MSc. SET Theses and Dissertations

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Toward improving energy security in Kenya via HTLS conductors and load balancing
(Strathmore University, 2024) Mkabane, C. C.
The Industrial and Commercial sectors of the economy are rapidly growing and hence, energy consumption and transmission line loading are also increasing. This explains the major breakdowns related to transmission lines since the initially installed lines are not able to handle the increased energy requirements. Previous studies have been done replacing ACSR with ACCC conductors to reduce thermal line losses. The project aims to replace ACSR with ACCC conductors in short lines, analyze the length of the line in which the ACCC conductor will cease to be beneficial, and analyze the mechanical properties of the line. The Kenyan grid will be analyzed in its present state by performing a load flow analysis and a sag and tension analysis. The short lines will then be replaced by ACCC conductors and the load flow and sag and tension analysis of the new system done. The two systems will then be compared. The length of the line in which the ACCC conductor will no longer be beneficial for application will be determined. The analysis will be performed using Power Factory DigSilent software.
Adoption of Behind-The-Meter Battery Storage Systems for residences in Kenya
(Strathmore University, 2024) Wambugu, A. W.
This research investigates the adoption of Behind-the-Meter Battery Energy Storage Systems (BTM BESS) in Kenyan urban households, focusing on mitigating the challenges posed by unreliable electricity supply, particularly for households engaged in work-from-home (WFH) activities. Electricity reliability challenges in Kenya's densely populated urban areas presents a significant challenge to the growing WFH workforce. While BTM BESS offer a potential solution for reliability, their affordability with grid-charging remains uncertain. The study develops a modified Levelized Cost of Storage (LCOS) model that integrates the WFH Reliability Metric and the WFH Income Value of Lost Load, offering a nuanced understanding of BTM BESS affordability tailored to the needs and economic realities of WFH individuals. Analysis reveals that BTM BESS becomes increasingly affordable as a reliability solution for WFH individuals, particularly those facing frequent outages or whose income is highly dependent on constant electricity access. This research contributes to the discourse on sustainable energy transitions by highlighting the economic and reliability advantages of BTM BESS for urban households in Kenya, proposing policy recommendations to enhance BTM BESS adoption and support the provision of reliable electricity as per Sustainable Development Goal 7 (SDG7).
A Model for predicting greenhouse gas emissions from motorcycles in Kenya
(Strathmore University, 2024) Cheruiyot, L. C.
In Kenya, inefficient public transport systems coupled with rough terrains have made motorcycles the most preferred means of transport. The transport sector is a leading emitter of greenhouse gases, the main driver of global climate change. This is due to the reliance on fossil fuels which require Internal Combustion Engines to operate. The threat posed by climate change and variability has fueled the ongoing energy transition from fossil fuels to green technologies through Emobility. Motorcycles have been described as low-hanging fruit in the E-mobility transition from fuel-based engines to electric-powered motors. However, this transition has shown little progress due to fewer and inadequate models to inform E-mobility policy and investment decisions. This study sought to develop a model for calculating GHG emissions from conventional and electric motorcycles under different scenarios. The scenarios were based on traffic conditions and engine efficiency. The study also aimed to analyze existing ICE and electric two-wheeler technologies in Kenya. A descriptive and experimental research design was adopted for the study. Primary data was collected using a structured questionnaire embedded in the Kobo Toolbox and was administered to motorcycle operators in Nairobi and Machakos counties. Secondary data was also collected from the NTSA database. The R-programming tool was used for data analysis and simulation of GHG emissions under different scenarios. The model was validated using experimental results to increase confidence in the findings. The study results provided comprehensive insights into the determinants of greenhouse gas emissions from both conventional Internal Combustion Engine (ICE) and electric motorcycles. Through an analysis of rider demographics, and electric and conventional motorcycle characteristics, the study revealed the multifaceted factors that contributed to the environmental impact of motorcycles. The specifics of electric motorcycle technologies, including battery characteristics, charging habits, and daily travel distances, were explored, offering valuable insights into the state of electric mobility in the country. Additionally, the study developed and applied a General Additive Model (GAM) for predicting motorcycle emissions, yielding high predictive accuracy and significant predictors. The model underscored the influence of fuel type and temporal trends on emissions, emphasizing the importance of considering both technological and temporal factors in policy formulation. Projection of emissions to 2045 revealed an alarming exponential increase, necessitating urgent intervention. Keywords: Predictive model, GHG emissions, electric two-wheelers
Modeling and optimization of renewable energy accessibility in underserved regions: a case of Mashuru area
(Strathmore University, 2024) Mutekhele, D. N.
Underserved regions have problems of inadequate access to energy. These regions are characterized by inadequate or no grid infrastructure, making it difficult to establish centralized energy systems. To address these challenges, various solutions have been undertaken such as the development of decentralized energy systems. Modeling tools are used to simulate energy systems. They, however, do not prioritize decentralized solutions. So far, less focus has been put on the evaluation of decentralized renewable energy modeling tools for promotion of access to energy in underserved regions. There is a demand for modeling tools to aid in the design of these solutions. The objective of this study is to develop, test and validate a renewable energy model that will optimize accessibility in underserved regions. To achieve the objectives of research, this work focused on Mashuru area in Kajiado County. This area has inadequate grid infrastructure and is characterized by a population with low income and poor purchasing power. This research project targeted small-scale farmers, who use diesel pumps to provide energy for irrigation. The study proposes an optimization model for renewable energy systems consisting of photovoltaics, batteries, supercapacitors, and a diesel generator which act as back up, to provide energy for powering pumping systems. Four technologies were considered; solar PV with battery backup, solar PV with super capacitor backup, standalone solar PV system and solar PV system with a generator set backup. It is based on the selection of an optimal PV system, battery, supercapacitor, and generator capacity while minimizing the LCOE. It employed a debt-versus-equity financial model to compute the WACC used for LCOE calculations. The study established that the total load was 1000 MWh. The optimal capacity requirements for various scenarios were; solar PV with battery backup (693.52kW), solar PV with super capacitor backup (679.71kW), standalone solar (668.45kW) and solar PV with genset backup (467.52kW). From the model results, the optimal LCOE was 8.95KES/kWh, 11.67KES/kWh, 14.47KES/kWh and 33.23KES/kWh for PV only, PV plus battery backup, PV plus supercapacitor back up and PV plus genset back up respectively. The model was validated using HOMER simulation tool to compare its performance and ascertain the correctness of the results.
A Model for assessing digital technology readiness in mini grids
(Strathmore University, 2024) Koskei, K. K.
In Sub-Saharan Africa, approximately 0.6 billion people lack access to electricity due to challenges with the centralized grid. Mini grids, seen as a solution for rural electrification, face sustainability issues, including technical limitations with renewable energy, outdated monitoring methods, and scalability matters. To address these challenges and strengthen their value proposition, the integration of current and emerging Digital Technologies, such as AI, IoT, Blockchain, and Cybersecurity, is recommended. This study aimed to address a critical gap in the current understanding of Digital Technology Readiness (DTR) in the context of mini grids in Kenya. Recognizing the necessity of a DTR assessment model tailored to end user preferences and the local environment, this research employed a combination of quantitative methods, inferential analysis, and fuzzy synthetic evaluation (FSE). The research methodology embraced the design thinking process and descriptive analysis to develop the DTR assessment model. Data collection involved the use of online questionnaires, employing purposive and snowball sampling techniques. These instruments sought insights from relevant stakeholders in the mini grid industry. Subsequently, surveys were conducted to validate and test the DTR assessment model, ensuring its validity and efficacy. The study findings, based on responses from diverse industry professionals, were instrumental in identifying 15 critical indicators that collectively contribute to digital technology readiness in the mini grids. Through factor analysis, these indicators were categorized into five main dimensions: Digital Literacy (DL), Digital Technology Usefulness (DTU), Digital Technology Preparedness (DTP), Digital Transformation (DT), and Digital Infrastructure Availability (DIA). The critical index values of these dimensions, in descending order, were as follows: DL (4.443), DTU (4.362), DTP (3.839), DT (3.642), and DIA (3.5). These critical index values serve as a valuable guide, emphasizing the key areas of focus in digital technology readiness. The output of this research was a DTR assessment model for mini grids. The model was tested by mini grid stakeholders and it was found to be valid and effective. The model will be used by the stakeholders to measure DT readiness in mini grids. This will aid in strategic decision-making and enhancing the industry's adaptability to the challenges and opportunities presented by Industry 4.0.
A Framework for sizing solar PV systems adaptable to off grid areas
(Strathmore University, 2024) Nyangoka, I. K.
Solar PV sizing is the process of determining the quantity and capacity of solar PV system components to meet a given energy demand. This process is needed to ensure that the components are not undersized resulting in insufficient energy or oversized increasing the system cost. There are several solar PV sizing frameworks currently in use in the market such as intuitive, numerical, and analytical frameworks. However, these frameworks have neglected some key adaptability factors unique to off-grid areas such as the ability of the household to pay and the type of roofing structure. This neglect has seen development of solar PV systems that are beyond the budget of most households in off-grid areas and with specifications that technically inhibit their effective use in the setup. Therefore, for enhanced adaptability, there is need to develop a new solar PV sizing framework that considers the unique adaptability factors of off grid areas. This study identified these unique adaptability factors and investigated how they influence the size of a solar PV system. Through the modification of the exiting numerical sizing framework, these adaptability factors were integrated in the sizing process within the context of this study. It was established that by integrating these factors, the resultant PV systems were more adaptable to off-grid areas in terms of cost, mobility, durability and reliability.
Energy supply optimization of the microgrids. A case of Wasini Island
(Strathmore University, 2024) Chiro, J. K.
The energy transition topic from fossil fuel to non-fossil fuel and non-energy efficient appliances to energy efficient appliances has been gaining momentum since the realization that human socio-economic activities form the major catalyst to climate change. It is therefore clear that conventional power generation sources can hold rapid growth of technological innovations that are putting more pressure on the existing regimes. The pressures put on existing regimes have resulted to innovations of microgrids that uses variable power generation sources from solar and wind to cushion rural communities who are likely to defer their electricity connection due to the rush of providing charging stations for plug in electric hybrid vehicle and EV, also use of cleaning cooking relying on the National grid which has a reserve of only 1100MW. This study looked at the essence of reliable and sustainable microgrids for rural areas such as the case study area. The island of Mkwiro - Wasini is not connected to the national electricity grid and its future access to electricity cannot be immediate. The study identifies multisource hybrid power supply systems that represent the use of renewable technology. In this study wind turbine generators, solar photovoltaic panels, tidal wave energy converters and storage batteries were used to build a block chain microgrid that is optimal based on the performance criteria in terms of cost and reliability. In this study, an energy supply model was designed and simulated using MATLAB 2016a as platform for particle swarm optimization (PSO) algorithm. The algorithm was simulated to generate an annualized system cost and energy index reliability of non-dominated solutions. Three energy supply models were designed to find the best configurations using particle swarm optimization technique. The intermittent nature of wind speed and solar insolation together with random load variation, a time series models were adopted to reflect the stochastic nature. A robustness and reliability test was conducted to determine the impacts of intermittent sources in the microgrid universal performance. Keywords: Energy Reliability, Microgrids, Particle swarm Optimization, wind generators, solar photovoltaic, tidal wave energy converters, annualized system cost and energy index reliability.
Modelling a sustainable electromobility infrastructure
(Strathmore University, 2024) Amiani, D. M.
The global transportation sector is undergoing a significant transformation as societies increasingly seek sustainable alternatives to conventional fossil fuelled. Electricity as an alternative for fossil fuels to power mobility in the wake of green transition has gained prominence. Energy inaccessibility due to inadequate availability of a robust infrastructure with reliable and efficient energy provision has become a drawback towards the transition from petroleum powered to electrified mobility. This study aimed to create a model that will foster the uptake of electromobility by formulating a model equation to determine the infrastructural requirements of establishing a sustainable e-mobility charging infrastructure. The study adopts desktop, descriptive and experimental research designs in sourcing for data and simulation of the model. To this end, Westlands region of Nairobi County was selected as an area with a blend of residential and most sought-after office space with capability for modern e-mobility infrastructure. Results depicted that the current EV infrastructural capacity (charging stations, charging station capacity and area coverage) is not sustainable (SI=2.316 (>0.29)) for the given population, energy demand, and coverage area. This is also the case in the short term (5 years) (SI=4.030 (>0.29)), medium term (10 years) (4.828 (>0.29)) as well as long term (15 years) (5.339 (>0.29)). The findings highlight the inadequacy and inflexibility of the current infrastructure to meet the evolving demands of a growing population, increasing energy demand, casting doubt on the viability of Kenya's e-mobility ecosystem in fostering sustainable transportation solutions. To ensure the continued sustainability of the e-mobility infrastructure, policymakers, government agencies, private sector stakeholders and urban planners should prioritize integrated planning and investment strategies in developing a cohesive framework to address current and future demands of e-mobility ecosystem. Key Words: Energy Access, Electromobility, Energy Transition, Sustainability
Modelling cooking energy demand for E-Cooking transition
(Strathmore University, 2024) Ogalo. S. A.
As per estimates, 92% of rural households still use a form of traditional biomass as their preferred cooking fuel. Children especially those under 5 years, suffer from respiratory infections due to the solid fuel combustion. Kenyan women as well as young children are especially exposed to indoor air pollution, which is connected with more than 15,000 unnecessary deaths annually. The Kenyan government and other non-governmental agencies have been on the forefront to promote adoption of electric cooking through various policies and set targets. Considering population expansion in the rural areas and greater electrification percentages, the expectation is that electric energy consumption would increase. This study employs the LEAP modelling tool to forecast rural residential cooking energy demand from 2020 to 2040. Results indicate that traditional biomass, continues to dominate cooking practices, contributing to indoor air pollution. However, alternative scenarios demonstrate the potential for accelerated shifts toward cleaner cooking technologies. Analysis of household surveys reveals a prevalent reliance on traditional biomass fuels such as fuelwood and charcoal, with minimal usage of electric cooking technologies. The LEAP modelling exercise simulates three scenarios: Business As Usual (BAU), Moderate Accelerated Shift (MAS), and High Accelerated Shift (HAS). Under the BAU scenario, traditional cooking methods persist, resulting in a steady increase in total energy demand, reaching 1,937.98 million Gigajoules by 2040. In contrast, the MAS scenario projects a moderate shift towards cleaner technologies, with total energy demand reaching 1,681.9 million Gigajoules by 2040. Notably, the HAS scenario envisions a proactive transition, with rapid adoption of electric cooking and other advanced technologies. This scenario leads to a significant reduction in traditional cooking methods, resulting in a 17% decrease in total energy demand by 2040. The findings highlight the importance of targeted interventions to promote the adoption of electric cooking and other clean energy technologies in rural Kenya. Keywords: LEAP model; clean energy; electric cooking fuel; sustainability; scenario analysis; electricity; fuel switching.

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