grid is also an option. This has not been included in this study.

Another option is that only the most important loads get power in a community that is

situated far away from the grid. Important loads might be the local health station, the

church and the school. Some street light on the main road / along the shops will also

make the area more secure and create a natural gathering point for the village’s

inhabitants. This option will give smaller systems with less investment cost.

Because that the investment costs for PV systems are so high people can’t afford to

have large systems meaning that the systems will be too small to generate enough

power for any income generating industries, except maybe home based sewing

industries.



4.3.2 Diesel generators Diesel generators are commonly used in many parts of Uganda. In several parts of the

country diesel generators are the only available electricity source, and in other places

they work as backup generation.

Benefits of diesel generators: ♦ Modular

♦ Relatively low investment cost

♦ Widely available technology

Disadvantages for diesel generators: ♦ Diesel generators give pollutants and

exhaust gases when producing electricity.

They also produce different levels of noise,

depending on type and size.

♦ Diesel is expensive in Uganda, and this is

not likely to decrease in the future.

♦ Needs skilled personnel to do frequent mainten

♦ Dependence on the availability of spare parts

There are many different sizes of generators and

simulations are in the range of 4 kW to several hu

cost curve, but become cheaper per kW the high

best when they work on a high load ratio. This

dimensioning because they will then use relatively

This is something that has been taken into acco

probably there will be a load growth in the trading

is very much an optimization problem.

Diesel generators need proper maintenance to w

This requires trained personnel to operate and m

rural areas. Another problem is the availability of s

most obvious problem with diesel generation is

energy, and will eventually have to be replaced w

a system of diesel generators will make a non oi


Picture 4-4 Example of a diesel generator

ance

the sizes that have been used in the

ndred kW. They do not follow a linear

er the rating. Diesel generators work

means that there is no use in over

more fuel because of low load ratio.

unt in the simulation program. Most

centre, so the choice of generator size

ork optimal and have a long lifetime.

aintain it. This might be a problem in

pare parts such as filters etc. But the

that it is not a sustainable source of

ith some other technology. Relying on

l producing country dependent on the

Spring 2004


importation of oil and the world price market of oil that is currently rising [10]. For a poor

country like Uganda, that has all of its petroleum products imported, this is an

undesirable situation. 15% of the country’s export earnings go to import of petroleum, at

the expense of development programmes. [14]



4.3.3 Grid extension There are several reasons to expand the transmission

and distribution system. [1]

Benefits of grid extension: ♦ Rural electrification

♦ To increase the security of the system by introducing

a more meshed configured system

♦ To allow the safe input from new power plants

♦ To reduce losses and improve operational economy

of the network

♦ To fulfil agreements with neighbouring countries.

There are also negative impacts that may arise from an ex

power production.

Disadvantages with grid extension: ♦ Environmental impacts from building and sustaining the

♦ People may illegally tap the grid and thereby cause po

can also be hazardous for people.

♦ High investment costs

When planning for a grid extension an anti-theft design mu

seen in the choice of conductors. Instead of choosing a c

stolen because its ability to be remelted, an expensive b

may be used. It may also be an alternative to use a

distribution lines which makes it impossible to use the p

instead of 230 V) [29] There are also several differen

chosen, since this is also something that is exposed for th

Environmental impacts that a grid extension in the Masind

• Impacts on flora and fauna

• Impacts on drainage and water resources

• Impact on landscape and visual amenity

• Impact on land use and agriculture

• Electric and magnetic fields


Picture 4-5 Rural electrification by grid extension

panded grid compared to local

grid.

orer quality of the power and this

st be considered. This may be

heaper conductor that may be

ut non remeltable transformer

higher voltage level on the

ower directly. (I.e. use 400 V

t types of poles that may be

eft.

i area may cause are[1]:

Spring 2004


• Clearances around lines

In general will the environmental impacts of new power lines occur within the line

corridor or near to it. There will also be some impacts on the nature if access roads have

to be built to get to the line corridor. When travelling around in Masindi district, it can

seen that except for around Kinyara sugar works where they farm sugar cane, large

areas of the countryside are not cultivated. These are mainly grasslands with low to

medium high vegetation except for in Budongo area where there is an old Mahogany

forest. In most parts of the district there will probably be few environmental problems

with grid extension concerning impacts on landscape, land use and agriculture. If lines

are drawn through sugar cropping or otherwise where there is high vegetation, there

must be a high limit of 1.8 m for the crops. This is a safety limit that should be followed.

There are also some impacts from the environment on the transmission lines, for

example growth of vegetation that can touch the conductors and during rainfalls, trees

can fall on the lines. Wind I s also a problem that can cause trees to fall on the lines in

line clearances are not followed. In some areas with salty soils, this can harm the

steyers that support the electric poles that are mostly made out of wood.

4.4 Conclusion A summary of the technology options are shown in Table 4-2.

Table 4-2 Summary of the technology option Technology PV Diesel generation Grid extension

Application area SHS, small scale

applications

Stand-alone systems, back-up

power

Areas with a high load, domestic and

industrial Usual size 50W- 7kW-200kW

Average cost US$280- US$ 7000-34500 US$20000 / km

+ Sustainable technology

Widely available technology

Available technology, no maintenance

-

High investment costs, frequent maintenance

Pollution, noise, frequent

maintenance

High investment cost, maintenance, deforestation and use of agricultural

landscape


5 Analysis of trading centres 23

5 Analysis of trading centres This chapter will give an overview of the socio-economic situation present in Masindi

district and the expected development. It will also give the criteria that where used to find

the different trading centres that where used in this study. Finally the background for the

assumed load profiles will be given and the profiles used in the simulation program will

be presented.

5.1 Masindi district Masindi district is one of 56 districts in Uganda. It is found in the north-west, close to

Lake Albert, and this district covers an area of approximately 9442.9 sq km. The district

has a total population of 469,865 where the urban population is 0.8% and the rural

population is 99.2%. The main economic activities carried out in the district are

agriculture, trading and others and the household main sources of livelihood are:

subsistence farming, commercial farming, trading, employment income, family support

and others. [2]

Considering that electrification work best as a mean for economic development when the

overall conditions are right for rural income growth and when it is complemented by

social and economic infrastructure development [28], the trading centres where chosen

because of their importance in developing Masindi district. These trading centres, which

are small communities, where chosen with the help from the district population office in

Masindi.

The trading centres studied in this report where chosen because of: ♦ Their importance for the development of Masindi district

♦ They are presently not connected to the main electrical grid and are not likely to be in a

near future

♦ They are likely to continue growing as trading centres

The last point is important as it is no idea to invest in powering areas that are likely to be

mitigated from in a not to distant future, by grid extension. Three different types of

trading centres will be studied further. These have different number of inhabitants and



thereby different loads. The different centres have about 20’000, 10’000 and 2000

inhabitants.

5.2 Socio-economic characteristics of Masindi A large portion of the population within Masindi district lacks skills or is semiskilled, so

most of the labour force is engaged in farming. Cottage industries engage very few

people and young men therefore turn to petty trade or motorcycle business. Young

women don’t engage in the visible economic activities to a high degree because of a

lack of education.

The most important source of livelihood in Masindi

district is agriculture and about 76% of the population is

engaged in farming activities. Most of the farmers are

family based with an area of 1-2 hectares under

cultivation and only 1 % of the population is employed in

commercial farming. [1] The only major mono cropping

plantation within the district is Kinyara Sugar Works who

crop sugarcane.

There is only one large industry in the area (Kinyara

sugar works) but there are several small-scale industries c

new ones are agro-based processing industries which inclu

hulling and furniture making. At the moment some of these a

lack of spare parts. [1]

When it comes to trading petty trading engages about 6.9%

about 3% of the economically active population. [2] The com

the district and salted and sun-dried fish across the border t

Congo.

The need for electricity in rural areas are mostly for lighti

richer areas also for fans, refrigeration and hot plates. Toda

used for lighting and the food that is used is bought fres


Picture 5-1 Small trading centre

oming up. The existing and

de maize and oil milling, rice

re out of production due to a

and formal trading employs

modity traded is crop within

o the Democratic Republic of

ng, TV and radio and in the

y candles and kerosene are

h or needs no refrigeration.

Spring 2004


When cooking, charcoal and fuel wood are used. Battery driven radios are used, but

replacement batteries can be hard to get and are relatively expensive. When I visited

Biizi trading centre, a community with a bit more than 2000 inhabitants, the water pump

was driven by hand power and there seemed to be no lights outside any shops.

5.3 Load assessment Load assessments have been done for the three types of trading centres investigated.

These assessment have been done with data about time usage and type of appliances

from UEDCL, and by assuming the usage of appliances during the day. To see a more

thorough description of the load profiles, see the CD in appendix 11.3.

Using data from UEDCL (see Table 5-2) the energy consumption for different types of

rural households has been calculated. These trading centres have been investigated

further as to represent a small, medium and large trading centre. Estimates given in

Table 5-1 are used for energy consumption for rural households in the simulations in

HOMER. Two different load profiles have been found for the three trading centres, one

for constrained demand and one for an unconstrained demand. Constrained demand

can signify different demands depending on the context. Generally it is a system

demand limited by generation supply for those that are already connected to the grid.

This demand does not really reflect the actual load growth over a given period of time.

Unconstrained demand is what they would use if there where enough generation

capacity and no power shortages. [31] Constrained demand can also signify the power

use according to a certain income level, and that is the meaning I have chosen to use

here. This has been done to represent different loads that can be expected.

Unconstrained use will in this case mean the power use if the customers where at a

higher income level.

These two load profiles have been used in the simulations to find the difference in

optimal systems to supply the trading centre with energy. The constrained demand is not

preferred situation, but one that the customers could “manage” if that is the only

alternative.



Table 5-1 Energy consumption for rural households in Uganda

Type of household Energy consumption (kWh)

Constrained demand Unconstrained demand

Rural high 3.453 6.077

Rural medium 0.612 1.556

Rural low 0,044 0,108

Following are the data from UEDCL for the time usage for different appliances in rural

households. It can be seen that there is a large difference between a rural high and a

rural low household and thereby will the load profiles that are built on these figures be

quite different. There is also quite a large difference between the constrained and

unconstrained demand.

Table 5-2 Time usage ( in hours) of electrical appliances in rural households

Rural high Rural medium Rural low Constrained

demand (h) Unconstrained demand (h)

Constrained demand (h)

Unconstrained demand (h)

Constrained demand (h)

Unconstrained demand (h)

Fan 6 15 5 15 Flat iron 0,5 0,8 Fridge 10 14 6 14 Hot plate 2 3,5 Lighting 4 8 2 6 3 6 Radio 4,9 7 3 6 2 6 TV 1,6 5 2 4 Power rating for appliances:

Fan 42 W Flat iron 1300 W Fridge 45 W Hot plate 1000 W Lighting 8 W Radio 10 W TV 35 W

Typical loads in a rural household are lights, small televisions and radio sets and irons.

Other rural loads are electric fencing, water pumping, small industries and institutions,

telecommunication, lighting at schools and churches, small shops and health centre

vaccine refrigeration. In the simulations only the loads from households have been used.

Usually the shops in a trading centre function as homes as well, with the same



standards, so these are included. Excluded are churches, schools and health centres.

These where considered as comparably small loads that will not affect the results to a

high degree.

Based on data from the census of 2002 in Uganda, there are on average 4.8 persons

per household [25]. This information together with Table 5-1 and Table 5-2 will give the

following table for the expected power consumption for the chosen trading centres. The

expected electric energy growth according to a study made by SWECO, is 7.9% per

year during the period 2001-2016 [1] and the consumption for 2010 is also given.

Below is given an explanation of the colours and from what load profiles the demands

calculated.

Table 5-3 Present power need in the Masindi region. Based on data from UEDCL

Energy consumption 2004 Constrained demand

Energy consumption 2010 Constrained demand

Energy consumption 2004 Unconstrained demand

Energy consumption 2010 Unconstrained demand

(kWh) kWh kWh kWh Kiryandongo 4339 6846,55 8563 13513,49 Bweyale 3773 5953,60 7446 11751,02 Kigumba 2097 3309,96 4400 6943,83 Katulikire 2060 3250,19 4321 6818,45 Kabango 645 1017,60 1634 2578,52 Butiaba 604 953,14 1530 2415,17 Masidi Town 577 909,98 1461 2305,82 Mutunda 548 865,22 1389 2192,41 Apodorwa 531 838,03 1346 2123,50 Biiso 518 816,90 1312 2069,95 Kyatiri 516 813,78 1307 2062,05 Nyabyeya 494 779,81 1252 1975,97 Pakanyi 469 739,77 1188 1874,53 Karuma 325 513,01 822 1297,83 Nyakabale 315 496,87 797 1257,00 Wanseko 306 483,26 775 1222,58 Kigezi 202 318,76 510 804,17 Bugoigo 201 316,48 506 798,42 Buliisa 180 284,15 454 716,85 Kijura 176 277,62 444 700,38 Biizi 20 32,20 50 79,04



Pink 0.2*rural high+0.4*rural medium+0.4*rural low

Purple 0.1*rural high+0.4*rural medium+0.5*rural low

Blue 0.4*rural medium+0.6*rural low

Turquoise 0.3*rural medium+0.7*rural low

Green 0.2*rural medium+0.8*rural low

Yellow 1.0*rural low



5.3.1 Small trading centre A typical load profile for a small trading centre can be seen in Figure 5-1. Two demand

profiles have been done, a constrained demand and an unconstrained demand. As can

be seen from the profile below, the maximum demand will occur during evening time and

there will be a smaller peak in the morning. The minimum load is after breakfast and

during night time. The peak is almost three times higher than the base load. The reason

for this is that there are very few large loads on during the day time in a domestic area

that a small trading centre mainly is. Most of the energy in rural areas is for lighting

purposes and entertainment meaning the energy will be consumed after sunset and

before sunrise i.e. between 6 p.m. and 7 a.m. The wattage behind the peak is mainly for

lighting and radio and a maybe a few television sets. There is almost no load during

night time and that is because that most lights are off besides from security lights and

larger loads like fridges are turned off during the night due to high energy prices.

Expensive loads like security lights and fridges may not even exist in a small rural area

that most often is a poor area as well. This information is found from talking to Ugandans

and after visiting some trading centres and seeing where they may live and the standard

under which they live in.

Load profile

0,00

2,00

4,00

6,00

8,00

10,00

1 3 5 7 9 11 13 15 17 19 21 23

Time (hours)

kWh

Constained Unconstrained

Figure 5-1 Load profiles for Biizi trading centre



The peak effect in the constrained situation is 12.8kW and the energy is 20kWh/day,

calculated from the simulation program HOMER. The same data for the unconstrained

situation is 12.8kW and 55kWh/day, respectively. The peak effect is the same in both

situations, but the energy consumed is largest in the unconstrained situation. This is

natural, because the consumers will not have more appliances in the unconstrained

situation, but they will use the ones they have for more hours per day.



5.3.2 Medium trading centre The load profile for a medium trading centre can be seen in the figure below. The

medium trading centre is represented by Mutunda, a community with about 10 000

inhabitants. The medium centre is assumed to consist of households that consume

energy according to rural medium and rural low energy pattern. The rural medium

pattern has more appliances than rural low, which can be seen in Table 5-2. The load

profile is created on the basis that the community consist of 60% rural low and 40% rural

medium households.

Load profile

0,00

50,00

100,00

150,00

1 3 5 7 9 11 13 15 17 19 21 23

Time (hours)

kWh

Constrained Unconstrained

Figure 5-2 Load profiles for Mutunda trading centre

In the load profile for Mutunda trading centre it can be seen that the energy consumption

in the unconstrained situation is more than twice the amount in the constrained

condition. (1.393MWh/day compared to 545MWh/day). This difference is mostly due to a

wish to have a fan on at night and also a refrigerator on at day in the unconstrained

situation compared to the constrained. The relatively high base load during night time

that weren’t there in the small example is due to the wish for having a fan on a night. The

unconstrained situation shows that much more power is consumed during the day

compared to the other situation. This is due to a wish for having radios and fans on

during day time.



5.3.3 Large trading centre A load profile that is developed for a large trading centre can be seen in the figure below.

To represent a large trading centre, Kiryandongo with about 20 000 inhabitants have

been chosen. In this profile, all the alternatives for rural energy demands have been

included. The compositions of demands are 10% rural high, 30% rural medium and 60%

rural low households. Explanation of these alternatives can be found in Table 5-2.

Load profile Kiryandongo

0,00

200,00

400,00

600,00

800,00

1000,00

1 3 5 7 9 11 13 15 17 19 21 23

Time (hours)

kWh

Constrained Unconstrianed

Figure 5-3 Load profiles for Kiryandongo trading centre

In this final profile the load profiles are more complex due to a more complex compound

of households. It can be seen here that the scale on the y-axis is very different from the

two previous examples and even though the trading centre have only twice as many

inhabitants as the medium example, the power drawn is 3 times as much. This trading

centre represents the one with most inhabitants, about 20 000, and it is assumed to be

the richest. This means that they have most appliances and also use them the longest

hours. It’s assumed that not all people in the trading centre have a job and a good

income, but the standard is though higher that in smaller trading centres in more rural

areas. The use of hotplates during lunch and dinner time is spread on four hours since

they will not all be used at the same time, even though Table 5-2 states that hotplates

are only used two hour per day. In this way it will seem that half the population use it two

hours each. The use of hotplates gives part of the explanation of the high peaks as they

are very power consuming. The base load is not much different from the load for the



medium trading centre. The consumers have a fan on during day time or night time,

depending on if it is a constrained or unconstrained demand. The fridge is on during day

time only.

5.3.4 Summary for the trading centres When seeing the load profiles for these trading centres, the problems with dimensioning

a system to supply these kinds of trading centres can be seen. Because of the low base

load and high peaks that arise under a relatively short period of time, grid extension is

not automatically the best way of supplying power even though there are quite a lot of

inhabitants. It will be seen in the simulation results from HOMER that the price of solar

systems and diesel will have a great impact on the optimal solution and the break-even

grid extension distance.


6 Basis and optimal solutions 34

6 Basis and optimal solutions The results from the simulations will be presented in this chapter. There will be a more

thorough presentation for the three different types of trading centres investigated and

finally a general discussion about the results found. First the equations that the result

from the simulation program is based on are presented. To get a better understanding of

the results, and to see the detailed simulation input variables, see the HOMER files on

the attached CD in appendix 11.3.

6.1 Simulation basis From HOMER several results can be found; optimal system configuration, total net

present cost (NPC) and break-even grid extension distance among others. As described

in chapter 3.4 sensitivity analyses is a tool in HOMER that can be used to find results in

risk analysis and system configuration based on the development of prices.

The demand in the simulations is divided in constrained and unconstrained demand.

The main economic output from HOMER is the total NPC. The NPC decides the ranking

of all system options and is calculated according to Equation 1. Net present cost can be

understood as the amount one would have to deposit in a bank today, for the amount to

match a given value some given time from now. If the interest rate is high, then the

required time for the amount to grow to the given value becomes shorter. This means

that for a high interest rate the more short-term projects become preferred. This is

usually bad for alternative energy sources that have a high investment cost and long pay

back time. [21]

Equation 1

),(,

proj

totannNPC RiCRF

CC =

where: Cann,tot = total annualized cost [$/yr] CRF() = capital recovery factor i = interest rate [%] Rproj = project lifetime [yr]



As can be seen from the equation above, the NPC is calculated from the total

annualized costs. The total annualized cost is in turn calculated from the sum of the

annualized costs of each component in the system plus other annualized costs. The

annualized cost consist of the operating-, capital- and replacement cost over the project

lifetime.

The capital recovery factor (CRF) that is used in the calculations is found according to

Equation 2. The CRF is used to calculate the present value of an annuity.

Equation 2

1)1()1(),(−+

+= N

N

iiiNiCRF

where: i = interest rate N = number of years

The break even grid extension distance is the distance from the existing grid that gives

the same net present cost of extending the grid as the net present cost for a stand-alone

system. [8] This equation is important as is gives an idea of when to expand the grid and

when to build a stand-alone system. The breakeven grid extension distance is calculated

according to Equation 3 [8]:

Equation 3

omprojcap

totpowerprojNPCgrid CRiCRFC

LCRiCRFCD

+

−=

),(**),(*

where CNPC = total net present cost of the stand-alone power system [$] CRF() = capital recovery factor i = interest rate [%] Rproj = project lifetime [yr] Ltot = total primary and deferrable load [kWh/yr] cpower = cost of power from the grid [$/kWh] ccap = capital cost of grid extension [$/km] com = O&M cost of grid extension [$/yr/km]



As can be seen from the simulations and Equation 3, the higher the cost for grid

extension, the shorter the break even grid extension distance gets and a stand-alone

system may be optimal for a trading centre that is situated far away. This of course is

dependent on the load of the trading centre. If the load is rising, then grid extension may

be optimal after all. Unfortunately, the simulation program does not have the feature of a

rising load demand. This makes it difficult to make realistic future prognoses.

The levelized cost of energy (COE) is the average cost of producing electricity and is

another output from HOMER. It is calculated using the following formula:

Equation 4

salesgriddefprim

totann

EEEC

COE,

,

++=

where: Cann,tot = total annualized cost of the system [$/yr] Eprim = primary load served [kWh/yr] Edef = deferrable load served [kWh/yr] Egrid,sales = total grid sales [kWh/yr]

In the simulations that will be done, Egrid,sales will be equal to zero since there is no

connection to the grid.



6.2 Small trading centre Biizi trading centre has been chosen to represent a small, rural community. Biizi has

2206 inhabitants according to the census of 2002 [25]. The characteristics for its load

profile is a very peaky load and very little or no load during large parts of the day. The

load profile can be seen in Figure 5-1. A problem with a small trading centre is that if

some consumers decide not to get connected to the new electricity supply system, then

the relative change in load and thereby the risk for the investor will become larger

compared to in a large trading centre where each customer represent a smaller part.

Following are the simulation results for Biizi trading centre. The results will be presented

with the optimal solution given different capital multipliers for the photovoltaic systems.

(PV Cap. Mult.) The first column, “PV Cap. Mult.”, can be understood as the part of the

actual price for PV systems. This is done to represent a change in price for PV systems

and to see how low the price must fall on solar systems for them to be comparable with

diesel systems. The next columns, “PV”, “Gen” and “Converter” columns, give the effect

of the optimal energy sources. “Total capital” is the investment cost for the solution and

“Total NPC” is the total net present cost which is explained in chapter 6.1. “COE” is the

levelized cost of energy is that is the average cost of producing electricity and the

column “Renewable fraction” gives how much of the total installed energy is renewable.

In the simulations a cost of US$0.09 / kWh is used. [26] For other input variables, see

appendix 11.2. “Capacity shortage” is the difference between the actual operating

capacity that the system can provide and the required operating capacity. Finally, the

“Diesel” column tells how much diesel is used and the “Gen (hrs)” tells how many hours

the respective diesel generators are running.

As can be seen in Table 6-1 and Table 6-2, the amount of PV depend strongly on the PV

price. Later it will be seen that they also depend on the diesel price and the load.



6.2.1 General solutions The optimal solutions for Biizi when there is a constrained demand are as given in Table

6-1. In the simulations, energy demand of 20kWh/d and 12.8kWp has been used. Input

parameters can be seen in appendix.

Table 6-1 Optimal solutions for Biizi trading centre, constrained demand

PV Cap. Mult.

PV (kW) Gen1 (kW)

Gen2 (kW)

Converter (kW)

Total capital

Total NPC

1.00 4 4 $ 8,000 $ 40,015 0.50 4 4 $ 8,000 $ 40,015 0.10 4 4 $ 8,000 $ 40,015 0.01 18.0 4 4 4 $ 9,811 $ 39,414 PV Cap. Mult.

COE ($/kWh)

Renewable fraction

Capacity shortage Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

1.00 0.453 0.00 0.13 2,379 1,095 944 0.50 0.453 0.00 0.13 2,379 1,095 944 0.10 0.453 0.00 0.13 2,379 1,095 944 0.01 0.446 0.84 0.13 2,216 1,095 766

The different results give almost the same total NPC, independent of the PV capital

multiplier. The reason that the different solutions have almost the same total NPC is that

the optimal systems are the same, except for the case when the PV capital multiplier is

0.01. The price for 20 kW PV is in that option very small and will affect the total cost little.

The optimal power generation in these options will therefore be diesel generation.

The optimal solutions for Biizi trading centre when there is an unconstrained demand are

as in Table 6-2. The simulation program has calculated with an energy use of 55 kWh/d

and 12 kWp. Other input parameters can be seen in appendix.

Table 6-2 Optimal solutions for Biizi trading centre with an unconstrained demand

PV Cap. Mult.

PV (kW) Gen1(kW)

Converter (kW)

Total capital

Total NPC

1.00 7 $ 7,000 $ 94,075 0.50 7 $ 7,000 $ 94,075 0.10 15 7 7 $ 20,245 $ 91,818 0.01 35 7 7 $ 12,285 $ 75,130



PV Cap. Mult.

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

1.00 0.414 0.00 0.16 6,282 3,285 0.50 0.414 0.00 0.16 6,282 3,285 0.10 0.398 0.67 0.15 5,032 2,747 0.01 0.323 0.84 0.14 4,549 2,398

For the unconstrained demand, the solutions are a bit different. It can be seen that the

investment cost increase when the PV capital multiplier decrease, but the total NPC

decrease at the same time. The reason for the increased capital cost is the increase in

installed solar power. It can be seen from these results that when PV is simulated with

its actual, present cost, no PV power will be used and only a 7 kW diesel generator will

produce energy. The capacity shortage is 0.16 and the total Net Present Cost (NPC) is $

94,075. To have no capacity shortage, the total NPC will arise to $ 108,677 and two

diesel generators, 4kW and 7kW respectively, will be used. (See Biizunconstrained.hmr

on the CD) The cost for PV systems has to come down to a tenth of the original cost for

it to be optimal to use PV according to the simulation. In this solution 15 kW of PV will be

used together with two 7 kW diesel generators. The total capital cost is more than twice

the first option, but the total NPC is lower and this decides the optimum in HOMER. The

reason that the net present cost goes down even though the capital cost rise, is that as

more PV comes into the system, the operating costs will go down, compared to a system

with diesel generation. A diesel system has a relatively low investment cost, but high

operating cost. Therefore the annualized costs that are the base for the net present cost

will be high for a diesel system, but lower for the solar system.

6.2.2 Load profiles and unmet load In the following figures, unmet load, capacity shortage and excess electricity is shown

compared to the load. Unmet load is the electrical load that cannot be met because of to

little generation. The capacity shortage is, as explained above, the difference between

the actual operating capacity that the system can provide and the required operating

capacity. A system has often a certain amount of operating reserve to cover unexpected

loads. The excess electricity is surplus energy that occurs when there is a surplus of

power being produced and no load is to be served. The excess electricity has to be

dumped in a load called a dump load, if there isn’t enough battery to absorb it all. A

dump load may be a set of light bulbs or a resistive heater.



The loads in the constrained situation are

peaky and there are only two peaks per day, a

smaller in the morning and a large in the

evening. When looking at these figures from the

simulations with the constrained demand in

Figure 6-1 and Figure 6-2, it can be seen that

there is almost no unmet load and capacity

shortage, in both cases. In the case of PV

multiplier 0.01, there will be an over dimensioning

of the system and a lot of excess energy. This will

actually mean that a lot of the produced energy

has to be used in a capacitor bank and without

any real use. This means that the price of PV

doesn’t need to be that low for PV to be an

economically viable alternative.

Figure 6-1 Load profile from simulations with constrained demand. PV multiplier 1.0

The following figures are from a simulation with

an unconstrained load profile. For such a small

trading centre, it can be seen that the load is

very “peaky”. It can also be seen that in the

system that has a PV multiplier of 0.01, there

will be a lot of excess electricity, compared to

none in the case with a PV multiplier of 1.0. At

the same time the unmet load and the capacity

shortage will be less in the case with the low

PV price. This is because the system has more

installed capacity.



Figure 6-3 Load profile from simulations with unconstrained demand. PV multiplier 1.0

Spring 2004


In Figure 6-3 it can be seen that during evening

peaks, there is both capacity shortage and

unmet load. This result is for a simulation with

the actual cost of PV. When the cost for PV is

down to a hundred, then the situation is

different as can be seen in Figure 6-4. When

the PV price is down to a hundred, four times

as much solar power is installed. A 4kW diesel

generator is also installed in this situation. All

together there will be excess electricity that has

to be dumped during the low loads during the

day.


6.2.3 Break even grid extension distance It can be seen from the break even grid extension distance that the distance changes

with a changing PV price. Logically, the break even distance becomes smaller when the

PV price decreases. Since it is relatively expensive with grid extension, it will become

more economically viable to build PV and diesel generator systems closer to the grid if

the price for these stand-alone systems gets lower.

The break even grid extension distances are in

the constrained condition 1.25 km and 1.23 km

for PV multiplier 1.0 and 0.01 respectively. The

total NPC is around $ 40 000 for both

situations.

In the results from the constrained demand, the

total NPC is almost the same weather the PV

multiplier is 1.0 or 0.01. This is because the load is

small and the systems are the same, except for the

PV.

Figure 6-5 Break even grid extension distance. Constrained demand, PV multiplier 1.0

(See Table 6-1) The PV prices



are so low though, that they barely have an

impact on the result.

When there is an unconstrained demand, the

break-even grid extension distances become

longer. The break even grid extension distance

for this small trading centre is between 2 and 3

kilometres with the given scenario.

If the results from the simulations with unconstrained

demand are compared, it can be seen that the

total NPC is lower when the PV capital

multiplier is 0.01 even though more diesel

generation capacity is installed. This is because

in this situation the system has a lot of PV,

which has a very low cost (1/100 compared to

the actual situation) but also diesel generators

to cover the load as much as possible. With a

low cost on PV, the net present cost will be lower

than in the situation with only diesel generators who

consume (expensive) diesel.

If the constrained demand is compared to the

unconstrained demand, it can be seen that the

break-even grid extension distance is shorter in

the constrained situation. This is reasonable

because in the constrained demand, the load is

smaller and the total NPC is lower than in the

unconstrained demand but the investment cost

for grid extension is still the same and relatively

high for such a low load. The pay back time for grid

extension will be very long with few customers.


Figure 6-7 Break-even grid extension distance. Unconstrained demand, PV multiplier 1.0

Figure 6-6 Break-even grid extension distance. Constrained demand, PV multiplier 0.01

Figure 6-8 Break-even grid extension distance. Unconstrained demand, PV multiplier 0.01

Spring 2004


6.2.4 Variations in diesel price When the diesel price rise, then PV systems become more and more economically

viable for small trading centres. The systems become more expensive if it’s calculated

with the present price of solar systems, so accordingly the break-even grid extension

distance become longer. It is shown by simulations that as diesel prices rice, stand-

alone systems based on solar systems are a good option. As the price for solar systems

a probable to decrease as well, this makes the solution of supplying a trading centre with

PV even better. Unfortunately, complicated simulations with so many variables can’t be

done in HOMER yet. Therefore, the following simulations are done with an increase in

diesel price but otherwise the same input parameters as in the previous simulations.

The diesel prices have been simulated up to three times the present price. From today’s

price of US$ 0.8, through US$ 1.6 up to US$ 2.4. (1DP = 1 times diesel price = US$0.8

etc.)

Cost as a function of diesel price Constrained

05000

100001500020000250003000035000400004500050000

Total capital Total NPC

US$

DG1 DG2 DG3

Figure 6-9 Cost as a function of diesel price when there is a constrained demand Table 6-3 Optimal results for variations in diesel price

PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

1DG 4 4 2DG 5.0 20 8.0 3DG 5.0 20 8.0

When there is a constrained demand, the optimal solutions will change when the diesel

price become twice the price today (US$0.8). As can be seen in Figure 6- the investment

costs will be much higher (more than three times) when the diesel price rise, but the net

present cost will be almost the same even though the diesel price is higher. This is

because there will now be a much less usage of diesel which constitute the major part of



the operating costs. The running costs for solar systems are almost non-existing. The

reason for the high investment costs is that solar power is presently expensive to

purchase.

Cost as a function of diesel price Unconstrained

0

50000

100000

150000

200000

250000


US$

DG1 DG2 DG3

Figure 6-10 Cost as a function of diesel price when there is an unconstrained demand

Table 6-4 Optimal results for variations in diesel price

PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

1DG 7 2DG 7 3DG 7

In the unconstrained condition, the solutions are not affected by a change in diesel price

up to three times the present price. This means that according to these simulations

energy from PV systems will not be an optimal way of supplying energy to Biizi trading

centre when there is an unconstrained demand. An objection to this is that when looking

at the simulation results in HOMER, it can be seen that the NPC is not so much higher

when 5kW PV is installed and the break-even grid extension distance is almost the

same, about 8 km. (See BiiziunconstrainedDG3.hmr on the CD) Adding environmental

concerns into the calculations, PV systems may be an option for powering Biizi at a

unconstrained demand after all.



6.2.5 Only PV or diesel There have also been simulations done on what the cost would be to supply Biizi trading

centre if only diesel generation or solar systems are available. This have been done to

see the differences and to be able to better compare the two options.

For Biizi trading centre with a constrained demand, the following results have been

found.

Table 6-5 Solutions when supplying Biizi with PV or diesel

PV Cap. Mult.

PV (kW) Battery

Converter (kW)

Total capital

Total NPC

COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 5.0 20 10 $ 25,040 $ 42,666 0.492 1.00 0.12 0.50 5.0 20 10 $ 15,040 $ 31,570 0.364 1.00 0.12 0.10 5.0 20 10 $ 7,040 $ 22,693 0.262 1.00 0.12 0.01 30.0 10 10 $ 5,940 $ 20,417 0.244 1.00 0.20

Gen1 (kW)

Gen2 (kW)

Total capital

Total NPC

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

4 4 $ 8,000 $ 40,015 0.453 0.00 0.13 2,379 1,095 944

From this table it can be seen that supplying the community with solar power almost give

the same net present cost as for diesel generation. The capital cost is though three

times higher which for a poor community may be a high obstacle to overcome. Since the

net present cost is almost the same for the two options the break even grid extension

distance is almost the same for diesel and PV 1.0. When looking at the simulation

results, it can be seen that for PV multiplier 1.0 there is little capacity shortage and very

little unmet load. There is a little excess electricity in the middle of the day. For the diesel

option there is little unmet load and capacity shortage as well, and no excess electricity.



Similar results have been found in simulations with an unconstrained demand.

Table 6-6 Solutions when powering with PV or diesel

PV Cap. Mult.

PV (kW) Battery

Converter (kW)


COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 15.0 35 7 $ 115,695 $ 157,701 0.714 1.00 0.19 0.50 15.0 35 7 $ 61,695 $ 97,396 0.441 1.00 0.19 0.10 15.0 35 7 $ 18,495 $ 49,152 0.222 1.00 0.19 0.01 25.0 25 7 $ 8,155 $ 36,822 0.167 1.00 0.19

Gen1 (kW)

Total capital

Total NPC

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

7 $ 7,000 $ 94,075 0.414 0.00 0.16 6,282 3,285

From this table it can be seen that supplying a larger load as with the unconstrained

demand, the NPC for the diesel system is much lower than the PV system with multiplier

1.0. The price of PV systems must be half the present price to be able to compete with

diesel systems. Because of the much higher net present cost for PV with multiplier 1.0,

the break even grid extension distance will be longer for this option compared to the

diesel system. From the simulation results it can be seen that the unmet load and

capacity shortage is almost the same for the two options, but that the excess electricity

is much higher during the day for the PV option.

As a small conclusion of the solutions for a small trading centre, the following can be

said. Diesel generation is the cheapest alternative, but if the price of diesel rise (which is

very likely with today’s oil situation) then solar systems may be a cost effective way of

producing electricity. Especially for a small trading centre, with a load profiles with large

peaks and little ground load, PV systems are a viable option if the relative high

investment cost can be covered. When adding environmental concerns, diesel

generation comes out negatively. If the trading centre is out of a certain distance from

the main grid, grid extension is not a good alternative as a start. It can be seen by the

simulation result that the break even grid extension distance is very short meaning that

for such a small community and scattered load, stand alone systems is a very good

alternative.

If an investment is done in this trading centre according to the unconstrained demand,

but the customers only can afford according to the constrained demand, alternatively,



that fewer customers connect to the system, then this will represent a risk for the

investor. From the simulation results it will be seen that the total capital cost will increase

from $7000 to $8000, but the total NPC will decrease from $94000 to 40000. This is

quite a difference and constitutes a large risk. In this case a positive risk, but if the

investment costs where based on the constrained demand, price difference could

constitute a large problem. In this case the system dimensioned for a constrained

demand would probably be able to serve an unconstrained demand with the difference

that the generators would have longer operating hors and consume more diesel. Some

more unmet load could be calculated. The cost of energy for the constrained demand is

$0.454 compared to $0.414 for the unconstrained demand.



6.3 Medium trading centre To represent a medium trading centre, Mutunda trading centre has been chosen. This

trading centre has according to the census of 2002 9704 inhabitants [25]. To see the

load profile for Mutunda, se Figure 5-2. Mutunda trading centre has in the unconstrained

demand a load during most parts of the day, but for the constrained demand there is a

small peak during the morning, a larger one during the middle of the day and a high

peak during evening time. In the medium trading centre, each consumer does not

represent a large part of the total number of consumers, as it did in the small trading

centre.

Following are the simulation results for the medium community. Here it will be shown

that the optimal solution is much more dependent on the diesel price than the small

trading centre. But still the fraction of solar energy is very much dependent on the price

of photovoltaic as well.

6.3.1 General solutions The optimal solutions when there is a constrained demand are as given in Table 6-7.

The energy demand used in the simulations is 545kWh/d and a 194kWp. The input

parameters can be seen in appendix 11.2.

Table 6-7 Optimal solutions for Mutunda with a constrained demand

PV Cap. Mult.

PV (kW) Gen1 (kW)

Gen2 (kW) Battery

Converter (kW)


1.00 66 66 $ 33,000 $ 917,137 0.50 66 66 $ 33,000 $ 917,137 0.10 100 66 250 50 $ 157,095 $ 588,957 0.01 350 66 250 50 $ 102,055 $ 453,560

PV

Cap. Mult.

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

1.00 0.372 0.00 0.06 78,32 4,38 733 0.50 0.372 0.00 0.06 78,32 4,38 733 0.10 0.250 0.76 0.12 21,671 1,147 0.01 0.192 0.93 0.11 17,598 932



As can be seen from the result, there will be no PV in the two first systems, resulting in

the exact same solutions. When the price is down to a hundred there will be a lot of

excess electricity as will be seen later and the system will be over dimensioned. The

optimal solution for Mutunda trading centre when there is a constrained demand is

therefore to supply it by diesel generation.

The optimal solutions when there is an unconstrained demand are as in Table 6-8. The

demand in the calculations has been set to 1393kWh/d and 190kWp. The energy

demand in the unconstrained demand is then more than twice the energy demand than

in the previous example. This can foremost be seen in the number of operating hours for

the diesel generators.

Table 6-8 Optimal solutions for Mutunda with an unconstrained demand

PV Cap. Mult.

PV (kW)

Gen1 (kW)

Gen2 (kW) Battery

Converter (kW)


1.00 40 40 1 0.6 $ 27,835 $ 1,856,216 0.50 50 66 10 50.0 $ 243,495 $ 1,824,035 0.10 300 66 150.0 $ 330,595 $ 1,421,868 0.01 450 66 150.0 $ 108,355 $ 1,106,953

PV Cap. Mult.

COE ($/kWh)

Renewable

fraction

Capacity shortage

Diesel (L) Gen1 (hrs) Gen2 (hrs)

1.00 0.323 0.00 0.17 157,785 8,760 5,344 0.50 0.328 0.21 0.20 134,881 8,376 0.10 0.253 0.70 0.17 92,469 5,961 0.01 0.197 0.79 0.17 86,723 5,569

In this solution it can be seen that PV power is used in all solutions except the first

option. Owing to the high energy demand, the generators have high levels of operating

hours. It can be seen, that like in the previous examples, the price of solar systems must

come down quite a bit to compete with the diesel systems.



6.3.2 Load profiles and unmet load Following are figures to illustrate the load profile, unmet load, capacity shortage and

excess electricity. For an explanation of these terms, see chapter 6.2.2. From Figure 6-

and Figure 6- it can be seen that for the PV multiplier 1.0 the load is almost fully met and

with capacity shortages only at the highest peaks during the evening. There is some

excess electricity, but not nearly as much as when the PV multiplier is 001.

F

c

c

e

s

o

B


or the unconstrained demand there is a much highe

apacity shortage when the PV multiplier is 1.0 compare

onstrained demand. The load profile also has a higher lo

nergy. According to this simulation there will be many c

hed during the high peaks. In the example where the PV

f excess electricity that has to be dumped.

ritt-Mari Langåsen


r fraction of unmet load and

d to the same multiplier in the

ad factor and consumes much

ustomers that have to be load

multiplier is 0.01 there is a lot
Figure 6-14 Load profile from simulations withunconstrained demand. PV multiplier 0.01

Spring 2004


6.3.3 Break even grid extension distance The break even grid extension distances are

naturally longer for this medium trading centre

compared to the small community. There are

also large differences in distances between the

actual cost of PV and the option with PV

multiplier of 0.01. (28 km compared to 9 km) It

can though be seen that for a trading centre of

this size, the grid can e extended quite far and

it is still more economic than to build a stand alone

system.


For the unconstrained demand the break even

distances are even longer (50 km and 22 km)

because the systems are here even larger and

therefore the systems more expensive. Because of its

size and high energy demand, the community

use a lot of diesel with gives the high net present

cost. This means that grid extension is really a

viable option, given that the load profile will be

more or less as expected during the simulations.

The fact that the trading centre has a relatively

high load factor during the day when if there is an

unconstrained demand, also makes it a good

candidate for grid extension. To have a high load factor

means that the average load is close to the maximum

load. The load factor is not very high here, but definitely

higher that in the other options.



Figure 6-17 Break even grid extension distance. Unconstrained demand, PV multiplier 1.0

Spring 2004


If the break even distances for the constrained

demand is compared to those for the

unconstrained demand it can be seen that they

are half the distance compared to the

unconstrained. This seems logical since the load

is less than half to.

Figure 6-18 Break even grid extension distance. Unconstrained demand, PV multiplier 0 01

6.3.4 Variations in diesel price For the medium trading centre there have also been done simulations for what happens

when the diesel prices rise. The same increases in diesel price as in chapter 5.3.1 have

been used.

Cost as a function of diesel price Constrained

0

500000

1000000

1500000

2000000

2500000


US$

DG1 DG2 DG3

Figure 6-19 Cost as a function of diesel price when there is a constrained demand


PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

1DG 40 66 2DG 5 66 40 5 3DG 5 66 40 5

With the constrained demand in the medium trading centre, the simulations show that

when the diesel price rises, there will be possibilities for solar systems to be

economically viable. In the constrained demand the installed PV will not cover much of



the load and it can be seen from Figure 6- that the total NPC will be high for the energy

systems as the diesel price rise. This is as mentioned before mostly due to the operating

cost for the diesel generators.

Cost as a function of diesel price

0500000

100000015000002000000250000030000003500000400000045000005000000


US$

DG1 DG2 DG3



PV (kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

1DG 5 40 40 5 10 2DG 5 7 66 2 5 3DG 90 4 40 24 10 75

For the unconstrained demand there is a dramatic change in the installed capacity of PV

when the price of diesel rise to three times the present. As can be seen from Figure 6-

the total NPC has not increased as much between change in diesel price from US$1.6 to

US$2.4 as between the price lift from US$0.8 to US$1.6. This is because now half of the

peak load can be covered by the installed effect of PV, which has very low running costs

that in turn affect the net present cost.

6.3.5 Only PV and diesel The following results are from simulations done if only diesel generation or solar systems

where available. The main conclusion that can be drawn from this is that in general,

diesel generation is the cheapest alternative.



If there is a constrained demand, the following results was achieved:

Table 6-11 Solutions when powering with PV and diesel

PV Cap. Mult.

PV (kW) Battery

Converter (kW) Total capital Total NPC

COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 125 400 100 $ 1,170,995 $ 1,558,089 0.717 1.00 0.19 0.50 125 400 100 $ 632,995 $ 956,495 0.440 1.00 0.19 0.10 125 400 100 $ 202,595 $ 475,220 0.219 1.00 0.19 0.01 200 250 100 $ 89,855 $ 352,859 0.162 1.00 0.19

Gen1 (kW)

Gen2 (kW)


COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

40 66 $ 30,240 $ 777,266 0.340 0.00 0.15 65,917 3,222 1,891

The total NPC for supplying the Mutunda community with only solar power is much

higher than with diesel, about twice the cost, and the upfront investment cost is much

higher.

If there is an unconstrained demand the following results is achieved:


PV Cap. Mult.

PV (kW) Battery


COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 325 600 150.0 $ 2,978,495 $ 3,814,217 0.703 1.00 0.19 0.50 325 600 150.0 $ 1,560,495 $ 2,228,461 0.411 1.00 0.19 0.10 325 600 150.0 $ 426,095 $ 959,856 0.177 1.00 0.19 0.01 675 400 100.0 $ 154,155 $ 642,310 0.117 1.00 0.20

Gen1 (kW)


COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

88 $ 19,412 $ 2,048,261 0.344 0.00 0.13 178,878 8,760

For the unconstrained demand the solution is more or less the same as in the above

example. The total NPC is much higher for the PV system and the investment cost is

almost infinitely higher. A thought is though that in these simulations the costs for the

solar systems is the cost for solar home systems. In reality, if such a large trading centre

would be supplied with power from solar power, it would probably be done in another

way.



If investments where made and another demand than the predicted would happen for

the medium trading centre, then a similar result as he one in the small trading centre

would occur, but with larger mounts. If the demand is calculated to be the unconstrained,

but the constrained occur, then the investment costs would be more ($33000 to $28000),

but the net present cost would be calculated to be much higher. As in the previous

example, the net present cost would be only half, $917000 compared to 1 850 000. If the

opposite occurs, then the expenditures will be higher, but the revenue from more

customers will also be there. As there is two 66 kW generators in the constrained

demand, but only two 40 kW generators in the unconstrained demand, it can be

assumed the load from an unconstrained demand could be covered with the constrained

solutions and that like previous, the generators would operate for longer hours. The cost

of energy is more expensive for the constrained demand, $0.372 compared to $0.323

based on respective demands.



6.4 Large trading centre To represent a large trading centre, Kiryandongo has been chosen. This trading centre

had according to the census of 2002 21852 inhabitants. [25] The load profile for

Kiryandongo can be seen in Figure 5-3. Kiryandongo has two large peaks during

morning and evening and high energy consumption.

6.4.1 General results The optimal solutions for Kiryandongo with a constrained demand are given in Table

6-13 and in the simulations a energy demand of 3.8MWh/d and a peak of 1241kWp have

been used. The input parameters can be seen in the appendix.

Table 6-13 Optimal solutions for Kiryandongo, constrained demand

PV Cap. Mult.

PV (kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW)


1.00 500 120 66 $ 86,160 $ 4,738,761 0.50 500 120 66 $ 86,160 $ 4,738,761 0.10 1000 160 200 200 500 $ 1,154,325 $ 3,888,303 0.01 2500 160 200 200 800 $ 601,485 $ 2,756,877

PV

Cap. Mult.

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs) Gen2 (hrs) Gen3 (hrs)

1.00 0.293 0.00 0.13 440,163 1,748 3,170 4,430 0.50 0.293 0.00 0.13 440,163 1,748 3,170 4,430 0.10 0.258 0.74 0.19 245,409 2,875 1,489 845 0.01 0.183 0.89 0.19 198,662 2,037 1,112 766

The optimal solutions for Kiryandongo with a constrained demand are given in Table

6-13 and in the simulations a energy demand of 6.3MWh/d and a peak of 1196kWp have

been used. The input parameters can be seen in the appendix.



Table 6-14 Optimal solutions for Kiryandongo, unconstrained demand

PV Cap. Mult.

PV (kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery


1.00 5 160 120 200 75 100 $ 158,135 $ 7,398,032 0.50 5 160 120 200 75 100 $ 148,135 $ 7,388,032 0.10 1000 40 160 120 75 1000 $ 1,309,975 $ 6,379,532 0.01 1200 40 160 120 50 1000 $ 533,985 $ 5,588,750 PV Cap. Mult.

COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs) Gen3 (hrs)

1.00 0.298 0.00 0.20 661,66 4,943 6,377 2,767 0.50 0.298 0.00 0.20 661,66 4,943 6,377 2,767 0.10 0.259 0.65 0.20 355,625 3,541 3,564 4,191 0.01 0.227 0.70 0.20 339,778 3,333 3,476 3,952

The most apparent with the results from the simulations of Kiryandongo trading centre, is

that the break even grid extension distance is very far. This indicates that supplying a

rural trading centre of this size with a stand alone system as designed in this simulation

is very expensive. This becomes more obvious when comparing the energy demand to

that of Biizi trading centre, which represents a small community. The energy demand

here is under the constrained condition over 4 MWh/day, compared to xxx. This gives a

much higher demand for energy which is a corner stone for making grid extension

economically viable.



6.4.2 Load profiles and unmet load

During the high peaks in the evening there seem to be some unmet load and capacity

shortage for a system with constrained demand and a PV multiplier of 1.0. When the PV

price goes down to a hundred there is a lot of excess energy but also some capacity

shortage and unmet load even here. This means though that there is a lot of solar power

and diesel generation installed, it is not enough to fully cover the accumulated effect. In

the simulations a maximum annual capacity shortage is set to 20%.

When there is an unconstrained demand, it seems t

shortage and unmet load. When the PV price is low, th

as with the constrained demand. It can be seen from

capacity intalled in the unconstrained situation, which c


Figure 6-22 Load profiles for simulation with constrained demand. PV multiplier 0.01


o be a higher fraction of capacity

ere is not so much excess energy

the tables above that there is less

an be seen from these figures.


Figure 6-23 Load profiles for simulation with unconstrained demand. PV multiplier 1.0

Spring 2004


6.4.3 Break even grid extension distance In the constrained option, the break-even grid extension distance is shorter, and this is

reasonable because the energy consumed is less, even though the peak effect is almost

the same.



As can be expected the break even grid

extension distances from the simulations with

an unconstrained demand are very long. This

means that the best way of supplying power to

a trading centre with a load demand as this will

be by grid extension. At least if the community

is within 200 km from the existing grid. This will

be the situation for most trading centres in Uganda. If

the price on solar systems fall there is still a

good chance that the best way for supplying

electricity is through grid extension since the

break even distance is 120 km when the PV

multiplier is down to 0.01.





6.4.4 Variations in diesel prices

As in the previous chapters, simulations with a changing diesel price have been done for

Kiryandongo trading centre as well. The prices are still the same, US$0.8 to US$2.4.

Cost as a function of diesel price

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000


US$

DP1 DP2 DP3



PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

DP1 500 120 66 DP2 500 66 120 DP3 100 500 66 120 100

For the profile with a constrained demand the price have to be three times the present

price for it to be economically viable with photovoltaic. The load will even though be

covered mostly by diesel generators. By looking at the simulation results in HOMER it

can be seen that there will be a little excess electricity in the morning and some capacity

shortage and unmet load in the high peaks in the evening when the diesel price is three

times the present price. The break even distance for the same diesel price is 477 km.



Cost as a function od diesel price Unconstrained

0

5000000

10000000

15000000

20000000

25000000


US$

DP1 DP2 DP3

Figure 6-30 Cost as a function of diesel price when there is an unconstrained demand


PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)

DP1 5 160 120 200 75 100 DP2 75 120 160 200 75 50 DP3 350 200 24 160 100 300

As in the previous examples, solar systems are more viable when there is a large load

that has to be covered by diesel generators that demand a lot of fuel. For a diesel price

three times todays price, there will be quite a lot of unmet load and capacity shortage

during the evening peaks. This can be seen also from Table 6-16 where the installed

effect is under 450kW. This can be compared to the peak effect of 1241kWp for

Kiryandongo trading centre. The break even distance for grid extension is 675 km.

6.4.5 Only PV or diesel

These are the results from the simulations with a constrained demand where only PV or

diesel generation supply power.


PV Cap. Mult.

PV (kW) Batteries


COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 900 1900 700 $ 8,425,995 $ 10,820,137 0.719 1.00 0.20 0.50 900 1900 700 $ 4,477,995 $ 6,404,914 0.426 1.00 0.20 0.10 900 1900 700 $ 1,319,595 $ 2,872,735 0.191 1.00 0.20 0.01 2200 1000 750 $ 605,855 $ 1,963,513 0.131 1.00 0.20



Gen1 (kW)

Gen2 (kW)

Gen3 (kW)


COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

Gen3 (hrs)

500 66 120 $ 86,160 $ 4,738,761 0.293 0.00 0.13 440,163 1,748 4,430 3,170

From the results of the simulations done to see the optimal solutions when supplying

power with only PV or diesel generation, it can be seen that diesel generation is the

most economical alternative. Diesel generation has less than half the net present cost

compared to PV with its present cost. This is a realistic result since the investment costs

for diesel is much lower than solar systems and it is a large load to be covered.

The results from the simulations for Kiryandongo with an unconstrained demand and

only PV systems or diesel generation can be seen below.


PV Cap. Mult.

PV (kW) Batteries


COE ($/kWh)

Renewable fraction

Capacity shortage

1.00 1500 2400 900 $ 13,850,995 $ 17,539,196 0.718 1.00 0.20 0.50 1500 2400 900 $ 7,262,995 $ 10,171,485 0.417 1.00 0.20 0.10 1500 2400 900 $ 1,992,595 $ 4,277,317 0.175 1.00 0.20 0.01 1800 2000 900 $ 773,155 $ 2,895,017 0.118 1.00 0.20

Gen1 (kW)

Gen2 (kW)

Gen3 (kW)


COE ($/kWh)

Renewable fraction

Capacity shortage

Diesel (L)

Gen1 (hrs)

Gen2 (hrs)

Gen3 (hrs)

200 160 160 $ 99,960 $ 7,406,441 0.296 0.00 0.19 679,925 2,628 8,379 3,218

The same conclusion as above can be drawn for the results from the simulation with an

unconstrained demand. Diesel is by far the cheapest option if only one energy source is

to be used. It though seem like there is less unmet load for the PV system.

Like in the previous chapters for a small and medium trading centre it is considered what

would happen if the load would differ from the assumed one. Here the results are

different and if a constrained demand would occur instead of the unconstrained, then

both the investment cost and the NPC would be lover, more or less half the cost for he

unconstrained solution. ($86000 and $4 700000 compared to $158000 n $7 400 000 for

the solution for the unconstrained demand.) The reason for the high investment cost is

the use of photovoltaic.



The solution or the unconstrained demand has less installed effect, but since the peak

effect it is derived from is almost the same as for the constrained demand, then it is

assumed that it could cover the load for the constrained demand and vice versa with the

difference in operating hours or the diesel generators. The cost of energy is almost the

same for the two options.



General results

After seeing the results from all these simulations some major conclusions can be

drawn. These are:

For a small trading centre, the best way to supply it with power is through diesel

generation. For a medium and large trading centre, the best way if grid extension is no

option, is through diesel generation with a bit of solar power.

If the diesel price rise, then the larger trading centres that has a large load and use a lot

of diesel, should install some solar power. This will give quite high investment costs, but

will lower the net present cost. Some diesel generation will still be installed.

If only solar power or diesel generation is available, then diesel generation is by far the

cheapest alternative with the current prices on photovoltaic. If the prices on PV systems

go down to 50%, then these systems can compete with diesel generation. Still the

investment cost will be much higher than for the diesel option, but the net present cost

will then be about the same and there are also positive effects because of no pollution

and noise.

The risk for an investor is quite large if the load will differ like between the constrained

and unconstrained demand.

To sum up the findings in this result chapter, a table over the results for the general

solutions for the three trading centres will be given. These are all results with a PV

multiplier of 1.0 and represent the cost for supplying a rural trading centre with the given

assumptions in this report.

Table 6-19 Summary of the solutions for rural electrification, PV multiplier 1.0

PV

(kW) Gen1 (kW)

Gen2 (kW)

Gen3 (kW) Battery

Converter (kW)


COE ($/kWh)

Biizi constrained 4 4 $ 8,000 $ 40,015 0.453 Biizi unconstrained 7 $ 7,000 $ 94,075 0.414 Mutunda constrained 66 66 $ 33,000 $ 917,137 0.372 Mutunda unconstrained 40 40 1 0.6 $ 27,835 $ 1,856,216 0.323 Kiryandongo constrained 500 120 66 $ 86,160 $ 4,738,761 0.293 Kiryandongo unconstrained 5 160 120 200 75 100 $ 158,135 $ 7,398,032 0.298


7 Grid extension 65

7 Grid extension In this section there will be a general discussion about grid

extension. The applicability of grid extension is discussed

and protection systems for the network parts are drafted.

Some questions for further studies if a grid extension is

relevant are stated finally.

7.1 Applicability Grid extension is probably the most viable alternative in the

long term in a larger electrification program for Uganda. Stan

the optimal technology option for small and remote villages, b

become larger it can be seen that the break-even grid extensi

specially if the load is relatively high and has a high load fact

Strategy for Accelerating Grid-based Renewable Power G

Environment that “The major challenge for Uganda is to ensur

strengthened to have the necessary capacity to handle the

Additional power will probably come from construction of

Considering that Uganda in several parts of the country have a

voltage levels and high transmission losses, there is lot of

rehabilitation of the grid. Some of this work has been initiated b

SWECO that have done a study on the transmission and sub

Uganda. According to this study, the potential load growth in r

determined by the changes in electrification policy more tha

behaviour. [1] As mentioned in chapter 4.3.3, the influx from p

areas to urban areas will change the need for sub transmission

to certain communities may be unnecessary.

The national grid in Uganda is already now under high stress be

capacity and transmission lines that have been without prop

district is far from the main power generation sites that are situ

and therefore have problems with keeping the bus bar voltag

The long and radial lines are able to carry very little load b


Picture 7-1 Warning sign at a grid pole in Kampala

d alone systems may be

ut as the trading centres

on distance is quite long,

or. It is said in Uganda’s

eneration for a Clean

e that the grid network is

additional power.” [19]

new hydro power sites.

problem with sustaining

work to be done on the

y UETCL with the help of

-transmission network in

ural areas is high and is

n changes in consumer

eople moving from rural

lines and grid extension

cause of little generation

er maintenance. Masindi

ate not far from Kampala

e at an acceptable level.

efore they cause severe

Spring 2004

7 Grid extension 66

voltage problems. For this reason autotransformers of 5 MVA are installed at Hoima and

Busunjo to boost up the voltage [31] but these have only short-term positive effects.

Since UEDCL is to be contracted out to the private sector, no formal expansion plans

are available because these will be the responsibility of the new owners.

Many rural areas face high transmission and distribution costs for several reasons: the

capacity of power lines is inefficiently used because of low population, densities and

demands are low, villages may have very peaky demand profiles and line losses tend to

be high. [28] The existing 33kV and 11kV subtransmission network is basically a

distribution network with long radial feedings and characterized with low, rural loads.

There are several reasons to expand the transmission system as mentioned in chapter

4.3.3. Some of these were rural electrification, to increase security and to reduce losses

and improve operational economy. The existing network may need to be upgraded and

reinforced with new lines, substations and voltage equipment due to the fact that the

lines are going to be overloaded or have an unfeasible capacity because of an increased

transfer demand. Some other reasons is that voltage level is too low for the transfer

demand or the reactive balance in not optimal which in turn increase losses in the

system. [1]

As mentioned a reason for the transmission losses in the Ugandan grid is due theft of

electricity directly from the grid. About 30% of the losses in the Ugandan grid are caused

by non-technical losses like theft. There is also a problem with theft and vandalism of

conductors and poles in some areas. It is therefore important with an anti-theft design

when planning for new lines. This can be to use a higher voltage level than most

electrical appliances work on so that it will be impossible to use the electricity without

transformers. [29] Other ways to protect from theft are:[1] ♦ To use anti-vandalism bolts in the lattice steel structures combined with a friction bond.

These bolts cannot be removed after being put into place and the tower cost is affected

with less than 1%.

♦ To use tubular steel towers that cannot be disassembled. These towers use little land

and are good for populated areas, but due to their slim design they cost up to 30-50%

more than standard lattice steel structures

♦ To use wooden poles with metal crossbars. They are not attractive for thefts and they

also have the lowest investment cost. These poles require high maintenance and can


7 Grid extension 67

become rotten or eaten by termites, but this can be minimised by chemical treatment.

They are not recommended for transmission system level.

♦ To use concrete poles. These poles have a high cost, 50% more than lattice steel towers,

and must be manufactured in one piece, but they are not attractive for theft and are slim

and use little land.

♦ To use welded lattice steel structures that cannot be disassembled and hence not so

easily stolen. They are expensive because they have to be welded at the manufacturer

making transport difficult. They are not common and favoured by utilities.

♦ To avoid ACSR conductors where the pure aluminium is attractive. It is possible to use

AAAC conductors instead which cannot be used in the process for pure aluminium.

These conductors are more costly and for a transmission line the use of these will

increase the total line cost by 3%.

The most realistic anti-vandalism solutions in Masindi is to use anti-vandalism bolts in

steel lattice towers or to use wooden poles. Also the use of AAAC conductors is

recommended. [1]

7.2 Protection schemes To have a more reliable system, a meshed system should be considered. This will

demand protection schemes so that only the part of the grid with the fault will be out, and

the meshed system will ensure electricity to such a large portion of the grid as possible.

When extending the grid, several protection schemes are needed for the various

network elements. The selection of protection schemes has to be made with the

consideration of availability of protection systems in Uganda and it should also be cost

effective. For the 132kV and 220kV system two independent protection systems based

on different measuring principles should be used.

The 132kV lines are presently equipped with the following protection [1]: ♦ A main protection that is a distance protection with three forwarding zones, reverse zone

and autoreclosing relay operation on three-phase autoreclosing scheme. This scheme

has a teleprotection scheme for operation of the remote feeder breaker in case of zone

one fault.

♦ A back-up protection that consist of static three-phase over current protection and a

directional earth fault protection scheme. 1

♦ Supplementary protection equipment like a fault locator for indication of the distance to a

fault. This is part of the overhead line protection scheme.


7 Grid extension 68

These protection schemes are acceptable and comply with safety requirements normal

for the voltage level. As a modernisation multi functional microprocessor controlled line

protection relays or additional line protection systems could be introduced. An increased

telecommunication capacity could allow upgrading of existing protection systems or

implementation of additional protection schemes that could increase protection reliability

and selectivity and to decrease tripping time.

For the 33kV lines, the protection scheme could be chosen depending on the importance

of the line. If the line is working as a transmission and supply line for a larger area with

substations, a protection system as one for a 132kV could be considered. If the line is

less important, a downgrading of the protection system to only a distance or over-current

protection may be an option. It is recommended to supplement the protection scheme

with an auto-reclosing function to avoid total shut down in case of temporary faults.

There is also a need for protection of the transformers for transmission and sub-

transmission level. The existing transformers are equipped with the following main

protection functions and this is considered sufficient [1]. ♦ Current differential relay

♦ Restricted earth fault relay for star windings

♦ Three phase over current and earth fault protection

♦ Bucholz relay

♦ Suddenoil pressure relay

♦ Winding temperature

♦ Oil temperature

The reliability in the Ugandan grid is low compared to European figures, but it could be

improved by relatively simple measures like bush cuttings, keeping the way of lines clear

of trees that may fall over the lines and replacement of wooden poles.

According to the district manager for UEDCL in Masindi, the most common fault that

makes lines fall out is rain, thunder, wind etc. There are most external faults that make

Masindi district loose power. When the new line from Apac to Masindi will be conducted,

there will be some more security concerning power reliability and hopefully less loss of

mains.

There are several issues that need to be addressed when extending the transmission

and sub-transmission grid. Some of these may be:


7 Grid extension 69

• How does new load and new lines affect the existing grid?

• How do added generating units affect the existing grid?

the grid enough with

dded?

The

with th e

• Will there be a need for more power or is rehabilitation of

booster transformers etc when new loads in Masindi is a

se were questions that were thought to be answered by doing a load flow analysis

e program SIMPOW by ABB. The data for the grid would be put in to a single lin

model and then a load flow analysis could be run. By adding new branches it is possible

to se how new loads affect the grid and from that it would also possible to se in what

order the grid should be expanded.

Instead, this could be recommended to do as a further study if a rural electrification by

grid extension is to proceed.


8 Discussion 70

8 Discussion Rural electrification is included in the energy policy of Uganda and it is stated that they

wish to enhance the living conditions of the rural population and to reduce the

inequalities in national access to electricity and social welfare. Rural electrification by

means of renewable energy and the use of the natural resources in Uganda is also

promoted. This means principally the use of water, solar and biomass. Rural

electrification programmes are all over the world and it is a big challenge for the

countries concerned to meet the future energy demands in a sustainable way.

A change in technology is linked to economical change which in turn is linked to social

change. [21] Given that most people may at first be reluctant to social change, it is not

hard to understand that a change in technology options is hard to drive through. This can

be seen in for example the use of charcoal stoves that are used even though some

people have access to both electrical stoves and gas stoves. In a larger scale, people

may be worried that introducing new forms of energy will affect places of work. For

example it can be mentioned that if the technology of making coal from bagasse would

be subsidised and promoted, then several people that are occupied with the work of

making charcoal would be out of work. Of course it would be more environmentally

sound to use coal from bagasse, but as mentioned people are reluctant to change,

especially when jobs are in danger.[30]

There are several reasons why rural electrification programs have a hard time becoming

viable. Some of these are [21]:

• Several projects compete for the same capital

• The projects are evaluated according to net present cost

• The cost of capital depend on associated time and risk

All together this means that projects that are known and considered safe, may get

capital. In this context, this would mean that diesel generation and grid extension would

be favoured compared to photovoltaic. Connected with a high interest rate, solar

systems that have a high initial cost get a non-favourable position as well. This means

that the Government, as an incentive to promote sustainable energy generation, could, if


8 Discussion 71

possible, put a lower interest rate on renewable energy projects as compared to diesel

system projects.

Uganda is in a special position as power generation is concerned because almost all of

the power produced in Uganda is from hydro power which is considered as a renewable

energy source. One problem is though that several of the new hydro sites that are under

consideration are situated in national parks. The energy that will be produced there will

be renewable, but will it be considered sustainable? Several environmentalist groups

have protested against this project. There is also a problem of getting financing from the

World Bank and foreign governments for the building of the hydro station at Bujagali falls

for example. The reason for this is the political risk connected to the governmental

stability in Uganda. Of course some of the planned hydro power sites are outside

national parks and not as large as the Bujagali falls project.

Considering all this, solar systems is put into a more favourable position compared to

grid extension where the power is produced from hydro power. However, more electricity

could be produced from other energy sources like biomass or geothermal energy,

making grid based electricity both renewable AND sustainable.

A problem one has to face when investing in new energy systems is concerning who

should be responsible for operation and maintenance, who will pay for the system and

will the customers come? This problem is relevant for all the technologies. For grid

extension, it is relatively easy to say that the grid utility will pay for the cost of extension,

and the customers will pay an interconnection fee and the price of energy used. But

should the interconnection fee be subsidised to promote rural electrification? And when it

comes to the electrical installations in the houses, who should pay for that? Should it be

included in the interconnection fee or separate? To ensure as many customers as

possible to connect to a new grid, all cost should probably be included as one. This

usually makes fees look less, and it becomes a bit like a plug-and-play package.

Solar systems are another question. If small solar home systems are considered, then

each customer could be responsible for their own system. Because of the high

investment cost, an official payment plan from an investing bank could be an option. This

could be a solution if a rural electrification program by solar power is promoted by the

government .A solution with a large PV system with a mini grid or where people can

come and charge their batteries, will still give a cost for the customer since the electric


8 Discussion 72

installations in the house has to be done, and a battery has to be bought, but the large

investment cost for the solar panels will be in the hands of a large investor.

Power from diesel generation should probably be phased out since it is a non-

sustainable solution. On the other hand it is an available technology in Uganda and has

also the lowest investment costs. If the diesel prices do not rise much in the near future,

it can be seen from the simulations that diesel is the most viable alternative. It could

function as a start for raising the living conditions for rural people and be phased out with

sustainable technologies further ahead.

A problem is for an investor to assess how much he is willing to pay to avoid a power

shortage. Since the area to be powered is mainly a residential area, with few or no

industries, then this sum is probably quite small. If there had been large industries

dependent on electricity for their production, they might have been willing to pay more

for power security and then there can be larger investments. Now, the customers will be

regular households that still have not got the same standard and power requirements as

the western world. There is for example no cost for undelivered energy in Uganda.

Some discussion could be made of the applicability of HOMER as a simulation tool and

the input parameters that have been used. There is always an uncertainty of prices for

the technology options and the resources. What is also introducing a large insecurity of

the results in this study are the load profiles that the simulations are based upon. These

have been done partly by collected data from Uganda, but also by own experience from

daily life in Scandinavia and Uganda. As a justification, it can be said that the same

grounds are used for all the load profiles so at least the simulations will be based on the

same assumptions. The fact that HOMER can not have a development of prices in its

simulations, have made the simulations time consuming and not always compatible with

real life situations.


9 Conclusion 73

9 Conclusion When looking at the energy situation in the world today, and the third world in particular,

it can easily bee seen that supplying people with reliable, clean and inexpensive energy

is a major challenge. Even so, giving people in poor areas access to energy is a big step

closer towards giving them a better future. Access to energy will lead to better health

care, better education possibilities and maybe even lead to small scale industries that

will give an economic development. Together, these things will lead to a better quality of

life for the people concerned.

It can also be seen from contracts being created between both companies and

governments that rural electrification is an important question in today’s world. As an

example can be mentioned a contract between the Tanzanian energy company

Tanseco, and the consultant company SWECO that are going to investigate and try to

find solutions for rural electrification in Tanzania. [27] SWECO has also as a task to

develop the transmission network in Uganda. [1] Other organizations that deal with this

type of questions are the World Bank, NORAD and Danida, to mention some.

Power from photovoltaic generation is most applicable in smaller communities that are

situated far from the main grid and have a dispersed load. Another application area for

photovoltaic systems is when there are a number of small but important loads that have

to be covered, for example vaccine refrigeration at a local health station, lighting at a

school or security lights along the main street in a trading centre. A negative side with

PV systems is the high investment cost for a relatively low effect. Many who install solar

systems become disappointed because they do not know the limitations of the system

and feel that the usage area of the electricity is smaller than wanted. For example does

a kettle for heating water usually use more effect (about 1000 W) than a solar home

system can produce. Other, larger, solar systems with mini-grids may be an option for

rural electrification and could be subject to further studies.

Diesel generation is a widely available technology that is known in most areas. Diesel

generators are used as back up system in many trading centres that are already

connected to the grid. They are available in many different sizes and it is relatively easy


9 Conclusion 74

to find a generator that can fit the actual load. For a trading centre far away from the

grid, there can be difficulties with the transport of diesel, making diesel generation less

preferable in such a situation. If transport problems are an issue for a trading centre,

then solar systems may be preferable. Diesel generation also have a negative

environmental impact, with pollution and noise. Frequent maintenance is required for a

diesel generator. It should though be easier to find a technician that can repair and do

the maintenance of a diesel generator compared to a solar system because of the

frequent use of generators.

Grid extension is most applicable when there is a large load to be covered or if the

trading centre is very close to the main grid. As it could be seen by the simulations, the

break even grid extension distance was quite long for both the medium and large trading

centre. (From 9 km and up.) Grid extension may have environmental effects like

deforestation and use of agricultural landscape but in Masindi this should not be a large

problem. As mentioned before the Ugandan grid is already under stress because of too

low generation capacity and transmission overloads. A new line is though being built

from Apac in the east to Masindi, giving Masindi a higher level of security in case of

congestions or loss of mains on the way up to Masindi, since it will now be fed with

power from two different directions. The rural loads that might be added to the main grid

through a grid extension are quite small. They will in any case not be built earlier than

new generation capacity is planned by the government to be built.

As a further study, rural electrification of a certain trading centre could be an option.

Then each household load etc. could be modelled and put in to another simulation tool

also developed by NREL. This tool is called VIPOR and is an optimization tool for village

power electrification system. This program, given a map over the village and data about

the loads, can design what loads should be supplied by for example SHS and what

loads should be connected to a grid. It is preferably used together with HOMER.

Another option is to further investigate the possibilities of using solar systems in a larger

scale for rural electrification. Not only as solar home systems, but also as power

generation for mini grids. Maybe solar thermal generation could be an alternative.


9 Conclusion 75

There are a lot of questions about financing rural electrification, but these have been

partly out of this scope in this thesis. Means of financing and investment plans and

investment risks could be the aim of further research.

It can be concluded that rural electrification is an important task to enhance the living

standard for millions of people and that there is a lot of further research to be done to

find optimal solutions for rural electrification in both Masindi district, Uganda and in

general in the third world. In this study simulations to give an idea of the costs have been

done, but to be really applicable, closer studies of the area to be electrified has to be

done, and more requirements have to be given for the proceeding of an electrification of

a rural area.


10 Literature references 76

10 Literature references [1] “Consultancy services for UEB. Transmission and subtransmission study”, NDF 103-3

Power III, SWECO, Dec 2003

[2] Arinda R., Okou R., “Technology options for powering Biizi trading centre – Masindi district”, Kampala, Uganda, March 2004

[3] Bahati Godfrey, “Geothermal energy in Uganda, country update”, Entebbe, Uganda, International Geothermal Conference, Reykjavik, Sept 2003

[4] Redwood-Sawyerr Jonas A S, “Widening access in the context of Power sector reform – an overview of the institutional challenges in Africa”, UNEP-IEA/AIE Meeting 21-22 May 2002, Paris, France

[5] Solar radiation data for Masindi district, Ministry of Water, Lands and Environment, Department of Meteorology, Kampala, March 2004

[6] Electricité de France, “Uganda load forecast review (Update 2001)”, January 2001

[7] “HOMER, Getting started guide, Version 2.0”, May 2003, NREL, USA

[8] Help in HOMER

[9] Oparaku O. U., “Rural area power supply in Nigeria: A cost comparison of the photovoltaic, diesel/gasoline generator and grid utility options”, Renewable Energy 28 (2003) 2089-2098, Elsevier Science Ltd

[10] Christofides Constantinos, “Autonomous photovoltaic power system or connection with electrical grid? A preliminary feasibility study for small and isolated communities”, 1989, Solar Cells 26 (1989) 165-175, Elsevier Sequoia

[11] Martzoukos S. H., Teplitz-Sembitzky W., “Optimal timing of transmission line investments in the face of uncertain demand, an option valuation approach”, 1992, Butterworth-Heinmann Ltd

[12] Flowers Larry, “Renewables for sustainable village power”, Presentad at International Conference of Village Elctrification through Renewable Energy, New Dehli India, NREL, March 1997

[13] “Energy and Poverty”, IEA: world energy outlook 2002

[14] Kamese Geoffrey, “Renewable energy technologies in Uganda: The potential for Geothermal Energy Development”, March 2004

[15] Ijumba N. M., “Application of distributed generation in optimised design and operation of rural power supply networks”

[16] Daley James M., Siciliano Robert L., “Application of emergency and standby generation for distributed generation: Part 1 – Concepts and hypotheses”, IEEE Transactions in industry applications, vol. 39 No. 4, July/August 2003


10 Literature references 77

[17] The Government of Uganda, “New strategy plan & implementation plan”, June 1999, Uganda

[18] MEMD, “Rural Electrification Strategy and Plan Covering the Period 2001 to 2010”, The Government of Uganda, February 2001

[19] Bbumba S. N. M., “Uganda’s strategy for accelerating grid-based renewable power generation for a cleaner environment”, Uganda

[20] “Energy after Rio: Prospects and Challenges”, Chapter 2 – Energy and Major Global Issues, UNDP, www.undo.org/seed/energy

[21] Gether Kaare, “Transition to Large Scale Use of Hydrogen and Sustainable Energy Services, Choices of technology and infrastructure under path dependence, feedback and nonlinearity”, Doctoral thesis at NTNU 2004:71

[22] Langåsen, Britt-Mari, “Distributed generation in developing countries”, Fall 2003, NTNU

[23] Bahati Godfrey, “Geothermal energy in Uganda, country update”, presented at the International Geothermal Conference, Reykjavik, September 2003, Entebbe Uganda.

[24] HOMER webpage, www.nrel.gov/homer

[25] Mean household size, OBOS, http://www.ubos.org/appendix3prov.pdf

[26] Electricity Regulatory Authority (ERA) webpage, http://www.era.or.ug/prices.asp

[27] SWECO webpage, http://www.sweco.se/templates/Page____11616.asp

[28] Goldemberg José, “Rural energy in developing countries”, chapter 10, WEA: Energy and the challenge of sustainability

[29] Andrew Mubonga, District Manager, Masindi district, UEDCL, personal contact

[30] Terry Jobling, Engineering Manader, Kinyara Sugar Works Ltd, personal contact

[31] Elisabeth Kabagante, Planning Engineer, UEDCL, personal contact

[32] Nyanzi Joseph Kubo, Principal Planning Engineer, UEDCL, Uganda, personal contact

[33] Fred Tugume, A.G. Coordinator for Geothermal Activities, Entebbe, Uganda, personal contact


http://www.undo.org/seed/energy

http://www.ubos.org/appendix3prov.pdf

http://www.era.or.ug/prices.asp

http://www.sweco.se/templates/Page____11616.asp

11 Appendix 78

11 Appendix

11.1 Interviews This chapter will include typed interviews and meetings with persons met in Uganda.

Addresses and other contact information where accurate in March 2004, but cannot be

guaranteed in the future.

Monday 16 feb: Meeting with Dr. Okure and Dr. da Silva.

Dr. Izael Pereira da Silva Lecturer Dept. of Electrical Engineering Makerere University Tel: +256-41-250415 Mob: +256-77-5057902 [email protected]

This was the first meeting at the university and we met Dr Okure and Dr Da Silva. They

had expected someone studying solar power (Torill) and had made a program for that

purpose during the day. It turned out that the communication between Makerere and

NTNU had been somewhat insufficient and that they had no idea who where coming and

what we where going to do research on and they had other expectations of our projects

than we had. This made things a bit insecure and a not so promising start.

Monday 16 february: Meeting with

Philippe Simonis Energy Advisor Amber Hose, Room A205 c/o GTZ Office Kampala P.O Box 10346 Tel/Fax: +256-41-234165 Mob: +256-75-791268 [email protected]

Mr Simonis had expected someone studying solar energy. He was a bit disappointed

when it turned out that no one studying that came to Makerere. He was not interested in

my original project because he didn’t think it was feasible. Mr Simonis instead suggested

that I should work with two of Dr. Da Silva’s students. They where going to look at a cost

analysis for electrifying a trading centre in the Masindi district. I was a bit disappointed


mailto:[email protected]


11 Appendix 79

that they wanted to change my project and said that I had to talk to my advisor first. They

had an understanding for this, but emphasised that they probably wouldn’t be able to

help me with my original project, but if I wanted to go through with it, they would try to

help me get in contact with the right people.

Tuesday 17 february: Meeting with Rachel Arinda and Richard Okou.

Rachel Arinda

Mob: +256-71-

Richard Okou

Mob: +256-71-

This was the first meeting with Rachel and Richard. They are two final year bachelor

students at Institute for Electric Engineering at Makerere University. They where going to

do a project for GTZ on the optimal way of electrifying a trading centre in Masindi. I

received some papers about the simulation program HOMER that they where going to

use and they told me what they knew about the project. They were going to travel up to

Masindi already the following weekend, and we decided to meet again the next day to go

to Mr Simonis as GTZ and to travel together up to Masindi on Saturday. I thought it was

a bit strange to go during a weekend, but being new in the country I thought they knew

best.

Thursday 19 february: Meeting 2 with :

Philippe Simonis Energy Advisor Amber Hose, Room A205 c/o GTZ Office Kampala P.O Box 10346 Tel/Fax: +256-41-234165 Mob: +256-75-791268 [email protected]

Meeting with Rachel and Richard at Mr. Simonis office about the project. During this

meeting I got some more information about the project in Masindi, and we also talked a

bit on how my project could be incorporated with this. We talked about that I maybe

could look at a larger part of Masindi and several trading centres. I.e doing the same as

Rachel and Richard on a larger scale. We decided that Mr Simonis should write an email

to Edgar Hertwich and Olav Fosso and tell them about the project.



11 Appendix 80

Friday 20 february: Meeting 3 with Mr. Simonis

Talked about my project and decided that I would look at the whole Masindi district, but

otherwise do the same as the two students. I was also going to do a load flow analysis

over the district.

Friday 20 february: Meeting at UEDCL

Franklin Kizito Oidu Manager Technical Services Amber House Plot 29/23 Kampala Road P.O.Box 7059 Kampala Tel: +256-31-360704 Fax: +256-41-349565 Mob: +256-41-259716 [email protected]@yahoo.co.uk

Met Mr. Oidu with R&R and spoke about the information we wanted for our project. He

seemed very helpful and became even more interested when he learnt that I studied at

NTNU where he had been for a year in 1986.

Nyanzi Joseph Kubo Principal Planning Engineer Amber House Plot 29/23 Kampala Road P.O. Box 7059 Kampala Tel: +256-31-360600 Fax: +256-41-349565 Mob: +256-77-404878

Mr. Kubo was the one helping us to get the information about the grid and also about

costs for grid extension. While in the office a man came down the roof, twice. He was up

there fixing wires. We got single line diagrams over the Masindi district and over the total

Uganda grid. He promised he would help me later with the load flow analysis. We talked

about IPPs but he didn’t know about any other in the area than the Kinyara sugar

factory. They had plans about expanding. As of now the produce 15 MW which they use

for their own consumption. He didn’t know about any plans for hydro power and the area

wasn’t suited for geothermal activities.




11 Appendix 81

Monday 23 february: Meeting with the LC5 in Masindi.

The LC5 is the chairman for the district of Masindi. We went to the LC5 to get

information about the population in Biizi and also about the economic activities. He

seemed “maatligt” interested in what we where doing and probably thought that we

should have been more prepared. He sent us to another person in the same building, Mr

Rashid Yawiya. He had the information we wanted about the trading centres but the

problem was that he didn’t know which where electrified and not. To get that information

we had to go to the planning office and then get back in touch.

The planning office promised to help finding important trading centres. The lay helping

us didn’t have time to do the work when we where there, but promised to find the

information, give it to the population office and then fax the information to GTZ. (After 3

weeks the info has still not come. Will go up there again.)

Week 9 Searched for information about prices for PV and generators.

Went to Mantrac to find prices about generators (why not the internet?) and to UltraTec

to get prices for PV. At Mantrac we spoke to a lady that where going to help us and find

the data we wanted about different generators. She couldn’t do it momentarily but she

said she would have it done within a couple of days. The person at Ultratec didn’t want

to give us the pricelist because he was afraid that we would publish his figures that are

wholesale. I asked if it is ok to get the info if the report has a confidential part but he

didn’t seem to understand or didn’t want to give out the price anyway. We didn’t get any

copies but had to write down the figures he said about different costs and life spans of

products.

Wednesday 10 march: Elisabeth Kabagante Planning Engineer UEDCL Amber House, Room B209 Tel: +256-31-360600 [email protected]

Ms. Kabagante works as a planner at UEDCL and will help me get information about the

load data for the district of interest. Decided that I would write an e-mail about the



11 Appendix 82

information I wanted and that we would make an appointment after that. Seemed

interested in what I am doing. She uses the programs ETAP and EPSS for load flow

analysis. She also gave us a copy of a part of an report which gave us figures about

operating hours that UEDCL use when planning and doing load profiles.

Wednesday 10 March: Meeting with

Luzze Fred Senior Technician Solar Energy for Africa Ltd. Plot 40, Bombo Road P.O. box 4155 Kampala Tel: +256-41-250125 Tel/Fax: +256-41-250131 Mob: +256-77-564405 [email protected]

During this meeting we got access to old orders that had been made by SPA. This gave

us an idea about the cost for solar and also what wattage one should put on different

devices. The man we talked to did not know how to design a system by him self, but he

used software that seemed excel based. We didn’t get any copies here either of

systems, but had to write down the data that were presented to us.

Friday 12 March: Meeting with

Dr. Izael Pereira da Silva Lecturer Dept. of Electrical Engineering Makerere University Tel: +256-41-250415 Mob: +256-77-5057902 [email protected]

Talked about the collaboration between Makerere University and NTNU, what have been

good and what needs to be improved. We said that communication prior to our arrival

was a key ingredient that where missing a bit this time. A better communication would be

in interest of both MUK and NTNU, the student and the professors if the collaboration is

to continue.

Tuesday 16 March: Meeting with

Elisabeth Kabagante Planning Engineer




11 Appendix 83

UEDCL Amber House, Room B209 Tel: +256-31-360600 [email protected]

This meeting was a result of an e-mail I had sent with questions to Elisabeth the 11 of

March. The questions where about data for a load flow analysis. First I got some

information at her office in Amber House and then we went to Lugugo and the UEDCL

office there. In Lugogo she showed me the load flow analysis program ETAP which she

used for analyses.

Tuesday 16 March: Meeting with

Nyanzi Joseph Kubo Principal Planning Engineer Amber House Plot 29/23 Kampala Road P.O. Box 7059 Kampala Tel: +256-31-360600 Fax: +256-41-349565 Mob: +256-77-404878

This was a “spontaneous” meeting and we talked a bit about different problems with grid

extension in rural areas.

Some of the problems when constructing new lines are:

-Theft of expensive parts (transformers are stolen and remelted. This means that the

cheaper aluminium transformers can’t be used, but instead they have to use the triple

SC (?) transformer)

-Vandalism

There are no costs for the distribution company when the power is not delivers, as in a

load shedding situation or a loss of means. Sometimes they pay if damage on devices or

loss of production can be proven. The larger industries wish for compensation for loss of

power and because of that production losses.

Different areas in Kampala have different load shedding priority, with the Mulago

Hospital coming first. This is not a priority you can pay for, but a natural selection.



11 Appendix 84

Wednesday 17 March: Meeting with:

Terry Jobling

Engineering manager

P.O Box 179

Masindi

Tel.: +256 036 600 410

[email protected]

Mr Jorling works at Kinyara Sugar Works Ltd and is the engineer there. Kinyara Sugar

Works is almost self sufficient with electricity and it is produced from both the burning of

bagasse which is a residue from the sugar production and from diesel generation.

They are at this moment building a new furnace for bagasse, since they are expanding

their production of sugar, but they will not utilise more of the bagasse for electricity

generation. The furnace that is being built can though be rebuilt with a generator to

recover steam for electricity generation.

According to studies done by Kinyara Sugar Works, it is not economically viable for them

to sell excess energy to the utility. There is a sub station just outside the factory, so

connecting to the grid is not a problem, but Mr Jorling said that if they where to sell

electricity to the grid, they would have to make contracts with certain amounts of power

to sell. If they for some reason should produce less energy and have contracts on

energy to sell, they would have to produce it by diesel generation. This would be to

expensive. If it where to be economically sound to sell excess electricity to the grid, the

grid price would have to be about twice the amount it is today.

Monday 22 March: Meeting with Andrew Mubonga, DM for UEDCL, Masindi

Andrew Mubonga

District Manager, Masindi district, UEDCL

Mob: +256-77-490596

This meeting was to find out some things about the reliability of the lines supporting

Masindi. Got some statistics from September to February, with some months missing.

During the rainy season there are often more outages because of falling trees, rain,

thunder etc.

Also got some general costs for grid extension. If there is an extension with no pole or

one pole, e.g. very close to the existing grid, then the customer can contact the district


11 Appendix 85

office and the will handle the extension. If there is a larger extension, then the main

office have to do a research of the investment.

The cost for a no-pole extension: 150 000 Ush

The cost for a one-pole extension: 350 000 Ush

These prices include a 50 000 Ush security deposit which the customer will get back if

there is a problem (?)

23 March: Meeting with

Herman Ssenyondwa Principal planning engineer UETCL P.O. Box 6088 Kampala [email protected]

I met with Mr Ssenyondwa and told hi about my project which where going to include a

load flow analysis over Masindi district, part from doing a cost analysis of electrifying a

trading centre. Got access to a report made by the Swedish company SWECO about the

transmission and subtransmission system in Uganda. But to get permission to have a

copy of this report I had to get a clearance from the head at UETCL. He was away on

business and I got in touch with … instead. The day after a got a clearance and could

copy parts of the report. Unfortunately it wasn’t in soft copy.

Wednesday 24 March: Meeting with Fred Tugume

Fred Tugume

A.G Coordinator for geothermal activities

Geological surveys & Minerals Department

Entebbe, Uganda

[email protected]

There are three different areas in Uganda around Kibiro that are being explored for

geothermal activities. They are still doing feasibility studies on these sights but there is

problems getting funding for the research. That is why there have been little

development of the studies since 1994. Mr Tugume said that the project was prioritised

by the World Bank that where funding the studies. The steam from he geothermal




11 Appendix 86

activities can be used for drying fish or tobacco in the area and that it is compatible with

small hydro power generation. He didn’t know how much power that where expected to

be used from the geothermal spots.


11 Appendix 87

11.2 HOMER input variables


11 Appendix 110

11.3 Attached CD with HOMER files and data


rural electrification in uganda

Documents