Energy modelling to support subnational sustainable planning in developing countries:

Energy modelling to support subnational sustainable planning in developing countries:
Energy modelling to support subnational
sustainable planning in developing
countries:
The case of Kakamega County in Kenya
Alexandros Korkovelos
Master of Science Thesis
KTH Industrial Engineering and Management
Energy Technology EGI-2015-066MSC
Department of Energy Systems Analysis
SE-100 44 STOCKHOLM
Abstract
Kenya is at the forefront of a socioeconomic transformation, aiming to turn into an industrialized middle
income country by 2030. Kenya Vision 2030 has identified energy as a key foundation and one of the
infrastructural “enablers” upon which the economic, social and political pillars of this long-term
development strategy will be built.
Predicting the future of energy systems however, involves risks due to various uncertainties. Therefore,
systematic energy planning at national and sub-national/County level is highly recommended through the
adoption of more realistic assumptions on the future evolution and profile of demand and robust prefeasibility of prospective projects including the integration of renewable energy sources, which the
country is endowed with.
This thesis provides a comprehensive analysis of the energy sector for Kakamega County in Western
Kenya. The current energy demand level was estimated for six selected sectors of the County namely
Residential, Industrial, Transportation, Commercial, Public and Agricultural. Additionally, the renewable
energy resources potential was assessed at local level using GIS and other available data. LEAP software
was used in order to model and project the energy demand and supply based on three 15-year scenarios
till 2030, developed to support the economic, social and environmental sustainability of the County.
This study intended to create a framework aiming to facilitate sub-national energy planning in developing
countries and it is expected that the findings will be complementary to already existing energy planning
models but also the base for future research towards energy poverty elimination.
Keywords: Rural electrification, Energy Access, Energy modelling, LEAP, Kakamega, Kenya.
[i]
Acknowledgments
The elaboration of this thesis would not be possible without the support of several people who contributed
in various ways. The following paragraph is a small appreciation of their help.
Initially, I would like to thank Professor Mark Howells for undertaking the supervision my MSc thesis as
well as Mr. Francesco Fuso Nerini, doctoral student in KTH whose support and guidance were
fundamental for the completion of my work.
I would also like to thank Mr. Tom Walsh and Dr. David Bauner from Renetech AB for their persistent
and educating guidance and for giving me the opportunity to work on a pragmatic project that fully meets
my personal expectations and goals.
Also, I would like to show my special appreciation to Mrs. Jenny Gabriela Peña Balderrama, a bright
energy modelling engineer who helped me gain the required expertise in LEAP.
Finally, I am deeply grateful to my family and friends for their unremitting support and understanding
during these two tremendous years of my life.
Alexandros Korkovelos
Stockholm, August 2015
[ii]
NOMENCLATURE
Here all the notations – abbreviations used in this thesis are described.
Abbreviations
NGO
Non-governmental organization
SE4ALL
Sustainable Energy for all
LEAP
Long range Energy Alternative Planning system
GDP
Gross Domestic Product
USD
United States Dollars
Ksh
Kenyan Shilling
MoEP
Kenyan Ministry of Energy & Petroleum
LPG
Liquefied Petroleum Gas
LNG
Liquefied Natural gas
KPC
Kenyan Pipeline Company Ltd.
KPLC
Kenyan Power & Lighting Company
RAE
Rural Electrification Authority
PV
Photovoltaic
KETRACO
Kenyan Electricity Transmission Company Ltd.
REP
Rural Electrification Programme
CBE
Cross Border electrification Scheme
ERC
Energy Regulatory Commission
LCPDP
Load Forecasting Power Development Plan
FiT
Feed in Tariff
IMF
International Monetary Fund
SWH
Solar Heating System
HPS
High Pressure Sodium
UN
United Nations
FAO
Food & Agriculture Organization of the UN
GIS
Geographic Information System
GHI
Global Horizontal Insolation
DNI
Direct Normal Insolation
ESMAP
Energy Sector Management Assistance Programme
PUoE
Productive Use of Energy
VAT
Value-added tax
WHO
World Health Organization
DSM
Demand Side Management
CAPEX
Capital Expenditure
OPEX
Operating Expenditure
[iii]
TABLE OF CONTENTS
ABSTRACT
I
ACKNOWLEDGMENTS
II
NOMENCLATURE
III
TABLE OF CONTENTS
IV
LIST OF TABLES
VI
LIST OF FIGURES
VII
LIST OF GRAPHS
VIII
INTRODUCTION
1
CHAPTER 1. GENERAL ENVIRONMENT AND ENERGY SECTOR IN KENYA
3
1.1 Background
3
1.2 The energy sector
4
1.3 Current challenges
12
CHAPTER 2. KAKAMEGA COUNTY CASE STUDY
13
2.1 Demographics and socioeconomic background
13
2.2 Energy demand
15
2.2.1 Residential sector (households)
15
2.2.2 Industrial sector
20
2.2.3 Transportation sectror
21
2.2.4 Commercial sector
24
2.2.5 Community sector & Public services
26
2.2.6 Agriculture, Aquaculture & Livestock
28
2.2.7 Estimated energy demand profile in Kakamega
29
2.3 Climatic conditions and resource assessment in Kakamega
31
2.3.1 Biomass and Bioenergy crops
31
2.3.2 Solar Resource
33
2.3.3 Hydro Resource
36
2.3.4 Wind Resource
36
2.3.5 Geothermal Resource
37
CHAPTER 3. ENERGY MODELLING
38
3.1 Energy modelling & methodology
38
3.2 Data structure
39
3.3.1 Residential sector
40
3.3.2 Industrial sector
42
3.3.3 Transportation sector
43
3.3.4 Commercial sector
43
3.3.5 Community sector & Public services
44
[iv]
3.3.6 Agriculture, Aquaculture & Livestock
44
3.4 Supply modelling
45
3.4.1 Electricity generation
45
3.4.2 Charcoal production
45
3.4.3 Ethanol production
46
3.5 Alternative scenarios
46
3.5.1 Productive use of energy expansion scenario (PUoE)
47
3.5.2 Sustainable energy for all (SE4ALL) scenario
49
CHAPTER 4. RESULTS
58
4.1 Energy demand
58
4.2 Energy supply
68
4.3 Resources
72
4.4 Environmental assessment
74
4.5 Cost assessment
76
DISCUSSION AND CONCLUSIONS
78
RECOMMENDATIONS AND FUTURE WORK
79
REFERENCES
80
APPENDIX A
84
APPENDIX B
86
APPENDIX C
88
APPENDIX D
89
APPENDIX E
91
[v]
List of Tables
Table 1. Sources of electric power generation in Kenya (MoEP 2015). ...................................................................... 5
Table 2. Development activities of 5000+ plan (MoEP 2015). .................................................................................. 12
Table 3. Percentage of fuel used for cooking purposes in Kakamega County (Eston Ngugi 2013). .......................... 16
Table 4. Cooking stoves technologies and efficiencies (Amarasekara 1994), (Boulkaid 2015). ............................... 16
Table 5. Percentage of lighting sources for households in Kakamega County (Eston Ngugi 2013). ......................... 18
Table 6. Estimated number of registered vehicles in Kakamega (KNBS 2014). ........................................................ 21
Table 7. Estimated number of new registered (2013) vehicles in Kakamega (KNBS 2014). .................................... 21
Table 8. Energy Intensities in transportation sector in Kakamega (UNEP n.d.), (Kimmo Erkkilä 2005), (US
Department of Energy n.d.), (US Department of Transportation n.d.) (OAK Ridge National Laboratory n.d.). ........ 23
Table 9. Energy consumption of various appliances utilized in schools (NREL 2000). ............................................ 27
Table 10. Farming activities and respective energy requirements .............................................................................. 28
Table 11. Aggregated energy consumption estimations per sector in Kakamega. ..................................................... 29
Table 12. Estimated fuel consumption in GWh per sector in Kakamega in 2014. ..................................................... 30
Table 13. Estimated land use in the county of Kakamega. ......................................................................................... 32
Table 14. Energy crops with high sustainability index (IRENA Global Atlas 2015). ................................................ 33
Table 15. Key assumptions for Kakamega’s energy model. ...................................................................................... 40
Table 16. Residential sector segmentation based on energy consuming activities and fuel mix. ............................... 41
Table 17. Residential sector expected changes by the end year (2030) under the suggested reference scenario. ...... 42
Table 18. Structure of the public (community) sector activities as introduced into the LEAP model. ....................... 44
Table 19. Charcoal production technology adoption by 2030 in Kakamega County, Kenya. .................................... 46
Table 20. Productive use of energy goals in health sector of Kakamega by 2030. .................................................... 48
Table 21. Productive use of energy goals in education in Kakamega by 2030. ......................................................... 49
Table 22. Acquisition cost of residential equipment. ................................................................................................. 51
Table 23. Cost of the main fuels involved in the energy sector of Kakamega. .......................................................... 52
Table 24. Projection of the population and GDP for the region of Kakamega at 2030. ............................................. 58
Table 25. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the reference (BAU) scenario.
.................................................................................................................................................................................... 61
Table 26. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the productive use of energy
(PUoE) scenario. ......................................................................................................................................................... 62
Table 27. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the sustainable energy for all
(SE4ALL) scenario. .................................................................................................................................................... 63
Table 28. Energy demand (in GWh) per household activity under reference & SE4ALL scenario by 2030. ............ 64
Table 29. Energy demand (in GWh) per vehicle category in Kakamega till 2030 (reference scenario). ................... 65
Table 30. Energy demand (in GWh) per vehicle category in Kakamega ................................................................... 66
Table 31. Energy demand projections (in GWh) per sub-category of the Community sector in Kakamega. ............. 67
Table 32. Energy demand projections (in TJ) per sub-category of the Community sector in Kakamega. ................. 73
[vi]
List of Figures
Figure 1. Geothermal sites in Kenya (GDC n.d.). ........................................................................................................ 6
Figure 2. Maximum wind speed map for Kenya (Sklivaniotis, Integrated Techno-Economic Comparative and
Socio-Economic Impact Study for Increasing Energy Access in Rural Kenya 2014). ................................................. 6
Figure 3. Solar insolation for Kenya (Sklivaniotis, Integrated Techno-Economic Comparative and Socio-Economic
Impact Study for Increasing Energy Access in Rural Kenya 2014). ............................................................................. 7
Figure 4. Transmission network planned extension in Kenya (MoEP 2015). .............................................................. 9
Figure 5. Energy sector structure in Kenya (Ministry of Energy and Petroleum MoEP n.d.). ................................... 10
Figure 6. Population density in Kakamega (CRA 2011). ........................................................................................... 14
Figure 7. Global Horizontal Irradiance annual average for Kakamega County (IRENA Global Atlas 2015). .......... 33
Figure 8. Global Horizontal Insolation in kWh/m2/day for Kakamega County (IRENA Global Atlas 2015). .......... 34
Figure 9. Direct Normal Insolation in kWh/m2/day for Kakamega County (IRENA Global Atlas 2015). ............... 35
Figure 10. Direct Normal Insolation on annual basis for Kenya in kWh/m2 (SolarGIS, 2015). ................................ 35
Figure 11. Wind speeds in m/s throughout Kakamega County (measurement at 10 m) (IRENA Global Atlas 2015).
.................................................................................................................................................................................... 36
Figure 12. Developed based on the Comprehensive Measurement of Energy Access for productive uses using the
multi-tier approach, Adaptation from the World Bank Group (2014)......................................................................... 39
Figure 13. Most economic rural electrification activity in Kakamega County, acquired from IRENA Global Atlas
(Masdar Institute of Science and Technology n.d.) ..................................................................................................... 54
Figure 14. Transmission lines in the area of Kakamega, Kenya, acquired from IRENA Global Atlas (Masdar
Institute of Science and Technology n.d.). .................................................................................................................. 54
Figure 15. Identified areas for the deployment of small hydro power plants in Kakamega, Kenya, base map acquired
from IRENA Global Atlas database. ........................................................................................................................... 55
Figure 16. Identified areas for the deployment of PV solar parks in Kakamega, Kenya, base map acquired from
IRENA Global Atlas database. ................................................................................................................................... 56
Figure 17. Category 1 health clinic energy consumption (USAID n.d.). ................................................................... 89
Figure 18. Category 2 health clinic energy consumption (USAID n.d.). ................................................................... 89
Figure 19. Category 3 health clinic energy consumption (USAID n.d.). ................................................................... 90
Figure 20. Barley sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). .......... 91
Figure 21. Cassava sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ........ 91
Figure 22. Jatropha sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ....... 92
Figure 23. Maize sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ........... 92
Figure 24. Miscanthous sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). 92
Figure 25. Rape sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ............. 93
Figure 26. Sorghum sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ...... 93
Figure 27. Soybean sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). ....... 93
Figure 28. Sugarcane sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). .... 94
Figure 29. Sunflower sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). .... 94
Figure 30. Wheat sustainability index characterization for Kakamega County (IRENA Global Atlas 2015). .......... 94
[vii]
List of Graphs
Graph 1. Estimation of the energy consumption profile in hotels and guest houses in Kakamega............................ 25
Graph 2. Estimation of the energy consumption profile in big open markets in Kakamega...................................... 25
Graph 3. Energy consumption by sector in Kakamega. ............................................................................................. 29
Graph 4. Average daily temperature range in Kakamega (oC) (NASA - Atmospheric Science Date Center n.d.). ... 31
Graph 5. Average monthly precipitation in Kakamega (oC) (NASA - Atmospheric Science Date Center n.d.). ...... 31
Graph 6. Monthly Average Insolation kWh/m2/day (NASA - Atmospheric Science Date Center n.d.). .................. 34
Graph 7. Wind speed frequency on annual basis for Kakamega region in m/s (measurement at 10 m) (NASA Atmospheric Science Date Center n.d.). ..................................................................................................................... 37
Graph 8. Monthly Average wind speed for Kakamega region in m/s (NASA - Atmospheric Science Date Center
n.d.). ............................................................................................................................................................................ 37
Graph 9. Total energy demand scenarios projection in Kakamega till 2030. ............................................................ 59
Graph 10. Total energy demand by sector in Kakamega till 2030 under reference scenario. .................................... 59
Graph 11. Total energy demand by sector in Kakamega till 2030 under PUoE scenario. ......................................... 60
Graph 12. Total energy demand by sector in Kakamega till 2030 under SE4ALL scenario. .................................... 60
Graph 13. Final energy demand by Sugar Company by 2030. .................................................................................. 64
Graph 14. Energy demand projections for the Commercial sector in Kakamega by 2030. ....................................... 65
Graph 15. Energy demand projections for the Community sector in Kakamega by 2030. ........................................ 66
Graph 16. Energy demand projections for Agriculture sector in Kakamega by 2030. .............................................. 68
Graph 17. Electricity generation requirements in Kakamega projected till 2030. ..................................................... 69
Graph 18. Electricity balance in Kakamega projected till 2030. ............................................................................... 69
Graph 19. Electricity generation mix in Kakamega by 2030. .................................................................................... 70
Graph 20. Charcoal balances in Kakamega till 2030 under the DSM scenario. ........................................................ 71
Graph 21. Ethanol energy balance in Kakamega till 2030 under the DSM scenario. ................................................ 72
Graph 22. Primary energy resources utilization in Kakamega till 2030 under the SE4ALL scenario. ...................... 73
Graph 23. Projection of imported fuels in Kakamega till 2030 under the SE4ALL scenario. ................................... 74
Graph 24. Share of estimated emissions by sector in Kakamega (2014). .................................................................. 75
Graph 25. Share of estimated emissions by sector in Kakameg under SE4ALL scenario. ........................................ 75
Graph 26. Global Warming Potential (GWP) among different scenarios for Kakamega till 2030. ........................... 76
[viii]
INTRODUCTION
Energy holds a privileged place in the construction of durable human societies (United Nations 2002). It
is a vital commodity highly interconnected with socioeconomic development and well-being. It is
therefore a crucial prerequisite for the achievement of continuous growth both on global and regional
level.
However, economic development, improvement of living standards and population growth are leading to
an exponential increase of energy consumption, especially in developing countries which are currently at
the forefront of a tremendous transformation. While this transformation should not be contestable, the
associated production and consumption of energy is encompassing several geopolitical and environmental
issues, such as land loss, climate change, resources depletion and environmental degradation (Urban
2009) (Emmanuel Bergasse 2013).
It is crucial that the path followed by the industrialized countries over the past decades should be avoided
and instead a more sustainable path should be envisioned. A sustainable energy transition, can create new
possibilities for developing countries in terms of growth while at the same time safeguarding natural
resources, environment and human health. Technological leap-frogging is assumed to play an important
role in this growth as it should give access to modern technologies and innovation, enabling this
sustainable transformation to happen (Urban 2009).
Renewable energy technologies are of significant importance in this attempt. Many developing countries
have seen them as an opportunity towards their prospective rapid growth, while their integration is
estimated to expand as a wider range of cost effective technologies will be employed. Renewables
potentially improve energy security by reducing the reliance on imported fuels and help to diversify the
power mix. Their decentralized manner, enables them to be deployed faster than centralized power plants
while providing local employment for deployment and maintenance. Furthermore, they can substantially
affect the energy access to remote communities (IEA 2014).
Globally, there are a number of initiatives trying to enhance this sustainable transition by building
cooperation between governments, private sector, communities, non-governmental organizations
(NGO’s) and other involved stakeholders in the energy sector. This thesis is elaborated under the
Sustainable Energy for All (SE4ALL) framework aiming at the following goals by 2030 (The World
Bank 2013).

Ensuring universal access to modern energy services

Double the global rate of improvements in energy efficiency

Double the share of renewable energy in the global energy mix
Energy models are a useful tool in order to analyze, plan and manage the energy transition imposed by the
aforementioned goals. They can highlight the most environmentally friendly, institutionally sound, social
acceptable and cost-effective solutions of the best mix of energy supply and demand options, for a
defined area. The development of long term projections and scenarios even though subjected to several
[1]
uncertainties, is a necessary step in order to support long-term regional sustainable development (Atom
Mirakyan 2015).
Predicting the future of energy systems in developing countries involves risks. Technological leapfrogging, ambiguity towards different trajectories of development, significant regional and income-related
differences between consumption patterns are issues which can critically affect the credibility of the
models (Urban 2009). There is therefore an evident need to properly adapt energy models towards a more
decentralized and flexible environment, better representing the energy future of developing countries.
Thesis scope and objectives
The scope of this thesis is to develop a model in LEAP software that will be able to perform a
comprehensive energy analysis on a sub national level in a developing country. Kakamega County in
western Kenya was selected as a suitable candidate for this analysis.
The objectives of the thesis are to:

Conduct a base line survey in order to understand and record the current energy situation.

Create a benchmark of energy uses and greenhouse gas emissions.

Assess the renewable energy resources potential in the county.

Model the energy demand & supply including projection till 2030.

Create scenarios suggesting relevant energy interventions at a regional level.
The overall objective of the thesis, is to develop an energy model able to analyze and optimize energy
systems at a sub national level, facilitating the decentralized and renewable energy generation and
supporting a multi-sectorial sustainable development.
In chapter one, an introduction in the Kenyan content is presented, highlighting the current socioeconomic
environment and the framework under which the energy sector is operating. Chapter two, focuses into the
subject area of Kakamega. A description of the County’s characteristics is followed by a comprehensive
bottom-up approach regarding energy consumption data collection for the county. The current energy
generation mechanisms and the renewables potential are also discussed. In chapter three, LEAP software
is introduced, analyzing the energy modelling procedure and the main factors affecting its implementation
in the Kakamega case study. The construction and purpose of various scenarios is also elaborated. Finally,
chapter four discusses on the results of the optimization process, specifically commenting on the possible
energy related interventions in the involved sectors.
[2]
CHAPTER 1. GENERAL ENVIRONMENT AND ENERGY
SECTOR IN KENYA
This chapter illustrates the general environment in Kenya in terms of socioeconomic conditions. Special
focus is given in the structure of the of the energy sector, whose understanding is fundamental for the
successful development of any energy plan in the country.
1.1 Background
Kenya, with a population of 44.08 million1 (2015), is a lower-middle income country and the biggest
economy within East Africa community with the gross domestic product (GDP) at 54.993 billion USD in
2013 (International Monetary Fund n.d.). That is, the GDP per capita for the same year was 1,245.5 USD
and an annual GDP growth of 5.7%. However, the past ten year average GDP growth was calculated as
5.2% which is a more representative index and will be used in this report (The World Bank n.d.)
(International Monetary Fund n.d.).
Although the economy of the country has experienced various shocks in the last ten years (e.g. global
economic recession, political instability, drought), it has sustained its capacity and seems ready to take off
leveraging its natural resources, its significant geopolitical location, the expanding skilled youthful
population and its dynamic private sector (The World Bank n.d.) (Barkan 2011).
Energy is a critical factor towards the socioeconomic development of the country. As at June 2014,
almost 68% of the population did not have access to electricity, with the percentage being higher in the
rural areas (MoEP 2015). Frequent electricity service interruptions are also evident and significantly
affect economic activities, imposing higher costs due to production loss and/or self-generation cost.
Therefore, higher levels of electricity access and service quality are necessary in order to enhance the
national energy mix and bust the social and economic development.
In 2011 it was estimated that the primary energy use per capita in Kenya was 480 kg of oil equivalent
(The World Bank n.d.). That implies an equivalent of 19,536 ktoe for the whole country in 20112. The
socioeconomic stability and the population increase is expected to have a direct impact on the energy use
in Kenya. In 2015 the primary energy use in the country is estimated to be 21,376 ktoe. Biomass accounts
for the 68% of the total energy consumption in the country (55% of which derives from farmland as
woody biomass and crop residue while 45% from forest), petroleum for 22%, electricity for 9% and coal
for roughly 1% (MoEP 2015).
1
2
In 2015 the population is estimated to be 44,076,000 (International Monetary Fund n.d.)
In 2011 the population of Kenya was 40,700,000 (International Monetary Fund n.d.)
[3]
1.2 The energy sector
Petroleum
According to the Ministry of Energy and petroleum (MoEP) of Kenya in 2013, the petroleum products
imported were 3,996 thousand tons. Almost 61.3% of the petroleum consumption accounted for retail
pump outlets and road transport, 14.24% for industrial and commercial use and 6.37% for power
generation. A refinery is operating in Mombasa producing roughly 12,400 tons LPG per year (2013)
covering almost 14% of the country’s demand (MoEP 2015). The refinery also produces premium and
regular motor spirit, automotive gas oil, dual purpose kerosene, fuel oil, grease and bitumen. It is
projected that the demand for petroleum products will show an annual increase of about 3.1% till 2030
(MoEP 2015). Petroleum exploration processes are in progress in the sedimentary basins both on and off
shore.
The major transport of petroleum products is being performed by the existing pipeline system (operated
by Kenya Pipeline Company Ltd. (KPC)) and road transportation, while roughly 1% was transported by
rail. The pipe line is connecting Mombasa – Nairobi – Nakuru – Eldoret and Kisumu while an extension
is planned from Nakuru through Nanyuki to Isiolo as well as to Taveta and Konza. LAPSSET project is
also under development interconnecting Lamu port with Ethiopia and South Sudan (for crude oil
transportation). Interconnections with Kampala (Uganda), Kigali (Rwanda) and Goma (DRC) are also
planned (MoEP 2015).
Coal
As in December 2014, all the coal used in Kenya (~208,900 tons) was imported and mainly used for
industrial purposes (cement manufacturers). Coal consumption is expected to be increased due to the
discovery of coal deposits in Mui basin in Kitui County and its exploitation plans for power generation
purposes (MoEP 2015).
Electricity generation
Even though the electricity sector accounts for 9% of the total energy consumption in Kenya, it
significantly affects the economy. The total generating capacity by December 2014 was 2,173 MW with
annual energy generation of 8,840 GWh between 2013/2014. Renewable energy sources account for
almost 68% of electricity generation while the rest 32% is deriving from fossil fuels. Table 1 provides a
detailed list of the main generation sources and their capacity.
In the period 2013/2014, 85 GWh were imported (mainly from Uganda and to a smaller extend from
Ethiopia and Tanzania) while 39 GWh were exported (37 GWh to Uganda, 2 GWh to Tanzania) (MoEP
2015). As at December 2014, the peak demand was 1,512 MW showing an increase of almost 33% over
the last five years (MoEP 2015).
[4]
Table 1. Sources of electric power generation in Kenya (MoEP 2015).
Hydropower
Hydropower is a significant source of electric power generation in Kenya. Large hydroelectric power
plants (above 10 MW) account for the majority of the production while small scale hydro projects have an
accumulated capacity of 25 MW (15 MW public – 10 MW private investments). According to the
Ministry of Energy and Petroleum there is a high potential of both large and small hydroelectric power in
the country reaching 6,000 MW of which 1,449 MW are of high economic significance.
The major drainage basins are Lake Victoria, Rift Valley, Athi River, Tana River, Ewaso Ng’iro North
River. However, competing interests over the land and water use, environmental degradation, lack of
adequate hydrological data and vandalism are some of the challenges needed to overcome in order to
harvest the full potential of hydropower in Kenya (MoEP 2015).
Geothermal
Geothermal power is the second biggest source of electricity in Kenya with a current installed and
operating capacity of 593.5 MW. According to the Ministry of Energy and Petroleum there is high
potential of geothermal power (~ 10,000 MWe) in the region of Rift Valley, due to significant volcanic
activity in the area (Figure 1). In October 2014, the biggest single turbine geothermal plant in the world
(Olkaria IV) was inaugurated in Naivasha County with a capacity of 140 MW (GDC n.d.). Further
exploration and development activities are taking place in indicative sites, aiming to step up and leverage
the geothermal potential.
[5]
Figure 1. Geothermal sites in Kenya (GDC n.d.).
Wind
By November 2014, wind power installed capacity was 25 MW in Kenya. The wind power potential is
estimated at 346 W/m2 with several locations however to experience wind speeds above the threshold of 6
m/s and indicate the large scale exploitation of wind power (Figure 2).
Figure 2. Maximum wind speed map for Kenya (Sklivaniotis, Integrated Techno-Economic Comparative and SocioEconomic Impact Study for Increasing Energy Access in Rural Kenya 2014).
[6]
The 300 MW Lake Turkana project is expected to be commissioned by 2017 while other feasibility
studies for additional 650 MW are in progress for Kinangop, Marsabit, Isiolo/Meru and Ngong locations
(MoEP 2015). Some pilot small wind power projects also exist so far in Kenya as part of hybrid systems
developed in areas where the grid extension is not currently possible (APPENDIX A).
Solar
Due to its geographic location Kenya’s potential for solar energy exploitation is high. The average solar
insolation ranges between 4-6 kWh/m2/day all year round, with moderate to high temperatures. Figure 3
schematically represents the solar potential for Kenyan territory. According to the annual report of Kenya
Power & Lighting Company (KPLC) by June 2014 there were 0.7 MW of solar power installed and all of
them under the collaboration with the Rural Electrification Authority (RAE) (KPLC 2014).
Figure 3. Solar insolation for Kenya (Sklivaniotis, Integrated Techno-Economic Comparative and Socio-Economic
Impact Study for Increasing Energy Access in Rural Kenya 2014).
It is evident that the on grid solar power has been stagnant in Kenya over the past few year while the offgrid, small PV applications (12-50 Wp) show a significant increase in popularity and high market
penetration mainly due to private sector activity (MoEP 2015) (Sklivaniotis, Integrated Techno-Economic
Comparative and Socio-Economic Impact Study for Increasing Energy Access in Rural Kenya 2014). As
at December 2014 there were 6 MW of solar home and small commercial PV systems (MoEP).
Regardless the national grid extension plan, it is estimated that the long term potential for solar home
systems (SHS) is about 30 MWp while the potential for other off-grid PV applications amounts to
roughly 6 MWp (Ondraczek, The sun rises in the east (of Africa): A comparison of the development and
status of solar energy markets in Kenya and Tanzania 2013) (APPENDIX A).
Solar water heating (SWH) is used at households, hospitals, hotels. There is an estimated demand of
800,000 units by 2020 (equivalent to 300,000 toe) implying an impressive 20% growth per year. This
[7]
demand derives mainly from domestic, institutional and small commercial applications, spurred by
Energy Regulations 2012 (MoEP 2015).
Other
Biomass is the major primary energy source in Kenya. Sustainable wood fuel supply management is
crucial in order to meet the growing demand and halt the environmental degradation in the country. A
strategy for introduction of biofuel blending (ethanol – gasoline) was developed in 2010, with facilities
constructed in Kisumu, Eldoret and Nakuru. Main source of feed stock is expected to be sugarcane and
sweet sorghum for ethanol and jatropha, castor, coconut, croton and cotton seed for biodiesel. Further
exploration is being performed towards this direction (MoEP 2015).
Biogas projects have also been initiated both by public and private initiative. According to the Ministry of
Energy and Petroleum, there is a 19,852 kW potential capacity from the floriculture industry (especially
from sisal production) (MoEP 2015).
A pre-feasibility study by Ministry of Energy and petroleum in 2007 indicated potential of 120 MWe
from sugar companies (through cogeneration) in Kenya under minor investments, and 200 MWe under
modest investments. Currently, Mumias Sugar company in Kakamega County has an installed capacity of
38 MW running on sugarcane bagasse, from which 26 MW are connected to the national grid. It is
estimated that the generation capacity by sugar companies will reach 60 MW by 2016 (MoEP 2015).
In October 2014, the Ministry of Energy and Petroleum granted the approval for a 100 MW electric
power plant running on wave energy. Nuclear power is on an initial development stage in Kenya,
however it is aspired that it will be the main contributor of electricity generation by 2030.
Electricity Transmission Network
As at June 2014, the transmission network in Kenya involved 1,434 km of 220 kV, 2,513 km of 132 kV,
132 kV double circuit with Uganda, 9 generation stations with capacity of 1,846 MVA and 45
transmission substations with capacity of 3,181 MVA.
The transmission capacity is severely constrained especially during peak hours (partly due to inadequate
reactive power and transmission constraints especially in Western regions and Nairobi) (MoEP 2015).
Power outages are a daily phenomenon across the country leading to reduced power reliability. In order to
temporarily cope with this problem an online, location based power outage notification service (Power
Alert) was introduced in 2014. However, additional transmission capacity development is necessary for
the electrification goals to be achieved in the future.
To this direction, the transmission entity KETRACO (established in 2008) has undertaken several
projects in order to reach geographical locations without access to electricity, enhance the evacuation
capacity for current and planned generating plants and facilitate regional connection with neighbor
countries (Kenya Electricity Transmission Company Ltd. KETRACO n.d.). It is expected the 5,000 km of
new transmission lines will be developed short term and 16,000 km by 2030 (Figure 4) (MoEP 2015).
The on-going and planned transmission projects are presented in APPENDIX B.
[8]
Figure 4. Transmission network planned extension in Kenya (MoEP 2015).
Electricity Distribution Network
Kenya Power & Lighting Company (KPLC) is the main licensee of the distribution network in Kenya
with Rural Electrification Authority (REA) undertaking responsibility for grid extension at low and
medium voltages in areas that is it consider unprofitable.
REA usually cooperates with each County authorities in order to enhance energy access through
electricity and gas reticulation and regulation. As at June 2014, there were 1,212 km of 66 kV lines,
20,788 km of 33 kV lines, 30,860 km of 11 kV lines and low voltage lines in Kenya with 3,311 MVA
primary distribution substations and 6,317 MVA total transformer capacity. Through the Rural
Electrification Programme (REP) and the Cross Border Electrification scheme (CBE) it is expected that a
big part of the population will be electrified in the next few years (MoEP 2015). As at 2014, 430,000
[9]
households were connected to grid electricity while 100% electrification of market centers and primary
schools is due to be completed by 2016 (The World Bank n.d.).
Key stakeholders involved in the Kenyan Energy sector
The Kenyan energy sector is vertically integrated with the Ministry of Energy and Petroleum (MoEP)
being the responsible body for energy policy while administrating a system of performance contracts with
the public sector entities:
Kenyan Pipeline Company Ltd. (KPC), Kenyan Petroleum Refinery Ltd. (KPRL) National Oil Company
of Kenya (NOCK), Kenya Electricity Generation Company (KenGen), Geothermal Development
Corporation (GDC), Kenya Nuclear Electricity Board (KNEB), Kenya Transmission Company
(KETRACO), Kenya Power and Lighting Company (KPLC) and Rural Electrification Authority (REA).
In addition, a semi-autonomous regulatory agency, the Energy Regulatory Commission (ERC)
formulates, enforces and reviews regulations, codes and standards for the energy sector and reviews and
adjusts electric power tariffs and tariff structures (The World Bank 2014).
The power sector has been significantly reformed since 1997 with the sector’s governance anchored by
the Energy policy 2004 and the Energy Act 2006. The generation, transmission and distribution entities
are separated and operate based on commercial principles and transparent financial relationships between
the involved stakeholders.
There is a strong private sector presence in the sector, with the independent power producers (IPPs)
comprising 23% of the electricity supply in the country (483 MW) while about 1.2 billion USD additional
investments have reached financial closed and new power generation plants are under development (The
World Bank 2014).
Figure 5. Energy sector structure in Kenya (Ministry of Energy and Petroleum MoEP n.d.).
[10]
Acts and policies that set the current framework in the Kenyan Energy Sector

Constitution of Kenya 2010

Sessional paper No.4 (2004)

Energy Act No.12 (2006)
Assigned responsibility for development of national energy plans to ERC. In 2009 established the 20 year
rolling Least Cost Power Development Plan (LCPDP) aiming at Load forecasting and Generation,
Transmission & Distribution planning. The last update was published in 2013 with the next one expected
in 2015.

Rural Electrification Master Plan

Millennium Development Goals

Feed-in Tariff (FiT) policy

Kenya National Climate Change Response Strategy

Kenia Vision 2030
Development plan started in 2012 aiming to transform Kenya into an industrialized middle income
country by 2030. Part of the VISION 2030 plan is to dramatically change the energy profile of the
country by providing universal and reliable electricity services in an affordable yet cost effective way.

Government 5000+ project
Power generation development plan started in October 2013 aiming in the addition of 5,000 MW of
generating capacity by April 2017. The plan is projected by that time to have reduced the generation cost
from 11.30 USD cents to 7.41 USD cents per kWh and the electricity tariff from 14.14 USD cents to 9
USD cents per kWh for commercial/industrial users and from 19.78 USD cents to 10.43 USD cents per
kWh for domestic consumers (Table 2) (MoEP 2015).

Geothermal Resources Act No.12 (1982)

Petroleum (Exploration and Production) Act Cap. 308

Environmental Management and Co-ordination Act, No. 8

Occupational Safety and Health Act of 2007

Water Act of 2002

Merchant Shipping Act & Kenya Maritime Authority Act of 2006
[11]
Table 2. Development activities of 5000+ plan (MoEP 2015).
1.3 Current challenges
Kenya is at the forefront of a socioeconomic transformation, aiming to turn into an industrialized middle
income country by 2030. Kenya Vision 2030 has identified energy as a key foundation and one of the
infrastructural “enablers” upon which the economic, social and political pillars of this long-term
development strategy will be built.
The three overarching objectives of the government are to increase electricity access, to provide efficient
and reliable electricity services and to secure adequate electricity supply at least cost. While the energy
sector has been significantly reformed over the last decade, the enabling environment to realize these
objectives is not yet fully in place. It is necessary that new policies and institutional arrangements should
be implemented in order to secure the optimal planning and put in place a robust framework for
competitive procurement of new capacity (The World Bank 2014).
In particular, governance in the public sector entities should be strengthened for greater accountability
and improved efficiency. The investment needs for electrification should be met by the government and
not by the public entities (like KPLC) in line with the international practice of successful electrification
programs. In addition, private sector participation should be mobilized through better procurement
processes and competitive bidding (The World Bank 2014).
Substantial investments are required in the transmission and distribution network in order to ensure the
achievement of good standards of service quality. However, decentralized electrification approaches
should be thoroughly taken into consideration through mini-grids and/or off grid systems in areas where
connection to the national system is not envisaged in the short and medium term (The World Bank 2014).
For that to be achieved, systematic energy planning (at national and sub-national/County level) is highly
recommended through the adoption of more realistic assumptions on the future evolution and profile of
demand and robust pre-feasibility of prospective projects including the integration of renewable energy
sources, which the country is endowed with. It was under this framework that this thesis was elaborated.
[12]
CHAPTER 2. KAKAMEGA COUNTY CASE STUDY
In this chapter Kakamega County in Western Kenya is introduced. A brief introduction on the
socioeconomic characteristics of the County is followed by a comprehensive analysis of the energy
demand in various sectors and an assessment of the available energy resources.
2.1 Demographics and socioeconomic background
Geographic Location
Kakamega County is located in the western part of Kenya, about 40 km northeast of the Lake Victoria
and 40 km from the borders with Uganda. It has a surface area of 3,020 square kilometers and an average
elevation of 1,535 meters. The County borders with Bungoma to the north, Trans Nzoia to the north east,
Uasin Gishu and Nandi Counties to the East, Vihiga to the South, Siaya to the south west and Busia to the
west. The County has 9 constituencies namely Butere, Mumias, Matungu, Khwisero, Shinyalu, Lurambi,
Ikolomani, Lugari and Malava (CRA 2011), (SoftKenya n.d.).
Demographics
Kakamega is the second most populous county in Kenya after Nairobi with as estimated population of
1,644,328 (2013 census) and a density of 544 ppl/km2, higher in the central and south parts of the county
(KNBS - SID 2013). The population growth is estimated at 2.5% per year. The urban population accounts
for 15.2% of the inhabitants while growing at a rate of 4.1% per year (CRA 2011), (Unicef n.d.).The
biggest towns in the region are:

Mumias: 99,987

Kakamega (Capital): 91,768

Butere: 12,780

Lumakanda: 10,580

Malava: 4,070
In Kakamega there are registered 355,679 households with the annual growth rate being 2.54%. The
average household size ranges between 5.5 people in rural areas and 4 people in urban areas. The average
value is 4.7 people per household that matches with the distribution of the estimated population to the
registered households. Household access to electricity is 5.6% with its usage mainly involve lighting and
small electric appliances. The monthly income of a household is ranging from 3,000 to 150,000 Ksh with
the average value being near 15,000 Ksh per month. 76.1% of the households have access to improved
water infrastructure, 54.1% has access to access to good/fair roads while only 4.9% of the households
have access to paved roads (KNBS 2014), (CRA 2011), (Ngetich 2013), (Eston Ngugi 2013).
[13]
Figure 6. Population density in Kakamega (CRA 2011).
Economy
Kakamega had the 5th higher local revenue among the Kenyan counties as in 2013/2014 with 3,500
Million Ksh. An equitable share of 6515.513 Million Ksh and a conditional grant of 840.74 Million Ksh
set the total revenue of the County at 10,856 Million Ksh for 2013/2014 (KNBS 2014).
Estimation of Kakamega’s GDP
According to IMF in 2013 the estimated GDP per capita in Kenya was 1245.5 USD. In absence of a
detailed county-scale estimation for the GDP, an estimation of the total GDP of the Kakamega can
therefore be made using the population of the county. Hence, it was estimated that the local GDP was
2.048 billion USD (190 billion Ksh) at the base year, set in 2013.
Main economic activities
The main economic activities are (SoftKenya n.d.):
3
4

Large and small scale sugar cane farming.

Sugar industry.

Small scale mixed farming of millet, tea, maize, sugar beans, sunflower & dairy products.

Commercial center: 5 Commercial Banks & 3 Microcredits.

Commercial businesses: Supermarkets, grocery shops, open markets.

Transportation business: Boda-Boda (35 registered societies, 1450 boda-boda cyclists).
3.1% of the 210 Billion Ksh distributed from Kenya Government according to the County Allocation of Revenue Act 2013.
4.2 % of the 20 Billion Ksh of conditional grant
[14]

Informal economy: Street venders, hawkers.
2.2 Energy demand
A bottom-up approach was followed, assuming and estimating disaggregated energy demands for selected
activities of each sector. The following paragraphs contain detailed description of these data.
2.2.1 Residential sector (households)
The residential sector holds a great percentage of the energy consumption in Kakamega. The following
paragraphs provide an analysis of the main activities in a typical household in Kakamega under an energy
consumption and fuel utilization approach.
Cooking
The main energy source for cooking in Kakamega is solid biomass (firewood and charcoal) as it accounts
for 95.7% of the energy fuels used. The constituencies of Lurambi and Shieywe near Kakamega town
show the highest charcoal adoption at 32% and 54% respectively. On the other hand, there are areas
where firewood is predominant reaching adoption rates above 95%. Table 3, shows in more detail the
allocation of fuels for cooking purposes in Kakamega County (Eston Ngugi 2013).
It is estimated that the average firewood consumption in Kenya is around 5-10 kg/household/day
(KEREA 2015). Another indication suggests 1.45 kg of firewood/person/day (Geoff rey Ndegwa 2011)
while a study conducted by Boulkaid in 2015 for Nyeri County shows similar intensities of approximately
1.3 kg of firewood/person/day (Boulkaid 2015). With the average household size being 4.7 persons, that
induces a daily average firewood consumption between 6 - 6.8 kg per household. The upper limit of 6.8
kg of firewood/household/day will be used now on in this paper. Consequently, the monthly household
consumption of firewood is 204 kg and the annual consumption 2,448 kg. Firewood has a heating value
ranging from 8 MJ/kg (if freshly harvested, moisture above 50%) to 16 MJ/kg (if it is air dried, moisture
around 15%) (Steve Sepp 2014). However, 84.1% of it is self-collected (especially in rural areas), which
implies no monetary cost but very low heating value (Dalberg 2013). When purchased, firewood price is
about 76 Ksh per head log or approximately 9 Ksh/kg (Wambua, Household energy consumption and
dependency on common pool forest resources: The case of Kakamega forest, Western Kenya 2011).
The charcoal consumption is higher in regions with increased income. Charcoal has higher heating value
than firewood, therefore lower quantities are necessary in order to cover the cooking needs. It is estimated
that an average household consumes about 750 kg of charcoal per year (Dalberg 2013) (Wambua,
Household energy consumption and dependency on common pool forest resources: The case of
Kakamega forest, Western Kenya 2011). That value is translated into 62.5 kg of charcoal per month and
roughly 2 kg of charcoal per day. The heating value of charcoal ranges between 27-33 MJ/kg according to
the fixed carbon and the carbonization process (Steve Sepp 2014). The price of charcoal is estimated at 14
Ksh/kg (average value between 9.8 – 18.3 Ksh/kg) (Dalberg 2013) (Wambua, Household energy
[15]
consumption and dependency on common pool forest resources: The case of Kakamega forest, Western
Kenya 2011).
Table 3. Percentage of fuel used for cooking purposes in Kakamega County (Eston Ngugi 2013).
Fuel
Percentage (%)
Firewood
86.8
Charcoal
8.9
Kerosene/Paraffin
2.3
LPG
0.8
Electricity
0.5
Biogas
0.4
Other
0.3
Both firewood and charcoal consumption depend on the cook stove utilized and its efficiency. The three
stone stove (open fire), traditional stove and improved stove are commonly found in Kakamega. Table 4,
presents a more detailed description of the stove technologies and their respective efficiencies.
Table 4. Cooking stoves technologies and efficiencies (Amarasekara 1994), (Boulkaid 2015).
Fuel
Firewood
Charcoal
Kerosene
LPG
Electricity
Biogas
Three stone fire
Efficiency
(%)
5 to 10
Traditional stoves
8 to 14
11
Improved stoves
20 to 30
25
Traditional stoves
8 to 14
11
Improved stoves
20 to 30
25
Stove
Mean eff. (%)
7.5
Wick type
25 to 35
30
Pressure type
35 to 55
45
LPG stove
40 to 60
50
Hot plates
55 to 75
65
Rice cooker
85
85
Pressure cooker
90
90
Electrical Kettle
80 to 90
85
Biogas stoves
45
45
Kerosene is used for cooking to a relatively low percentage even though it is a more efficient solution.
The higher cost and the availability are mainly to be blamed. It is estimated that the average consumption
of kerosene for cooking is about 200 liters per household per year (Dalberg 2013). That is translated to
16.7 liters per month or 0.54 liters per day. The lower heating value of Kerosene is 43 MJ/kg (The
[16]
Engineering Toolbox 2015) and its price is following a declining path since 2012, set at 54.35 Ksh/liter as
in March 2015 (Energy Regulatory Commission (ERC) 2015).
LPG has a very low adoption rate in Kakamega of about 0.8% (Eston Ngugi 2013). The high price
(especially in comparison with the cheaper charcoal) and the limited storage, distribution and retail
capacity in the County are the basic barriers for that. It is estimated that on average households consume
128.6 liters of LPG per year for cooking (Dalberg 2013). That implies an average value of 10.7 liters per
household per month or 0.35 liters per day. The specific heat of LPG (including propane/butane) is 46.4
MJ/kg (The Engineering Toolbox 2015) while its price at 256.2 Ksh/liter as in April 2013 (Dalberg
2013).
According to the data described above, a comparative analysis was performed in order to estimate the
average daily energy required by a household for cooking purposes in Kakamega. Therefore, depending
on fuel and cooking technology (stove) it was estimated that a household in Kakamega requires about 7.8
MJ per day for cooking purposes. That approximation was used in order to determine the electricity
consumed for cooking by electrified households in the County.
According to the statistics, about 1,778 out of 19,918 electrified households use electricity for cooking in
Kakamega. In order to cover the estimated 7.8 MJ of necessary energy per household for daily cooking
and with an average efficiency of 0.8 for electric cooking devices, 2.7 kWh are needed. That means that
the average electricity consumption is about 986 kWh per year per household or 82 kWh per month for
cooking purposes, while the electricity price 2.5 KES/kWh (under fixed tariff of 120 KES and charge 2.5
KES/kWh for the first domestic users tier) (ERC 2013).
Finally, Biogas is used by 0.4% of the households for cooking purposes. The calorific value of biogas is
estimated at 20 MJ/kg and the biogas stove efficiency 45%, so the daily average consumption of biogas is
around 0.865 m3. That means 26.8 m3 per month or 321.8 m3 per year for an average household.
Assuming a yield of 0.035 m3 of biogas per kg of slurry (3.5% efficiency), that is translated to 24.7 kg of
slurry per day. If 50% of the slurry is water that implies that manure should be supplied at 12.4 kg per day
per household. It is estimated that cows produce 4.5 kg of dung/head per day from which 60% is
recoverable. That accounts to 4-5 cows per household as a fuel feed for biogas cooking (author’s
estimation).
Lighting
The main source of lighting for households in Kakamega is kerosene, which accounts 93.4% of the
lighting fuels and is utilized in multiple devices with predominant the tin lamps and lanterns (Eston Ngugi
2013). It is estimated that 5.5% of households use electricity for lighting, percentage that matches with
the total electrification rate in the County (5.6%). That means that the households that have access to
electricity, mainly use it for lighting purposes. In Lurambi constituency the percentage of electricity usage
for lighting reaches 21% while in Mumias North the percentage is around 50%. Table 5, presents the
lighting sources allocation according to the household preference in the County (Eston Ngugi 2013).
[17]
In a survey contacted in 2014 in western Kenya, it was found that the average monthly expenditure on
kerosene for lighting was around 891.4 Ksh per household (Sklivaniotis, Integrated Techno-Economic
Comparative and Socio-Economic Impact Study for Increasing Energy Access in Rural Kenya 2014).
That accounts for 5.9% of the average household income (15,000 Ksh/month) (Denise Law, Kopernik
2013). According to the price of kerosene in Kakamega at that time (May-June 2014) which was 84.68
KES per liter (Energy Regulatory Commission (ERC) 2015), it was estimated that the monthly
consumption of Kerosene is about 10.5 liters. That is translated to 126 liters of kerosene per year or 0.34
liter per day for an average household in Kakamega.
In order to give a practical dimension to these numbers, 0.34 liters of kerosene can provide 340 lumenhours per day (if an average efficacy of kerosene lamps is assumed to be 1000 lumen-hours per liter)
(Mills 2003). With average 4.5 hours of lighting necessity per day that accounts to 75.6 lumens available.
That is a very low number in terms of usefulness.
On the other hand, for the electrified households, the lighting needs slightly change. It is assumed that an
average electrified household in Kakamega has 3-6 lamps (Plas 1994). The assumption involves 25W
tungsten incandescent light bulbs providing light for 4-5 hours per day. That implies a daily electricity
consumption of about 0.56 kWh for lighting purposes of a household. The average monthly and annual
consumption are respectively estimated at 17.4 kWh and 209 kWh. It should be mentioned at this point
that under this scenario the luminous flux available is roughly thirteen times higher (1000 lumen) than in
kerosene fueled appliances which is an appropriate light source for everyday activities.
Table 5. Percentage of lighting sources for households in Kakamega County (Eston Ngugi 2013).
Source
Percentage (%)
Tin lamp
63.9
Lantern
28.2
Electricity
5.5
Gas lamp
0.8
Solar
0.7
Pressure lamp
0.5
Fuel wood
0.4
Other sources of lighting such as solar power, dry cell (torches), firewood and candles are also evident in
Kenya but no concrete data were found for Kakamega County. As a matter of fact, solar powered lighting
is a solution for 1.4% of Kenyan households. The same percentage accounts for dry cell devices. It is
interesting to notice that in lack of electricity access, households turn mainly to kerosene, dry cell and
firewood in order to cover their lighting needs. Biogas is also an option but at a very small extent
(Lighting Africa 2012) (Jann Lay 2012).
[18]
Water heating
It is estimated that the water usage for bathing and clothes washing is about 13.5 liters/day/household
(IIED 2001). Assuming that the necessary temperature of the water should be 45 oC that would imply an
energy requirement of 1,129 kJ/day/household5 or 0.31 kWh/day/household. Electric heaters show an
overall efficiency of 0.96 so that would imply a total energy requirement of 117 kWh/year/household.
Additionally, the efficiency of solar water heating systems (SWH) is estimated at 0.55 while the firewood
burners for water heating have an efficiency of 0.25. Therefore, the primary energy requirement for the
two technologies is about 749 MJ/year/household and 1,648 MJ/year/households respectively. That is,
around 165 kg of firewood/year/household for water heating purposes.
Other Uses
The estimated electricity consumed by a household on an annual basis in Kenya is 844 kWh in urban and
544 kWh in rural areas (Sklivaniotis, Integrated Techno-Economic Comparative and Socio-Economic
Impact Study for Increasing Energy Access in Rural Kenya 2014). The electrification rate in Kakamega is
quite low (5.6%) and even more, electricity is available mainly in the biggest towns (Kakamega –
Mumias). Therefore, the energy profile of electrified households is better described by the average 694
kWh per year. That is further enhanced by a survey of 2012, indicating that the mean electricity
expenditure of a household in Western Kenya is roughly 300 Ksh per month (Jann Lay 2012). According
to the Energy Regulatory Commission of Kenya (ERC) tariff policy, an electricity consumption of 57.8
kWh/month would cost 265 Ksh (under fixed tariff of 120 Ksh and charge 2.5 Ksh/kWh for the first
domestic users tier) (ERC 2013).
In addition, as described in a previous paragraph, electricity is mainly used for lighting and minor
additional services like radio, TV etc. Cooking is still based on traditional sources of energy (firewood,
charcoal). It was also estimated that the average electricity consumption of a household for lighting is
about 17.4 kWh per month and for water heating 9.75 kWh per month. With a simple calculation it can be
approximated that “other” uses of electricity account for 31 kWh per month or 368 kWh per year for a
household. That is also translated to roughly 1 kWh per day for appliances except lighting. TV (188 W),
radio (10 W) or fridge (1,200 Wh/day) could be possible uses.
Conclusion
According to the assumptions made, the residential sector shows quite high energy consumption levels.
The high consumption mainly derives from the use of inefficient technologies (consequently high
requirements of primary energy inputs) in the daily activities (mainly cooking and lighting).
Disaggregated data are available in Table 12 at the end of this chapter.
5
Water temperature set at 45 oC while ambient temperature at 25 oC. Also the specific heat capacity of water was set at 4.18
kJ/kg.K.
[19]
2.2.2 Industrial sector
Sugar production is the main industrial activity in the Kakamega. Three companies are active in this field;
Mumias Sugar Company Limited, West Kenya Sugar Company and Butali Sugar Mills. It is estimated
that sugar factories consume 28 kWh of electricity per ton of sugarcane crushed and 570 kg of steam per
ton of sugar produced (C. Mbohwa 2013) (Francis X. Johnson 2012). The steam used in sugar processing
is typically at pressure between 1.2-2 bars and temperature of 200-300 oC, having a specific enthalpy
around 2,972 MJ/ton of steam (Mbohwa 2009) (Francis X. Johnson 2012). Under this assumption, the
heat requirements in sugar processing can be estimated at about 1.694 GJ/ton of sugar produced. It should
be mentioned at this point that the majority of sugar factories cover their energy needs through
cogeneration, using bagasse as fuel. The electricity production highly depends on the power house
specifications. It is estimated that for small – medium plants it ranges between 24 kWh (Mukhongo 2012)
and 30 kWh per sugarcane crushed (C. Mbohwa 2013). A more analytical energy balance for each factory
in Kakamega is presented in the following paragraphs.
Mumias Sugar Factory Limited (MSFL)
Mumias sugar factory has the highest capacity for sugarcane crushing in Kenya and accounted to the
36.1% of the county’s total sugar production in 2012. More specifically, 1,964,063 tons of sugarcane
were crushed in the factory producing 181,372 tons of sugar and 743,791 tons of bagasse6 (KETS 2013).
According to that data, the electricity consumption of the factory is about 55 GWh/year while the heat
consumption about 307,224 GJ per year. It is estimated that the energy intensity is 2.786 GJ/ton of sugar
produced with the energy mix involving 60.8% if form of heat and 39.2% as electricity. It should be
mentioned at this point that the factory has an installed capacity of 38 MW (35 MW effective capacity) 26
MW of which are connected to the national grid. The generation process runs 7200 hours per year under a
low capacity factor of 0.3 (MoEP 2015) (CDM 2006).
West Kenya Sugar Company (WKSC)
As in 2012, WKSC crushed 539,329 tons of sugarcane producing 49,565 tons of sugar and 204,244 tons
of bagasse (KETS 2013). Following the same assumptions as in the previous case that would imply an
electricity consumption of 15.1 GWh/year and a heat consumption of 83,963 GJ/year. It is estimated that
the energy intensity is 2.791 GJ/ton of sugar produced with the energy mix involving 60.7% if form of
heat and 39.3% as electricity.
Butali Sugar Mills
Finally, according to KETS report, Butali Sugar Mills crushed 415,546 tons of sugarcane and produced
42,671 tons of sugar and 157,367 tons of bagasse in 2012 (KETS 2013). Following the same process as in
the previous cases it is estimated that the electricity consumption for the plant is about 11.6 GWh/year
while the heat consumption about 72,284 GJ/year. It is estimated that the energy intensity is 2.676 GJ/ton
of sugar produced with the energy mix involving 63.3% if form of heat and 36.7% as electricity.
6
Sugarcane to bagasse ratio was found to be 37.87% (Mukhongo 2012).
[20]
Conclusion
Therefore, the total industrial electricity consumption in Kakamega is 81.7 GWh/year while the heat
requirements for industrial processes is estimated at 463,471 GJ per year.
2.2.3 Transportation sectror
The number of vehicles is estimated according to the national data on registered vehicles and the
population (Kakamega/Kenya – 3.7%) ratio. Table 6, gives an initial estimation of the number of vehicles
in Kakamega while Table 7 provides the number of new registered cars in the county (KNBS 2014)
(KNBS 2014).
Table 6. Estimated number of registered vehicles in Kakamega (KNBS 2014).
Motor cars
Utilities, parcel vans, pick ups
Lorries, Vans & heavy Vans
Busses, Mini Busses
Motor & Auto cycles
Trailers
Other motor vehicles
Total
26263
9331
4350
3539
27314
1470
2176
74443
Table 7. Estimated number of new registered (2013) vehicles in Kakamega (KNBS 2014).
Motor cars
Utilities, parcel vans, pick ups
Lorries, Vans & heavy Vans
Busses, Mini Busses
Motor & Auto cycles
Trailers
Other motor vehicles
Total
2405
363
354
85
4742
147
124
8220
In order to calculate the energy consumption in the transportation sector, a number of parameters were
introduced and assumed. Therefore, by taking into account the average annual distance covered, the fuel
economy for each segment and the load factors, the final energy intensities were estimated as shown in
Table 8 by using the following equation:
𝑙𝑖𝑡𝑒𝑟
1
𝐸𝑛𝑒𝑟𝑔𝑦 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (
)=
𝑣𝑒ℎ_𝑘𝑚
𝑝𝑎𝑠𝑠_𝑘𝑚
𝐹𝑢𝑒𝑙 𝑒𝑐𝑜𝑛𝑜𝑚𝑦 (
) 𝑥 𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 (𝑝𝑎𝑠𝑠_𝑘𝑚/𝑣𝑒ℎ_𝑘𝑚)
𝑙𝑖𝑡𝑒𝑟
Two categories were suggested, namely passenger transportation and freight transportation.
[21]
The first category involves all the means of transport that are used in passenger mobility (motor cycles,
cars, busses) and are highly correlated with the population and income level. An average value of 1,482
passenger-km/person/year was estimated based on the following equation:
3
𝑃𝑎𝑠𝑠𝑘𝑚𝑝𝑒𝑟𝑠𝑜𝑛𝑦𝑒𝑎𝑟
1
=
× ∑[𝑁𝑜𝑉𝑖 × 𝑚𝑖𝑙𝑒𝑎𝑔𝑒𝑖 × 𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟𝑖 ]
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
𝑖=1
Where,
NoV: Number of Vehicles
Mileage: Distance covered (in km) in one year
Load factor: Pass_km/Veh_km
And i=1 stands for motor cars, i=2 stands for busses and mini busses and i=3 stands for motor cycles.
It should be mentioned at this point that walking and cycling were also introduced as means of transport
in this category as high portion of the population in Kakamega is based on them. These two activities do
not contribute to the energy consumption in actual terms, but they should be taken into consideration in
the policy formation as they highly affect the future of the sector.
The second category includes freight transport in Kakamega. Following the same principle as before, the
energy intensities were calculated for all the vehicles involved (utilities, parcel vans, pick-ups, lorries,
trailers and other) on the base of GDP contribution. The average value was estimated 1.27 ton-km/USD
and used in the model to calculate the total energy consumption.
4
𝑇𝑜𝑛𝑘𝑚𝑦𝑒𝑎𝑟
1
=
× ∑[𝑁𝑜𝑉𝑘 × 𝑚𝑖𝑙𝑒𝑎𝑔𝑒𝑘 × 𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟𝑘 ]
𝐶𝑜𝑢𝑛𝑡𝑦 ′ 𝑠 𝐺𝐷𝑃
𝑘=1
Where, k=1 stands for utilities, parcel vans and pick-ups, k=2 stands for lorries, vans and heavy vans, k=3
stands for trailers and k=4 stands for mixed other vehicles.
It was observed that motorcycles and conventional cars are the most common vehicles in the roads of
Kakamega. The average growth rate of registered vehicles was calculated for the years 2004-2013 to
12.5% per year with motorcycles having the highest rate of 36.3% (U.S Department of Energy n.d.), (Oak
Ridge National Laboratory n.d.), (United Nations Environment Programme n.d.), (UNEP 2006).
Conclusion
The energy consumption was estimated as 745.9 GWh for gasoline and 898.5 GWh for diesel, accounting
for 1,644 GWh/year for the total transportation sector in Kakamega. It should be mentioned at this point
that measuring the long run fuel demand in transportation sector is a quite challenging task. This thesis
adopted a basic approach in order to have some initial preliminary results. It is suggested that several
factor should be added (fuel price, income, taxation, road investment policies etc.) in order to provide
higher accuracy on the projections. Here is some suggested literature (Olof Johansson 1997).
[22]
Table 8. Energy Intensities in transportation sector in Kakamega (UNEP n.d.), (Kimmo Erkkilä 2005), (US Department of Energy n.d.), (US Department of
Transportation n.d.) (OAK Ridge National Laboratory n.d.).
Transportation
Number
Percentage
of Vehicles
Motor cars
26263
Gasoline (75%)
19697.25
Diesel (25%)
6565.75
Busses, Mini Busses (Matatu)
3539
Diesel
3539
Motor & Auto cycles
27314
Gasoline
27314
35%
Growth rate
Vehicle-km
(% )
per year
9.75
17082
Fuel economy
Load factor
(liter per vehicle-km) (Pass-km/veh-km)
0.0288
0.091
6.97
22000
37%
36.26
3574
(liter/pass-km)
2.5
0.072
5%
Energy Intensity
0.0364
15
0.280
0.0187
1.5
0.054
0.0360
Walking
Cycling
(ton-km/veh-km)
Utilities, parcel vans, pick ups
9331
Gasoline (75%)
6998.25
13%
3.85
21306
(liter/ton-km)
2.5
0.123
0.0493
Diesel (25%)
2332.75
Lorries, Vans & heavy Vans
4350
Gasoline (25%)
1087.5
0.126
0.0167
Diesel (75%)
3262.5
0.157
0.0209
Trailers
1470
Diesel
1470
Other motor vehicles
2176
Gasoline (50%)
1088
Diesel (50%)
1088
Total
74443
0.156
6%
2%
7.02
21671
16.65
0.0624
7.5
50000
15
0.375
3%
100%
6.99
16093
12.5
[23]
0.0250
8.5
0.094
0.0110
0.212
0.0249
2.2.4 Commercial sector
The commercial sector in Kakamega mainly comprises of small and medium scale enterprises. These
enterprises are characterized by low production cost, accessibility to raw materials, proximity to the
markets and generally low level of mechanization which entails low productivity. Examples of such
enterprises found across the county are engaged in activities like pottery, crude sugar production, brickmaking, liquor production, quarrying and masonry, carpentry, traditional medicine production, charcoal
production, basketry and weaving, baking, bicycle repair, flour-grinding, and shoe-making and repair
(Makokha 2014). The division is made regarding the energy consumption into these two categories
respectively.
Small scale enterprises
In this category the energy demand is respectively low and mainly regards low load electrical
applications. Lighting and professional equipment few hours per day compose the consumption profile,
while the total energy demand is up to 10 kWh/day. It was assumed that there are 1,000 businesses of this
kind (according to data retrieved from Open Street Map for the region) in the county accounting for 3,650
MWh/year.
Medium/large scale enterprises
This category contains commercial activities with higher energy consumption between 10 kWh/day and
100 kWh/day. Such activities involve super markets, open markets, shopping malls, banks, hotels etc.
Hotel – Guest houses - Restaurants
The energy consumption in hotels and guest houses in Kakamega follows the profile presented in Graph
1. It is estimated that there are 174 hotels – guest houses in the area with a capacity of 1,340 beds and an
occupancy rate of 30% (KNBS 2014). It is assumed that the average electricity consumption is 30
kWh/day while heat required for water heating7 is about 30 MJ/day and for cooking8 10 MJ/day. That
would imply a total consumption of 1,905 MWh/year of electricity and 2,540 GJ/year of heat.
Super markets, Open markets & Shopping malls
Electrification of markets is one of the direct plans in Kakamega (governor speech 2015). The energy
demand is mainly derived from electricity requirements for refrigeration, cooling and lighting. Heat is
necessary for cooking/baking purposes. The energy profile of markets in Kakamega in assumed as shown
in Graph 2. It is estimated that the electricity consumption is 50 kWh/day while 25 MJ/day are necessary
for heating purposes. It is assumed that there are 300 markets of this king in the county. That implies an
electricity consumption of 5,475 MWh/year and heat consumption of 2,738 GJ/year.
7
It was assumed that the average water consumption for a 10-minute shower is 35 liters and that hotels have 5 guests per day on
average. Water temperature set at 60 oC while ambient temperature at 25 oC. Also the specific heat capacity of water was set at
4.18 kJ/kg.K. Efficiency of the boiler at 0.85.
8 Average energy required for cooking assumed at 1.7 MJ/day. Also 5 guest/day was assumed for each hotel.
[24]
Graph 1. Estimation of the energy consumption profile in hotels and guest houses in Kakamega .
Graph 2. Estimation of the energy consumption profile in big open markets in Kakamega.
Banks and other businesses
This category includes mainly service oriented commercial activities, whose energy demand derives
mainly from electricity requirements for office equipment and lighting. It is assumed that 25 kWh/day
cover these needs with 20% devoted to lighting and 80% to equipment (computers, monitors etc.). With
250 businesses of this type active in Kakamega, that implies an energy consumption of about 2,281
MWh/year.
Conclusion
In total, the commercial sector in Kakamega shows an electricity demand of 13,356 MWh/year and a heat
demand of 5,278 GJ/year.
[25]
2.2.5 Community sector & Public services
This section involves the energy demand of activities addressing community needs. More specifically, the
energy requirements are estimated for Hospitals and health clinics, Schools, Community buildings &
Public offices and Street lighting.
Hospitals & health clinics
It is found that in Kakamega there are 119 health facilities from which 17 are hospitals, 32 clinics the rest
health centers and dispensaries (Kakamega County n.d.) (Google maps 2015) (Open Street Map).
According to USAID, health facilities can be categorized into three groups according to their energy
requirements. In this report it is assumed that hospitals in Kakamega fall into the third category having a
daily energy requirement of 25 kWh while clinics, health centers and dispensaries have an average energy
requirement of 10 kWh per day (combination of category 1 and 2) (USAID n.d.). That is, the energy
consumption for hospitals and health clinics in Kakamega is estimated at 1,445 kWh per day or 527.4
MWh per year. Further explanation of the categories can be found in APPENDIX D.
Educational buildings
Primary & secondary education
Recent data shows that 24% of the population in Kakamega are children between the ages of 6-13 and
11% children between 13-17 years old (Republic of Kenya 2006). The attendance to primary education is
very high in the region and in this report is assumed 100%. On the other hand attendance to secondary
education is estimated at 55% (UNESCO-IBE 2011). Under these assumptions, the primary education
students in Kakamega in 2013 are estimated 394,639 while the secondary education students 99,482.
With 300-500 students registered in each school, that implies a rough estimation of 1000 primary schools
and 300 secondary schools in the County. In this report the latest data obtained on the number of schools
will be used. Therefore the number of primary schools is 938 while the secondary schools are 276
(Kakamega County n.d.).
The estimated 1.515 kWh/day (Table 9) can be an indicative value of average energy consumption in
schools in Kakamega. Under this assumption the total estimated energy demand is estimated at 1,840
kWh/day or 671.6 MWh/year.
Higher education
In terms of higher education, Kakamega is the home of Masinde Muliro University of Science and
Technology (MMUST). According to Dimitriadis, the energy consumption of a university building
averages at 0.12 kWh/day/m2 (Dimitriadis 2011). Assuming that MMUST area is 5,000 m2 that would
imply an energy consumption of 600 kWh/day or 219 MWh/year.
Therefore, the total energy demand of educational buildings in Kakamega is assumed 1,418 MWh/year.
[26]
Table 9. Energy consumption of various appliances utilized in schools (NREL 2000).
Appliances
Power (Watts)
Duration (h/day)
Energy/day (Wh)
Lights (fluorescent)
5-30
2-12
122.5
Overhead fan
40
4-12
320
TV (19” color)
60
1-4
150
VCR/DVD
30
1-4
75
AM/FM Radio
15
1-12
97.5
150W
2-8
750
-
-
1515
Computer
(desktop+screen)
Electric
consumption
Street Lighting
Kakamega has approximately 400 km of main roads including (A1, B2, C33, C40 and C41) and 200 km
additional road connections within the county (Google maps 2015). It is assumed that 25% of these roads
are lit so that 150 km are covered by street lighting. With one light pole per 30 meters that means that
5,000 lamps light up the streets in the county every night. Assuming that 70W - HPS (High Pressure
Sodium) lamps are used, operating 4,000 hours/year, that implies an annual energy consumption of 1,400
MWh/year.
Community buildings
In this category are included all the energy demands of activities that serve community needs and are not
in the previous categories, namely public offices, police stations, military buildings, sport facilities,
libraries etc. It is assumed that there are approximately 100 building serving such purposes in Kakamega
base on data retrieved from open street map. The energy requirements of such buildings is set at 50
kwh/day with 15% devoted to lighting and 75% to electric appliances (computer, cooling, special
equipment etc.). That would imply an electrical demand of 1,825 Mwh/year.
Conclusions
The total energy consumption in the Community sector is 4.6 GWh/year, which account all for electricity.
It should be mentioned at this point that additional fuels (charcoal, briquettes, oil products etc.) were not
included in this sector. That is one of the limitations of this study and should be revised in future work.
[27]
2.2.6 Agriculture, Aquaculture & Livestock
Agriculture activities contribute by 10%9 to Kakamega’s GDP, while accounting for 0.54% of the
County’s energy consumption. In the absence of validated data for Kakamega, this value was estimated
based on the national energy shares between sectors in Kenya (J.K. Kiplagata 2011).
About 90% (Lübker 2013) of the population in Kakamega depend directly or indirectly from activities
related to agriculture such as farming, agro-processing, post-harvest and storage facilities, marketing,
retail and infrastructure. All these activities are part of the value chain and show an increasing demand for
modern energy services and related equipment. Energy needs in agricultural activities include direct and
indirect inputs. The first term refers to energy requirements for land preparation, cultivation, irrigation,
harvesting, post-harvest processing, food production, storage and transport of agricultural quantities. The
latter, refers to needs in the form of sequestered energy in fertilizers, herbicides, pesticides, and
insecticides.
The aggregated energy consumption of agricultural activities in Kakamega, is estimated at 31 GWh per
year. The form of energy required highly depends on the activity and it can vary between electrical,
mechanical (motive power) and thermal energy. It is assumed that 50% of the energy demand derives
from motive power, 30% by electrical energy and 20% by thermal energy requirements in the sector.
Table 10, presents examples of activities and their respective energy requirements.
Table 10. Farming activities and respective energy requirements
Activity
Land preparation/tilling
Seeding
Harvesting
Drip-feed/sprinkler irrigation
Grain milling
Oil pressing
Drying (fruits, vegetables, coffee,
tea, meat, fish, spices)
Smoking (fish, meat, cheese)
Food & drink cooling
(milk chilling/pasteurisation)
Ice-making (fish storage)
Water heating
Sawmilling
Improved warehousing
Water circulation and purification
(fish hatcheries and fish farms)
Lighting
9These
Modern Energy form
Motive power
Motive power
Motive power
Electrical
Mechanical/electrical
Mechanical/electrical
Thermal
Thermal
Electrical
Electrical
Thermal
Mechanical/electrical
Electrical
Mechanical/electrical
Lighting
are assumptions made by the author due to the absence of validated data of these values in Kakamega. The assumptions
reflect regional trends in Eastern African counties. It should be noticed that the LEAP model was developed on these values.
[28]
2.2.7 Estimated energy demand profile in Kakamega
According to the disaggregated analysis presented above, the following results were estimated for the
sectorial energy consumption in Kakamega.
Table 11. Aggregated energy consumption estimations per sector in Kakamega.
Sector
Units
Percentage
(ktoe)
(GWh)
(% )
Residential
330.2
3841.0
66.74
Transportation
141.4
1644.0
28.57
Industrial
19.0
221.3
3.85
Agriculture
2.7
31.0
0.54
Commercial
1.1
13.0
0.23
Community
0.4
4.6
0.08
Total
494.8
5755
100.00
Graph 3. Energy consumption by sector in Kakamega.
[29]
Table 12. Estimated fuel consumption in GWh per sector in Kakamega in 2014.
Sectors
Residential
Electrified
Unelectrified
Industry
Mumias Sugar Company Limited
West Kenya Sugar Company
Butali Sugar Mills
Commercial
Small Scale Businesses
Medium or Large businesses
Agriculture
Agro_demand
Community
Education
Health
Community build.
Street L
Transportation
Passenger Transportation
Freight Transportation
Total Demand
Electricity
Gasoline Kerosene Diesel LPG
(grid)
13.5
434.8
3.3
13.5
2.1
0.3
432.7
3.0
11.5
1.8
9.7
6.2
15.5 6.2
15.5 4.5
0.9
0.5
1.8
1.3
1,124.0
520.3 610.5
135.4 513.5
384.9 35.8
1,124.0
434.8 535.8 3.3
[30]
Wood
3,153.7
84.2
3,069.6
-
Charcoal Biogas Bagasse
230.7
51.9
178.8
230.7
4.2
0.2
4.0
4.2
221.3
147.4
40.3
33.6
221.3
Electricity
Heat
(solar)
1.0
0.3
0.7
1.5
1.5
9.3
9.3
0.1
0.1
1.2 10.8
Total
3,841.3
152.4
3,688.8
221.3
147.4
40.3
33.6
13.0
1.8
11.2
31.0
31.0
4.6
0.9
0.5
1.8
1.4
1,644.3
745.9
898.5
5,755.6
2.3 Climatic conditions and resource assessment in Kakamega
The average temperature in Kakamega is 20.8 oC with the monthly average been shown in Graph 4. The
average hours of sunlight are 12.1 per day and the average precipitation is 2.923 mm/day (monthly
average shown in Graph 5) (NASA - Atmospheric Science Date Center n.d.). This is a good indicator for
rain-fed crops described later on the report.
Graph 4. Average daily temperature range in Kakamega (oC) (NASA - Atmospheric Science Date Center n.d.).
Graph 5. Average monthly precipitation in Kakamega (oC) (NASA - Atmospheric Science Date Center n.d.).
2.3.1 Biomass and Bioenergy crops
Kakamega has a total area of 3,020 km2 from which around 30% is devoted to cash crops (mainly sugar
cane (60%) in the south region and partially sunflower, coffee and tea in the rest of the county).
According to the KETS report in 2013 the average yield of sugar cane in the region of Kakamega was 55
tons/hectare or 5,500 tons/km2. Based on the same report, the estimated land devoted in the cultivation of
sugar cane in Kakamega was 540 km2 (KETS 2013). Cultivation of maize is also very important in the
county and it occupies almost 70% of the land of north Kakamega (Lugari and North Kakamega districts).
[31]
The gazette forest land is 282 km2 mainly comprising by five areas namely Viz, Lugari, Turbo, Nzoia,
Malava and Kakamega forest) (Ngetich 2013). The forest area accounts for 9.3% of the county’s territory,
number which is slightly below the marginal value of 10% of tree coverage, set by the National Forest
Policy in 2014 (Ministry of Environment, Water and Natural Resources 2014). That is, the
aforementioned forest areas should not be accounted as potential biomass source in the County.
Additionally, by taking into consideration the population and the area of the biggest towns in Kakamega it
was calculated that on average every urban citizen occupies 5x10-4 km2. That is, with the urban
population being 15.2%, the total urban area of Kakamega is roughly 128 km2. Finally, it was estimated
that the total area not occupied by human activities showing the potential for natural biomass is about 936
km2.
Table 13. Estimated land use in the county of Kakamega.
Land Use
Area (sq. km) % of total
Cash crops
906
30.0
Food crops
768
25.4
Gazetted forests
282
9.3
Urban
128
4.2
Other
936
31.0
Kakamega
3020
100
Woody biomass
According to FAO, the area of Kakamega has a woody biomass availability to a rate of 12,500 tons/km2
(Drigo 2005). That means that the available woody biomass in the county (based on the 963 km2 value) is
estimated around 11.7 million tons or approximately 93.6 million GJ10.
Bagasse and Vegetal Wastes (Molasses)
The calculation of bagasse availability in the county was based on the sugar cane production on the base
year. Therefore, in 2013 around 2.9 million tons of sugar cane were crushed yielding 1,105,382 tons of
bagasse (KETS 2013). The calorific value of bagasse depends on the moisture content and lies between 8
– 14 MJ/kg (The Engineering Toolbox 2015). Hence, the available bagasse in Kakamega was estimated to
be approximately 12.5 million GJ.
Following the same procedure based on the sugar cane crushed in Kakamega on the base year the
availability of vegetal waste (specifically molasses) was estimated. Every ton of sugar cane crushed yields
3.5% of molasses (Francis X. Johnson 2012) which have a calorific value of 12.13 MJ/kg (The
Engineering Toolbox 2015). In 2013, approximately 102.2 thousand tons of molasses were produced
implying an available energy equivalent of 1.24 million GJ.
Sustainability index for various energy crops
As described above, Kakamega has a very good potential for rain-fed crops. Table 14 presents which
energy crops could be cultivated in the County. Figures 20 – 30 (APPENDIX E) illustrate the geographic
area where each energy crop yields a high sustainability index for high input, rain-fed conditions.
10
Using an average heating value of 8MJ/kg.
[32]
Table 14. Energy crops with high sustainability index (IRENA Global Atlas 2015).
Crop
Sustainability Index
Crop
Sustainability Index
Barley
High sustainability index
Reed canary grass
No
Cassava
High sustainability index
Sorghum
High sustainability index
Coconut
No
Soybean
High sustainability index
Jatropha
High sustainability index
Sugar beet
No
Maize
High sustainability index
Sugarcane
High sustainability index
Miscanthus
High sustainability index
Sunflower
High sustainability index
Palm oil
No
Switch grass
No
Rape
High sustainability index
Wheat
High sustainability index
2.3.2 Solar Resource
Global Horizontal Insolation
According to GIS data retrieved from IRENA Global Atlas, the average annual global horizontal
irradiance in Kakamega is approximately 237 W/m2 as shown in Figure 7. This provides a potential of
about 5.7 kWh/m2/day of global horizontal insolation (GHI) as shown in Figure 8.
Figure 7. Global Horizontal Irradiance annual average for Kakamega County (IRENA Global Atlas 2015).
[33]
Figure 8. Global Horizontal Insolation in kWh/m2/day for Kakamega County (IRENA Global Atlas 2015).
Furthermore, NASA Laboratory, providing 22 year average data (1982 – 2004) gives a monthly average
global horizontal Insolation for Kakamega which averages at 5.9 kWh/m2/day (Graph 6).
Graph 6. Monthly Average Insolation kWh/m2/day (NASA - Atmospheric Science Date Center n.d.).
Direct Normal Insolation
According to GIS data retrieved from IRENA Global Atlas, the average annual Direct Normal Insolation
(DNI) in Kakamega is between 4 – 4.5 kWh/m2/ day as shown in Figure 9.
[34]
Figure 9. Direct Normal Insolation in kWh/m2/day for Kakamega County (IRENA Global Atlas 2015).
SolarGIS ranges the DNI potential between 1700 – 1900 kWh/m2 per year, which implies an average
value of about 4.9 kWh/m2/day as shown in Figure 10.
Figure 10. Direct Normal Insolation on annual basis for Kenya in kWh/m2 (SolarGIS, 2015).
Therefore, the average global horizontal insolation for the region of Kakamega was estimated to be 5.8
kWh/m2/day while the direct normal insolation 4.6 kWh/m2/day (value correlated with concentrated solar
[35]
power technologies). In order to estimate the solar potential in the county the area of 18711 km2 was
assumed to be suitable for solar technologies deployment. Hence, the available solar energy in the county
was estimated at 1.43 billion GJ per year.
2.3.3 Hydro Resource
River Nzoia and River Yala are the major water resources in the County. However, there are about fifteen
tributaries to Nzoia River (Ikhamala, Isuikhu, Lusumu, Kipkareen, Sasala, Laini Luandati, Sergoit,
Lumakanda, Vivatsi and many other smaller). It is estimated the monthly average of the water sources
ranges between 2.8 – 10.8 m3/s. River Nzoia has an estimated annual average discharge between 52 – 70
m3/s (Ngetich 2013).
In order to quantify the hydro potential in the county a number of assumptions were made. Kakamega lies
within a latitude of 250 – 2,000 meters (SoftKenya n.d.). Therefore a net head of 250 meters for the
prospective power plants can be a plausible scenario. By accumulating the monthly average flows of the
aforementioned rivers and assuming an efficiency of 0.85, the hydropower equation12 yields a power
potential of around 280 MW in the county. It was further assumed that 1 MW yields approximately 5.22
GWh13 per year (Renetech 2015). Hence, under these assumptions the energy potential from small hydro
in Kakamega was found to be 5.3 million GJ per year.
2.3.4 Wind Resource
The wind potential is not high in Kakamega and it ranges between 3 - 4.2 m/s throughout the year. Figure
11 illustrates the annual average wind speed in the County measured at 10 m height while Graph 7 the
wind speed frequency throughout the year. Nasa Laboratory etc. provides a monthly average of the wind
speeds in the area measured at 50 m height (Graph 8). The average wind speed is estimated at 4.07 m/s
which implies a low potential for commercial exploitation of the wind resource in the area.
Figure 11. Wind speeds in m/s throughout Kakamega County (measurement at 10 m) (IRENA Global Atlas 2015).
11
This value represents the 20% of the land area in Kakamega that is not directly occupied by human activities according to table
13.
12 P=Q*ρ*g*h*η where Q: water flow (m3/s), ρ: density of water (kg/m3), g: acceleration of gravity (m/s), h: net head (m), η:
efficiency.
13 Based on Renetech AB calculations on a similar project.
[36]
Graph 7. Wind speed frequency on annual basis for Kakamega region in m/s (measurement at 10 m) (NASA Atmospheric Science Date Center n.d.).
Graph 8. Monthly Average wind speed for Kakamega region in m/s (NASA - Atmospheric Science Date Center
n.d.).
2.3.5 Geothermal Resource
There are no finding of geothermal sources available to a commercial level in Kakamega. Therefore, such
a solution will not be further researched under the framework of this thesis.
Conclusions
The preliminary assessment of the renewable energy resources in Kakamega showed that solar, small
hydro and biomass have high potential. The forms under which these sources of energy could be
leveraged in the future, are explicitly described in CHAPTER 3. ENERGY MODELLING.
[37]
CHAPTER 3. ENERGY MODELLING
This chapter elaborates on the energy modelling process followed in this thesis. It specifically describes
the LEAP model structure developed for the representation of the energy sector in Kakamega and the
insertion of data under current, reference and alternative scenarios.
3.1 Energy modelling & methodology
Energy models are a useful tool in order to analyze, plan and manage the energy transition which is
evident around the world nowadays. There is a wide range of energy models, employing different
techniques and serving various purposes. However, according to Frauke Urban, there are roughly twelve
models suitable for the evaluation of energy systems behavior in developing countries (AIM, ASF,
IMAGE-TIMER, MARIA, MARKAL, MiniCAM, LEAP, PowerPlan, RETScreen, MESSAGE, SGM,
WEM) (Urban 2009). Long-range Energy Alternative Planning system (or LEAP) is one of them and was
selected for the purposes of this thesis.
LEAP is a scenario based energy/environment accounting modelling tool that can follow a top-down, a
bottom-up or a mixed/hybrid approach of energy systems representation. Through the collection of
disaggregated data on different energy related activities it allows the analysis of explicitly set
technological specification and detailed end-user choices. That implies that the model is independent
from market behavior and production frontiers. Furthermore, its ability to incorporate electrification rates,
urban-rural classification, power sector performance and traditional biofuels in the analysis, makes it
suitable for regional energy modelling in developing counties (Urban 2009) (Stockholm Environment
Institute 2015).
However, there are some characteristics of the energy sector in developing countries which are not taken
into consideration such as the impact of the informal economy on GDP, supply shortages, structural
economic changes and investment decisions. In order to cope with these issues, a different approach was
selected during the model formation, inspired by the multi-tier methodology of energy access
measurement, proposed by ESMAP.
This methodology proposes an alternative, more comprehensive approach of assessing the energy access
of the subject areas. Instead of the traditional binary metrics (Access or No Access), eight attributes and
five tiers are used in order to provide a more disaggregated and detailed analysis of energy access. It is
believed that information exchange between this methodology and the LEAP model, can provide a broad
understanding of how energy interventions can be translated into development goals, and thus was highly
encouraged in this thesis.
Therefore, under the scope of this thesis, LEAP was used in order to create a benchmark of energy uses
and greenhouse gas emissions over six sectors in the selected area (Kakamega County, Kenya), make
projections of the energy supply and demand over a fifteen year planning horizon and identify the cost
requirements for the suggested renewable energy integration. The following paragraphs provide a detailed
description of the modelling process.
[38]
3.2 Data structure
As mentioned previously, six main sectors were selected at the initial stage of data collection. The
modelling process required however a more disaggregated approach, which was inspired by the multi-tier
methodology of energy access measurement. Figure 12 illustrates the level of disaggregation followed in
the model formation. This structure allows each sector to be processed and analyzed individually but also
offers aggregated results if necessary. In order to get a better understanding of the modelling process a
more detailed description of each sector characteristics is presented below.
Figure 12. Developed based on the Comprehensive Measurement of Energy Access for productive uses using the
multi-tier approach, Adaptation from the World Bank Group (2014)
General basic parameters
The initial stage of the model formation involved the regulation of the model’s basic parameters. Thus,
the energy units were set in GJ, the currency in United States Dollars (USD), the base year at 2013, the
first scenario year at 2015 and time series years at 2017, 2022 and 2030. Furthermore, a sub national scale
was selected involving energy and non-energy sector environmental loadings.
Key assumptions
The following step involved the insertion of macroeconomic and demographic characteristics of the
selected area, in the form of key assumptions. The selection of these characteristics is very important for
the development of the model, as they are the base on which the demand tree is structured. Table 15
presents the key assumptions that were taken into consideration in this model. The data were collected
through literature review and assumptions as described in CHAPTER 2. KAKAMEGA COUNTY CASE
STUDY.
[39]
Table 15. Key assumptions for Kakamega’s energy model.
Population
1644328
people
2.5
%
GDP per capita
1245.5
USD
County's GDP
2.048
Billion USD
Population growth
GDP growth
5.2
%
Households
349857
HH
Household's size
4.7
people
Household grouth
2.54
%
Urbanization
15.2
%
Urbanization growth
4.1
%
Roads in County
600
km
74443
Veh
Vehicles
Vehicle_km
2604212719
Veh_km
Passenger_km
2435848474
Pass_km
3.3 Demand modelling
For each sector a current and a reference scenario were created based on business as usual patterns and
expected development of the aforementioned indicators. Here is how every sector in Kakamega is
expected to develop till 2030.
3.3.1 Residential sector
The residential sector model structure was based on the number of households existing in Kakamega. The
households were then divided into electrified and un-electrified. This division was selected as the two
categories show different energy consumption patterns and different fuel mix. Table 16, shows the
differences between these two categories and how that was imported in to the model. It should be
mentioned at this point that no further urban/rural division was introduced, as it would add complexity but
not added value to the model, due to the small size of the selected area.
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. In this scenario the population growth (2.5% per year) is the main driver of the energy
consumption increase in the residential sector as it is directly connected to the number of households. In
addition to that, the electrification rate is expected to increase to 9.5% by 2017, 19% by 2022 and 38% by
2030. The increasing electrification rate is expected to affect the fuel mix, basically in the prospective
electrified households.
Lighting seems to be the main end use of electricity in the region implying a decrease of kerosene
consumption in the following years. Furthermore, a turn towards cleaner and more efficient ways of
cooking is expected in the County. That, because of the government’s acts against deforestation, the
increase in popularity of efficient cook stoves and the individual income growth. All these factors indicate
a decrease in firewood consumption, while the adoption of other fuels is expected to expand (mainly
charcoal and electricity (grid and or off-grid solar). Finally, an increase in water heating capacity is
expected in the County mainly due to governmental plan.
[40]
It should be mentioned here that fuel stacking is a significant characteristic to consider in energy
modelling, especially in developing counties. Predicting the fuel transition is a quite challenging task
especially on a local level. Therefore, due to the lack of explicit data for Kakamega, the fuel transition
was estimated based on the aforementioned expected changes in the county. These estimations were
applied to the end year of the simulation (2030), leaving the interpolation method on LEAP to change
them gradually over the years. The reference scenario in numbers is shown in Table 17.
Table 16. Residential sector segmentation based on energy consuming activities and fuel mix.
Current scenario
Residential
355679
Electrified
5.60%
Cooking
100%
Share
Final Consumption
Units
44
39.7
8.9
5.4
1.5
0.5
750
2448
986
200
128.6
321.8
kg/year
kg/year
kwh/year
l/year
l/year
m^3/year
100
209
kwh/year HouseHold
50
15
35
117
749
165
kwh/year HouseHold
MJ/year HouseHold
kg/year HouseHold
100
336
kwh/year HouseHold
Firewood
Charcoal
kerozene
LPG
Biogas
86.9
9
2.5
1
0.6
2448
750
200
128.6
321.8
kg/year
kg/year
l/year
l/year
m^3/year
Kerozene
Solar
99.3
0.7
126
24.64
l/year HouseHold
kwh/year HouseHold
0
5
95
117
749
165
kwh/year HouseHold
MJ/year HouseHold
kg/year HouseHold
Charcoal
Firewood
Electricity
Kerozene
LPG
Biogas
Lighting
100%
Electricity
Water Heating
45%
Electricity
Solar
Firewood
Other
100%
Electricity
Unelectrified
94.40%
Cooking
100%
Lighting
100%
Water Heating
20%
Electricity
Solar
Firewood
[41]
Base
(per)
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
Table 17. Residential sector expected changes by the end year (2030) under the suggested reference scenario.
Reference scenario
Residential
growing
Electrified
growing
Share
Final Consumption
Units
49
10
30
7
2.5
1.5
750
2448
986
200
128.6
321.8
kg/year
kg/year
kwh/year
l/year
l/year
m^3/year
100
209
kwh/year HouseHold
60
10
30
117
749
165
kwh/year HouseHold
MJ/year HouseHold
kg/year HouseHold
100
336
kwh/year HouseHold
Firewood
Charcoal
kerosene
LPG
Biogas
75
18
3.5
2
1.5
2448
750
200
128.6
321.8
kg/year
kg/year
l/year
l/year
m^3/year
Kerosene
Solar
70
30
126
24.64
l/year HouseHold
kwh/year HouseHold
Water Heating
50%
Electricity
Solar
Firewood
0
10
90
117
749
165
kwh/year HouseHold
MJ/year HouseHold
kg/year HouseHold
Cooking
100%
Charcoal
Firewood
Electricity
Kerosene
LPG
Biogas
Lighting
100%
Electricity
Water Heating
75%
Electricity
Solar
Firewood
Other
100%
Electricity
Unelectrified
decreasing
Cooking
100%
Lighting
100%
Base
(per)
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
HouseHold
3.3.2 Industrial sector
In the industrial sector the model structure was based on the activity level and final energy intensity. For
the three sugar companies identified in the area, the annual production of sugar (tons/year) was used in
order to estimate the energy consumption in terms of heat and electricity throughout the year.
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. In this scenario the sugar production growth is the main driver of the energy consumption in the
industrial sector. The main expected changes are presented below:
Mumias

Sugar production increase at an annual rate of 2.7%

An energy intensity drop is expected to 2.9 GJ/metric ton by 2022 and 2.88 GJ/metric ton by
2030 due to few efficiency measures taken by the company.
[42]
West Kenya Sugar Company (WKSC)

Sugar production increase at an annual rate of 3.2%

The energy intensity is expected to grow on an annual basis at 0.7% due to aging.
Butali Sugar Mills

Sugar production increase at an annual rate of 2.45%

The energy intensity is expected to be stable till 2030.
3.3.3 Transportation sector
As described in chapter 2 the transportation sector model was structured based on two indicators,
passenger transportation and vehicle transportation. Further segmentation was introduced based on the
type of vehicle and the respective fuel used (gasoline – diesel).
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. In this scenario there are different factors affecting the energy consumption in the
transportation sector. The main expected changes are presented below.
Passenger transportation
The number of vehicles responsible for passenger mobility in the county is highly related with the
population and the income level. In order to depict this change an elasticity of 1.15 was set between
income growth rate and passenger-km (Olof Johansson 1997). That is, 1% of income growth will impose
a 15% increase in the passenger-km value, implying an increase of vehicles used. In parallel the vehicle
fleet is further affected by the population growth.
Freight transportation
The activity of freight transport is related with the economic activity of the county in terms of GDP. As
the GDP grows (incorporating also a population growth) the energy intensity in terms of ton-km/USD is
also growing. Energy intensity is correlated with the energy consumption which also shows a growing
projection.
3.3.4 Commercial sector
In the commercial sector the base on which the model structure was developed is the number of buildings
devoted to commercial activity. The segregation was also based on the enterprise size. It was assumed
that small enterprises require a small amount of electricity in order to cover their energy needs, while
medium and large enterprises require both electricity and heat. The energy intensities were set based on
the estimations explained in chapter 2.
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. In this scenario the growing number of enterprises is the main driver of the energy consumption
in the commercial sector. More specifically, it is expected that the number of small stores will be 1200 by
2022 and 1500 by 2030. Similarly, the number of medium/large commercial stores is expected to reach
750 by 2022 and 800 by 2030. Not any significant changes in energy consumption are expected in this
sector.
[43]
3.3.5 Community sector & Public services
The segregation in the public sector involved the following areas: education, health, community buildings
and street lighting. Here the base of the model was the number of building devoted for the relevant
activities. Street lighting energy consumption was based on the electrification coverage of the streets in
Kakamega. Table 18 shows in detail the energy intensities for the selected categories.
Table 18. Structure of the public (community) sector activities as introduced into the LEAP model.
Community
Activity (Buildings)
Intensity
Units
25
kwh/day
15
Units
Base
(per)
9125
kwh/year
building
kwh/day
5475
kwh/year
building
7.5
kwh/day
2738
kwh/year
building
Electricity
1.515
kwh/day
553
kwh/year
building
Electricity
0.12
kwh/day/m2
43.8
Lighting
Office equipment
Share
100
25
75
18250
4562.5
13687.5
kwh/year
kwh/year
kwh/year
kWh/km/year 2799900
kWh/year
Hospitals/Health Clinics
Hospitals
17
Electricity
Health Clinics
32
Electricity
Health Centers/Dispensaries
70
Electricity
Education Buildings
(one univ. of 5000m^2)
Primary/Secondary
1214
Higher Education
5000
Commuity Buildings
100
Street Lighting
300
Electricity
(km of coverage)
Electricity
9333
kwh/year/m2 building
building
building
building
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. In the education subsector, an elasticity of 1 was set between the population growth and the
number of buildings. Similarly, in the health sector an elasticity factor was set to 1 as very small
improvements are expected in the health provision over the next years. The number of community
buildings is not expected to significantly increase, thus a small 1% annual growth was induced. Finally,
street lighting projections involved the installation of 5,000 new lamps by 2030, while by that year it is
expected that 20% of the municipality lamps will be powered by solar.
3.3.6 Agriculture, Aquaculture & Livestock
A bottom-up modelling of the agricultural sector is very challenging as it incorporates factors that are
volatile and highly dependent on individual activities. Therefore, in this case a top-down approach was
followed by taking into consideration the total energy consumption in the sector. The consumption was
then shared by three forms of energy: Mechanical (motive power), thermal and electrical. The base on
which the model was built was the energy intensity in terms of kWh per USD yielded.
Reference scenario
The reference scenario describes the energy consumption projections in the sector under business as usual
activities. Agriculture is related with the GDP through its added value. Under this scenario it is assumed
that by 2030 agriculture will remain contributing at about 10% to the county’s GDP. It is also assumed
[44]
that the level of mechanization will show an increase, implying a respective increase in diesel
consumption in the sector. Thus, the useful energy intensity is expected to grow with a rate of 1.5% per
year while no changes are expected in the equipment efficiency.
3.4 Supply modelling
The modelling of energy generation in various forms is structured in LEAP under the transformation
section. In Kakamega, three main energy conversion activities were identified: Electricity generation,
charcoal production and ethanol production. Each of them is explicitly described in the following
paragraphs.
3.4.1 Electricity generation
Mumias Sugar Company cogeneration plant is the solely source of utility electricity generation in
Kakamega. Its generation capacity is 26 MW with an availability of 7,200 hours/year. According to the
MoEP annual report 2015, the electricity generation of Mumias plant was 57 GWh in the period 20132014 implying a capacity factor of 0.3 on a yearlong basis (MoEP 2015). Bagasse, deriving from sugar
production, is used as a fuel for the plant. As its availability is based on the harvesting seasons, storage is
required in order to maintain the capacity factor at a constant level. No additional capacity is expected in
the following years and the dispatch rule was defined as proportional to the plant’s capacity. It should be
mentioned at this point that the electricity generated in the plant is exported to the national grid.
Therefore, transmission and distribution losses needed to be included in the analysis. More specifically,
the losses due to that factor were set at 18% at the base year (EAPIC 2014) with an estimated reduction to
12% by 2030.
3.4.2 Charcoal production
Charcoal supply is acquiring significant economic importance in Kakamega due to the increasing
urbanization, which implicitly imposes a fuel transition from firewood towards alternative, more
accessible fuels. However, even though its efficiency and environmental performance is better than
firewood’s, its growing adoption rate has caused concerns regarding the sustainability of its production.
According to the Kenyan Ministry of Environment, Water and Natural resources, 156,000 bags of
charcoal were licensed through the movement permit system in Kakamega in 2013 (MoEWNR 2013).
That is, 5,46014 tons of charcoal produced in the county, which accounted for 18.7% of the indigenous
charcoal consumption in that year. The remaining demand is covered by imports mainly from Elgeyo
Marakwet, Turkana and neighbor counties (Eldoret, Busia and Narok) (MoEWNR 2013) (Wambua,
Household energy consumption and dependency on common pool forest resources: The case og
Kakamega forest, Western Kenya 2011). It should be mentioned at this point that charcoal distribution
channels In Kakamega involve wholesalers, retailers and street vendors (hawkers), with the latter being
the main source of indigenous charcoal production. That consequently implies that charcoal production in
Kakamega follows an informal norm.
Even though there are several technologies for charcoal production, the traditional earth mound kilns are
predominant in Kakamega. It was assumed that 95% of the production in 2013 was conducted using this
technology with an estimated efficiency of 17.5% (UNDP - KFS 2011). The rest 5% of the production
14
A bag of charcoal weights approximately 35 kg.
[45]
was assumed to come from improved kilns with a respective efficiency of 28% (UNDP - KFS 2011).
Finally, Mekko kiln (Biochar) alternative technology was included in the model, even though there was
no actual utilization of it in Kakamega in 2013. However, its impressive efficiency of 62.5% (UNDP KFS 2011) and its side-effect advantages (mobility, gasses recycling, heat recovery) are expected to
facilitate its market adoption in the following years, both for small and large scale production. Ultimately,
at the reference scenario, the import ratio of charcoal is expected to remain near 20% in Kakamega by
2030, while new technology adoption is expected to develop as shown in Table 19.
Table 19. Charcoal production technology adoption by 2030 in Kakamega County, Kenya.
Technology
2013
2017
2022
2030
Traditional earth mounds kilns (%)
95
90
83
77
Improved kilns (%)
5
8
12
15
Mekko kilns (%)
0
2
5
8
3.4.3 Ethanol production
One of the promising energy resources in Kakamega is ethanol. Ethanol is solely produced by Mumias
Sugar Company based on molasses deriving as by-product of the sugar production process. It is estimated
that the final molasses weight is 4% of the sugar cane (3.0 - 4.5) and that one tone of molasses yields
approximately 220 liters of ethanol (Francis X. Johnson 2012) (BUTUNYI 2009). In 2013, Mumias Sugar
Company crushed 1,964,063 tons of sugar cane producing 68,742 tons of molasses and therefore
approximately 15.1 million liters of ethanol (KETS 2013). It should be mentioned at this point that the
full capacity of the company is 22 million liters of ethanol per year (BiofuelsDigest 2010) (Andae 2015).
The conversion efficiency15 on an energy base was set at 38.9% for this process.
As there is no current domestic use, all the ethanol produced is primarily exported either for alcohol
production in the beverage industry either as resource in various industries in the country. However,
ethanol could be introduced either in transportation sector (ethanol – petrol blends) or in residential –
commercial sector in order to cover cooking and lighting fuel needs. Further investigation of these
scenarios can be found in the following chapters.
3.5 Alternative scenarios
The formation of the new policy scenarios for Kakamega was basically inspired by the SE4ALL
fundamental goals regarding universal access to modern energy services, improved efficiency and
increase share of renewables in the energy balance. In addition, priority was given to the productive use
of energy, which is regarded as a significant driver of economic growth and social progress in the area.
Therefore, under this framework two main scenarios were created:

Productive use of energy expansion scenario

Sustainable energy for all scenario
o Demand Side Management scenario
o Renewable energy generation scenario
A more detailed description of their basic parameters is presented in the following paragraphs.
15
Based on the calorific value of molasses as 12 MJ/kg and ethanol energy density as 21.2 MJ/liter.
[46]
3.5.1 Productive use of energy expansion scenario (PUoE)
Energy has a significant role in achieving goals related to both human and economic development. In the
context of providing access to modern energy services in rural areas of developing countries, a productive
use of energy involves the application of energy aiming to create goods and/or services which directly or
indirectly lead to value creation (R. Anil Cabraal 2005). In economic terms that means that such activities
add value which is taxable in form of VAT if they are part of the formal economy (Anna Brüderle 2011 ).
That mainly concerns agricultural, commercial and industrial activities. In social terms, productive use of
energy means that energy is used in order to enhance human welfare resulting people being more
productive in the activities they are involved (better health services, improved education, gender equality,
women empowerment etc.) (R. Anil Cabraal 2005).
The productive use of energy expansion scenario was used in order to project the energy demand in
Kakamega if the energy intensity and utilization in productive activities increase with higher rate than in
the business as usual scenario. More specifically, under PUoE scenario the following changes are
expected in each sector respectively.
Agriculture

The contribution of agriculture in the County’s GDP increases from 10% to 25% by 2030.
The introduction of modern forms of energy can underpin the creation and upgrading of agricultural value
chains, facilitate the diversification of economic structures and reduce vulnerability to multiple stresses
and external shocks. That is expected to increase the productivity in the sector which however comes
along with an increase in the useful energy intensity (kWh/USD produced). An elasticity factor of 1.35
was selected in order to define the correlation between the useful energy intensity and the GDP growth.
That is, more energy is required per USD produced in the sector. It should be mentioned at this point that
this value does not reflect real data but it was used here in order to facilitate the model development.
Commercial
The development of the commercial sector is critical for the economic reformation aimed for Kakamega.
Under the productive use of energy expansion scenario two trends are assessed regarding both the
quantity and the quality of energy demand projections. Therefore the following goals were set.
[47]
Small scale enterprises

Promote entrepreneurship and micro/small business creation in the County. 500 new small
enterprises by 2022 and 1000 by 2030.

Increase the energy intensity from 5 kWh per day to 10 kWh per day.
Medium/large scale enterprises

Increase the energy intensity in all the respective activities in this subsector by 15% till 2030.
Community
As aforementioned, the productive use of energy under a social framework indicates that energy is
utilized in order to enhance the welfare in a society. Under these terms the following goals were set for
the public sector.
Hospitals & health clinics

Increase the inpatient availability service at the level of 15 beds per 10,000 people by 2030.

Increase the energy consumption per institution by 30% till 2030.
According to the current situation in Kakamega the availability of inpatient services is estimated at 3 beds
per 10,000 people. According to the World Health Organization that index lies far below the necessary
requirements for adequate healthcare, which depending of the country is about 20-30 beds per 10,000
people (WHO 2009). Under the PUoE scenario the goal set at 15 beds per 10,000 people by 2030. That
means that the number and the inpatient availability16 (capacity) of the institutions will grow at the level
shown in Table 20. Furthermore, a 30% increase in energy consumption was set in order to represent the
acquisition of additional equipment. It should be also mentioned that under this scenario the number of
health institutions was found to grow elastically with the population at a range of 2.23.
Table 20. Productive use of energy goals in health sector of Kakamega by 2030.
Current situation
Type of building
PUoE scenario by 2030
Number Number of beds/building Energy intensity/building Number Number of beds/building Energy intensity/building
Hospitals
17
20
25 kWh/day
34
70
114 kWh/day
Health Centers
32
10
15 kWh/day
50
15
29.3 kWh/day
Dispensaries
72
2
5 kWh/day
203
4
13 kWh/day
Inpatient availability
5 beds/10000 people
15 beds/ 10000 people
Educational buildings

Increase in primary and secondary educational buildings which follows an elastic correlation
(value: 1.7) with the population.

Double and triple the energy consumption in primary and secondary educational buildings
respectively by 2030.
The increased number of buildings will provide better educational environment as schools will be less
crowded. Special focus should be given on the secondary education by increasing the attendance from
55% to 80% by 2030. The expected number of schools and their relative intensities under the PUoE
scenario are presented in Table 21.
16
The increase in capacity means that hospitals will increase their daily consumption by 3.5 times, health clinics by 1.5 time and
health centers and dispensaries by 2 times.
[48]
Table 21. Productive use of energy goals in education in Kakamega by 2030.
Current situation
PUoE scenario by 2030
Type of building Number Number of students Energy intensity/building Number Number of students Energy intensity/building
Primary
938
420
1.52 kWh/day
1664
350
3 kWh/day
Secondary
276
360
1.52 kWh/day
715
290
5 kWh/day
Higher
1
-
600 kWh/day
1
-
600 kWh/day
Community buildings

Increase by 50% the energy intensity in the office equipment by 2030.
Street Lighting

100% lighting coverage of the main roads identified in Kakamega by 2030.
The electrification of the roads will increase security in the county and will allow to the extension of the
commercial activities beyond dusk.
3.5.2 Sustainable energy for all (SE4ALL) scenario
Demand Side Management (DSM) scenario
Kakamega is at the forefront of an ambitious socioeconomic development. That consequently implies that
the energy consumption in the county is expected to increase significantly over the next fifteen years. In
order this transition to be stable and effective, proper management of the increasing demand is necessary.
It terms of scenario formation that is translated into three fundamental goals:
1) The promotion of new, more efficient technologies
2) The introduction and expansion of new fuels in the market
3) Proper management of natural resources
[49]
It is evident that in Kakamega the energy demand is mainly derived by the residential sector. Therefore,
primary goal in the development plan should be to propose alternative consumption patterns that will
improve the activities’ performance in terms of energy consumption and relieve the environmental stress
caused by the local resource depletion (mainly deforestation). Hence, under the DSM scenario, the
following trends are expected.
Residential sector

Increase the electrification rate in households to 15% by 2017, 30% by 2022 and 70% by 2030.
The higher electrification rate is expected to lower the consumption of firewood and kerosene, as it will
replace their utilization in cooking and lighting activities respectively. Electricity based residential
equipment shows higher efficiency while it will eliminate to a great extend the health problems related
with the currently used fuels. Furthermore the following goals are suggested.
Electrified households

Promote the use of efficient firewood and charcoal based cook stoves (min. efficiency of 25%).

Facilitate the adoption of new fuels in cooking mainly electricity (60%), ethanol17 (15%) and
charcoal (10%) by 2030.
Un-electrified households

Reduce of the use of firewood as fuel for cooking by 50% by 2030.

Promote efficient firewood and charcoal fired cook stoves (min. efficiency of 25%)

Facilitate the adoption of new fuels in cooking, charcoal (35%), ethanol (10%) and LPG (8%).

Facilitate the adoption of solar technologies for lighting to an extent of 50% by 2030.

Facilitate the adoption of solar technologies for water heating to an extent of 60% by 2030.
Commercial sector

Reduce the amount of firewood used for heating purposes by 30% by 2030.

Promote alternative fuels for heat generation like electricity and/or bio-charcoal from agricultural
waste in order to cover 50% of the sector’s demand by 2030.
Agriculture

Increase electricity penetration to a level of 50% of the total useful energy demand in the sector.
Transportation

Promote public transportation by increasing the number of busses to a level of serving 20% of the
total local passenger transport by 2030.
Charcoal Production

Promote the adoption of improved kilns in charcoal production by 10% by 2017, 20% by 2022
and 30% by 2030.

Promote the adoption of Mekko kilns in charcoal production by 5% by 2017, 10% by 2022 and
20% by 2030.
17
Ethanol energy density was assumed at 21.2 MJ/liter while the stove efficiency at 0.6 based on the performance of KIKE cook
stoveΗ πηγή που καθορίστηκε δεν είναι έγκυρη..
[50]
Costing data
The following paragraph elaborates on the cost factors taken into consideration in order to have an initial
cost estimation of the transformation proposed in Kakamega based on the DSM scenario. This scenario
involves radical changes mainly in the energy consumption pattern of the residential sector. These
changes are associated with a cost deriving by both the capital (acquisition) cost of new equipment and
also operational cost based on the fuel used. These costs are presented in Table 22 and Table 23
respectively.
Table 22. Acquisition cost of residential equipment.
Costing Data - Residential sector
Cost per unit Lifetime (years)
Cooking
(stoves)
Firewood
300 Ksh
1
Charcoal
800 Ksh
2
Ethanol
2500 Ksh
6
Kerosene
3500 Ksh
6
LPG
6000 Ksh
6
Electricity
10000 Ksh
15
Biogas
15000 Ksh
15
Kerosene
290 Ksh
8 months
Electricity
35 Ksh
1
Solar
3500 Ksh
5
Lighting
(lanterns)
Water Heating
(complete systems) Electricity 9000 Ksh/unit
15
Solar
15000 Ksh/unit
15
Firewood
-
-
It is estimated that in order to increase the efficiency of cook stoves based on firewood and charcoal to a
minimum 25%, an incremental cost of about 600 Ksh per stove is required (Boulkaid 2015). In lighting,
the costs were estimated by taking into account the average number of units per household and their
respective operative duration. A typical household in Kakamega possesses 1.8 kerosene lamps (779
Ksh/household/year) or when electrified uses on average 4.5 light bulbs (156 Ksh/household/year). On
the other hand, solar lamps show high initial cost however they do not have any operational cost and their
lifespan is significantly higher (Sklivaniotis, Integrated Techno-Economic Comparative and SocioEconomic Impact Study for Increasing Energy Access in Rural Kenya 2014). In water heating, a high
initial cost (acquisition and installation) is evident especially in solar based systems which however are
free of operational costs (Waruru 2014) (Climate Innovation Center 2012).
An equally important parameter that should be taken into consideration in the cost evaluation is the
current and projected price of the main fuels involved in the energy sector in Kakamega. It should be
mentioned at this point that the projection of fuel prices in a span of fifteen years is a quite challenging
task as there are numerous factors associated with their fluctuation. Table 23, presents the authors
assumptions based on lasting literature review and the experience acquired during the duration of the
project.
[51]
Table 23. Cost of the main fuels involved in the energy sector of Kakamega.
Fuel Price
Current
Estimated
(2013-2014)
2030
Firewood (purchased)
9 Ksh/kg
15 Ksh/kg
Charcoal
14 Ksh/kg
16 Ksh/kg
Type
Kerosene
84.7 Ksh/liter 55.4 Ksh/liter
LPG
256.2 Ksh/liter 213.5 Ksh/liter
Electricity
12.6 Ksh/kWh 9.6 Ksh/kWh
Gasoline
107.4 Ksh/liter 107.4 Ksh/liter
Diesel
101.2 Ksh/liter 101.2 Ksh/liter
Molasses
1.2 Ksh/kg
1.2 Ksh/kg
Bio-charcoal
23 Ksh/kg
18 Ksh/kg
Ethanol
200 Ksh/liter
133 Ksh/liter
Bagasse
-
-
Biomass residues
-
-
Biogas
-
-
The extensive use of firewood in Kakamega has raised serious concerns regarding its origin, which is
associated with the high deforestation rate in the area. Restriction measures are expected from the
National Forest Policy (Ministry of Environment, Water and Natural Resources 2014) and will likely
impose an increase in the price of the purchased firewood over the next years. This trend will internally
affect the charcoal production as well. Therefore, its price is also expected to slightly increase. On the
other hand, the adoption of new production methods base of biomass residues and the increase in
awareness of bio-charcoal products (briquettes) is expected to reduce their price in the next fifteen years
(GVEP International 2015), (International Biochar Initiative 2014).
Kerosene shows a continuous decrease in price with the current level being 63.7 Ksh/liter (as in June
2015, in Kakamega) (Energy Regulatory Commission (ERC) 2015). Kerosene price is expected to further
decrease by 15% by 2030. Similar trend is expected for the price of LPG as well, which according to
Dalberg the end user cost of LPG has the potential to decrease by 20% by 2020 (Dalberg 2013). Gasoline
price shows a general stabilized level in the last three years at around 107 Ksh/liter. On the other hand,
diesel price has decreased by 18% over the last year with the current level being 85.5 Ksh/liter (Energy
Regulatory Commission (ERC) 2015). Due to their erratic price fluctuation, both fuels were selected to
follow projections equal to their current cost.
Ethanol is a critical fuel in the proposed DSM scenario as it will replace to a significant percent the
traditional fuels. That strategy was selected due to the indigenous ethanol production in the County. The
current price is quite high at around 200 Ksh/liter (Nderitu 2015) however with governmental support
(through policies, regulations and subsidies) it is expected that the end user price will be reduced by 50%
until 2030. This statement is further enhanced by the close proximity of the producer to the new market
(reduced logistic costs) and the increasing purchasing power of the local population.
The price of electricity is also a very important parameter for the estimation of the intervention cost
necessary for the successful implementation of the DSM scenario. The Energy Regulatory Commission of
Kenya has established several tariff levels for different customer segments. The tariff for domestic users
varies from 2.5 – 19.6 Ksh/kWh while the average price for commercial and industrial users is 14.2
[52]
Ksh/kWh. For simplicity, an average price of 12.6 Ksh/kWh was assumed as a representative tariff in this
project (ERC 2013). According to the 5000+ plan, proposed by the Kenyan government in 2013, the
electricity prices are expected to be reduced by 92% and 57% in the two customer segments respectively,
by 2017 (Ministry of Energy and Petroleum MoEP n.d.). Therefore, the expected average electricity price
is about 9.6 Ksh/kWh, value that was assumed as a plausible goal for Kakamega County by 2030.
Finally, bagasse and biomass residue may have a minor cost associated with their collection and
transportation but such costs were assumed as negligible in this project.
Renewable energy generation (RENE) scenario
This scenario refers to the exploitation of the county’s renewable energy sources in order to generate
electricity in a local, decentralized and commercially viable manner that will enhance the self-dependency
of the county and will support the aforementioned development. As described in Chapter 2, Kakamega
shows high solar and hydro potential. Therefore, this scenario elaborates on the deployment of
photovoltaic technology and small hydro projects in the county.
An initial assessment of the location, the capacity and the economic viability of such projects was
performed based on GIS maps provided by IRENA Global Atlas. Figure 13, illustrates a map showing the
most economic rural electrification activity for the region of Western Kenya. The comparative analysis
was performed based on the levelized cost of electricity expected by four selected electrification options:
Grid extension, off grid photovoltaic systems, diesel generators and small hydro. A more detailed
description of the modelling process can be found in the suggested literature (S. Szabo 2011) (Lily
Parshall 2009) (IRENA Global Atlas 2015).
Utilizing the map as a base, the following assumptions were made. The grid extension seems to be the
most economical solution in the south part of the county, mainly due to the existence of the cogeneration
plant in Mumias which is connected to the national grid, as well as in the central-north part of the county
due to the passing high voltage transmission line connecting Eldoret and Tororo, Uganda (Figure 14).
Furthermore, photovoltaic systems were identified as most profitable solution in three main regions of the
County while mini hydro projects could be commercially deployable in five identified areas. A
description of the proposed projects follows in the next paragraphs. It should be mentioned at this point
that the aforementioned assumptions are based on the author’s preliminary assessment. Further analysis
should necessarily be contacted in order to validate the suitability of these locations.
[53]
Figure 13. Most economic rural electrification activity in Kakamega County, acquired from IRENA Global Atlas
(Masdar Institute of Science and Technology n.d.)
Figure 14. Transmission lines in the area of Kakamega, Kenya, acquired from IRENA Global Atlas (Masdar
Institute of Science and Technology n.d.).
Small hydro exploitation
In chapter 2 it was calculated that Kakamega has potentially 280 MW of available capacity deriving from
small hydro projects deployment. Five main areas where identified, where small hydro power plants
could be economically sustainable according to IRENA (Figure 15).
[54]
Figure 15. Identified areas for the deployment of small hydro power plants in Kakamega, Kenya, base map acquired
from IRENA Global Atlas database.
Under the renewable energy generation scenario a capacity of 29 MW is suggested to be deployed by
2030. The scenario involves the commissioning of 3 small hydro power plants with individual capacity of
2 MW constructed by 2022 in the central part of the county (points A, B and C in Figure 15). Additional
capacity is suggested with the construction of an 8 MW hydro power plant by 2025 (point D) and a 15
MW power plant by 2028 in the north of the county (point F).
Solar energy exploitation
Grid-connected power plants
Based on the most economically effective solution for electrification discussed in the previous paragraph,
three main areas were identified for the deployment of photovoltaic technology in Kakamega presented in
Figure 16.
More specifically under the renewable energy generation scenario a capacity of 30 MW is suggested to be
deployed by 2030. The scenario involves the commissioning of a 15 MW solar power plant by 2020
constructed in the north part of the county. It is assumed that the proximity to the transmission lines is an
additional advantage for this location and will facilitate the interconnection with the national grid.
Secondly, a 10 MW solar power plant by 2024 constructed northwest of Mumias town and a 5 MW plant
by 2027 northwest of Kakamega town. It is estimated that such projects require approximately 20,000 m2
per MW deployed (Mevin Chandel 2013), (Scatec Solar n.d.), (Tata Power Solar n.d.). Thus, the land use
for the proposed projects is estimated at roughly 600,000 m2 (60 ha).
Off-grid systems
In terms of off-grid solar systems, the renewable energy generation scenario involves the deployment of
individual PV systems covering partially the needs of social institutions (educational and health
buildings). The scenario suggests that the capacity of such systems will reach 1 MWp by 2030 setting the
goal of 375 Wp of PV modules on every roof top.
[55]
Figure 16. Identified areas for the deployment of PV solar parks in Kakamega, Kenya, base map acquired from
IRENA Global Atlas database.
Costing data
The following paragraph elaborates on the cost factors taken into consideration in order to have an initial
cost estimation of the transformation proposed in Kakamega based on the renewable energy scenario.
This scenario involves changes in the energy generation pattern of the County. A preliminary assessment
of the associated costs in LEAP, involves capital costs (feasibility studies, development of the site,
construction of the generating facility and applicable taxes and waivers (VAT, import duties etc.) and
operation & maintenance costs (fixed and variable). These costs are presented below for the respective
technologies.
Photovoltaic systems
PV is one of the fastest growing renewable energy technologies at the moment, trend that is continuously
reducing the cost of its implementation. It should be noticed that the cost of PV systems is certainly
affected by numerous factors such as technology, location, financial environment etc. however an in
depth analysis of the cost structure associated with PV system deployment is out of the scope of this
thesis. Thus, at this stage the cost estimation will be principally based on capital (CAPEX) and operating
& maintenance (OPEX) costs as they were retrieved for the Kenyan region.
According to IRENA estimations, the total installed PV system cost for utility scale systems in 2015 is
around 2,700 USD/kW showing a declining trend, expecting to reach 1,800 USD/kW by 2020 (IRENA
2012). A study conducted in 2013, estimated that the investment cost for a utility scale 10 MW solar
power plant in Mombasa, Kenya requires an investment cost of 2,566 USD/kW (Ondraczek, Are we there
yet? Improving solar PV economics and power planning in developing countries: The case of Kenya
2013). According to MoEP, the initial cost PV systems in the range of 0-10 MW is estimated at 2,670
USD/kW (MoEP, Ramboll, ECA, ERC, The World Bank 2014). Therefore, an average cost of 2,650
USD/kW installed was considered representative of utility scale systems for the region of Kakamega and
was used in the model.
Systems with much lower capacity (<< 1 MW) for residential/commercial sectors, show higher costs
mainly due to their storage requirements and no benefit from economies of scale. According to US
[56]
Department of Energy, it is estimated that such installations necessitate an initial cost of nearly 3,500
USD/kW (IRENA 2012).
In addition, the fixed operation & maintenance costs were set at 1.5% of the initial cost whereas there are
not associated variable costs. The capacity factor of such systems was set as 0.25, the lifetime at 25 years
and the discount rate at 8% (MoEP, Ramboll, ECA, ERC, The World Bank 2014) (Ondraczek, Are we
there yet? Improving solar PV economics and power planning in developing countries: The case of Kenya
2013).
Small hydropower systems
Hydropower is a capital intensive technology showing long lead times of deployment due to significant
feasibility assessments, design planning and construction work requirements. The cost breakdown
includes two main categories regarding infrastructure development costs on the one hand and electromechanical equipment costs on the other. Additional costs may derive from necessary environmental
impact analyses and mitigation measures (wildlife, water quality etc.), licensing and miscellaneous
activities (IRENA 2015).
According to IRENA, small hydro power plants (< 10 MW) in Africa have an initial cost of 3,700
USD/kW installed (IRENA 2015). MoEP estimates that in the Kenyan content, the capital cost for hydro
power plants in the range 1-10 MW is roughly 2,500 USD/kW (MoEP, Ramboll, ECA, ERC, The World
Bank 2014). According to Renetech AB, the estimated cost for a hydro plant in the same capacity range is
3,200 USD/kW (Renetech 2015). The average value of 3,130 USD/kW was assumed as representative
and was further used in the LEAP model. The fixed operation & maintenance costs was set at 2.7%, the
capacity factor at 0.6, the lifetime at 20 years and the discount rate at 8% (IRENA 2015) (MoEP,
Ramboll, ECA, ERC, The World Bank 2014).
[57]
CHAPTER 4. RESULTS
This chapter presents the results of the simulation process as yielded by the LEAP model developed for
Kakamega.
Kakamega is at the forefront of a socio-economic transformation spurred by the national and regional
development plans. While the scope of this thesis is to translate the consequences of this transformation in
terms of energy requirements, it is also worthy to review how the macroeconomic and demographic
characteristics of the County are expected to evolve. Table 24 presents the projection of the main
parameters based on which the model was structured.
Table 24. Projection of the population and GDP for the region of Kakamega at 2030.
2015
2030
Population
1,685,400
2,441,000
people
Households
358,700
522,600
HH
GDP per capita
1274.1
1792.1
USD
County's GDP
2.147
4.375
Billion USD
4.1 Energy demand
The energy demand in Kakamega is mainly characterized by the huge consumption of the residential and
transportation sector. That is a pattern usually evident in developing regions especially in sub-Saharan
Africa, and is predominantly caused by the consumption of low energy content fuels and/or the use of
inefficient technologies. As expected, the continuous growing population and the prospective economic
development in such regions tend to increase the energy demand, however usually under an unsustainable
and questionable environment. While access to energy services should not be contestable, alternative
pathways should be identified and amplified in order to achieve a sustainable and prosperous future in
these regions.
Under this perspective, the energy demand for Kakamega County was subjected to different scenarios,
whose projections are illustrated in Graph 9. Under the reference scenario (BAU) the demand shows an
increase from 5.9 TWh in 2015 to 9.1 TWh in 2030. The demand is further increased by 0.39 TWh under
the PUoE scenario, which aims to induce however important improvements in the social and economic
content of the County. This transformation does not necessarily mean that the energy consumption need
to be skyrocketed. In fact, with the adoption of the SE4ALL scenario (combination of DSM and RENE)
the energy demand can be stabilized at around 5.8 TWh by 2030 without restricting the aforementioned
development goals. That is mainly due to the reduced energy demand in the residential sector caused by
the fuel switching strategy proposed and the promotion of more efficient technologies.
The energy consumption by sector is also expected to differentiate under the alternative scenarios. Graphs
10, 11 & 12 show the share of every sector in the total energy demand of Kakamega by the year 2030. As
expected the transportation sector will predominantly account for more than 50% of the energy demand in
the County by the year 2030 under the combination of all the scenarios. That is due to the difficulty
[58]
encountered in the formation of policy and the implementation of measures towards the reduction of
energy consumption in this sector.
Graph 9. Total energy demand scenarios projection in Kakamega till 2030.
Graph 10. Total energy demand by sector in Kakamega till 2030 under reference scenario.
[59]
Graph 11. Total energy demand by sector in Kakamega till 2030 under PUoE scenario.
Graph 12. Total energy demand by sector in Kakamega till 2030 under SE4ALL scenario.
Tables 25, 26 and 27 provide a disaggregated description of the energy consumption in Kakamega per
fuel and sector, as projected in 2030 under the reference and the two proposed alternative scenarios
(PUoE and SE4ALL).
[60]
Table 25. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the reference (BAU) scenario.
Sectors
Electricity
(grid)
Gasoline
Residential
142.5
-
Electrified
142.5
-
Kerosene
Diesel
LPG
Wood
Charcoal Biogas Bagasse
446.1
-
5.7
-
21.6
-
-
424.5
-
-
-
-
-
-
-
-
-
Mumias Sugar Company Limited
-
-
-
-
-
-
-
West Kenya Sugar Company
-
-
-
-
-
-
Butali Sugar Mills
-
-
-
-
-
-
13.5
-
-
-
-
2.7
-
-
-
10.7
-
-
16.8
-
-
16.8
-
-
Community
6.5
-
Education
1.3
Health
0.8
Community build.
Street L
Unelectrified
Industry
Commercial
Small Scale Businesses
Medium or Large businesses
Agriculture
Agro_demand
Transportation
Electricity
(solar)
Heat
Total
3,934.1
701.2
5.9
-
6.5
-
5,242.0
2.7
862.7
525.7
2.0
-
3.1
-
1,560.4
3.0
3,071.4
175.5
3.9
-
3.4
-
3,681.7
346.3
-
-
346.3
-
222.2
-
-
222.2
-
-
74.7
-
-
74.7
-
-
49.4
-
-
49.4
-
-
-
-
-
1.6
15.1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
1.6
12.4
42.0
-
-
-
-
-
-
25.2
84.0
42.0
-
-
-
-
-
-
25.2
84.0
-
-
-
-
-
-
-
0.5
-
7.0
-
-
-
-
-
-
-
-
-
-
1.3
-
-
-
-
-
-
-
-
-
-
0.8
2.1
-
-
-
-
-
-
-
-
-
-
2.1
2.2
-
-
-
-
-
-
-
-
0.5
-
2.8
2.7
-
2,091.4
-
1,324.9
-
-
-
-
-
-
-
3,416.3
Passenger Transportation
-
814.8
-
368.0
-
-
-
-
-
-
-
1,182.8
Freight Transportation
-
1,276.7
-
956.9
-
-
-
-
-
-
-
2,233.6
179.2
2,091.4
446.1
26.8
9,110.8
Total Demand
1,366.9
[61]
5.7
3,934.1
701.2
5.9
346.3
7.1
Table 26. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the productive use of energy (PUoE) scenario.
Sectors
Electricity
(grid)
Electricity
Gasoline
Kerosene
Diesel
LPG
Wood
Charcoal Biogas Bagasse
(solar)
Heat
Total
Residential
142.5
-
446.1
-
5.7
3,934.1
701.2
5.9
-
6.5
-
5,242.0
Electrified
142.5
-
21.6
-
2.7
862.7
525.7
2.0
-
3.1
-
1,560.4
-
-
424.5
-
3.0
3,071.4
175.5
3.4
-
3,681.7
-
-
-
-
-
-
-
-
346.3
-
-
346.3
Mumias Sugar Company Limited
-
-
-
-
-
-
-
-
222.2
-
-
222.2
West Kenya Sugar Company
-
-
-
-
-
-
-
-
74.7
-
-
74.7
Unelectrified
Industry
Butali Sugar Mills
3.9
-
-
-
-
-
-
-
-
-
49.4
-
-
49.4
19.3
-
-
-
-
-
-
-
-
-
1.9
21.2
7.3
-
-
-
-
-
-
-
-
-
-
12.0
-
-
-
-
-
-
-
-
-
1.9
13.9
92.2
-
-
230.6
-
-
-
-
-
-
138.3
461.2
92.2
-
-
230.6
-
-
-
-
-
-
138.3
461.2
Community
13.7
-
-
-
-
-
-
-
-
1.1
-
14.8
Education
3.4
-
-
-
-
-
-
-
-
-
-
3.4
Health
2.9
-
-
-
-
-
-
-
-
-
-
2.9
Community build.
2.9
-
-
-
-
-
-
-
-
-
-
2.9
Street L
4.5
-
-
-
-
-
-
-
-
1.1
-
5.6
Commercial
Small Scale Businesses
Medium or Large businesses
Agriculture
Agro_demand
Transportation
-
7.3
2,091.4
-
1,324.9
-
-
-
-
-
-
-
3,416.3
Passenger Transportation
-
814.8
-
368.0
-
-
-
-
-
-
-
1,182.8
Freight Transportation
-
1,276.7
-
956.9
-
-
-
-
-
-
-
2,233.6
267.7
2,091.4
446.1
140.2
9,501.8
Total Demand
1,555.5
[62]
5.7
3,934.1
701.2
5.9
346.3
7.6
Table 27. Estimated fuel consumption in GWh per sector in Kakamega in 2030 under the sustainable energy for all (SE4ALL) scenario.
Sectors
Electricity
(grid)
Gasoline
Residential
446.8
-
Electrified
446.8
-
Kerosene
Diesel
147.1
-
-
39.8
-
107.3
-
-
Mumias Sugar Company Limited
-
West Kenya Sugar Company
Butali Sugar Mills
Unelectrified
Industry
Commercial
Small Scale Businesses
Medium or Large businesses
LPG
Wood
Charcoal Biogas
Ethanol Bagasse
16.3
640.1
290.5
5.6
92.9
-
-
5.0
194.8
123.3
3.7
72.3
-
11.3
445.2
167.3
1.9
20.6
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
19.3
-
-
-
-
7.3
-
-
-
Electricity
(solar)
Heat
Total
17.4
-
1,656.7
-
5.7
-
891.3
-
11.7
-
765.4
-
346.3
-
-
346.3
-
-
222.2
-
-
222.2
-
-
-
74.7
-
-
74.7
-
-
-
49.4
-
-
49.4
-
-
-
-
-
-
1.9
21.2
-
-
-
-
-
-
-
-
7.3
12.0
-
-
-
-
-
-
-
-
-
-
1.9
13.9
136.0
-
-
163.2
-
-
-
-
-
-
-
108.8
408.1
136.0
-
-
163.2
-
-
-
-
-
-
-
108.8
408.1
Community
5.8
-
-
-
-
-
-
-
-
-
8.8
-
14.6
Education
1.8
-
-
-
-
-
-
-
-
-
1.6
-
3.4
Health
1.5
-
-
-
-
-
-
-
-
-
1.5
-
2.9
Community build.
1.5
-
-
-
-
-
-
-
-
-
1.5
-
2.9
Street L
1.1
-
-
-
-
-
-
-
-
-
4.3
-
5.4
Agriculture
Agro_demand
Transportation
-
1,971.2
-
1,377.0
-
-
-
-
-
-
-
-
3,348.2
Passenger Transportation
-
694.5
-
420.1
-
-
-
-
-
-
-
-
1,114.6
Freight Transportation
-
1,276.7
-
956.9
-
-
-
-
-
-
-
-
2,233.6
608.0
1,971.2
147.1
16.3
640.1
290.5
92.9
346.3
26.2
110.7
5,795.2
Total Demand
1,540.2
[63]
5.6
Residential sector
The implementation of the proposed scenarios is expected to transform the energy consumption profile in
the residential sector. More specifically, the electrification rate is expected to reach 70% by 2030 which
means that 365,820 households are expected to be given access to electricity by that year. In addition, the
promotion of efficient technologies that was proposed by the DSM scenario, is expected to lower the
energy demand in the sector by almost 53.7%. The cumulative energy demand including all fuels, is
expected to reach 891.3 GWh for electrified households and 765.4 GWh for un-electrified households.
Table 28, presents the energy demand by activity for both categories under the reference and SE4ALL
scenario respectively.
Table 28. Energy demand (in GWh) per household activity under reference & SE4ALL scenario by 2030.
Electrified Households
Reference
Un-electrified households
SE4ALL
Reference
SE4ALL
2015
2030
2015
2030
2015
2030
2015 2030
Cooking
191
1400.5
225.3
596.8
3241.1
3166.4 2889.9
Lighting
5.7
41.5
7
76.5
416.1
408.3
Water heating
4.4
45.3
6
83.4
45.3
107
49.4
32.1
Other
10
73.1
13.6
134.6
-
-
-
-
632
382.2 101.4
Industrial sector and Transportation
The energy demand in both sectors is expected to increase over the next fifteen years however no specific
goals were set for these sectors. In the industrial sector the demand will increase in proportion with the
sugar production in the County, which is expected to reach 422.7 ktons of sugar by 2030, an increase of
54.5% over the current stage (273.6 ktons). Graph 13 illustrates the energy demand in final units by 2030,
for the three sugar companies operating in Kakamega.
Graph 13. Final energy demand by Sugar Company by 2030.
In the transportation sector the demand will increase in proportion with the population and the economic
development in the County. More specifically, the energy correlated with the passenger transportation is
[64]
expected to increase by 53.7% under the reference scenario or by 45.8% under the SE4ALL scenario
which implies intensification of the public transport (increased share of busses from 6% to 20% by 2030).
On the other hand, the prospective development in the commercial and agricultural sector is expected to
impose an increase of 134.8% in the energy demand related to freight transportation. Table 29, presents
the projected energy demand per vehicle category in Kakamega over the next fifteen years.
Table 29. Energy demand (in GWh) per vehicle category in Kakamega till 2030 (reference scenario).
2015
2020
2025
2030
Passenger Transportation
Cars
342.3
401.3
470.1
550.3
Motor Cycles
397.9
453.5
516.8
589.0
Busses/Mini Busses
29.4
33.5
38.2
43.5
Walking/Cycling
Freight Transportation
Utilities/Parcel vans/Pick ups
750.1
997.0 1,325.2 1,761.5
Lorries/Heavy vans
139.6
185.6
246.7
327.9
Trailers
24.7
32.8
43.7
58.0
Other Vehicles
36.7
48.8
64.8
86.2
Total
1720.6 2152.4 2705.4 3416.3
Commercial sector
As described in CHAPTER 3. ENERGY MODELLING, the productive use of energy expansion, is a
necessary prerequisite for the economic development of the County. The quantitative and qualitative
increase of energy in the commercial sector was one of the main goals of the PUoE scenario. Graph 14,
compares the energy demand in the sector under the reference and the PUoE scenario while Table 30
provides a more detailed description of the energy demand projection over the sub categories of the
sector.
Graph 14. Energy demand projections for the Commercial sector in Kakamega by 2030.
[65]
It is observed that the PUoE scenario implementation will impose an increase of about 55% in the energy
demand of the sector. The increase is more evident in small scale businesses which intend to constitute
the backbone of Kakamega’s economy by 2030 with 1,000 new small enterprises established within the
next fifteen years. In addition, the increased energy intensity proposed for the medium and large
businesses is expected to improve the quality of services, assist in the expansion of operations and
ultimately lead to revenue increase.
Table 30. Energy demand (in GWh) per vehicle category in Kakamega
Reference
PUoE
2015
2030
2015
2030
Medium or Large businesses
11.3
12.4
11.4
13.9
Hotels_Guest houses_Restaurants
2.6
2.9
2.7
3.3
Super Market_Open Markets_Shopping Malls
6.4
7
6.5
7.7
Service Businesses
2.3
2.5
2.3
2.9
Small Scale Businesses
Total
1.9
2.7
2.3
7.3
13.2
15.1
13.7
21.2
Community Sector & Public services
The access to energy services is of vital importance for the community sector as it fundamentally
enhances the welfare of the society. Substantial improvements were proposed by the alternative scenarios
for this sector with aim to increase the quality in education, health and public services. The energy
demand projections under different scenarios are presented in Graph 15.
Graph 15. Energy demand projections for the Community sector in Kakamega by 2030.
Under the reference scenario the energy demand is expected to grow with a rate of 45.8%, proportionally
with the population increase projected for the County. However, the implementation of the goals
proposed by the PUoE scenario require an increase of 169% in the energy demand. More specifically, the
education sector is expected to require 3.4 GWh by 2030, the health sector 2.1 GWh, the community
[66]
buildings 2.9 GWh and street lighting services 5.6 GWh. It should be mentioned in this point that the
same demand pattern was followed in the formation of the RENE scenario as well. While the two
scenarios (PUoE and RENE) show similar demand increase, their transformation profile changes
significantly due to the increased adoption of solar powered technologies in the latter one. Table 31,
provides a detailed description of the energy demand projections of various activities taken into
consideration if the community sector.
Table 31. Energy demand projections (in GWh) per sub-category of the Community sector in Kakamega.
Reference
Education
PUoE
2015
2030
2015
2030
0.9
1.3
1.0
3.4
Primary
0.5
0.8
0.6
1.8
Secondary
0.15
0.23
0.2
1.3
Higher
0.2
0.3
0.2
0.3
Health
0.6
0.8
0.6
2.9
Health centers/Dispensaries
0.2
0.3
0.2
1
Health Clinics
0.2
0.3
0.2
0.5
Hospitals
0.2
0.2
0.2
1.4
Community buildings
1.8
2.1
1.9
2.9
Street lighting
1.6
2.8
1.9
5.6
Total
4.9
7.0
5.4
14.8
Agriculture, Aquaculture & Livestock
The adoption and expansion of modern forms of energy in agriculture, is essential for Kakamega, whose
economic welfare and development is highly interconnected with this sector. Therefore, special focus was
given in the quantitative and qualitative increase of energy in the sector, aiming to improve productivity
and upgrade of agricultural value chains. In order to make this possible, the PUoE scenario proposed that
agriculture should contribute by 25% in the County’s GDP by 2030. This goal was also escorted by an
increase in energy intensity of the main agricultural activities, which basically implied the utilization of
more energy per activity.
[67]
Graph 16. Energy demand projections for Agriculture sector in Kakamega by 2030.
The modernization and energy intensification of the agriculture sector is expected to increase the demand
to a significant extent. More specifically, the total energy demand in the sector is expected to increase
from 39.4 GWh in 2015 to 461.2 GWh in 2030 under the PUoE scenario. The demand is slightly lower
under the DSM scenario, which implies the adoption of electricity (more efficient form of energy) to a
greater extent. Graph 16, illustrates the different demand projections under the four alternative scenarios.
4.2 Energy supply
The transformation of the energy sector in Kakamega cannot be integrated without improvements being
implemented in the supply side as well. This chapter presents the energy generation projections under
different scenarios over the next fifteen years.
Electricity
Adequate electricity generation is fundamental for the successful implementation of the proposed
scenarios, however the requirements are expected to vary. More specifically, as in 2014 the electricity
requirements in Kakamega were estimated at 44.4 GWh. The reference scenario indicates that the
electricity demand will be 4.7 times higher by 2030 reaching 209.3 GWh. The requirements are even
higher under the PUoE scenario reaching 333.6 GHW by 2030. Finally, the intensive electrification plan
proposed by the SE4ALL (and more specifically DSM) scenario will increase electricity requirements of
the County to 730.8 GWh by 2030. Graph 17, shows the aforementioned projections over the next fifteen
years.
[68]
Graph 17. Electricity generation requirements in Kakamega projected till 2030.
In order to cope with the increasing electricity demand, 59 MW of additional capacity were proposed with
the RENE scenario. More specifically, a capacity of 30 MW of solar PV power plants is expected to be
constructed in the County and be gradually commissioned by 2030.
Graph 18. Electricity balance in Kakamega projected till 2030.
The estimated electricity output is 52.6 GWh per year by 2030. Small and mini hydro power plants are
expected to reach a capacity of 29MW by 2028 generating 152.4 GWh per year (by 2028). The generation
capacity in the County is expected to cover 37.9% of the indigenous electricity requirements in 2030,
[69]
with the imports increasing significantly every year. Graph 18, illustrates the balances between
indigenous production and imports of electricity in Kakamega over the next fifteen years under the RENE
scenario. Graph 19, depicts the expected electricity generation mix in the County under the same
scenario.
Graph 19. Electricity generation mix in Kakamega by 2030.
Charcoal
Charcoal is a significant fuel for Kakamega, as it is expected to assist in the transition from traditional to
modern fuels especially in the residential sector. Its availability is therefore necessary for this transition to
be smooth and effective. As in 2014, the requirements for charcoal in the County accounted for 882.4 TJ.
Under the reference scenario the expected demand for charcoal by 2030 is 2,640 TJ. The demand is
significantly higher (3,129 TJ) under the PUoE expansion scenario which suggests increased use of
charcoal as fuel in the commercial sector. However, the implementation of the DSM scenario will induce
the charcoal demand to stabilize and create a plateau after 2026 over 1,850 TJ/year. Graph 20, illustrates
the charcoal balance accounts till 2030 under the DSM scenario.
It should me mentioned at this point that the charcoal production capacity of Kakamega in not unlimited.
Deforestation issues in the County set restrictions and require that the increasing demand (and the
proportionally increasing production capacity) should be covered by the adoption of modern, more
efficient technologies in the sector (improved kilns, Mekko kilns etc.). Under the DSM scenario it is
expected that the indigenous charcoal production will cover 43.5% of the total domestic demand in 2030,
almost double the share at the current stage, without compromising the sustainability of the gazette forests
in the region.
[70]
Graph 20. Charcoal balances in Kakamega till 2030 under the DSM scenario.
Ethanol
As described in CHAPTER 3. ENERGY MODELLING, Ethanol is solely produced by Mumias Sugar
Company based on molasses deriving as by-product of the sugar production process. The full capacity of
the company is 22 million liters of ethanol per year which is translated to 465.95 TJ per year. However,
the plant’s production availability was set and kept at 68.4% in order to consort with the real data found
for the base year (2013). Therefore the ethanol production capacity is constant at 318.7 TJ (around 15
million liters) per year till 2030. Under the reference scenario all the ethanol produced in the County is
exported. On the other hand, under the DSM scenario part of the produced ethanol is circulated within the
borders of the County in order to cover the domestic requirements, deriving by the increasing use of
ethanol stoves in the residential sector. Graph 21 illustrates the ethanol balances till 2030 as projected
under the DSM scenario for Kakamega.
[71]
Graph 21. Ethanol energy balance in Kakamega till 2030 under the DSM scenario.
4.3 Resources
The prospective energy transformation in Kakamega is highly interconnected with the ability of the
County to have access to essential resources. As described in CHAPTER 2. KAKAMEGA COUNTY CASE
STUDY, the main primary energy resources in Kakamega are solar, hydro and different sources of
biomass (wood, bagasse, agroforestry residues). The secondary resources are mainly comprising by
charcoal, kerosene, gasoline, diesel, LPG, electricity and biogas. While access to secondary resources
should not be contestable, it is necessary for the County do strengthen its self-sustainability by following
pathways that will effectively utilize available resources and minimize the dependency on imported fuels.
As in 2014, the total resource requirements in Kakamega were estimated at 22,300 TJ with the primary
resources accounting 64% and secondary the rest 36%. As an initial remark, it should be mentioned that
the total energy resource requirements are expected to grow under any scenario. More specifically, under
the reference scenario, it is expected that they will reach 35,900 TJ by 2030 with primary and secondary
resources accounting for 55% and 45% respectively. As expected the PUoE scenario will impose an
increase in the resource requirements in the County, which however could be mitigated by the
implementation of the SE4ALL scenario. In fact, the combination of these scenarios is expected to reduce
the total resource requirements in Kakamega by 27.6% (in respect with the reference scenario) by 2030 to
26,000 TJ. Table 32, provides a comprehensive description of the resources projections over the next
fifteen year in Kakamega.
[72]
Table 32. Energy demand projections (in TJ) per sub-category of the Community sector in Kakamega.
Primary Resources
Wood
Bagasse
Molasses
Solar
Hydro
Biomass
Secondary Resources
Kerosene
Gasoline
Diesel
LPG
Charcoal
Ethanol
Biogas
Electricity
Total
Reference Scenario
2015
2022
2030
14,931
17,388
19,596
13,016
15,199
17,118
1,063
1,323
1,575
819.3
819.3
819.3
5.5
13
25.4
0
0
0
26.1
33.1
57.8
8,260
11,184
16,337
1,575
1,665
1,606
4,181
5,450
7,529
2,071
3,115
4,921
12,2
15.4
20.4
723.9
1,124
2,050
318.7
318.7
318.7
15.4
18
21.3
0.7
115.3
507.4
23,190
28,571
35,932
2015
15,051
16,117
1,077
819.3
5.6
0
31.7
8,275
1,575
4,180
2,084
12.2
723.9
318.7
15.4
2.4
23,326
PUoE scenario
2022
17,889
15,644
1,323
819.3
13.8
0
88.7
11,528
1,665
5,450
3,261
15.4
1,216
318.7
18
221.5
29,417
2030
21,877
19,153
1,575
819.3
27.4
0
302.2
17,839
1,606
7,529
5,600
20.4
2,427
318.7
21.3
955.1
39,716
SE4ALL scenario
2015
2022
2030
14,148
12,025
10,087
12,155
9,506
6,524
1,115
1,323
1,575
819.3
819.3
819.3
12.2
147.5
284
0
113.5
549
46.2
115.1
337
8,302
11,201
15,915
1,447
1,105
529.7
4,142
5,251
7,096
2,103
3,324
5,545
21
48
58.7
857
1,330
1,035
297.7
195.3
318.7
15.3
17.6
15.7
14.1
316
1,615
22,450
23,226
26,002
Graph 22, shows the projected utilization of the indigenous energy resources under the SE4ALL scenario
till 2030. A reduction is evident mainly due to the replacement of wood as fuel in the residential sector
and the adoption of more efficient transformation technologies (cook stoves, charcoal kilns etc.) while the
majority of the rest primary resources seem to follow an increasing trend.
Graph 22. Primary energy resources utilization in Kakamega till 2030 under the SE4ALL scenario.
Graph 23, shows the projection of imported fuels in Kakamega under the SE4ALL scenario till 2030. As
in 2015, the total imported fuels in Kakamega accounted for 8,599 TJ. Gasoline and diesel dominate the
imports as they are correlated with a sector (transportation), whose energy demand is difficult to mitigate.
Hence, an increase in necessary imports in these two fuels is necessary in order to meet the demand in
Kakamega. On the other hand, kerosene and (imported) charcoal are expected to decrease over the next
fifteen years. Finally, electricity imports are expected to reach 449 GWh by 2030 accounting for the
[73]
62.1% of the total electricity requirements that year. The total imported fuels are expected to account for
15,915 TJ by 2030.
Graph 23. Projection of imported fuels in Kakamega till 2030 under the SE4ALL scenario.
4.4 Environmental assessment
The sustainable energy transition described in the previous paragraphs will create new possibilities for
Kakamega in terms of growth while at the same time it will secure the precious natural resources, the
environment and human welfare. The Global Warming Potential (GWP) index (100 years horizon) was
selected in order to present the environmental assessment of the energy sector in Kakamega (EPA n.d.),
(IPCC n.d.).
As in 2014, the total emissions were estimated at 708.7 ktons of CO2e, with 58.9% coming from the
transportation sector, 37.2% from the residential sector, 3% from the transformation sector (energy
production), 0.6% from agricultural activities and 0.2% from the industrial sector. Graph 24, illustrates
the share of estimated emissions in Kakamega in 2014.
More specifically, in the residential sector, the un-electrified households are responsible for the 92% of
the emissions due to the use of traditional and severely polluting fuels such as firewood, charcoal,
kerosene etc. Cooking is the most polluting activity followed by lighting. In transportation, 55% of the
emissions are deriving from freight transport (Lorries, heavy vans, utility cars) while the rest 45% is
associated with passenger transport (cars and motorcycles). In the transformation sector 51.4% of the
emissions are correlated with heat generation, 46.2% with charcoal production and only 2.4% with
electricity generation.
It should be mentioned at this point that emissions associated with electricity are a critical point in this
analysis. LEAP provides the possibility of assigning environmental loadings to various generation
technologies. In Kakamega, the existing and proposed electricity generation is deriving from renewable
[74]
energy sources implying very low to zero emission levels. According to the proposed alternative
scenarios, electricity is expected to show increasing penetration rates in the following years. That is, the
imported from the grid electricity is expected to increase. However, environmental loadings for imported
electricity is not an option in LEAP. Therefore, the emissions associated with the imported electricity
were calculated manually according to the emission factors associated with the national grid in Kenya.
The methodology used, associated the emission factors of carbon dioxide (CO2), methane (CH4) and
nitrous oxide (N2O) to the electricity consumed (in this case imported). In particular, one kWh of
electricity imported was associated with 0.332 kgCO2, 13.07x10-6 kgCH4 and 2.62x10-6 kg N2O (Matthew
Brander 2011), (Mott MacDonald 2010), (IEA 2014). The aggregated GWP index was then estimated for
the different scenarios.
Graph 24. Share of estimated emissions by sector in Kakamega (2014).
Graph 25. Share of estimated emissions by sector in Kakameg under SE4ALL scenario.
[75]
According to the reference scenario, GWP index is expected to increase significantly reaching 1,450
ktons CO2e while the increase is even higher under the PUoE scenario, reaching 1,655 ktons CO2e by
2030. However, the adoption and implementation of the SE4ALL scenario, will lead to lower greenhouse
gas emissions over the next fifteen years for the County, resulting to 1,303 ktons CO2e in 2030.
According to the SE4ALL scenario it is expected that 65.6% of the emissions will be associated with the
transportation sector, 19% with the residential sector, 8.5% with the transformation sector (energy
production), 6.1% with agricultural activities and 0.41% with the commercial sector, 0.2% with the
industrial sector and 0.12% with the public sector. Graph 25, illustrates the share of estimated emissions
in Kakamega in 2030 under the SE4ALL scenario.
Comparing the SE4ALL and the reference scenarios, the accumulated GHG emission savings were
estimated at 297 ktons CO2e by 2030. Σφάλμα! Το αρχείο προέλευσης της αναφοράς δεν βρέθηκε.,
compares the Global Warming Potential (GWP) index (100 years horizon) between the proposed
scenarios for Kakamega till 2030.
Graph 26. Global Warming Potential (GWP) among different scenarios for Kakamega till 2030.
4.5 Cost assessment
The transformation proposed for Kakamega with the PUoE and SE4ALL scenarios, involve radical
changes both in the energy consumption and generation pattern of the County. These changes are
associated with a various social costs deriving by both the capital (acquisition) costs of new equipment
[76]
and operating costs. A preliminary assessment was performed in order to roughly estimate these costs and
provide a monetary perspective to the aforementioned transformation. The values presented below were
calculated under a discount rate of 5%.
Thus, it was estimated that the changes in the residential sector proposed by the DSM scenario will
impose an accumulative cost of 11.6 million USD. Furthermore, the RENE scenario proposal for 1MW
additional PV capacity in the community sector is expected to have an additional cost of 0.5 million USD.
Therefore, the total social costs related with the demand management in Kakamega require a cost of 12.1
million USD.
The construction of the proposed 59 MW of small hydro and photovoltaic capacity by 2030 is expected to
have an additional cost of 104.6 million USD.
On the other hand, it is estimated that the changes in the indigenously produced resources, the decreasing
imports of certain fuels and additional exports will lead to a reduction in the resource expenses by 243.4
million USD in regards with the reference scenario.
Consequently, the proposed transformations in Kakamega under the combination of the scenarios are
expected to have a positive balance characterized by a net present value of 126.7 million USD.
[77]
DISCUSSION AND CONCLUSIONS
A discussion of the results and the conclusions that the author has drawn during the Master of Science
thesis are presented in this chapter. The conclusions are based from the analysis with the intention to
answer the formulation of questions that is presented in Introduction.
In this study, a comprehensive analysis of the energy sector for Kakamega County in Western Kenya was
performed. The study initiated with a base line survey recording and estimating the current energy
demand level in the county for six selected sectors: Residential, Industrial, Transportation, Commercial,
Public and Agricultural. As an outcome, a benchmark of energy uses and their respective technological
solutions was created. Additionally, the renewable energy resources potential was assessed at local level
using GIS and other available data.
At a secondary level, the study proceeded with the County’s energy supply and demand modelling in
LEAP. The model was created based on the data acquired from the previous stage of the study while three
15-year projection scenarios were developed. The first scenario (Reference) was used to describe the
energy projections in the County under Business As Usual activities. The second scenario (Productive
Use of Energy Expansion) incorporated energy related enhancements in various activities, aiming to
increase their associated productivity. Finally, the third scenario (Sustainable Energy for All) induced
changes targeting at universal access to modern energy services, increased energy efficiency and
maximized share of renewables in the County’s energy mix.
The implementation of the scenarios followed the same hierarchy, being inserted into the model in the
form of twenty five energy related interventions with specified target goals and timelines. The
disaggregated nature of the model, mandates that the results should be analyzed according to the
respective question needed to be answered. However, here are some of the key elements outlining the
pathway suggested by the aforementioned scenarios.
The electrification rate of households in Kakamega is estimated to reach 70% by 2030. Significant
increase in the intensity, quality and productive use of energy is expected in agriculture, commercial and
public sector aiming to amplify the socioeconomic conditions in the County. However, despite the
increased energy requirements, the total demand in 2030 is expected to be retained at current levels
(approx. 5.8 TWh/year) due to demand side management interventions.
In addition, the primary requirements of kerosene are expected to be reduced by 67%, of wood by 62%
and of charcoal by 49.5% by 2030. Furthermore, the indigenous electricity generation is expected to
cover 37.9% of the County’s requirements, all deriving from renewable sources. While the GHG
emissions will continue to show an increasing trend over the next years, it should be noticed that the
suggested alternative scenarios will induce a 10.1% reduction of them by 2030 in comparison with the
reference (BAU) scenario. Finally, a preliminary cost assessment showed that the proposed interventions
will yield a beneficiary cost balance with net present value of 126.7 million USD.
To conclude, this thesis intended to create a framework aiming to facilitate sub-national energy planning
in developing countries through a holistic approach securing the economic, social and environmental
sustainability of the selected region. While further enhancements are necessary, it is expected that the
findings will be complementary to already existing energy planning models but also the base for future
research towards energy poverty elimination.
[78]
RECOMMENDATIONS AND FUTURE WORK
Paying respect to the global initiatives towards energy poverty elimination through sustainable
development and promoting the continuous research on the field of renewable energy assessment and
implementation, the following future work is suggested.
This thesis was elaborated on findings, estimations and assumptions derived mainly from literature
review. While cross referencing was fundamentally followed, validation of the data through a field study
is necessary and would add significant value to the work performed. Therefore is highly suggested as a
next step.
Further improvement of the LEAP model is also recommended by increasing the disaggregation level of
energy activities in all sectors. Another option would be the incorporation of the multi-tier methodology
of energy access measurement, proposed by ESMAP, into the LEAP model. That would allow a more
detailed and comprehensive representation of energy access in all sectors.
Finally, a more elaborated analysis based on GIS approach could be performed in order to accurately
quantify the renewable resource potential and identify the optimal electrification option in the selected
region.
[79]
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[83]
APPENDIX A
Existing isolated mini-grids in Kenya (MoEP 2015), (ECA & Trama TecnoAmbiental 2014).
Site & County
Year Commissioned
Number of
customers
Installed
Capacity (kW)
Wajir (Wajir)
1988
3,360
1,746 - Diesel
Merti (Isiolo)
2007
287
Habaswein (Wajir)
2010
779
Lodwar (Turkana)
2007
1,610
Hola (Tana River)
2007
1,300
2011
2,194
2007
3,270
El Wak (Mandera)
2009
535
Baragoi (Turkana)
Mfangano (Homa
Bay)
Garissa (Garissa)
(KenGen)
Lamu (Lamu)
(KenGen)
2009
199
2011
101
1996
NA
6,700 - Diesel
1989
NA
2,900 - Diesel
Rhamu (Mandera)
2012
210 (1,770
potential)
Eldas (Wajir)
2012
80 (1,370
potential)
184 - Diesel
50 - Solar
184 - Diesel
30 - Solar
Takaba (Mandera)
2012
Marsabit
(Marsabit)
Mandera
(Mandera)
Lokichoggio
(Turkana)
Lokori (Turkana)
Faza (Lamu)
Laisamis
(Marsabit)
North Horr
(Marsabit)
Lokitaung
(Turkana)
Kiunga (Lamu)
Hulugho (Garissa)
Dadaab (Garissa)
Maikona
(Marsabit)
Lokiriama
128 - Diesel
10 - Solar (2011)
640 - Diesel
20 - Wind (2012)
30 - Solar (2012)
1,440 - Diesel
60 – Solar (2012)
800 - Diesel
50 - Solar (2012)
800 - Diesel
500 - Wind (2011)
1,600 - Diesel
300 - Solar (2012)
360 - Diesel
50 - Solar (2012)
128 - Diesel
584 - Diesel
11 - Solar (2013)
Proposed actions
800, 850 – Solar
300 - Wind
100 – Solar
100 – Wind
100 – Solar
100 – Wind
250, 500 – Solar
Wind
100 – Solar
200, 650 – Solar
Wind
100 – Solar
Wind
100 Solar, Wind
100 – Solar
Connection to the
national grid
Connection to the
national grid
50, 150 - Solar
30, 150 – Solar
184 - Diesel
50 - Solar
2014
153 (2,526
potential)
10,980
potential
8,261 potential
2014
1,681 potential
380 - Diesel
2014
1,456 potential
184 - Diesel
80 - Solar
150 – Solar
2014
1,883 potential
184 - Diesel
100 – Solar
100 – Wind
2014
7,239 potential
184 - Diesel
150 – Solar
Installation in
progress
Installation in
progress
Installation in
progress
762
Households
759
Households
10,064
Households
1,208
Households
482
184 - Diesel
150 – Solar
184 - Diesel
150 – Solar
2012
In progress
In progress
[84]
640 - Diesel
184 - Diesel
640 - Diesel
640 - Diesel
380 - Diesel
50, 150 – Solar
150 – Solar
Wind
150 – Solar
100 – Solar
100 – Wind
100 – Solar
100 - Wind
100 – Solar,
Wind
150 – Solar
(Turkana)
Households
3,217
Households
Banisa (Mandera)
In progress
Kamoliriban
(Mandera)
In progress
NA
NA
Kotulo (Mandera)
In progress
NA
NA
Khorondile (Wajir)
In progress
NA
NA
Kakuma (Turkana)
In progress
NA
NA
[85]
184 - Diesel
100 – Solar
100 – Wind
Not specific plans
for Renewables
Not specific plans
for Renewables
Not specific plans
for Renewables
600 - Wind
APPENDIX B
Transmission network extension activity (Kenya Electricity Transmission Company Ltd. KETRACO
n.d.).
Connected locations
National level
Voltage
Thika – Kiganjo (Nyaga)
32 km of 132 kV single circuit line
Assosiated substations
35 km of 132 kV line
23 MVA substation
37 km of 132 kV line
5 MVA substation
33 km of 132 kV line
23 MVA substation
68 km of 132 kV line
23 MVA substation
65 km of 132 kV
15 MVA substation
79 km of 132 kV line
23 MVA substation
44 km of 132 kV line
23 MVA substation
60 km of 132 kV line
23 MVA substation
250 km of 132 kV line
23 MVA substation
Ishiara – Kieni – Embu
Sultan Hamud – Wote – Kitui
Bomet – Sotik
Olkaria – Narok
Lessos – Kabarnet
Nanyuki – Nyahururu
Kisii – Awendo
Eldoret – Kitale
Kindaruma – Mwingi – Garissa
Nairobi Metropolitan ring:
Sub-stations
Nairobi Metropolitan ring:
Suswa – Isinya line
Rabai – Malindi – Garsen – Lamu
Olkaria – Lessos – Kisumu
Loiyangalani – Suswa
Mombasa – Nairobi
Kilimambogo – Thika – Githambo
Kindaruma – Athi River
Sondu – Kendu – Homa Bay
Baringo – Rongai
Arusha – Nairobi
Konza – Machakos
Konza – Kajiado
Meru – Isiolo – Nayuki
Status
In progress
In progress
In progress
In progress
In progress
In progress
In progress
In progress
In progress
In progress
Part of 100 km of 400 kV line
In progress
Part of 100 km of 400 kV line
In progress
320 km of 220 kV line
Associated substations
300 km of 220 kV double circuit
line
90 MVA substation
430 km of 400 kV line
475 km of 220/400 kV
Associated substations
17 km of double circuit 132 kV line
50 km 132 kV line
132 kV double substation
150 km of 220 kV line
75 MVA substation
80 km 132 kV single circuit
2x23 MVA substation
80 km of 220 kV double circuit
2x75 MVA substation
260 km of 400 kV line
45 km of 132 kV line
23 MVA substation
45 km of 132 kV line
23 MVA substation
75 km of 132 kV line
23 MVA substation
[86]
In progress
In progress
In progress
In progress
In progress
Planned
Planned
Planned
Planned
Planned
Planned
Planned
Substation at Mariakani & Isinya
Suaswa & Loiyangalani
Nairobi Metropolitan ring:
Suswa – Ngong line
400/220 kV substations
400/220 kV substations
45 km of double circuit line
2x23 MVA substation
220/66 kV substations
Planned
Planned
Planned
Regional level
Lessos (Kenya) – Tororo (Uganda)
Eastern Africa Electricity Highway
Kenya – Tanzania
127 km of 400 kV line
2x75 MVA transformers
220 kV line upgrading
700 km of 500 kV HVDC
400 kV substation
500 km of 400 kV
[87]
In progress
In progress
Planned
APPENDIX C
Full list of stakeholders in the Kenyan Energy Sector.
1. Ministry of Energy & Petroleum (MoEP)
2. Energy Regulatory Commission (ERC)
3. Energy Tribunal
4. National Electrification and Renewable Energy Authority
5. The National Energy Institute
6. Rural Electrification Authority (REA)
7. County Energy Authorities
8. Kenya Electricity Generating Company Limited (KenGen)
9. Geothermal Development Company Limited (GDC)
10. Independent Power Producers (IPPs) – 9 IPPs accounting for 24% of the installed capacity (as at
December 2014):

Iberafrica Power (E.A.) Company Limited (thermal power plant) – 108.5 MW

Tsavo Power Company Limited (thermal power plant) – 74 MW

Mumias Sugar Company Limited (co-generation) – 26 MW

Orpower 4 Inc (geothermal power plant) – 110 MW

Rabai Power Company Limited (thermal power plant) – 90 MW

Imenti Tea Factory Company Limited (mini-hydro) – 0.3 MW

Gikira Hydro (mini-hydro) – 0.514 MW

Thika Power Limited (thermal power plant) – 87 MW

(Gulf Power Limited (thermal power plant) not in KPLC report)
11. Kenya Electricity Transmission Company Limited (KETRACO)
12. The Kenya Power & Lighting Company Limited (KPLC)
13. Kenya Petroleum Refineries Limited (KPRL)
14. Kenya Pipeline Company Limited (KPC)
15. National Oil Corporation of Kenya Limited (NOCK)
16. Oil Marketing Companies (OMCs)
17. Petroleum Institute of East Africa (PIEA)
18. Oil Exploration and Production Companies (OIEPs)
19. Kenya Nuclear Electricity Board (Corporation) (KNEB)
20. Centre of Energy Efficiency and Conservation (CEEC)
21. Kenya Revenue Authority (KRA)
22. National Environmental Management Authority (NEMA)
23. Directorate of Occupational Safety and Health Services (DOSHS)
24. Water Resources Management Authority (WRMA)
Other players Kenya Railways (KR), Kenya Truckers Association (KTA), Kenya Association of
Manufacturers (KAM), Kenya Bureau of Standards (KEBS), Kenya Maritime Authority (KMA) and of course
consumers.
[88]
APPENDIX D
Health clinics categorization according to energy requirements.
Figure 17. Category 1 health clinic energy consumption (USAID n.d.).
Figure 18. Category 2 health clinic energy consumption (USAID n.d.).
[89]
Figure 19. Category 3 health clinic energy consumption (USAID n.d.).
[90]
APPENDIX E
Sustainability index (on GIS maps) of various rain-fed energy crops in Kakamega County.
Figure 20. Barley sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 21. Cassava sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
[91]
Figure 22. Jatropha sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 23. Maize sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 24. Miscanthous sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
[92]
Figure 25. Rape sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 26. Sorghum sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 27. Soybean sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
[93]
Figure 28. Sugarcane sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 29. Sunflower sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
Figure 30. Wheat sustainability index characterization for Kakamega County (IRENA Global Atlas 2015).
[94]
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