Browsing by Author "Getachew Bekele (Assoc. Prof.)"

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    Assessing the Potential of Demand-Side Energy Management in the Cement Industry
    (Addis Ababa University, 2025-05) Alebachew Tilahun; Getachew Bekele (Assoc. Prof.); Palm, Björn (Prof.); Khatiwada, Dilip (Assoc. Prof.)
    Ensuring reliable energy access is crucial for sustainable development and economic growth. The challenges posed by the growing energy demand can be approached through supply-side energy management. However, this task has become increasingly challenging due to the high fluctuating electricity demand and the growing share of intermittent renewable energy sources in the electricity supply mix. In response to these challenges, demand-side management (DSM) has emerged as a key strategy in modern energy systems to address grid stability, enhance energy efficiency, and promote sustainability. This PhD study investigates an industrial demand-side energy management system, focusing on improving energy efficiency, implementing demand response strategies and promoting onsite power generation in the cement industry. The cement industry is notably recognized as one of the most energy-intensive and emission-heavy industries. Hence, it is important to assess the potential of demand-side energy management in this sector. The energy efficiency improvement opportunities are explored with the help of a benchmarking tool, while a mixed integer non-linear programming model is developed to evaluate the sector's energy demand flexibility, aiming to achieve energy savings, grid balancing, and climate mitigation goals. The potential and viability of waste heat recovery power generation are investigated by estimating the power output capacity, life cycle costs, levelized cost of energy, and net present values for three different waste heat recovery technology options. The energy and environmental performances of the Ethiopian cement industry are first assessed and compared against best practices with the help of a Benchmarking and Energy Saving Tool for Cement (BEST-Cement). The results reveal that all the surveyed plants are less efficient, with an average energy saving potential of 36% indicating a significant potential for energy efficiency improvement. Then, potential energy efficiency measures (EEMs) have been identified and analyzed using a bottomup energy conservation supply curve (ECSC) model. The findings show that the cost-effective electrical energy and fuel-saving potentials of these measures are estimated to be 99 Gigawatt hours per year which is about 11.5% of the plants’ annual electrical energy consumption and, 2.7 Petajoules per year which is 12.5% of the plants’ annual fuel consumption, respectively. The cost-effective fuel measures have an annual average CO2 emission reduction potential of 254 kilo-tonnes per year which covers about 5% of the total CO2 emission. The technical potential for saving electrical energy and fuel of the measures in each category amounted to 33% and 14%, respectively, of the annual energy consumption of the surveyed cement plants. Sensitivity analysis is conducted using the key parameters that show some discrepancy in the base case results. To assess the energy demand flexibility potential of the cement industry, an energy consumption optimization model of the industrial demand response for conventional power grids has been developed, aiming to flatten the hourly demand curve of the grid by minimizing the industrial customer's hourly peak loads and maximizing the shifting of demand to off-peak periods.The result demonstrates that the demand flexibility potential of the case study cement plants is about 495 MWh per day, constituting approximately 28% of the daily total electrical energy used by these cement plants, proving that the cement industry is a potential candidate for demand response strategies. By adapting the proposed model, the loads of the case study plants during the peak period of the day are reduced by an average of 75%. In addition, an overall reduction of 188 tonnes of CO2 emissions per day has been achieved in case study plants. Furthermore, the cost of consumed electrical energy for a day decreased on average by 14% in these plants. Thus, the proposed model can minimize the impact on grid instability and the cost of energy consumption of an industrial customer. Some scenarios have been suggested in the study including the variation of the capacity factor, considering onsite electrical power generation such as solar power plants and waste heat recovery power plants, which can enhance the demand response obtained from the cement subsector. Moreover, cement manufacturing is a highly energy-intensive process, with over half of the thermal energy used in the production chain being lost. Consequently, exploring ways to capture and utilize this wasted heat to generate electricity and meet industrial energy requirements is crucial. The study investigates the potential for Waste Heat Recovery (WHR) power generation within the Ethiopian cement industry. The levelized cost of energy (LCOE) and the Net Present Values (NPV) of the steam Rankine cycle-based, Organic Rankine cycle-based and Kalina Rankine cycle-based waste heat recovery power plant options are evaluated. The findings reveal that the steam Rankine cycle-based waste heat recovery power plant is the only feasible plant in the Ethiopian cement plant, with the net present value of 0.35 million USD, and about 0.04 USD per kWh of levelized cost of energy. The power capacity of the feasible plant is about 8.9 MW for the studied cement plant with an annual production capacity of 2.3 Mt of cement. This amount can cover roughly 18% of the case study cement plant’s electricity demand.. The associated reduced CO2 emissions potential is not significant, as the hydropower sources dominate the national power grid. In summary, this doctoral research underscores the feasibility of adapting demand-side management (DSM) strategies in the cement industry. The main findings are compared with similar studies and international benchmarks, confirming the practical applicability. Detailed sensitivity analysis has been conducted to ensure that the results derived from the base case assumptions remain reliable despite potential fluctuations in the variations of influencing parameters. Consequently, this thesis can be a valuable resource for energy policymakers and industry players seeking to develop effective DSM strategies and policies. The methodologies and frameworks employed can be applied to similar energy-intensive sectors worldwide, aiding in formulating DSM strategies for the energy system.
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    Long-Term Least-Cost Electrification Pathways For Ethiopia: A Geospatial Modeling Approach High-Resolution Spatial Analysis to Bridge the Electricity Access Gap
    (Addis Ababa University, 2025-11) Adugnaw Lake; Getachew Bekele (Assoc. Prof.); Erik O. Ahlgren (Prof.); Yibeltal T. Wassie (PhD)
    In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) to guide global development efforts towards 2030. Among these, SDG 7 aims to "ensure access to affordable, reliable, sustainable, and modern energy for all by 2030." Electricity is essential for the development of many sectors, including households, health and education facilities, and productive enterprises. Yet, as of 2022, approximately 685 million people worldwide remained without electricity access, nearly 83% of whom lived in Sub-Saharan Africa, where only about half the population was electrified. In this region, rapid population growth and dispersed settlements further complicate the challenge. Achieving universal electricity access necessitates strategies tailored to the unique context of each population settlement, accounting for settlement distribution, economic activities, and resource availability. To this end, policymakers and planners increasingly employ geospatial and techno-economic assessments to inform energy policies and national electrification targets. However, geospatial electrification models require large volumes of reliable georeferenced data, from infrastructure locations to electricity consumers, which are often limited or unavailable in many developing countries. This paucity of spatial information, combined with several methodological limitations, can undermine model outcomes. These limitations include low-resolution data that mask settlement-level variations; simplified demand assumptions that overlook local socio-economic realities; and short-term planning horizons that fail to capture dynamic, long-term investment pathways. In response, this thesis develops and applies a geospatial framework using the Open-Source Spatial Electrification Tool (OnSSET) to produce phased, least-cost electrification pathways for Ethiopia through 2050. The research is guided by three specific objectives: (1) To analyze how and to what extent geospatial factors affect the feasibility of extending the national power grid to unelectrified settlements. This objective is pursued by conducting a geospatial analysis that quantifies the spatial constraints of grid extension based on factors such as distance from road and substation, terrain slope, elevation, and land cover. The results indicate that geospatial factors may increase grid extension costs by 2.3% to 29% across Ethiopia.The second specific objective is (2) To develop long-term, spatially disaggregated electricity demand projections for rural electrification planning. Existing literature offers limited insights into spatial heterogeneity in electricity demand, reducing its applicability to spatial electrification planning. This objective is pursued by projecting the electricity demand of households, productive users, and community institutions. Alternative scenarios are developed by considering electricity demand growth in rural areas of developing countries under different drivers of demand, such as population growth, urbanization, rural electricity access, and economic growth, using a multiple regression model. OnSSET is employed to spatially disaggregate electricity demand, using high-resolution data on mean gridded GDP and the International Wealth Index to classify settlements by economic status. The scenario results generate aggregate national electricity demand projections and also show how demand is expected to evolve over time for each consumer group within each settlement. The results show that electricity demand is spatially heterogeneous, with projected household demand ranging from Tier 1 to Tier 4. The final objective is (3) To explore long-term, least-cost electrification technology mix dynamics and evaluate investment needs to enhance electricity access in rural areas. A geospatial optimization model is developed using OnSSET to determine the optimal electricity supply option that provides electricity at the lowest levelized cost of electricity (LCOE) under varying demand and grid generation cost scenarios. The model integrates spatially disaggregated electricity demand, georeferenced existing grid infrastructures, renewable energy resources (solar, wind, and hydro), geospatial cost penalty factors, and techno-economic parameters. The model identifies the least-cost option for each settlement by comparing the LCOE of grid extension, mini-grid (solar, wind, and hydro), and standalone photovoltaic systems. The results reveal a dynamic technology mix that shifts over time: grid extension can be the least-cost solution for over 82% of the population planned to be electrified by 2030, with its share declining by 2050, while mini-grids become the least-cost option for about 26% of the population. This integrated, geospatial modeling, data-driven approach informs cost-effective, sustainable, and equitable electrification strategies tailored to Ethiopia’s diverse regional contexts.