Environmental and techno-economic assessment of power system expansion for projected demand levels in Kenya using TIMES modeling framework

https://doi.org/10.1016/j.esd.2021.05.006Get rights and content

Highlights

  • Kenya can meet its NDC emission reduction by applying a carbon emission cap.

  • Energy security is still achievable under abatement efforts.

  • Carbon emission cap increases the adoption of renewable energy resources.

  • Geothermal and hydro resources provides comparatively affordable energy while limiting GHG emissions.

  • Policies that will foster the uptake of renewable energy should be enacted.

Abstract

This study develops a new national-scale bottom-up energy system optimization model called Kenya-TIMES. The model evaluates the implication of greenhouse gas emission's reduction on the techno-economic and environmental evolution of Kenya's power system under three government-projected electricity demand levels, which covers the 2020–2045 period. To assess the implications of greenhouse gas emission reduction measures, a business as usual and a carbon emission cap scenarios were developed. The model shows that energy security can be achieved under the two scenarios, and for all the three demand levels. The generation mix suggested by the model is dominated by renewable sources under the carbon emission cap scenario compared to the business as usual scenario. The higher share of renewable technologies under the carbon emission cap scenario results in lower emission but increased electricity cost. Consequently, to meet its emission reduction targets, the Kenyan government need to enact and implement policies that will enhance deployment of renewable energy technologies. The findings indicate that the Kenyan government should prioritize developing geothermal and hydropower resources in the short- to medium-term, which can provide affordable and secure energy while limiting GHG emissions.

Introduction

The provision of secure, reliable, affordable, and environmentally-benign energy is required to address global challenges related to sustainable development, including poor health services and quality of education, high levels of poverty, climate-change associated risks, food insecurity, and gender disparities (Bazilian et al., 2012). Understanding current and future energy needs, particularly in developing countries where these issues are acute, is a global concern. In 2019, 770 million people, mainly in low- and middle-income countries in Asia and Sub-Saharan Africa (SSA), lacked access to electricity (IEA, 2020a). Further, 2.6 billion people, mainly from the same regions, lacked access to clean cooking energy (IEA, 2020a). Developing countries are challenged to find a balance between attaining universal energy access for their population at an affordable cost while limiting greenhouse gas (GHG) emissions. To achieve this, effective energy planning, policy assessment, and robust forecasts for both demand and supply functions are critical (Musonye et al., 2020).

Over the last 50 years, energy stakeholders in advanced economies have developed and improved energy planning modeling tools to assist in making informed decisions concerning energy-sector planning and development at the global, regional, and national levels (Debnath & Mourshed, 2018). These modeling tools either adopt a top-down or bottom-up approach, simulation, optimization or hybrid methodology, covers local, national, regional or global geographic areas, and on a short-term, medium-term or long-term time horizons (Van Beeck, 1999). The top-down modeling tools are largely aggregated macro-economic tools, which focus on market processes rather than technology detail while bottom-up tools are technology-rich tools, which focuses on energy technologies and how they can be substituted based on the relative cost to provide the required energy services (van Vuuren et al., 2009). Simulation tools are descriptive models, which describe an energy system based on a set of rules that do not necessarily lead to a full equilibrium (van Vuuren et al., 2009). Conversely, optimization tools apply a methodology where a number of decision variables are computed that minimize or maximize an objective function subject to constraints. The main difference is that simulation models intend to envisage the performance of a given energy system, given certain assumptions, while optimisation models seek for the optimal system design (Lund et al., 2017). Hybrid tools combine both simulation and optimization methodologies. There exists no standard definition of the number of years that form time horizons. However, the commonly used period is 5 years or less for short-term, 5 to 15 years for medium-term and 10 years or more for long-term (Van Beeck, 1999). The geographical coverage reflects the level at which the analysis takes place.

Electricity, unlike other energy carriers, can provide an array of energy services hence, plays a central role in energy access (Morrissey, 2017). As a result, efforts to enhance energy access have focused more on the provision of electricity. Economically-developing countries can leverage existing energy modeling platforms to perform robust demand-supply planning that is critical in achieving universal access to modern energy services at minimum cost (Musonye et al., 2020).

Most of Kenya's population lacks access to modern energy services. The 2018 statistics indicate that Kenyan households utilized 192,915 TJ of biomass in the form of wood fuel and charcoal, out of the total 488,780 TJ of primary energy consumed (KNBS, 2019). The consumption was mainly in rural and informal urban-settlement households. Electricity generation accounted for only 8%, or 39,786 TJ of total primary energy consumed. Electricity generated from domestically available resources — wind, solar, hydro, and geothermal — accounted for 34,213 TJ, while that generated from imported fossil fuels accounted for 5573 TJ (KNBS, 2019). By December 2019, the total installed power capacity was 2846 MW, with an estimated electrification of 75% (IEA, 2019). Kenya's access rate value is higher than the average SSA value of 45% (IEA, 2020a); this result is, however, qualified, as it is directly equated to the connectivity rate, yet not all connected customers consume electricity (Taneja, 2018). The 2019 per capita annual electricity consumption of 217 kWh (Ritchie & Roser, 2020) is low compared to the average per capita annual electricity consumption for all African countries of 600 kWh (EIA, 2020), and the world-wide per capita average annual electricity consumption of 3200kWh (EIA, 2020). In addition to its low access rate, Kenya faces other challenges, including a demand-supply mismatch, urban-rural access disparities, an insufficient and unreliable electricity supply and high costs of electricity (Avila et al., 2017).

The government has rolled out various plans to accelerate electricity access: the Least Cost Power Development Plan, which is reviewed every two years, the Last Mile Connectivity Project, the Slum Electrification Program, the Kenya National Electrification Strategy (KNES), the Rural Electrification Project, the Kenya Electricity Modernisation Project, and the Boresha Umeme Network Upgrade Project (KPLC, 2018). Despite the advances made by these programs, Kenya still seems to lag in meeting the goal of universal energy access by 2022 established in the Kenya National Electrification Strategy (MoEP, 2018). Furthermore, some connected customers are either unable to consume electricity or have to limit consumption due to high prices, while those who can afford the cost of electricity are subjected to regular blackouts (Taneja, 2018).

The Kenyan government currently lacks an appropriate application of energy modeling tools (Musonye et al., 2020). These tools are critical in achieving optimal, integrated energy planning and policy formulation, hence secure, affordable, and reliable universal energy access. Instead of building local modeling expertise, the government relies on expatriates to make forecasts and plan the energy system, as is evident from the three previous government national energy master plans (ERC, 2010; EPRA, 2018; Lahmeyer International, 2016).

Recently, researchers have attempted to simulate various aspects of Kenya's power system. The Open Source Spatial Electrification Toolkit (OnSSET) and Open Source Energy Modeling SYStem (OSeMOSYS) were used to investigate pathways that would allow Kenya to reach its electrification demand by 2030 (Moksnes et al., 2017). Irungu simulated the cost implications and the associated GHGs emission for three possible development pathways using the Long-range Energy Alternative and Planning (LEAP) (Irungu et al., 2018). Carvallo et al. used the Solar and Wind energy Integrated with Transmission and Conventional sources (SWITCH-Kenya) to explore low-carbon development pathways for Kenya between 2020 and 2035 (Carvallo et al., 2017). Kenya's Energy and Petroleum Regulatory Authority (EPRA) has been in charge of developing Kenya's energy plans. EPRA contracted expatriates who used the Lahmeyer International Power System Operational/Expansion Planning (LIPS-OP/XP) model, an in-house developed tool, to simulate and forecast Kenya's power development plan for the period 2015–2035 (Lahmeyer International, 2016).

These studies found that while energy models were indeed viable tools in energy demand-supply planning and forecasting, there was still room for improvement in the country's energy modeling studies and planning. Some of the areas yet to be addressed by the previous research include assessing the techno-economic implication related to the three government projected demand levels. For instance, no published study has assessed the impact of subsidies, emission mitigation measures, technology learning curves, and power importation on generation technology mix, GHGs emission and their mitigation costs, and assessment of the overall power system cost associated with meeting the three projected demand levels. Further, an evaluation of the short term operational constraints, for example, the hourly variability of renewable resources, hourly load curve, unit commitment, operating reserves, and ramp rates in the long-term power planning is yet to be assessed by any study. Also, all of the previous studies either used simulation tools, econometric tools or less technologically detailed optimization tools to assess a limited number of scenarios, instead of using advanced optimization tools to identify optimal solutions.

This brief review underscores Kenya's complex energy situation and its challenges. These challenges stem from the lack of robust demand-supply forecasting and planning, and energy investment decisions that are politically driven, and that ignore the existing generation-expansion model recommendations (Newell & Phillips, 2016). Moreover, energy modeling expertise for the government's institution mandated with energy planning is inadequate, and the energy-planning model LIPS-OP/XP that the government currently uses as a guide is insufficient (Carvallo et al., 2017).

Kenya should develop and adopt a technology-rich, data-driven, and integrated demand-supply energy model at a national level for effective energy system planning and operation. For ease of development and regular model updates, the government can utilize any of the existing energy modeling tools by acquiring a perpetual license, and then build and retain a pool of local experts to update the model on an as-need-be basis. A well-documented account of how national energy planning models for SSA countries can be developed is found in Musonye et al. (2020).

The study presented in this paper uses the International Energy Agency's (IEA) TIMES-VEDA energy modeling framework to develop a national-scale, bottom-up energy system optimization model for Kenya — the Kenya-TIMES. The TIMES modeling framework is an economic modeling platform, which provides a technology-rich basis for representing energy dynamics over a multi-period time horizon (Loulou, Lehtila, et al., 2016; Loulou, Remne, et al., 2016). The TIMES modeling framework is highly detailed and has the capability to evaluate various demand-supply energy planning-related themes. Some of the themes that can be assessed using the TIMES framework include endogenously forecasted energy demand, generation expansion pathways and policy instruments, endogenous technology learning, energy storage, energy and GHG emission trading, short term operational constraints — for example, the hourly intermittency of renewable sources and the resultant ramp-up and ramp-down rates of peaking and baseload plants — on the long term planning, among others (Loulou, Remne, et al., 2016). The framework also allows for imputing age-dependent emission factors for technologies, flexible time slices disaggregated into seasonal, weekly and daily periods, discrete investment and retirement of technologies, among others. Lastly, the TIMES framework has been tried and tested exhaustively over the years and the methodology is well documented. Even though the current study does not assess all the listed themes, the Kenya-TIMES model development is a continuous process. Resultantly, the use of TIMES framework provides an opportunity for further development and refinement of the Kenya-TIMES model with the further acquisition of data.

This is the first time the TIMES modeling framework has been applied to assess Kenya's power-generation expansion scenarios. Furthermore, the study is the first attempt of its kind to investigate the techno-economic-environmental aspects of Kenya's three forecast power demand levels. This study aims to evaluate the impacts of meeting GHG emissions reduction target as guided by the Nationally Determined Contribution (NDC) under the three government's projected power demand levels. This assessment is done using the Business As Usual (BAU) and the Carbon Emission Cap (CEC) scenarios. Consequently, the study evaluates the GHG emissions, technological choices, and economic implications associated with meeting the three demand levels using domestic and imported primary energy resources under the BAU and CEC scenarios. The analysis covers the 2020–2045 period, with the base year set in 2018. The current study is restricted to the grid-connected supply. The Kenya-TIMES model was developed in a data-scarce environment. The data was collected through literature searches, field visits, and interviews with the authorities in the various power utilities. Because some of the required data were not available at the time of the study, the model could be further refined in the future with additional information. The rest of the paper consists of an overview of the current energy status for Kenya (Section 2), the methodology (Section 3), results (Section 4), discussion (Section 5) and conclusions (Section 6).

Section snippets

The current installed power generation capacity and consumption

Until 2003, Kenya's electricity generation relied solely on hydropower and imported crude oil and petroleum products, with hydropower generating 60%, and crude oil and petroleum products 40% of total consumed power (EPRA, 2018). With the recent commissioning of geothermal power plants, wind turbines, and off-grid renewable sources, the dependency on crude oil has decreased.

Currently, the grid-connected total installed power capacity is 2846 MW (KNBS, 2019; MoEP, 2020). The mini- and micro-grid

Materials and method

The methodology used in this study is designed to assess the GHG emission reduction target's impact on the technological choices and economic cost for the different demand-supply expansion pathways. The method integrates available energy resources, current and future conversion technologies, and demand projections under the BAU and CEC scenarios. This analysis only considers grid-connected generation comprising of government and Independent Power Producer (IPP)-owned power plants. Electricity

Results

This section presents Kenya-TIMES modeling outcome for Kenya's power demand-supply expansion pathways under the BAU and CEC scenarios for the three projected demand levels for 25 years. The results are organized by technology choice, economic and environmental analyses.

Discussion

Overall, the Kenya-TIMES results shows that energy security can be achieved for Kenya's energy system for the three demand levels, with 100% of primary energy carriers being supplied from domestic sources under the BAU scenario. However, to be able to meet the three levels of demand beyond 2030 under BAU, the government will need to fast-track the development of domestically-supplied coal-run power plants. Without any emission abatement policies, Kenya's energy system will not be able to meet

Conclusion

This study developed the Kenya-TIMES, a national scale, bottom-up energy optimization model. Here, the model has been used to assess the implication of greenhouse gas (GHG) emission reduction targets on the techno-economic parameters of Kenya's energy system for the three government's projected power demand levels, and made recommendations on how this reduction can be achieved. The emission reduction targets are guided by Kenya's NDC. This study is the first of its kind in which the TIMES

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This project was supported by the Kenya Electricity Generating Company PLC, the GRÓ-Geothermal Training Program of Iceland, and Reykjavik University. We express our gratitude to Anne Kiburi, Fenwicks Musonye, Victor Otieno, Francis Makhanu and Winnie Apiyo for their assistance during data collection.

References (61)

  • J. Tattini et al.

    Reaching carbon neutral transport sector in Denmark – evidence from the incorporation of modal shift into the TIMES energy system modeling framework

    Energy Policy

    (2018)
  • S.A. Ur Rehman et al.

    Energy-environment-economy nexus in Pakistan: Lessons from a PAK-TIMES model

    Energy Policy

    (2019)
  • D.P. van Vuuren et al.

    Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials

    Energy Policy

    (2009)
  • C. Yang et al.

    Achieving California’s 80% greenhouse gas reduction target in 2050: Technology, policy, and scenario analysis using CA-TIMES energy-economic systems model

    Energy Policy

    (2015)
  • N.Y. Amponsah et al.

    Greenhouse gas emissions from renewable energy sources: a review of lifecycle considerations

    Renewable and Sustainable Energy Reviews

    (2014)
  • N. Avila et al.

    The energy challenge in sub-Saharan Africa: A guide for advocates and policymakers: Part 1: Generating energy for sustainable and equitable development

    (2017)
  • C. Calvillo et al.

    Potential for the use of TIMES in assessing energy system impacts of improved energy efficiency: Using the TIMES Model in developing energy policy (Report)

    (2017)
  • J.P. Carvallo et al.

    Sustainable low-carbon expansion for the power sector of an emerging economy: the case of Kenya

    Environmental Science & Technology

    (2017)
  • C. Cosmi et al.

    Integration of country Energy system models in a Pan European framework for supporting EU policies

  • H.E. Daly et al.

    UK TIMES model overview 15

    (2014)
  • S. Di Leo et al.

    Energy systems modelling to support key strategic decisions in energy and climate change at regional scale

    Renewable and Sustainable Energy Reviews

    (2014)
  • EIA, 2020. EIA International Energy Outlook 2020 - Issue in Focus - U.S. Energy Information Administration (EIA) [WWW...
  • EPRA

    Least cost power development plan for Kenya, 2018. (Energy Planning Report)

    (2018)
  • ERC-Energy Regulatory Commission

    Least Cost Power Development Plan for Kenya, 2010. (Energy Planning Report)

    (2010)
  • EU-European Union, 2020. European Union: monitoring and Evaluation of the RES directives implementation in EU27 and...
  • Government of Kenya, 2020. About Vision 2030 | Kenya Vision 2030 [WWW Document]. Kenya Vis. 2030. URL...
  • IEA-International Energy Agency, 2019. Kenya Energy Outlook. Analysis from Africa Energy Outlook 2019. (Accessed...
  • IEA-International Energy Agency, 2020a. Access to electricity – SDG7: Data and Projections – Analysis [WWW Document]....
  • IEA-International Energy Agency, 2020b. IEA-ETSAP | Energy Systems Analysis [WWW Document]. ETSAP. URL...
  • IEA-International Energy Agency, 2020c. IEA-ETSAP | Energy Supply Technologies Data [WWW Document]. URL...
  • Cited by (0)

    View full text