Kenya Distribution Master Plan The Kenya Power & Lighting Company Limited Final Report

Final Report
Volume I
Prepared by
Parsons Brinckerhoff
Volume I: Distribution Master Plan Report Volume II: Design Manual
Volume III: Environmental Impact Assessment Scoping Report
Document History and Status Report Issue Date of Issue Prepared By: Checked By: Approved By:
2 4 April 2013
M Fraser O. Nanka-Bruce A Topari G Vukojevic M Safranek L Veitch J Kuchimanchi B Brewin E Tyldesley
M. Fraser K. Jackson
1 6 February 2013
0 19 November 2012
Report Title : Distribution Master Plan Report Report Status : Final Job No : 3511648A Date : April 2013 Prepared by : Authors listed below Checked by : M. Fraser Approved by : K. Jackson
Distribution Master Plan Report Prepared for KPLC Final Report
CONTENTS
1 INTRODUCTION .................................................................................................... 1.1
2 BACKGROUND ..................................................................................................... 2.1
3 MASTER PLAN APPROACH ................................................................................ 3.1
3.1 Overview ................................................................................................................ 3.1 3.2 Counterpart Team .................................................................................................. 3.2 3.3 Data Gathering ....................................................................................................... 3.2 3.4 Software Selection ................................................................................................. 3.2 3.5 Network Modelling and Assessment of Existing Networks....................................... 3.2 3.6 County Visits .......................................................................................................... 3.3 3.7 Demand Forecasting .............................................................................................. 3.3 3.8 Short-medium term planning .................................................................................. 3.3 3.9 Long term planning ................................................................................................ 3.3
4 DEMAND FORECAST ........................................................................................... 4.1
4.1 Background ............................................................................................................ 4.1 4.2 LCPDP Demand Forecast ...................................................................................... 4.1 4.3 Deriving the LCPDP Demand Forecast ................................................................... 4.4 4.4 County Statistics .................................................................................................... 4.7 4.5 County Demand Forecast ..................................................................................... 4.14 4.6 Disaggregation to Substation Level ...................................................................... 4.17
5 PLANNING CRITERIA AND GUIDELINES ............................................................ 5.1
5.1 Introduction ............................................................................................................ 5.1 5.2 Planning Criteria..................................................................................................... 5.1 5.3 Planning Guidelines ............................................................................................... 5.5 5.4 Planning Recommendations ................................................................................. 5.15
6 DESIGN STANDARDS .......................................................................................... 6.1
7 NETWORK DATA AND MODELLING ................................................................... 7.1
7.1 Network Data and Assumptions.............................................................................. 7.1 7.2 Network Models ..................................................................................................... 7.1
8 ASSESSMENT OF EXISTING NETWORKS .......................................................... 8.1
8.1 General .................................................................................................................. 8.1 8.2 Nairobi Region ....................................................................................................... 8.1 8.3 Mount Kenya Region .............................................................................................. 8.6 8.4 Coast Region ......................................................................................................... 8.9 8.5 Western Region ................................................................................................... 8.11
9 SHORT-MEDIUM TERM PLAN.............................................................................. 9.1
9.1 Basis for Expansion Plan ........................................................................................ 9.1 9.2 Nairobi Region ....................................................................................................... 9.1 9.3 Mount Kenya Region ............................................................................................ 9.10 9.4 Coast Region ....................................................................................................... 9.19 9.5 Western Region ................................................................................................... 9.27
10 ENVIRONMENTAL IMPACT ASSESSMENT SCOPING STUDIES ..................... 10.1
10.1 Background .......................................................................................................... 10.1 10.2 Purpose of this Scoping Report and Approach to the Scoping Study..................... 10.1 10.3 Distribution Master Plan Project Activities ............................................................. 10.2 10.4 Consultation ......................................................................................................... 10.2 10.5 Likely Environmental and Social Impacts of Distribution Master Plan Projects ....... 10.3 10.6 Mitigation and Monitoring ..................................................................................... 10.4 10.7 Environmental and Social Capacity Building ......................................................... 10.4
11 INTERCONNECTION OF OFF-GRID AREAS ..................................................... 11.1
11.1 Off-Grid Power Plants .......................................................................................... 11.1 11.2 Relevant Transmission Network Developments .................................................... 11.3 11.3 Assessment of Grid Connection ........................................................................... 11.3 11.4 Summary of results .............................................................................................. 11.8
12 LONG TERM PLAN AND INVESTMENT PLAN .................................................. 12.1
12.1 Introduction .......................................................................................................... 12.1 12.2 Generic Models .................................................................................................... 12.1 12.3 Investment Plan ................................................................................................... 12.7
13 CONCLUSIONS AND RECOMMENDATIONS .................................................... 13.1
13.1 Conclusions ......................................................................................................... 13.1 13.2 Recommendations ............................................................................................... 13.3
APPENDICES APPENDIX A PLANNING GUIDELINES
APPENDIX B NAIROBI REGION NETWORK STUDIES
APPENDIX C MT. KENYA REGION NETWORK STUDIES
APPENDIX D COAST REGION NETWORK STUDIES
APPENDIX E WESTERN REGION NETWORK STUDIES
APPENDIX F COMMITTED AND PROPOSED PROJECTS
LIST OF ABBREVIATIONS
AVR Automatic Voltage Regulator
BBHs Branch Business Heads
BSP Bulk Supply Point
CAA Civil Aviation Authority
EIA Environmental Impact Assessment
EN European Standards
FDB Facilities Database
IEC International Electro-technical Commission
IPP Independent Power Producer
KenGen Kenya Generating Company
KenInvest Kenya Investment Authority
KETRACO Kenya Electricity Transmission Co. Ltd
KFS Kenya Forestry Service
KPLC Kenya Power and Light Company (Kenya Power)
KNBS Kenya National Bureau of Statistics
KSh Kenyan Shillings
kV Kilovolt
kW Kilo-Watt
kWh Kilo-Watt-hour
LRMC Long Run Marginal Cost
LV Low Voltage
NOPs Normally Open Points
OHL Overhead Lines
SHE Safety, Health and Environment
SVC Static VAR Compensator
ToR Terms of Reference
USD United States Dollar
EXECUTIVE SUMMARY
Objectives
Parsons Brinckerhoff (PB) was appointed by The Kenya Power & Lighting Company Limited (KPLC) to conduct a
Distribution Master Plan Study to address the country’s distribution requirements up to 2030.
The main objectives of the study were to;
conduct a detailed assessment of KPLC’s distribution system requirements over the 2012-2030
planning period and develop a Distribution Master Plan and
undertake an environmental scoping study for the investments recommended in the short-medium term
(3-5 years).
Investment Requirements
The study indicates the need for the following investment in distribution infrastructure:
A. Short-medium term investment
1. Proposed 66 kV and 33 kV investment
The study identifies the need for approximately 300, 66 kV and 33 kV distribution projects beyond those that are
already committed or under construction, for completion over the 2013-2017 period. The estimated investment
required for the 66 kV and 33 kV projects over the 2013 – 207 period is $ 149 million as indicated in Table E-1-1.
Details of the proposed projects are provided in the report.
Table E-1-1: Proposed 66 kV and 33 kV investment in short-medium term (USD ‘000)
2. Proposed Bulk Supply Point (BSP) investment
Table E-1-2 shows an estimated investment requirement in new BSPs and reinforcement of existing BSPs
(beyond the committed BSP projects) of $107 million over the 2013-2017 period. This is for the BSP substations
only and does not include the cost of transmission lines required to connect the new BSPs to the grid.
Table E-1-2: Proposed BSP investment in short-medium term (USD ‘000)
Substation/Feeder Project Status 2013 2014 2015 2016 2017 Grand Total
Feeders (66 kV and 33 kV) Proposed 3,110 9,698 20,269 17,714 7,107 57,899
Primary substations Proposed 5,454 15,983 21,211 28,004 20,640 91,291
Grand Total 8,564 25,681 41,480 45,718 27,746 149,189
Substation/Feeder Project Status 2014 2015 2016 2017 Grand Total
Bulk Supply Points Proposed 16,728 35,717 39,980 15,002 107,428
DISTRIBUTION MASTER PLAN
3. Estimated 11 kV and LV investment
The above figures do not include investment at the 11 kV and LV levels. Due to the scale of these networks it
was not practical to model them in their entirety. The estimated investment costs at these voltage levels were
calculated based on representative 11 kV and LV networks. The estimated level of investment is shown in Table
E-1-3. and is considerably more than that required at 66 kV and 33 kV. This difference is primarily due to the
scale of the infrastructure at these voltage levels. The cost of all 33/0.433 kV and 11/0.433 kV distribution
transformers is included within these figures.
Table E-1-3: Estimated 11 kV and LV investment in short-medium term (USD million)
Equipment 2013 2014 2015 2016 2017
11 kV and LV networks (including all
distribution transformers)
B. Long-term investment
The estimated long term annual investment in all distribution infrastructure from 66 kV to LV is indicated in Table
E-1-4.
Table E-1-4: Estimated long term investment - 66 kV – LV (USD million)
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
407 420 496 538 604 676 791 893 992 1,119 1,272 1,437 1,611
Key Drivers
Development of the power sector in Kenya is underpinned by Vision 2030, which is Kenya’s development
blueprint covering the period 2008 to 2030. The objective of Vision 2030 is to help transform Kenya into a
“middle-income country providing a high quality of life to all of its citizens by the year 2030”. It identifies energy
and electricity as a key element of Kenya’s sustained economic growth and transformation.
The power sector in Kenya requires high levels of investment to ensure that the rapidly growing demand is
adequately met. Major investments are being made in generation and transmission, however for benefits to be
realised, similar levels of investment are required in the distribution sub-sector. It is essential that this investment
is carefully planned to ensure efficient use of scarce resources.
Under the new constitution, power will be devolved to each of the forty seven counties. This is expected to result
in more equitable sharing of resources across the country, with increased levels of investment particularly in the
more remote counties that have historically been neglected. There exist wide ranging levels of household
electrification across the country, from around 75 % of households in Nairobi County to less than 2 % of
households in Tana River County1. Electrification levels are increasing across the country and following
devolution, it is expected that the rate of increase will be highest in those counties that are currently poorly served
with electricity infrastructure.
The Distribution Master Plan includes a forecast of electricity demand for each of the counties and identifies the
distribution infrastructure required to meet that demand, whilst achieving acceptable levels of power quality and
reliability, based on well established principles of least-cost planning.
Approach
Throughout the period of the Master Plan Study, there was close collaboration between the PB team and the
KPLC counterpart team. The process as shown in Figure E.1 began with a detailed data gathering exercise at
central, regional and sub-regional level.
Figure E.1: Distribution Master Plan Process
The scope of the study included provision of suitable software to conduct the detailed network analysis and to
provide an ongoing tool for KPLC to use in the future. Selection and procurement of the software was conducted
in parallel with data gathering.
This was followed by network modelling and assessment of existing network issues and constraints, leading to
short-medium term planning studies covering the period from 2012-2017. The network modelling and analysis
for the various KPLC regions was conducted in parallel in order to minimise the time required for these detailed,
data-intensive tasks.
1 Based on the 2009 Kenya Population and Housing Census Report
Data gathering at central and regional level
Software selection Network modelling Assess existing Network issues
Short-medium term planning studies
Planning and design guidelines
Demand forecasting
County level meetings (in each of the 47 counties Hosted by KPLC BBHs)
ESIA scoping studies
Localised technical issues
3 week intensive training course in UK Including visits to industry
Investment plan
Off-grid assessment
Distribution Master Plan Study Prepared for KPLC Final Report
Apart from the network data and models, key inputs to the planning studies were; the demand forecast and the
planning and design guidelines.
Long term planning was based on the development of ‘generic networks’ to represent the different typical network
topologies, i.e. urban, rural, Nairobi and off-grid configurations. These were used along with the county level
demand forecast to determine the longer term investment requirements.
A key aspect of the Master Plan study was consultation at a county level. This was used to inform the
Environmental Impact Assessment scoping studies and also provided valuable insights into technical issues at a
local level.
The Master Plan study draws together the key aspects of the assignment which are briefly described below.
Counterpart Team
An important factor leading to the successful completion of the Master Plan Study was the appointment by KPLC
of a dedicated team of knowledgeable counterparts. The counterpart team was pivotal during the data gathering
stage and provided invaluable local knowledge during development of the short-medium term plans.
The assignment included an intensive three-week training course in the UK for the counterparts. This covered
most of the key aspects of the study and included hands-on training in the use of the network planning software.
Data Gathering
Data gathering was conducted at central, regional and sub-regional level. County level meetings were also held
as described below.
Initial data collection was conducted at KPLC’s central offices in Nairobi. This included centrally stored network
data and diagrams, relevant reports, branch sales data and relevant information from Kenya National Bureau of
Statistics (KNBS). The initial data gathering stage was followed in April with data gathering at the regional and
sub-regional level. This was conducted in three teams, each comprising members from the PB and KPLC
counterpart study teams. The teams covered the various KPLC regions as follows:
Nairobi Region
Western Region
The data obtained in the regions and sub-regions was used to try to complete the many gaps in the data obtained
initially from the central offices. Most of the remaining gaps were resolved through collaboration with the KPLC
counterpart team, thereby minimising the number of assumptions that needed to be made with respect to the
network models.
Software Selection
The scope of the study included the procurement of a suitable software package, both for conducting the network
analysis during the study and for handing over to KPLC at the end of the study. Two potential software packages
were assessed and following a presentation by PB, KPLC chose NEPLAN.
Network Modelling and Assessment of Existing Networks
The next stage was to develop detailed NEPLAN models of the 33 kV and 66 kV networks. These were initially
used to assess the performance of the existing networks in each of the four KPLC regions and to identify specific
network constraints. Representative 11 kV feeders were also modelled and assessed using NEPLAN.
County Visits
Stakeholder meetings were held within each of the 47 counties, in order to gather data for the EIA scoping study
and to understand key technical issues at a county level. County meetings were initially held in three ‘pilot’
counties. Following the pilot meetings, the agenda was refined and used as the basis for the remaining
meetings.
Demand Forecasting
The demand forecast contained within the Least Cost Power Development Plan (LCPDP) dated March 2011 was
used for the study. This was disaggregated to county level using a process that is described in detail in this
report. County demand growth rates were applied to the respective substations to determine substation
demands for the network studies. County growth rates were also used to determine investment requirements at a
county level.
Network expansion planning
The NEPLAN models were used in conjunction with GIS mapping to produce detailed short-medium term
expansion plans for the 66 kV and 33 kV networks in each of the four KPLC regions.
Generic or typical network configurations were developed to derive average incremental investment costs ($/kW)
for the various network topologies; urban, rural, Nairobi and off-grid. The county demand growth rates were then
used to calculate long term investment requirements.
The investment required at 11 kV and LV was determined at a county level both in the short-medium term and
long term using the average incremental cost approach.
Conclusions
The main conclusions that may be drawn from the study are briefly described below.
Short-Medium Term Plan
A short-medium term (2013-2017) distribution network reinforcement and expansion plan was developed for each
of the regions. This plan is based on detailed network analysis and identifies proposed 66 kV and 33 kV projects
that are required beyond the ongoing and committed projects. The need for additional BSPs, beyond the
committed transmission projects, and reinforcement of existing BSPs in order to support the distribution network
has also been identified.
The need for the projects is driven by the demand forecast which has been disaggregated to county level and
then applied to the individual substations across the network. The proposed projects should be considered to be
the minimum requirement to meet the forecast demand whilst complying with the planning criteria.
Across the network a total of approximately 300 projects are proposed for completion by 2017. These include
66 kV and 33 kV feeders, new and reinforced substations and reactive compensation. The estimated total
investment cost of these projects is $ 149 million. A further $ 107 million is required for new BSPs and
reinforcement of existing BSPs over the same period. The corresponding investment in the 11 kV and LV
networks is included in the overall investment plan described below.
Interconnection of Off-grid Areas
The national grid does not currently extend to all parts of Kenya, and large parts of the country, particularly in the
north, remain off-grid. The total energy generated by the off-grid diesel power plants is relatively low, and for the
year 2010/11 represented just 0.8 % of the total electricity sales in the country.
Studies were conducted to assess the technical requirements and economic viability of extending the grid to each
of the off-grid areas. The studies include a comparison of the levelised cost of electricity for the grid connected
and off-grid options. The results indicate an economic case for extension of the grid to; Lodwar, Marsabit, Wajir,
Habaswein and Hola.2 These sites would be interconnected to the grid through extension of the transmission
network and therefore these projects are not included within the list of proposed distribution projects.
Environmental Impact Assessment
Environmental impact assessment scoping studies have been conducted for the projects proposed in the
Distribution Master Plan.
The scoping studies provide an initial assessment of the potential environmental and social impacts associated
with each of the projects currently proposed. The assessment has been based on desk study data used to
inform the county level baseline summaries as well as on feedback obtained from the in-county consultation
meetings. The impacts (and associated mitigation proposals) are necessarily high level since the exact project
locations and extents are as yet to be determined.
Based on the consultation feedback and high level assessments conducted to date, the negative impacts of
many of the proposed projects are expected to be minimal and the overall benefits of the projects are expected to
greatly outweigh the adverse impacts.
The EIA Scoping Report is intended to provide a basis for project-specific discussions with the relevant
authorities and stakeholders to help determine whether an Environmental Impact Assessment (EIA) is required
for each of the projects which come forward as part of the Distribution Master Plan.
Long Term Plan and Investment Plan
Generic models were developed to represent typical rural, urban and off-grid areas. These models were used to
derive average incremental investment costs which were applied to the demand forecast to obtain estimated long
term investment requirements for each of the counties.
The specific projects identified through detailed network studies only cover 66 kV and 33 kV network
requirements. The generic models were therefore also used to derive corresponding investments requirements
for the 11 kV and LV networks.
The total distribution infrastructure investment requirements for each county are provided in the report. This
includes estimated investment requirements for;
2 Garissa and Lamu were excluded from this analysis as there are ongoing projects to connect these areas to the grid.
the specific proposed short-medium term (2013-2017) 66 kV and 33 kV projects. Note that this
excludes the costs associated with committed distribution projects,
the estimated 11 kV and LV investment required in the short-medium term based on the generic
models and
the total estimated distribution investment requirements in the long term (2018-2030), based on the
generic models.
The total annual investment in distribution infrastructure increases from $ 183 million in 2013 to $ 1.6 billion by
2030. The net present value of this investment at a discount rate of 12 % is $3.9 billion3.
Recommendations
Planning Data
During the data collection phase of this project it was observed that essential network planning data is not always
readily available. In order to improve the efficiency and effectiveness of the network planning process, it is
essential that improvements are made in the quality and accessibility of network and metering information
available to the planning department.
KPLC’s network database was found to be only 60-70 % complete and in many cases conflicts were found
between the database and network schematics. For effective planning, it is important that the database and
schematics reflect as closely as possible the state of the network.
The lack of consistent primary substation and feeder load information is a significant issue and one that should
be addressed in order to improve the planning process and assist with prudent investment decisions.
Coordinated Approach
Distribution planning by necessity should be done at the local level within each region. Knowledge of the local
conditions, customers, and the existing network are all vital to distribution planning engineers. However there
should be a similar approach throughout the KPLC offices, applying national standards and planning criteria.
Currently, there is a wide variance in terms of training, tools, experience, and methodology of distribution
planning across the regions.
Standardised Planning
Procedures should be implemented and regular training provided to ensure that they are widely understood and
applied. Additionally, feedback from all regions should be used to update the standards and guidelines as
necessary.
Planning and Analysis Software
Implementation of planning and analysis software such as NEPLAN needs to be part of the overall
implementation of a coordinated network planning process. These packages are tools for planners to evaluate
different present and future scenarios to arrive at an optimum solution. However, without a coordinated network
planning process, these tools are of little benefit.
3 In money of 2012.
It is recommended that KPLC invests further in training for distribution planning and that this training is rolled out
to each of the regions.
It is recommended that KPLC expands the use of NEPLAN to distribution planning engineers throughout Kenya
and continues the development of the network models. They need to be maintained in a controlled centralised
manner, ensuring that all network changes are captured.
Dedicated Distribution Planning Section
KPLC should establish a dedicated central distribution planning section. This section would be responsible for;
Ensuring that network planning data is as complete and accurate as possible.
Ensuring a coordinated approach to planning across the regions.
Controlling the use of network planning software models across the regions.
Design Manual
The new Design Manual is a draft document and should be reviewed by KPLC before using it for network design.
The existing DSGM is likely to have evolved over some considerable time and may in many respects provide
adequate guidance.
The new Design Manual covers a number of areas as described above, including aspects not covered in the
DSGM. It is recommended that the new Design Manual is adopted over time. Some aspects may be found by
KPLC to be more useful and applicable than others and therefore is suggested that the document is gradually
amended and increasingly adopted by KPLC.
The report includes recommendations for training workshops associated with the Design Manual and also
includes recommendations for the development of related documentation including; construction and
maintenance manuals and technical specifications.
Updating the Distribution Master Plan
The Master Plan is based on many assumptions, not least the demand forecast. It should be reviewed annually
and modified as necessary to reflect changes in the underlying assumptions. The network planning software
(NEPLAN) provides the flexibility to readily incorporate changes and assess new requirements.
SECTION 1
INTRODUCTION
Distribution Master Plan Study Prepared for KPLC Final Report Page 1.1
1 INTRODUCTION
Parsons Brinckerhoff (PB) was appointed by The Kenya Power & Lighting Company Limited (KPLC) to conduct a
Distribution Master Plan Study to address the country’s distribution requirements up to 2030.
The main objectives of the study were to;
conduct a detailed assessment of KPLC’s distribution system requirements over the 2012-2030
planning period and develop a Distribution Master Plan and
undertake an environmental scoping study for the investments recommended in the short-medium term
(3-5 years).
1.1 Key Drivers
Development of the power sector in Kenya is underpinned by Vision 2030, which is Kenya’s development
blueprint covering the period 2008 to 2030. The objective of Vision 2030 is to help transform Kenya into a,
“middle-income country providing a high quality of life to all of its citizens by the year 2030”. It identifies energy
and electricity as a key element of Kenya’s sustained economic growth and transformation.
The power sector in Kenya requires high levels of investment to ensure that the rapidly growing demand is
adequately met. Major investments are being made in generation and transmission, however for benefits to be
realised, similar levels of investment are required in the distribution sub-sector. It is essential that this investment
is carefully planned to ensure efficient use of scarce resources.
households in Tana River County4. Electrification levels are increasing across the country and following
The Distribution Master Plan includes a forecast of electricity demand for each of the counties and identifies the
distribution infrastructure required to meet that demand, whilst achieving acceptable levels of power quality and
reliability, based on well established principles of least-cost planning.
1.2 Study Documents
The study documents have been compiled in three volumes as follows:
Volume I: Distribution Master Plan Report
Volume II: Design Manual
Volume III: Scoping Studies for Environmental Impact Assessment
4 Based on the 2009 Kenya Population and Housing Census Report
This report (Volume I), documents the work undertaken to produce a distribution master plan for Kenya and
includes a section to cover each of the main aspects of the study.
The first sections include a brief description of the distribution system in Kenya and describe the approach taken
by PB to conduct the study. The following section describes the demand forecast on which the Master Plan
Study was based and the process by which the national forecast was disaggregated to county level. The next
two sections cover planning criteria and design standards respectively. These also make reference to a Design
Manual which has been produced as a separate volume. These are followed by sections covering network data,
modelling and technical assessment of the existing networks. Network development in the short-medium term is
then covered in detail, including network planning studies spanning the first five years of the planning period.
The next section of the report includes a summary of the scoping study for the Environmental Impact
Assessment. The full scoping study is included in a separate volume. This is followed by a section which
considers extension of the grid to areas that are presently off-grid and includes technical and economic
assessment of grid extension versus continuing with off-grid solutions. The report then covers the approach to
long-term planning. The results of the short-medium term and long-term distribution network development plans
are brought together in the form of an investment plan, which identifies the projected investment requirements for
each of the counties over the planning period.
SECTION 2
BACKGROUND
2 BACKGROUND
2.1 Electricity Sector
Figure 2-1 provides an overview of the Electricity Sector in Kenya and shows the relationships between the key
players.
Figure 2-1: Kenya Electricity Sector
The Electricity Sector falls under the Ministry of Energy (MoE), which provides policy direction. The Energy
Regulatory Commission (ERC) is responsible for formulating and enforcing regulations, licensing power
companies, providing customer protection, approving power purchase agreements (PPAs) and conducting tariff
reviews.
Kenya Electricity Generating Company (KenGen) is the largest generating company and is majority government
owned. The remaining grid-connected generation is provided by privately owned independent power producers
(IPPs). The Geothermal Development Company (GDC) develops geothermal steam fields for subsequent use by
generation companies.
KPLC, which is 51 % government owned, owns and operates both the transmission and distribution networks
throughout Kenya. KPLC is responsible for purchase of all bulk electricity and is the sole supplier to end use
customers throughout the country. KPLC also operates the majority of the off-grid diesel power plants on behalf
of the Rural Electrification Programme.
Ministry of Energy
E nergy R
egulatory C om
Retail Tariff Approval
BACKGROUND
KPLC is the system operator and is responsible for generation scheduling and dispatch, frequency control,
voltage control, outage management and system security. Generation dispatch and control of the transmission
network is via the national control centre (NCC). The distribution networks are controlled regionally though each
of the four regional control centres (RCCs).
Kenya Electricity Transmission Company (KETRACO) is 100 % government owned, with responsibility for
constructing new transmission lines across the country and then handing them over to KPLC to operate and
maintain.
The Rural Electricity Authority (REA) has responsibility for implementing the government’s Rural Electrification
Programme, which includes extension of the 33 kV and 11 kV distribution networks to facilitate the connection of
public facilities and private customers in rural areas.
Kenya is interconnected with Uganda at 132 kV, for import and export. Kenya supplies some border towns in
Tanzania at 33 kV and receives power from Ethiopia to supply a border town in the far north of the country.
2.2 Distribution Network Configuration
KPLC’s distribution network includes the main interconnected grid in addition to a number of small off-grid
networks. The grid is operated in four distinct regions; Nairobi, Coast, Western and Mt. Kenya as shown in
Figure 2-2 with a further split into sub-regions as shown in Table 2-1. The extent of the grid covers the main
population centres in the four geographical regions.
2.2.1 Nairobi
Within the Nairobi Region, the network configuration differs from that of the other KPLC regions. The region is
supplied from the transmission network via several 220/66 kV and 132/66 kV transmission substations or bulk
supply points (BSPs). A number of 66 kV feeders emanate from each BSP and each 66 kV feeder supplies one
or more primary (66/11 kV) substations. Each primary substation supplies a number of 11 kV feeders, which in
turn supply 11/0.433 kV distribution substations. Larger customers may be supplied at 11 kV or 66 kV.
The Nairobi network is interconnected both at 66 kV and 11 kV, with normally open points to allow transfer of
load across BSPs or primary substations respectively.
The 66 kV feeders are mostly overhead using single and double circuit wood-pole construction. The 11 kV
feeders are also mostly overhead using single circuit wood or concrete pole construction.
In Nairobi city centre, where there are space constraints or issues with clearances, underground cables are used
for 11 kV and a few for 66 kV.
There are a few 66/33 kV substations on the outskirts of Nairobi, which supply neighbouring areas via long 33 kV
feeders.
BACKGROUND
Figure 2-2: Extent and Regional Split of the Distribution Network
SECTION 2
BACKGROUND
Table 2-1: KPLC Regions
Mt. Kenya South
2.2.2 Other Regions
The distribution network in the regions outside Nairobi is less interconnected, with many radial 33 kV feeders and
generally with long distances between BSPs. Standard BSP design typically consists of 2 x 132/33 kV two
winding transformers, however some BSPs are equipped with only a single transformer.
A number of 33 kV feeders emanate from each BSP and each 33 kV feeder generally supplies one or more
primary (33/11 kV) substations and many distribution (33/0.433 kV) substations. The 11 kV feeders emanating
from the primary substations in turn supply distribution substations.
In general, the primary substations are supplied from a single BSP, although some 33/11 kV substations have an
alternative supply at 33 kV from a different 132 kV substation. There are normally open points to avoid parallel
supply from different BSPs.
The 33 kV and 11 kV feeders are generally overhead using single circuit wood-pole or concrete-pole
construction. Underground cables are used as necessary due to space constraints in urban centres, although
their present level of use outside Nairobi is minimal.
In the urban centres, the 11 kV networks are interconnected where possible to provide alternative supply,
however in the rural areas the 11 kV feeders are radial.
SECTION 2
BACKGROUND
2.2.3 Off-grid Areas
Most of Kenya is off-grid as indicated in Figure 2-2, however the eight off-grid counties5, whilst geographically
large, account for just 7 percent of the population. It is important to note however that most of the rural areas
within the grid connected counties do not currently enjoy electricity access due to the limited extent of the grid.
This issue is being addressed by the Rural Electrification Authority (REA), which is undertaking donor and
government funded projects to extend the 33 kV and 11 kV networks. These projects are primarily aimed at
providing supplies for public facilities such as schools, health centres, trading centres and bore-holes.
In the larger towns in the off-grid counties, small isolated 11 kV networks are supplied via diesel generators, with
in some cases backup from renewable power sources such as solar photo-voltaic (PV). The REA is in the
process of extending some of these networks to nearby towns or villages by means of 33 kV ‘transmission’.
2.3 On-going Projects
Many projects are currently underway including new substations and feeders and reinforcement of existing
substations and feeders. These are aimed at extending the distribution network to new areas to increase
coverage and reinforcement of the existing network to accommodate demand growth and improve power quality
and reliability. The projects are grouped under various initiatives including the Energy Sector Recovery Project
(ESRP) and Kenya Electricity Expansion Project (KEEP) and include a combination of donor, government and
self funding by KPLC.
2.4 Network Planning Issues
The distribution network suffers from poor reliability and quality of supply, which is generally due to under-
investment. Some of the key issues identified during the study are briefly described below.
2.4.1 Feeder Length
Many parts of the distribution network are supplied over extremely long, radial 33 kV and 11 kV feeders, with no
alternative source of supply. In some cases, 33 kV feeders may be hundreds of km long, with many spurs,
resulting in a total length (in extreme cases) in excess of 1000 km supplied from a single source. A fault on such
a long feeder will have wide-spread impact, be difficult to locate and therefore will result in a long restoration time.
These parts of the network are not surprisingly subjected to frequent and prolonged outages.
Due to excessive feeder lengths and use of undersized conductors, voltage levels on feeders, particularly
outside of the urban areas are typically poor and significantly under the required standard. Automatic
voltage regulators (AVRs) have been installed on feeders in the past, however many of these have failed and
have subsequently been bypassed.
Excessively long, undersized feeders also result in high losses. As a distribution operator, losses must be paid
for as they represent a proportion of the energy purchased. Furthermore, distribution infrastructure must be sized
for both the delivered power and power losses. For these two reasons, there is a financial incentive to reduce
losses.
5 Lamu, Tana River, Garissa, Mandera, Wajir, Marsabit, Samburu and Turkana
SECTION 2
BACKGROUND
Economically, losses represent part of the generated energy and generation and transmission infrastructure must
be sized for both the delivered power and losses. Losses therefore have both energy and capacity cost
components and loss reduction measures on distribution networks tend to be self financing.
Generally however, provided distribution plant and feeders are not overloaded and voltage levels are within
normal limits, the level of losses will be acceptable. The Distribution Master Plan includes studies to identify
excessive feeder loading and poor voltage regulation and includes measures to address these issues. The
Master Plan also includes planning guidelines for feeders, identifying maximum MVA.km curves for the standard
and proposed conductor sizes.
2.4.2 Bulk Supply Points
A related issue concerns the number of BSPs across the system. In many parts of the country, the BSPs are
sparsely distributed; with no available alternative should that BSP fail. This issue of course has a direct bearing
on the 33 kV feeder lengths and the associated reliability and power quality issues.
KETRACO is currently investing in new transmission lines and associated substations, both in Nairobi and in
other parts of the country. These new BSPs will relieve loading on existing overloaded BSPs and will allow for
shorter feeder lengths and greater levels of interconnection. These measures will therefore improve voltage
levels and reliability.
In some cases the new BSPs will result in extension of the grid to currently off-grid areas, improving reliability of
supply to those areas and reducing the cost of supply by displacing expensive diesel generating plant.
The Master Plan includes studies to identify the need for and location of additional BSPs beyond those already
under construction or committed.
2.4.3 Security of Supply
Much of the distribution network does not have adequate capacity to effectively manage the present
demand, and many of the primary substations are loaded well beyond firm capacity. Adequate excess capacity
is unavailable, making it difficult to manage contingencies and meet future demand.
KPLC aims to achieve N-1 security of supply at least for major substations. However many primary substations
(and BSPs) are equipped with just a single transformer and even those with two or three transformers are often
loaded such that no spare capacity exists to cater for a transformer failure. This is a particular issue for parts of
the network with no alternative means of supply.6
In the event of such a failure, it is often necessary to source a spare transformer from another part of the network,
resulting in a prolonged outage before supply is restored.
The Master Plan identifies transformer loading across the network and makes recommendations for a graduated
move
6 In urban areas such as Nairobi, where a greater level of interconnection exists, there is often scope to switch a feeder onto an alternative BSP or primary substation at 66 kV and 11 kV respectively. In the more rural, radially fed areas, there is no scope for this type of switching and transformer redundancy is therefore particularly critical.
SECTION 3
MASTER PLAN APPROACH
3 MASTER PLAN APPROACH
3.1 Overview
The process by which the Distribution Master Plan was completed is shown in Figure 3-1.
Figure 3-1: Master Plan Process
Throughout the period of the Master Plan Study, there was close collaboration between the PB team and the
KPLC counterpart team. The process began with a detailed data gathering exercise at a central, regional and
sub-regional level.
The scope of the study included provision of suitable software to conduct the detailed network analysis and to
provide an ongoing tool for KPLC to use in the future. Selection and procurement of the software was conducted
in parallel with data gathering as indicated in Figure 3-1.
This was followed by network modelling and assessment of existing network issues and constraints, leading to
short-medium term planning studies covering the period from 2012-2017. The network modelling and analysis
for the various KPLC regions was conducted in parallel in order to minimise the time required for these detailed,
data-intensive tasks.
Apart from the network data and models, key inputs to the planning studies were; the demand forecast and the
planning and design guidelines.
Long term planning was based on the development of ‘generic networks’ to represent the different typical network
topologies, i.e. urban, rural, Nairobi and off-grid configurations. These were used along with the county level
demand forecast to determine the longer term investment requirements.
Data gathering at central and regional level
Software selection Network modelling Assess existing Network issues
Short-medium term planning studies
Planning and design guidelines
Demand forecasting
County level meetings (in each of the 47 counties Hosted by KPLC BBHs)
ESIA scoping studies
Localised technical issues
3 week intensive training course in UK Including visits to industry
Investment plan
Off-grid assessment
SECTION 3
A key aspect of the Master Plan study was consultation at a county level. This consultation was used to inform
the Environmental Impact Assessment scoping studies and also provided valuable insights into technical issues
at a local level.
The Master Plan study draws together the key aspects of the assignment which are briefly described below.
3.2 Counterpart Team
An important factor leading to the successful completion of the Master Plan Study was the appointment by KPLC
of a dedicated team of knowledgeable counterparts. The counterpart team was pivotal during the data gathering
stage and provided invaluable local knowledge during development of the short-medium term plans.
The assignment included an intensive three-week training course in the UK. This covered most of the key
aspects of the study and included hands-on training in the use of the network planning software.
3.3 Data Gathering
Data gathering was conducted at central, regional and sub-regional level. County level meetings were also held
as described below.
Initial data collection was conducted at KPLC’s central offices in Nairobi. This included centrally stored network
data and diagrams, relevant reports, branch sales data and relevant information from Kenya National Bureau of
Statistics (KNBS).
The initial data gathering stage was followed in April with data gathering at the regional and sub-regional level.
This was conducted in three teams, each comprising members from the PB and KPLC counterpart study teams.
The teams covered the various KPLC regions as follows:
Nairobi Region
Western Region
The data obtained in the regions and sub-regions was used to try to complete the many gaps in the data obtained
initially from the central offices. Most of the remaining gaps were resolved through collaboration with the KPLC
counterpart team, thereby minimising the number of assumptions that needed to be made with respect to the
network models.
3.4 Software Selection
The scope of the study included the procurement of a suitable software package, both for conducting the network
analysis during the study and for handing over to KPLC at the end of the study. Two potential software packages
were assessed and following a presentation by PB, KPLC chose NEPLAN.
3.5 Network Modelling and Assessment of Existing Networks
The next stage was to develop detailed NEPLAN models of the 33 kV and 66 kV networks. These were initially
used to assess the performance of the existing networks in each of the four KPLC regions and to identify specific
network constraints.
SECTION 3
Representative 11 kV feeders for each of the four regions were also modelled and assessed using NEPLAN.
3.6 County Visits
Stakeholder meetings were held within each of the 47 counties, in order to gather data for the EIA scoping study
and to understand key technical issues at a county level.
County meetings were initially held in three ‘pilot’ counties. Following the pilot meetings, the agenda was refined
and used as the basis for the remaining meetings.
3.7 Demand Forecasting
The demand forecast contained within the Least Cost Power Development Plan (LCPDP) dated March 2011 was
used for the study. This was disaggregated to county level using a process that is described in detail in this
report. County demand growth rates were applied to the respective substations to determine substation
demands for the network studies. County growth rates were also used to determine investment requirements at a
county level.
3.8 Short-medium term planning
The existing network models were used as the basis for the short-medium term planning studies. Ongoing and
committed projects were added to the models and studies were conducted for each of the years up to 2017. The
outcome of these studies was a list of projects that would be required beyond the ongoing and committed
projects, in order to meet the forecast demand whilst satisfying the planning criteria.
The short-medium term investment requirements at 33 kV and 66 kV were determined directly from these lists of
projects by applying estimated project costs.
3.9 Long term planning
Generic or typical network configurations were developed to derive average incremental investment costs ($/kW)
for the various network topologies; urban, rural, Nairobi and off-grid. The county demand growth rates were then
used to calculate long term investment requirements
The investment required at 11 kV and LV was determined at a county level both in the short-medium term and
long term using the average incremental cost approach.
SECTION 4
DEMAND FORECAST
SECTION 4
DEMAND FORECAST
4 DEMAND FORECAST
4.1 Background
Vision 2030 is Kenya’s development blueprint covering the period 2008 to 2030. The objective of Vision 2030 is
to help transform Kenya into a, “middle-income country providing a high quality of life to all of its citizens by the
year 2030”. The Vision outlines the Government of Kenya’s economic growth objectives. These objectives were
used to develop the official Kenyan demand forecast using an econometric model which links electricity sales to
economic growth and the price of electricity. This demand forecast is presented in the Least Cost Power
Development Plan (LCPDP) publications.
The LCPDP (and associated demand forecast) is updated annually by a committee comprising officers from the
Ministry of Energy (MoE), Energy Regulatory Commission (ERC), Kenya Electricity Generating Company
(KenGen), Kenya Power and Lighting Company (KPLC), Kenya Electricity Transmission Company (KETRACO),
Geothermal Development Company (GDC), Rural Electrification Authority (REA), The Ministry of State for
Planning, National Development and 2030, Kenya Vision 2030 Board, Kenya Investment Authority (KenInvest),
Kenya Private Sector Alliance (KEPSA) and the Kenya National Bureau of Statistics (KNBS). The LCPDP is the
key power generation and transmission system planning document in Kenya. The key message within the
LCPDP is that, “there is a need to plan for sufficient electricity capacity additions to meet the growth aspirations
of the Vision 2030”. Vision 2030 identifies energy and electricity as a key element of Kenya’s sustained
economic growth and transformation.
households in Tana River County. Electrification levels are increasing across the country and following
In accordance with the terms of reference, the LCPDP demand forecast is to be applied in the Master Plan study.
The terms of reference for the Master Plan Study include the need to identify the distribution infrastructure
requirements to meet the needs of each county. A key element of this process is to develop a demand forecast
for each county as this will be the main driver for investment. In this section of the report we describe the
process by which the LCPDP forecast has been disaggregated to county level.
4.2 LCPDP Demand Forecast
The demand forecast that will be applied in the Distribution Master Plan Study is described in detail in the LCPDP
for the period 2011-2031, dated March 2011.
The LCPDP forecast is based on a number of inputs including; GDP growth rate projections, population growth
rates, levels of urbanisation, levels of electrification and specific consumption.
SECTION 4
DEMAND FORECAST
The results of the forecast are shown below. The energy (GWh) forecast refers to generated energy and
therefore includes transmission and distribution losses as described in the LCPDP and noted in Table 4-1. The
following levels of losses were taken from the LCPDP.
The peak (MW) demand forecast was calculated from the energy forecast using the load factors shown and
refers to generated power.
The forecast shown in Table 4-1 does not include the Vision 2030 Flagship Projects. Some of these are
particularly energy intensive and the estimated energy and capacity requirements along with the expected
completion dates for these projects are shown in Table 4-2.
Table 4-1: LCPDP Forecast – Excluding Flagship Projects7
Source: LCPDP, March 2011.
The resultant total forecast including the Flagship Projects and also allowing for an element of gradually
decreasing suppressed demand is shown in Table 4-3. According to the LCPDP report, the forecast includes
100 MW of suppressed demand in 2010, gradually reducing to zero by 2015. Again the figures shown are at the
generation level and therefore include transmission and distribution losses.
7 The LCPDP forecast is at generation level and therefore includes transmission and distribution losses.
SECTION 4
DEMAND FORECAST
Table 4-2: Energy Intensive Vision 2030 Flagship Projects
SECTION 4
DEMAND FORECAST
4.3 Deriving the LCPDP Demand Forecast
4.3.1 Introduction
In order to disaggregate the LCPDP demand forecast to county level, it was first necessary to understand how
the LCPDP forecast was derived. This was achieved by reconstructing the LCPDP forecast using the data and
assumptions provided in the LCPDP report. Once the LCPDP forecast had been derived on a national level, it
was possible to derive county level forecasts using the same methodology.
The objective in this sub-section is to re-create the LCPDP forecast. Using the methodology described in the
LCPDP report, a national level energy demand forecast was re-created for each consumer category including;
domestic urban, domestic rural, industrial & commercial and street lighting. The sum of the demand forecasts for
the various consumer categories derived in this way was then compared with the published LCPDP forecast to
confirm that a reasonable reconstruction had been achieved.
4.3.2 Methodology
The model presented in this section uses economic and technical assumptions set out in the LCPDP report and
aims to recreate the final Load Forecast set out in the LCPDP by applying appropriate logical linkages to specific
statistical inputs found in both 2009 Kenyan census statistics and Kenya Power Annual Report 2011 data. The
flow diagram below shows the steps followed by the model to calculate the energy and peak demand forecasts
for the Kenyan network.
Domestic Demand Assumptions
Many of the model’s assumptions are taken directly from the LCPDP. Population is assumed to grow from 38.6
million according to the 2009 Kenya Population and Housing Census Volume 1C (2009 Census) to 60.5 million
by 2030 according to the LCPDP.
Similarly, growth in urbanization levels of consumers is a key demographic input and driver of demand and sales.
This is assumed to rise from 32 % in 2009, to 63 % in 2031 as stated in the LCPDP.
The number of persons per household differs between urban and rural domestic settings. The number of persons
per urban household is expected to decrease from a present average of 5, to 4.3 by 2031. The number of
persons per rural household is expected to decrease from a present average of 6.5, to 5.9 by 2031.
The LCPDP differentiates between high, middle and low income groups in terms of future supply rates and
specific consumption. The LCPDP does not however specifically identify the proportions of the population falling
into each of these income groups in the urban and rural areas. These proportions were therefore estimated. The
weighted average specific consumption calculated from the estimated proportions was then compared with the
average specific consumption from the KPLC 2010/11 Annual Report. The proportions in the various income
groups were then adjusted until the calculated weighted average consumption approximated the actual average
specific consumption. The proportions derived in this way are as shown in Table 4-4.
SECTION 4
DEMAND FORECAST
Table 4-4: Estimated Proportions of Income Groups
Income Group Weighting
Urban High Income 10 %
Urban Middle Income 20 %
Urban Low Income 20 %
Rural High Income 5 %
Rural Middle Income 15 %
Rural Low Income 30 %
The calculated weighted average specific consumption for urban and rural areas was then as shown in Table 4-5.
Table 4-5: Estimated Specific Consumption
2009 2031
Urban (kWh/household/year) 1410 1338
Rural (kWh/household/year) 514 748
The electrification rates assumed for 2031 are given in the LCPDP and differ between the high, middle and low
income urban and rural consumers. Using the estimated proportions in each of the income groups and the 2031
supply rates in each of those groups as quoted in the LCPDP it was possible to derive a weighted average supply
rate for urban and rural households. This was assumed to reach 98 % for urban households and 58 % for rural
households by 2031.
Industrial and Commercial Demand Assumptions
The Industrial and commercial demand in the LCPDP forecast, is based on GDP growth. The level of economic
growth is a key driver for increased electricity consumption. The assumed coefficient factors are defined in the
LCPDP as consumption growth rate divided by the real GDP growth rate. The table below shows the assumed
coefficient factors that are used in the LCPDP model.
Year Electricity Intensity 2012 1.25 2013 1.32 2014 1.38
2015-2031 1.44
SECTION 4
DEMAND FORECAST
Annual GDP growth rates were adjusted to obtain the required total demand in line with the LCPDP. The
resultant average annual GDP growth required to match the LCPDP forecast was 9.7 %.
Wider Assumptions
The LCPDP states that the number of lamps used for street lighting is assumed to grow at 80% of the growth rate
of the number of domestic consumers. There is also an assumed technical improvement increasing the efficiency
of the lamps and resulting in specific consumption decreasing by 1 % per year.
Levels of energy losses were assumed to be the level set out in the LCPDP at 14 %.
Load Factors were also assumed to be as stated in the LCPDP. The load factor for the domestic sector and
street lighting was assumed to be falling from 55 % to 45 % between 2012 and 2031. The commercial and
industrial load factors were assumed to be constant at 76 %.
4.3.4 Demand Forecast
The resultant demand forecast derived as described above is shown in Table 4-6.
The right-hand column, which is sales plus transmission and distribution losses, shows a close match with Table
4-3 (LCPDP Reference Scenario GWh).
Table 4-6: Derived LCPDP Forecast (GWh)
4.3.5 Summary
The load forecast model presented in this section of the report can be summarised as follows:
Domestic Urban
Domestic Rural
Ind & Commercial
Flagship Projects
Generation (GWh)
2012 1434 103 5977 21 7535 282 9090 2013 1601 120 6805 23 8548 565 10597 2014 1786 138 7862 25 9812 847 12394 2015 1994 160 9662 28 11843 1130 15085 2016 2226 185 11088 30 13529 1412 17374 2017 2485 214 12685 33 15417 1695 19897 2018 2775 248 14511 36 17570 1977 22729 2019 3099 286 16287 39 19712 2259 25549 2020 3461 331 18515 43 22351 2542 28945 2021 3866 383 20915 47 25212 2824 32600 2022 4319 443 23626 51 28440 3107 36682 2023 4826 513 26517 56 31912 3389 41048 2024 5393 593 29954 60 36001 3672 46131 2025 6028 686 33836 66 40616 3954 51825 2026 6738 794 38124 71 45727 4236 58097 2027 7533 919 42955 77 51484 4519 65120 2028 8424 1063 48460 84 58030 4801 73060 2029 9421 1230 54671 91 65412 5084 81972 2030 10539 1423 61599 98 73658 5366 91888 2031 11623 1646 70025 105 83399 5648 103544
SECTION 4
DEMAND FORECAST
The load forecast uses the assumptions stated in LCPDP and has attempted to replicate the results
produced in the Vision 2030 flagship load forecast presented in the LCPDP March 2011.
Where possible, all input data used was provided by the LCPDP. Where the LCPDP data was not
available, relevant input data was extracted from the Kenyan Census 2009 or from the Kenya Power
Annual Reports 2011.
The GDP growth rate assumptions take into account the growth created by the flagship projects due for
completion 2012-2021.
4.4 County Statistics
Figure 4-1 shows the forty seven counties formed under the new constitution.
4.4.1 County Electrification Levels
Key statistics from the 2009 Population and Housing Census (Table 4-7) were used as the basis for
disaggregation of the LCPDP demand forecast to the county level. The 2009 Census data was obtained from the
Kenya National Bureau of Statistics (KNBS) and is the most up to date information available.
SECTION 4
DEMAND FORECAST
Figure 4-1: Kenya County Map
SECTION 4
DEMAND FORECAST
Table 4-7: Key Statistics from 2009 Census
Figure 4-2 and Figure 4-3 show the 2009 electrification levels (electrified households as a percentage of the total
households) per county for urban and rural areas respectively. It may be seen that there is a wide range in
household electrification in both urban and rural areas. The total urban household electrification level for Kenya
was approximately 50 % in 2009, whilst the corresponding rural figure was 5 %.
Region Sub-region County
Electrified (%) Urban
Electrified (%) Rural
Western Central Rift Baringo 445,828 63,168 508,996 14% 38.5% 4.7% Western West Kenya Bomet 694,881 108,268 803,149 15% 12.3% 2.9% Western West Kenya Bungoma 1,222,743 280,568 1,503,311 21% 14.4% 1.8% Western West Kenya Busia 592,092 112,992 705,084 18% 23.5% 2.1% Western North Rift Elgeyo Marakwet 309,965 46,420 356,385 15% 21.8% 4.6% Mt Kenya Mt Kenya North Embu 501,734 99,724 601,458 19% 47.4% 7.3% Off grid Off grid Garissa 343,133 102,332 445,465 26% 43.2% 0.5% Western West Kenya Homa Bay 817,725 129,084 946,809 16% 13.9% 1.4% Mt Kenya Mt Kenya North Isiolo 79,059 58,020 137,079 46% 37.0% 2.6% Nairobi Nairobi Kajiado 405,817 348,480 754,297 50% 66.8% 12.6% Western West Kenya Kakamega 1,127,102 225,576 1,352,678 19% 21.7% 2.2% Western West Kenya Kericho 361,012 203,080 564,092 40% 22.6% 4.6% Mt Kenya Mt Kenya North Kerinyaga 595,326 110,220 705,546 18% 46.2% 10.0% Nairobi Nairobi Kiambu 711,091 1,242,852 1,953,943 67% 67.2% 29.0% Coast Coast Kilifi 599,368 288,956 888,324 36% 39.4% 3.9% Western West Kenya Kisii 975,842 248,228 1,224,070 23% 23.3% 3.1% Western West Kenya Kisumu 479,856 498,488 978,344 55% 30.8% 3.1% Mt Kenya Mt Kenya South Kitui 806,633 135,468 942,101 16% 23.4% 1.1% Coast Coast Kwale 436,907 116,352 553,259 24% 33.5% 3.4% Mt Kenya Mt Kenya North Laikipia 347,090 117,060 464,150 28% 50.7% 4.6% Off grid Off grid Lamu 81,738 19,172 100,910 22% 62.8% 4.4% Nairobi Nairobi Machakos 536,853 601,104 1,137,957 57% 27.4% 3.4% Nairobi Nairobi Makueni 751,074 106,700 857,774 14% 24.7% 2.7% Off grid Off grid Mandera 480,255 93,260 573,515 19% 12.9% 0.2% Off grid Off grid Marsabit 212,177 47,188 259,365 21% 33.7% 0.6% Mt Kenya Mt Kenya North Meru 1,335,820 141,596 1,477,416 11% 60.1% 7.8% Western West Kenya Migori 535,072 265,464 800,536 37% 12.5% 1.1% Coast Coast Mombasa - 1,074,800 1,074,800 100% 59.0% 0.0% Mt Kenya Mt Kenya South Muranga 1,034,160 171,588 1,205,748 16% 34.7% 9.9% Nairobi Nairobi Nairobi - 3,940,064 3,940,064 100% 72.4% 0.0% Western Central Rift Nakuru 922,248 854,452 1,776,700 52% 56.0% 10.1% Western North Rift Nandi 612,542 94,980 707,522 15% 21.4% 3.6% Western Central Rift Narok 717,775 66,008 783,783 10% 43.6% 1.8% Western West Kenya Nyamira 427,573 61,648 489,221 14% 16.1% 4.4% Mt Kenya Mt Kenya North Nyandarua 537,863 117,760 655,623 20% 27.2% 6.2% Mt Kenya Mt Kenya North Nyeri 692,475 217,472 909,947 27% 55.3% 15.6% Off grid Off grid Samburu 180,137 36,108 216,245 19% 26.6% 1.4% Western West Kenya Siaya 830,358 89,448 919,806 11% 19.7% 2.4% Coast Coast Taita Taveta 247,934 73,352 321,286 26% 35.6% 7.8% Off grid Off grid Tana River 185,815 31,516 217,331 17% 10.1% 1.0% Mt Kenya Mt Kenya North Tharaka Nithi 309,697 91,640 401,337 26% 21.4% 3.8% Western North Rift Trans Nzoia 605,689 164,988 770,677 24% 27.5% 2.9% Off grid Off grid Turkana 470,357 92,460 562,817 19% 10.4% 0.6% Western North Rift Uasin Gishu 497,246 385,976 883,222 48% 51.3% 6.6% Western West Kenya Vihiga 395,378 156,896 552,274 32% 10.8% 5.3% Off grid Off grid Wajir 346,634 59,288 405,922 17% 20.2% 0.1% Western North Rift West Pokot 398,198 36,216 434,414 10% 21.1% 0.6%
Kenya 25,198,270 13,626,480 38,824,750 35% 50.4% 5.1%
SECTION 4
DEMAND FORECAST
Figure 4-2: Urban Household Electrification Levels
Figure 4-3: Rural Household Electrification Levels
The electrification levels are shown geographically in Figure 4-4 which indicates that the large counties in the
north and east of the country have the lowest electrification levels. There are however wide ranges in
electrification levels within individual counties. In the large off grid counties to the north and east of the country,
only the larger towns are served by isolated diesel power plants, generally feeding a small 11 kV network within
the town. Table 4-7 indicates that eight of the counties are currently off-grid, although in some of them the grid
extends to part of the county. This is covered in more detail in a later section of the report.
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SECTION 4
DEMAND FORECAST
Figure 4-4: Electrification Levels across the Country
4.4.2 Existing Demand
Disaggregation of the existing demand was based on 2011 sales data provided by KPLC from 84 branches. The
branch sales data provided was limited to Domestic and Small Commercial Sales which account for around 40 %
of the total sales. The remaining 60 % of sales are in the Industrial and Large Commercial categories. For
these, only a regional split was provided as follows:
SECTION 4
DEMAND FORECAST
In the absence of more information, the Industrial & Large Commercial sales were shared across the counties
within each of the KPLC regions in the same proportion as the Domestic and Small Commercial Sales. The
resultant regional split of sales and sales by county are shown in Table 4-8 and Table 4-9 respectively.
Table 4-8: Regional Split of 2011 Sales
KPLC Region Share of Industrial & Large Commercial sales NAIROBI 33% COAST 13% WESTERN 9% MT KENYA 4% TOTAL 60%
Region Total Coast 17% Mt Kenya 8% Nairobi 57% Off grid 1% Western 17% Grand Total 100%
SECTION 4
DEMAND FORECAST
Table 4-9: 2011 Sales by County (GWh)
Region Sub-region County Sum of 2011 Domestic
Sales (GWh) Sum of 2011 Industrial & Commercial (GWh)
Sum of 2011 Total sales (GWh)
Coast Coast Kilifi 25 140 166 Kwale 16 88 103 Mombasa 102 572 674 Taita Taveta 9 49 58
Coast Total 152 849 1001 Mt Kenya Mt Kenya North Embu 13 37 50
Isiolo 6 15 21 Kerinyaga 8 23 31 Laikipia 15 42 57 Meru 13 36 49 Nyandarua 10 27 37 Nyeri 27 74 102 Tharaka Nithi 6 16 22
Mt Kenya South Kitui 8 23 32 Muranga 10 28 38
Mt Kenya Total 117 321 438 Nairobi Nairobi Kajiado 33 99 132
Kiambu 171 516 688 Machakos 14 41 55 Makueni 2 5 7 Nairobi 610 1836 2446
Nairobi Total 830 2498 3328 Off grid Off grid Garissa 7 4 11
Lamu 4 2 6 Mandera 2 1 4 Marsabit 2 1 3 Samburu 2 4 6 Tana River 1 1 2 Turkana 2 1 4 Wajir 2 2 4
Off grid Total 22 17 39 Western Central Rift Baringo 2 6 8
Nakuru 79 211 290 Narok 5 13 18
North Rift Elgeyo Marakwet 1 2 3 Nandi 7 19 26 Trans Nzoia 12 32 44 Uasin Gishu 40 108 148 West Pokot 2 5 7
West Kenya Bomet 2 5 8 Bungoma 5 13 18 Busia 6 17 24 Homa Bay 5 12 16 Kakamega 16 44 60 Kericho 11 30 41 Kisii 16 43 59 Kisumu 40 107 147 Migori 8 22 30 Nyamira 2 6 9 Siaya 7 19 26 Vihiga 6 15 21
Western Total 274 729 1003 Grand Total 1394 4415 5809
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DEMAND FORECAST
4.5 County Demand Forecast
The LCPDP demand forecast methodology was applied to obtain a county level forecast as described below. A
separate domestic urban, domestic rural and industrial & commercial forecast was produced as follows.
4.5.1 Domestic Urban
The starting point for the domestic urban forecast was the county urban population from the 2009 Census. The
total urban population in 2009 was 13.6 million, representing 32 % of the total population. According to the
LCPDP, the total Kenya population was expected to rise to 60.5 million by 2031, with 63 % living in urban areas.
This information was used to forecast urban population for each county.
The next step was to calculate the number of urban households per county. The LCPDP quotes a present
average level of 5 persons/urban household, decreasing steadily to an average of 4.3 persons by 2031. This
was used to calculate the number of urban households per county up to 2031.
The 2009 Census includes urban electrification levels per county which is the connected urban households as a
percentage of the total urban households. There is a wide variation across the counties and the level for the
whole country is 50 %. The LCPDP forecasts urban electrification levels increasing to 95-100 % by 2031. For
the county level forecast, a weighted average 98 % electrification level was assumed for all counties by 2031.
The number of electrified households per county up to 2031 was thus derived.
The specific consumption figures from the LCPDP for urban consumers were then applied to obtain annual urban
domestic sales per county up to 2031.
4.5.2 Domestic Rural
The starting point for the domestic rural forecast was the county rural population from the 2009 Census. The
total rural population in 2009 was 25.2 million, representing 68 % of the total population. According to the
LCPDP, the total Kenya population was expected to rise to 60.5 million by 2031, with 37 % living in rural areas.
This information was used to forecast rural population for each county.
The next step was to calculate the number of rural households per county. The LCPDP quotes a present
average level of 6.5 persons/rural household, decreasing steadily to an average of 5.9 persons by 2031. This
was used to calculate the number of rural households per county up to 2031.
The 2009 Census includes rural electrification levels per county which is the connected rural households as a
percentage of the total rural households. There is a wide variation across the counties and the level for the whole
country is 5 %. The LCPDP forecasts rural electrification levels increasing to 50 -100 % by 2031. For the county
level forecast, a weighted average 58 % rural electrification level was assumed for all counties by 2031. The
number of electrified households per county up to 2031 was thus derived.
The specific consumption figures from the LCPDP for rural consumers were then applied to obtain annual rural
domestic sales per county up to 2031.
4.5.3 Industrial and Commercial
The sum of the demand forecasts for the counties must equal the total LCPDP forecast. On this basis, the
industrial and commercial forecast for the country was calculated by subtracting the total domestic urban and
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DEMAND FORECAST
rural forecasts from the total LCPDP forecast. The total industrial and commercial forecast was then
disaggregated to the counties in accordance with the present split of industrial and commercial demand across
the country.
4.5.4 Total Demand Forecast
The annual energy consumption (GWh) per county was calculated as the sum of the domestic urban, domestic
rural and industrial & commercial forecasts.
The peak demand forecast (MW) for each county was calculated from the energy consumption forecast by
applying separate load factors for domestic and industrial & commercial demand in accordance with the LCPDP.
4.5.5 Inclusion of Flagship Projects
The total system demand (at the generation level) by 2030 including the Flagship Projects is 91,946 GWh
(15,026 MW), compared with 54,761 GWh (9,458 MW) without the Flagship Projects, a difference of 37,185 GWh
(5,568 MW). The sum of the estimated energy and capacity requirements indicated in Table 4-10 of 6,394 GWh
(876 MW) accounts for only 17 % of the difference between the ‘with Flagship Projects’ and ‘without Flagship
Projects’ forecasts. The basis for the remaining difference in growth between the two forecasts is understood to
be due to increased economic activity across the country resulting from the Vision 2030 Flagship Projects.
Table 4-10: Energy intensive Flagship Projects8
The main energy intensive projects are expected to influence demand in certain counties as described below:
The planned ICT Park at Konza will have significant electricity requirements and will result in increased
demand in Kajiado and Machakos Counties.
Iron and steel melting is highly energy intensive and will therefore significantly impact demand in Meru
County
The planned resort cities at Isiolo, Kilifi and Ukunda will impact demand in Isiolo, Kilifi and Kwale
Counties.
8 Note that the energy and power values for Lamu Port including resort cities were increased from those shown in the LCPDP as the values appeared too low for the type of development.
Flagship Projects Ref Completion Energy (GWh) Capacity (MW) ICT Park 1 2015 2930 440 2nd Container Terminal and Mombasa Free Port 2 2014 746 2 Juba-Lamu Railway 3 2014 19 9 Lamu Port including resort cities 4 2014 200 30 Special Economic Zones (Mombasa, Kisumu, Lamu) 5 2015 333 50 Iron & steel smelting industry in Meru area 6 2015 2097 315 Mombasa-Nairobi-Malaba-Kisumu Railway 7 2017 27 18 Light rail for Nairobi and suburbs 8 2017 16 8 Resort cities (Isiolo, Kilifi and Ukunda) 9 2017 200 30 Total 6568 902
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DEMAND FORECAST
The new Lamu port will attract additional development in Lamu County and the proposed Lamu-South
Sudan-Ethiopia transport corridor will result in increased demand for electricity both due to the traction
power requirements of the rolling stock and more importantly, due to opening remote areas of the
country for development. The railway will pass through or near to Tana River, Garissa, Isiolo,
Samburu, Marsabit and Turkana Counties.
The planned Mombasa-Nairobi-Malaba-Kisumu railway will improve transport links in the already
relative well connected parts of the country.
Due to their level of demand, it was assumed that Konza ICT Park and Iron and Steel Smelting in Meru would
require direct connection to the transmission network. Demand associated with these two flagship projects was
therefore excluded from the distribution level demand forecast. The remaining flagship project demand was
added to the demand of the specific counties indicated above.
According to the KPLC 2010/11 Annual Report, approximately 5 % of the Industrial and Large Commercial sales
were to 132 kV customers. It is assumed that this percentage will steadily increase to 10 % by 2021 and then
remain at that level until the end of the planning period. This is to take account of increasing levels of
industrialisation over time.
Figure 4-5 shows the relative scale of peak demand across the country and also illustrates the forecast level of
growth over the planning period.
The 2011 figures were derived from the sales data, whilst the 2012-2030 figures were calculated based on the
LCPDP methodology described above.
The demand forecast for each county at MV distribution level (allowing for an estimated 7 percent losses in the
LV and 11 kV distribution systems) is shown in Table 4-11.
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DEMAND FORECAST
Figure 4-5: County Peak Demand and Growth (MW)
4.6 Disaggregation to Substation Level
Annual growth rates derived from the above county level forecast were applied to the peak substation demands
for the purpose of conducting the network modelling over the short-medium term.
The demand forecast was also used to derive the long-term investment requirements for each of the counties as
described later in this report.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Nairobi Off grid Central Rift North Rift West Kenya
Coast Mt Kenya Nairobi Off grid Western
Sum of 2011 (From sales) Sum of 2030
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DEMAND FORECAST
Table 4-11: County Peak Demand Forecast (MW)9
9 At MV distribution level, i.e. including LV losses, but excluding demand connected at 132 kV.
Values
Sum of 2012
Sum of 2013
Sum of 2014
Sum of 2015
Sum of 2017
Sum of 2020
Sum of 2025
Sum of 2030
Coast Coast Kilifi 26.3 34.4 39.2 45.3 54.9 80.3 112.4 198.5 356.3 Kwale 16.5 18.9 21.6 25.1 30.9 49.5 68.2 119.0 213.0 Mombasa 107.3 149.0 168.5 289.5 342.1 407.9 533.0 865.4 1466.5 Taita Taveta 9.2 11.2 12.8 14.8 18.1 24.0 34.8 63.8 116.9
Coast Total 159.3 213.4 242.1 374.7 445.9 561.6 748.5 1246.7 2152.6 Mt Kenya Mt Kenya North Embu 8.4 11.2 12.6 14.4 17.2 22.2 31.7 56.9 102.3
Isiolo 3.4 4.5 5.1 6.2 7.4 9.4 21.8 32.4 51.9 Kerinyaga 5.1 9.5 10.7 12.1 14.1 17.9 25.2 44.3 77.9 Laikipia 9.4 12.4 14.0 16.0 19.1 24.6 35.0 62.7 112.7 Meru 8.1 15.5 17.4 19.6 22.8 28.8 40.3 70.2 123.1 Nyandarua 6.2 8.4 9.6 11.1 13.3 17.7 25.7 47.4 87.6 Nyeri 16.9 24.9 28.0 31.7 37.4 47.5 66.8 116.8 205.4 Tharak