Development of a Power Generation and Transmission Master Plan, Kenya

Transcription

1 Final Report Development of a Power Generation and Transmission Master Plan, Kenya Long Term Plan Renewable Energy May 2016 Ministry of Energy and Petroleum

2 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 Page i

3 Development of a Power Generation and Transmission Master Plan, Kenya Long Term Plan Renewable Energy May 2016 Prepared for: Prepared by: Ministry of Energy and Petroleum Nyayo House, Kenyatta Avenue, P.O. Box 30582, Nairobi, Kenya Lahmeyer International GmbH Friedberger Str Bad Vilbel, Germany Inspection status: Approved Revision History: Revision Date Author Department Checked by Approved by Description v v PGTMP project team PGTMP project team IED, EFLA, LI GE7, GE5, GE6, GW IED, EFLA, LI GE7, GE5, GE6, GW Dr. Tim Hoffmann Karsten Schmitt Daniel d Hoop Dr. Tim Hoffmann Draft PGTMP LTP RE Final PGTMP LTP RE Page ii

4 Table of Contents 1 EXECUTIVE SUMMARY INTRODUCTION Objectives of report Structure of report Methodology and assumptions RENEWABLE ENERGY RESOURCES IN KENYA Hydropower energy Available data and current situation Medium and long term potential Solar energy photovoltaic (PV) Available data and current situation in Kenya Medium and long term potential Recommendation for expansion plan Solar energy concentrated solar power (CSP) Available data and current situation in Kenya Medium and long term potential Recommendation for expansion plan Wind energy Available data and current situation in Kenya Medium and long term potential Recommendation for expansion plan Biomass, biogas and waste-to-energy Available data and current situation in Kenya Medium and long term potential Recommendation for expansion plan Geothermal energy Available data and current situation in Kenya Medium and long term potential Recommendation for expansion plan ANALYSIS OF RENEWABLE ENERGY EXPANSION Methodology Definition of Renewable Energy scenarios Results Capacity and fuel mix Renewable Energy scenarios - comparison Conclusions DISCUSSION OF RENEWABLE ENERGY INCENTIVE POLICIES Page iii

5 5.1 Direct versus Complementary Measures: Direct Public or Governmental Action vs. Indirect Regulatory Action: Stages of the Value Chain: Affected Market Parties and Market Variables: Description and Discussion of Relevant Incentive Schemes Direct Subsidies I Investment Subsidies Direct Subsidies II Feed-In tariff systems Competitive Bidding / Tendering Quota Obligations and Tradable Certificates The feed-in tariff for renewable energy in Kenya Renewable energy international good practice benchmarking Regulatory and policy options available when the main grid reaches the mini-grid Bagasse-based cogeneration from sugar industries in Mauritius Sri-Lanka Net-metering policy 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 Page iv

6 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 Figure 3-3: Annual generated electricity by hydropower plant from 1999 to Figure 3-4: Monthly electricity generation of the four hydropower production groups 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 Figure 3-7: Figure 3-8: Monthly electricity generation of existing large hydropower plants, average hydrology Monthly electricity generation of existing large hydropower plants, low hydrology17 Figure 3-9: Masinga HPP annual electricity generation Figure 3-10: Masinga HPP selected annual generation curves on monthly basis Figure 3-11: Kamburu HPP annual electricity generation Figure 3-12: Kamburu HPP selected annual generation curves on monthly basis Figure 3-13: Gitaru HPP annual electricity generation Figure 3-14: Gitaru HPP selected annual generation curves on monthly basis Figure 3-15: Kindaruma HPP annual electricity generation Figure 3-16: Kindaruma HPP selected annual generation curves on monthly basis Figure 3-17: Kiambere HPP annual electricity generation Figure 3-18: Kiambere HPP selected annual generation curves on monthly basis Figure 3-19: Tana HPP annual electricity generation Figure 3-20: Tana HPP selected annual generation curves on monthly basis Figure 3-21: Turkwel HPP annual electricity generation Figure 3-22: Turkwel HPP selected annual generation curves on monthly basis Figure 3-23: Sondu Miriu HPP annual electricity generation Figure 3-24: Sondu Miriu HPP selected annual generation curves on monthly basis Figure 3-25: Sang Oro HPP selected annual generation curves on monthly basis Figure 3-26: GHI map for Kenya Figure 3-27: Average daily PV production patterns per month Figure 3-28: DNI map for Kenya Figure 3-29: Mean wind speed map of Kenya Figure 3-30: Potential wind capacity development in Kenya Figure 3-31: Kinangop wind farm average daily production patterns per month Figure 3-32: Kipeto wind farm average daily production patterns per month Figure 3-33: Lake Turkana wind farm average daily production patterns per month Figure 3-34: Meru wind farm average daily production patterns per month Figure 3-35: Generic wind farm average daily production patterns per month Page v

7 Figure 3-36: Simple schematic drawing of single flash power plant Figure 3-37: Simple schematic drawing of binary geothermal power plant Figure 3-38: Binary bottoming unit in single flash power plant Figure 4-1: Additional wind and solar PV development Figure 4-2: Power generation differences vs. moderate RE scenario Figure 4-3: Incremental cost and LRMC of RE expansion Figure 5-1: Figure 5-2: Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Capacity and Production Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Price and Quantity Page vi

8 List of Tables Table 3-2: Average monthly rainfall at selected stations in Kenya... 8 Table 3-3: Average annual evaporation - reservoirs of large hydropower plants... 9 Table 3-4: Areas, major rivers, identified hydropower potential of the six catchment areas... 9 Table 3-5: Existing large hydropower plants in Kenya Table 3-6: Table 3-7: Table 3-8: Table 3-9: Monthly available capacity (MW) of existing large hydropower plants, average hydrology Monthly available capacity (MW) of existing large hydropower plants, low hydrology Monthly electricity generation of existing large hydropower plants, average hydrology Monthly electricity generation of existing large hydropower plants, low hydrology19 Table 3-10: Masinga reservoir characteristics Table 3-11: Masinga HPP statistical characteristics Table 3-12: Kamburu reservoir characteristics Table 3-13: Kamburu HPP statistical characteristics Table 3-14: Gitaru reservoir characteristics Table 3-15: Gitaru HPP statistical characteristics Table 3-16: Kindaruma HPP statistical characteristics Table 3-17 Kiambere reservoir characteristics Table 3-18: Kiambere HPP statistical characteristics Table 3-19: Tana HPP statistical characteristics Table 3-20: Turkwel reservoir characteristics Table 3-21: Turkwel HPP statistical characteristics Table 3-22: Sondo Miriu HPP statistical characteristics Table 3-23: Existing small hydropower plants in Kenya Table 6-34: Potential large hydropower projects (long-list) Table 3-24: Details of identified large hydropower candidates (short-list) Table 3-25: Planned small hydropower projects Table 3-26: Cumulated expansion small hydropower (incl. existing plants) Table 3-27: Strengths and weaknesses of PV energy systems Table 3-28: Main Solar PV projects submitted to FiT scheme Table 3-29: Strengths and weaknesses of CSP energy systems Table 3-30: Energy yield of sample wind turbines Table 3-31: Wind farm candidates Table 3-32: Main biomass projects submitted to the FiT scheme Table 3-33: Present sugar mills in Kenya and their technical potential Table 3-34: Cumulated expansion cogeneration (incl. existing plants) Table 3-35: Strengths and weaknesses of geothermal energy Page vii

9 Table 3-36: Present Geothermal power plants Table 3-37: Geothermal power plants to be implemented in medium-term period Table 3-38: Geothermal potential by field Table 4-1: Table 4-2: Additional (to existing and committed plants) RE development in the moderate, accelerated and slowed down RE scenarios ( ) Comparison of results: moderate, accelerated and slowed down RE expansion scenarios Table 4-3: Changes in CODs due to different RE developments Table 4-4: RE shares in generation and consumption (average ) Table 4-5: Cost implications of RE scenarios Table 5-1: Current Feed-in-Tariff Structure Page viii

10 Abbreviations and Acronyms 10YP A AC ACSR ADF AFD AGO AIS AVR BB BOO CAPEX CCGT CEEC CHP Cif COD Cogen CPP DANIDA DC DCR DIN DUC EAC EAPP EE EECA EIB ENDSA EPC ERC ESIA FGD FiT Fob GAMS GDC GDP GE GEF GIS 10 year plan Ampere Alternating Current Aluminium Clad Steel/Reinforced African Development Fund Agence Française de Développement Automotive gas oil Air Insulated Switchgear Automatic Voltage Regulation Busbar Build own operate Capital expenditure Combined Cycle Gas Turbine Committee for European Economic Cooperation Combined Heat and Power Cost insurance freight Commercial operation date Co-Generation Coal Power Plant Danish International Development Agency Direct Current Discount rate German Institute for Standardization Dynamic unit cost East African Community East African Power Pool Energy Efficiency Energy Efficiency and Conversation Agency European Investment Bank Ewasa Ng iiro South River Basin Development Authority Engineering Procurement Construction Energy Regulation Commission European Semiconductor Industry Association Flue gas desulphurisation Feed in Tariff Free on board General Algebraic Modeling System Geothermal Development Company Gross Domestic Product General Electric Global Environment Facility Geographic Information System Page ix

11 GIS GoK GOV GT GW GWh HFO HPP HV HVDC Hz I&C IAEA IDO IEA IED IPE IPP IR ISO ITCZ JICA KAM KenGen KeNRA KETRACO KfW km km3 KNBS KNEB KPC KPLC KTDA kv Kvar KVDA KW kwh LCPDP LDC LEC LF LFO Gas Insulated Switchgear Government of Kenya Governor Gas Turbine Gigawatt Giga Watt-hour Heavy Fuel Oil Hydro Power Plant High Voltage High Voltage Direct Current Hertz Instrument and Control System International Atomic Energy Agency Industrial diesel oil International Energy Agency Innovation Energie Développement Indicator Power Efficiency Independent Power Producer Inception Report International Organisation for Standardization Intertropical Convergence Zone Japan International Cooperation Agency Kenya Association of Manufacturers Kenya Electricity Generating Company Kenya National Resources Alliance Kenya Transmission Company KfW Development Bank German development bank; was: Kreditanstalt für Wiederaufbau) kilometre cubic kilometre Kenya National Bureau of Statistics Kenya Nuclear Electricity Board Kenya Pipeline Company Limited Kenya Power and Lighting Company Kenya Tea Development Agency kilo Volt Kilo volt ampere reactive Kerio Valley Development Authority Kilowatt kilowatt-hour Least Cost Power Development Plan Load Dispatch Center Levelized electricity cost Load Flow Light Fuel Oil Page x

12 LI LIPS-OP LMTPSP LNG LOLP LPG LTP LTWP LV m M&E MAED MIP MOE MOEP MORDA MSD MTP MV MVA Mvar Lahmeyer International GmbH Lahmeyer International Power System - Operation Planning Long and Medium Term Power System Plans Liquefied Natural Gas Loss of Load Probability Liquefied Petroleum Gas Long Term Plan Lake Turkana Wind Park Low Voltage metre Mechanical & Electrical Model for Analysis of Energy Demand (MAED-D for kwh, MAED-L for Kw) Mixed Integer Linear Optimization Problem Ministry of Energy (changed in 2013 to Ministry of Energy and Petroleum) Ministry of Energy and Petroleum Ministry of Regional Development Authorities Medium speed diesel engine Medium Term Plan Medium Voltage Megavolt Ampere Megavolt Ampere Reactive MW Mega Watt (10^6 Watts) MWh NCV NG NGO NIB NPP NPV NSSF NTP NWCPC O&M OHL OPEX P PB PF PGTMP PPA PSS/E PV Q Megawatt Hours Net calorific value Natural Gas Non-Governmental Organization National Irrigation Board Nuclear Power Plant Net Present Value National Social Security Fund Notice-to-Proceed National Water and Conservation and Pipeline Corporation Operation & Maintenance Overhead Line Operational Expenditure Active Power Parsons and Brinckerhoff Power Factor Power Generation and Transmission Master Plan Power Purchase Agreement Power System Simulator for Engineering Photovoltaic Reactive Power Page xi

13 Qc Ql QM RE REA RES RES-E RfP RMS RMU S/S SBQC SC SCADA SHPP SHS SKM SLD SMP SPP SPV ST T/L TA TARDA TJ TNA TOR TPP TR TRF UNDP WACC WASP WB WP WTG Reactive Power Capacitive Reactive Power Inductive Quality Management Renewable Energy Rural Electrification Authority Renewable Energy Sources Renewable Energy Sources Electricity Generation Request for Proposal Root-Mean-Square Value Ring Main Unit(s) Substation Selection Based on Consideration of Quality and Cost Short Circuit Supervisory Control and Data Acquisition Small Hydro Power Plants Solar Home Systems Sinclair Knight Merz Single Line Diagram System Marginal Price Steam Power Plant Special Purpose Vehicle Steam Turbine Transmission Line Technical Assistance Tana & Athi River Development Authority Terra-joule Training Need Assessment Terms of Reference Thermal Power Plant Transformer Training Results Form United Nations Development Programme Weighted average cost of capital Wien Automatic System Planning World Bank Wind Park Wing turbine generators Page xii

14 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 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 Tana, 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 will also give further hints in relation to possible shortcomings resulting from droughts Page 1

15 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. 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 will 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 will 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 will thus be 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 planning expansion planning the existing Mumias and Kwale are considered. Beyond 2017, linear extrapolation of biomass capacity is assumed. 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 will not be considered in the long-term planning as a candidates. 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 ex Page 2

16 pected 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 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. In the years before High Grand Falls hydropower plant will start operation, other hydropower plants cater for this reserve at the expense of spilled water. This leads to a decrease in generation from hydropower plants.. 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 Page 3

17 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 Plan 2 (LTP) for the period 2015 (base year) to 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. 2 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 Page 4

18 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 is complements the longterm 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. 2.3 Methodology and assumptions The resource assessment investigates the renewable energy potential in Kenya for the following renewable energies: Hydro power, Page 5

19 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 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 By incorporating RE in a wider power system-wide framework, the analysis accounts for interdependencies of all power supply options, not only renewables Page 6

20 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 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 saves as basis for the assessment 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 (ITCZ), 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 For the sake of comparison, the average monthly rainfall of the country as a whole is also presented Page 7

21 Average monthly rainfall [mm] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Table 3-1: Average monthly rainfall at selected stations in Kenya Representative Rainfall Gauging Stations Total Average country total East and northeast of the country Central part of the country Northwest and south west of the country Garissa (1979 to 2010) Mandera (1979 to 2010) Wilson Airport/Moi Air Base (1979 to 2010) Kakamega (1979 to 2010) Kericho (1979 to 2010) , ,005 Source: NWMP JICA study based on data from Water Resources Management Authority (WRMA) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Garissa Mandera Wilson Airport/ Moi Air Base Kakamega Kericho Kenya 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: Page 8

22 Table 3-2: Average annual evaporation - reservoirs of large hydropower plants Hydropower plant Masinga & Kamburu HPPs Gitaru & Kindaruma HPPs Kiambere HPP Turkwel HPP Average annual evaporation 1,900 mm/a 1,975 mm/a 2,050 mm/a 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, identified hydropower potential of the six catchment areas 3 Catchment area Area [km²] Major Rivers Identified hydropower potential [MW] 4 Lake Victoria North Lake Victoria South 18,374 Nzoia R., Yala R ,734 Nyando R., Sondu R., Kuja (Gucha) R., Mara R. 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. 0 TOTAL: 575,451 1, 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, 6 out of the 9 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 Source: NWMP JICA based on data from WRMA 4 Source: NWMP - JICA Page 9

23 Figure 3-2: Areas and major rivers of the six catchment areas and location of existing large hydropower plants 5 5 Source of base map: National Water Master Plan Page 10

24 Annual generated electricity [GWh/a] 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 plants 6, 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% until In 2015, the total effective capacity of large hydropower plants was 785 MW. Additionally, 17 MW of small hydropower capacity was installed. 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 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 The capacity factor of the aggregated hydropower capacity dropped from 49% in 1999 to 27% in 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 ,500 Small HPPs 4,000 70% Tana 3,500 60% Sang'oro Sondo 3,000 50% Turkwel 2,500 40% Kiambere 2,000 1,500 30% Kindaruma Gitaru 1,000 20% Kamburu % 0% Masinga Share of generated electricity by hydropower on total generated electricity [%] Capacity factor of aggregated hydropower capacity [%] Figure 3-3: Annual generated electricity by hydropower plant from 1999 to In the framework of the present study, hydropower plants with an effective capacity of at least 20 MW are defined as large hydropower plants Page 11

25 Monthly generated electricity [GWh/month] 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 HPP 7, Mesco HPP, Sagana HPP (total effective capacity of 29 MW in 2014) 3) Turkwel HPP located in the Rift Valley Catchment Area (effective capacity of 105 MW) 4) Lake Victoria South Catchment Area with Sondo Miriu HPP 8, Sang oro HPP 9, Sosiani HPP and Gogo HPPs (total effective capacity of 82 MW in 2014) The following figure presents the monthly generated electricity by hydropower by each group from 2000 to 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 Total Seven forks (Masinga, Kamburu, Gitaru, Kindaruma**, Kiambere HPPs) Rift Valley (Turkwel HPP) Lake Victoria South (Sondo Miriu*, Sang'oro*, Sosiani and Gogo HPPs) Upper Tana (Tana, Wanjii, Ndula, Mesco, Sagana HPPs) * COD of Sondo Miriu in 2008 and COD of Sang'oro in 2012) ** Upgrade of Kindaruma from 40 MW to 71 MW in 2012 Figure 3-4: 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 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 Commissioning of Sondo Miriu HPP was in Commissioning of Sang oro HPP was in Page 12

26 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 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 River Owner COD Rehabilitation and/or upgrading Effective capacity [MW] 20 Tana HPP Tana KenGen , 1955, 2010 Masinga HPP Tana Tana KenGen Kamburu Tana Tana KenGen HPP Gitaru HPP Tana Tana KenGen Kindaruma Tana Tana KenGen HPP Kiambere Tana Tana KenGen HPP Turkwel HPP Rift Valley Turkwel KenGen Sondu Miriu Lake Victoria Sondu KenGen HPP South Sang oro HPP Lake Victoria Sondu KenGen South Total 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 plant 10. 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 av- 10 e.g. considering upgradation of Kindaruma HPP and rehabilitation of Tana HPP Page 13

27 Available capacity [MW] Available capacity [MW] erage, 728 MW of the 785 MW effective hydropower capacity are available considering average hydrology conditions. The monthly available capacity during low hydrology is defined as the Percentile 95 (P95) exceedance probability value of the detected monthly maximum production output 11. 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 Sang'oro Sondo Turkwel Kiambere Kindaruma Gitaru Kamburu Masinga Tana Figure 3-5: Monthly available capacity existing large hydropower plants, average hydrology Sang'oro Sondo Turkwel Kiambere Kindaruma Gitaru Kamburu Masinga Tana Figure 3-6: Monthly available capacity existing large hydropower plants, low hydrology 11 With a probability of 95% the respective capacity is available Page 14

28 Table 3-5: Monthly available capacity (MW) of existing large hydropower plants, average hydrology Plant name January February Match April May June July August October September November December Annual average Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang oro HPP Total Page 15

29 Table 3-6: Monthly available capacity (MW) of existing large hydropower plants, low hydrology Plant name January February Match April May June July August October September November December Annual average Tana HPP Masinga HPP Kamburu HPP Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang oro HPP Total Page 16

30 Average daily electricity generation [MWh/day] Average daily electricity generation [MWh/day] Monthly electricity generation: 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 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 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Sang'oro Sondo Turkwel Kiambere Kindaruma Gitaru Kamburu Masinga Tana Figure 3-7: Monthly electricity generation of existing large hydropower plants, average hydrology 5, , , , , Sang'oro Sondo Turkwel Kiambere Kindaruma Gitaru Kamburu Masinga Tana Figure 3-8: hydrology Monthly electricity generation of existing large hydropower plants, low Page 17

31 Table 3-7: Monthly electricity generation of existing large hydropower plants, average hydrology Plant name January February Match April May June July August Tana HPP Masinga HPP Kamburu HPP GWh October September November December Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang oro HPP Total ,690 Annual sum Page 18

32 Table 3-8: Monthly electricity generation of existing large hydropower plants, low hydrology Plant name January February Match April May June July August Tana HPP Masinga HPP Kamburu HPP GWh October September November December Gitaru HPP Kindaruma HPP Kiambere HPP Turkwel HPP Sondu Miriu HPP Sang oro HPP Total ,498 Annual sum Page 19

33 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 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: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 60 m 2,220 m 40 MW 2 Kaplan turbines 1,056.5 m 1,031 m Page 20

34 Table 3-9: Masinga reservoir characteristics Reservoir water elevation Reservoir volume Reservoir Area m million m 3 km² 1, , , , , , , , , , , , , , , , , , , , ,051 1, ,052 1, ,053 1, ,054 1, ,055 1, ,056 1, , , 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 Page 21

35 Monthly generated electricity [GWh/month] Annual generated electricity [GWh/a] Figure 3-9: Masinga HPP annual electricity generation average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-10: 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 This analysis saves as basis for the determination of the available capacity of Masinga HPP Page 22

36 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 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: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 52 m 900 m 94 MW 3 Francis turbines 1,006.5 m 990 m Page 23

37 Table 3-11: Kamburu reservoir characteristics Reservoir Water Elevation Reservoir volume Reservoir Area m Mio m 3 km² , , , , , , , , 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 Page 24

38 Monthly generated electricity [GWh/month] Annual generated electricity [GWh/a] Figure 3-11: Kamburu HPP annual electricity generation average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-12: 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 Page 25

39 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 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 m and 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: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 30.5 m 580 m 225 MW 3 Francis turbines m 919 m Page 26

40 Annual generated electricity [GWh/a] Table 3-13: Gitaru reservoir characteristics Reservoir Water Elevation Reservoir volume Reservoir Area m Mio m 3 km² 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 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 Figure 3-13: Gitaru HPP annual electricity generation Page 27

41 Monthly generated electricity [GWh/month] average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-14: 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 The annual generated electricity was 487 GWh in 2000 and 417 GWh in 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 Table 3-14: Gitaru HPP statistical characteristics Parameter Monthly available capacity [MW] Percentile P Percentile P Percentile P Percentile P Minimum value 130 Maximum value 230 Mean value 199 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 Page 28

42 Annual generated electricity [GWh/a] The maximum dam height is 28.7 m, maximum and minimum operating levels are m and m, respectively. At maximum storage level, the reservoir volume amounts to 7 million m³. Project features: Maximum dam height: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 28.7 m 549 m 72 MW 3 Francis turbines m 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 Figure 3-15: Kindaruma HPP annual electricity generation Page 29

43 Monthly generated electricity [GWh/month] 25 average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-16: 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 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 Page 30

44 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 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: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 110 m 1,000 m 164 MW 2 Kaplan turbines 700 m 665 m Table 3-16 Kiambere reservoir characteristics Reservoir Water Elevation Reservoir volume m million m Page 31

45 Annual generated electricity [GWh/a] Reservoir Water Elevation Reservoir volume m million m 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 Figure 3-17: Kiambere HPP annual electricity generation Page 32

46 Monthly generated electricity [GWh/month] average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-18: 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 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 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 Table 3-17: Kiambere HPP statistical characteristics Parameter Monthly available capacity [MW] Percentile P95 85 Percentile P Percentile P Percentile P Minimum value 72 Maximum value 172 Mean value Page 33

47 Annual generated electricity [GWh/a] 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 The rehabilitated power station comprises four Francis turbines with an overall installed capacity of 20 MW. 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 *shut down of Tana HPP for several months in 2010 because of rehabilitation Figure 3-19: Tana HPP annual electricity generation Page 34

48 Monthly generated electricity [GWh/month] average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-20: 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 Table 3-18: Tana HPP statistical characteristics Parameter Monthly available capacity [MW] Percentile P Percentile P Percentile P Percentile P Minimum value 4.4 Maximum value 24.8 Mean value Page 35

49 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: Dam length: Installed capacity: Number of turbines: Full supply level: Minimum operating level: 153 m 150 m 106 MW 2 vertical shaft Francis turbines 1,150 m 1,098 m Table 3-19: Turkwel reservoir characteristics Reservoir water elevation Reservoir volume Reservoir area m million m 3 km² 1, , , , , , , , , , , , Page 36

50 Reservoir water elevation Reservoir volume Reservoir area m million m 3 km² 1, , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,139 1, ,140 1, ,141 1, ,142 1, ,143 1, ,144 1, ,145 1, ,146 1, ,147 1, ,148 1, ,149 1, ,150 1, Page 37

51 Monthly generated electricity [GWh/month] Annual generated electricity [GWh/a] 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. 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 Figure 3-21: Turkwel HPP annual electricity generation average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-22: Turkwel HPP selected annual generation curves on monthly basis 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 Page 38

52 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 Table 3-20: Turkwel HPP statistical characteristics Parameter Monthly available capacity [MW] Percentile P95 91 Percentile P90 96 Percentile P Percentile P 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: Dam length: Installed capacity: 18 m 70 m 60 MW Number of turbines: Page 39

53 Monthly generated electricity [GWh/month Annual generated electricity [GWh/a] 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 average ( ) Figure 3-23: Sondu Miriu HPP annual electricity generation average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-24: Sondu Miriu HPP selected annual generation curves on monthly basis Page 40

54 The figures clearly show that the power plant was affected by the drought period which Kenya experienced in 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 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 Table 3-21: Sondo Miriu HPP statistical characteristics Parameter 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 The following figure shows the generation curves from 2013 and 2014 and the average annual generation curves on monthly basis Statistical characteristics of Sang Oro HPP are not presented, since data quantity is insufficient for an accurate statistical analysis (commissioning in 2012) Page 41

55 Monthly generated electricity [GWh/month average ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3-25: 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 operates 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 Sagana HPP KenGen Mesco HPP KenGen Wanjii HPP KenGen Gogo HPP KenGen Imenti HPP Gikira Kenya Tea Development Authority Power Technology Solutions Page 42

56 James Findlay (K) Ltd. James Finlay Tea HPP company Brooke Bond HPP Unilever Tea Company 0.63 Savani HPP Eastern Produce Diguna HPP Missionary Tenwek HPP Tenwek Missionary 0.32 Hospital Mujwa HPP Missionary 0.07 Community MHPs Community Total 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 Planned large hydropower schemes 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 Page 43

57 Table 3-23: Potential large hydropower projects (long-list) 13 Name Installed capacity [MW] Catchment area Purpose Authority in charge of High Grand Falls Stage Tana Water supply, flood control, irrigation, hydropower High Grand Falls Stage Tana Water supply, flood control, irrigation, hydropower MORDA 14 MORDA 14 Karura 90 Tana Hydropower KenGen Arror 60 Rift Valley Irrigation, hydropower KVDA 15 Embobut 45 Rift Valley Water supply, irrigation, hydropower KVDA 15 Kimware 20 Rift Valley Water supply, irrigation, KVDA 15 hydropower Oletukat Rift Valley Water supply, hydropower ENSDA 17 Leshota Rift Valley Water supply, irrigation, hydropower Oldorko Rift Valley Water supply, irrigation, hydropower Nandi Forest 50 Lake Victoria North Water supply, irrigation, hydropower ENSDA 17 ENSDA 17 MORDA 14 Hemsted Bridge 60 Lake Victoria North Water supply, irrigation, -- hydropower Nzoia I 16 Lake Victoria North Water supply, irrigation, NWCPC 18 flood control, hydropower Nzoia II 25 Lake Victoria North Flood control, hydropower NIB 19 Magwagwa 120 Lake Victoria South Water supply, irrigation, hydropower MORDA 14 Upgrade of Gogo Falls Lake Victoria South Hydropower KenGen Munyu 40 Athi Irrigation, hydropower TARDA 21 Thwake 20 Athi Water supply, irrigation, hydropower Total 1,484 TARDA 21 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 Page 44

58 Table 3-24: Details of identified large hydropower candidates (short-list) Name Unit Karura High Grand Falls Nandi Forest Magwagwa Arror Catchment area Tana Tana Lake Victoria North Lake Victoria South Rift Valley Purpose Hydropower Water supply, flood control, irrigation, hydropower Water supply, irrigation, hydropower Water supply, irrigation, hydropower Irrigation, water supply, hydropower Responsible authority/institution KenGen MORDA 22 MORDA 23 MORDA 24 KVDA 25 Installed capacity MW (+200 MW for stage 2) Number of units # 2 5 (+2 for stage 2) Unit type Kaplan Francis Pelton 26 Francis Pelton Average annual electricity generation GWh 235 Average capacity factor % 30% 1,213 (+57 GWh for stage 2) 28% (21% considering both stages) % 49% 36% Dam height m Dam crest length m 2,270 2,500 1, Net head m Reservoir volume at full supply level million m³ 152 5, Ministry of Regional Development Authorities 23 Ministry of Regional Development Authorities 24 Ministry of Regional Development Authorities 25 Kerio Valley Development Authority 26 As per final study report Page 45

59 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 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 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 are further assessed as potential future candidates. However, as multipurpose dams the responsibility for their scheduling and implementation is not solely within the power sector. Long Term Plan Renewable Energy Page 46

60 Planned small hydropower schemes There is a large pipeline of small hydropower projects promoted under the FiT scheme. Feasibility studies of twenty-one projects comprising a total capacity of 89 MW are already approved. PPA negotiations of thirteen of these projects with a total capacity of 41 MW are completed successfully. Seven projects with a total capacity of 24 MW are under construction and one project is already completed. Furthermore, feasibility studies of additional eleven small hydropower projects comprising a total capacity of 76 MW are on-going. The following table provides a brief overview of the various small hydropower plant projects. Table 3-25: Planned small hydropower projects 27 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, construction in advanced stage GenPro-Teremi Falls 5 Teremi Falls Mt. Elgon PPA signed, construction on-going Gura (KTDA) 6 Gura Nyeri PPA signed, construction on-going KTDA Ltd, North Mathioya- Metumi KTDA Ltd, Lower Nyamindi 6 North Mathioya North Muranga PPA signed, construction on-going 2 Nymanindi Nymanindi PPA signed, construction on-going KTDA Ltd, Iraru 2 Iraru Iraru PPA signed, construction on-going KTDA Ltd, South Maara 2 Mara South Mara PPA signed, construction on-going KTDA Ltd, Nyambunde 2 Nyambunde Nyambunde PPA signed, construction contract signed KTDA Ltd, Lower Nyamindi KTDA Ltd, Kipsonoi- Settet 2 Lower Nyamindi Nyamindi PPA signed, construction contract signed 4 Kipsonoi Kipsonoi PPA signed, in tendering process for EPC contractor Tindinyo Falls Resort 2 Yala Tindinyo PPA signed 27 Data source: list of power generation projects coordinated by KPLC (status: October 2015), FiT Database (status: August 2015) Long Term Plan Renewable Energy Page 47

61 Name Capacity [MW] River Nearest urban centre Status Kleen Energy Limited 6 Rupingazi Embu PPA signed Mt Kenya Community Based Organisation Hydel 15 Kagumoine, Kairo, Njega 1 Kathita River Meru PPA signed Embu, Kirinyaga ongoing PPA negotiations KTDA Gucha 3.6 Gucha Gucha awaiting PPA negotiations KTDA Chemosit and Kiptiget 3.3 Chemosit, Kiptiget Global Sustainable 11 Yala, Kaptis Kaimosi Kericho Kaimosi awaiting PPA negotiations awaiting PPA negotiations KTDA Ltd. - Itare river 1.3 Itare Kabianga awaiting PPA negotiations KTDA Yurith/Chemosit 0.9 Yurith/Chemosit Cheptuyet awaiting PPA negotiations Que Energy/ western hydro 10 Webuye Falls Webuye awaiting PPA negotiations Global Sustainable Ltd 5 Nzoia Bungoma FS under review, awaiting PPA negotiations Everest Contractors Ltd 16 Sagana Mutundu undertaking FS Everest Contractors Ltd 10 Sagana Thanga Thini undertaking FS Power Technologies 10 Tana Nuhito, Mukurueni undertaking FS KTDA River Rupangizi 2.5 undertaking FS Everest Contractors Ltd 12 Gura Othaya undertaking FS Karuga Gitugi Electrification Project Kirigori Electrification Project 2 South Mathioya Gitugi, Muranga 3 South Mathioya Gitugi, Muranga undertaking FS undertaking FS KTDA 2.5 River Rupangizi Embu undertaking FS VSHydro Kenya Ltd 5 River Chania Nyeri undertaking FS VSHydro Kenya Ltd 5 River Nithi Nithi undertaking FS Frozen Lemon Energy Ltd 2.6 River unywa, Mt Elgon Bungoma undertaking FS In the framework of the generation expansion planning projects, small hydropower projects with completed PPA negotiations are considered to be implemented until Furthermore, projects Long Term Plan Renewable Energy Page 48

62 whose feasibility studies are already approved are estimated to be commissioned until 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 35%. As a result, an average firm capacity factor of 30% is considered as reasonable for small hydropower plants in the generation expansion planning. 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] Generation [GWh] 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% in 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 Tana, Masinga, Long Term Plan Renewable Energy Page 49

63 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. 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 In 2014, Germany, China, and Japan accounted for over half of the Long Term Plan Renewable Energy Page 50

64 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 China 28. The strengths and weaknesses of PV technology are summarised in the table below: Table 3-27: Strengths and weaknesses of PV energy systems Strengths Mature technology - high reliability and long lifetimes (power output warranties from PV panels are now commonly for 25 years) Automatic operation with very low maintenance requirements No fuel required (i.e. negligible variable OPEX) Modular nature of PV allowing for a complete range of system sizes as application dictates Low environmental impact compared to conventional energy sources Weaknesses Performance is dependent on sunshine levels and local weather conditions Fluctuating power production/no power production at night High capital/initial investment costs Specific training and infrastructure needs in case of limited experience Use of toxic materials in some PV panels No economic storage options for large-scale plants 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. 28 Source: IEA-Photovoltaic Power System Programme (PVPS) 2014 Snapshot of Global PV Markets Long Term Plan Renewable Energy Page 51

65 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 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 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 29 Long Term Plan Renewable Energy Page 52

66 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. Moreover, on a private basis, there is a 72 kwp plant at a flower plant and a 1 MWp plant on a tea-processing farm 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 small project pipeline under the FiT scheme going through PPA negotiations. This PV project pipeline amounts to an overall installed capacity of 240 MWp of solar PV power distributed amongst ten projects (see Table 3-28). Table 3-28: Main Solar PV projects submitted to FiT scheme 31 Name Site Installed capacity [MW] Status Viteki International Holdings Malindi 40 PPA negotiations on-going Subuiga International Chemelil 15 PPA negotiations on-going Kenergy Renewables Ltd Rumuruti 23 PPA negotiations on-going Dafre Holdings Company Ltd /Makindu solar ltd Makueni 30 PPA negotiations on-going Alten Kenya Limited Kesses 40 PPA negotiations on-going Marco Borero Co Ltd. Kieni 1.5 PPA negotiations on-going Solarjoule Ltd Naivasha 10 PPA negotiations on-going Strathmore University Nairobi 0.6 Construction completed Cedata Eldoret 40 PPA negotiations on-going Solienke Eldoret 40 PPA negotiations on-going Total 240 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. 30 Info on both plants under the following source: UNEP Risø Centre, Prospects for investment in large-scale, grid-connected solar power in Africa, June Status: January 2015 Long Term Plan Renewable Energy Page 53

67 Power output [% of rated] 70% 60% 50% 40% 30% 20% 10% 0% hour of day January February March April May June July August September October November December annual 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 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. 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. Long Term Plan Renewable Energy Page 54

68 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 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 Technical maturity of some CSP solutions (particularly parabolic trough) Enables storage of heat and allows dispatchability Limited fuel requirements (low additional costs for fuel and delivery logistics) Low environmental impact compared with conventional energy sources Weaknesses Requires direct irradiance (i.e. dry climate without clouds) very site-specific Has moving parts, requiring higher CAPEX and OPEX than static photovoltaics Require cooling by either water or large condensers Higher capital costs than photovoltaics Long Term Plan Renewable Energy Page 55

69 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 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, Long Term Plan Renewable Energy Page 56

70 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 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 will 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. 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 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%. Long Term Plan Renewable Energy Page 57

71 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 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 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 The wind farm comprises six Vestas V kw. Meanwhile the original two turbine have already retired. In 2015, the Ngong wind farm was expanded by 20.4 MW 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, envi- Long Term Plan Renewable Energy Page 58

72 ronmental 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 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 V m 2 MW 5550 MWh 2775 h Vestas V 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 approximately 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 3-29Error! Reference source not found. 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. Long Term Plan Renewable Energy Page 59

73 Figure 3-29: Mean wind speed map of Kenya 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. Long Term Plan Renewable Energy Page 60

74 Table 3-31: Wind farm candidates Wind farm Installed capacity [MW] Annual generated electricity [GWh] Capacity factor [%] Earliest COD* Ngong 1 - Phase III % 2018 Aeolus Kinangop % 2018 Kipeto - Phase I % 2017 Lake Turkana - Phase I, Stage % 2017 Prunus % 2019 Meru Phase I % 2018 Ol-Danyat Energy 10 not provided not provided 2017 Kipeto - Phase II % 2018 Lake Turkana - Phase I, Stage % 2018 Malindi % 2018 Lake Turkana - Phase I, Stage % 2019 Limuru Wind - Transcentury 50 not provided not provided 2019 Kajiado Wind - Chagem Power 50 not provided not provided 2019 Meru Phase II % 2024 Marsabit Phase I 300 1,043 40% 2025 Lake Turkana - Phase II, Stage % 2025 Lake Turkana - Phase II, Stage % 2026 Lake Turkana - Phase II, Stage % 2027 Marsabit Phase II 300 1,043 40% 2027 Lake Turkana - Phase III, Stage % 2030 Lake Turkana - Phase III, Stage % 2031 Lake Turkana - Phase III, Stage % 2032 * earliest COD as identified in PESTEL analysis (please see Chapter 6 of the LTP report) Taking into account the earliest CODs 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 The potential wind expansion is visualised in the figure below. Long Term Plan Renewable Energy Page 61

75 Installed capacity [MW] 2,500 2,000 Kajiado Wind - Chagem Power Limuru Wind - Transcentury Malindi 1,500 1, Ol-Danyat Energy Marsabit Meru Prunus Lake Turkana - Phase III Lake Turkana - Phase II Lake Turkana - Phase I Kipeto Aeolus Kinangop Ngong Figure 3-30: Potential wind capacity development in Kenya 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 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 ( ). 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 sites 32 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 32 MERRA data are given for a dense grid of 55m x 75 km around the globe. Long Term Plan Renewable Energy Page 62

76 Power output [% of rated] correlated to the average of , for which measurement data is available, in order to represent long-term average wind speeds. 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 I Phase II (7 MW) and Ngong II (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. 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% hour of day January February March April May June July August September October November December annual Figure 3-31: Kinangop wind farm average daily production patterns per month Long Term Plan Renewable Energy Page 63

77 Power output [% of rated] Power output [% of rated] 120.0% 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% hour of day January February March April May June July August September October November December annual Figure 3-32: Kipeto wind farm average daily production patterns per month 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% hour of day January February March April May June July August September October November December annual Figure 3-33: Lake Turkana wind farm average daily production patterns per month Long Term Plan Renewable Energy Page 64

78 Power output [% of rated] 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% hour of day January February March April May June July August September October November December annual 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%. Long Term Plan Renewable Energy Page 65

79 Power output [% of rated] 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% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% hour of day January February March April May June July August September October November December annual 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) 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 will thus consider wind power development as a scenario parameter. Based on a reference case that reflects the pipeline of already committed wind power projects, scenarios will determine the impacts of an accelerated and slowed-down deployment of wind resources in Kenya. Results will help to determine adequate development corridors and highlight potential excess cost due to the promotion of wind power. Long Term Plan Renewable Energy Page 66

80 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. Long Term Plan Renewable Energy Page 67

81 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 is connected to the grid. 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 is expected to be commissioned in 2015 with a capacity of 18 MW from bagasse co-generation, out of which 10 MW will be supplied to the grid. Besides these two sugar mills, there is no other biomass power project expected in the short term. 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 Ken- Long Term Plan Renewable Energy Page 68

82 ya. Though evaluated for many years no actual projects have been realised. This is a situation similar to many other African countries Medium and long term potential At present, there are eight biomass and biogas projects proposed under the FiT scheme which are at an advanced stage. They are displayed in the table below. Table 3-32: Main biomass projects submitted to the FiT scheme Name Technology Site Installed capacity [MW] Status Biojoule Kenya Limited Biogas Naivasha 2.0 construction on-going Biogas Holdings Ltd Biogas Kilifi 0.3 construction on-going Sustainable Energy ManagementBiomass Nairobi 40.0 PPA negotiations on-going Kwale Int. Sugar Co. Ltd Biomass Kwale 18.0 commissioned; supply to grid (10 MW) assumed for 2017 Cummins Biomass Baringo Kabarnet 10.0 construction on-going Viability Africa Biomass Garissa 1.0 Feasibility study completed REA Vipingo Plantations Limited Biomass Kibwezi 1.4 PPA negotiations on-going Total 72.7 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 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. 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: Cummins CK has been involved in the innovative Marigat project in the Baringo County of Kenya, which will generate 12 MW of electricity by making use of the invasive Prosopis Juliflora plant as feedstock. The initial phase of the 10.8 MW biofuel plant in Baringo, which will burn the noxious mathenge (local name of Prosopis Puliflora) tree to generate electricity has been completed. The power plant completed in the initial phase has an installed capacity of 2.4 MW. A PPA has been signed with Kenya Power to supply 2 MW to the national grid. The second phase will see Cummins inject 7 MW into the national grid. The ERC license was issued in January Long Term Plan Renewable Energy Page 69

83 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 dual-fuel 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. 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. 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 MW and export around 100 MW to the grid. Long Term Plan Renewable Energy Page 70

84 Table 3-33: Present sugar mills in Kenya and their technical potential Name Capacity Ton Cane per day [TCD] Technical potential Capacity [MW] Chemelil 3, Mumias 8, Nzoia 3, South Nyanza 2, West Kenya 3, Muhoroni 2,200 9 Kibos 1, Butali 1, Transmara 1, Sukari 1, Kwale 3, TOTAL 32, 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, 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. 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. These assumptions result in the expansion path as depicted in the following table. Long Term Plan Renewable Energy Page 71

85 Table 3-34: Cumulated expansion cogeneration (incl. existing plants) 2035 Capacity [MW] Generation [GWh] Recommendation for expansion plan The future of successfully implemented biomass projects in Kenya will strongly depend on the development of the agricultural sector. The long-term expansion planning will consider the existing Mumias and Kwale. Beyond 2017, linear extrapolation of biomass capacity is assumed. 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 will not be considered in the long-term planning as a candidates. 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 ( 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. Long Term Plan Renewable Energy Page 72

86 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 steams contains noncondensable gases, e.g. hydrogen sulphides, carbon dioxide and methane which may be emitted to the atmosphere. The CO 2 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 3-35: Strengths and weaknesses of geothermal energy Strengths Weaknesses Large resources in volcanic areas (rift zones) Site-specific technology (taylor-made ) Provision of base load power at low operating cost Reliable and constantly available during the year Mature technology High drilling risk and drilling cost Reservoir characteristics may change over the life of a power plant Rather long implementation time Scaling, corrosion and requirement to clean noncondensable gases (NCG) will result in additional cost 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 Long Term Plan Renewable Energy Page 73

87 Figure 3-36: Simple schematic drawing of single flash power plant 33 The geothermal liquid (brine) from the separator is at high temperature, usually in the range of 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 side 34. 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. 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 This type of technology is applied within the Kenyan plants of Olkaria 3 owned and operated by OrPower4. 33 Source: EFLA 34 Geothermal steam contains considerable quantities of hydrogen sulphides and CO 2 which leads to metal erosion and corrosion. Long Term Plan Renewable Energy Page 74

88 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. 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 Long Term Plan Renewable Energy Page 75

89 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 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 Nairobi 35. In 2014, geothermal capacity provided 32% of the power generation. Today, the total geothermal net capacity amounts 622 MW (see Table 3-36). 79% 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. 35 Besides a 2.5 MW binary plant in the Eburru field. Long Term Plan Renewable Energy Page 76

90 Table 3-36: Present Geothermal power plants Name Owner Net capacity [MW] COD Olkaria 1 Unit 1-3 KenGen Olkaria 1 Unit 4-5 KenGen Olkaria 2 KenGen Olkaria 3 Unit 1-6 OrPower Olkaria 3 Unit 7-9 OrPower /2014 Olkaria 4 KenGen OrPower Wellhead 4 OrPower Olkaria Wellheads (OW37, OW43, OW ) KenGen Eburru Hill KenGen Total 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 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 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 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 473 MW of geothermal power could be implemented during the medium-term period (until 2019) since implementation is already on-going. A breakdown of the project information is provided in the table belowerror! Reference source not found.. Long Term Plan Renewable Energy Page 77

91 Table 3-37: Geothermal power plants to be implemented in medium-term period 36 Project Name Owner Capacity [MW] Project Status Olkaria Wellheads KenGen 20 Construction on-going, partly commissioned Menengai 1 Stage 1 Quantum, OrPower , Sosian Energy 103 Procurement and EPC contracting Olkaria 1 (Unit 6) KenGen 70 Design and procurement in progress Olkaria 5 KenGen 140 Design and procurement in progress Olkaria 6 KenGen 140 Design and procurement in progress TOTAL 473 Earliest COD It is estimated that further 2,435 MW geothermal capacity can be implemented during the LTP period until 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. Table 3-38: Geothermal potential by field Field Existing capacity Medium term potential 39 Medium and long term potential 40 Theoretical potential 41 MW MW MW MW Olkaria ,500 Menengai ,600 Eburru Longonot Akiira Suswa Baringo Silali ,000 Emuruangogolak 0 0 no projects defined 650 Arus 0 0 no projects defined 200 Badlands 0 0 no projects defined Considering medium-term period until Estimated based on results of candidates assessment (see Chapter 6.5) 38 Consortium consisting of Ormat, Civicon, Symbion 39 Considering medium-term period until Estimates based on results of candidates assessment (see Chapter 6.5) 41 Estimated potential as presented in GDC strategic plan (April 2013) or based on additional information received 42 Comprising the fields Silali Korosi and Paka Long Term Plan Renewable Energy Page 78

92 Field Existing capacity Medium term potential 39 Medium and long term potential 40 Theoretical potential 41 MW MW MW MW 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 Total ,488 9,880-10, 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 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. Long Term Plan Renewable Energy Page 79

93 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 Long Term Plan Renewable Energy Page 80

94 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 ). 2) Small hydro: Small hydropower projects with completed PPA negotiations are considered to be implemented until Furthermore, projects whose feasibility studies are already approved are estimated to be commissioned until From 2025 onwards, linear extrapolation of small hydropower capacity is assumed (see Section ). 3) Geothermal: It is expected that an overall capacity of roughly 500 MW of geothermal power will be implemented until 2020 since implementation is already ongoing (projects are: Menengai 1 Phase I Stage 1, KenGen Olkaria Wellheads II, Olkaria 1 Unit 6, Olkaria 5, Olkaria 6, and Eburru 2; see also Section 3.6.2). The further utilisation of the geothermal resource is variable and determined by the generation system optimisation. 4) Biomass: The future expansion of cogeneration from biomass resources (mainly bagasse) strongly depends on the development of the agricultural sector in Kenya. The expansion planning will consider the existing Mumias and Kwale facilities. Beyond 2017, linear extrapolation of biomass capacity is assumed (see also Section 3.5.2). Long Term Plan Renewable Energy Page 81

95 5) Wind: Roughly 520 MW of wind power capacity are considered as already committed, and to become operational until the year 2020 (the considered projects are: Ngong 1 Phase III, Aelous Kinangop, Kipeto Phase I, Lake Turkana Phase I, Stage 1, Meru Phase I, Kipeto Phase II, Lake Turkana Phase I, Stage 2, and Lake Turkana Phase I, Stage 3). Either construction already commenced or the projects have reached financial close. Until 2035 an additional development of 600 MW is assumed for the moderate RE scenario. The moderate scenario assumes a slight reduction in momentum of wind development between 2020 and 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 The moderate RE scenario, thus, foresees solar PV development to start in 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 3.3.2). Until 2035 about 250 MW of solar PV capacity will 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 Also solar PV efforts are intensified. 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, and solar PV capacity to 100 MW until Table 4-1 and Figure 4-1 depict additional RE development in the considered scenarios during the period between 2020 and The three scenarios create a bandwidth of possible wind and solar PV development until The following analysis will identify the impacts of these different development pathways. Results will provide a valuable basis for future decision-making regarding the development of wind and solar PV. Long Term Plan Renewable Energy Page 82

96 Generic PV Generic wind Technology RE path Table 4-1: Additional (to existing and committed plants) RE development in the moderate, accelerated and slowed down RE scenarios ( ) Moderate Slowed down ,000 1, Moderate Generic biomass Accelerated Accelerated Slowed down Generic small HPP Additional wind development Additional solar PV development Figure 4-1: Additional wind and solar PV development Long Term Plan Renewable Energy Page 83

97 4.3 Results 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 ( ) 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 5,500 MW and does not differ considerably between the considered RE scenarios (see first row in Table 4-2). This is more than two times the existing generation system. Hence, the generation system has to more than triple during the 20-year study period. About 30% (1.6 GW) of the needed firm capacity is already committed. Main expansion through 2,345 MW base load geothermal capacity from 2024 onwards. In 2035, geothermal capacity represents between 29% (accelerated RE) and 35% (slowed down RE) of the total installed system capacity providing between 54% (accelerated RE) and 63% (slowed down RE) of the annual generated electricity. A detailed discussion of the differences will follow in Section Expansion of back-up and peaking capacity by 1,610 MW to 1,680 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, Olkaria 5 & 6, Lamu), overcapacities occur during the years 2019 to 2027 in all considered RE scenarios. This results in: Underused investment for this period reflected by a strong increase of system LEC. LEC increase by up to 34% (moderate RE), up to 38% (accelerated RE), and up to 33% (slowed down RE) compared to LEC in 2015; All three scenarios lead to excess electricity generation (which has to be dumped or might be exported); Long Term Plan Renewable Energy Page 84

98 Electricity generation/ consumption [GWh] Electricity generation/ consumption [GWh] Electricity generation/ consumption [GWh] Firm capacity/ load [MW] Firm capacity/ load [MW] Firm capacity/ load [MW] 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 7,000 6,000 5,000 4,000 3,000 2,000 1, Generic wind expansion (firm capacity) Back-up capacity - candidate Coal - candidate Large HPP (firm capacity) - candidate GEO - candidate Generic small HPP expansion (firm capacity) Generic cogeneration expansion (firm capacity) Committed wind (firm capacity) Committed coal Committed imports Committed GEO Wind firm - existing Existing cogeneration (firm capacity) Existing gas turbines Existing diesel engines Existing HPP (firm capacity) Existing GEO Peak load Peak load + reserve margin Existing system Existing + committed + small HPP + cogeneration expansion 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1, Generic wind expansion (firm capacity) Back-up capacity - candidate Coal - candidate Large HPP (firm capacity) - candidate GEO - candidate Generic small HPP expansion (firm capacity) Generic cogeneration expansion (firm capacity) Committed wind (firm capacity) Committed coal Committed imports Committed GEO Wind firm - existing Existing cogeneration (firm capacity) Existing gas turbines Existing diesel engines Existing HPP (firm capacity) Existing GEO Peak load Peak load + reserve margin Existing system Existing + committed + small HPP + cogeneration expansion 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1, Generic wind expansion (firm capacity) Back-up capacity - candidate Coal - candidate Large HPP (firm capacity) - candidate GEO - candidate Generic small HPP expansion (firm capacity) Generic cogeneration expansion (firm capacity) Committed wind (firm capacity) Committed coal Committed imports Committed GEO Wind firm - existing Existing cogeneration (firm capacity) Existing gas turbines Existing diesel engines Existing HPP (firm capacity) Existing GEO Peak load Peak load + reserve margin Existing system Existing + committed + small HPP + cogeneration expansion Electricity generation versus elelctricity consumption: 40,000 Unserved energy 40,000 Unserved energy 40,000 Unserved energy 35,000 PV Wind 35,000 PV Wind 35,000 PV Wind 30,000 25,000 Generic back-up capacity Cogeneration Import 30,000 25,000 Generic back-up capacity Cogeneration Import 30,000 25,000 Generic back-up capacity Cogeneration Import 20,000 Gas turbines (gasoil) 20,000 Gas turbines (gasoil) 20,000 Gas turbines (gasoil) 15,000 10,000 5, Diesel engines Coal Hydropower Geothermal Electricity consumption Excess energy 15,000 10,000 5, Diesel engines Coal Hydropower Geothermal Electricity consumption Excess energy 15,000 10,000 5, Diesel engines Coal Hydropower Geothermal Electricity consumption Excess energy Long Term Plan Renewable Energy Page 85

99 Electricity generation & load [MWh/h] Electricity generation & load [MWh/h] Electricity generation & load [MWh/h] Capacity factor [%] Capacity factor [%] Capacity factor [%] Share on energy mix [%] Share on energy mix [%] Share on energy mix [%] Accelerated RE expansion Moderate RE expansion Slowed down RE expansion Share on generation mix by technology: 100% PV 100% PV 100% PV 90% Wind 90% Wind 90% Wind 80% 70% 60% 50% 40% 30% 20% 10% 0% Generic back-up capacity Cogeneration Import Gas turbines (gasoil) Diesel engines Coal Hydropower Geothermal RE total Excess energy 80% 70% 60% 50% 40% 30% 20% 10% 0% Generic back-up capacity Cogeneration Import Gas turbines (gasoil) Diesel engines Coal Hydropower Geothermal RE total Excess energy 80% 70% 60% 50% 40% 30% 20% 10% 0% Generic back-up capacity Cogeneration Import Gas turbines (gasoil) Diesel engines Coal Hydropower Geothermal RE total Excess energy Capacity factor by technology: 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Sample dispatch in the period : Unserved energy PV Wind Back-up capacity Diesel engines HPP Coal Geothermal Cogeneration Import Demand Excess energy Demand + primary reserve provision Demand + primary reserve requirement 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Unserved energy PV Wind Back-up capacity Diesel engines HPP Coal Geothermal Cogeneration Import Demand Excess energy Demand + primary reserve provision Demand + primary reserve requirement % 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Geothermal Hydropower Coal Diesel engines Gas turbines (gasoil) Import Cogeneration Generic back-up capacity Wind PV Unserved energy PV Wind Back-up capacity Diesel engines HPP Coal Geothermal Cogeneration Import Demand Excess energy Demand + primary reserve provision Demand + primary reserve requirement Long Term Plan Renewable Energy Page 86

100 Comparatively low capacity factors of dispatchable power plants: o 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 2023 is 75% to 85% in all considered RE scenarios. Medium speed diesel engines and gas turbines are not utilised. They only provide back-up capacity. In the long-term, nearly 85% of the electricity demand will be covered by renewable energy sources. 60% is generated by geothermal power plants, followed by hydropower with 15% and wind power with 11%. Cogeneration and PV contribute 3% to the annual energy needs. The remaining electricity demand is mainly covered by imports (7%) and coal (4%). 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 13 and 25% 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 differences in (i) the fuel-specific power generation, and (ii) in the shares of RE generation in total power generation and consumption in the years 2020 to 2035 (see figure on the next page). The figures in the left column display changes of power generation of the accelerated and the slowed down RE scenarios versus the moderate RE scenario measured in absolute terms (GWh). The figures in the right column show changes of RE shares in total generation and consumption versus the moderate RE scenario measured in relative terms (%). The top row contains results of the accelerated RE scenario, the bottom row those of the slowed down RE scenario. Detailed result tables are provided in Annex 4. Long Term Plan Renewable Energy Page 87

101 Slowed down RE Accelerated RE Difference to Moderate RE scenario [GWh] Difference to Moderate RE scenario Difference to Moderate RE scenario [GWh] Difference to Moderate RE scenario , % 2,000 1,500 1, % 1.5% % -0.5% -1,000-1,500-2, % -2.5% -2, % Wind PV Hydropower Cogeneration Geothermal Coal Diesel Engines Gas turbines (gasoil) Import RE share in total consumption RE share in total generation 2, % 2,000 1,500 1, % 1.5% % -0.5% -1,000-1,500-2, % -2.5% -2, % Geothermal Hydropower Coal Diesel Engines Gas turbines (gasoil) Import Cogeneration Wind PV RE share in total consumption RE share in total generation Figure 4-2: Power generation differences vs. moderate RE scenario Long Term Plan Renewable Energy Page 88

102 Not surprisingly, the accelerated RE scenario leads to higher power generation based on wind and solar resources than the moderate RE case. From 2022, 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). To a much lesser extent coal based generation (only some 25% of the reduction of geothermal generation). 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. Table 4-3 shows the impacts of the RE scenarios on the CODs of considered geothermal expansion candidates. After 2029 the accelerated RE scenario delays CODs of geothermal candidates by one or two years compared to the CODs in the moderate RE case. Table 4-3: Changes in CODs due to different RE developments COD COD - Difference to Reference Moderate RE Accelerated RE Slowed down RE Menengai 2 Phase I - Stage delayed by 1 year(s) Eburru delayed by 1 year(s) Olkaria advanced by 1 year(s) Menengai 2 Phase I - Stage advanced by 1 year(s) Suswa Phase I - Stage delayed by 2 year(s) Suswa 2 Phase II - Stage advanced by 1 year(s) Suswa 2 Phase II - Stage delayed by 1 year(s) Suswa 2 Phase II - Stage saved Baringo Silali Phase I, Stage 4 na additional 2034 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 about half a percentage point) compared to the reference scenario (see the top right figure). For the year 2025 the RE share in total generation is even lower than in the reference case. The increased utilisation of volatile wind and solar resources induces larger reserve requirements. To a large extent these requirements are provided by hydropower plants that need to run below their minimum to utilise all water (minimum outflow) more often compared to the reference 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). Long Term Plan Renewable Energy Page 89

103 The effects of the accelerated RE scenario on the RE share in total generation and consumption is nearly the same. The accelerated RE scenario increases the overall share of renewables in total generation and consumption only after This is mainly caused by the commissioning of High Grand Falls hydropower plant which is then able to cover parts of the reserve requirements caused by wind and solar power expansion. 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 and consumption: Compared to the moderate case the slowed down RE scenario does not induce a substantial reduction of the overall RE shares. Between 2031 and 2032 the overall RE shares are even higher than in the accelerated 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. 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 large share of renewables in total generation and consumption calculated as the average shares in the period from 2015 to On average, the accelerated RE case increases the RE share in consumption by roughly 0.5%, the RE share in total generation by approximately 0.3% 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 or consumption in Kenya. Reduced development of wind and PV is compensated by advanced utilisation of geothermal resources. Table 4-4: RE shares in generation and consumption (average ) Moderate RE Accelerated RE Slowed down RE share Difference to Reference share Difference to Reference RE share in total consumption 84.1% 84.6% 0.5% 84.0% -0.1% RE share in total generation 83.2% 83.6% 0.3% 83.2% 0.0% Long Term Plan Renewable Energy Page 90

104 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. 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 The column labelled Present value reflects the sum of the discounted annual cost figures over the whole planning horizon from 2015 to As compared to the moderate case, the accelerated RE scenario significantly increases annual capital cost by 0.8% in 2020 up to 15.0% in Over the period from 2015 to 2035 the present value of all annual capital cost is 5% higher than in case of the moderate scenario. The impact on fixed and variable O&M cost is almost negligible. 43 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 including the reference scenario. For the sake of comparability net present values include also the first years. Long Term Plan Renewable Energy Page 91

105 Table 4-5: Cost implications of RE scenarios Unit Present Value* Capital cost (Investment & rehabilitation) Moderate RE MUSD 9,099 1,037 1,129 1,289 1,308 1,442 1,495 1,618 1,658 1,920 1,928 2,175 2,373 2,547 2,804 2,985 3,137 Accelerated RE MUSD 9,525 1,045 1,137 1,353 1,345 1,529 1,517 1,715 1,730 2,054 2,036 2,399 2,683 2,859 3,196 3,419 3,613 Difference to Moderate RE 4.7% 0.8% 0.7% 5.0% 2.8% 6.0% 1.4% 6.0% 4.3% 7.0% 5.6% 10.3% 13.0% 12.2% 14.0% 14.5% 15.2% Low RE MUSD 8,861 1,029 1,121 1,281 1,301 1,386 1,512 1,558 1,612 1,861 1,845 2,034 2,171 2,382 2,557 2,706 2,845 Difference to Moderate RE -2.6% -0.8% -0.7% -0.6% -0.6% -3.9% 1.1% -3.7% -2.8% -3.0% -4.3% -6.5% -8.5% -6.5% -8.8% -9.4% -9.3% O&M cost (fixed and variable) Moderate RE MUSD 2, Accelerated RE MUSD 2, Difference to Moderate RE 0.8% 0.0% 0.0% 0.5% 0.4% 0.8% -0.8% 1.1% 1.1% 1.5% 0.8% 1.7% 2.5% 1.7% 2.6% 3.2% 4.0% Low RE MUSD 2, Difference to Moderate RE -0.2% -0.1% 0.0% -0.1% -0.1% -0.1% -0.1% -0.4% -0.4% -0.4% -0.5% -0.8% 1.0% 1.3% -0.8% -1.5% -1.9% Fuel cost Total cost Moderate RE MUSD Accelerated RE MUSD Difference to Moderate RE -2.3% 1.9% 0.6% -2.9% -2.3% -3.7% 9.1% -4.9% -5.6% -9.0% -8.8% -18.1% -16.2% -0.5% 3.8% 0.9% 0.2% Low RE MUSD Difference to Moderate RE -0.3% 2.7% 0.2% 1.0% 0.6% 1.7% 0.4% 2.1% 2.8% 4.4% 7.0% 9.2% -19.8% -25.8% -6.5% -9.1% 3.4% Moderate RE MUSD 12,490 1,483 1,618 1,824 1,862 2,018 2,109 2,264 2,342 2,589 2,641 2,937 3,179 3,402 3,709 3,963 4,198 Accelerated RE MUSD 12,928 1,492 1,626 1,889 1,899 2,107 2,133 2,364 2,416 2,726 2,745 3,153 3,489 3,726 4,124 4,425 4,710 Difference to Moderate RE 3.5% 0.6% 0.5% 3.5% 2.0% 4.4% 1.1% 4.4% 3.1% 5.3% 3.9% 7.4% 9.8% 9.5% 11.2% 11.7% 12.2% Low RE MUSD 12,245 1,476 1,610 1,817 1,855 1,963 2,125 2,204 2,296 2,531 2,562 2,801 2,963 3,221 3,450 3,662 3,893 Difference to Moderate RE -2.0% -0.5% -0.5% -0.4% -0.4% -2.7% 0.8% -2.7% -2.0% -2.2% -3.0% -4.7% -6.8% -5.3% -7.0% -7.6% -7.3% System LEC *Discount rate: 12% Moderate RE USD/MWh Accelerated RE USD/MWh Difference to Moderate RE 3.5% 0.6% 0.5% 3.5% 2.0% 4.4% 1.1% 4.4% 3.1% 5.3% 3.9% 7.4% 9.8% 9.5% 11.2% 11.7% 12.2% Low RE USD/MWh Difference to Moderate RE -2.0% -0.5% -0.5% -0.4% -0.4% -2.7% 0.8% -2.7% -2.0% -2.2% -3.0% -4.7% -6.8% -5.3% -7.0% -7.6% -7.3% Long Term Plan Renewable Energy Page 92

106 Accelerated wind and solar development reduces consumption of fossil fuels, mainly coal reflected by decreasing fuel cost. Total cost are higher in the accelerated RE case than in the moderate scenario: Cumulated and discounted cost are 4% higher than in the moderate scenario. Total cost development is dominated by the sharp increase in capital cost in the later years. On average, specific generation cost are as well 4% higher, reflected by a system LEC of USD/MWh compared to 10.4 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 0.5% to 10% lower than in the moderate scenario. In total, cumulated and discounted annual capital cost are 3% lower. Again, effects on fixed and variable O&M cost are negligible. The slowed down RE scenario reduces fuel consumption and cost. Substantial fuel cost savings may be expected in the later years, caused by the advanced commissioning of additional geothermal projects. Also total cost are lower as compared to the moderate case. Over the whole planning horizon savings of about 30 MUSD could be realised, which represents a reduction in total cost of roughly 0.3% 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 ( ). 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 4-3. Increasing the share of wind and solar from the slowed down scenario to the moderate scenario is quite costly in the early years. The moderate scenario stipulates a larger share of solar PV in these years, wind generation is same in both cases. The observed high specific incremental cost are thus caused by the deployment of a relatively costly technology. From 2024 on the moderate scenario utilises more wind power than the slowed down RE scenario. Specific cost of wind generation (LEC) are more cost-competitive than those of solar PV. It is thus not surprising, that incremental cost further decrease after Annual incremental cost range between 18 USDcent/kWh and 100 USDcent/kWh. The overall LRMC for switching from the slowed down RE scenario to the moderate case amount to roughly 30 USDcent/kWh (indicated by the dotted orange line in Figure 4-3. As indicated before, increasing the share of wind and solar power generation will at similar costs (in specific terms) when the share of already existing generation such kind gets higher. This is well reflected by the simulation results Page 93

107 Figure 4-3: Incremental cost and LRMC of RE expansion Switching from the moderate scenario to the accelerated RE scenario results in similar specific cost. Although the switch from moderate to accelerated scenario leads to lower incremental costs in the early years, 44 the specific incremental cost of treh two cases converge at around 20 USDcent/kWh. Resulting LRMC for switching from the moderate case to the accelerated RE scenario are calculated at almost 27 USDcent/kWh, only slightly below the switch from slowed down to reference scenario. 4.4 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, 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. In the years before High Grand Falls hydropower plant will start operation, other hydropower plants cater for this reserve at the expense of spilled water. This leads to a decrease in generation from hydropower plants. 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: 44 The accelerated RE scenario foresees a larger increase of wind generation in the early years. Together with the stipulated solar PV development this leads to relatively high cost in the beginning, however incremental cost are lower than in the previous case, since wind developmend dampens the sharp increase in incremental cost Page 94

108 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, cost-effective and emission-free energy source for Kenya Page 95

109 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-E (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 will be mentioned as well. 5.2 Direct Public or Governmental Action vs. Indirect Regulatory Action: As stated above, the direct promotion of RES-E 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) Page 96

110 or assign the responsibility to develop renewables to a third party. The latter case involves various forms of public procurement and public private partnership. 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. 45 Governmental support for electricity from renewable energy sources can thereby affect the following simplified stages: research and development (R&D), investment in RES-E, 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-E mainly affect the market for renewable energy. For example feed-in tariffs may be used to provide favourable revenues for RES-E investments or quota obligations for or renewable portfolio standards for suppliers safeguard a certain RES-E share in the electricity market. Besides the direct market regulation also fiscal incentives (e.g. reduced income taxes on revenues from RES-E 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-E could be promoted by fiscal incentives such as reduced tax rates (e.g. VAT) 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 45 Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECN- C Page 97

111 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 Investment Subsidies Fiscal Measures Quota Obligation Capacity-based (kw) Figure 5-1: Classification of Renewable Energy Policy Support Mechanisms by Supply, Demand, Capacity and Production 46 Price (e.g. per kwh) Feed-in systems Fiscal Measures Tendering / Bidding Subsidies Fiscal Measures Subsidies to Consumers 46 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 Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECN- Supplyside Demandside Supplyside Demandside 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 Quantity 47 C Page 98

112 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 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-E 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: Page 99

113 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 Direct Subsidies II Feed-In tariff systems Feed-in tariff schemes directly affect the revenues of RES-E projects. The system determines the price paid for a unit of produced electricity. Generally, such systems are applied to grid-connected RES-E capacities. Additional costs caused by the feed-in tariffs are normally recovered by the grid operator via the respective tariff structure. 48 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-E 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). 49 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. 50 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 48 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. 49 Menanteau, P.; Finon, D.; Lamy, M. (2003): Prices versus quantities: choosing policies for promoting the development of renewable energy, Energy Policy (31, 8), pp 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 Page 100

114 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-E 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: Page 101

115 The system should provide a sufficient level of promotion but should also minimize the associated economic cost 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.) 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 Non- Fossil 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 Page 102

116 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 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. 51 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) 51 Van Dijk, A. et al. (2003): Renewable Energy Policies and Market Developments, ECN research paper: ECN- C Page 103

117 Many bidding rounds are needed to achieve goals, especially when projects are relatively small 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-E 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. 52 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 52 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 Page 104

118 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. 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: 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. 5.4 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 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 Page 105

119 End-users fee connection and tariff are uniform for all KPLC customers. Pre-financing facility exists for connection charges ( Stima loan ). Table 5-1: Current Feed-in-Tariff Structure Technology Eligibility <10 MW Standard FiT Eligibility >10 MW Standard FiT Wind US$ US$ Geothermal US$ Hydro US$ US$ US$ US$ Biomass US$ US$ Biogas US$ Solar (Grid) US$ US$ Solar (Off-grid) 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 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 Page 106

120 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 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 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 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 53. 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 53 Draft Energy and petroleum policy Jan Page 107

121 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. 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 assessment 54 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 at- 54 EUEI-PDF Kenya 2013 Project Renewable Energy Regulatory Capacity Development. Assessment of a net metering programme in Kenya Page 108

122 tractive. Without any cap in the systems, in 5 years Kenya could have net-metering capacity installed that generates 100 MW at its peak. 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 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 Bank 55 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. 55 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 Page 109

123 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. This options 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 EWURA 56, 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 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 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 legisla- 56 EWURA Climate Technology Centre & Network Page 110

124 tions which made provisions for fiscal incentives for energy conservation and utilisation within sugar factories and energy export to the public grid. 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, 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 cogeneration 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 farmers Sri-Lanka Net-metering policy Sri Lanka established a net metering policy in January 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 incen- 58 Deepchand, K. Commercial scale cogeneration of bagasse energy in Mauritius. Energy for Sustainable Development. (2001 ), y_in_mauritius (accessed: 15 January 2016) 59 Assessment of a net metering programme in Kenya. Volume 1: Main report, March 2014 (Ministry of Energy and Petroleum, EUEI-PDF) Page 111

125 tive 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 commercial 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 lead to a considerable increase in the installation of solar PV systems of an average of 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 programme 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 Page 112

126 Page 113

127 n ANNEX 1 EXECUTIVE SUMMARY ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank Annex Page 1

128 n ANNEX 2 INTRODUCTION ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank Annex Page 2

129 n ANNEX 3 RENEWABLE ENERGY RESOURCES IN KENYA ANNEXES There is no annex to this chapter. The rest of the page is intentionally left blank Annex Page 3

130 n ANNEX 4 ANALYSIS OF RENEWABLE ENERGY EXPANSION ANNEXES Annex Page 4

131 n Annex Table 1: Moderate RE expansion annual data demand, capacity, reliability criteria Unit Peak versus capacity: Peak load MW 1,570 1,709 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 Peak load national MW 1,570 1,679 1,804 1,942 2,090 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 Peak load export Rwanda MW Peak load + reserve margin MW 1,853 1,981 2,107 2,249 2,379 2,830 3,067 3,300 3,497 3,701 3,996 4,245 4,513 4,842 5,150 5,610 5,984 6,375 6,786 7,238 7,756 Reserve margin ,017 1,073 Share on peak load % 18% 16% 15% 14% 12% 25% 25% 25% 24% 22% 22% 21% 20% 20% 19% 19% 18% 17% 17% 16% 16% Installed capacity: Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 1,892 2,182 2,582 2,882 3,082 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,260 1,610 Wind MW ,060 1,140 PV MW Total MW 2,213 2,275 2,477 2,858 3,226 3,576 3,847 4,194 4,301 4,484 4,842 5,105 5,372 5,925 6,235 6,773 7,221 7,693 8,241 8,812 9,422 Firm capacity: Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 1,892 2,182 2,582 2,882 3,082 Hydropower MW ,126 1,286 1,288 1,291 1,294 1,296 1,299 1,302 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,260 1,610 Wind MW PV MW Total MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,483 3,561 3,705 4,052 4,275 4,522 4,927 5,166 5,636 5,997 6,382 6,815 7,260 7,760 LOLE h/a Annex Page 5

132 n Annex Table 2: Moderate RE expansion annual data consumption and generation Unit Consumption versus generation: Electricity consumption GWh 9,453 10,356 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 Electricity consumption national GWh 9,453 10,093 10,821 11,593 12,420 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 Electricity consumption export Rwanda GWh Electricity generation: GWh Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,148 5,318 5,430 5,627 7,119 8,556 9,613 10,506 10,879 11,584 13,421 15,176 17,149 19,469 21,404 23,194 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,913 3,145 3,391 3,816 3,712 3,748 3,789 3,869 5,472 5,547 5,572 5,586 5,620 5,642 5,666 5,688 Coal GWh ,342 1,687 1,431 1,455 1,544 1,826 1,361 2,117 2,011 1,868 1,638 1,257 1,315 1,533 Diesel engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh 2,663 2,701 2,693 2,678 2,665 2,668 2,668 2,671 2,682 2,663 2,669 2,673 2,670 2,674 2,666 2,661 2,678 Cogeneration GWh Generic back-up capacity GWh Wind GWh ,845 2,326 2,326 2,326 2,413 2,413 2,500 2,500 2,587 2,587 2,659 2,659 2,833 3,094 3,355 3,703 4,051 4,337 PV GWh Total GWh 9,453 10,356 11,084 11,872 13,285 13,838 14,733 15,682 16,667 17,947 19,483 20,822 22,164 23,776 25,383 27,417 29,387 31,519 33,896 36,384 38,893 Unserved energy GWh Excess energy GWh ,254 1,402 1, Share on total generation % 0% 0% 0% 0% 9% 10% 7% 5% 3% 4% 4% 3% 3% 0% 0% 0% 0% 0% 1% 1% 1% Vented GEO steam* GWh , , ,916 2,466 2,332 Share on potential maximum GEO generation % 0% 0% 0% 7% 21% 16% 13% 11% 8% 9% 10% 10% 9% 5% 1% 2% 3% 5% 9% 10% 9% Annex Page 6

133 n Annex Table 3: Moderate RE expansion cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 2, ,066 1,198 1,286 Hydropower MUSD 2, Coal MUSD 1, Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD 1, PV MUSD Total MUSD 9, ,037 1,129 1,289 1,308 1,442 1,495 1,618 1,658 1,920 1,928 2,175 2,373 2,547 2,804 2,985 3,137 O&M fixed Geothermal MUSD 1, Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD PV MUSD Total MUSD 2, Annex Page 7

134 Annex Table 4: Moderate RE expansion cost summary (2/2) Unit NPV O&M variable (other than fuel) Geothermal MUSD Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD 1, Cogeneration MUSD Generic back-up capacit MUSD Wind MUSD PV Total MUSD Fuel cost Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Generic back-up capacit MUSD Total MUSD Unserved energy cost MUSD Total cost MUSD 12, ,074 1,321 1,483 1,618 1,824 1,862 2,018 2,109 2,264 2,342 2,589 2,641 2,937 3,179 3,402 3,709 3,963 4,198 System LEC USDcent/kWh Annex Page 8

135 Annex Table 5: Accelerated RE expansion annual data demand, capacity, reliability criteria Unit Peak versus capacity: Peak load MW 1,570 1,709 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 Peak load national MW 1,570 1,679 1,804 1,942 2,090 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 Peak load export Rwanda MW Peak load + reserve margin MW 1,853 1,981 2,107 2,249 2,379 2,830 3,067 3,300 3,497 3,701 3,992 4,245 4,513 4,842 5,149 5,600 5,974 6,392 6,804 7,246 7,765 Reserve margin ,025 1,083 Share on peak load % 18% 16% 15% 14% 12% 25% 25% 25% 24% 22% 22% 21% 20% 20% 19% 18% 18% 18% 17% 16% 16% Installed capacity: Geothermal MW ,087 1,287 1,387 1,387 1,387 1,652 1,892 2,082 2,482 2,782 2,982 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,050 1,050 1,260 1,610 Wind MW ,010 1,160 1,360 1,560 1,740 PV MW Total MW 2,213 2,275 2,477 2,858 3,226 3,581 3,852 4,229 4,336 4,554 4,852 5,210 5,487 6,085 6,390 6,953 7,496 8,103 8,781 9,422 ##### Firm capacity: Geothermal MW ,087 1,287 1,387 1,387 1,387 1,652 1,892 2,082 2,482 2,782 2,982 Hydropower MW ,126 1,286 1,288 1,291 1,294 1,296 1,299 1,302 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,050 1,050 1,260 1,610 Wind MW PV MW Total MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,490 3,567 3,718 4,005 4,294 4,541 4,952 5,166 5,603 5,983 6,427 6,885 7,285 7,810 LOLE h/a Annex Page 9

136 Annex Table 6: Accelerated RE expansion annual data consumption and generation Unit Consumption versus generation: Electricity consumption GWh 9,453 10,356 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 Electricity consumption national GWh 9,453 10,093 10,821 11,593 12,420 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 Electricity consumption export Rwanda GWh Electricity generation: GWh Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,142 5,309 5,415 5,607 7,063 8,149 9,517 10,411 10,612 11,322 13,176 14,654 16,060 18,060 19,787 21,316 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,888 3,155 3,355 3,778 3,661 3,756 3,705 3,803 5,471 5,546 5,564 5,596 5,614 5,635 5,629 5,657 Coal GWh ,294 1,637 1,370 1,584 1,460 1,715 1,215 1,903 1,621 1,484 1,511 1,193 1,211 1,343 Diesel engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh 2,663 2,698 2,690 2,680 2,665 2,666 2,670 2,667 2,679 2,666 2,665 2,664 2,663 2,671 2,662 2,658 2,673 Cogeneration GWh Generic back-up capacity GWh Wind GWh ,845 2,326 2,326 2,326 2,500 2,500 2,675 2,675 2,849 2,849 3,007 3,007 3,355 3,877 4,399 5,096 5,792 6,425 PV GWh Total GWh 9,453 10,356 11,084 11,872 13,285 13,825 14,747 15,692 16,666 17,986 19,432 20,866 22,216 23,817 25,390 27,452 29,469 31,595 34,115 36,736 39,329 Unserved energy GWh Excess energy GWh ,254 1,413 1, Share on total generation % 0% 0% 0% 0% 9% 10% 7% 5% 3% 4% 3% 4% 3% 0% 0% 0% 1% 1% 2% 2% 2% Vented GEO steam* GWh , ,141 1, ,016 1,183 2,497 3,254 3,382 Share on potential maximum GEO generation % 0% 0% 0% 7% 21% 16% 13% 11% 8% 10% 9% 11% 9% 8% 1% 4% 6% 7% 12% 14% 14% * assuming that all geothermal power plants are equipped with single-flash technology (no flexible handling of geothermal steam possible) Annex Page 10

137 Annex Table 7: Accelerated RE expansion cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 2, ,023 1,154 1,242 Hydropower MUSD 2, Coal MUSD 1, Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD 1, PV MUSD Total MUSD 9, ,045 1,137 1,353 1,345 1,529 1,517 1,715 1,730 2,054 2,036 2,399 2,683 2,859 3,196 3,419 3,613 O&M fixed Geothermal MUSD 1, Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD PV MUSD Total MUSD 2, Annex Page 11

138 Annex Table 8: Accelerated RE expansion cost summary (2/2) Unit NPV O&M variable (other than fuel) Geothermal MUSD Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD 1, Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD PV Total MUSD Fuel cost Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Generic back-up capacity MUSD Total MUSD Unserved energy cost MUSD Total cost MUSD 12, ,074 1,321 1,492 1,626 1,889 1,899 2,107 2,133 2,364 2,416 2,726 2,745 3,153 3,489 3,726 4,124 4,425 4,710 System LEC USDcent/kWh Annex Page 12

139 Annex Table 9: Slowed down RE expansion annual data demand, capacity, reliability criteria Unit Peak versus capacity: Peak load MW 1,570 1,709 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 Peak load national MW 1,570 1,679 1,804 1,942 2,090 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 Peak load export Rwanda MW Peak load + reserve margin MW 1,853 1,981 2,107 2,249 2,379 2,830 3,067 3,300 3,497 3,711 3,996 4,245 4,513 4,842 5,150 5,610 5,984 6,369 6,793 7,245 7,773 Reserve margin ,024 1,090 Share on peak load % 18% 16% 15% 14% 12% 25% 25% 25% 24% 23% 22% 21% 20% 20% 19% 19% 18% 17% 17% 16% 16% Installed capacity: Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 2,032 2,382 2,682 2,982 3,182 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,260 1,680 Wind MW PV MW Total MW 2,213 2,275 2,477 2,858 3,226 3,571 3,842 4,189 4,296 4,519 4,832 5,065 5,322 5,860 6,150 6,653 7,086 7,488 7,986 8,457 9,042 Firm capacity: Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 2,032 2,382 2,682 2,982 3,182 Hydropower MW ,126 1,286 1,288 1,291 1,294 1,296 1,299 1,302 Coal MW Diesel engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Generic back-up capacity MW ,260 1,680 Wind MW PV MW Total MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,483 3,561 3,769 4,052 4,269 4,516 4,921 5,160 5,623 6,035 6,399 6,852 7,278 7,830 LOLE h/a Annex Page 13

140 Annex Table 10: Slowed down RE expansion annual data consumption and generation Unit Consumption versus generation: Electricity consumption GWh 9,453 10,356 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 Electricity consumption national GWh 9,453 10,093 10,821 11,593 12,420 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 Electricity consumption export Rwanda GWh Electricity generation: GWh Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,149 5,320 5,426 5,627 7,094 8,559 9,645 10,533 10,960 11,607 13,483 16,120 18,365 20,497 22,648 24,536 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,921 3,148 3,392 3,821 3,750 3,751 3,807 3,903 5,472 5,547 5,572 5,600 5,618 5,633 5,670 5,693 Coal GWh ,353 1,693 1,452 1,461 1,581 1,889 1,425 2,280 2,229 1,534 1,284 1,244 1,306 1,695 Diesel engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh 2,663 2,698 2,691 2,676 2,663 2,667 2,667 2,671 2,681 2,667 2,671 2,677 2,664 2,663 2,669 2,665 2,680 Cogeneration GWh Generic back-up capacity GWh Wind GWh ,845 2,326 2,326 2,326 2,413 2,413 2,413 2,500 2,500 2,500 2,572 2,572 2,659 2,659 2,746 2,833 2,920 2,944 PV GWh Total GWh 9,453 10,356 11,084 11,872 13,285 13,850 14,731 15,678 16,667 17,877 19,477 20,797 22,156 23,770 25,380 27,409 29,407 31,566 33,838 36,252 38,749 Unserved energy GWh Excess energy GWh ,254 1,406 1, Share on total generation % 0% 0% 0% 0% 9% 10% 7% 5% 3% 3% 4% 3% 2% 0% 0% 0% 0% 1% 1% 1% 1% Vented GEO steam* GWh , , ,364 1,716 2,050 1,819 Share on potential maximum GEO generation % 0% 0% 0% 7% 21% 16% 13% 11% 8% 10% 10% 10% 8% 5% 1% 1% 4% 7% 8% 8% 7% * assuming that all geothermal power plants are equipped with single-flash technology (no flexible handling of geothermal steam possible) Annex Page 14

141 Annex Table 11: Slowed down RE expansion cost summary (1/2) Unit NPV Capital cost (Investment & rehabilitation) Geothermal MUSD 2, ,110 1,242 1,330 Hydropower MUSD 2, Coal MUSD 1, Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD PV MUSD Total MUSD 8, ,029 1,121 1,281 1,301 1,386 1,512 1,558 1,612 1,861 1,845 2,034 2,171 2,382 2,557 2,706 2,845 O&M fixed Geothermal MUSD 1, Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD Cogeneration MUSD Generic back-up capacity MUSD Wind MUSD PV MUSD Total MUSD 2, Annex Page 15

142 Annex Table 12: Slowed down RE expansion cost summary (2/2) Unit NPV O&M variable (other than fuel) Geothermal MUSD Hydropower MUSD Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Import MUSD 1, Cogeneration MUSD Generic back-up capacit MUSD Wind MUSD PV Total MUSD Fuel cost Coal MUSD Diesel engines MUSD Gas turbines (gasoil) MUSD Generic back-up capacit MUSD Total MUSD Unserved energy cost MUSD Total cost MUSD 12, ,074 1,321 1,476 1,610 1,817 1,855 1,963 2,125 2,204 2,296 2,531 2,562 2,801 2,963 3,221 3,450 3,662 3,893 System LEC USDcent/kWh Annex Page 16

143 Annex Table 13: Comparison of RE scenarios installed capacity Moderate Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 1,892 2,182 2,582 2,882 3,082 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel Engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Wind MW ,060 1,140 PV MW Reserve/Peaking capacity MW ,260 1,610 Installed capacity: MW 2,213 2,275 2,477 2,858 3,226 3,576 3,847 4,194 4,301 4,484 4,842 5,105 5,372 5,925 6,235 6,773 7,221 7,693 8,241 8,812 9,422 Firm capacity MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,483 3,561 3,705 4,052 4,275 4,522 4,927 5,166 5,636 5,997 6,382 6,815 7,260 7,760 Accelerated RE Geothermal MW ,087 1,287 1,387 1,387 1,387 1,652 1,892 2,082 2,482 2,782 2,982 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel Engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Wind MW ,010 1,160 1,360 1,560 1,740 PV MW Reserve/Peaking capacity MW ,050 1,050 1,260 1,610 Installed capacity: MW 2,213 2,275 2,477 2,858 3,226 3,581 3,852 4,229 4,336 4,554 4,852 5,210 5,487 6,085 6,390 6,953 7,496 8,103 8,781 9,422 10,172 Firm capacity MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,490 3,567 3,718 4,005 4,294 4,541 4,952 5,166 5,603 5,983 6,427 6,885 7,285 7,810 Slowed down RE Geothermal MW ,147 1,287 1,387 1,387 1,412 1,652 2,032 2,382 2,682 2,982 3,182 Hydropower MW ,499 1,706 1,715 1,723 1,732 1,741 1,750 1,759 Coal MW Diesel Engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Wind MW PV MW Reserve/Peaking capacity MW ,260 1,680 Installed capacity: MW 2,213 2,275 2,477 2,858 3,226 3,571 3,842 4,189 4,296 4,519 4,832 5,065 5,322 5,860 6,150 6,653 7,086 7,488 7,986 8,457 9,042 Firm capacity MW 2,020 2,059 2,132 2,293 2,576 2,910 3,170 3,483 3,561 3,769 4,052 4,269 4,516 4,921 5,160 5,623 6,035 6,399 6,852 7,278 7,830 Accelerated RE difference to moderate Geothermal MW Hydropower MW Coal MW Diesel Engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Wind MW PV MW Reserve/Peaking capacity MW Installed capacity: MW Firm capacity MW Slowed down RE difference to moderate Geothermal MW Hydropower MW Coal MW Diesel Engines MW Gas turbines (gasoil) MW Import MW Cogeneration MW Wind MW PV MW Reserve/Peaking capacity MW Installed capacity: MW Firm capacity MW Annex Page 17

144 Annex Table 14: Comparison of RE scenarios annual generation Moderate Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,148 5,318 5,430 5,627 7,119 8,556 9,613 10,506 10,879 11,584 13,421 15,176 17,149 19,469 21,404 23,194 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,913 3,145 3,391 3,816 3,712 3,748 3,789 3,869 5,472 5,547 5,572 5,586 5,620 5,642 5,666 5,688 Coal GWh ,342 1,687 1,431 1,455 1,544 1,826 1,361 2,117 2,011 1,868 1,638 1,257 1,315 1,533 Diesel Engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh ,663 2,701 2,693 2,678 2,665 2,668 2,668 2,671 2,682 2,663 2,669 2,673 2,670 2,674 2,666 2,661 2,678 Cogeneration GWh Wind GWh ,845 2,326 2,326 2,326 2,413 2,413 2,500 2,500 2,587 2,587 2,659 2,659 2,833 3,094 3,355 3,703 4,051 4,337 PV GWh Reserve/Peaking capacity GWh Electricity generation GWh 9,453 10,356 11,084 11,872 13,285 13,838 14,733 15,682 16,667 17,947 19,483 20,822 22,164 23,776 25,383 27,417 29,387 31,519 33,896 36,384 38,893 Electricity consumption GWh 9,453 10,356 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 Excess generation GWh ,254 1,402 1, Accelerated RE Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,142 5,309 5,415 5,607 7,063 8,149 9,517 10,411 10,612 11,322 13,176 14,654 16,060 18,060 19,787 21,316 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,888 3,155 3,355 3,778 3,661 3,756 3,705 3,803 5,471 5,546 5,564 5,596 5,614 5,635 5,629 5,657 Coal GWh ,294 1,637 1,370 1,584 1,460 1,715 1,215 1,903 1,621 1,484 1,511 1,193 1,211 1,343 Diesel Engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh ,663 2,698 2,690 2,680 2,665 2,666 2,670 2,667 2,679 2,666 2,665 2,664 2,663 2,671 2,662 2,658 2,673 Cogeneration GWh Wind GWh ,845 2,326 2,326 2,326 2,500 2,500 2,675 2,675 2,849 2,849 3,007 3,007 3,355 3,877 4,399 5,096 5,792 6,425 PV GWh Reserve/Peaking capacity GWh Electricity generation GWh 9,453 10,356 11,084 11,872 13,285 13,825 14,747 15,692 16,666 17,986 19,432 20,866 22,216 23,817 25,390 27,452 29,469 31,595 34,115 36,736 39,329 Electricity consumption GWh 9,453 10,356 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 Excess generation GWh ,254 1,413 1, Slowed down RE Geothermal GWh 5,072 5,250 5,250 5,691 4,832 5,149 5,320 5,426 5,627 7,094 8,559 9,645 10,533 10,960 11,607 13,483 16,120 18,365 20,497 22,648 24,536 Hydropower GWh 3,718 3,740 3,764 3,780 3,168 2,921 3,148 3,392 3,821 3,750 3,751 3,807 3,903 5,472 5,547 5,572 5,600 5,618 5,633 5,670 5,693 Coal GWh ,353 1,693 1,452 1,461 1,581 1,889 1,425 2,280 2,229 1,534 1,284 1,244 1,306 1,695 Diesel Engines GWh 585 1,143 1, Gas turbines (gasoil) GWh Import GWh ,663 2,698 2,691 2,676 2,663 2,667 2,667 2,671 2,681 2,667 2,671 2,677 2,664 2,663 2,669 2,665 2,680 Cogeneration GWh Wind GWh ,845 2,326 2,326 2,326 2,413 2,413 2,413 2,500 2,500 2,500 2,572 2,572 2,659 2,659 2,746 2,833 2,920 2,944 PV GWh Reserve/Peaking capacity GWh Electricity generation GWh 9,453 10,356 11,084 11,872 13,285 13,850 14,731 15,678 16,667 17,877 19,477 20,797 22,156 23,770 25,380 27,409 29,407 31,566 33,838 36,252 38,749 Electricity consumption GWh 9,453 10,356 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 Excess generation GWh ,254 1,406 1, Accelerated RE difference to moderate Geothermal ,089-1,409-1,616-1,878 Hydropower Coal Diesel Engines Gas turbines (gasoil) Import Cogeneration Wind ,044 1,392 1,741 2,089 PV Reserve/Peaking capacity Electricity generation Excess generation Slowed down RE difference to moderate Geothermal ,216 1,028 1,244 1,342 Hydropower Coal Diesel Engines Gas turbines (gasoil) Import Cogeneration Wind ,131-1,392 PV Reserve/Peaking capacity Electricity generation Excess generation Annex Page 18

145 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 Annex Page 19

146 *** Annex Page 20