Final Report
Development of a Power Generation and Transmission Master Plan, Kenya Long Term Plan – Renewable Energy 2015 - 2035
October 2016
Ministry of Energy and Petroleum
© Lahmeyer International GmbH, 2016 The information contained in this document is solely for the use of the Client identified on the cover sheet for the purpose for which it has been prepared. Lahmeyer International GmbH undertakes no duty to or accepts any responsibility to any third party who may rely upon this document. All rights reserved. No section or element of this document may be removed from this document, reproduced, electronically stored or transmitted in any form without written permission of Lahmeyer International GmbH.
The photo on the title page shows a collection of photos from power generation and network assets in Kenya and figures from the planning process
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page i
Development of a Power Generation and Transmission Master Plan, Kenya Long Term Plan – Renewable Energy 2015 – 2035 October 2016
Prepared for: Ministry of Energy and Petroleum Nyayo House, Kenyatta Avenue, P.O. Box 30582, Nairobi, Kenya Prepared by: Lahmeyer International GmbH Friedberger Str. 173 61118 Bad Vilbel, Germany
Inspection status: Approved
Revision History: Revision
Date
Author
Department
Checked by
Approved by
Description
v20160226 26.02.2016
PGTMP project team
IED, EFLA, LI GE7, GE5, GE6, GW
Dr. Tim Hoffmann
Daniel d’Hoop
Draft PGTMP LTP RE
v20160528 28.05.2016
PGTMP project team
IED, EFLA, LI GE7, GE5, GE6, GW
Karsten Schmitt
Dr. Tim Hoffmann
Final PGTMP LTP RE
v20161030 30.10.2016
PGTMP project team
IED, EFLA, LI GE7, GE5, GE6, GW
Karsten Schmitt
Dr. Tim Hoffmann
Final PGTMP LTP RE Update
v20161128 28.11.2016
PGTMP project team
IED, EFLA, LI GE7, GE5, GE6, GW
Karsten Schmitt
Dr. Tim Hoffmann
Final PGTMP LTP RE Update
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page ii
Table of Contents 1
EXECUTIVE SUMMARY ................................................................................................................ 1
2
INTRODUCTION ........................................................................................................................... 4
2.1
Objectives of report ................................................................................................................. 4
2.2
Structure of report ................................................................................................................... 5
2.3
Methodology and assumptions ................................................................................................ 6
3 3.1
RENEWABLE ENERGY RESOURCES IN KENYA .............................................................................. 7 Hydropower energy ................................................................................................................. 7
3.1.1
Available data and current situation ................................................................................... 7
3.1.2
Medium and long term potential ...................................................................................... 43
3.1.3
Recommendation for expansion plan ............................................................................... 49
3.2
Solar energy – photovoltaic (PV) ............................................................................................ 50
3.2.1
Available data and current situation in Kenya .................................................................. 51
3.2.2
Medium and long term potential ...................................................................................... 52
3.2.3
Recommendation for expansion plan ............................................................................... 53
3.3
Solar energy – concentrated solar power (CSP) ..................................................................... 54
3.3.1
Available data and current situation in Kenya .................................................................. 55
3.3.2
Medium and long term potential ...................................................................................... 56
3.3.3
Recommendation for expansion plan ............................................................................... 56
3.4
Wind energy ........................................................................................................................... 57
3.4.1
Available data and current situation in Kenya .................................................................. 57
3.4.2
Medium and long term potential ...................................................................................... 58
3.4.3
Recommendation for expansion plan ............................................................................... 65
3.5
Biomass, biogas and waste-to-energy.................................................................................... 66
3.5.1
Available data and current situation in Kenya .................................................................. 67
3.5.2
Medium and long term potential ...................................................................................... 68
3.5.3
Recommendation for expansion plan ............................................................................... 70
3.6
Geothermal energy ................................................................................................................ 71
3.6.1
Available data and current situation in Kenya .................................................................. 75
3.6.2
Medium and long term potential ...................................................................................... 75
3.6.3
Recommendation for expansion plan ............................................................................... 77
4
ANALYSIS OF RENEWABLE ENERGY EXPANSION ...................................................................... 78
4.1
Methodology .......................................................................................................................... 78
4.2
Definition of Renewable Energy scenarios............................................................................. 79
4.3
Results .................................................................................................................................... 83
4.3.1
Capacity and fuel mix ........................................................................................................ 83
4.3.2
Renewable energy scenarios - comparison ...................................................................... 87
4.4
Conclusions............................................................................................................................. 95
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page iii
5
DISCUSSION OF RENEWABLE ENERGY INCENTIVE POLICIES..................................................... 97
5.1
Direct versus complementary measures:............................................................................... 97
5.2
Direct public or governmental action vs. indirect regulatory action: .................................... 97
5.2.1
Stages of the value chain: ................................................................................................. 98
5.2.2
Affected market parties and market variables: ................................................................ 98
5.3
Description and discussion of relevant incentive schemes.................................................. 100
5.3.1
Direct Subsidies I – Investment Subsidies ....................................................................... 100
5.3.2
Direct Subsidies II – Feed-In tariff systems ..................................................................... 101
5.3.3
Competitive bidding / tendering ..................................................................................... 103
5.3.4
Quota obligations and tradable certificates ................................................................... 105
5.4
The feed-in tariff for renewable energy in Kenya ................................................................ 106
5.5
Renewable energy international good practice benchmarking ........................................... 110
5.5.1
Regulatory and policy options available when the main grid reaches the mini-grid...... 110
5.5.2
Bagasse-based cogeneration from sugar industries in Mauritius................................... 111
5.5.3
Sri-Lanka – Net-metering policy ...................................................................................... 112
List of Annexes ANNEX 1
EXECUTIVE SUMMARY – ANNEXES ........................................................................ 1
ANNEX 2
INTRODUCTION – ANNEXES ................................................................................... 2
ANNEX 3
RENEWABLE ENERGY RESOURCES IN KENYA – ANNEXES ...................................... 3
ANNEX 4
ANALYSIS OF RENEWABLE ENERGY EXPANSION – ANNEXES ................................. 4
ANNEX 5
DISCUSSION OF RENEWABLE ENERGY INCENTIVE POLICIES – ANNEXES ............. 19
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page iv
List of Figures Figure 3-1:
Average monthly rainfall at selected stations in Kenya .............................................. 8
Figure 3-2:
Areas and major rivers of the six catchment areas and location of existing large hydropower plants .................................................................................................... 10
Figure 3-3:
Annual generated electricity by hydropower plant from 1999 to 2014 ................... 11
Figure 3-4:
Monthly electricity generation of the four hydropower production groups ............ 12
Figure 3-5:
Monthly available capacity existing large hydropower plants, average hydrology .. 14
Figure 3-6:
Monthly available capacity existing large hydropower plants, low hydrology ......... 14
Figure 3-7:
Monthly electricity generation of existing large hydropower plants, average hydrology ................................................................................................................... 17
Figure 3-8:
Monthly electricity generation of existing large hydropower plants, low hydrology17
Figure 3-9:
Masinga HPP annual electricity generation .............................................................. 22
Figure 3-10:
Masinga HPP selected annual generation curves on monthly basis ......................... 22
Figure 3-11:
Kamburu HPP annual electricity generation ............................................................. 25
Figure 3-12:
Kamburu HPP selected annual generation curves on monthly basis ........................ 25
Figure 3-13:
Gitaru HPP annual electricity generation .................................................................. 27
Figure 3-14:
Gitaru HPP selected annual generation curves on monthly basis ............................ 28
Figure 3-15:
Kindaruma HPP annual electricity generation .......................................................... 30
Figure 3-16:
Kindaruma HPP selected annual generation curves on monthly basis ..................... 30
Figure 3-17:
Kiambere HPP annual electricity generation............................................................. 33
Figure 3-18:
Kiambere HPP selected annual generation curves on monthly basis ....................... 33
Figure 3-19:
Tana HPP annual electricity generation .................................................................... 34
Figure 3-20:
Tana HPP selected annual generation curves on monthly basis ............................... 35
Figure 3-21:
Turkwel HPP annual electricity generation ............................................................... 38
Figure 3-22:
Turkwel HPP selected annual generation curves on monthly basis .......................... 38
Figure 3-23:
Sondu Miriu HPP annual electricity generation ........................................................ 40
Figure 3-24:
Sondu Miriu HPP selected annual generation curves on monthly basis ................... 40
Figure 3-25:
Sang’Oro HPP selected annual generation curves on monthly basis ........................ 42
Figure 3-26:
GHI map for Kenya .................................................................................................... 51
Figure 3-27:
Average daily PV production patterns per month .................................................... 53
Figure 3-28:
DNI map for Kenya .................................................................................................... 55
Figure 3-29:
Mean wind speed map of Kenya ............................................................................... 59
Figure 3-30:
Potential wind capacity development in Kenya in the long term ............................. 61
Figure 3-31:
Kinangop wind farm – average daily production patterns per month...................... 62
Figure 3-32:
Kipeto wind farm – average daily production patterns per month .......................... 63
Figure 3-33:
Lake Turkana wind farm – average daily production patterns per month ............... 63
Figure 3-34:
Meru wind farm – average daily production patterns per month ............................ 64
Figure 3-35:
Generic wind farm – average daily production patterns per month ........................ 65
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page v
Figure 3-36:
Simple schematic drawing of single flash power plant ............................................. 72
Figure 3-37:
Simple schematic drawing of binary geothermal power plant ................................. 73
Figure 3-38:
Binary bottoming unit in single flash power plant .................................................... 74
Figure 4-1:
Additional wind and solar PV development .............................................................. 82
Figure 4-2:
Power generation – accelerated RE vs. moderate RE scenario 2020–2035.............. 87
Figure 4-3:
Power generation – slowed down RE vs. moderate RE scenario 2020–2035 ........... 88
Figure 4-4:
Change of RE generation share – difference to moderate RE scenario .................... 88
Figure 4-5:
Excess energy – difference to moderate RE scenario ............................................... 89
Figure 4-6:
Incremental cost and LRMC of RE expansion ............................................................ 95
Figure 5-1:
Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Capacity and Production ........................................................................................... 99
Figure 5-2:
Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Price and Quantity ..................................................................................................... 99
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page vi
List of Tables Table 3-1:
Average monthly rainfall at selected stations in Kenya .............................................. 8
Table 3-2:
Average annual evaporation - reservoirs of large hydropower plants ....................... 9
Table 3-3:
Areas, major rivers, hydropower potential of the six catchment areas...................... 9
Table 3-4:
Existing large hydropower plants in Kenya ............................................................... 13
Table 3-5:
Monthly available capacity (MW) of existing large hydropower plants, average hydrology ................................................................................................................... 15
Table 3-6:
Monthly available capacity (MW) of existing large hydropower plants, low hydrology ................................................................................................................... 16
Table 3-7:
Monthly electricity generation of existing large hydropower plants, average hydrology ................................................................................................................... 18
Table 3-8:
Monthly electricity generation of existing large hydropower plants, low hydrology19
Table 3-9:
Masinga reservoir characteristics.............................................................................. 21
Table 3-10:
Masinga HPP statistical characteristics ..................................................................... 23
Table 3-11:
Kamburu reservoir characteristics ............................................................................ 24
Table 3-12:
Kamburu HPP statistical characteristics .................................................................... 26
Table 3-13:
Gitaru reservoir characteristics ................................................................................. 27
Table 3-14:
Gitaru HPP statistical characteristics ......................................................................... 28
Table 3-15:
Kindaruma HPP statistical characteristics ................................................................. 31
Table 3-16
Kiambere reservoir characteristics ............................................................................ 32
Table 3-17:
Kiambere HPP statistical characteristics ................................................................... 34
Table 3-18:
Tana HPP statistical characteristics ........................................................................... 35
Table 3-19:
Turkwel reservoir characteristics .............................................................................. 36
Table 3-20:
Turkwel HPP statistical characteristics ...................................................................... 39
Table 3-21:
Sondo Miriu HPP statistical characteristics ............................................................... 41
Table 3-22:
Existing small hydropower plants in Kenya ............................................................... 42
Table 3-23:
Large hydropower projects (long-list) ....................................................................... 44
Table 3-24:
Details of identified large hydropower candidates (short-list) ................................. 45
Table 3-25:
Small hydropower projects (committed and planned) ............................................. 47
Table 3-26:
Cumulated expansion small hydropower (incl. existing plants) – 2035 .................... 48
Table 3-27:
Strengths and weaknesses of PV energy systems ..................................................... 50
Table 3-28:
Main Solar PV projects submitted to FiT scheme ..................................................... 52
Table 3-29:
Strengths and weaknesses of CSP energy systems ................................................... 54
Table 3-30:
Energy yield of sample wind turbines ....................................................................... 58
Table 3-31:
Wind farm projects (committed and planned) ......................................................... 60
Table 3-32:
Present sugar mills in Kenya and their technical potential ....................................... 69
Table 3-33:
Cumulated expansion cogeneration (incl. existing plants) – 2035............................ 70
Table 3-34:
Strengths and weaknesses of geothermal energy .................................................... 71
Table 3-35:
Present Geothermal power plants ............................................................................ 75
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page vii
Table 3-36:
Geothermal power plants for medium-term period ................................................. 76
Table 3-37:
Geothermal potential by field ................................................................................... 77
Table 4-1:
RE development in the moderate, accelerated and slowed down RE scenarios (2015-2035) ............................................................................................................... 81
Table 4-2:
Comparison of results: moderate, accelerated and slowed down RE expansion scenarios .................................................................................................................... 85
Table 4-3:
Changes in CODs due to different RE developments ................................................ 90
Table 4-4:
RE shares in generation (average 2015-2035) .......................................................... 91
Table 4-5:
Cost implications of RE scenarios .............................................................................. 93
Table 5-1:
Current Feed-in-Tariff Structure.............................................................................. 107
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page viii
Abbreviations and Acronyms 10 year plan
EPC
Engineering Procurement Construction
A
Ampere
ERB
AC
Alternating Current
Electricity Regulatory Board (predecessor ERC)
ACSR
Aluminium Clad Steel/Reinforced
ERC
Energy Regulation Commission
ADF
African Development Fund
ESIA
AFD
Agence Française de Développement
European Semiconductor Industry Association
AGO
Automotive Gas Oil
ESRP
Energy Sector Recovery Project
AIS
Air Insulated Switchgear
EUE
Estimated Unserved Energy
AVR
Automatic Voltage Regulation
EUR
Euro
BB
Busbar
FCC
Fuel Cost Charge
BOO
Build Own Operate
FERFA
BOOT
Build Own Operate Transfer
Foreign Exchange Rate Fluctuation Adjustment
CAPEX
Capital Expenditure
FGD
Flue gas desulphurisation
CBS
Central Bureau of Statistics (predecessor KNBS)
FiT
Feed in Tariff
Fob
Free on board
CCGT
Combined Cycle Gas Turbine
GAMS
General Algebraic Modelling System
CEEC
Committee for European Economic Cooperation
GDC
Geothermal Development Company
GDP
Gross Domestic Product General Electric
10YP
CHP
Combined Heat and Power
GE
Cif
Cost Insurance Freight
GEF
Global Environment Facility
COD
Commercial Operation Date
GEO
Geothermal (energy)
Cogen
Co-Generation
GHG
Greenhouse Gas
Common Market for Eastern and Southern Africa
GHI
Global Horizontal Irradiation
GIS
Geographic Information System
CPI
Corruption Perception Index
GIS
Gas Insulated Switchgear
CPP
Coal Power Plant
CSP
Concentrating Solar Power
DANIDA
Danish International Development Agency
GIZ / GTZ German Development Cooperation (Deutsche Gesellschaft für International Zusammmenarbeit)
DC
Direct Current
DCR
Discount Rate
DIN
German Institute for Standardization
DNI
Direct Normal Irradiation
DUC
Dynamic Unit Cost
EAC
East African Community
EAPMP
East African Power Master Plan Study
EAPP
East African Power Pool
EE
Energy Efficiency
EECA
Energy Efficiency and Conversation Agency
EFLA
Company: Consulting Engineers
EGIS
Company: Engineering and Consulting
EIA
Environmental Impact Assessment
EIB
European Investment Bank
ENDSA
Ewasa Ng’iiro South River Basin Development Authority
COMESA
ENS
Energy Not Served
GJ
Gigajoule
GoK
Government of Kenya
GOV
Governor
GPOBA
Global Partnership Output Based Aid
GT
Gas Turbine
GW
Gigawatt
GWh
Giga Watt-hour
HDI
Human Development Index
HFO
Heavy Fuel Oil
HGFL
High Grand Falls
HPP
Hydro Power Plant
HSD
High Speed Diesel Engine
HV
High Voltage
HVDC
High Voltage Direct Current
Hz
Hertz
I&C
Instrument and Control System
IAEA
International Atomic Energy Agency
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page ix
Internal Combustion Engine (here: MSD, HSD)
LFO
Light Fuel Oil
LI
Lahmeyer International GmbH
ICT
Information, Communication & Technology
IDO
Industrial Diesel Oil
LIPS-OP/XP Lahmeyer International Power System Operation Planning / Expansion Planning
IEA
International Energy Agency
LNG
Liquefied Natural Gas
IED
Innovation Energie Développement
LOLE
Loss of Load Expectation
IMF
International Monetary Fund
LOLP
Loss of Load Probability
IPE
Indicator Power Efficiency
LPG
Liquefied Petroleum Gas
IPP
Independent Power Producer
LTP
Long Term Plan
IPS
Industrial Promotion Services
LTWP
Lake Turkana Wind Park
IR
Inception Report
LV
Low Voltage
ISO
International Organisation for Standardization
m
metre
M&E
Mechanical & Electrical
ITCZ
Intertropical Convergence Zone
MAED
JICA
Japan International Cooperation Agency
Model for Analysis of Energy Demand (MAED-D for kWh, MAED-L for Kw)
JKIA
Jomo Kenyatta International Airport
MEWNR
KAM
Kenya Association of Manufacturers
Ministry of Environment, Water and Natural Resources
KenGen
Kenya Electricity Generating Company
MIP
Mixed Integer Linear Optimization Problem
KENINVEST Kenya Investment Authority
MJ
Megajoule
KeNRA
Kenya National Resources Alliance
MOE
KEPSA
Kenya Private Sector Alliance
Ministry of Energy (changed in 2013 to Ministry of Energy and Petroleum)
KES
Kenyan Shilling
MOEP
Ministry of Energy and Petroleum
MOIED
Ministry of Industrialization and Enterprise Development
ICE
KETRACO Kenya Transmission Company KfW
KfW Development Bank German development bank; was: Kreditanstalt für Wiederaufbau)
MORDA
Ministry of Regional Development Authorities
KISCOL
Kwale International Sugar Company Ltd
MSD
Medium Speed Diesel Engine
km
kilometre
MSW
Municipal Solid Wastes
km3
cubic kilometre
MTP
Medium Term Plan
KNBS
Kenya National Bureau of Statistics
MUSD
Million USD
KNEB
Kenya Nuclear Electricity Board
MV
Medium Voltage
KOSF
Kipevu Oil Storage Facility
MVA
Megavolt Ampere
KPC
Kenya Pipeline Company Limited
Mvar
Megavolt Ampere Reactive
KPLC
Kenya Power and Lighting Company
MW
Mega Watt
KPRL
Kenya Petroleum Refineries Limited
MWh
Megawatt Hours
KRC
Kenya Railways Corporation
NBI
Nile Basin Initiative
KTDA
Kenya Tea Development Agency
NCC
National Control Center
kV
kilo Volt
NCV
Net calorific value
Kvar
Kilo volt ampere reactive
NELSAP
KVDA
Kerio Valley Development Authority
Nile Equatorial Lakes Subsidiary Action Program
KW
Kilowatt
NEMA
kWh
kilowatt-hour
National Environment Management Authority
LAPSSET
Lamu Port, Southern Sudan and Ethiopia Transport
NG
Natural Gas
NGO
Non-Governmental Organization
LCPDP
Least Cost Power Development Plan
NIB
National Irrigation Board
LDC
Load Dispatch Center
NPP
Nuclear Power Plant
LEC
Levelised electricity cost
NPV
Net Present Value
LF
Load Flow
NSSF
National Social Security Fund
NTC
Net Transfer Capacity
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
(10^6 Watts)
28.11.2016
Page x
NTP
Notice-to-Proceed
SBQC
NWCPC
National |Water and Conservation and Pipeline Corporation
Selection Based on Consideration of Quality and Cost
SC
Short Circuit
National Water Resources Management Strategy
SCADA
Supervisory Control and Data Acquisition
SHPP
Small Hydro Power Plants
O&M
Operation & Maintenance
SHS
Solar Home Systems
ODA
Official Development Assistance
SKM
Sinclair Knight Merz
OECD
Organisation for Economic Co-operation and Development
SLA
Service Level Agreement
SLD
Single Line Diagram
OHL
Overhead Line
SME
Small and Medium Sized Enterprises
OPEX
Operational Expenditure
SMP
System Marginal Price
OPIC
Overseas Private Investment Corporation
SPP
Steam Power Plant
P
Active Power
SPV
Special Purpose Vehicle
PB
Parsons and Brinckerhoff
ST
Steam Turbine
PESTEL
Political, Economic, Social, Technical, Environmental and Legal criteria
SWERA
Solar and Wind Energy Resource Assessment
PF
Power Factor
T/L
Transmission Line
PGTMP
Power Generation and Transmission Master Plan
TA
Technical Assistance
PPA
Power Purchase Agreement
TARDA
Tana & Athi River Development Authority
PSS/E
Power System Simulator for Engineering
TJ
Terra-joule
PV
Photovoltaic
TNA
Training Need Assessment
Q
Reactive Power
TOR
Terms of Reference
Qc
Reactive Power Capacitive
TPP
Thermal Power Plant
QEWC
Qatar Water & Electricity Company
TR
Transformer
Ql
Reactive Power Inductive
TRF
Training Results Form
QM
Quality Management
UNDP
United Nations Development Programme
RAP
Resettlement Action Plan
UNEP
United Nations Environment Programme
RE
Renewable Energy
US
United States of America
REA
Rural Electrification Authority
USD
United States Dollar
REP
Rural Electrification Programme
VBA
Visual Basic for Applications
RES
Renewable Energy Sources
WACC
Weighted average cost of capital
RfP
Request for Proposal
WASP
Wien Automatic System Planning
RMS
Root-Mean-Square Value
WB
World Bank
RMU
Ring Main Unit(s)
WEO
World Energy Outlook
S/S
Substation
WTG
Wing turbine generators
NWRMS
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page xi
1
EXECUTIVE SUMMARY
In 2013, the Ministry of Energy and Petroleum (MOEP) contracted Lahmeyer International (LI) to provide consultancy services for the development of the Power Generation and Transmission Master Plan (PGTMP) for the Republic of Kenya. This report provides the Renewable Energy (RE) component of the respective Long Term Plan (LTP) for the period 2015 (base year) to 2035. It encompasses the following:
This executive summary focuses on the main results.
The introduction provides the objectives, structure and methodology of the report.
An evaluation of renewable energy resources in Kenya depicts the potentials for hydro power, wind and solar power, biomass and geothermal energy and derives recommendations for long-term system expansion planning in Kenya.
A model-based analysis uses the derived potentials in order to determine feasible expansion pathways for renewables to be used for long-term system expansion planning in the framework of the PGTMP for Kenya.
An overview on different renewable energy incentive schemes is provided and discussed.
Main results of the resource assessment are: Hydro power Large hydropower plants with dams provide valuable peaking capacity at low operating cost. Since there is already a large pipeline of traditional base load plants, hydropower plants with storage facilities may play a major role in load following measurements in the future Kenyan power system. Large hydropower plants with dams are also able to contribute to primary reserve regulations. Today, only Kiambere and Gitaru are able to provide primary reserve. It is recommended to analyse the opportunity to equip the existing hydropower plants Masinga, Kamburu, Kindaruma and Turkwel with the respective IT infrastructure in order to ensure sufficient primary reserve capacity in the future generation system. As experienced in the past, it is essential to consider sufficient backup capacity which does not rely on the present hydrology and is able to compensate the lacking hydropower capacity during drought periods. In the generation expansion planning process it is thus recommended to define the so-called firm capacity of hydropower plants (e.g. considering P90 conditions) which is considered in the peak demand – supply balancing. Sensitivity analysis by simulating the detected generation expansion plan considering low hydrology conditions does also give further hints in relation to possible shortcomings resulting from droughts. Small hydropower schemes provide great benefits in remote areas and ensure electricity supply of villages, small businesses and farms. From the system point of view, small hydropower plants are considered as baseload capacity without participation in load following measurements.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 1
Solar energy The total solar energy potential in Kenya is several thousand times the expected Kenyan electricity demand. For long-term expansion planning potential solar PV development is analysed by a scenario analysis. Expansion pathways of generic PV projects are assessed regarding their technical and economic implications. Resulting solar PV capacities in 2035 may reach 100 MW to 500 MW. Due to currently rather unclear development prospects of CSP projects and the considerable amount of more (cost-) competitive renewable alternatives (especially geothermal and wind) in Kenya, CSP are not be addressed in the long-term expansion planning. However, it is strongly recommended to closely monitor the global development of the technology in future years. Wind energy A considerable potential for wind power development exists in Kenya. Several wind power projects are already committed or planned for the coming years. Taking into account the earliest commissioning of these projects, the wind power capacity could reach almost 2,500 MW by 2035 – more than the currently installed total capacity in the Kenyan system and more than 50% of the total technical wind potential. Such utilisation might have significant impacts on the operation of the power system in future years. Depending on the generation characteristics of wind plants, additional reserve capacity might be required to safeguard the adequate operation of the power system. This might lead to substantial excess cost. Wind development is thus considered as a scenario parameter. Results of the following expansion planning help to determine adequate development corridors and highlight potential excess cost due to the promotion of wind power. Biomass The future of successfully implemented biomass projects in Kenya will strongly depend on the development of the agricultural sector. For the long-term generic linear expansion of biomass (mainly bagasse based) capacity is assumed to start in 2020 (since it will need some lead time to be developed). This will add to the existing plants (Mumias, Kwale, Cummins (under construction) and Biojoule. For various operational issues there total capacity is assumed to be reduced in 2016 to 2018, but fully available from 2019 onwards. Power generation from municipal solid waste are not expected to play a significant role in the future. Their profitable operation depends on benefits beyond the power sector such waste collection and hygiene. Consequently, this option is not considered in the long-term planning as a candidate. Geothermal energy Already today, geothermal power contributes significantly to the Kenyan generation mix. Considering the tremendous potential of 8,000 to 12,000 MW along the Kenyan Rift Valley, it can be expected that geothermal power will play an essential role in the future Kenyan power system. Deep knowledge and expertise in geothermal exploration, drilling, power plant implementation and op-
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 2
eration is already present in the country today. However, drilling risks, high upfront costs and a rather long implementation period have to be taken into account in the planning. Geothermal is considered as “conventional" renewable energy source which is already well developed in Kenya and can compete with other sources. In the expansion planning this is done through the fully identified candidates (see Chapter 6 in the LTP), which are drawn by the system according to their costs and plant characteristics (including earliest year of system integration). Geothermal power provides reliable base load power at low operating cost. Single flash technology which is mainly being utilised in Kenya today, is restricted in providing flexible power due to technical reasons. Binary systems, however, are able to be operated very flexible. With regard to future geothermal expansion and considering the power system needs (load following, regulation control), it is thus recommended to analyse the opportunity for installing binary power plants. The possibility of implementing binary bottoming unit in a single flash plant should also be evaluated. Results of the renewable energy expansion planning The analysis of different RE expansion pathways revealed several important implications regarding the development of the Kenyan power generation system and the associated cost. First and foremost, a more ambitious development of wind and solar potentials in Kenya does not necessarily lead to an increased share of renewables, neither in generation nor in consumption. This is mainly caused by two reasons: (i) Additional wind and solar capacities postpone the commissioning of geothermal projects. So, wind and solar generation directly crowds out another renewable energy source, and (ii) volatile wind and solar generation increases the reserve requirements in the system. Results also revealed the potential to include wind and solar power: The generation system may well be operated when larger wind and solar capacities exist. Against this background, wind and solar generation might be interpreted as a long-term alternative to the geothermal resource in Kenya. Despite the additional cost of an over-ambitious development, these resources may contribute to the future generation in Kenya:
They can slow down the depletion of the geothermal resources in Kenya and are thus able to save parts of the resource for future use – beyond the current planning horizon. However, the actual depletion of geothermal fields and the future value of (saved) geothermal sources are difficult to estimate. Therefore, this reason may not be sufficient to justify solar and wind development alone.
They enable a diversification of the Kenyan fuel mix and thereby reduce the dependency on the geothermal resource and on other, mostly conventional fossil fuels. As wind and solar potentials are available in different regions of the country, this can also contribute to a more decentralised structure of power supply.
To introduce new opportunities for the Kenyan manufacturing and service sectors – thereby enabling creation of added value and job opportunities on a regional level.
However, the results underpin the important role of the geothermal resource as an available, costeffective and emission-free energy source for Kenya.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 3
2
INTRODUCTION
In 2013, the Ministry of Energy and Petroleum (MOEP, further also referred to as “the client”) contracted Lahmeyer International (LI, further also referred to as “the consultant”)1 to provide consultancy services for the development of the Power Generation and Transmission Master Plan (PGTMP) for the Republic of Kenya. This report provides the Renewable Energy (RE) component of the Long Term Plan2 (LTP) for the period 2015 (base year) to 2035. This chapter includes the following sections:
The objectives of the report (section2.1)
The structure of the report (section 2.2)
Introduction to the methodology and assumptions (section 2.3)
Note: The results provided in this report are not statements of what will happen but of what might happen, given the described assumptions, input data and methodologies. In particular, given the high uncertainty of for instance the development of economic, political, and technical framework the reader should carefully study the described assumptions before using any of the results. Therefore, this critical review and regular update of the assumptions applied in this report is essential for any planning process based thereupon.
2.1
Objectives of report
The overall objective of the report is: The identification and analysis of renewable energy potential within the Kenyan power sector and respective suitable measures and recommendations to realize this potential (to
1
Lahmeyer International conducts this project with Innovation Energie Développement (IED), France. The LTP is the identification and analysis of suitable expansion paths of the Kenyan power system for that period, complying with the defined planning criteria and framework. The Energy Efficiency and Renewable Energy tasks are an integral part of the overall Master Plan (e.g. providing input for the demand forecast and generation optimisation). It was agreed with the client that these subjects will be considered as such, i.e. in practice as tasks of the Master Plan closely depending on the other Master Plan tasks. Hence, this report complements the PGTMP LTP report and vice versa. 2
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 4
contribute to the Power Generation and Transmission Master Plan – Long Term Plan) in a feasible and sustainable manner. This broad objective encompasses the following:
To identify and analyse Kenya’s technical renewable energy potential,
To identify and analyse current and planned renewable energy projects in Kenya,
To investigate the realisable potential of different renewable energy options for Kenya in the long-run until 2035,
To model their expected contribution to the future power generation and the probable operation of the generation system to meet the forecasted demand,
To analyse the economic implications of the renewable energy development, and
To identify potential trade-offs in the utilisation of renewable energies in Kenya
2.2
Structure of report
This report consists of the following main sections: 1)
Executive summary, summarising the main results and recommendations of the report;
2)
Introduction, providing the report’s objectives and structure, and a general overview of the approach and assumptions and tools applied;
3)
Renewable energy potentials in Kenya, analysing long-term technical potentials, and deriving renewable energy options for the period until the year 2035;
4)
Analysis of renewable energy expansion in Kenya, providing a scenario analysis of potential renewable energy development pathways in Kenya. The analysis complements the long-term PGTMP for Kenya by identifying appropriate renewable targets;
5)
Discussion of renewable energy incentive schemes, introducing alternative measures to promote the development of renewable energy.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 5
2.3
Methodology and assumptions
The resource assessment investigates the renewable energy potential in Kenya for the following renewable energies:
Hydro power,
Wind power,
Solar PV and CSP,
Biomass, and
Geothermal energy.
Based on a thorough review of existing studies and literature as well as currently discussed renewable energy projects in Kenya, potential lower and upper limits of renewable energy development are derived. The renewable energy assessment investigates the potential future generation capacity development regarding the time horizon until 2035. It provides an analysis of the present situation and an outlook into the future development of the various renewable energy sources and technologies in order to derive RE expansion scenarios. A subsequent scenario analysis using the modelling toolbox developed for the PGTMP evaluates the possibilities to realise the identified potentials. The results of this analysis contribute mainly to setting appropriate renewable energy targets until 2035. By incorporating RE in a wider power system-wide framework, the analysis accounts for interdependencies of all power supply options, not only renewables.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 6
3
RENEWABLE ENERGY RESOURCES IN KENYA
3.1
Hydropower energy
This section provides an overview and evaluation of the hydropower development in Kenya. Meteorological and hydrological conditions are analysed. Furthermore, existing hydropower schemes are introduced with regard to technical parameters and past operation. Additionally, this section gives an overview of planned water resource management schemes in the medium and long term as well as provides an evaluation of the significance of hydropower development in the future Kenyan electricity system.
3.1.1
Available data and current situation
The following section provides an overview of the current status of hydropower development in Kenya. The analysis is based on information received from KenGen and KPLC. Furthermore, the National Water Master Plan covering a study period until 2030 serves as basis for the assessment.
3.1.1.1 Meteorological and hydrological framework Kenya is characterised by a diverse landscape from sea-level at the coast to over 5,000 m in the highlands which are bisected from north to south by the Great Rift Valley. Influenced by the complex topography, the proximity to the Indian Ocean and other large water bodies, as the Lake Victoria, as well as the oscillating movement of the Intertropical Convergence Zone, the climate in Kenya varies from humid tropical at the coastline to humid and sub-humid in the Highlands and western regions to arid in the northern and north-eastern areas. Seasonal variations in rainfall Most places in Kenya experience a bimodal rainfall pattern. The “long rains” start in March and runs through May and the “short rains” occur from September to November. The most intense monsoon period is recorded in May. Due to the wet Congo air mass, the western parts of the country also receive considerable rainfall from June to September while the remaining regions in Kenya experience a dry period during these months. The average annual rainfall in Kenya is estimated at 710 mm (based on measurements of 36 synoptic stations at various places in Kenya from 1979 to 2010). However, the rainfall strongly varies over the country from 0 to 265 mm in the arid and semi-arid regions (east and north-east of the country) to 2,005 mm in the wettest areas (western parts of the country). The following table and figure provide an overview of average monthly rainfall measured at selected stations in Kenya from 1979 to 2010. For the sake of comparison, the average monthly rainfall of the country as a whole is also presented.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 7
Northwest and south west of the country
Jul
Aug
Sep
Oct
Nov
Dec
-
35
30
55 110 150 80
15
15
20
50
90
60
710
10
2
45
80
15
3
2
5
5
30 100 50
347
0
5
20
90
35
0
0
0
0
55
10
265
50
45
90 175 145 30
15
20
25
50 140 90
875
Garissa (1979 to 2010) Mandera (1979 to 2010) Wilson Airport/ Moi Air Base (1979 to 2010) Kakamega (1979 to 2010) Kericho (1979 to 2010)
May
Total
Apr
Jun
Central part of the country
Mar
East and northeast of the country
Representative Rainfall Gauging Stations
Feb
Average country total
Average monthly rainfall at selected stations in Kenya Jan
Table 3-1:
50
75 100 165 255 250 160 150 210 175 155 150 85
1,930
110 95 170 250 260 165 165 200 175 170 150 95
2,005
Average monthly rainfall [mm]
Source: NWMP – JICA study based on data from Water Resources Management Authority (WRMA)
250
Garissa
200
Mandera Wilson Airport/ Moi Air Base Kakamega
150
100
Kericho 50
Kenya 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-1:
Average monthly rainfall at selected stations in Kenya
Evaporation The average annual evaporation shows a strong variation over the country from 1,215 mm at Kimakia forest station to 3,945 mm at Lokori, which is located at Lake Turkana. The average annual evaporation of the reservoirs of large hydropower plants (HPPs) are as follows:
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 8
Table 3-2:
Average annual evaporation - reservoirs of large hydropower plants
Hydropower plant
Average annual evaporation
Masinga & Kamburu HPPs
1,900 mm/a
Gitaru & Kindaruma HPPs
1,975 mm/a
Kiambere HPP
2,050 mm/a
Turkwel HPP
2,745 mm/a
Catchment areas As defined by the National Water Resources Management Strategy (NWRMS), Kenya is divided into six catchment areas. The areas, main rivers and identified hydropower potential are summarised in the table below.
Table 3-3:
Areas, major rivers, hydropower potential of the six catchment areas3
Catchment area
Area [km²]
Major Rivers
Identified hydropower potential 4 [MW]
Lake Victoria North
18,374
Nzoia R., Yala R.
151
Lake Victoria South
31,734
Nyando R., Sondu R., Kuja (Gucha) R., Mara R.
178
Rift Valley
130,452
Turkwel R., Kerio R., Ewaso Ngiro South R.
305
Tana
126,026
Tana R.
790
Athi
58,639
Athi R., Lumi R.
60
Ewaso Ng’iro North
210,226
Ewaso Ngiro North R., Daua R.
TOTAL:
575,451
0 1,484
Figure 3-2 shows the major rivers of the six catchment areas and location of existing large hydropower plants in Kenya. As can be seen, six out of the nine large HPPs are located in the Tana catchment area, with the Tana River being the major source of water supply for the respective reservoirs.
3 4
Source: NWMP – JICA based on data from WRMA Source: NWMP – JICA
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 9
Figure 3-2:
5
Areas and major rivers of the six catchment areas and location of existing large hydropower plants5
Source of base map: National Water Master Plan
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 10
3.1.1.2 Existing hydropower schemes In the 1990s, the Kenyan power generation system was dominated by hydropower with a share of 70% of the total installed generation capacity and 80% of the total electricity generation. Due to several droughts in the past decade, the hydropower plants could, at times, not provide sufficient electricity any more. This resulted in an intensified construction of thermal power plants that are independent of the fluctuations in hydrology. Only two large hydropower plants6, namely Sondu Miriu (60 MW) and Sang’oro (21 MW) have been commissioned since then. Thus, the share of hydropower in the total installed system capacity has decreased to 36 to 37%% until 2014 / 2015. In 2015, the total effective capacity of large hydropower plants was 785 MW. Additionally, some 14 MW of small hydropower capacity was available. Figure 3-3 illustrates the annual generated electricity by hydropower plants and the share of annual generated electricity by hydropower in the total generated electricity from 1999 to 2014. The figure clearly shows the impact of drought periods on electricity production by hydropower in Kenya. The generated electricity by hydropower decreased from 2,914 GWh (65% of the total generated electricity) in 1999 to 1,585 GWh (37% of the total generated electricity) in the drought year 2000. The capacity factor of the aggregated hydropower capacity dropped from 49% in 1999 to 27% in 2000. From 2008 to the drought year 2009, the generated electricity by hydropower decreased from 3,253 GWh (53% of the total generated electricity) to 2,097 GWh (34% of the total generated electricity). The capacity factor of aggregated hydropower decreased from 50% in 2008 to 33% in 2009. 4,500
Small HPPs
70% Tana
4,000 60%
Annual generated electricity [GWh/a]
3,500
Sang'oro Sondo
3,000
50% Turkwel
2,500
40%
2,000
Kiambere Kindaruma
30% Gitaru
1,500 20%
Kamburu
1,000 Masinga
10%
500
0
0% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 3-3:
Share of generated electricity by hydropower on total generated electricity [%] Capacity factor of aggregated hydropower capacity [%]
Annual generated electricity by hydropower plant from 1999 to 2014
6
In the framework of the present study, hydropower plants with an effective capacity of at least 20 MW are defined as large hydropower plants.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 11
With regards to the location of hydropower plants in Kenya, there are four overall groups, namely 1)
“Seven Forks” located in the Tana Catchment Area with Masinga HPP, Kamburu HPP, Gitaru HPP, Kindaruma HPP and Kiambere HPP (a total effective capacity of 581 MW)
2)
“Upper Tana” HPPs located in the upper reach of the Tana River with Tana HPP, Wanjii HPP, Ndula HPP7, Mesco HPP, Sagana HPP (total effective capacity of 29 MW)
3)
Turkwel HPP located in the Rift Valley Catchment Area (effective capacity of 105 MW)
4)
Lake Victoria South Catchment Area with Sondo Miriu HPP8, Sang’oro HPP9, Sosiani HPP and Gogo HPPs (total effective capacity of 82 MW)
The following figure presents the monthly generated electricity by hydropower by each group from 2000 to 2014. As already seen in Figure 3-3, this figure emphasizes again the impact of the drought periods in 2000 and 2009 on the electricity generation of hydropower plants. 400
Total
375
Monthly generated electricity [GWh/month]
350 325
Seven forks (Masinga, Kamburu, Gitaru, Kindaruma**, Kiambere HPPs) Rift Valley (Turkwel HPP)
300 275 250 225 200 175
Lake Victoria South (Sondo Miriu*, Sang'oro*, Sosiani and Gogo HPPs)
150 125
100
Upper Tana (Tana, Wanjii, Ndula, Mesco, Sagana HPPs)
75 50 25
Figure 3-4:
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
0
* COD of Sondo Miriu in 2008 and COD of Sang'oro in 2012) ** Upgrade of Kindaruma from 40 MW to 71 MW in 2012
Monthly electricity generation of the four hydropower production groups
Due to the large capacity of the Seven Forks HPPs, this group shows by far the largest contribution to electricity generation by hydropower in the electricity system. The average capacity factor of the Seven Fork is 48% (average from 2000 to 2014). However, the group was strongly affected by the drought periods reflected in low average capacity factors of 26% in 2000 and 29% in 2009. As a result of the commissioning of Sondo Miriu HPP (60 MW) in the Lake Victoria South Catchment Area, this group shows a strong increase in electricity generation from 2008 onwards. The “upper 7
Ndula HPP has been phased out in 2011. Commissioning of Sondo Miriu HPP was in 2008. 9 Commissioning of Sang’oro HPP was in 2012. 8
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 12
Tana” group shows a slight increase from 2011 onwards caused by the rehabilitation of Tana HPP in 2010 resulting in an upgradation of this HPP from 10.4 MW to 20 MW. The fourth group, “Rift Valley” comprises only one HPP, namely Turkwel HPP (105 MW). The average capacity factor was 44% from 2000 to 2014. From 2000 to 2003 the power plant shows its lowest average annual capacity factors ranging from 16% (in 2002) to 29% (in 2001) caused by a low hydrology. An overview of existing large hydropower plants is illustrated in the following table.
Table 3-4:
Existing large hydropower plants in Kenya
Plant name
Catchment area
Tana HPP
Tana
Masinga HPP
Tana
Kamburu HPP
Owner
COD
KenGen
1932
Tana
KenGen
1981
1953, 1955, 2010 -
Tana
Tana
KenGen
1974
1976
90
Gitaru HPP
Tana
Tana
KenGen
1978
1999
216
Kindaruma HPP
Tana
Tana
KenGen
1968
2012
70.5
Kiambere HPP
Tana
Tana
KenGen
1988
-
164
Rift Valley
Turkwel
KenGen
1991
-
105
Sondu Miriu HPP
Lake Victoria South
Sondu
KenGen
2008
-
60
Sang’oro HPP
Lake Victoria South
Sondu
KenGen
2012
-
20
Turkwel HPP
River
Rehabilitation and/or upgrading
Total
Effective capacity [MW]
20 40
785.5
Monthly available hydropower capacity The available hydropower capacity is an essential input parameter for generation expansion planning and typically varies during the year resulting from variations in the hydrology. For estimating the monthly available capacity of existing large hydropower plants, the monthly maximum values of half-hourly production data from 2009 to 2014 have been determined taking into account the actual installed capacity of the respective hydropower plant10. Due to the fact that drought periods heavily reduce the available hydropower capacity, the monthly available capacity of each hydropower plant has been determined both for average and low hydrology conditions. Considering average hydrology, the monthly available capacity of each existing large hydropower plant represents the average value of the monthly maxima of the dataset analysed. On annual average, 728 MW of the 785 MW effective hydropower capacity are available considering average hydrology conditions.
10
E.g. considering upgradation of Kindaruma HPP and rehabilitation of Tana HPP
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 13
The monthly available capacity during low hydrology is defined as the Percentile 95 (P95) exceedance probability value of the detected monthly maximum production output11. The resulting summarised annual average available hydropower capacity is thus estimated at 525 MW (28% lower compared to average hydrology conditions). An overview of the results is presented in the following graphs and tables. 800
Sang'oro
Available capacity [MW]
700
Sondo
600 Turkwel
500
Kiambere
400
300
Kindaruma
200
Gitaru
100
Kamburu
0
Masinga Tana
Figure 3-5:
Monthly available capacity existing large hydropower plants, average hydrology
Available capacity [MW]
600
Sang'oro
500
Sondo
400
Turkwel
300
Kiambere
200
Kindaruma Gitaru
100
Kamburu 0 Masinga Tana
Figure 3-6:
11
Monthly available capacity existing large hydropower plants, low hydrology
With a probability of 95% the respective capacity is available.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 14
Table 3-5: Plant name Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang’oro HPP Total
Monthly available capacity (MW) of existing large hydropower plants, average hydrology January
February
Match
April
May
June
July
August
September
October
November
December
Annual average
15
14
15
17
19
20
14
17
16
17
18
17
16
36
36
35
35
35
36
29
28
31
30
33
36
33
85
86
83
87
87
86
86
84
84
85
86
86
85
193
203
193
202
197
210
204
199
199
193
206
190
199
70
64
65
70
68
69
67
68
68
69
69
70
68
142
150
152
160
146
150
143
143
143
155
155
151
149
102
102
103
103
102
100
95
102
102
94
92
101
100
59
51
50
59
60
60
60
60
60
59
59
59
58
20
16
18
19
20
20
20
20
20
20
19
20
19
721
721
714
751
732
750
718
720
724
722
736
730
728
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 15
Table 3-6: Plant name Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang’oro HPP Total
Monthly available capacity (MW) of existing large hydropower plants, low hydrology January
February
Match
April
May
June
July
August
September
October
November
December
Annual average
7
6
7
8
8
9
7
8
7
8
8
8
7
11
11
10
10
10
11
9
8
9
9
10
11
10
75
75
73
76
76
75
75
73
74
74
75
75
75
134
140
134
140
136
146
142
138
138
134
143
132
138
61
57
57
62
59
61
59
60
60
61
60
62
60
81
86
87
91
83
85
82
82
82
89
89
86
85
93
93
94
94
93
91
87
93
94
85
84
92
91
46
40
39
46
47
47
47
47
47
46
46
46
45
14
12
13
14
15
15
15
15
15
15
14
15
14
521
519
514
540
527
539
521
523
525
520
528
526
525
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 16
Monthly electricity generation
Average daily electricity generation [MWh/day]
Similar to the available capacity, the monthly electricity production of hydropower plants heavily relies on the present hydrology. In order to determine accurate generation output values on monthly basis, past generation data of the existing hydropower plants from the years 1990 to 2014 have been studied. Again, the analysis focuses both on average and low hydrology conditions. In case of upgradation or rehabilitation of hydropower plants, the power output has been scaled in relation to the actual effective capacity of the hydropower plant during that time. The monthly electricity generation under average hydrology conditions is represented by the monthly average from 1990 to 2014. Considering low hydrology, the P95 exceedance probability value has been taken into account. On annual average, electricity generation from hydropower plants during low hydrology is reduced by 41% compared to average hydrology conditions. An overview of the results is presented in the following graphs and tables. 11,000 10,000
Sang'oro
9,000
Sondo
8,000
Turkwel
7,000 6,000
Kiambere
5,000
Kindaruma
4,000 3,000
Gitaru
2,000
Kamburu
1,000
Masinga
0
Tana
Average daily electricity generation [MWh/day]
Figure 3-7:
Monthly electricity generation of existing large hydropower plants, average hydrology
5,000.0
Sang'oro 4,000.0
Sondo Turkwel
3,000.0
Kiambere
Kindaruma
2,000.0
Gitaru 1,000.0
Kamburu Masinga
0.0
Tana
Figure 3-8:
Monthly electricity generation of existing large hydropower plants, low hydrology
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 17
Table 3-7: Plant name
Monthly electricity generation of existing large hydropower plants, average hydrology January
February
Match
April
May
June
July
August
September
October
November
December
Annual sum
GWh Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang’oro HPP Total
9
6
6
8
10
10
9
9
7
9
10
11
106
17
15
18
13
11
12
16
17
15
14
11
15
173
35
28
34
33
39
34
36
34
32
34
34
34
407
82
70
83
75
86
78
80
76
72
79
78
77
936
29
24
29
27
31
28
29
28
26
28
27
26
331
77
65
81
71
76
70
75
74
69
75
72
76
883
32
27
30
27
29
28
34
34
34
34
32
30
373
25
11
16
24
35
38
35
39
36
37
35
30
364
8
4
4
7
7
13
10
14
14
14
12
11
117
315
251
302
286
324
311
323
326
306
323
312
310
3,690
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 18
Table 3-8:
Plant name
Monthly electricity generation of existing large hydropower plants, low hydrology
January
February
Match
April
May
June
July
August
September
October
November
December
Annual sum
GWh Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang’oro HPP Total
4
2
3
4
5
5
4
4
3
4
5
5
47
3
3
3
2
2
2
3
3
3
2
2
3
30
15
12
15
14
17
15
16
15
14
15
15
15
178
37
32
38
34
39
35
36
35
33
36
35
35
425
12
10
12
12
13
12
12
12
11
12
12
11
142
37
31
39
34
36
34
36
35
33
36
35
36
423
11
9
10
9
10
9
11
12
11
11
11
10
125
7
3
4
7
9
10
9
11
10
10
9
8
97
2
1
1
2
2
3
3
4
4
4
3
3
31
129
104
125
118
133
125
130
130
122
130
126
126
1,498
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 19
Characteristics of existing large hydropower plants In the following, the existing large hydropower projects of each catchment area are described and their key parameters are summarised.
1)
Tana catchment area
The Tana catchment area is located in the south-eastern part of Kenya and covers an area of 126,026 km² (22% of Kenya’s total area). The Tana River originates from Mt. Kenya and is the longest river in the country with a total length of approximately 1,000 km. The five large hydropower plants Masinga, Kamburu, Gitaru, Kindaruma and Kiambere (so-called “Seven Forks”) are situated along the upstream reach of the Tana River in a cascade, i.e. the river flows through each upstream dam and the downstream reservoir is as short as possible to maximise the utilisation of the total available head and thus hydropower potential along the river. They have a total effective capacity of 581 MW and generated 2,136 GWh in 2014 (equal to 24% of the total generated electricity in Kenya in 2014). The HPPs located in the Tana catchment area are as follows:
a)
Masinga HPP
The Masinga Dam at the Tana River represents the first hydropower scheme in the Seven Forks cascade. The functionality of the dam includes protection from high floods, power generation as well as irrigation of agricultural areas. The dam was constructed in 1981 with a total installed capacity of 40 MW. The maximum dam height is 60 m, maximum and minimum operating levels are 1,056.5 m and 1,031 m, respectively. At maximum storage level, the reservoir volume amounts to 1,560 million m³ covering a surface of 116.4 km² (useful volume: 1,350 million m³). The reservoir operation mainly follows the power generation needs. The power station comprises two vertical Kaplan turbines with a gross capacity of 20 MW each and a gross operating height between 22 and 48 m. Project features: Maximum dam height:
60 m
Dam length:
2,220 m
Installed capacity:
40 MW
Number of turbines:
2 Kaplan turbines
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 20
Full supply level:
1,056.5 m
Minimum operating level:
1,031 m
Table 3-9:
Masinga reservoir characteristics
Reservoir water elevation
Reservoir volume 3
Reservoir Area
m
million m
km²
1,031
121
15.0
1,032
130
15.4
1,033
150
17.8
1,034
170
20.2
1,035
193
22.6
1,036
218
25.6
1,037
245
28.5
1,038
276
31.6
1,039
310
34.7
1,040
346
37.8
1,041
386
41.6
1,042
432
45.4
1,043
485
49.2
1,044
540
53.2
1,045
600
57.2
1,046
660
61.2
1,047
720
65.2
1,048
780
69.2
1,049
840
73.8
1,050
920
78.5
1,051
1,000
83.6
1,052
1,080
89.2
1,053
1,180
96.0
1,054
1,280
102.4
1,055
1,380
108.0
1,056
1,500
113.6
1,056.5
1,560
116.4
Figure 3-9 shows the annual generated electricity of Masinga HPP from 1991 to 2014 as well as the average annual generated electricity. Figure 3-10 presents the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 21
Annual generated electricity [GWh/a]
250.0 200.0 150.0 100.0 50.0
0.0
Figure 3-9:
Masinga HPP annual electricity generation
Monthly generated electricity [GWh/month]
25.0 average (1991-2014) 20.0 1995 1996
15.0
2000
10.0
2004 2009
5.0
2014
0.0 Jan
Figure 3-10:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Masinga HPP selected annual generation curves on monthly basis
The figures above clearly show the impact of drought periods in 2000/2001 and 2009 on the electricity generation of the HPP. In 1995 and 1996, the power plant recorded the highest production with 234 GWh of electricity produced in each year. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014. This analysis saves as basis for the determination of the available capacity of Masinga HPP.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 22
Table 3-10:
Masinga HPP statistical characteristics
Parameter
Monthly available capacity [MW]
Percentile P95
10
Percentile P90
24
Percentile P75
30
Percentile P50
36
Minimum value
0
Maximum value
44
Mean value
33
b)
Kamburu
Located 110 km north-east of Nairobi, Kamburu HPP is the first underground power station in the Seven Forks complex. The power plant was commissioned in 1976. The maximum dam height is 52 m, maximum and minimum operating levels are 1,006.5 m and 990 m, respectively. At maximum storage level, the reservoir volume amounts to 154 million m³ covering a surface of 14.3 km² (useful volume: 125 million m³). The reservoir operation follows the power generation needs. The power plant consists of three vertical Francis turbines with gross capacities of 31.4 MW each and a gross operating head between 60 and 76 m. Project features: Maximum dam height:
52 m
Dam length:
900 m
Installed capacity:
94 MW
Number of turbines:
3 Francis turbines
Full supply level:
1,006.5 m
Minimum operating level:
990 m
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 23
Table 3-11:
Kamburu reservoir characteristics
Reservoir Water Elevation
Reservoir volume 3
Reservoir Area
m
Mio m
km²
990
22
3.5
991
25
3.9
992
29
4.4
993
33
5.0
994
38
5.5
995
43
6.0
996
50
6.5
997
58
7.1
998
65
7.8
999
74
8.4
1,000
83
9.1
1,001
93
9.8
1,002
103
10.5
1,003
113
11.3
1,004
124
12.1
1,005
135
13.0
1,006
148
13.9
1,006.5
154
14.3
From Kamburu, water is conveyed to the Gitaru hydropower station via a 2.9 km tailrace tunnel. Figure 3-11 presents the annual generated electricity of the power plant from 1991 to 2014 as well as the average annual generated electricity. Figure 3-12 shows the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 24
Annual generated electricity [GWh/a]
600.0 500.0 400.0 300.0 200.0 100.0 0.0
Monthly generated electricity [GWh/month]
Figure 3-11:
Kamburu HPP annual electricity generation
50 average (1991-2014) 1995
40
2013 30 2000 2004
20
2009 10 2014 0 Jan
Figure 3-12:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Kamburu HPP selected annual generation curves on monthly basis
In 1995 and 2013, Kamburu HPP recorded the highest electricity production with 510 GWh and 511 GWh respectively. In 2000 and 2009 Kamburu HPP generated only 124 GWh and 235 GWh due to a low hydrology caused by the droughts Kenya experienced in these years. Based on the recorded generation data from 1991 to 2014, the average annual generated electricity of the power plant was 408 GWh. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 25
Table 3-12:
Kamburu HPP statistical characteristics
Parameter
Monthly available capacity [MW]
Percentile P95
75
Percentile P90
76
Percentile P75
82
Percentile P50
87
Minimum value
60
Maximum value
96
Mean value
85
c)
Gitaru
The Gitaru Dam is situated 3 km downstream of Kamburu HPP and was commissioned in 1978. With 225 MW, the underground power station is Kenya’s largest hydropower plant in terms of installed capacity. The maximum dam height is 30.5 m, maximum and minimum operating levels are 924.5 m and 919.0 m, respectively. At maximum storage level, the reservoir volume amounts to 22 million m³ covering a surface of 18.6 km² (useful volume: 10.5 million m³). Due to the comparatively small reservoir (e.g. Kamburu HPP’s maximum reservoir volume amounts to 154 million m³), Gitaru HPP relies on steady discharges from the Kamburu and Masinga HPP located upstream. The discharge from Gitaru hydropower plant is conveyed through a 5 km tailrace tunnel to the Kindaruma reservoir. The power plant comprises one 80 MW and two 72.5 MW Francis turbines (gross operating head between 129 m and 135 m). Project features: Maximum dam height:
30.5 m
Dam length:
580 m
Installed capacity:
225 MW
Number of turbines:
3 Francis turbines
Full supply level:
924.5 m
Minimum operating level:
919 m
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 26
Table 3-13: Gitaru reservoir characteristics Reservoir Water Elevation
Reservoir volume 3
Reservoir Area
m
Mio m
km²
919
8
-
920
10
-
921
12
-
922
14
-
923
16
-
924
19
-
924.5
22
18.6
The discharge from Gitaru hydropower plant is conveyed through a 5 km tailrace tunnel to Kindaruma reservoir. The hydropower plant generated 741 GWh in 2010, 724 GWh in 2011, 944 GWh in 2012, 1,017 GWh in 2013, and 706 GWh in 2014.
Annual generated electricity [GWh/a]
Figure 3-13 shows the annual generated electricity of Gitaru HPP from 1991 to 2014 and the average annual generated electricity. Figure 3-14 presents the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
1200.0 1000.0
800.0 600.0 400.0
200.0 0.0
Figure 3-13:
Gitaru HPP annual electricity generation
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 27
Monthly generated electricity [GWh/month]
120
average (1991-2014)
100
2007 80 2013 60
2000 2004
40
2009 20 2014 0 Jan
Figure 3-14:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Gitaru HPP selected annual generation curves on monthly basis
Considering the years from 1991 to 2014, Gitaru HPP recorded the highest amount of annual generated electricity in 2007 with 1,032 GWh and in 2013 with 1,017 GWh. Gitaru HPP was also strongly affected by the drought periods in 2000 and 2009. The annual generated electricity was 487 GWh in 2000 and 417 GWh in 2009. The average annual generated electricity between 1991 and 2014 was 789 GWh. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Table 3-14: Parameter
Gitaru HPP statistical characteristics Monthly available capacity [MW]
Percentile P95
138
Percentile P90
169
Percentile P75
188
Percentile P50
206
Minimum value
130
Maximum value
230
Mean value
199
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 28
d)
Kindaruma
Kindaruma Dam is located 5 km downstream of the Gitaru reservoir and is the oldest hydropower plant in the Seven Forks Scheme. It was commissioned in 1968. The maximum dam height is 28.7 m, maximum and minimum operating levels are 780.5 m and 776.8 m, respectively. At maximum storage level, the reservoir volume amounts to 7 million m³. Project features: Maximum dam height:
28.7 m
Dam length:
549 m
Installed capacity:
72 MW
Number of turbines:
3 Francis turbines
Full supply level:
780.5 m
Minimum operating level:
776.8 m
Originally, the power house comprised two Kaplan turbines with an installed capacity of 20 MW each. In 2012, these turbines were upgraded and a third turbine was additionally installed resulting in an overall installed capacity of 72 MW. The gross operating head is between 31 and 35 m. Figure 3-15 shows the annual generated electricity of Kindaruma HPP from 1991 to 2014 and the average annual generated electricity. Figure 3-16 presents the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 29
Annual generated electricity [GWh/a]
300.0 250.0 200.0 150.0 100.0 50.0 0.0
Figure 3-15:
Kindaruma HPP annual electricity generation
Monthly generated electricity [GWh/month]
25 average (1991-2014) 1993
20
2007 2013
15
2000 2002
10
2009 2014
5
0 Jan
Figure 3-16:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Kindaruma HPP selected annual generation curves on monthly basis
The figures clearly show the impact of drought periods in 2000/2001 and 2009 on the electricity generation of the HPP. Considering the years from 1991 to 2014, the highest electricity production of the power plant is recorded in 2007 with 250 GWh and in 2013 with 247 GWh. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 30
Table 3-15:
Kindaruma HPP statistical characteristics
Parameter
Monthly available capacity [MW]
Percentile P95
60
Percentile P90
64
Percentile P75
68
Percentile P50
71
Minimum value
39
Maximum value
81
Mean value
70
e)
Kiambere
Kiambere HPP is located downstream of the Kindaruma HPP and is the latest hydropower plant in the Seven Forks complex. The power plant has a total installed capacity of 164 MW and was commissioned in 1988. The maximum dam height is 110 m while maximum and minimum operating levels are 700 m and 665 m respectively. At maximum storage level, the reservoir volume amounts to 585 million m³ (useful volume: 477 million m³). The reservoir operation mainly follows the power generation needs. Project features: Maximum dam height:
110 m
Dam length:
1,000 m
Installed capacity:
164 MW
Number of turbines:
2 Kaplan turbines
Full supply level:
700 m
Minimum operating level:
665 m
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 31
Table 3-16
Kiambere reservoir characteristics
Reservoir Water Elevation m 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700
Reservoir volume 3 million m 108 112 119 125 133 140 147 155 164 173 182 190 200 210 223 236 245 253 266 278 292 305 320 335 359 370 386 400 420 440 460 480 505 527 553 585
The power station is equipped with two Kaplan turbines of 82 MW gross capacity, each with a gross operating head between 114 and 149 m. Figure 3-17 shows the annual generated electricity of Kiambere HPP from 1991 to 2014 and the average annual generated electricity. Figure 3-18 presents the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 32
Annual generated electricity [GWh/a]
1200.0 1000.0
800.0 600.0 400.0 200.0 0.0
Monthly generated electricity [GWh/month]
Figure 3-17:
Kiambere HPP annual electricity generation
100.0
average (1991-2014) 1992
80.0
1998 60.0
2000 2001
40.0
2013 20.0
2014
0.0 Jan
Figure 3-18:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Kiambere HPP selected annual generation curves on monthly basis
The highest amount of generated electricity was recorded 1,089 GWh in 1998 and 1,131 GWh in 2013. In 2000 and 2001, the power plant was strongly affected by the drought period that Kenya experienced. The power plant generated 478 GWh in 2000 and 482 GWh in 2001. Considering the years from 1991 to 2014, the average annual generated electricity amounts to 886 GWh. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 33
Table 3-17: Parameter
Kiambere HPP statistical characteristics Monthly available capacity [MW]
Percentile P95
85
Percentile P90
119
Percentile P75
146
Percentile P50
154
Minimum value
72
Maximum value
172
Mean value
149
f)
Tana
Tana HPP is located 80 km north-east of Nairobi utilising the flow of Merila and Maragua rivers for electricity generation. The run-of-river (RoR) power plant was commissioned in 1932 and redeveloped in 2010. The rehabilitated power station comprises four Francis turbines with an overall installed capacity of 20 MW.
Annual generated electricity [GWh/a]
Figure 3-19 shows the annual generated electricity of Tana HPP from 1991 to 2014 as well as the average annual generated electricity. Figure 3-20 presents the annual generation curves, on monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1991 to 2014 is also presented.
120.0 100.0 80.0 60.0 40.0
20.0 0.0
*shut down of Tana HPP for several months in 2010 because of rehabilitation
Figure 3-19:
Tana HPP annual electricity generation
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 34
Monthly generated electricity [GWh/month]
14.0
12.0
average (1991-2014)
10.0
2000
8.0
2007
6.0
2009 2012
4.0
2013
2.0
2014
0.0 Jan
Figure 3-20:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Tana HPP selected annual generation curves on monthly basis
The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Table 3-18: Parameter
Tana HPP statistical characteristics Monthly available capacity [MW]
Percentile P95
7.3
Percentile P90
7.4
Percentile P75
12.6
Percentile P50
18.7
Minimum value
4.4
Maximum value
24.8
Mean value
16.3
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 35
2)
Rift Valley catchment area
The Rift Valley area is situated in the central and western part of Kenya with a total area of 130,452 km² (23% of the total area of Kenya). There is one large hydropower plant in the Rift Valley catchment area, namely Turkwel HPP.
g)
Turkwel
Turkwel Dam commissioned in 1991 is the tallest dam in Kenya and is located in the Turkwel River in West Poko County. The dam is used both for electricity generation and irrigation purposes. The power station has an installed capacity of 106 MW. The maximum dam height is 153 m while maximum and minimum operating levels are 1,150 m and 1,098 m, respectively. At maximum storage level, the reservoir volume amounts to 1,645 billion m³ covering a surface of 66.1 km² (useful volume: 1,531 billion m³). Project features: Maximum dam height:
153 m
Dam length:
150 m
Installed capacity:
106 MW
Number of turbines:
2 vertical shaft Francis turbines
Full supply level:
1,150 m
Minimum operating level:
1,098 m
Table 3-19:
Turkwel reservoir characteristics
Reservoir water elevation
Reservoir volume 3
Reservoir area
m
million m
1,098
98
km² 7.8
1,099
106
8.2
1,100
114
8.6
1,101
123
9.1
1,102
133
9.6
1,103
143
1.2
1,104
153
10.7
1,105
164
10.7
1,106
175
11.8
1,107
187
12.4
1,108
200
13.0
1,109
213
13.6
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 36
Reservoir water elevation
Reservoir volume 3
Reservoir area
m
million m
1,110
227
km² 14.2
1,111
242
15.0
1,112
257
15.7
1,113
273
16.4
1,114
290
17.2
1,115
308
17.9
1,116
326
18.8
1,117
345
19.6
1,118
365
20.5
1,119
386
21.4
1,120
408
22.3
1,121
431
23.3
1,122
455
24.4
1,123
479
25.4
1,124
505
26.5
1,125
532
27.5
1,126
561
28.7
1,127
590
29.8
1,128
620
31.0
1,129
652
32.1
1,130
684
33.3
1,131
718
34.6
1,132
754
35.8
1,133
790
37.0
1,134
828
38.3
1,135
867
39.5
1,136
907
41.0
1,137
949
42.5
1,138
992
44.0
1,139
1,036
45.4
1,140
1,083
46.9
1,141
1,130
48.7
1,142
1,180
50.5
1,143
1,232
52.3
1,144
1,285
54.2
1,145
1,340
56.0
1,146
1,397
58.0
1,147
1,456
60.0
1,148
1,517
62.0
1,149
1,580
64.1
1,150
1,645
66.1
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 37
The power plant comprises two vertical shaft Francis turbines of 53.7 MW gross capacity each with a gross operating head between 300 and 350 m.
Annual generated electricity [GWh/a]
The figure below shows the annual generated electricity of Turkwel HPP from 1991 to 2014 as well as the average annual generated electricity. Figure 3-22 presents the annual generation curves on monthly basis of selected years. Additionally, the average generation curve calculated on the basis of the generation data from 1992 to 2014 is presented.
700.0 600.0
500.0 400.0 300.0 200.0 100.0
0.0
Figure 3-21:
Turkwel HPP annual electricity generation
Monthly generated electricity [GWh/month]
70 60
average (1992-2014)
50
1993
40
2002
30
2003 2007
20
2013
10
2014
0 Jan
Figure 3-22:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Turkwel HPP selected annual generation curves on monthly basis
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 38
Considering the years from 1992 to 2014, the highest electricity production is recorded for the years 2013 (623 GWh) and 2014 (642 GWh). In 2002, the power plant generated only 145 GWh. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Table 3-20:
Turkwel HPP statistical characteristics
Parameter
Monthly available capacity [MW]
Percentile P95
91
Percentile P90
96
Percentile P75
100
Percentile P50
102
Minimum value
52
Maximum value
105
Mean value
100
3)
Lake Victoria South Catchment Area
The Lake Victoria South Catchment area is situated in the south-western part of Kenya and has an area of 31,734 km² (5.5% of the country). Two large hydropower plants are located in the Lake Victoria South Catchment Area, namely Sondu Miriu and Sang’Oro HPP.
h)
Sondu Miriu HPP
The power plant was commissioned in 2008 with a total capacity of 60 MW and is situated on the Sondu River. Since the scheme is based on a run-of-river technology it does not have a large reservoir, but relies on the flow of the river. The water intake passes a 6.2 km headrace tunnel to the Nyakach escarpment. From there, a 1.2 km penstock takes the water from the top of the escarpment down to the power station resulting in a gross head of approximately 200 m. Before the water is discharged back to the Sondu River, it is conveyed through an open channel with an overall length of 5 km to Sang’Oro HPP. Project features: Maximum dam height:
18 m
Dam length:
70 m
Installed capacity:
60 MW
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 39
Number of turbines:
2
The power plant comprises two turbines with 30 MW gross capacity each. The figure below shows the annual generated electricity of Sondu Miriu HPP from 2008 to 2014 and the average annual generated electricity. Figure 3-24 presents the annual generation curves on, monthly basis, for selected years. Additionally, the average generation curve calculated on the basis of the generation data from 2008 to 2014 is presented.
Annual generated electricity [GWh/a]
600.0 500.0 400.0 300.0 200.0 100.0 0.0 2008
Figure 3-23:
2009
2010
2011
2012
2013
2014
average (2008-2014)
Sondu Miriu HPP annual electricity generation
Monthly generated electricity [GWh/month
45 40
average (20082014)
35
2008
30 2009 25 20
2010
15 2012
10 5
2014
0 Jan
Figure 3-24:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sondu Miriu HPP selected annual generation curves on monthly basis
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 40
The figures clearly show that the power plant was affected by the drought period which Kenya experienced in 2009. In this year the power plant generated 219 GWh which is low compared to the average annual generated electricity estimated at 362 GWh (based on generation data from 2008 to 2014). The highest amount of electricity generated by Sondu Miriu HPP was in 481 GWh in 2010. The following table provides an overview of selected exceedance probability values as well as minimum, maximum and mean values of the monthly maximum generation output based on halfhourly production data from 2009 to 2014.
Table 3-21: Parameter
Sondo Miriu HPP statistical characteristics Monthly available capacity [MW]
Percentile P95
45
Percentile P90
52
Percentile P75
62
Percentile P50
62
Minimum value
32
Maximum value
70
Mean value
60
i)
Sang’Oro HPP
The run-of-river HPP was commissioned in 2012 and uses the tail water of Sondu Miriu HPP. It has an installed capacity of 20 MW and comprises of two turbines. The plant provides annual energy of at least 53 GWh per year and a minimum available capacity of 12 MW (low hydrology). The power plant generated 128 GWh in 2013 and 110 GWh in 2014. The following figure shows the generation curves from 2013 and 2014 and the average annual generation curves on monthly basis12.
12
Statistical characteristics of Sang’Oro HPP are not presented, since data quantity is insufficient for an accurate statistical analysis (commissioning in 2012).
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 41
Monthly generated electricity [GWh/month
16.0 14.0 average (2013-2014)
12.0 10.0 8.0
2013
6.0 4.0 2014
2.0 0.0 Jan
Figure 3-25:
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sang’Oro HPP selected annual generation curves on monthly basis
Small hydropower plants In 2015, the capacity of installed small hydropower plants was approximately 17 MW. As per KPLC annual report (financial year 2014/2015) 14 MW of small hydropower capacity provides electricity to the national grid. Small hydropower schemes provide great benefits in areas far from the national electricity grid and ensure electricity supply to villages, small businesses and farms. The existing schemes are mainly owned by KenGen, but private entrepreneurs and communities also operate small hydropower plants. The table below provides an overview of existing small hydropower schemes in Kenya.
Table 3-22:
Existing small hydropower plants in Kenya
Plant name
Owner
COD
Installed capacity [MW]
Sosiani HPP (Selby Falls)
KenGen
1952
0.40
Sagana HPP
KenGen
1954
1.50
Mesco HPP
KenGen
1930
0.38
Wanjii HPP
KenGen
1952
7.40
Gogo HPP
KenGen Kenya Tea Development Authority Power Technology Solutions James Finlay Tea
1958
2.00
2009
0.90
2015
0.51
1934-1999
2.21
Imenti HPP Gikira James Findlay (K) Ltd.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 42
Plant name HPP
Owner
COD
Installed capacity [MW]
company
Brooke Bond HPP Savani HPP Diguna HPP Tenwek HPP Mujwa HPP Community MHPs
Unilever Tea Company Eastern Produce
0.63 1927
0.10
Missionary Tenwek Missionary Hospital Missionary
1997
0.40
Community
2002
0.32 0.07 0.02
Total
3.1.2
16.84
Medium and long term potential
This sub-chapter provides an overview of large and small hydropower projects planned to be developed in the medium and long term period.
3.1.2.1 Large hydropower projects (planned) Electricity generation by hydropower power is strongly affected by the prevailing hydrology. As Kenya has already experienced in the past, the dependency may lead to power shortages during drought periods, in case that the lacking hydropower capacity cannot be compensated e.g. by thermal power plants. However, hydropower plants have the advantage that they provide power at low operating costs. Large schemes with storage facilities are able to react very fast to variations in electricity demand caused by short ramp-up times and are thus very valuable for the provision of peak load capacity at low costs. In the long-term, there are several plans of developing large hydrological schemes mainly under the auspices of the Ministry of Environment, Water and Natural Resources. The majority of the projects are planned to be constructed as multipurpose scheme. The following table provides an overview of the various planned large hydropower projects.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 43
Table 3-23:
Large hydropower projects (long-list)13
Name
Installed capacity
Catchment area
Purpose
Authority in charge of
[MW] High Grand Falls Stage 1
500
Tana
Water supply, flood control, irrigation, hydropower
MORDA
High Grand Falls Stage 2
200
Tana
Water supply, flood control, irrigation, hydropower
MORDA
Karura
90
Tana
Hydropower
Arror
60
Rift Valley
Irrigation, hydropower
KVDA
Embobut
45
Rift Valley
Water supply, irrigation, hydropower
KVDA
15
Kimware
20
Rift Valley
Water supply, irrigation, hydropower
KVDA
15
36
Rift Valley
Water supply, hydropower
ENSDA
16
54
Rift Valley
Water supply, irrigation, hydropower
ENSDA
16
90
Rift Valley
Water supply, irrigation, hydropower
ENSDA
Nandi Forest
50
Lake Victoria North
Water supply, irrigation, hydropower
MORDA
Hemsted Bridge
60
Lake Victoria North
Water supply, irrigation, hydropower
Nzoia I
16
Lake Victoria North
Water supply, irrigation, flood control, hydropower
NWCPC
Nzoia II
25
Lake Victoria North
Flood control, hydropower
NIB
Magwagwa
120
Lake Victoria South
Water supply, irrigation, hydropower
MORDA
Lake Victoria South
Hydropower
Oletukat Leshota
Oldorko
16
Upgrade of Gogo Falls
+58
20
14
KenGen 15
17
17
17
14
-18
19
14
KenGen
Munyu
40
Athi
Irrigation, hydropower
TARDA
Thwake
20
Athi
Water supply, irrigation, hydropower
TARDA
Total
14
21
21
1,484
It was agreed with the client that the large hydropower plants High Grand Falls, Karura, Nandi Forest, Arror and Magwagwa are considered as candidates in the expansion planning. Details of these projects are presented in the table below. 13
Data source: National Water Master Plan 2012 provided by Nippon Koei Co. and adjusted with updated information received 14 Ministry of Regional Development Authorities 15 Kerio Valley Development Authority 16 Component of the Lower Ewaso Ng’iro cascade 17 Ewaso Ng’iro South River Basin Development Authority 18 National Water and Conservation and Pipeline Corporation 19 National Irrigation Board 20 Upgrade from 2 MW to 60 MW 21 Tana & Athi Rivers Development Authority
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 44
Table 3-24:
Details of identified large hydropower candidates (short-list)
Name
Unit
Catchment area Purpose Responsible authority/institution
Karura
High Grand Falls
Nandi Forest
Magwagwa
Arror
Tana
Tana
Lake Victoria North
Lake Victoria South
Rift Valley
Hydropower
Water supply, flood control, irrigation, hydropower
Water supply, irrigation, hydropower
Water supply, irrigation, hydropower
Irrigation, water supply, hydropower
KenGen
Installed capacity
MW
Number of units
#
MORDA
22
90
500 (+200 MW for stage 2)
2
5 (+2 for stage 2)
Kaplan
Francis
GWh
235
1,213 (+57 GWh for stage 2)
Average capacity factor
%
30%
Dam height
m
Dam crest length Net head Reservoir volume at full supply level
23
50
MORDA
24
KVDA
120
2
25
60
3
3
Francis
Pelton
185
510
190
28% (21% considering both stages)
43%
49%
36%
45
115
69
95
91
m
2,270
2,500
1,509
450
615
m
37
100
482
210
115
million m³
152
5,700
228
445
64
Unit type Average annual electricity generation
MORDA
Pelton
26
22
Ministry of Regional Development Authorities Ministry of Regional Development Authorities 24 Ministry of Regional Development Authorities 25 Kerio Valley Development Authority 26 As per final study report 23
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 45
The multipurpose dam project High Grand Falls is aimed to provide irrigation and to supply drinking and commercial/industrial water in the Ukambani and Tana River regions in addition to electricity generation. In its initial stage the power house will comprise five Francis turbines resulting in a total capacity of 500 MW. The annual electricity generation is estimated at 1,213 GWh (capacity factor: 28%). The concrete dam is designed for the installation of two further turbines rated at 100 MW each as additional peak power. Feasibility study, detailed design and tender documentation of the project are already completed and financial agreement is on-going. The proposed hydropower scheme Karura is developed by KenGen and planned to be used solely for power generation. The power plant will be embedded in the existing Seven Forks cascade between Kindaruma and Kiambere HPP. Considering results of the feasibility study completed in December 2015, Karura HPP will be equipped with two Kaplan turbines and generating 235 GWh annually (capacity factor: 30%). Nandi Forest is a further multipurpose project promoted under the auspices of the Ministry of Water and Irrigation. The dam is planned to be located on the Yala River in the western part of the country. The scheme is aimed to be used for irrigation, water supply and power generation. The two Pelton turbines rated at 25 MW each will provide 185 GWh electrical energy annually resulting in an average capacity factor of 43%. The feasibility study of the dam was completed in 2011. It is envisaged to restructure the project for Public Private Partnership (PPP) funding. The multipurpose project Magwagwa is planned to be located on the Sondu River in the upstream of the existing Sondu Miriu hydropower plant. The scheme is aimed to provide irrigation, water supply and electrical power. The power house will comprise three Francis turbines with a total capacity of 120 MW and generating 510 GWh electrical energy annually (capacity factor: 49%). It is also expected that the dam will stabilise the flow of the Sondu River which has positive effects on the existing Sondo Miriu and Sang’oro power stations. Similar to Nandi Forest dam it is envisaged to restructure the project for PPP funding. The multipurpose project Arror is planned to be situated on the Arror River about 75 km northeast of Eldoret. The first feasibility study was carried out in 1990 by an Italian consultant. In 2012, this study has been revised and adapted. The scheme is aimed to provide irrigation, water supply and electrical power. The proposed design of the power house comprises three Pelton turbines with a total capacity of 60 MW generating 189.5 GWh annually (capacity factor: 36%). It is envisaged to implement the project through PPP funding. In the framework of the generation expansion planning only Karura HPP and High Grand Falls are considered as “secured candidates”, which can be scheduled in the planning process. Karura HPP is within the responsibility of MOEP and the planning process of High Grand Falls is considered quite advanced. The remaining large hydropower plants should be further assessed as “potential future candidates”. However, as multipurpose dams the responsibility for their scheduling and implementation is not solely within the power sector.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 46
3.1.2.2 Small hydropower projects (committed and planned) There is a large pipeline of small hydropower projects promoted under the FiT scheme. Feasibility studies of 27 projects comprising a total capacity of 115 MW are already submitted end of 2016. PPA negotiations of thirteen of these projects with a total capacity of 37 MW are completed successfully, most of the PPAs signed. Four projects with a total capacity of 15 MW are under construction and four projects have been already completed in recent time (not all of them having a finalised PPA). Furthermore, feasibility studies of more than 20 small hydropower projects comprising a total capacity of more than 100 MW are on-going. The following table provides a brief overview of the various small hydropower plant projects (with at least the status of feasibility study submitted).
Table 3-25:
Small hydropower projects27 (committed and planned)
Name
Capacity [MW]
River
Nearest urban centre
Status
Unilever Tea Kenya Ltd.
3
Kerenge, Tagabi, Jamji
Kericho
awaiting PPA negotiations, construction already completed
KTDA Ltd, Chania
1
Chania
Mataara
PPA signed, commissioned, supply to grid expected for 2017
GenPro-Teremi Falls
3
Teremi Falls
Mt. Elgon
PPA signed, construction on-going
Gura (KTDA)
6
Gura
Nyeri
PPA signed, construction on-going
KTDA Ltd, North Mathioya- Metumi
6
North Mathioya
North Muranga
PPA not established, construction on-going
KTDA Ltd, Lower Nyamindi
2
Nyamindi
Nyamindi
PPA signed, construction on-going
KTDA Ltd, Iraru
2
Iraru
Iraru
PPA signed, construction on-going
KTDA Ltd, South Mara
2
Mara
South Mara
PPA signed, construction on-going
KTDA Ltd, Kipsonoi-Settet
4
Kipsonoi
Kipsonoi
PPA signed, in tendering process for EPC contractor
Tindinyo Falls Resort
2
Yala
Tindinyo
PPA signed
Kleen Energy Limited
6
Rupingazi
Embu
PPA signed
Mt. Kenya Community Based Organisation
1
Kathita River
Meru
PPA signed
Hydel
15
Kagumoine, Kairo, Njega
Embu, Kirinyaga
ongoing PPA negotiations
KTDA Gucha
4
Gucha
Gucha
ongoing PPA negotiations
KTDA Chemosit and Kiptiget
3
Chemosit, Kiptiget
Kericho
awaiting PPA negotiations
Global Sustainable
19
Yala, Kaptis Kaimosi
Kaimosi
awaiting PPA negotiations
KTDA Ltd. - Itare river
1
Itare
Kabianga
awaiting PPA negotiations
27
Data source: list of power generation projects coordinated by KPLC (status: October 2015), FiT Database (status: August 2015 and status 2016)
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 47
Name
Capacity [MW]
River
Nearest urban centre
Status
KTDA Yurith/Chemosit
1
Yurith/Chemosit
Cheptuyet
awaiting PPA negotiations
Que Energy/ western hydro
10
Webuye Falls
Webuye
PPA negotiations finalised
Global Sustainable Ltd
5
Nzoia
Bungoma
ongoing PPA negotiations
Greenlight Holdings
2
River Nyamindi
Kutus
awaiting PPA negotiations
Frontier Investment Management
6
River Nithi
Tharaka Nithi
awaiting PPA negotiations
Mutunguru Hydroelectric Company Ltd
8
Mutonga
Chogoria, Meru
awaiting PPA
Hydro Project Service Peters Kianthumbi
1
Kirima
Meru
awaiting PPA negotiations
KTDA Yurith
4
Yurith River
Kamas
awaiting PPA negotiations
In the framework of the generation expansion planning projects, small hydropower projects with completed PPA negotiations are considered to be implemented until 2020 (actual supply to the grid estimated for each project which is either commissioned or under construction). Furthermore, projects whose feasibility studies were already approved end of 2015 are estimated to be commissioned until 2025. From 2025 onwards, linear extrapolation of small hydropower capacity is assumed. As per KPLC annual reports, the average annual capacity factor of small hydropower plants varied between 44% and 54% in the past five years. During the drought period in 2009/2010, however, the average annual capacity factor dropped to some 35%. For the expansion planning their energy generation is fixed at their monthly capacity factor varying between 46 and 56% (assuming an average annual capacity factor of 50% which is similar to average generation in recent years). Considering low hydrology conditions, the monthly average capacity factor varies from 25 to 31%. Firm capacity is assumed with 25% of installed capacity. These assumptions result in the expansion path as depicted in the following table.
Table 3-26:
Cumulated expansion small hydropower (incl. existing plants) – 2035
Capacity [MW] Existing & committed Generic expansion Total Generation [GWh] Capacity [MW] Existing & committed Generic expansion Total Generation [GWh]
2015 2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
14 0 14 61
31 0 31 135
38 0 38 167
49 0 49 215
49 9 58 254
49 18 67 293
49 27 76 333
49 36 85 372
49 45 94 412
49 54 103 451
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
49 63 112 490
49 72 121 529
49 81 130 568
49 90 139 607
49 99 148 646
49 107 156 685
49 116 165 724
49 125 174 763
49 134 183 802
49 143 192 841
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 48
3.1.3
Recommendation for expansion plan
In the past, power generation was dominated by hydropower. As a result, electricity supply in Kenya heavily relied on the present hydrology. During drought years, this led to a strong utilisation of expensive fossil-fuelled thermal power plants. With the objective to become more independent from the effects of hydrology, the Government of Kenya (GoK) strived for a strong diversification of the generation mix. As a result, the share of the effective hydropower capacity in the total effective generation capacity decreased from 64% in 2000 to 36% to 37% in 2014 / 2015. Building of dams to create reservoirs always leads to changes in the natural ecosystem of the area where the river is located. For this reason, it is important to evaluate and to manage carefully social and environmental impacts which arise with the construction of dam projects. Due to the fact that most of the planned large hydropower schemes are foreseen as multipurpose projects, a successful implementation requires a close and efficient cooperation between the various agencies and institutions in Kenya. Nevertheless, hydropower plants have the advantage that they provide power at low operating costs. Large schemes with storage facilities are able to react very fast to variations in electricity demand, caused by short ramp-up times and are thus very valuable for the provision of peak load capacity, at low costs. Hydropower plants with dams are also generally very suitable for the provision of primary reserve due to their ability to quickly control their water sheds and the possibility to rapidly adapt their power output. Generally, all large Kenyan HPPs, except Run-of-the-River (RoR) HPPs, could potentially provide primary reserve. Today, only the existing hydropower plants Kiambere and Gitaru are taking part in providing regulation reserve to the power system. It is recommended to analyse the opportunity to equip the existing hydropower plants Masinga, Kamburu, Kindaruma and Turkwel with the respective IT infrastructure in order to ensure sufficient primary reserve capacity in the future generation system. With the objective to secure power supply during drought periods, it is recommended to consider adequate backup capacity in the generation expansion planning that does not rely on hydrology and is able to compensate the lacking hydropower capacity if necessary. Furthermore, it has to be considered that most large hydropower plant candidates are multipurpose projects carried out under auspices of the Ministry of Water and Irrigation and the Ministry of Environment and Natural Resources. Thus, it is recommended to regularly evaluate the current implementation status of these projects. For the sake of conservativeness, only very promising multipurpose projects or projects in advanced stage of implementation should be considered. For this reason, only Karura HPP (under auspices of KenGen) and High Grand Falls HPP are taken into account in the generation expansion planning process conducted in the framework of the present study. Small hydropower schemes provide great benefits in remote areas and ensure electricity supply of villages, small businesses and farms. From the system point of view, small hydropower plants are considered as baseload capacity without participation in load following measurements.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 49
3.2
Solar energy – photovoltaic (PV)
Photovoltaics (PV) devices convert solar energy directly into electrical energy. The amount of energy that can be produced is proportional to the amount of solar energy available on a specific site. PV has a seasonal variation in electricity production, with the peaks generally following months with the highest solar irradiation. Due to the stable climate, PV systems operating along the equator typically have a fairly consistent exploitable solar potential throughout the year. Electricity production varies on a daily basis, with no generation when the sun has set. Short-term fluctuations of weather conditions, including clouds and rainfall, impact the hourly amount of electricity that is produced. A major drawback of PV plants is the intermittent production, since the electricity production occurs based on the resource and not on demand, i.e. there is no opportunity to use it as base or peak load power supply. In addition, large capacities of fluctuating nature constitute a challenge to electrical grid stability. Classically, solar PV was deemed a suitable technology only for isolated grids and/or rural areas, due to its modularity, availability of solar resource, and applicability for smaller applications. Its development in this type of application has contributed to the maturity of the technology and facilitated its adoption on a larger scale for grid connection in the long term. As result, current large scale grid connected PV systems are becoming competitive with conventional sources with regard to their levelised cost of electricity (LCOE; see also Chapter 6 in the LTP report). Solar PV generation costs are continuously decreasing, and the industry is growing rapidly worldwide. The cumulative installed capacity of solar PV reached roughly 177 GWp, at the end of 2014, up from only 1.5 GWp in 2000. In 2014, Germany, China, and Japan accounted for over half of the global cumulative capacity, followed by Italy and the United States. In 2014, an additional 40 GWp were installed in the world, half of it being in Japan and China28. The strengths and weaknesses of PV technology are summarised in the table below:
Table 3-27:
Strengths and weaknesses of PV energy systems
Strengths
Weaknesses
Mature technology - high reliability and long lifetimes (power output warranties from PV panels are now commonly for 25 years)
Performance is dependent on sunshine levels and local weather conditions
Automatic operation with very low maintenance requirements
Fluctuating power production/no power production at night
No fuel required (i.e. negligible variable OPEX)
High capital/initial investment costs
Modular nature of PV allowing for a complete range of system sizes as application dictates
Specific training and infrastructure needs in case of limited experience
Low environmental impact compared to conventional energy sources
Use of toxic materials in some PV panels No economic storage options for large-scale plants
28
Source: IEA-Photovoltaic Power System Programme (PVPS) 2014 Snapshot of Global PV Markets
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 50
3.2.1
Available data and current situation in Kenya
Thanks to its latitude across the equator (4.5° South and 5° North), Kenya is endowed with very high solar resources, among the highest 10 of Sub-Saharan African countries. In favourable regions, the global horizontal irradiation (GHI) is up to 2,400 kWh/m²/year.
Figure 3-26:
GHI map for Kenya
A publicly available Solar and Wind Energy Resource Assessment (SWERA) mapping exercise was completed and published by UNEP, with GEF funding in 2008. It compiles information relating to the solar and wind energy resource, including data capturing and analysis, computation and mapping using GIS and other technologies to produce national solar and wind atlases for Kenya. Moreover, a comprehensive report funded by the World Bank on Renewable Energy Resource Potential
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 51
in Kenya, carried out by Economic Consulting Associates and Rambol in August 2012, provides useful background information on renewable energy, resources potential and current projects. Although the total installed capacity of PV power in Kenya amounted to 50 MWp in 2013 (4.2% of the installed renewable capacity)29, there is no PV power installation of significant capacity which connected to the grid. The 575 kWp system at the main United Nations compound in Nairobi is intended for on-site consumption but is connected to the grid. A 60 kWp solar PV system at the SOS Children’s Home in Mombasa is connected to the grid as a pilot project. The first project under FIT scheme, a 600 kW project at Strathmore University, is connected to the grid since 2015. Moreover, on a private basis, there is a 72 kWp plant at a flower plant and a 1 MWp plant on a teaprocessing farm30.
3.2.2
Medium and long term potential
The total solar energy potential in Kenya is several thousand times the expected Kenyan electricity demand. Calculating the theoretical technical potential based on the resources is therefore not relevant. A more realistic representation of potential can be obtained from the candidate IPPs. At present, there is a project pipeline under the FiT scheme with completed feasibility studies, amounting to an overall capacity of more than 500 MW distributed amongst some 20 projects. Eleven of these projects have finalised or ongoing PPA negotiations, amounting to an overall installed capacity of some 300 MW (see Table 3-28).
Table 3-28:
Main Solar PV projects submitted to FiT scheme31
Name
Site
Status
0.6
Nairobi
PPA signed, commissioned, supplying to the grid
50/55
Garissa
PPA signed
Viteki International Holding
40
Malindi
PPA negotiations finalised
Dafre Holdings Company Ltd /Makindu solar ltd
30
Makueni
PPA negotiations finalised
Kenergy Renewables Ltd
40
Ramuruti
PPA negotiations finalised
Alten Kenya Limited
40
Kesses
PPA negotiations finalised
Solienke/ Radiant Solar
40
Eldoret
PPA negotiations finalised
Marco Borero Co Ltd.
2
Kieni
PPA negotiations ongoing
Maara Energy Company
2
Various
PPA negotiations ongoing
Solarjoule
10
Naivasha
PPA negotiations ongoing
Cedate/Eldosol
40
Eldoret
PPA negotiations ongoing
Total
299
Strathmore University Rural Electrification Authority
Capacity [MW]
29
http://resourceirena.irena.org/gateway/dashboard/ Info on both plants under the following source: UNEP Risø Centre, Prospects for investment in large-scale, grid-connected solar power in Africa, June 2014 31 Status: January 2015 30
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 52
With regard to technical maturity for power production and supply to the grid, photovoltaics could be an option in the medium or long term, as the technology is mature and is used in numerous countries and climates, both as large generators (>50 MWp) and as small scale roof-top domestic generators (3 to 5 kWp). For the assessment of the most probable operation of PV power in the generation system, the Consultant studied irradiation data of fifteen representative sites in Kenya. On this basis, a representative aggregated PV generation curve has been derived. The average daily production patterns per month are presented in the figure below.
70%
January February
Power output [% of rated]
60%
March
50%
April May
40%
June
July
30%
August 20%
September October
10%
November December
0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
annual
hour of day
Figure 3-27:
Average daily PV production patterns per month
It is noted that another possible solar energy application is direct water heating, which does not produce electricity, but could have a major impact on decreasing the peak demand, through the replacement of the present numerous domestic electric water heaters by solar thermal heater. This issue is further addressed in the Energy Efficiency report.
3.2.3
Recommendation for expansion plan
The total solar energy potential in Kenya is several thousand times the expected Kenyan electricity demand. Calculating the theoretical technical potential based on the resources is therefore not very meaningful. For long-term expansion planning potential solar PV development is analysed by a scenario analysis. Expansion pathways of generic PV projects are assessed regarding their technical and economic implications.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 53
3.3
Solar energy – concentrated solar power (CSP)
Concentrated Solar Power (CSP) plants are thermal power plants that collect solar energy by using mirrors to concentrate direct sunlight onto a receiver. The receiver collects and transfers the solar thermal energy to a heat transfer fluid which can be used to generate electricity in a steam turbine. CSP plants typically include a thermal energy storage system. This allows for dispatchable electricity generation, including possible generation during night time and periods with passing clouds. CSP generation requires direct normal irradiation (DNI) to operate (i.e. a direct angle of incidence at clear skies without clouds) and there is also a significant operation and maintenance component for the solar field and balance of plant. As with conventional thermal power plants, CSP plants often utilise water for the cooling of the steam cycle. CSP plants are typically sited in water scarce areas, therefore dry cooling solutions are often the preferred cooling option with regard to efficient and sustainable use of local water resources. Dry-cooled CSP plants typically use 90% less water than their wet cooled counterparts. However, such plants are typically about 5-10% more expensive than wet cooled plants. The primary and most significant advantage of CSP over PV is that CSP can directly integrate lowcost thermal energy storage technology. This means that CSP plants can produce stable electricity over long periods and can readily control the output of the plant. This is a significant advantage for a renewable energy source, as most renewable sources do not have cost effective energy storage solutions. The development of commercial CSP plants is still in its infancy with approximately 4 GW (compared to 150GW of PV) of installed capacity worldwide up to 2014, with United States and Spain having about 1.5 GW and 2.3 MW of installed capacities respectively. However it is expected to grow in future as an additional 11 GW of capacity is in planning or under development for operation by 2020. Compared to PV, one of the reasons for the slower development of CSP is its high levelised electricity cost. In general, the costs of CSP have dropped in recent years, but not as significantly as those of PV. Combined with long lead times, CSP deployment is expected to rapidly increase only after 2020 when it will become competitive with peak production costs. In Sub-Saharan Africa, South Africa leads the early development of CSP, having already allocated 400 MW towards CSP development with a potential pipeline of a further 1 GW over the next few years. Despite the large potential that this technology could have in some parts of Africa, a reduction in levelised cost of electricity (LCOE) is essential to improve CSP competitiveness against some of the currently cheaper renewable alternatives.
Table 3-29:
Strengths and weaknesses of CSP energy systems Strengths
Weaknesses
Technical maturity of some CSP solutions (particularly parabolic trough)
Requires direct irradiance (i.e. dry climate without clouds) → very site-specific
Enables storage of heat and allows dispatchability
Has moving parts, requiring higher CAPEX and OPEX than static photovoltaics
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 54
Strengths
Weaknesses
Limited fuel requirements (low additional costs for fuel and delivery logistics)
Require cooling by either water or large condensers
Low environmental impact compared with conventional energy sources
Higher capital costs than photovoltaics
3.3.1
Available data and current situation in Kenya
The map in Figure 3-28 shows the solar direct normal irradiance in the various regions of the country. As mentioned earlier, Kenya is endowed with very high solar resources and is among the highest 10 of Sub-Saharan African countries. Its solar direct normal irradiance is around 2,300 kWh/m²/year in favourable regions. However, there are presently no operational CSP plants in Kenya.
Figure 3-28:
DNI map for Kenya
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 55
3.3.2
Medium and long term potential
CSP plants are very site specific. The plants require flat areas with direct solar irradiance, which means clear skies without clouds. Generally these areas have a very low population density, meaning a very low power demand, often requiring the construction of power transmission lines to load centres. As mentioned in the introduction to this technology, capital costs of CSP plants are still high compared to other renewable technologies. Moreover, unlike most of the photovoltaic plants, CSP plants require heavy workload to manage the solar field and balance of plant, which implies higher operation and maintenance costs than static PV plants. In addition, for cooling purposes, either water or more expensive air cooling condensers are needed. From a strictly economic perspective, CSP does not immediately appear as a viable option for grid connected power generation in Kenya in the short to medium term. Medium term cost reduction is possible, but will depend largely on how the cost of CSP develops versus alternatives. The existing CSP advantage of dispatchability is significant, but it is difficult to directly translate it into economic terms. Reflecting this dispatchability benefit on the LCOE will depend on the local regulations and grid requirements for dispatchable renewable energy production.
3.3.3
Recommendation for expansion plan
Due to currently rather unclear development prospects of CSP projects and the considerable amount of more (cost-) competitive renewable alternatives (especially geothermal and wind) in Kenya, CSP is not be addressed in the long-term expansion planning. However, it is strongly recommended to closely monitor the global development of the technology in future years.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 56
3.4
Wind energy
There are several types of wind turbines for generating electricity. However, in recent times, the horizontal axis three bladed turbine has become the most common configuration. Modern wind turbines vary in size with two market ranges:
Small units rated at just a few hundred watts up to 50-80 kW in capacity, used mainly for rural and stand-alone systems and
Large units, from 150 kW up to 7 MW in capacity, used for large-scale, grid-connected systems.
However, in established markets, commercial proven utility scale wind turbine capacities (not considering off-shore applications) usually range from 1.5 MW up to 3.5 MW. As the small scale units are mainly used for off-grid applications, such as water pumping, they are not considered any further in this report. Grid-connected wind turbines already have a considerable impact in developed countries and are increasing in some developing countries as well. This is mainly through large-scale installations, either on land (on-shore) or in the sea on the continental shelf (off-shore). However, wind turbines generate electricity intermittently in correlation to the underlying fluctuation of the wind. Because wind turbines do not produce power constantly and at their rated power (which is only achieved at higher wind speeds), capacity factors are typically between 20 to 55%. One of the principal areas of concerns of wind energy is its variable power output, accommodation of which can be a challenge for the power network as the share of intermittent generation on the grid rises. On a world level, decreasing costs are leading to a fast development of wind power. According to the Global Wind Energy Council (GWEC), a total global capacity of 51 GW was added in 2014 and the total installed capacity presently reaches 370 GW.
3.4.1
Available data and current situation in Kenya
A high-level and remote Solar and Wind Energy Resource Assessment (SWERA) mapping exercise was completed and published in 2008. This provides general information on the areas with the highest wind potential. Moreover, a wind energy data analysis and development programme conducted in 2013 by WinDForce Management Services Pvt. Ltd indicates a total potential of 1,600 GW and a technical potential of 4,600 MW. This represents about two times the present overall installed capacity in Kenya. At present, the only grid connected wind power plant is the Ngong Wind Farm, operated by KenGen. The first two wind turbines of Ngong Wind Farm were commissioned back in 1993 as a donation of the Belgium government. In May 2008, the construction of a 5.1 MW wind farm at Ngong hill started and was commissioned in 2009. The wind farm comprises six Vestas V52-850 kW. Meanwhile the original two turbines have already retired. In 2015, the Ngong wind farm was expanded by 20.4 MW.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 57
3.4.2
Medium and long term potential
The overall medium and long-term wind energy potential of a country depends on several factors. The wind resource potential is one of the most important driving factor but other factors have to be considered as well. These are, among others:
Wind speed
Land use
Feed in tariff
Availability of grid access
Turbine technology
A pure view on the distribution of wind power density across a country gives a good idea about the total theoretical wind energy potential, but the technical potential depends on the land use, environmental and social restrictions and the available infrastructure, such as grid access. The price of feed-in-tariffs (FiT) or PPAs also have a significant influence on the technical and feasible potential. Nonetheless, even sites with lower average wind speeds can be attractive for wind farm development if the right turbine technology is chosen. During the past five to ten years, wind turbine manufacturer have realised that sites with excellent wind speeds are often limited and there is a need to make low wind sites feasible and attractive for wind farm development as well. Turbines are now also being developed, with rotor-diameters between 100 and 130 m and hub heights of up to 140 m, which are suitable for low wind sites. Due to the increased diameter, a wind turbine can produce significantly more energy under the same wind conditions. The following comparison show the energy yield of a Vestas V80 2 MW, a Vestas 100 2 MW, and a Vestas V117 3 MW turbine for a wind speed of 6 m/s at 100 m height.
Table 3-30:
Energy yield of sample wind turbines
Turbine Type
Rotor-Diameter
Rated Power
Yield
Full-load hours
Vestas V80
80 m
2 MW
4400 MWh
2200 h
Vestas V100
100 m
2 MW
5550 MWh
2775 h
Vestas V117
117 m
3 MW
8200 MWh
2733 h
Incentive schemes and governmental support have a significant role to play in the realisation of wind energy potential. For example, in 2014 Germany installed 4,200 MW additional on-shore wind energy, the highest additional installation of wind capacities within the last 22 years. A combination of factors, such as political will to push renewable energies, as well as an attractive FiT of ap-
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 58
proximately €9 cent/kWh (9.8 KES/kWh) and modern large-scale wind turbines which are able to exploit average wind conditions as well, can lead to the establishment of a stable market. In 2013, WinDForce Management Services Pvt. Ltd conducted a wind energy data analysis and development programme indicating the wind potentials for Kenya. Almost one third of the country’s area offers excellent wind potential with wind power densities greater than 350 W/m². Figure 3-29 visualises mean wind speeds in Kenya. As can be seen, the wind speeds in a large part of the country correspond well with the turbine technology described earlier, pointing towards a high potential for wind energy development in the years to come.
Figure 3-29:
Mean wind speed map of Kenya
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 59
Wind capacity expansion Being a domestic renewable energy source, wind power projects are given a high profile in Kenya by both the government and private sector. The table below provides an overview of already committed and planned wind farm candidates.
Table 3-31:
Wind farm projects (committed and planned) Installed capacity [MW] 100
Annual generated electricity [GWh] 482
Capacity factor [%] 55%
Earliest year for 32 system integration 2017
Kipeto - Phase I
50
201
46%
2018
Lake Turkana - Phase I, Stage 2
100
482
55%
Ngong 1 - Phase III
10
30
35%
Kinangop
60
176
34%
Ol-Danyat Energy
10
not provided
not provided
2019
Kipeto - Phase II
50
201
46%
2019
Lake Turkana - Phase I, Stage 3
100
482
55%
Meru Phase I
80
222
32%
2020
Prunus
51
154
35%
2021
Limuru Wind - Transcentury
50
not provided
not provided
2022
Kajiado Wind - Chagem Power
50
not provided
not provided
2022
Malindi
50
126
29%
2024
Meru Phase II
320
888
not provided
2024
Marsabit Phase I
300
1,043
40%
2025
Lake Turkana - Phase II, Stage 1
100
482
55%
2025
Lake Turkana - Phase II, Stage 2
100
482
55%
2026
Lake Turkana - Phase II, Stage 3
150
723
55%
2027
Marsabit Phase II
300
1,043
40%
2027
Lake Turkana - Phase III, Stage 1
100
482
55%
2030
Lake Turkana - Phase III, Stage 2
100
482
55%
2031
Lake Turkana - Phase III, Stage 3
150
723
55%
2032
Wind farm Lake Turkana - Phase I, Stage 1
2018
33
2019 2019
2019
34
33
Taking into account the earliest years for system integration as a result of the generation candidates assessment (please see Chapter 6 of the LTP report), the wind power capacity could reach almost 2,500 MW by 2035. The potential wind expansion is visualised in the figure below.
32
Earliest year for system integration as identified in PESTEL analysis (please see Chapter 6 of the LTP report) Same as Stage 1 but stepwise system integration assumed 34 Project cancelled for location but assets assumed to be utilised in Kenya 33
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 60
Figure 3-30:
Potential wind capacity development in Kenya in the long term
Wind generation profiles For the assessment of the most probable power output characteristics of the planned wind farms, the Consultant studied the wind data from the measurement campaign of the Ministry of Energy and Petroleum which was conducted in 2009-2014. Wind measurement raw data of the measurement campaign were screened and the period with the highest amount of available data was identified and chosen for further analysis (15.07.2011 – 14.07.2012). For this period, 31 sites were evaluated (only sites with more than 99% data availability). In order to determine power generation of the wind farms Kipeto, Lake Turkana and Meru, the closest measurement sites were selected (Kipeto: Gikonyokie; Lake Turkana: North Horr, Marsabit, Meru: Kieni) since the actual wind measurement data of the projected wind farms were not provided to the Consultant. Subsequently, hourly MERRA reanalysis data for grid points closest to the projected sites35 were assessed in order to determine the long term correlation with the measurement data for these sites. This was done in order to compensate for inter-annual changes in wind resource. Accordingly, the measurement data were adapted to represent the long-term average of wind speeds. For the Kinangop wind farm, wind measurement data from a measurement site very close to the planned wind farm were analysed. Here, measurement data from the year 2007 were chosen and correlated to the average of 2004-2007, for which measurement data is available, in order to represent long-term average wind speeds. 35
MERRA data are given for a dense grid of 55m x 75 km around the globe.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 61
For the determination of the wind energy output of the wind farms, respective planned wind turbine generator models have been considered. Consequently, for the Kinangop, Kipeto and Meru wind farms, the GE1.6 WTGs, and for the Lake Turkana wind farm, the Vestas V52 WTGs were analysed. These turbines represent the technical concept of the respective wind farms. The achieved wind power production figures were scaled up to the announced capacity factors (Kinangop: 33.5%, Kipeto: 45.9%, Lake Turkana: 55.0%, Meru: 32%). This methodology had to be applied due to the lack of measurement data for the real sites. However, this methodology provides for a correct consideration of the electricity generation potential as well as a very good estimate of the hourly wind injection profile. Due to the relatively small size of the existing Ngong wind farm (6 MW) and the new Ngong 1 – Phase II (7 MW) and Ngong 2 (14 MW) wind farms, no extra analyses were carried out. Their production was considered by extrapolating generation of the other wind farms installed in the respective years. In Figure 3-31 to Figure 3-33, the average daily production profiles of the four wind farms Kinangop, Kipeto, Lake Turkana and Meru are visualised on a monthly basis. Additionally the annual average profile is given.
Power output [% of rated]
90.0%
January
80.0%
February
70.0%
March April
60.0%
May
50.0%
June
40.0%
July
30.0%
August September
20.0%
October
10.0%
November
0.0%
December
1
3
5
7
9
11 13 15 17 19 21 23
annual
hour of day
Figure 3-31:
Kinangop wind farm – average daily production patterns per month
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 62
Power output [% of rated]
120.0%
January
February
100.0%
March April
80.0%
May
June
60.0%
July August
40.0%
September October
20.0%
November December
0.0%
1
3
5
7
9
11 13 15 17 19 21 23
annual
hour of day
Figure 3-32:
Kipeto wind farm – average daily production patterns per month
Power output [% of rated]
100.0%
January
90.0%
February
80.0%
March
70.0%
April
60.0%
May June
50.0%
July
40.0%
August
30.0%
September
20.0%
October
10.0%
November
0.0%
December 1
3
5
7
9
11 13 15 17 19 21 23
annual
hour of day
Figure 3-33:
Lake Turkana wind farm – average daily production patterns per month
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 63
80.0%
January
February
Power output [% of rated]
70.0%
March
60.0%
April
50.0%
May June
40.0%
July
30.0%
August
20.0%
September October
10.0%
November December
0.0% 1
3
5
7
9
11 13 15 17 19 21 23
annual
hour of day
Figure 3-34:
Meru wind farm – average daily production patterns per month
Depending on the geographical location and the respective climatic conditions, the four analysed wind farms show different power output characteristics. The Kinangop wind farm has an extensive night-day profile with its highest output levels during the night. Depending on the seasons, this homogenous profile is either on a low (5% during day, 25% during night) or on a very high level (35% during day, 80% during night). The annual average curve shows approximately 20% output during day time and up to 55% of rated power output during night time. The Kipeto wind farm also has an extensive day-night profile where the largest power output is during day time at around 6 pm. This profile generally prevails in all individual months. While the average daily output ranges from 10% to 45% of rated power output in June, it achieves levels between 40% and 100% in February. On an annual average, the average daily production pattern ranges from 20% to 65% of rated power output. The Lake Turkana wind farm has a less prominent daily generation profile. In general, the daily power output peaks in the morning hours. The daily profile is not as homogenous in the individual months compared to the two other wind farms. However, Lake Turkana wind farm generally exhibits very high levels of power output. Even in the worst month (November), it is always above 20%, and on average, up to 40%. On an annual average, the average daily production ranges between 45% and 70% of its rated power output, which, even on an international scale, is a very high value. Meru wind farm shows strong variations during the year. In December and January the wind farm has an extensive day night profile where the largest power output is during day time around 1 pm. In contrast to this, the profile appears very flat in the months August and September. On an annual average, daily productions patterns range from 25 to 42%.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 64
In order to simulate the generic wind expansion in the generation modelling, a generic wind generation curve was also derived based on wind measurement data of selected sites. The results are depicted in the figure below. 80.0%
January February
Power output [% of rated]
70.0%
March
60.0%
April
50.0%
May June
40.0%
July
30.0%
August
20.0%
September October
10.0%
November
December
0.0% 1
3
5
7
9
11 13 15 17 19 21 23
annual
hour of day
Figure 3-35:
Generic wind farm – average daily production patterns per month
The wind generation curves introduced above are considered in the generation expansion modelling of the LTP (please see Chapter 7 of the LTP report).
3.4.3
Recommendation for expansion plan
A considerable potential for wind power development exists in Kenya. Regardless of the economic implications, the utilisation of this potential might have significant impacts on the operation of the power system in future years. Depending on the generation characteristics of wind plants, additional reserve capacity might be required to safeguard the adequate operation of the power system. This might lead to substantial excess cost. The expansion planning thus considers wind power development as a scenario parameter. Based on a reference case that reflects the pipeline of planned wind power projects, scenarios determine the impacts of an accelerated and slowed-down deployment of wind resources in Kenya. Results help to determine adequate development corridors and highlight potential excess cost due to the promotion of wind power.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 65
3.5
Biomass, biogas and waste-to-energy
Biomass energy usually means renewable energy coming from sources such as wood and wood residues, agricultural crops and residues, animal and human wastes. The conversion technology depends on the biomass itself and is influenced by demand side requirements. The final result of the conversion process is direct heat and electricity or a solid, liquid or gaseous fuel. This flexibility is one of the advantages of biomass compared to other renewable energy sources. There are numerous commercially available technologies for the conversion process and the utilization of the resulting energy’s for heating or for power generation. Cogeneration incorporates the simultaneous utilization for both heating and power electricity generation. Solid biomass, rich in lignin can be used in an incinerator where the produced flue gas provides heat and electricity or in a gasification process to provide a syngas for further use. Solid/liquid biomass, which is poor in lignin, is commonly used in fermenters and with the produced biogas also heat and electricity can be provided for further use. Syngas is a mixture of hydrogen, methane and carbon monoxide with amounts of other gases. Before further use it needs to be cleaned in a separate process step. Syngas has less than half of the energy density of natural gas. Biogas is a mixture of methane and carbon dioxide with small amounts of other gases and needs a further cleaning step before it is usable. Biogas is similar to landfill gas, which is produced by the anaerobic decomposition of organic material in landfill sites. Agricultural and agro-industrial residues and wastes have the potential to generate heat and/or power. The best example in several countries is power generation from bagasse, which is presently used for power generation in two sugar mills in Kenya: Mumias and Kwale. Besides the sugar bagasse, there could be some potential in the tea industry as well, which could co-generate about 1 MW in the 100 factories using their own wood plantations for drying. A study conducted by GTZ in 2010 shows a biogas energy potential mainly for heat production and a rather small potential for power production. However, some biogas power projects are presently being submitted to the FiT scheme. Municipal Solid Wastes (MSW) constitutes a potential source of material and energy as well. Because of it heterogeneous components, it is necessary to pre-treat this wastes (or collect it separated by source) before it can be used. The objective is to recycle as much as possible and use the remaining material with a high calorific value in an incinerator or gasification process to provide heat, electricity or syngas. The wet material can be used in a fermentation process to produce biogas.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 66
3.5.1
Available data and current situation in Kenya
Unlike solar or wind energy, the grid connected power potential from biomass resources has not been studied in detail in Kenya. Besides the World Bank funded study quoted earlier, some information can be found in the GTZ funded study from 2010 “Agro-Industrial Biogas in Kenya: Potentials, Estimates for Tariffs, Policy and Business Recommendations”. The latter shows a theoretical potential mostly for heat and a rather small potential for power production regarding the medium term perspective of the present study. Regarding cogeneration from bagasse, the only significant survey of the sugar mills was done in 2007 by Afrepren, an NGO implementing a GEF funded program named “Cogen for Africa”. In 2013, 68 MW of bioenergy were installed in Kenya (contributing with 5.7% to the overall installed capacity of renewables in Kenya). However, out of this capacity, only one plant, Mumias, with a capacity of 26 MW was connected to the grid (and currently not supplying for operational issues). Below the existing plants and plants under construction are detailed:
62% of the Mumias sugar mill company is owned by the private sector and 38% by the public sector. It has 1,700 employees. Since 2006, before the FiT scheme was in place, it was the first sugar mill in Kenya to produce surplus power and supply to the grid. The factory has a capacity of 2.4 MTCY (Million tons of cane per year) and presently produces about 2 MTCY. The export power capacity is up to 26 MW, but the average power is 10 MW. The main obstacle is the lack of cane. 70% of the cane comes from small growers located around the mill. The input is not constant and the cane is of a low quality since it is too young and grown in rain fed areas, instead of irrigated fields. It seems that the root problem of the Kenyan sugar industry is zoning, as too many mills are granted a license, with regard to the cane available in the plantations around the mills. Originally, it was planned to have a 40 km radius around each mill, while the real density of the number of mills that have been built is located is higher than planned. This explains the lack of good quality cane. Moreover, the buyback power rate at 6 US cents/kWh from KPLC is too low for the sugar mills, when calculated on a cost basis, considering free bagasse, in comparison to the utility avoided cost. This rate was agreed before the FiT scheme. At present, the Mumias sugar mill has to buy surplus bagasse from other sugar mills, which do not export power. The transport cost of this bagasse was not foreseen and the power is therefore sold at loss to KPLC.
Another sugar complex on the East coast, the Kwale sugar plant was commissioned in 2015 with a capacity of 18 MW from bagasse co-generation, out of which 10 MW are foreseen to be supplied to the grid which is expected to start in 2017. Besides these two sugar mills, there is no other biomass power project expected in the short term. In addition, the 2 MW Biojoule biomass power plant was commissioned in the beginning of 2016.
The Cummins plant is under construction. It is assumed to provide 10 MW of grid supply in 2017. Cummins CK has been involved in the innovative Marigat project in the Baringo County, which will generate up to 12 MW of electricity by making use of the invasive “mathenge” (local name of Prosopis Puliflora) tree as feedstock. Some technical problems have been phased during construction so that a stage wise commissioning is possible.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 67
Regarding Municipal Solid Wastes (MSW), the available studies and information mostly deal with Nairobi (though project ideas exist also for other cities such as Kisumu). Various donor funded studies cover this topic such as the above cited GIZ publication (which contains a brief section on MSW in Kenya), the UNEP funded Integrated Solid Waste Management (ISWM) Plan 2010 for the City Council of Nairobi, the JICA funded Study on solid waste management in Nairobi City in the Republic of Kenya (1998) as well as a feasibility study prepared by KenGen (not available for this study). The sources also deal with various ways of potential electricity production from solid waste in Kenya. Though evaluated for many years no actual projects have been realised. This is a situation similar to many other African countries.
3.5.2
Medium and long term potential
At present, there are eight biomass and biogas projects proposed under the FiT scheme with at least submitted feasibility study. This pipeline however contains the above mentioned three projects which are either commissioned or expected to start grid supply in the near future. Other projects are on hold (e.g. the below mentioned project on waste to energy in Nairobi) or only at feasibility study level. Hence, there are no projects at advanced development stage to be considered as additional committed capacity in the medium term. Regarding Municipal Solid Wastes (MSW), there is according to local press a project idea from the German firm, Sustainable Energy Management Company (SEMC) to invest USD 400 million into the construction of a solid waste recycling plant in Nairobi’s Dandora suburb. The plant, which would generate up to 70 MW of power, would absorb 2,000 tonnes of solid waste daily. However, the Nairobi City County has not confirmed this information so far. According to press reports the project was put on hold due to lack of finance. Furthermore, power generation from incineration of solid wastes represent very high costs, as compared to other MSW options, and are only recommended when mature waste management systems already exist. Further, it largely depends on responsibilities outside the power sector such realising benefits beyond electricity production (e.g. waste collection and hygiene) and securing suitable amounts and quality of waste. Hence, it is considered that power production from municipal solid waste does not constitute a secured option for large power production in the medium term in Kenya. As far as biogas is concerned, several biogas power plants are expected to be further developed and to feed into the grid beyond the medium term period:
Del Monte Kenya is planning for a 6 MW biogas cogeneration project at its Thika facility, using solid wastes from pineapple, field residues and waste water. The biogas will be used in a dualfuel boiler to replace oil for the steam production needed at the cannery and outside of production time. The biogas will be used in the combined heat and power system to produce electricity that will be fed into the national grid.
Tropical Power Kenya is a grid-connected biogas plant comprising of a 2.8 MW anaerobic digester that will consume an annual 50,000 tons of organic waste sourced from a neighbouring horticulture farms. The plant will also house a 10 MW grid-connected solar PV Plant.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 68
Under the AFD funded Regional Technical Assistance Programme (RTAP), five biogas projects have been identified among the Kenyan Association of Manufacturers (KAM) with a power capacity of 21 MW.
Regarding the medium term perspective of power production from biogas, the study conducted by GIZ in 2010 shows a theoretical potential mostly for heat and rather small potential for power production. GIZ-ENDEV is starting a new biogas project in small food processing industries, such as dairy farms. Some agro industries which are concentrating (ex: companies purchased by Danone) are opening doors for possible bigger projects with power surplus. However, in the medium term, biogas is not expected to provide significant power surplus to the grid in Kenya. On the contrary, excess power generation from bagasse in the sugar milling industry represents a significant potential for biomass energy. There are presently 11 sugar mills in Kenya processing about 6 million tons of cane per year. All the sugar mills already cogenerate heat and power for their own needs, however, only Mumias exports extra power to the grid. The new Kwale plant is expected to deliver 10 MW to the grid in 2017. On the basis of the technology and experience of Mumias, it is estimated that the present Kenyan sugar mills could install a total capacity of 136 MW and export around 100 MW to the grid.
Table 3-32:
Present sugar mills in Kenya and their technical potential
Name
Capacity Ton Cane per day [TCD]
Technical potential Capacity [MW]
Chemelil
3,500
14.3
Mumias
8,400
34.2
Nzoia
3,250
13.2
South Nyanza
2,400
9.8
West Kenya
3,000
12.2
Muhoroni
2,200
9
Kibos
1,800
7.3
Butali
1,500
6.1
Transmara
1,500
6.1
Sukari
1,500
6.1
Kwale
3,000
18
TOTAL
32,000
136.3
Cogeneration from sugar mills has many advantages. It is widely used in many countries (it represents, for example, up to 20% of power production in Mauritius). The technology is well proven and very efficient, maximising the excess power available for the grid. In Kenya, where the milling season is all year round, it could provide the base production. Compared to other IPPs, the power plants are often owned and managed by the sugar companies, as a business diversification. It could be much more developed in Kenya, subject to a better institutional environment. Among the present limiting factors are the low level of cane production (soils, variety, and lack of irrigation) and the demotivation of small growers, as they are paid after 30 days and many turn to other crops for quicker cash.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 69
The present sugar production only covers two thirds of the sugar domestic demand. The sugar imported from COMESA incl. Malawi is cheaper than the local one, not to mention the illegal imports. There is however a government policy to increase sugar production and reach self-sufficiency. Therefore in the long term, the Kenyan sugar mills could export an amount of power to the grid representing up to 150 MW. Additionally, the ethanol produced by the mills could be sold for replacement of fuelwood in stoves, or blending in the gasoline, as it is planned under a new law. Besides the sugar bagasse, there could be some potential in the tea industry, who could co generate about 1 MW in the 100 factories using their own wood plantations for drying. The factories have 1 acre of wood per 8 acres of tea. In the long term, biomass cogeneration (also considered as industrial energy efficiency and could benefit from related support programmes) could represent a significant share of power production in rural areas. Compared to stand alone power production for the plant, the marginal investment to produce excess power for the grid is generally quite cost effective for the agro-industries. This is a major advantage from a macroeconomic point of view. These assumptions result in the expansion path as depicted in the following table.
Table 3-33:
Cumulated expansion cogeneration (incl. existing plants) – 2035
Capacity [MW] Existing & committed actually available Generic expansion Total Generation [GWh] Capacity [MW] Existing & committed actually available Generic expansion Total
515
3.5.3
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
23 2 0 2 9
43 12 0 12 53
43 33 0 33 145
43 43 0 43 188
43 43 11 54 237
43 43 22 65 285
43 43 33 76 333
43 43 44 87 382
43 43 55 98 430
43 43 66 109 478
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
43 43 74 117 515
43 43 83 126 551
43 43 91 134 587
43 43 99 142 623
43 43 108 151 660
43 43 116 159 696
43 43 124 167 732
43 43 132 175 768
43 43 141 184 805
43 43 149 192 841
Recommendation for expansion plan
The future of successfully implemented biomass projects in Kenya will strongly depend on the development of the agricultural sector. The expansion planning considers the existing Mumias, Kwale, Cummins (under construction) and Biojoule (supply to the grid assumed for 2018, 2017, 2017, and 2016 respectively). Due to the uncertainty whether full capacity will be immediately available from these plants the total available biomass capacity is reduced by 10 MW in 2017 and 2018. No projects are at advanced development stage to be considered as additional committed capacity in the medium term. Generic expansion of biomass (mainly bagasse based) capacity (which will need some lead time to be developed) is assumed to start in 2020 with annually 11 MW. Power generation from municipal solid waste are not expected to play a significant role in the future. Their profitable operation depends on benefits beyond the power sector such waste collection and hygiene. Consequently, this option is not considered in the long-term planning as a candidate.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 70
3.6
Geothermal energy
Geothermal energy can be available as heat emitted from within the earth, usually in the form of hot water, steam or two phase flow. The potential of the geothermal energy is site-dependent and is harnessed primarily to produce electricity but can also be used for direct heating or drying purposes. Medium temperature resources (150° C+) can be used for electricity generation, while low temperature resources (50-100° C) can be used for various direct uses such as district heating and industrial processing. Geothermal energy is theoretically an inexhaustible energy source, with the centre of the earth having cooled down by only about 2% over the earth’s lifetime of about 4 billion years. Geothermal heat is generally extracted through production wells from the hot permeable flow zone that are 2 to 3 km deep. With regard to the operation of a steam field, however, drilling of so-called make-up wells becomes necessary due to degradation of the reservoir over the years. Geothermal energy sources are not considered to cause intermittency in their utilisation within a power system; one or more wells may be shut down for maintenance while other wells are producing. The reliability of geothermal energy is good and the low operational costs are the main reasons why geothermal power plants are normally used for base load power. Present geothermal technology is flexible and manageable for developing a power plant that takes notice of the field characterisation. Accumulated knowledge of power plant operation, design and modelling makes it possible to avoid improper practices. During geothermal power plant implementation, drilling and testing of the exploration and production wells have an impact on the environment. The impact will mainly be waste water from the mud system, steam plumes and 100°C hot brine from the test separator. After commissioning of the power plant, the brine from separators and condensate from the condenser is jointly reinjected into the reservoir to reduce drawdown and maintain pressure in the reservoir. Geothermal steam contains non condensable gases, e.g. hydrogen sulphides, carbon dioxide and methane which may be emitted to the atmosphere. The CO2 may be used for agriculture, dry ice production, beer and soda water production. A summary of strengths and weaknesses of geothermal energy is provided in the table below.
Table 3-34:
Strengths and weaknesses of geothermal energy Strengths
Weaknesses
Large resources in volcanic areas (rift zones)
Site-specific technology (tailor-made )
Provision of base load power at low operating cost
High drilling risk and drilling cost
Reliable and constantly available during the year
Reservoir characteristics may change over the life of a power plant
Mature technology
Rather long implementation time Scaling, corrosion and requirement to clean noncondensable gases (NCG) will result in additional cost
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 71
There are different types of plants under commercial operation (both world-wide and in Kenya). The decision which technology is implemented strongly depends on the characteristics of the resource. In the following, the two technologies which are already implemented in Kenya are introduced.
1)
Single-flash power plants
Single-flash power plants use “flashing” of hot water at high pressure and temperature in a separation system where pressure is released resulting in a mixture of steam and water. The steam is separated from the water (brine) and expanded through a steam turbine, which drives a generator. A typical arrangement of a single flash power plant can be seen in Figure 3-36.
Figure 3-36:
Simple schematic drawing of single flash power plant36
The geothermal liquid (brine) from the separator is at high temperature, usually in the range of 160-180°C which can be utilised for direct use applications by installing a heat exchanger. Investment cost of single-flash power plants are generally lower compared to other geothermal technologies. Since main parts of the equipment (e.g. turbines) are in direct contact with the acidic geothermal steam, O&M costs are on the high side37. Single flash technology is typically used for the provision of constant power. If required, load following measurements are possible to a limited extend by venting steam to the atmosphere. This type of technology is currently applied in the Olkaria 1, Olkaria 2 and Olkaria 4 plants in the Kenyan power system. 36
Source: EFLA Geothermal steam contains considerable quantities of hydrogen sulphides and CO 2 which leads to metal erosion and corrosion. 37
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 72
2)
Binary cycle stand-alone geothermal power plants
Binary standalone power plants are build-up of one or many small units (typically 1-10 MW each). This option is typically utilised in geothermal fields with medium temperature fluids, but in some cases it is applied in hot geothermal reservoirs as well. A typical arrangement of such a power plant can be seen in Figure 3-37. This type of technology is applied within the Kenyan plants of Olkaria 3 owned and operated by OrPower4.
Figure 3-37:
Simple schematic drawing of binary geothermal power plant
Binary systems are based on the thermodynamic Organic Rankine Cycle (ORC). The geothermal fluid from the production well passes through a heat exchanger and transfers heat to the working fluid of the binary cycle (green line). The cooled geothermal fluid is pumped back to the geothermal reservoir. The vaporised working fluid is injected into the turbine and expands, whereby the rotation of the turbine is induced. In a condenser the expanded working fluid is cooled down and condenses to liquid. Binary systems are closed-loop configurations. As a result this technology has nearly zero emissions. In contrast to single flash plants, binary systems are able to be operated very flexible. This is reached by adjusting the geothermal flow control valve in that way that the steam supply from the well is reduced. As a consequence, less energy passes the preheater and the evaporator. With the reduced energy transfer, the ORC binary circulation has to be reduced accordingly resulting in reduced electricity production. Due to the pressure drop downstream of the geothermal flow control valve (which is a result from reducing the valve opening), the geothermal fluid is partly flashed. In order to be able to utilise the flashed steam from the gathering system as well, separators are typically installed.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 73
3)
Bottoming unit in single-flash power plants
Binary cycle can be incorporated as a bottoming unit in single-flash power plants. Within this setup, the geothermal brine is utilised from the separators. This increases the thermal efficiency and thus the net power output of the power plant. A bottoming unit power plant can be arranged similar to the one presented in Figure 3-37; except that the heat source for the system is not in the form of a geothermal fluid obtained direct from a well but in the form of a brine from a separator in a single-flash or double-flash power plant as seen Figure 3-38.
Figure 3-38:
Binary bottoming unit in single flash power plant
If the geothermal liquid from the preheater is at high enough temperature, it can be utilised for direct use applications by installing a heat exchanger. For the time being, none of the Kenyan geothermal power plants applies this technology.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 74
3.6.1
Available data and current situation in Kenya
Geothermal energy is a well-developed industry in Kenya. Projects have been implemented by both KenGen and the IPP OrPower. Geothermal power is currently mainly being utilised in the Greater Olkaria Field located in the Hell’s Gate National Park 120 km north-west of Nairobi38. In 2015, geothermal capacity provided nearly 50% of the total power generation, up from 32% in 2014. Today, the total geothermal capacity amounts to nearly 650 MW (see Table 3-35). The majority of the installed geothermal capacity is owned and operated by KenGen. These power plants are equipped with single flash steam technology. The remaining capacity is owned and operated by independent power producers (IPP) using binary steam cycle technology. Due to the low short-run marginal costs, geothermal power plants generally run as base load.
Table 3-35:
Present Geothermal power plants
Name
Owner
Installed capacity [MW]
COD
Olkaria 1 – Unit 1-3
KenGen
45
1981
Olkaria 1 – Unit 4-5
KenGen
140
2014
Olkaria 2
KenGen
105
2003
Olkaria 3 – Unit 1-6
OrPower
48
2000
Olkaria 3 – Unit 7-9
OrPower
62
2013/2014
Olkaria 4
KenGen
140
2014
OrPower Wellhead 4
OrPower
24
2015
Olkaria Wellheads (OW37, 43, 914-915)
KenGen
56
2012-2015
Eburru Hill
KenGen
3
2012
Olkaria Wellheads II
KenGen
20
2016
Total
3.6.2
643
Medium and long term potential
Kenya is endowed with tremendous geothermal potential estimated at 8,000 to 12,000 MW along the Kenyan Rift Valley. A specific master plan for geothermal development of Kenya was announced by GDC with the support from JICA in 2015. This study will also provide the most recent status of geothermal resource potential in Kenya. Today, geothermal power is only being harnessed in the Olkaria and Eburru field. In the medium and long term new geothermal reservoirs, such as Menengai, Suswa, Longonot, Akiira and Baringo 38
Besides a 2.5 MW binary plant in the Eburru field.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 75
Silali (comprising the fields Silali, Korosi and Paka) are planned to be developed. Other potential geothermal prospects within the Kenya Rift that have not been studied in great depth include Emuruangogolak, Arus, Badlands, Namarunu, Chepchuk, Magadi and Barrier. Geothermal studies are planned for these prospects until 2017. The actually applicable medium and long term potential has been derived based on the current development status of the geothermal power plant pipeline. According to their achieved development stage by the time of the assessment under the present report, it is expected that an overall capacity of 539 MW of geothermal power could be implemented during the medium-term period since they are already at advanced stage of construction or planning. A breakdown of the project information is provided in the table below.
Table 3-36:
Geothermal power plants for medium-term period39
Project Name
Owner
Capacity [MW]
Project Status
Earliest year for system 40 integration
Project COD
Olkaria Wellheads
KenGen
20
Commissioned
2016
May 2016
Menengai 1 – Stage 1
Quantum, Or41 Power 2020 , Sosian Energy
103
Procurement and EPC contracting
2019
End 2018
Olkaria 1 (Unit 6)
KenGen
70
Production drilling completed; financial close
2019
Dec. 2018
Olkaria 5
KenGen
140
Production drilling completed; financial close
2019
Mid 2019
Olkaria top42 ping unit
KenGen
60
Steam available, study on-going
2019
End 2018
Olkaria 1 rehabilitation
KenGen
6
Financing committed, tendering in progress
2019 – 2020
End 2018, Mid / end 2019
Olkaria 6
KenGen
140
Production drilling completed
2021
2 half 2020
TOTAL
nd
539
It is estimated that some further 2,400 MW geothermal capacity can be implemented during the LTP period until 2035. The following table provides an overview of the geothermal field development and potential considering the current status of the identified geothermal projects. In addition, the theoretical potential of each field is illustrated. 39
Considering medium-term period until 2020 Estimated based on results of candidates assessment (see Chapter 6.5). Year considers full system integration Project COD 41 Consortium consisting of Ormat, Civicon, Symbion 42 Considered in expansion planning according to system needs in medium term period or beyond 40
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 76
Table 3-37:
Geothermal potential by field
Field
Existing capacity
Medium term poten43 tial
Medium and long 43 term potential
Theoretical 44 potential
MW
MW
MW
MW
620
436
856
1,500
Menengai
0
103
763
1,600
Eburru
2
0
25
30
Longonot
0
0
140
700
Akiira
0
0
140
350
0
0
450
600-750
0
0
600
3,000
Emuruangogolak
0
0
no projects defined
650
Arus
0
0
no projects defined
200
Badlands
0
0
no projects defined
200
Namarunu
0
0
no projects defined
400
Chepchuk
0
0
no projects defined
100
Magadi
0
0
no projects defined
100
Barrier
0
0
no projects defined
450
622
539
2,974
9,880-10,030
Olkaria
Suswa Baringo Silali
Total
3.6.3
45
Recommendation for expansion plan
Already today, geothermal power contributes significantly to the Kenyan generation mix. Considering the tremendous potential of around 10 GW along the Kenyan Rift Valley, it can be expected that geothermal power will play an essential role in the future Kenyan power system. Deep knowledge and expertise in geothermal exploration, drilling, power plant implementation and operation is already present in the country today. However, drilling risks, high upfront costs and a rather long implementation period have to be taken into account in the planning. Geothermal is considered as “conventional" renewable energy source which is already well developed in Kenya and can compete with other sources. In the expansion planning this is done through the fully identified candidates, which are drawn by the system according to their costs and plant characteristics (including earliest year of system integration). Geothermal power provides reliable base load power at low operating cost. Single flash technology which is mainly being utilised in Kenya today, is restricted in providing flexible power due to technical reasons. Binary systems, however, are able to be operated very flexible. With regard to future geothermal expansion and considering the power system needs (load following, regulation control), it is thus recommended to analyse the opportunity for installing binary power plants. The possibility of implementing binary bottoming unit in a single flash plant should also be evaluated.
43
Estimates based on results of candidates assessment (see Chapter 6.5) Estimated potential as presented in GDC strategic plan (April 2013) or additional information received 45 Comprising the fields Silali Korosi and Paka 44
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 77
4
ANALYSIS OF RENEWABLE ENERGY EXPANSION
4.1
Methodology
Deriving adequate expansion pathways for power generation from renewable energy requires a detailed analysis of the techno-economic implications of RE deployment. In order to assess these implications a scenario analysis covering different RE developments is carried out. The methodology follows the approach of the long-term PGTMP and is done using optimisation techniques to arrive at the least cost solution for a given set of assumptions. These include among others development of demand, existing and the committed generation system. For this, the generation expansion planning is done along the following steps: 1) Input processing for simulation and optimisation models (into Excel based data interface): a) Demand forecast hourly load curves for the study period (based on: generic load curves, annual demand for electricity and peak load); b) Existing generation system: configurations based on existing power plants; c) Renewable energy: potential expansion paths by energy source and for intermittent RE generic production curves (hourly, seasonal, representative sites); d) Pre-screened and prioritised generation candidates; e) Reliability requirements of the system; f)
Economic assumptions to calculate comparable cost streams.
2) Demand supply balancing: the demand supply balance of the power system is derived from the evaluation of the existing and committed power plants and the demand forecast. For each year of the study period, annual peak demand and available capacity as well as total energy demand and possible total energy generation (as firm energy) are matched. The net capacity and energy deficit constitutes the minimum amount of additional capacity needed in the system. This balance will provide the framework for scheduling of generation capacity additions. 3) Scenario definition: scenarios are defined in order to investigate specific questions related to the robustness of results; furthermore scenario analyses are used to investigate the impacts of different RE expansion developments – the RE scenario definition is presented in the subsequent section. 4) Generation system optimisation: the optimisation follows a two steps approach which is done by two different generation system simulation and optimisation tools. The tools are interlinked. a) Identification of a long-list of preferable generation capacity expansion paths: (net present value costs) optimisation of the long-term capacity expansion by means of the software
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 78
LIPS-XP (Lahmeyer International Power System Expansion Planning) considering power plant operation characteristics, hourly dispatch for the hourly load curves, candidates expansion restrictions (tunnels), general reserve requirements, (optimised) maintenance schedules, RE expansion paths, costs of energy not served, loss of load probabilities. b) Identification of the optimum expansion path: optimisation (net present value costs) for the operational system configuration of the previously identified long-list (preferable expansion paths) by means of the software LIPS-OP considering same assumptions as for LIPS-XP (Lahmeyer International Power System Operation Planning) 5) Evaluation of the optimum expansion path in terms of energy mix, system reliability and costs (levelised electricity costs).
4.2
Definition of Renewable Energy scenarios
The analysis is based on three different expansion scenarios:
A moderate RE scenario: The moderate RE expansion scenario builds on reasonable future development of RE capacities in Kenya. Main source for the definition of RE expansion is the evaluation of technology-specific potentials as presented in Section 3. Different technologies and resources are considered. 1) Large hydro: For the scenario analysis only Karura HPP and High Grand Falls are considered, which can be scheduled in the planning process. Karura HPP is within the responsibility of MOEP and the planning process of High Grand Falls is considered quite advanced (see Section 3.1.2.1). 2) Small hydro: Small hydropower projects with completed PPA negotiations are considered to be implemented until 2020. Furthermore, projects whose feasibility studies were already approved end of 2015 are estimated to be commissioned until 2025. From 2025 onwards, linear extrapolation of small hydropower capacity is assumed (see Section 3.1.2.2). 3) Geothermal: It is expected that an overall capacity of some 500 MW of geothermal power will be implemented until 2020 since implementation is already on-going (see Section 3.6.2). The further utilisation of the geothermal resource is variable and determined by the generation system optimisation. 4) Biomass: The expansion planning considers the existing Mumias, Kwale, Cummins (under construction) and Biojoule. Due to the uncertainty whether full capacity will be immediately available from these plants the total available biomass capacity is reduced by 10 MW in 2017 and 2018. Generic expansion of biomass (mainly bagasse based) capacity is assumed to start in 2020 with annually 11 MW (see also Section 3.5.2).
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 79
5) Wind: Roughly 520 MW of wind power capacity are considered as already committed, and to become operational until the year 2020 (see also Section 3.4.2). Until 2035, an additional development of 600 MW is assumed for the moderate RE scenario. The moderate scenario assumes a reduction in momentum of wind development between 2020 and 2029. The reason for this assumption is that capacities from already committed projects will lead to over-capacities and even excess generation in the Kenyan system. In order to maintain (or continue to gain) experience with the technology in the country, a slight expansion of wind power capacity is assumed for this period. After 2030 wind development accelerates again. 6) Solar: The total solar energy potential in Kenya is certainly exceeding the expected electricity demand of Kenya. However, currently no PV power installation of significant capacity is connected to the Kenyan grid. In comparison to wind power, solar PV is the more expensive technology in terms of generation cost. Given the expected overcapacities in the Kenyan system and the already existing pipeline of committed wind projects, no significant expansion of solar PV is required until 2020 (in addition to the 50 MW PV plant committed for 2019). The moderate RE scenario, thus, foresees solar PV development to start in 2020. The overall capacity additions until 2035 are aligned to the current pipeline of projects that applied for the feed-in tariff for solar PV in Kenya (see also Section 3.2.2). Until 2035 about 250 MW of solar PV capacity is assumed to be developed in case of the moderate RE scenario. In addition, the assumptions of the reference scenario of the LTP study (i.e., reference demand development, average hydrology, no energy efficiency measures) are applied.
An accelerated RE scenario: This scenario mimics intensified efforts to develop wind and solar (PV) resources in Kenya. Regarding large and small hydropower, geothermal projects and cogeneration projects from biomass, the assumptions of the moderate RE scenario hold. In case of wind power, the development of new projects is intensified after 2020 to reach an additional capacity of 1,200 MW until 2035 (600 MW more than assumed for the moderate RE scenario). Also solar PV efforts are intensified (doubling to 500 MW in 2035 compared to moderate RE scenario). The scenario builds on the main assumptions of the reference scenario of the LTP.
A slowed down RE scenario: The scenario also builds on the main assumptions of the moderate RE scenario, however, wind and solar development is less ambitious. Additional wind capacity amounts to 200 MW (a third of the moderate RE scenario wind expansion), and solar PV capacity to 100 MW until 2035 (40% of the moderate RE scenario PV expansion).
Table 4-1 and Figure 4-1 depict additional RE development in the considered scenarios during the period between 2015 and 2035. The three scenarios create a bandwidth of possible wind and solar PV development until 2035. The following analysis identifies the impacts of these different development pathways. Results provide a valuable basis for future decision-making regarding the development of wind and solar PV.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 80
Table 4-1:
RE development in the moderate, accelerated and slowed down RE scenarios (2015-2035)
Existing & committed capacity: Small HPP Cogeneration PV Wind
a ctua l a va i l a bl e for gri d i ns tal l ed for gri d a ctua l a va i l a bl e for gri d a ctua l a va i l a bl e for gri d a ctua l a va i l a bl e for gri d
Unit 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 MW 14 14 31 38 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 MW 21 23 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 MW 0 2 12 33 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 MW 1 1 1 1 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 MW 26 26 126 276 496 576 576 576 576 576 576 576 576 570 570 570 570 570 570 570 550
Generic expansion: Small HPP Cogeneration PV
Wind
RE scenario all all s l owed down modera te a ccel era ted s l owed down modera te a ccel era ted
Unit 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 MW 0 0 0 0 0 9 18 27 36 45 54 63 72 81 90 99 107 116 125 134 143 MW 0 0 0 0 0 11 22 33 44 55 66 74 83 91 99 108 116 124 132 141 149 MW 0 0 0 0 0 0 0 5 5 10 10 15 15 20 20 30 40 50 65 80 100 MW 0 0 0 0 0 5 5 10 10 20 20 30 40 60 80 100 120 140 170 210 250 MW 0 0 0 0 0 10 10 20 20 40 40 60 80 120 160 200 240 280 340 420 500 MW 0 0 0 0 0 0 0 25 25 25 50 50 50 75 75 100 100 125 150 175 200 MW 0 0 0 0 0 0 0 25 25 50 50 75 75 100 100 150 225 300 400 500 600 MW 0 0 0 0 0 0 0 50 50 100 100 150 150 200 200 300 450 600 800 1,000 1,200
Total (available for grid): Small HPP Cogeneration PV
Wind
RE scenario all all s l owed down modera te a ccel era ted s l owed down modera te a ccel era ted
Unit 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 MW 14 14 31 38 49 58 67 76 85 94 103 112 121 130 139 148 156 165 174 183 192 MW 0 2 12 33 43 54 65 76 87 98 109 117 126 134 142 151 159 167 175 184 192 MW 1 1 1 1 51 51 51 56 56 61 61 66 66 71 71 81 91 101 116 131 151 MW 1 1 1 1 51 56 56 61 61 71 71 81 91 111 131 151 171 191 221 261 301 MW 1 1 1 1 51 61 61 71 71 91 91 111 131 171 211 251 291 331 391 471 551 MW 26 26 126 276 496 576 576 601 601 601 626 626 626 645 645 670 670 695 720 745 750 MW 26 26 126 276 496 576 576 601 601 626 626 651 651 670 670 720 795 870 970 1,070 1,150 MW 26 26 126 276 496 576 576 626 626 676 676 726 726 770 770 870 1,020 1,170 1,370 1,570 1,750
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 81
Additional wind development
Figure 4-1:
Additional wind and solar PV development
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
Additional solar PV development
28.11.2016
Page 82
4.3
Results
4.3.1
Capacity and fuel mix
The figures on the following pages provide an overview on the results of the three scenarios (see Table 4-2). Figures in the first row show the expansion of firm capacity in comparison with the forecasted peak load (with and without reserve margin). The second row presents the annual generation mix contrasted with the forecasted electricity consumption. It further illustrates the annual excess electricity generation. The annual share by technology on the generation mix is depicted in the third row. The following row illustrates the development of the average capacity factors by technology in the three RE expansion scenarios. Finally, the last row presents an hourly dispatch of a sample week (21-27.06.2030) for each of the considered cases. Key results of the simulation of the three scenarios are summarised in the following:
The energy mix of the generation expansion plan is diverse, secure with regard to supply and costs of fuel and “clean” in terms of renewable energy utilisation and, thus emissions.
The forecasted need for new firm capacity until 2035 is about 6,500 MW and does not differ considerably between the considered RE scenarios (see first row in Table 4-2). This is more than three times the existing generation system. Hence, the generation system has to more than triple during the 20-year study period. About 30% of the needed firm capacity is already committed.
Main expansion through 1,695 MW base load geothermal capacity from 2026 onwards. In 2035, geothermal capacity represents between 27% (accelerated RE) and 33% (slowed down RE) of the total installed system capacity providing between 51% (accelerated RE) and 59% (slowed down RE) of the annual generated electricity. A detailed discussion of the differences is included in Section 4.3.2.
Expansion of back-up and peaking capacity by 1,820 MW to 1,890 MW mainly providing the required cold reserve. In the generation modelling the capacity is represented by gasoil fuelled gas turbines. Flexible imports or peaking hydropower plants may constitute a favourable alternative.
Due to the large amount of committed power supply projects (namely HVDC, Turkana, Lamu, geothermal power plants in Olkaria and Menengai), overcapacities occur during the years 2019 to 2024 in all considered RE scenarios. This results in:
Underused investment for this period reflected by a strong increase of system LEC. LEC increase for all RE scenarios by up to 26% compared to LEC in 2015;
All three scenarios lead to excess electricity generation (which has to be dumped or might be exported);
Comparatively low capacity factors of dispatchable power plants:
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 83
o
Geothermal power plants regularly have to reduce their power output to their minimum capacity during hours of low demand. The average capacity factor from 2019 to 2024 is 75% to 77% in all considered RE scenarios.
o
Medium speed diesel engines and gas turbines are not utilised. They only provide back-up capacity.
In the long-term, more than 85% of the electricity demand will be covered by renewable energy sources. 50% to 60% is generated by geothermal power plants, followed by hydropower with 15% to 16% and wind power with 8% (slowed down RE scenario) to 17% (accelerated RE scenario). Cogeneration contributes 2%. PV contributes between 1% (slowed down RE scenario) and 2% (accelerated RE scenario) to the annual energy needs. The remaining electricity demand is mainly covered by imports (7%) and coal (5% to 7%).
Due to the large amount of geothermal capacity with nearly zero operating costs as well as further must-run capacity (HVDC, RE sources) in the system, the utilisation of coal units is comparatively low during the entire study period. The capacity factor varies between 11 and 37%.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 84
Table 4-2:
Comparison of results: moderate, accelerated and slowed down RE expansion scenarios
Accelerated RE expansion
Moderate RE expansion
Slowed down RE expansion
Firm capacity versus peak demand: 8,000
Generic wind expansion (firm capacity)
8,000
Generic wind expansion (firm capacity)
7,500
Generic small HPP expansion (firm capacity)
7,500
Generic small HPP expansion (firm capacity)
Generic cogeneration expansion (firm capacity)
7,000
6,500
Large HPP (firm capacity) - candidate
6,000
GEO - candidate
6,000
GEO - candidate
Committed small HPP (firm capacity)
5,500
2,500
Existing diesel engines
1,500
Existing large HPP (firm capacity)
1,000 500
2,500
Existing GEO
1,500
Peak load
1,000
1,000
Existing large HPP (firm capacity) Existing GEO Peak load
500
Peak load + reserve margin
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2015
Existing system
0
Existing system
0
Existing + committed system
Existing GEO
Existing diesel engines
Peak load + reserve margin
2016
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
Existing large HPP (firm capacity)
1,500
Peak load
500
Existing gas turbines
2,000
Existing diesel engines
Peak load + reserve margin Existing system
2,500
Existing gas turbines
2,000
Existing small HPP (firm capacity) Existing cogeneration (firm capacity)
Existing cogeneration (firm capacity)
Existing gas turbines
2,000
Existing small HPP (firm capacity)
3,000
Existing wind (firm capacity)
3,000
Existing + committed system
2035
Existing cogeneration (firm capacity)
Committed GEO
3,500
2034
Existing small HPP (firm capacity)
Existing wind (firm capacity)
2033
3,000
Committed GEO
3,500
Committed imports
4,000
2032
Existing wind (firm capacity)
4,000
Committed coal
2031
3,500
Committed imports
Committed wind (firm capacity)
4,500
2030
Committed GEO
Committed coal
2029
4,000
4,500
2028
Committed imports
Committed cogeneration (firm capacity)
5,000
2027
Committed coal
Committed wind (firm capacity)
2026
4,500
5,000
2025
Committed wind (firm capacity)
2024
5,000
Committed small HPP (firm capacity)
5,500
Committed cogeneration (firm capacity)
2023
Committed cogeneration (firm capacity)
Firm capacity / Load [MW]
Firm capacity / Load [MW]
5,500
Committed small HPP (firm capacity)
2022
GEO - candidate
Large HPP (firm capacity) - candidate
2021
6,000
Back-up capacity - candidate
6,500
2020
Large HPP (firm capacity) - candidate
2019
6,500
Generic cogeneration expansion (firm capacity)
7,000
Back-up capacity - candidate
2018
Back-up capacity - candidate
2017
7,000
Generic cogeneration expansion (firm capacity)
2016
Generic small HPP expansion (firm capacity)
2015
Generic wind expansion (firm capacity)
7,500
Firm capacity / Load [MW]
8,000
Existing + committed system
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy g
28.11.2016
PV Wind Generic back-up capacity Cogeneration Import Gas turbines (gasoil) Diesel engines Coal Hydropower Geothermal
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
Electricity consumption
2017
Excess energy
Unserved energy
2016
2035
2034
2033
2032
2031
2030
2029
2028
2027
Electricity consumption
40,000.0 38,000.0 36,000.0 34,000.0 32,000.0 30,000.0 28,000.0 26,000.0 24,000.0 22,000.0 20,000.0 18,000.0 16,000.0 14,000.0 12,000.0 10,000.0 8,000.0 6,000.0 4,000.0 2,000.0 0.0
2015
Geothermal
Electricity generation/ consumption [GWh]
Hydropower
2026
Excess energy
Coal
2015
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
Electricity consumption
Diesel engines
2025
Geothermal
Gas turbines (gasoil)
2024
Hydropower
Import
2023
Coal
Generic back-up capacity Cogeneration
2022
Diesel engines
Wind
2021
Gas turbines (gasoil)
PV
2020
Import
Unserved energy
2019
Generic back-up capacity Cogeneration
Electricity generation/ consumption [GWh]
Wind
40,000.0 38,000.0 36,000.0 34,000.0 32,000.0 30,000.0 28,000.0 26,000.0 24,000.0 22,000.0 20,000.0 18,000.0 16,000.0 14,000.0 12,000.0 10,000.0 8,000.0 6,000.0 4,000.0 2,000.0 0.0
2018
PV
2017
Unserved energy
2016
40,000.0 38,000.0 36,000.0 34,000.0 32,000.0 30,000.0 28,000.0 26,000.0 24,000.0 22,000.0 20,000.0 18,000.0 16,000.0 14,000.0 12,000.0 10,000.0 8,000.0 6,000.0 4,000.0 2,000.0 0.0 2015
Electricity generation/ consumption [GWh]
Electricity generation versus elelctricity consumption:
Excess energy
Page 85
Accelerated RE expansion
Moderate RE expansion
Slowed down RE expansion
PV Wind Generic back-up capacity Cogeneration Import Gas turbines (gasoil)
Diesel engines Coal Hydropower Geothermal
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
Excess energy
2017
RE total
2016
Share on energy mix [%]
100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
2015
2035
2034
2033
2032
2031
2030
2029
2028
2027
Excess energy
2026
RE total
2015
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
RE total
Geothermal
2025
Geothermal
Hydropower
2024
Hydropower
Coal
2023
Coal
Gas turbines (gasoil) Diesel engines
2022
Diesel engines
Import
2021
Gas turbines (gasoil)
Generic back-up capacity Cogeneration
2020
Import
Share on energy mix [%]
Cogeneration
Wind
2019
Generic back-up capacity
PV
2018
Wind
100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
2017
PV
2016
100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
2015
Share on energy mix [%]
Share on generation mix by technology:
Excess energy
100.0% 95.0% 90.0% 85.0% 80.0% 75.0% 70.0% 65.0% 60.0% 55.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Capacity factor [%]
100.0% 95.0% 90.0% 85.0% 80.0% 75.0% 70.0% 65.0% 60.0% 55.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%
Capacity factor [%]
Capacity factor [%]
Capacity factor by technology: 100.0% 95.0% 90.0% 85.0% 80.0% 75.0% 70.0% 65.0% 60.0% 55.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Geothermal
Hydropower
Coal
Diesel engines
Geothermal
Hydropower
Coal
Diesel engines
Gas turbines (gasoil)
Import
Cogeneration
Generic back-up capacity
Gas turbines (gasoil)
Import
Cogeneration
Generic back-up capacity
Wind
PV
Wind
PV
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Geothermal Gas turbines (gasoil) Wind
Hydropower Import PV
Coal Cogeneration
Diesel engines Generic back-up capacity
4,600.0
PV
PV
4,400.0 Wind
Wind
4,200.0 4,000.0
Generic back-up capacity
Generic back-up capacity
3,800.0 Cogeneration
3,600.0
Cogeneration
3,400.0
Import
Diesel engines
Coal Hydropower Geothermal
Power Output [MW]
Gas turbines (gasoil)
Dump Generation
3,200.0
Import
3,000.0
Gas turbines (gasoil)
2,800.0
Diesel engines
2,600.0 2,400.0
Coal
2,200.0
Hydropower
2,000.0 1,800.0
Geothermal
1,600.0 1,400.0
Excess energy
1,200.0
Load
Load
1,000.0
Primary Reserve Requirement
800.0
Primary Reserve Requirement
600.0 Primary Reserve Secondary Reserve Requirement
Primary Reserve
400.0 200.0
Secondary Reserve Requirement
Secondary Reserve
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165
0.0 hour of week
hour of week
g
Unserved Energy
4,800.0
28.11.2016
Secondary Reserve
5,200.0 5,000.0 4,800.0 4,600.0 4,400.0 4,200.0 4,000.0 3,800.0 3,600.0 3,400.0 3,200.0 3,000.0 2,800.0 2,600.0 2,400.0 2,200.0 2,000.0 1,800.0 1,600.0 1,400.0 1,200.0 1,000.0 800.0 600.0 400.0 200.0 0.0
Unserved Energy PV
Wind Generic back-up capacity Cogeneration
Import Gas turbines (gasoil) Diesel engines
Coal Hydropower Geothermal
Dump Generation Load Primary Reserve Requirement Primary Reserve
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165
5,000.0
Unserved Energy
Power Output [MW]
5,200.0 5,000.0 4,800.0 4,600.0 4,400.0 4,200.0 4,000.0 3,800.0 3,600.0 3,400.0 3,200.0 3,000.0 2,800.0 2,600.0 2,400.0 2,200.0 2,000.0 1,800.0 1,600.0 1,400.0 1,200.0 1,000.0 800.0 600.0 400.0 200.0 0.0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165
Power Output [MW]
Sample dispatch in the period 21.-27.06.2030:
Secondary Reserve Requirement Secondary Reserve
hour of week
Page 86
4.3.2
Renewable energy scenarios - comparison
The following figures depict a more comprehensive evaluation of the differences between the three RE scenarios. The moderate RE scenario serves as a benchmark in order to highlight the relative differences between the three expansion pathways. The figures display for the relevant period 2020 to 2035 the differences in the 1.
Fuel-specific power generation (Figure 4-2 and Figure 4-3): changes of annual power generation of the accelerated and the slowed down RE scenarios versus the moderate RE scenario – measured in absolute terms (GWh)
2.
Shares of RE generation in total power generation (Figure 4-4): changes of annual RE shares in total generation versus the moderate RE scenario – measured in percentage point changes of the share (%), and
3.
Changes to excess energy (Figure 4-5): changes of total annual excess energy versus the moderate RE scenario – measured in relative terms (%).
Detailed result tables are provided in Annex 4.
Figure 4-2:
Power generation – accelerated RE vs. moderate RE scenario 2020–2035
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 87
Figure 4-3:
Power generation – slowed down RE vs. moderate RE scenario 2020–2035
Figure 4-4:
Change of RE generation share – difference to moderate RE scenario
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 88
120% 100% 80% 60% 40%
20%
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
-20%
2021
0%
2020
Excess energy- relative difference to moderate RE scenario
140%
-40% -60% Accelerated RE
Figure 4-5:
Slowed down RE
Excess energy – difference to moderate RE scenario
Not surprisingly, the accelerated RE scenario leads to higher power generation based on wind and solar resources than the moderate RE case. From 2020, the first year of the accelerated RE development, power generation from wind exceeds developments of the moderate scenario (see blue line in the figure). In 2035, wind-based generation is roughly 2,100 GWh higher than in the moderate scenario. Also generation from solar PV is higher than in the moderate RE scenario (see yellow line in the figure). In 2035 solar PV capacity is twice as high as in the moderate case, which also doubles PV-based power generation. At the end of the planning period solar generation in the accelerated RE scenario exceeds the one in the moderate case by approximately 430 GWh. For the whole study period the increased wind and solar development comes almost entirely at the expense of geothermal generation (see the dark green line in the figure). Coal based generation is decreased to a much lesser extent (only 7% of the reduction of geothermal generation) and only for particular periods. In other words: Increasing the contribution of one renewable resource (e.g. wind) directly crowds out another renewable source (i.e. geothermal). On the one hand, additional wind and solar generation substitutes the utilisation of existing geothermal plants. On the other hand, and of higher relevance, it delays investments in new geothermal power stations. Obviously this delays their contribution to total, geothermal and RE based generation. This effect explains the strong shift from increased RE share to reduced RE share (compared to moderate RE scenario) in 2031 (see Figure 4-4) where the delay of one large geothermal plant results in a reduced geothermal generation by more than 1,000 GWh per year. Table 4-3 shows the impacts of the RE scenarios on the CODs of considered geothermal expansion candidates. After 2030 the accelerated RE scenario delays CODs of geothermal candidates by one or two years compared to the CODs in the moderate RE case.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 89
Table 4-3:
Changes in CODs due to different RE developments
Olkaria 9
COD Moderate RE 2032
COD - difference to moderate RE Accelerated RE Slowed down RE delayed by 1 year
Eburru 2
2032
delayed by 2 years
Menengai 2 Phase I - Stage 4
2031
delayed by 1 year
Menengai 4 Phase II - Stage 2
na
additional 2035
Suswa Phase I - Stage 1
2033
delayed by 2 years
Suswa Phase I - Stage 2
2035
Suswa 2 Phase II - Stage 1
2035
Baringo Silali Phase I, Stage 3
2033
delayed by 1 year or more delayed by 2 years
AGIL Longonot Stage 1
2034
delayed by 1 year
advanced by 1 year
As a consequence, the accelerated RE scenario does not substantially increase the share of RE generation in Kenya’s total power generation. The RE share in generation is only slightly higher (by on average less than half a percentage point for the period 2020 to 2035) compared to the moderate RE scenario (see Figure 4-4). For the years 2031 to 2034 the RE share in total generation is even lower than in the moderate RE case. The increased utilisation of volatile wind and solar resources induces additional reserve requirements. In the simulation this can be observed in the reduced hydropower generation that need to run below their minimum to utilise all water (minimum outflow) more often compared to the moderate RE case. This leads to not utilised (spilled) water. Thus, increased wind and solar development would not only substitute geothermal generation but also reduce hydropower generation (however to a much lower extent). The accelerated RE scenario increases excess energy since more must take generation is included in the system. This increase can be enormous, even doubling excess energy for some years (the Figure 4-5). Evidently, the effects of the slowed down RE scenario point to the opposite direction. If less wind and solar generation capacity is introduced to the system (compared to the moderate case), the emerging supply gap is covered by advanced commissioning of geothermal power plants (see again Table 4-3). Again, the simulation results emphasise the relationship between different renewable energy sources in the Kenyan power supply system. Volatile renewable sources (complemented with conventional thermal capacity) and geothermal resources appear as substitutes. This outcome is backed by the development of total RE shares in generation: Compared to the moderate case the slowed down RE scenario does not induce a substantial reduction of the overall RE shares. Between 2031 and 2034 the overall RE shares are even higher than in the moderate RE case caused by advanced realisation of geothermal projects and less reserve requirements due to lower wind and PV development targets. This leads to a lower utilisation of coal-fired plants vis-à-vis the moderate case. A reduced expansion of must take RE capacity in the slowed down RE scenario reduces excess energy. The three considered RE scenarios do not distinctly differ in the share of renewable energy in the Kenyan power system. As shown in Table 4-4 the moderate RE scenario already leads to a rather
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 90
large share of renewables in total generation and consumption – calculated as the average shares in the period from 2015 to 2035. On average, the accelerated RE case increases the RE share in generation by roughly 0.2%, as compared to the moderate scenario. Even a less ambitious development of wind and solar resources – as stipulated by the slowed down RE scenario – does not lead to decreasing shares of all RE generation in total generation in Kenya. Reduced development of wind and PV is compensated by advanced utilisation of geothermal resources.
Table 4-4:
RE shares in generation (average 2015-2035) Moderate RE
RE share in total generation
82.4%
Accelerated RE
Slowed down RE
share
Difference to Moderate RE
share
Difference to Moderate RE
82.6%
0.2%
82.4%
0.0%
As a first important result the analysis revealed the potential of wind and solar power to substitute a portion of the huge geothermal contribution to the Kenyan power supply system. The simulation shows that the Kenyan generation system can be well suited to include a substantial amount of volatile power generation from wind and solar resources. Such resources can be interpreted as an alternative option to maintain large RE shares in the Kenyan system. Although Kenya disposes of considerable geothermal resources as well as an adequate project pipeline to exploit them, wind and solar power can serve as an insurance. Wind and solar resources can help:
To slow down the depletion of the geothermal resources in Kenya. They are able to save parts of the resource for future use – beyond the current planning horizon. However, the actual depletion of geothermal fields and the future value of (saved) geothermal sources are difficult to estimate. Therefore, this reason may not be sufficient to justify solar and wind development alone.
To diversify the Kenyan fuel mix – thereby reducing the dependency on the geothermal resource and on other, mostly conventional fossil fuels. As wind and solar potentials are available in different regions of the country, this can also contribute to a more decentralised structure of power supply.
To introduce new opportunities for the Kenyan manufacturing and service sectors – thereby enabling creation of added value and job opportunities on a regional level.
Notwithstanding the potential benefits of increased wind and solar generation in Kenya, the accelerated development induces excess cost. Table 4-5 gives an overview on the cost implications.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 91
The table provides information on the development of annual capital and O&M cost, as well as fuel cost and resulting system-wide power generation cost (LEC) for the years 2020 to 2035. The column labelled ‘Present value’ reflects the sum of the discounted annual cost figures over the whole planning horizon from 2015 to 2035.46 As compared to the moderate case, the accelerated RE scenario increases annual capital cost by 0.1% in 2020 up to 6% in 2035. Over the period from 2015 to 2035 the present value of all annual capital cost is 1% higher than in case of the moderate scenario. The impact on fixed and variable O&M cost is lower with an increase of 0.5% for the present value. The present value of the fuel cost is higher by about 1%. For half of the years in the period 2020 to 2035 the fuel costs decrease with the accelerated development of wind and PV. However, in a few years the fuel consumption increases considerably due to the delay of geothermal capacity and the resulting higher dispatch of coal units and generic back-up units (which are assumed to run on gasoil) during those years.
46
Only annual values as of 2020 are reported since both considered RE scenarios fully enfold after this year. Values from 2015 to 2019 are (almost) similar in all scenarios. For the sake of comparability present values include also the first years.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 92
Table 4-5:
Cost implications of RE scenarios Unit
Capital cost (Investment & rehabilitation) Moderate RE Accelerated RE
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
1,136 1,137
1,247 1,256
1,355 1,364
1,356 1,373
1,399 1,416
1,458 1,483
1,542 1,569
1,695 1,733
1,728 1,769
1,857 1,915
1,973 1,991
2,143 2,188
2,305 2,337
2,427 2,472
2,604 2,748
997 998
1.0%
0.1%
0.1%
0.7%
0.6%
1.3%
1.2%
1.8%
1.8%
2.2%
2.4%
3.1%
0.9%
2.1%
1.4%
1.9%
5.5%
MUSD
8,471
996
1,134
1,246
1,354
1,347
1,397
1,448
1,530
1,681
1,710
1,831
1,934
2,090
2,230
2,366
2,521
-0.7%
-0.1%
-0.1%
-0.1%
-0.1%
-0.6%
-0.1%
-0.7%
-0.7%
-0.8%
-1.0%
-1.4%
-2.0%
-2.5%
-3.2%
-2.5%
-3.2%
MUSD MUSD
2,942 2,958
432 432
475 475
501 502
524 525
527 531
536 540
555 561
582 589
593 601
605 614
645 658
697 695
753 758
805 806
848 850
906 941
0.5%
0.0%
0.0%
0.3%
0.2%
0.7%
0.7%
1.1%
1.1%
1.4%
1.5%
2.0%
-0.4%
0.7%
0.1%
0.2%
3.8%
MUSD
2,930
432
475
501
524
525
535
552
580
589
602
641
688
740
786
835
889
-0.4%
0.0%
0.0%
0.0%
0.0%
-0.4%
-0.1%
-0.4%
-0.4%
-0.5%
-0.5%
-0.7%
-1.3%
-1.8%
-2.3%
-1.5%
-1.9%
579 587
2 2
15 16
32 35
55 56
79 77
98 94
121 116
127 123
98 89
130 113
159 134
141 179
131 147
136 187
204 265
227 224
1.4%
-0.1%
7.2%
6.9%
0.9%
-3.3%
-3.9%
-3.8%
-3.0%
-9.3%
-12.8%
-15.6%
27.0%
11.7%
37.4%
30.0%
-1.0%
597
2
16
33
56
82
98
122
129
102
137
170
160
149
170
204
251
3.1%
0.1%
2.0%
0.9%
2.0%
3.5%
-0.1%
1.3%
1.7%
4.1%
5.6%
7.0%
14.1%
13.6%
24.5%
0.3%
10.9%
MUSD MUSD
12,048 12,157
1,430 1,432
1,626 1,628
1,780 1,792
1,935 1,945
1,962 1,980
2,032 2,049
2,133 2,160
2,251 2,281
2,386 2,423
2,463 2,497
2,661 2,707
2,812 2,864
3,028 3,093
3,246 3,330
3,479 3,587
3,737 3,913
0.9%
0.1%
0.1%
0.7%
0.5%
0.9%
0.9%
1.3%
1.3%
1.5%
1.4%
1.7%
1.9%
2.1%
2.6%
3.1%
4.7%
MUSD
11,997
1,429
1,625
1,779
1,934
1,954
2,029
2,123
2,240
2,373
2,449
2,642
2,783
2,979
3,186
3,405
3,661
-0.4%
-0.1%
-0.1%
0.0%
0.0%
-0.4%
-0.1%
-0.5%
-0.5%
-0.6%
-0.6%
-0.7%
-1.0%
-1.6%
-1.9%
-2.1%
-2.0%
USD/MWh USD/MWh
10.07 10.16
10.70 10.71
11.26 11.28
11.51 11.59
11.69 11.75
11.08 11.19
10.56 10.65
10.37 10.50
10.24 10.38
10.06 10.21
9.71 9.85
9.72 9.89
9.59 9.77
9.65 9.85
9.66 9.91
9.67 9.98
9.71 10.17
0.9%
0.1%
0.1%
0.7%
0.5%
0.9%
0.9%
1.3%
1.3%
1.5%
1.4%
1.7%
1.9%
2.1%
2.6%
3.1%
4.7%
USD/MWh
10.03
10.69
11.26
11.50
11.68
11.04
10.55
10.32
10.19
10.00
9.66
9.65
9.49
9.49
9.48
9.47
9.51
-0.4%
-0.1%
-0.1%
0.0%
0.0%
-0.4%
-0.1%
-0.5%
-0.5%
-0.6%
-0.6%
-0.7%
-1.0%
-1.6%
-1.9%
-2.1%
-2.0%
Difference to Moderate RE
Slowed down RE
2021
8,527 8,612
Difference to Moderate RE
O&M cost (fixed and variable) Moderate RE Accelerated RE
2020
MUSD MUSD
Difference to Moderate RE
Slowed down RE
Present Value*
Difference to Moderate RE
Fuel cost Moderate RE Accelerated RE
MUSD MUSD
Difference to Moderate RE
Slowed down RE
MUSD
Difference to Moderate RE
Total cost Moderate RE Accelerated RE Difference to Moderate RE
Slowed down RE Difference to Moderate RE
System LEC Moderate RE Accelerated RE Difference to Moderate RE
Slowed down RE Difference to Moderate RE
*Discount rate: 12%
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 - 2035 – Renewable Energy f
28.11.2016
Page 93
Total costs are higher in the accelerated RE case than in the moderate scenario: cumulated and discounted costs are 1% higher than in the moderate scenario. Total cost development is dominated by the increase in capital cost, mainly in the later years. On average, specific generation cost are as well 1% higher, reflected by a system LEC of 10.2 USD/MWh compared to 10.1 USD/MWh in the moderate case. In contrast, slowing down the development of wind and solar resources may reduce cost. Annual capital cost would be up to 2% lower than in the moderate scenario. In total, cumulated and discounted annual capital costs are 1% lower. Fixed and variable O&M cost are lower (by 0.7% of the present value). The slowed down RE scenario increases fuel consumption and cost. This is mainly caused by higher dispatch of coal units in the last years of the study period. The fuel cost effect of the advanced commissioning of additional geothermal projects (in comparison to the moderate scenario) is apparently not as strong as between the accelerated and moderate scenario. Total costs of the slowed down RE scenario are lower as compared to the moderate case. Over the whole planning horizon savings of about 50 MUSD could be realised, which represents a reduction in total cost of roughly 0.4% versus the moderate case. The results show that scope and timing of wind and solar development markedly affects cost of the Kenyan power supply system. Results indicate that cost of adding an amount of renewable generation keeps at a similar level for different shares of already existing renewable generation in the system. This effect is further evaluated by calculation of incremental cost between the three scenarios. For each year (absolute) differences in total cost are related to differences in wind and solar generation. Furthermore annual differences in cost and generation are discounted and cumulated for the entire period (2015-2035). Relating cumulated and discounted cost to cumulated and discounted generation yields the expected long-run marginal cost (LRMC) for increasing the development of wind and solar power (from one scenario to another). Results of this analysis are presented in Figure 4-6. In general, the development of incremental costs is very similar for both increases of wind and PV capacities (from slowed down RE to moderate RE and from moderate to accelerated RE). Increasing the share of wind and solar is slightly more costly in the early years compared to the later years. The higher incremental costs in 2020 and 2021 derive from the development of solar PV in these years while wind generation remains the same. Thus, the observed high specific incremental costs are caused by the deployment of a relatively costly technology. In the long term the two cost curves converge to a lower cost level. This is caused by an increasing utilisation of wind power in all scenarios as well as the assumed cost degradation for specific costs of newly installed PV and wind plants. Annual incremental cost range between 847 USDcent/kWh and 21 USDcent/kWh in the beginning and 5 to 7 USDcent/kWh in the long term. When comparing the cost implications of the two switches between different PV and wind development scenarios the following can be observed. The switch from slowed down to moderate scenario appears to be less costly (on a per kWh basis) than the switch from moderate to accelerated scenario. The overall LRMC for the first amount to roughly 6 USDcent/kWh (indicated by the dot47
The very low incremental costs in 2022 for the shift from slowed down RE to moderate derive from a reduction of coal consumption in that particular year.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 94
ted orange line in Figure 4-6). The overall LRMC for latter case are higher and amount to roughly 7 USDcent/kWh (indicated by the dotted blue line in Figure 4-6). Concluding, increasing the share of wind and solar power generation will be more costly (in specific terms) when the share of already existing generation such kind gets higher. This is well reflected by the simulation results.
Incremental cost [USDcent/kWh]
25.0 20.0 15.0 10.0 5.0
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
Incremental Cost - Moderate RE -> Accelerated RE
Incremental Cost - Slowed-down RE -> Moderate RE
LRMC - Moderate RE -> Accelerated RE
LRMC - Slowed-down RE -> Moderate RE
Figure 4-6:
4.4
2021
2020
0.0
Incremental cost and LRMC of RE expansion
Conclusions
The analysis of different RE expansion pathways revealed several important implications regarding the development of the Kenyan power generation system and the associated cost. First and foremost, a more ambitious development of wind and solar potentials in Kenya does not necessarily lead to an increased share of renewables in generation. This is mainly caused by two reasons: (i) Additional wind and solar capacities postpone the commissioning of geothermal projects. So, wind and solar generation directly crowds out another renewable energy source, and (to a much lesser extent) (ii) volatile wind and solar generation increases the reserve requirements in the system. Results also revealed the potential to include wind and solar power: The generation system may well be operated when larger wind and solar capacities exist. Against this background, wind and solar generation might be interpreted as a long-term alternative to the geothermal resource in Kenya. Despite the additional cost of an over-ambitious development, these resources may contribute to the future generation in Kenya:
They can slow down the depletion of the geothermal resources in Kenya and are thus able to save parts of the resource for future use – beyond the current planning horizon. However, the actual depletion of geothermal fields and the future value of (saved) geothermal sources are difficult to estimate. Therefore, this reason may not be sufficient to justify solar and wind development alone.
They enable a diversification of the Kenyan fuel mix and thereby reduce the dependency on the geothermal resource and on other, mostly conventional fossil fuels. As wind and
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 95
solar potentials are available in different regions of the country, this can also contribute to a more decentralised structure of power supply.
To introduce new opportunities for the Kenyan manufacturing and service sectors – thereby enabling creation of added value and job opportunities on a regional level.
However, the results underpin the important role of the geothermal resource as an available, cost-effective and emission-free energy source for Kenya.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 96
5
DISCUSSION OF RENEWABLE ENERGY INCENTIVE POLICIES
Once renewable energy development targets are defined, measures to safeguard the intended development need to be identified and designed. Regarding renewable energies a wide range of instruments may provide the necessary financial and institutional support. To date, countries worldwide employ various support schemes. Although all of the measures are designed to promote the use of renewable energy sources, policies vary considerably in their design and conception. In order to provide an overview of different measures, it is useful to categorize or structure policies and instruments using important different distinctive features. The following list provides starting points for such a categorization or distinction of potential policies and instruments.
Instruments may directly be targeted at the renewable energy sector or they could only complement the development of RES (indirect measures).
Instruments could be direct public- or governmental action or result from indirect regulatory action. Instruments may be differentiated along the value chain of renewable energy production. A distinction between market parties and market variables is also possible.
The following section describes the mentioned features.
5.1
Direct versus complementary measures:
A first general categorization can be made between instruments that directly affect investments in or the use of renewable energies and measures that mainly target the framework conditions for renewable energies, e.g. the removal of barriers such as lacking public acceptance, the streamlining of planning and permission procedures etc. Regarding the achievement of given targets and given the general background of the project the direct instruments are certainly more important and hence the focus of this report lies on them. Nevertheless, indirect measures may be valuable and sometimes necessary complements. So whenever needed, indirect instruments are mentioned as well.
5.2
Direct public or governmental action vs. indirect regulatory action:
As stated above, the direct promotion of RES development usually aims at increasing the market penetration of renewable energy technologies. Countries have two different general options to achieve this. These are:
Public- or Governmental Action: In this case the state or the government directly takes action concerning the investment in and construction of renewable energy facilities. The state might become active directly via a public or governmental body (ministry or agency)
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 97
or assign the responsibility to develop renewables to a third party. The latter case involves various forms of public procurement and public private partnership.
5.2.1
Regulatory Action: The government may take regulatory action to safeguard the achievement of the targets. In most of the cases regulation shall thereby ensure a market for energy produced from renewable sources. Regulatory approaches might involve subsidies, fiscal measures or other market-based instruments but also command and control regulation (e.g. mandatory standards).
Stages of the value chain:
Another possible characterization of policy instruments is the stage of the renewable energy value chain the policy affects.48 Governmental support for electricity from renewable energy sources can thereby affect the following simplified stages: research and development (R&D), investment in RES, the production and the consumption of electricity.
Instruments targeting R&D only indirectly affect the energy or electricity market. They aim at strengthening the manufacturing industry and at creating knowledge and knowhow. Possible policy instruments are R&D subsidies, grants for demonstration facilities, special loans etc.
Other instruments may directly promote investments in renewable energy projects. The most common instruments are direct investment subsidies, tax exemptions / reductions for investments (e.g. reduced import duties for renewable energy goods etc.) or soft loans.
Instruments targeting the production of RES mainly affect the market for renewable energy. For example feed-in tariffs may be used to provide favourable revenues for RES investments or quota obligations for or renewable portfolio standards for suppliers safeguard a certain RES share in the electricity market. Besides the direct market regulation also fiscal incentives (e.g. reduced income taxes on revenues from RES production and trade) are possible.
Finally, instruments might target electricity consumption. Consumers might be obliged to satisfy a certain share of their demand by renewable energy or the consumption of RES could be promoted by fiscal incentives such as reduced tax rates (e.g. VAT).
5.2.2
Affected market parties and market variables:
Some of the measures stimulate the supply of renewable electricity, while others directly affect the demand. Furthermore, support schemes can be distinguished according to the supported 48
Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECNC--03-029.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 98
activity, i.e., either capacity installation is promoted or the generation of green electricity. Figure 5-1 depicts a possible categorization along these dimensions. A further categorization approach differentiates between price and quantity. Instruments may either influence the price for renewable energy or prescribe a minimum quantity of renewable energy to be produced or consumed. A possible categorization is presented in Figure 5-2.
Generation-based (kWh)
Feed-in systems Fiscal Measures Tendering / Bidding Subsidies
Quota Obligations Green Pricing Fiscal Measures Demandside
Supplyside
Investment Subsidies Fiscal Measures
Quota Obligation
Capacity-based (kW)
Figure 5-1:
Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Capacity and Production49 Price (e.g. per kWh)
Feed-in systems Fiscal Measures Tendering / Bidding Subsidies
Fiscal Measures Subsidies to Consumers Demandside
Supplyside Investment Subsidies Fiscal Measures Quota Obligations Tendering / Bidding
Quota Obligation
Quantity (e.g. kWh)
Figure 5-2:
Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Price and Quantity50
49
Uyterlinde, M. et al. (2003): Challenges for investment in renewable electricity in the European Union: Background report in the ADMIRE REBUS project, ECN report, ECN-C--03-081. 50 Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECNC--03-029.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 99
5.3
Description and discussion of relevant incentive schemes
Over the last years the choice and the design of policy measures for the promotion of renewable energies has changed. More specifically, there has been a shift – as more generally in environmental policy design – from command-and-control policies to market-based instruments such as taxes, subsidies (also in form of predetermined feed-in tariffs), and tradable quotas. In the context of renewable energy promotion, taxation of energy in many EU countries meanwhile comes along with tax breaks or tax exemptions to renewable energy working as implicit subsidies to correct relative prices with respect to energy security and environmental. In addition, direct subsidies for renewable energy are warranted – typically differentiated by the type of green energy, i.e., hydropower, wind, biomass, solar, etc. A relatively new strand of policy regulation is the use of tradable green quotas where energy suppliers are required to produce a certain share of energy services from renewable energy but are flexible to trade these shares between each other in order to exploit potential difference in specific compliance costs. Recent surveys for Europe show that feed-in tariffs are the most common promotion measure followed by quota obligation systems with tradable green certificates (TGC). Especially for PV investment subsidies have been an important instrument in Europe. In contrast, tender schemes and fiscal measures only play a minor role.
5.3.1
Direct Subsidies I – Investment Subsidies
A straightforward way to influence the relative cost of renewable and conventional thermal power generation are investment subsidies. Financial support for investment in RES technologies has a long history and is still is a common and widespread instrument for the support of renewable energy. The main mechanism of this instrument is as well straightforward: Subsidies reduce the effective investment cost of a project to a level that shall ensure the economic and financial viability of a project. Subsidies are usually used in cases where relatively high initial investment cost constitutes the main barrier to investment. This is one reason why investment subsidies have been a popular measure to promote the investment in relatively capital intensive solar power projects. In most cases investment subsidies are used to complement other support instruments. The may easily range between 20-50% of investment costs. When subsidies are paid on the specific (per unit) output of a grid-connected power plant the system might also be considered a feed-in tariff scheme. Summarizing the features of investment subsidies the following advantages and problems of the scheme can be identified: Advantages:
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 100
Subsidies lower payback periods and thus reduce investment risks
They reduce funding requirements and thereby eases funding
They are a well-known instrument and are easy to implement and administer
Problems:
Subsidies may create windfall profits (when subsidy set too high or when investment would have been realized anyway)
They only promote investment; no incentive to operate capacities in an efficient way is provided
5.3.2
Direct Subsidies II – Feed-In tariff systems
Feed-in tariff schemes directly affect the revenues of RES projects. The system determines the price paid for a unit of produced electricity. Generally, such systems are applied to grid-connected RES capacities. Additional costs caused by the feed-in tariffs are normally recovered by the grid operator via the respective tariff structure. 51 Feed-in tariff systems feature two important characteristics: 1) The system ensures the economic and financial viability of the renewable energy project. This is probably the most important justification of financial support schemes for renewable energy projects. The feed-in tariff shall guarantee that investments in RES facilities are economically viable and provide a competitive rate of return. 2) The system shall reduce the investment risk to a manageable level. The tariff level determines to a large extent the revenues of the whole investment project. Under a feed-in tariff regime, fluctuations in revenues of PV projects only depend on fluctuations in generation and not on potentially volatile electricity prices. A crucial prerequisite of a feed-in tariff system is the guarantee of an adequate long-term off-take of the produced electricity (either via a PPA or via legislation).52 Depending on the technology and the respective market conditions in a region or country these PPAs typically have durations ranging from 10 to over 25 years.53 As indicated earlier the granted tariffs can be differentiated by technology type but also by project size, resource quality or project location. The system can be 51
European Commission (2008), The support of electricity from renewable energy sources - Accompanying document to the Proposal for a Directive to the European Parliament and of the Council on the promotion of the use of energy from renewable sources, COM(2008) 19 final. 52 Menanteau, P.; Finon, D.; Lamy, M. (2003): Prices versus quantities: choosing policies for promoting the development of renewable energy, Energy Policy (31, 8), pp. 799–812. 53 Klein, A.; et al. (2008): Evaluation of Different Feed-in Tariff Design Options: Best Practice Paper for the International Feed-in Cooperation, 2nd Edition. Berlin, Germany: BMU. October 2008.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 101
designed to account for technological change by automatically adjusting payment levels. In many existing feed-in tariff systems the tariffs for new installations (new vintages) decline automatically in subsequent years. Feed-in tariff systems can generally be divided into two groups: 1) Systems with fixed tariffs: tariffs are usually based on cost figures (renewable energy production costs or avoided costs of conventional supply) and, 2) Premium systems in which the tariff is based on market prices for electricity (e.g. electricity wholesale prices) plus a specific additional premium. This premium may either be fixed or variable. A fixed premium can lead to fluctuations in revenues caused by volatile electricity prices. Recently systems with variable premiums emerged (e.g. Netherlands) in which the premium is adjusted to compensate for fluctuations of the electricity price. The majority of premium based systems do not fully alleviate the revenue risk. Investors and developers would need detailed information on the potential future development of electricity prices in order to evaluate the solar project correctly. In addition, premium systems favour dispatchable electricity generation, since usually electricity prices are higher in peak-load periods. Although solar PV will probably feed in electricity during peak-load (see Section 2) premium systems would rather be suitable for biomass or CSP facilities (with storage) that can be flexibly dispatched. The design of an adequate feed-in tariff system is subject to economic and administrative tradeoffs. On the one hand, the system must be effective, i.e. the financial support provided by the tariff must be high enough to attract investors and thus achieve the given target. The remuneration of electricity fed into the grid directly affects the return of the whole investment project. In order to foster investments the tariff must be high enough to ensure an attractive rate of return. On the other hand, the tariff system shall achieve given targets in an efficient way. The feed-in tariff system usually causes additional costs compared to a situation without the promotion of renewable energy. These costs need to be re-financed via the electricity bill of customers or subsidized via the national budget. Over-subsidization may help to achieve specific RES targets in due time but could impose a huge burden on the economy. As shown later in this report several measures are possible to avoid over-subsidization and high costs. In order to be attractive to investors, the tariff system should account for specific needs of investors and cover a wide range of possible project options. Especially the approaches to differentiate the feed-in tariff system regarding technological- or site-specific aspects aim at providing favourable conditions for a wide range of technologies and locations. Nevertheless, the more diverse and specific the tariff scheme is, the more information is needed on the part of regulation. From an administrative and regulatory point of view diverse systems are much more difficult to design in order to be effective and efficient. The level of diversion is positively correlated with the level of information needs as well as administrative efforts. The inherent danger of (economic) efficiency losses is much higher in diverse and fragmented systems than in harmonized and simple ones. Summarizing the design of feed-in tariffs is subject to two important trade-offs:
The system should provide a sufficient level of promotion but should also minimize the associated economic cost
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 102
The system should facilitate diverse promotion and account for the perspective of project developers and investors but should also be administratively manageable and efficient.
Considering the features of feed-in tariff schemes the following advantages and problems of the scheme can be identified: Advantages:
The systems can be effective as well as efficient when prices (tariffs) are set at the correct level
They are flexible: The system allows for a targeted promotion of different technologies, even different technology bands (e.g. project sizes, location etc.)
They feature lowest market risk, revenues are known (almost certainty), the feed-in tariff promotes bankability of projects
The systems have proven the ability to create viable markets and industries
Problems:
Feed-in tariff schemes needs well informed regulation and experience with the promoted technology
When they are introduced to existing markets the system might need additional complementing measures in early stages
They might lead to higher cost for economy (in the short term) but: cost might be justified by other policy goals (e.g. industry or technology development etc.)
5.3.3
Competitive bidding / tendering
Competitive bidding is used to select either developers for one or more specific sites or beneficiaries for investment subsidies or production support (e.g. feed-in-tariffs under the former NonFossil Fuel Obligation regime in the United Kingdom). Competitive bidding is not necessarily linked to the promotion of renewable energy projects. Bidding procedures are most common in the area of private participation in the energy supply sectors (e.g. to involve independent power producers – IPPs). The systems shall thereby facilitate an efficient allocation of scarce resources such as promotional funding, land or grid capacities. In most of the cases bidding procedures are a form of direct governmental action. The government or a public-/ governmental body (e.g. a designated renewable energy authority) issues invitations to tender or requests for proposals. These documents specify the main characteristics a potential power generation facility should have. Interested potential developers then submit their
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 103
bids. Previously specified criteria are used to evaluate and rank the bids and select one or more developers. The criteria for judgment of the bids are set before each bidding round. Many options exist to evaluate or select winning bidders; usually auctions are used to select the bids. The competition facilitated by the system ensures that the level of support decreases with cost reduction caused for example by technological development. Competitive bidding mechanisms can be most effective in driving down the price of renewable energy projects (as experiences in the UK under the NFFO and Ireland have demonstrated). The bidding mechanism is an effective instrument only if the awarded projects are indeed carried out. The UK experience has demonstrated that is not always the case. Many wind projects that were successful in the bidding round were not implemented because of problems caused by planning and land-use procedures. Policy might take additional efforts to ensure the actual implementation of the accepted bids. In addition, the costs of the scheme are relatively predictable and the maximum is known. This usually increases acceptance from a political point of view. However, the effectiveness might be reduced whenever the competition in the bidding rounds yields only very low prices.54 Competitive bidding is mostly used when regulation has no or only little information on the techno-economic characteristics of RE technologies. In many cases bidding rounds shall not only facilitate investments but also generate reliable estimates of costs and energy yields. Summarizing the features of competitive bidding as a promotion scheme the following advantages and problems can be identified: Advantages:
The bidding procedure usually has a clear scale and scope; capacities and allocated budget etc. can usually be well determined.
The scheme generates valuable information regarding potential cost and performance of projects
The main advantage is: Ability to drive down cost (if really competitive)
Problems:
The scheme does not automatically guarantee that the winning projects will indeed be realized (e.g. due to land-use issues or lengthy planning procedures or strategic behaviour of investors)
Many bidding rounds are needed to achieve goals, especially when projects are relatively small
54
Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECNC--03-029.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 104
5.3.4
The scheme is only useful for larger projects; it is not applicable to small- and medium scale solar projects
Quota obligations and tradable certificates
A relatively new instrument is the quota system, or a Renewable Portfolio Standard. Under such a scheme the government only provides a market framework for the production, trade, distribution or consumption of a certain amount of energy from renewable sources. The obligation is imposed either on consumption (mostly via distribution companies) or production. Depending on the concrete design of the system regulation may also establish ‘technology bands’ that could be used to protect technologies from strong competition by lower cost options. Especially, quota obligations on the supply can be defined per technology (group). This approach guarantees a technology mix, promoting also technologies that are currently less cost effective. The guarantee of a market favouring less cost effective options, implies that the overall cost effectiveness of the system is lower. In contrast to the central price driven regulation of feed-in tariff systems, quota obligation systems with tradable green certificates (TGC) make use of decentralized market mechanisms in order to meet overall national RES targets in an efficient way. The quota system implicitly assigns a scarcity price to the “greenness” of electricity as an explicit policy objective. Despite the mentioned ‘banding’ there is usually no differentiation between alternative renewable energies. Quota obligations on the consumption usually do not specify technologies- and therefore generally lead to a selection of the cheapest options under the obligation. In other words: It is left to the market to sort out which type and quantity of renewable energy will serve most efficiently the policy objective of green electricity. From a theoretical and purely economic perspective such systems are considered efficient.55 If market participants fail to achieve their quota obligation, a penalty or fine will be put on each kWh shortfall. The effectiveness of a quota system now strongly depends on the level of this penalty. If penalty levels are set too low, non-compliance is the cheaper option for the obliged parties. The consequence is that the necessary investment to achieve a national target will most probably not be triggered. In addition, a quota system does not promote investments above those necessary to meet the obligation. The effectiveness of the quota on consumption on inducing additional installation depends also on the frontiers of the markets. If the system provides for flexibility across different markets (or even countries or regions) – i.e. if ‘foreign’ trade of certificates is allowed – a quota system could promote investments in RE capacities abroad. A policy goal that aims at domestic investments will possibly not be achieved under such a scheme.
55
See again the theoretical considerations in Menanteau, P.; Finon, D.; Lamy, M. (2003): Prices versus quantities: choosing policies for promoting the development of renewable energy, Energy Policy (31, 8), pp. 799– 812.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 105
However, green certificates may pose a higher risk for investors and long-term, currently high cost technologies are not easily developed under such schemes. Considering the features of this scheme the following advantages and problems can be identified: Advantages:
The system is usually considered as being effective: Targets will be met (but only if the system is designed and administered correctly, i.e., penalties for non-compliance have to be set at a sufficiently high level).
Theoretically the system is efficient when the quota is tradable (green certificates): Decentralized market mechanisms will sort out high cost projects; low cost for economy.
Low information requirements for regulation are induced.
Problems:
5.4
The system leads to a substantial market risk for developers and producers; future revenues from project are difficult to determinate.
No (limited) technology diversification in broader renewable energy context is possible; only the low-cost options are promoted.
The system creates a rough climate for new technologies, smaller projects and developers.
Windfall profits are possible.
The feed-in tariff for renewable energy in Kenya
The first Kenyan FIT policy was enacted in 2008 and only included wind, hydropower and bioenergy generation of electricity. The 2010 version of the FIT modified the existing tariffs and included geothermal, solar and biogas electricity. A second revision has been undertaken in December 2012. The FiT policy is revised every 3 years. The FiT guarantees power purchase agreement with power utility, Kenya Power & Lighting Company (KPLC). The FIT are not fixed but are calculated on a technology-specific basis using the principle of cost plus reasonable investor return. Except for solar PV the policy considers only grid connected power plants. The tariffs shall apply for 20 years from the date of the first commissioning. End-users fee connection and tariff are uniform for all KPLC customers. Pre-financing facility exists for connection charges (“Stima loan”).
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 106
Table 5-1: Technology
Current Feed-in-Tariff Structure Eligibility <10 MW
Standard FiT
Eligibility >10 Standard FiT MW
Wind Geothermal Hydro
0.5 -10 0.11 US$ 10.1-50 0.11 US$ 35 – 70 0.08 US$ 0.5 – 1 0.12 US$ 10.1-20 0.08 US$ 1-5 0.10 US$ 5 - 10 0.08 US$ Biomass 0.5-10 0.10 US$ 10.1-40 0.10 US$ Biogas 0.2-10 0.10 US$ Solar (Grid) 0.5-10 0.12 US$ 10.1-40 0.12 US$ Solar (Off-grid) 0.5-10 0.20 US$ Source: Adaptation from Draft National Energy and Petroleum Policy (Jan 2014) Generally, the Kenyan regulation provides for a sound implementation of a feed-in tariff system. The system provides connection and dispatch guarantees for projects, stipulates a standardized PPA template and defines a basis for cost recovery. However, the current feed-in-tariff structure presents some bottlenecks that hinder a wider development of renewable energy:
The feed-in tariff policy stipulates a review and potential revision of the tariffs after three years after issuing. This period may be too long against the background of cost developments (for components and equipment) on world markets. Tariffs should be monitored regularly and potential revisions could be scheduled after 12 – 18 months.
The Policy and institutional framework is uncompleted for small scale mini-grids: There are no clear tariffs for off-grid and mini-grid systems, in particular below 500kW;
The regulatory framework for off-grid electrification, i.e. small scale renewable energy projects, is not clear as the FIT considers only solar PV as off-grid technology;
Negotiating PPA may be a very lengthy process while the purchase of license and concessions may be another obstacle for the implementation of those projects. Especially, potential limits of the priority dispatch for projects larger than 10MW might induce an additional and non-negligible development risk for such projects.
The connection to the grid, whose costs are borne by the IPP, can be very expensive for some sites (depending on the distance to the network and suitable substations) and may further slow the negotiation process;
Absorbing additional production capacity on the grid may call for an upgrade of the network.
A common feature of feed-in tariff systems is their decentralized character. Regulation defines tariffs and adequate framework conditions for developers. The market then decides how many projects will be realized as well as their size and location. In other words: Regulation only has an indirect influence on the amount of electricity generation under a feed-in tariff scheme. The
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 107
quantitative analysis of different renewable energy development pathways for Kenia (provided in Section 4) revealed an important result: Due to the large amount of currently committed power plants and the direct substitution of one renewable resource (geothermal) by other renewable resources (solar and wind), an incentive system that does not allow to directly influence the quantity of renewable generation might lead to undesired effects. Uncontrolled growth of some renewable projects could sharpen the effects presented in Section 4, and – as the FiT is linked to guaranteed dispatch – additionally increase the cost in the sector. Although such excess cost might be justified (see Sections 4.3.2 and 5.3.2) a comprehensive cost-benefit analysis would be required. For next revision of the FiT policy (the short- to medium term future) it could be considered to limit the eligibility of large scale wind or solar projects under the FiT. Incentives for small-sized renewable generation projects may substantially contribute to (rural) electrification efforts – especially when paired with a net-metering policy. Net-metering for small scale renewable energy sources projects with PPA is under consideration in the Energy Bill 2014. Especially small-scale embedded generation can be promoted by net metering. A net metering scheme usually remunerates embedded generation at retail prices. Selfconsumption of generated electricity directly leads to cost savings. Excess generation fed into the distribution grid, when embedded generation exceeds consumption, may either be remunerated or accounted against future electricity consumption from the grid. Net metering generally requires grid parity at retail prices The net-metering is an incentive policy that would be a low cost and low risk way to promote grid connected solar PV and other small scale renewable energy technology such as wind and biogas. The new Energy Bill contains provisions for the Establishment of a “framework for connection of electricity generated from solar and wind energy to national and isolated grids, through direct sale or net-metering”56. Net-metering can apply to all renewable energy technologies embedded in the distribution systems; however most of the net-metering systems are likely to be solar PV. No major technical constraints or effect of the system load profile are expected. Embedded or distributed generation provides several advantages or benefits to the power supply system. The most important are: Self-generation lowers demand for electricity consumption from the distribution grid. Consequently, load may considerably be reduced if self-generation in distribution grids increases. In regions or parts of the distribution grid where load reaches critical levels regarding grid capacity, embedded generation can help to ameliorate stability issues and load shedding. Especially when distributed assets are able to generate power during peakload periods – either for self-consumption or to feed-in excess power into the distribution grids. In case renewable energy sources (wind and solar) are used, potential benefits depend on the relationship between demand or load characteristics of consumers and the specific generation profile of wind and solar assets.
56
Draft Energy and petroleum policy Jan 2015
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 108
Embedded generation based on renewable resources may contribute to fuel savings and fuel efficiency. While the fuel savings from additional renewables are limited in Kenya, fuel efficiency might be a considerable effect. Transmission is prone to losses in the transmission lines, substations and other electrical components. The avoided losses may directly be interpreted as benefits from embedded generation. The decentralized use of renewable energy sources can help to reach renewable energy development targets. A regulatory framework conducive to distributed renewable generation enables access to significant renewable energy potentials that might not be utilised in cases where only utility-scale projects are incentivised. Embedded generation can lead to considerable cost savings or additional revenues for operators of respective assets. If grid parity is reached, i.e. electricity prices are higher than cost of self-generation, operators are able to realise benefits. However, there are also some important prerequisites for the successful development of embedded generation: Limited capacity of distribution grids may hinder the development of embedded generation capacity. The distribution networks need to incorporate input from generation assets without jeopardising grid stability and security of supply. Especially large embedded generation projects may not always be connected to distribution grids without difficulties. In case of small-scale embedded generation (e.g. in residential buildings or households) appropriate metering equipment needs to be installed. In addition, the net-metering system has to be managed and administered which could lead to additional cost burden on the utility. Embedded generation might reach a level where specific demand nodes become generation nodes. This may directly affect higher voltage levels and compromise system stability and security of supply and require additional instrumentation and control systems. When increased self-generation of electricity results in overall cost savings – e.g. for households – embedded generation could lead to so-called rebound effects. Monetary savings could have adverse effects on households’ behaviour concerning the rational use of energy. High up-front investments and rather long payback periods are a barrier for private households’ engagement in self-generation of power. The payback period depends on the cost of the equipment, the electricity end-use price, the tariff, the energy yield of the system and the financing structure (financed by own funds and/or loans). Especially private households might rate such payback periods as a relatively high risk or are not completely aware of all the future benefits. In such a situation investments sometimes are not realised although they would be economically viable. According to the recent assessment57 of the impact of net-metering policy “there is a strong market for net metering in Kenya”, both for households and businesses that see it as financially attractive. Without any cap in the systems, in 5 years “Kenya could have net-metering capacity installed that generates 100 MW at its peak”.
57
EUEI-PDF Kenya 2013 Project Renewable Energy Regulatory Capacity Development. Assessment of a net metering programme in Kenya.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 109
5.5
Renewable energy international good practice benchmarking
As mentioned before, the new Energy Bill is likely to improve the overall framework for renewable energy. This chapter briefly describes some international good practices in the field of renewable energy that can be taken as source of inspiration.
5.5.1
Regulatory and policy options available when the main grid reaches the minigrid
The possible increase in the renewable energy mini-grids, as an interim solution to access in rural areas, require to set-up in advance a regulatory framework for the integration of mini-grids into the main grid. Regulations and policies should consider in advance the commercial options available to the Small power producers (SPP) once the main grid arrives in its perimeter in order to encourage investors in isolated mini-grids. A recent study financed by the World Bank58 gives the existing pre-connection options available, these are namely five options: 1. The SPP converts itself in Small power distributors (SPD) that buys electricity to the operator of the national grid and resells to retail customers. This option has been adopted in Asian countries such as Nepal, Bangladesh, Vietnam, and Cambodia and all of them were successful in scaling-up electrification. In particular in Cambodia the absence of a policy clearly indicating what to do once the grid has reached the area led to under-investments by the private MG operators. The Cambodian decided to allow the MG operators that meet sufficient technical standards to connect to the national grid and becomes SPD. “As of 2013, the Cambodian regulator has issued the licenses for 82 distribution utilities that were formerly isolated diesel powered mini-grids”. 2. The SPP leaves the retail sales business and keep only sells electricity to the utility at wholesale This option is viable if the cost of electricity production by the SPP, the new Feed in Tariff that the SPP now connected will receive for selling the generated electricity to the national grid, and the capacity factor at which the SPP will be able to operate. In Tanzania, where FiT for SPPs are based on the avoided cost, only small hydro projects and some biomass projects in the agro-industry sector are likely to be commercially viable while in Thailand with technology based FiT, technology such as solar and wind may be viable even in on-grid capacity. 3. The SPP simultaneously plays the SPD and SPP options, in the sense that it sells electricity to retail customers and to the national grid.
58
From the Bottom Up, How Small Power producers and Mini-grids can deliver electrification and renewable energy in Africa, Bernard Tenenbaum, Chris Greacen, Tilak Siyambalapitiya, and James Knuckles
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 110
This option is generally adapted for the countries that face generation capacity shortages and need at the same time to increase electrification in rural areas or where the grid is weak and the mini-grid may function as end of line voltage support to avoid brown or blackouts. 4. The SPP sells its distribution grid to the national grid operator and receives a compensation (Buyout option) 5. In the absence of any of the above the mini-grid is abandoned or moved to another area. In the case that a mini-grid does not meet the technical standard this may be the only option. In 2013, Tanzania appeared to be the first country in Africa to take into consideration the SPDs, the SPP rules under consideration by the Tanzania regulatory authority EWURA59, SPDs are explicitly allowed to apply to EWURA for the right to operate as:
An SPP selling to a distribution network operator (DNO) that is connected to the main grid
An SPD that purchases electricity in bulk from a DNO connected to the main grid and resells it to the SPD’s retail customers
A combination of an SPP and an SPD.
5.5.2
Bagasse-based cogeneration from sugar industries in Mauritius
Mauritius can be considered an African success story in co-generation from bagasse. About 56% of the current electricity generation in Mauritius comes from 4 power plants making use of bagasse, a by-product of sugar cane, and coal (40.5 MW to 83 MW) and one power plant operating only on coal (34.5 MW). Around 683,000 tonnes of coal and 1,000,000 tonnes of bagasse have been used for electricity production in 201360. The sugar industry in Mauritius is currently self-sufficient in electricity and sells the excess electricity generated to the national grid. It is to be noted that bagasse can be environmentally hazardous, if not used since during decomposition it releases methane which is a greenhouse gas 25 times more potent than carbon dioxide. Following the success achieved in large-scale firm power generation at one sugar factory and in continuous power generation from two other factories at the end of the eighties, the Government of Mauritius decided to clearly define its policy vis-à-vis bagasse electricity and enacted legislations which made provisions for fiscal incentives for energy conservation and utilisation within sugar factories and energy export to the public grid.
59 60
EWURA 2013 Climate Technology Centre & Network
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 111
The Mauritian Government has played an instrumental role in the development of bagasse cogeneration. In 1985, the Sugar Sector Package Deal Act (1985) was enacted to encourage the production of bagasse for the generation of electricity. The Sugar Industry Efficiency Act (1988) provided tax incentives for investments in electricity generation and incentives to encourage small planters to provide bagasse for electricity generation. The Bagasse Energy Development Programme was initiated in 1991 for the sugar industry. In 1994, the Government of Mauritius abolished the sugar export duty as an incentive for the industry. A year later, foreign exchange controls were removed and the centralization of the sugar industry was accelerated. Specific incentives in the past have included : (a) Performance-linked rebates on export duty payable by millers for efficiency in energy conservation to generate surplus bagasse and in energy generation, preferably, firm power; (b) income tax exemption on revenue derived from sale of power, and capital allowances in such investment; (c) raising of tax-free debentures; and (d) bagasse energy pricing. Bagasse-based co-generation development in Mauritius has delivered a number of benefits, including reduced dependence on imported oil, diversification in electricity generation, and improved efficiency in the power sector in general and increased incomes for smallholder sugar farmers. In recent years, the revenue from the sale of excess electricity from co-generation has enabled Mauritian sugar factories to remain profitable. A notable achievement has been the use of a wide variety of innovative revenue sharing measures. For example, the Mauritian co-generation industry has worked closely with the Government to ensure that substantial monetary benefits from the sale of electricity from cogeneration flow to all key stakeholders of the sugar economy, including the poor, smallholder, sugar farmers61.
5.5.3
Sri-Lanka – Net-metering policy
Sri Lanka established a net metering policy in January 200962. In addition to the Feed-in-Tariff policy established in the country since 1996, net metering was seen as an easy way to increase renewable generated electricity. In the initial phase the net-metering was allowed on small size systems but the limit was increased to 10 in July 2012, with an aggregate system limit of 10% of demand. There is no time-of-use tariff and the customer can carry forward the surplus credits up to 10 years even if he moves to a new location. There are no financial incentives for net metering in Sri Lanka. For residential high-income customers the electricity prices and decreasing equipment costs of PV are already a sufficient incentive to consider solar PV. For other categories of customers with lower or subsidized electricity prices, net metering may not be economically viable at the moment. In order to assess the com-
61
Deepchand, K. Commercial scale cogeneration of bagasse energy in Mauritius. Energy for Sustainable Development. (2001 ), http://www.researchgate.net/publication/245480882_Commercial_scale_cogeneration_of_bagasse_energ y_in_Mauritius (accessed: 15 January 2016) 62 Assessment of a net metering program in Kenya. Volume 1: Main report, March 2014 (Ministry of Energy and Petroleum, EUEI-PDF)
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 112
mercial viability of their net-metering investment, an Excel tool is made available to the customers. Despite the country has no specific incentives for net metering, the high electricity prices have led to a considerable increase in the installation of solar PV systems of an average of 2 – 4.5 kW (700 kW between June 2013 and March 2014 in aggregate across approximately 300 customers. No adverse impacts have been experienced so far and the net metering program has thus been expanded to allow system sizes of up to 10 MW. There is a challenge that in the remote case, if all high income domestic customers adopt net metering in Sri Lanka, the utility could suffer from high loss. Another potential future challenge that could also apply for Kenya due to the similarity of their peak consumption pattern, has been noted in the fact that there is a USD 0.03/kWh differential between daytime marginal electricity costs when solar PV exports to the grid versus peak evening consumption. In a high-uptake scenario where the utility would be compensated for this in addition to possible “banking” charges, the attractiveness of net metering for customers would probably decrease significantly.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy test
28.11.2016
Page 113
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Page 114
ANNEX 1 EXECUTIVE SUMMARY – ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 1
ANNEX 2 INTRODUCTION – ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 2
ANNEX 3 RENEWABLE ENERGY RESOURCES IN KENYA – ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 3
ANNEX 4 ANALYSIS OF RENEWABLE ENERGY EXPANSION – ANNEXES
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 4
Annex Table 1: Moderate RE expansion – annual data demand, capacity, reliability criteria Peak load Peak load + reserve margin Reserve margin Share on peak load Installed capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Firm capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total LOLE
Unit MW MW % MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW h/a
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 1,570 1,679 1,834 1,972 2,120 2,259 2,451 2,633 2,823 3,022 3,282 3,511 3,751 4,040 4,320 4,732 5,071 5,431 5,813 6,220 6,683 1,853 1,960 2,115 2,252 2,415 2,555 3,047 3,279 3,515 3,705 3,992 4,255 4,526 4,855 5,156 5,620 5,989 6,381 6,801 7,263 7,779 283 281 281 280 294 296 595 647 693 683 710 744 775 815 836 888 918 950 988 1,043 1,096 18% 17% 15% 14% 14% 13% 24% 25% 25% 23% 22% 21% 21% 20% 19% 19% 18% 17% 17% 17% 16% 614 799
634 799
634 816
619 823
934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446 614 627
634 627
634 631
619 633
934 635
721 54
691 54
691 54
691 54
1
6
17
635 54 400 22
6 6 28 61 124 0 0 0 0 0 2,021 2,012 2,043 2,073 2,804 0 3 12 28 0
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 1,824 2,129 2,379 2,549 2,849 843 852 861 870 879 977 986 995 1,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 327 654 981 981 981 981 981 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 244 244 244 77 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 159 167 175 184 192 140 350 490 490 560 840 980 1,050 1,190 1,610 1,890 576 576 601 601 626 626 651 651 670 670 720 795 870 970 1,070 1,150 56 56 61 61 71 71 81 91 111 131 151 171 191 221 261 301 3,570 3,983 4,333 4,622 4,597 4,846 5,168 5,475 6,028 6,303 6,840 7,277 7,764 8,301 8,882 9,521 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 1,824 2,129 2,379 2,549 638 640 642 644 647 720 722 724 1,119 1,278 1,280 1,283 1,285 1,287 1,289 327 654 981 981 981 981 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 244 244 244 77 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 27 33 38 44 49 55 59 63 67 71 75 79 84 88 92 140 350 490 490 560 840 980 1,050 1,190 1,610 144 144 150 150 156 156 163 163 168 168 180 199 218 243 268 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2,851 3,252 3,566 3,841 3,776 3,994 4,277 4,563 4,967 5,171 5,640 5,990 6,390 6,811 7,266 0 0 0 0 1 1 1 1 1 2 2 2 2 1 1
28.11.2016
2,849 1,291 981
400 96 1,890 288 0 7,795 1
Annex Page 5
Annex Table 2: Moderate RE expansion – annual data consumption and generation Electricity consumption Electricity generation: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Unserved energy Excess energy Share on total generation
Spilled water Share on potential generation of HPPs with dams
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh % GWh
4,941 5,154 5,158 5,031 5,892 6,073 7,111 7,177 7,264 7,372 7,577 3,741 3,737 3,810 3,832 3,894 3,934 3,584 3,681 3,788 3,902 4,356 283 628 1,058 1,536 1,863 692 1,115 1,503 1,600 4 17 16 16 30 30 51 0 0 0 6 0 0 0 0 0 2,641 2,655 2,654 2,653 2,662 2,663 2,681 9 53 145 188 237 285 333 382 430 478 1 78 78 560 1,243 2,132 2,357 2,357 2,444 2,444 2,531 2,531 1 1 1 1 87 96 96 104 104 122 122 9,453 10,093 11,084 11,857 14,839 15,368 16,385 17,037 17,732 18,586 19,660 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2,156 2,001 1,952 1,570 1,177 887 418 0% 0% 0% 0% 15% 13% 12% 9% 7% 5% 2% 10 14 15 24 10 10 399 341 274 199 19 0% 143
0% 96
0% 92
8,158 9,381 9,979 10,740 12,104 14,257 16,326 18,172 19,531 21,739 4,396 4,432 5,698 5,786 5,798 5,850 5,903 5,943 5,976 6,015 2,258 2,279 1,879 2,584 2,900 2,328 2,027 1,892 2,319 2,189 79 114 56 49 104 84 86 97 47 2,692 2,709 2,695 2,678 2,695 2,697 2,694 2,702 2,720 2,713 515 551 587 623 660 696 732 768 805 841 3 8 5 6 24 52 60 85 249 361 2,618 2,618 2,689 2,689 2,863 3,124 3,386 3,734 4,082 4,368 139 156 190 225 259 294 328 380 449 517 20,857 22,248 23,778 25,381 27,407 29,381 31,543 33,774 36,178 38,742 0 0 0 0 0 0 0 0 0 0 280 265 60 24 34 69 159 179 217 251 1% 1% 0% 0% 0% 0% 1% 1% 1% 1% 18 22 7 15 42 30 16 14 20 21
1% 0% 0% 13% 11% 9% 6% 1% 1% 1% 98 1,848 1,827 1,949 1,883 1,796 1,688 1,483 1,399 1,335
0% 737
0% 473
1% 517
1% 0% 0% 0% 0% 849 1,306 1,531 1,580 1,857
% 3% 2% 2% 2% 24% 23% 22% 21% 20% 19% 16% 15% 12% * assuming that all geothermal power plants are equipped with single-flash technology (no flexible handling of geothermal steam possible)
7%
4%
4%
6%
Vented GEO steam*
% GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 9,453 10,093 11,084 11,856 12,683 13,367 14,433 15,467 16,555 17,699 19,242 20,577 21,983 23,718 25,358 27,374 29,312 31,384 33,595 35,960 38,491
Share on potential maximum GEO generation
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
7%
8%
7%
8%
Annex Page 6
Annex Table 3: Moderate RE expansion – cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 3,127 Hydropower MUSD 2,009 Coal MUSD 910 Diesel engines MUSD 880 Gas turbines (gasoil) MUSD 42 Import MUSD 285 Cogeneration MUSD 182 Generic back-up capacity MUSD 147 Wind MUSD 861 PV MUSD 84 Total MUSD 8,527 O&M fixed Geothermal MUSD 1,089 Hydropower MUSD 191 Coal MUSD 385 Diesel engines MUSD 139 Gas turbines (gasoil) MUSD 6 Import MUSD 72 Cogeneration MUSD 80 Generic back-up capacity MUSD 87 Wind MUSD 246 PV MUSD 11 Total MUSD 2,022
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 249 272 0 149 9 0 0 0 7 0 686
256 272 0 149 9 0 1 0 7 0 693
256 278 0 137 9 0 5 0 34 0 717
256 203 0 137 9 0 13 0 74 0 691
395 207 0 124 9 63 16 0 133 11 958
404 464 464 464 464 445 466 526 497 525 601 730 864 973 1,049 1,181 211 200 203 206 209 253 256 259 430 441 444 412 415 418 422 425 0 101 201 302 302 302 302 302 302 302 302 302 302 302 302 302 124 109 109 109 98 98 98 98 98 77 77 55 55 55 18 0 9 9 4 4 0 0 0 0 0 0 0 0 0 0 0 0 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 21 25 29 33 38 42 45 48 51 54 58 61 64 67 70 73 0 0 0 0 0 15 38 54 54 61 92 107 115 130 176 207 154 154 160 160 167 167 173 173 178 178 191 210 229 254 278 298 12 12 13 13 15 15 17 19 22 26 30 33 37 42 49 56 997 1,136 1,247 1,355 1,356 1,399 1,458 1,542 1,695 1,728 1,857 1,973 2,143 2,305 2,427 2,604
87 22
90 22
90 22
88 23
131 23
134 23
28 1
22 1
22 1
22 1
0
2
5
20 1 10 6
2 0 137
10 0 147
21 0 159
38 1 231
2 0 140
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
156 24 43 18 1 10 11
156 24 65 16 1 10 13
156 24 65 14
156 26 65 14
165 26 65 14
186 26 65 14
186 34 65 14
195 37 65 11
221 38 65 11
266 38 65 8
313 38 65 8
351 38 65 8
376 39 65 2
422 39 65
20 1 10 8
156 23 22 18 1 10 10
10 15
44 1 242
44 1 284
46 2 309
46 2 331
48 2 333
10 16 3 48 2 339
10 18 7 50 2 356
10 19 10 50 2 382
10 20 10 51 3 393
10 21 12 51 3 406
10 23 18 55 4 444
10 24 20 61 5 496
10 25 22 66 5 552
10 26 25 74 6 602
10 28 34 81 7 642
10 29 40 88 8 699
28.11.2016
Annex Page 7
Annex Table 4: Moderate RE expansion – cost summary (2/2) Unit
NPV
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD
0 15 14 33 0 1,331 20 4 0 0 920
0 2
0 2
0 2
0 2
0 2
0 2
10 0
13 0
14 0
0 2 2 0
0 2 3 1
0 2 3 1
0 3 2 0
0 3 3 0
0 3 4 1
0 3 3 1
0 3 3 1
0 3 2 1
0 3 3 0
0
1
0 0 186 2
0 2 1 0 0 186 3
0 2 2 0
0
0 0 185 2
0 2 1 0 0 186 3
0 3 3
6 0
0 2 0 0 0 186 2
186 4
0 0 8
0 0 12
0 0 16
0 0 17
0 0 188
0 0 190
0 0 190
0 0 191
0 0 193
0 0 194
188 4 0 0 0 197
188 4 0 0 0 198
190 5 0 0 0 200
189 5 0 0 0 199
187 5 0 0 0 199
189 6 0 0 0 202
189 6 1 0 0 202
189 6 1 0 0 202
189 7 1 0 0 203
190 7 3 0 0 206
190 7 5 0 0 207
MUSD MUSD MUSD MUSD MUSD MUSD MUSD USDcent/kWh
525 281 1 98 579 0 12,048
75 0
111 0
128 1
0 0
2 0
14 2 0
31 2 0
52 3 0
76 3
91 6
110 9
111 14
89 7
121 7
136 14
111 12
98 12
93 14
114 7
108
43 0 43 0 876 9.26
75 0 916 9.08
111 0 990 8.93
1 1 3 2 2 8 18 21 29 83 118 130 0 2 15 32 55 79 98 121 127 98 130 159 141 131 136 204 227 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 997 1,378 1,430 1,626 1,780 1,935 1,962 2,032 2,133 2,251 2,386 2,463 2,661 2,812 3,028 3,246 3,479 3,737 8.41 10.86 10.70 11.26 11.51 11.69 11.08 10.56 10.37 10.24 10.06 9.71 9.72 9.59 9.65 9.66 9.67 9.71
O&M variable (other than fuel) Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Fuel cost Coal Diesel engines Gas turbines (gasoil) Generic back-up capacity Total Unserved energy cost Total cost System LEC
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 8
Annex Table 5: Accelerated RE expansion – annual data demand, capacity, reliability criteria Peak load Peak load + reserve margin Reserve margin Share on peak load Installed capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Firm capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total LOLE
Unit MW MW % MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW h/a
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 1,570 1,679 1,834 1,972 2,120 2,259 2,451 2,633 2,823 3,022 3,282 3,511 3,751 4,040 4,320 4,732 5,071 5,431 5,813 6,220 1,853 1,960 2,115 2,252 2,415 2,555 3,047 3,279 3,515 3,705 3,992 4,255 4,526 4,855 5,156 5,620 6,004 6,389 6,811 7,270 283 281 281 280 294 296 595 647 693 683 710 744 775 815 836 888 933 958 998 1,049 18% 17% 15% 14% 14% 13% 24% 25% 25% 23% 22% 21% 21% 20% 19% 19% 18% 18% 17% 17% 614 799
634 799
634 816
619 823
934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446 614 627
634 627
634 631
619 633
934 635
721 54
691 54
691 54
691 54
1
6
17
635 54 400 22
6 6 28 61 124 0 0 0 0 0 2,021 2,012 2,043 2,073 2,804 0 3 12 28 0
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 843 852 861 870 879 977 986 995 1,499 1,706 1,715 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 140 350 490 490 560 840 576 576 626 626 676 676 726 726 770 770 870 61 61 71 71 91 91 111 131 171 211 251 3,575 3,988 4,368 4,657 4,667 4,916 5,273 5,590 6,188 6,483 7,090 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 638 640 642 644 647 720 722 724 1,119 1,278 1,280 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 27 33 38 44 49 55 59 63 67 71 75 140 350 490 490 560 840 144 144 156 156 169 169 181 181 193 193 218 0 0 0 0 0 0 0 0 0 0 0 2,851 3,252 3,572 3,848 3,788 4,007 4,296 4,582 4,992 5,196 5,677 0 0 0 0 1 1 1 1 1 2 1
28.11.2016
1,624 1,964 2,104 2,229 1,723 1,732 1,741 1,750 981 981 981 981 244 244 244 77
2035 6,683 7,762 1,079 16% 2,749 1,759 981
400 400 400 400 400 159 167 175 184 192 1,190 1,190 1,400 1,820 1,820 1,020 1,170 1,370 1,570 1,750 291 331 391 471 551 7,632 8,179 8,806 9,482 10,201 1,624 1,964 2,104 2,229 1,283 1,285 1,287 1,289 981 981 981 981 244 244 244 77
2,749 1,291 981
400 400 400 400 79 84 88 92 1,190 1,190 1,400 1,820 255 293 343 393 0 0 0 0 6,056 6,440 6,846 7,281 1 1 1 2
400 96 1,820 438 0 7,775 2
Annex Page 9
Annex Table 6: Accelerated RE expansion – annual data consumption and generation Electricity consumption Electricity generation: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Unserved energy Excess energy Share on total generation
Spilled water Share on potential generation of HPPs with dams
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh % GWh
4,941 5,154 5,158 5,031 5,892 6,073 7,100 7,143 7,235 7,337 7,543 3,741 3,737 3,810 3,832 3,894 3,934 3,588 3,678 3,787 3,882 4,354 312 686 1,091 1,496 1,789 692 1,115 1,503 1,599 4 17 12 9 18 24 49 0 0 0 7 0 0 0 0 0 2,641 2,655 2,649 2,644 2,650 2,658 2,677 9 53 145 188 237 285 333 382 430 478 2 78 78 560 1,243 2,133 2,357 2,357 2,531 2,531 2,705 2,705 1 1 1 1 87 104 104 122 122 156 156 9,453 10,093 11,084 11,857 14,839 15,377 16,408 17,146 17,816 18,687 19,754 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2,156 2,009 1,975 1,678 1,261 988 512 0% 0% 0% 0% 15% 13% 12% 10% 7% 5% 3% 10 14 15 24 10 10 395 344 275 220 21 0% 143
0% 96
0% 92
8,073 9,260 9,769 10,643 11,968 12,748 14,981 16,077 17,089 19,939 4,392 4,428 5,698 5,786 5,798 5,871 5,903 5,942 5,975 5,987 2,147 2,183 1,680 2,215 2,390 2,745 2,064 2,201 2,531 1,793 81 115 54 46 93 111 104 138 60 2,690 2,703 2,688 2,675 2,688 2,711 2,701 2,725 2,724 2,703 515 551 587 623 660 696 732 768 805 841 3 8 5 7 22 98 90 175 391 384 2,879 2,879 3,038 3,038 3,386 3,908 4,430 5,127 5,823 6,457 190 225 294 362 431 500 569 672 810 948 20,971 22,352 23,812 25,396 27,436 29,388 31,575 33,825 36,208 39,052 0 0 0 0 0 0 0 0 0 0 394 369 94 38 63 75 191 230 248 561 2% 2% 0% 0% 0% 0% 1% 1% 1% 1% 23 26 8 15 42 9 15 15 21 48
1% 0% 0% 13% 11% 9% 7% 1% 1% 1% 98 1,848 1,828 1,960 1,917 1,825 1,723 1,517 1,484 1,456
0% 947
0% 571
1% 653
0% 0% 0% 0% 1% 702 1,285 1,348 1,372 2,829
% 3% 2% 2% 2% 24% 23% 22% 21% 20% 19% 17% 16% 14% * assuming that all geothermal power plants are equipped with single-flash technology (no flexible handling of geothermal steam possible)
9%
5%
5%
5%
Vented GEO steam*
% GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 9,453 10,093 11,084 11,856 12,683 13,367 14,433 15,467 16,555 17,699 19,242 20,577 21,983 23,718 25,358 27,374 29,312 31,384 33,595 35,960 38,491
Share on potential maximum GEO generation
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
8%
8%
7%
12%
Annex Page 10
Annex Table 7: Accelerated RE expansion – cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 3,072 Hydropower MUSD 2,009 Coal MUSD 910 Diesel engines MUSD 880 Gas turbines (gasoil) MUSD 42 Import MUSD 285 Cogeneration MUSD 182 Generic back-up capacity MUSD 156 Wind MUSD 957 PV MUSD 120 Total MUSD 8,612 O&M fixed Geothermal MUSD 1,070 Hydropower MUSD 191 Coal MUSD 385 Diesel engines MUSD 139 Gas turbines (gasoil) MUSD 6 Import MUSD 72 Cogeneration MUSD 80 Generic back-up capacity MUSD 93 Wind MUSD 275 PV MUSD 16 Total MUSD 2,038
2015
2016
2017
2018
2019
249 272 0 149 9 0 0 0 7 0 686
256 272 0 149 9 0 1 0 7 0 693
256 278 0 137 9 0 5 0 34 0 717
256 203 0 137 9 0 13 0 74 0 691
395 207 0 124 9 63 16 0 133 11 958
404 464 464 464 464 445 466 526 497 525 601 645 791 851 907 1,137 211 200 203 206 209 253 256 259 430 441 444 412 415 418 422 425 0 101 201 302 302 302 302 302 302 302 302 302 302 302 302 302 124 109 109 109 98 98 98 98 98 77 77 55 55 55 18 0 9 9 4 4 0 0 0 0 0 0 0 0 0 0 0 0 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 21 25 29 33 38 42 45 48 51 54 58 61 64 67 70 73 0 0 0 0 0 15 38 54 54 61 92 130 130 153 199 199 154 154 167 167 180 180 193 193 204 204 230 267 305 355 404 448 13 13 15 15 19 19 23 27 34 41 49 56 63 73 87 101 998 1,137 1,256 1,364 1,373 1,416 1,483 1,569 1,733 1,769 1,915 1,991 2,188 2,337 2,472 2,748
87 22
90 22
90 22
88 23
131 23
134 23
28 1
22 1
22 1
22 1
0
2
5
20 1 10 6
2 0 137
10 0 147
21 0 159
38 1 231
2 0 140
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
156 24 43 18 1 10 11
156 24 65 16 1 10 13
156 24 65 14
156 26 65 14
165 26 65 14
186 26 65 14
186 34 65 14
195 37 65 11
221 38 65 11
236 38 65 8
287 38 65 8
309 38 65 8
328 39 65 2
407 39 65
20 1 10 8
156 23 22 18 1 10 10
10 15
44 2 242
44 2 285
48 2 312
48 2 333
51 2 337
10 16 3 51 2 343
10 18 7 55 3 363
10 19 10 55 3 389
10 20 10 59 5 403
10 21 12 59 6 416
10 23 18 66 7 458
10 24 25 78 8 491
10 25 25 89 9 556
10 26 29 104 10 600
10 28 38 120 12 641
10 29 38 133 15 735
28.11.2016
Annex Page 11
Annex Table 8: Accelerated RE expansion – cost summary (2/2) Unit
NPV
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD
0 15 14 33 0 1,330 20 5 0 0 919
0 2
0 2
0 2
0 2
0 2
0 2
10 0
13 0
14 0
0 2 2 0
0 2 3 1
0 2 3 1
0 3 2 0
0 3 3 0
0 3 3 1
0 3 4 1
0 3 3 1
0 3 3 1
0 3 3 1
0
1
0 0 186 2
0 2 1 0 0 185 3
0 2 2 0
0
0 0 185 2
0 2 1 0 0 185 3
0 3 2
6 0
0 2 0 0 0 185 2
186 4
0 0 8
0 0 12
0 0 16
0 0 17
0 0 188
0 0 190
0 0 190
0 0 191
0 0 192
0 0 194
187 4 0 0 0 196
188 4 0 0 0 198
189 5 0 0 0 200
188 5 0 0 0 198
187 5 0 0 0 199
188 6 0 0 0 201
190 6 1 0 0 204
189 6 1 0 0 203
191 7 2 0 0 206
191 7 5 0 0 209
189 7 5 0 0 206
MUSD MUSD MUSD MUSD MUSD MUSD MUSD USDcent/kWh
514 281 1 140 587 0 12,157
75 0
111 0
128 1
0 0
2 0
15 1 0
34 1 0
54 2 0
74 3
88 5
105 9
107 14
81 7
104 6
113 13
130 16
100 15
107 20
124 9
92
43 0 43 0 876 9.26
75 0 916 9.08
111 0 990 8.93
1 1 3 2 3 8 32 31 60 132 133 130 0 2 16 35 56 77 94 116 123 89 113 134 179 147 187 265 224 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 997 1,378 1,432 1,628 1,792 1,945 1,980 2,049 2,160 2,281 2,423 2,497 2,707 2,864 3,093 3,330 3,587 3,913 8.41 10.86 10.71 11.28 11.59 11.75 11.19 10.65 10.50 10.38 10.21 9.85 9.89 9.77 9.85 9.91 9.98 10.17
O&M variable (other than fuel) Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Fuel cost Coal Diesel engines Gas turbines (gasoil) Generic back-up capacity Total Unserved energy cost Total cost System LEC
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 12
Annex Table 9: Slowed down RE expansion – annual data demand, capacity, reliability criteria Peak load Peak load + reserve margin Reserve margin Share on peak load Installed capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Firm capacity: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total LOLE
Unit MW MW % MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW MW h/a
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 1,570 1,679 1,834 1,972 2,120 2,259 2,451 2,633 2,823 3,022 3,282 3,511 3,751 4,040 4,320 4,732 5,071 5,431 5,813 6,220 6,683 1,853 1,960 2,115 2,252 2,415 2,555 3,047 3,279 3,515 3,705 3,992 4,255 4,526 4,855 5,156 5,620 5,999 6,391 6,811 7,280 7,786 283 281 281 280 294 296 595 647 693 683 710 744 775 815 836 888 928 960 998 1,060 1,103 18% 17% 15% 14% 14% 13% 24% 25% 25% 23% 22% 21% 21% 20% 19% 19% 18% 18% 17% 17% 17% 614 799
634 799
634 816
619 823
934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446 614 627
634 627
634 631
619 633
934 635
721 54
691 54
691 54
691 54
1
6
17
635 54 400 22
6 6 28 61 124 0 0 0 0 0 2,021 2,012 2,043 2,073 2,804 0 3 12 28 0
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 843 852 861 870 879 977 986 995 1,499 1,706 1,715 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 140 350 490 490 560 840 576 576 601 601 601 626 626 626 645 645 670 51 51 56 56 61 61 66 66 71 71 81 3,565 3,978 4,328 4,617 4,562 4,836 5,128 5,425 5,963 6,218 6,720 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 638 640 642 644 647 720 722 724 1,119 1,278 1,280 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 27 33 38 44 49 55 59 63 67 71 75 140 350 490 490 560 840 144 144 150 150 150 156 156 156 161 161 168 0 0 0 0 0 0 0 0 0 0 0 2,851 3,252 3,566 3,841 3,770 3,994 4,271 4,557 4,961 5,164 5,627 0 0 0 0 1 1 1 1 1 2 2
28.11.2016
1,824 2,129 2,379 2,649 2,949 1,723 1,732 1,741 1,750 1,759 981 981 981 981 981 244 244 244 77 400 400 400 400 400 159 167 175 184 192 1,050 1,120 1,260 1,610 1,890 670 695 720 745 750 91 101 116 131 151 7,142 7,569 8,016 8,527 9,071 1,824 2,129 2,379 2,649 2,949 1,283 1,285 1,287 1,289 1,291 981 981 981 981 981 244 244 244 77 400 400 400 400 400 79 84 88 92 96 1,050 1,120 1,260 1,610 1,890 168 174 180 186 188 0 0 0 0 0 6,028 6,416 6,819 7,284 7,795 1 1 1 1 1
Annex Page 13
Annex Table 10: Slowed down RE expansion – annual data consumption and generation Electricity consumption Electricity generation: Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Unserved energy Excess energy Share on total generation
Spilled water Share on potential generation of HPPs with dams
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh % GWh
4,941 5,154 5,158 5,031 5,892 6,074 7,111 7,179 7,258 7,376 7,592 3,741 3,737 3,810 3,832 3,894 3,934 3,585 3,682 3,793 3,917 4,355 290 630 1,080 1,591 1,862 692 1,115 1,503 1,599 4 17 16 18 30 30 51 0 0 0 6 0 0 0 0 0 2,641 2,655 2,653 2,654 2,661 2,662 2,678 9 53 145 188 237 285 333 382 430 478 2 78 78 560 1,243 2,133 2,357 2,357 2,444 2,444 2,444 2,531 1 1 1 1 87 87 87 96 96 104 104 9,453 10,093 11,084 11,857 14,839 15,361 16,384 17,037 17,744 18,556 19,653 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2,156 1,993 1,950 1,569 1,189 857 411 0% 0% 0% 0% 15% 13% 12% 9% 7% 5% 2% 10 14 15 24 10 10 398 341 269 184 20 0% 143
0% 96
0% 92
8,197 9,427 10,035 10,760 12,135 14,368 16,637 18,530 20,619 22,833 4,397 4,434 5,698 5,786 5,798 5,871 5,903 5,950 5,981 6,022 2,294 2,321 1,977 2,748 3,141 2,742 2,388 2,467 2,506 2,601 79 119 55 50 108 88 92 114 48 2,693 2,714 2,692 2,680 2,705 2,699 2,699 2,708 2,720 2,731 515 551 587 623 660 696 732 768 805 841 3 7 4 7 24 54 64 102 231 380 2,531 2,531 2,603 2,603 2,690 2,690 2,777 2,864 2,951 2,975 113 113 122 122 139 156 173 199 225 259 20,824 22,217 23,772 25,379 27,400 29,363 31,466 33,702 36,086 38,642 0 0 0 0 0 0 0 0 0 0 247 235 54 22 27 51 83 107 125 151 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 17 20 7 15 42 9 15 7 16 14
1% 0% 0% 13% 11% 9% 6% 1% 1% 1% 98 1,848 1,826 1,949 1,880 1,802 1,684 1,468 1,359 1,289
0% 682
0% 453
1% 486
0% 739
0% 0% 0% 0% 995 1,173 1,321 1,592
% 3% 2% 2% 2% 24% 23% 22% 21% 20% 19% 16% 14% 12% * assuming that all geothermal power plants are equipped with single-flash technology (no flexible handling of geothermal steam possible)
6%
4%
4%
5%
6%
Vented GEO steam*
% GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 9,453 10,093 11,084 11,856 12,683 13,367 14,433 15,467 16,555 17,699 19,242 20,577 21,983 23,718 25,358 27,374 29,312 31,384 33,595 35,960 38,491
Share on potential maximum GEO generation
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
6%
6%
7%
Annex Page 14
Annex Table 11: Slowed down RE expansion– cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 3,135 Hydropower MUSD 2,009 Coal MUSD 910 Diesel engines MUSD 880 Gas turbines (gasoil) MUSD 42 Import MUSD 285 Cogeneration MUSD 182 Generic back-up capacity MUSD 150 Wind MUSD 816 PV MUSD 62 Total MUSD 8,471 O&M fixed Geothermal MUSD 1,092 Hydropower MUSD 191 Coal MUSD 385 Diesel engines MUSD 139 Gas turbines (gasoil) MUSD 6 Import MUSD 72 Cogeneration MUSD 80 Generic back-up capacity MUSD 89 Wind MUSD 232 PV MUSD 8 Total MUSD 2,009
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 249 272 0 149 9 0 0 0 7 0 686
256 272 0 149 9 0 1 0 7 0 693
256 278 0 137 9 0 5 0 34 0 717
256 203 0 137 9 0 13 0 74 0 691
395 207 0 124 9 63 16 0 133 11 958
404 464 464 464 464 445 466 526 497 525 601 730 864 973 1,093 1,225 211 200 203 206 209 253 256 259 430 441 444 412 415 418 422 425 0 101 201 302 302 302 302 302 302 302 302 302 302 302 302 302 124 109 109 109 98 98 98 98 98 77 77 55 55 55 18 0 9 9 4 4 0 0 0 0 0 0 0 0 0 0 0 0 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 21 25 29 33 38 42 45 48 51 54 58 61 64 67 70 73 0 0 0 0 0 15 38 54 54 61 92 115 122 138 176 207 154 154 160 160 160 167 167 167 172 172 178 178 184 191 197 198 11 11 12 12 13 13 14 14 15 15 17 18 20 23 25 29 996 1,134 1,246 1,354 1,347 1,397 1,448 1,530 1,681 1,710 1,831 1,934 2,090 2,230 2,366 2,521
87 22
90 22
90 22
88 23
131 23
134 23
28 1
22 1
22 1
22 1
0
2
5
20 1 10 6
2 0 137
10 0 147
21 0 159
38 1 231
2 0 140
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
156 24 43 18 1 10 11
156 24 65 16 1 10 13
156 24 65 14
156 26 65 14
165 26 65 14
186 26 65 14
186 34 65 14
195 37 65 11
221 38 65 11
266 38 65 8
313 38 65 8
351 38 65 8
392 39 65 2
437 39 65
20 1 10 8
156 23 22 18 1 10 10
10 15
44 1 242
44 1 284
46 1 309
46 1 331
46 2 331
10 16 3 48 2 339
10 18 7 48 2 354
10 19 10 48 2 380
10 20 10 49 2 390
10 21 12 49 2 403
10 23 18 51 2 438
10 24 22 51 2 486
10 25 23 53 3 537
10 26 26 55 3 582
10 28 34 57 3 629
10 29 40 57 4 680
28.11.2016
Annex Page 15
Annex Table 12: Slowed down RE expansion – cost summary (2/2) Unit
NPV
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD MUSD
0 15 15 33 0 1,332 20 4 0 0 921
0 2
0 2
0 2
0 2
0 2
0 2
10 0
13 0
14 0
0 2 2 0
0 2 3 1
0 2 3 1
0 3 3 0
0 3 4 0
0 3 4 1
0 3 4 1
0 3 3 1
0 3 3 1
0 3 3 0
0
1
0 0 186 2
0 2 1 0 0 186 3
0 2 2 0
0
0 0 185 2
0 2 1 0 0 186 3
0 3 3
6 0
0 2 0 0 0 186 2
186 4
0 0 8
0 0 12
0 0 16
0 0 17
0 0 188
0 0 190
0 0 190
0 0 191
0 0 193
0 0 194
187 4 0 0 0 196
189 4 0 0 0 199
190 5 0 0 0 201
188 5 0 0 0 199
188 5 0 0 0 200
189 6 0 0 0 203
189 6 1 0 0 202
189 6 1 0 0 202
190 7 1 0 0 204
190 7 3 0 0 206
191 7 5 0 0 209
MUSD MUSD MUSD MUSD MUSD MUSD MUSD USDcent/kWh
557 281 1 99 597 0 11,997
75 0
111 0
128 1
0 0
2 0
14 2 0
31 2 0
53 3 0
79 3
91 6
112 9
112 14
94 7
128 7
147 15
130 12
114 13
119 17
121 7
127
43 0 43 0 876 9.26
75 0 916 9.08
111 0 990 8.93
1 1 3 2 3 8 18 22 34 76 125 130 0 2 16 33 56 82 98 122 129 102 137 170 160 149 170 204 251 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 997 1,378 1,429 1,625 1,779 1,934 1,954 2,029 2,123 2,240 2,373 2,449 2,642 2,783 2,979 3,186 3,405 3,661 8.41 10.86 10.69 11.26 11.50 11.68 11.04 10.55 10.32 10.19 10.00 9.66 9.65 9.49 9.49 9.48 9.47 9.51
O&M variable (other than fuel) Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total Fuel cost Coal Diesel engines Gas turbines (gasoil) Generic back-up capacity Total Unserved energy cost Total cost System LEC
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 16
Annex Table 13: Comparison of RE scenarios – installed capacity Moderate Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
Unit MW MW MW MW MW MW MW MW MW MW MW
2015 614 799
2016 634 799
2017 634 816
2018 619 823
2019 934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 1,824 2,129 2,379 2,549 2,849 843 852 861 870 879 977 986 995 1,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 327 654 981 981 981 981 981 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 244 244 244 77 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 159 167 175 184 192 140 350 490 490 560 840 980 1,050 1,190 1,610 1,890 576 576 601 601 626 626 651 651 670 670 720 795 870 970 1,070 1,150 56 56 61 61 71 71 81 91 111 131 151 171 191 221 261 301 3,570 3,983 4,333 4,622 4,597 4,846 5,168 5,475 6,028 6,303 6,840 7,277 7,764 8,301 8,882 9,521
Accelerated RE 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 843 852 861 870 879 977 986 995 1,499 1,706 1,715 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 140 350 490 490 560 840 576 576 626 626 676 676 726 726 770 770 870 61 61 71 71 91 91 111 131 171 211 251 3,575 3,988 4,368 4,657 4,667 4,916 5,273 5,590 6,188 6,483 7,090
2031 2032 2033 2034 2035 1,624 1,964 2,104 2,229 2,749 1,723 1,732 1,741 1,750 1,759 981 981 981 981 981 244 244 244 77
2031 2032 2033 2034 2035 1,824 2,129 2,379 2,649 2,949 1,723 1,732 1,741 1,750 1,759 981 981 981 981 981 244 244 244 77
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 954 1,094 1,094 1,094 1,094 1,094 1,154 1,294 1,294 1,354 1,524 843 852 861 870 879 977 986 995 1,499 1,706 1,715 327 654 981 981 981 981 981 981 981 981 635 561 561 502 449 449 449 449 449 359 359 54 54 27 27 400 400 400 400 400 400 400 400 400 400 400 54 65 76 87 98 109 117 126 134 142 151 140 350 490 490 560 840 576 576 601 601 601 626 626 626 645 645 670 51 51 56 56 61 61 66 66 71 71 81 3,565 3,978 4,328 4,617 4,562 4,836 5,128 5,425 5,963 6,218 6,720
2015 0 0 0 0 0 0 0 0 0 0 0
2016 0 0 0 0 0 0 0 0 0 0 0
2017 0 0 0 0 0 0 0 0 0 0 0
2018 0 0 0 0 0 0 0 0 0 0 0
2019 0 0 0 0 0 0 0 0 0 0 0
2020 0 0 0 0 0 0 0 0 0 5 5
2021 0 0 0 0 0 0 0 0 0 5 5
2022 0 0 0 0 0 0 0 0 25 10 35
2023 0 0 0 0 0 0 0 0 25 10 35
2024 0 0 0 0 0 0 0 0 50 20 70
2025 0 0 0 0 0 0 0 0 50 20 70
2026 0 0 0 0 0 0 0 0 75 30 105
2027 0 0 0 0 0 0 0 0 75 40 115
2028 0 0 0 0 0 0 0 0 100 60 160
2029 0 0 0 0 0 0 0 0 100 80 180
2030 0 0 0 0 0 0 0 0 150 100 250
2031 -200 0 0 0 0 0 0 210 225 120 355
2032 -165 0 0 0 0 0 0 140 300 140 415
2033 -275 0 0 0 0 0 0 210 400 170 505
2034 -320 0 0 0 0 0 0 210 500 210 600
2035 -100 0 0 0 0 0 0 -70 600 250 680
Slowed down RE difference to moderate Unit 2015 Geothermal MW 0 Hydropower MW 0 Coal MW 0 Diesel engines MW 0 Gas turbines (gasoil) MW 0 Import MW 0 Cogeneration MW 0 Generic back-up capacity MW 0 Wind MW 0 PV MW 0 Total MW 0
2016 0 0 0 0 0 0 0 0 0 0 0
2017 0 0 0 0 0 0 0 0 0 0 0
2018 0 0 0 0 0 0 0 0 0 0 0
2019 0 0 0 0 0 0 0 0 0 0 0
2020 0 0 0 0 0 0 0 0 0 -5 -5
2021 0 0 0 0 0 0 0 0 0 -5 -5
2022 0 0 0 0 0 0 0 0 0 -5 -5
2023 0 0 0 0 0 0 0 0 0 -5 -5
2024 0 0 0 0 0 0 0 0 -25 -10 -35
2025 0 0 0 0 0 0 0 0 0 -10 -10
2026 0 0 0 0 0 0 0 0 -25 -15 -40
2027 0 0 0 0 0 0 0 0 -25 -25 -50
2028 0 0 0 0 0 0 0 0 -25 -40 -65
2029 0 0 0 0 0 0 0 0 -25 -60 -85
2030 0 0 0 0 0 0 0 0 -50 -70 -120
2031 0 0 0 0 0 0 0 70 -125 -80 -135
2032 0 0 0 0 0 0 0 70 -175 -90 -195
2033 0 0 0 0 0 0 0 70 -250 -105 -285
2034 100 0 0 0 0 0 0 0 -325 -130 -355
2035 100 0 0 0 0 0 0 0 -400 -150 -450
Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
Unit MW MW MW MW MW MW MW MW MW MW MW
2015 614 799
2016 634 799
2017 634 816
2018 619 823
2019 934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
26 26 126 276 496 1 1 1 1 51 2,213 2,205 2,332 2,496 3,446
400 400 400 400 400 159 167 175 184 192 1,190 1,190 1,400 1,820 1,820 1,020 1,170 1,370 1,570 1,750 291 331 391 471 551 7,632 8,179 8,806 9,482 10,201
Slowed down RE Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
Unit MW MW MW MW MW MW MW MW MW MW MW
Accelerated RE difference to moderate Unit Geothermal MW Hydropower MW Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW Wind MW PV MW Total MW
2015 614 799
2016 634 799
2017 634 816
2018 619 823
2019 934 834
721 54
691 54
691 54
691 54
2
12
33
635 54 400 43
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
400 400 400 400 400 159 167 175 184 192 1,050 1,120 1,260 1,610 1,890 670 695 720 745 750 91 101 116 131 151 7,142 7,569 8,016 8,527 9,071
Annex Page 17
Annex Table 14: Comparison of RE scenarios – annual generation Moderate Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 4,941 5,154 5,158 5,031 5,892 6,073 7,111 7,177 7,264 7,372 7,577 3,741 3,737 3,810 3,832 3,894 3,934 3,584 3,681 3,788 3,902 4,356 283 628 1,058 1,536 1,863 692 1,115 1,503 1,600 4 17 16 16 30 30 51 0 0 0 6 0 0 0 0 0 2,641 2,655 2,654 2,653 2,662 2,663 2,681 9 53 145 188 237 285 333 382 430 478 1 78 78 560 1,243 2,132 2,357 2,357 2,444 2,444 2,531 2,531 1 1 1 1 87 96 96 104 104 122 122 9,453 10,093 11,084 11,857 14,839 15,368 16,385 17,037 17,732 18,586 19,660
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 4,941 5,154 5,158 5,031 5,892 6,073 7,100 7,143 7,235 7,337 7,543 3,741 3,737 3,810 3,832 3,894 3,934 3,588 3,678 3,787 3,882 4,354 312 686 1,091 1,496 1,789 692 1,115 1,503 1,599 4 17 12 9 18 24 49 0 0 0 7 0 0 0 0 0 2,641 2,655 2,649 2,644 2,650 2,658 2,677 9 53 145 188 237 285 333 382 430 478 2 78 78 560 1,243 2,133 2,357 2,357 2,531 2,531 2,705 2,705 1 1 1 1 87 104 104 122 122 156 156 9,453 10,093 11,084 11,857 14,839 15,377 16,408 17,146 17,816 18,687 19,754
Unit GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh GWh
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 4,941 5,154 5,158 5,031 5,892 6,074 7,111 7,179 7,258 7,376 7,592 3,741 3,737 3,810 3,832 3,894 3,934 3,585 3,682 3,793 3,917 4,355 290 630 1,080 1,591 1,862 692 1,115 1,503 1,599 4 17 16 18 30 30 51 0 0 0 6 0 0 0 0 0 2,641 2,655 2,653 2,654 2,661 2,662 2,678 9 53 145 188 237 285 333 382 430 478 2 78 78 560 1,243 2,133 2,357 2,357 2,444 2,444 2,444 2,531 1 1 1 1 87 87 87 96 96 104 104 9,453 10,093 11,084 11,857 14,839 15,361 16,384 17,037 17,744 18,556 19,653
2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 8,158 9,381 9,979 10,740 12,104 14,257 16,326 18,172 19,531 21,739 4,396 4,432 5,698 5,786 5,798 5,850 5,903 5,943 5,976 6,015 2,258 2,279 1,879 2,584 2,900 2,328 2,027 1,892 2,319 2,189 79 114 56 49 104 84 86 97 47 2,692 2,709 2,695 2,678 2,695 2,697 2,694 2,702 2,720 2,713 515 551 587 623 660 696 732 768 805 841 3 8 5 6 24 52 60 85 249 361 2,618 2,618 2,689 2,689 2,863 3,124 3,386 3,734 4,082 4,368 139 156 190 225 259 294 328 380 449 517 20,857 22,248 23,778 25,381 27,407 29,381 31,543 33,774 36,178 38,742
Accelerated RE Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 8,073 9,260 9,769 10,643 11,968 12,748 14,981 16,077 17,089 19,939 4,392 4,428 5,698 5,786 5,798 5,871 5,903 5,942 5,975 5,987 2,147 2,183 1,680 2,215 2,390 2,745 2,064 2,201 2,531 1,793 81 115 54 46 93 111 104 138 60 2,690 2,703 2,688 2,675 2,688 2,711 2,701 2,725 2,724 2,703 515 551 587 623 660 696 732 768 805 841 3 8 5 7 22 98 90 175 391 384 2,879 2,879 3,038 3,038 3,386 3,908 4,430 5,127 5,823 6,457 190 225 294 362 431 500 569 672 810 948 20,971 22,352 23,812 25,396 27,436 29,388 31,575 33,825 36,208 39,052
Slowed down RE Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Total
Accelerated RE difference to moderate Unit Geothermal GWh Hydropower GWh Coal GWh Diesel engines GWh Gas turbines (gasoil) GWh Import GWh Cogeneration GWh Generic back-up capacity GWh Wind GWh PV GWh Total GWh
2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 8,197 9,427 10,035 10,760 12,135 14,368 16,637 18,530 20,619 22,833 4,397 4,434 5,698 5,786 5,798 5,871 5,903 5,950 5,981 6,022 2,294 2,321 1,977 2,748 3,141 2,742 2,388 2,467 2,506 2,601 79 119 55 50 108 88 92 114 48 2,693 2,714 2,692 2,680 2,705 2,699 2,699 2,708 2,720 2,731 515 551 587 623 660 696 732 768 805 841 3 7 4 7 24 54 64 102 231 380 2,531 2,531 2,603 2,603 2,690 2,690 2,777 2,864 2,951 2,975 113 113 122 122 139 156 173 199 225 259 20,824 22,217 23,772 25,379 27,400 29,363 31,466 33,702 36,086 38,642
2015 0 0 0 0 0 0 0 0 0 0 0
2016 0 0 0 0 0 0 0 0 0 0 0
2017 0 0 0 0 0 0 0 0 0 0 0
2018 0 0 0 0 0 0 0 0 0 0 0
2019 0 0 0 0 0 0 0 0 0 0 0
2020 -1 0 0 0 0 0 0 0 0 9 8
2021 -11 4 29 -3 0 -5 0 0 0 9 22
2022 -34 -3 58 -7 0 -10 0 0 88 17 108
2023 -29 -1 34 -12 0 -13 0 0 88 17 84
2024 -35 -20 -40 -6 0 -5 0 0 175 34 101
2025 -34 -2 -74 -3 0 -3 0 0 175 34 94
2026 -85 -4 -111 2 0 -2 0 0 262 52 113
2027 -121 -4 -95 1 0 -6 0 0 262 69 105
2028 -210 0 -199 -3 0 -6 0 0 349 103 33
2029 -97 0 -369 -4 0 -2 0 1 349 138 14
2030 2031 2032 2033 2034 2035 -136 -1,509 -1,346 -2,095 -2,442 -1,800 0 21 0 -1 -1 -27 -510 417 37 308 212 -396 -11 28 18 41 13 0 0 0 0 0 0 0 -7 14 6 22 4 -10 0 0 0 0 0 0 -2 45 30 90 141 23 523 784 1,045 1,393 1,741 2,089 172 207 241 293 361 430 29 7 32 52 30 310
Slowed down RE difference to moderate Unit 2015 Geothermal GWh 0 Hydropower GWh 0 Coal GWh 0 Diesel engines GWh 0 Gas turbines (gasoil) GWh 0 Import GWh 0 Cogeneration GWh 0 Generic back-up capacity GWh 0 Wind GWh 0 PV GWh 0 Total GWh 0
2016 0 0 0 -1 0 0 0 0 0 0 0
2017 0 0 0 0 0 0 0 0 0 0 0
2018 0 0 0 0 0 0 0 0 0 0 0
2019 0 0 0 0 0 0 0 0 0 0 0
2020 1 0 0 0 0 0 0 0 0 -9 -8
2021 0 1 6 0 0 -1 0 0 0 -9 -2
2022 2 1 2 2 0 1 0 0 0 -9 -1
2023 -6 4 23 0 0 -1 0 0 0 -9 11
2024 4 15 55 0 0 -1 0 0 -87 -17 -30
2025 15 -1 -1 0 0 -3 0 0 0 -17 -7
2026 40 1 36 0 0 1 0 0 -87 -26 -33
2027 46 2 42 5 0 5 0 0 -87 -43 -30
2028 55 0 98 -1 0 -3 0 0 -87 -69 -6
2029 20 0 164 1 0 3 0 0 -87 -103 -2
2030 31 0 241 4 0 10 0 1 -174 -120 -7
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
2031 110 21 415 4 0 2 0 2 -435 -138 -18
2032 311 1 361 6 0 5 0 4 -609 -155 -77
2033 2034 2035 358 1,088 1,094 7 5 7 575 188 412 17 1 0 0 0 0 5 0 18 0 0 0 17 -18 20 -870 -1,131 -1,392 -181 -224 -258 -72 -92 -100
Annex Page 18
ANNEX 5 DISCUSSION OF RENEWABLE ENERGY INCENTIVE POLICIES – ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank.
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 19
***
Power Generation and Transmission Master Plan, Kenya Long Term Plan 2015 – 2035 – Renewable Energy
28.11.2016
Annex Page 20
