Browsing by Author "Yared Bekele"

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    Determinants of Commercial Banks’ Lending: The Case of Selected Private Commercial Banks in Ethiopia
    (A.A.U., 2020-03) Yared Bekele; Habtamu Birhanu
    The study was aimed to investigate the determinants of commercial banks’ lending on the selected private commercial banks in Ethiopia based on Bank specific, regulatory and macro-economic factors of the country. In order to achieve research objective the researcher used quantitative research approach. The analyses were performed using panel data derived from the financial statements of sampled Ethiopian commercial banks and macro-economic data from National bank of Ethiopia. Ordinary least square (OLS) technique was applied to determine the impact of those predictor variables on commercial bank lending. In the study volume of deposit, cash reserve requirement, legal reserve requirement, non-performing loan, liquidity ratio, capital adequacy ratio, equity investment, and GDP of the country were considered as independent variables whereas commercial banks’ lending or loan and advances was considered as dependent variable. The result of the regression analysis reveals that Ethiopian commercial banks’ lending is determined by volume of deposit, non-performing loans, legal reserve requirement and GDP growth, positively. On the other hand capital adequacy, legal reserve requirement, equity investment and liquidity have insignificant impact on Ethiopian commercial banks’ lending or loan and advances. The study suggests that Ethiopian commercial banks should enhance their strategies in mobilizing deposits from the public and have to strive to strength their asset quality. In addition commercial banks’ should administer their lending activity by considering internal factors, existing economic situation, competitive environment, regulatory measures and their target customers.
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    Optimizing Operation and Control Strategies for Distributed Energy Resource-Integrated Microgrids: Evolutionary and Neuroevolutionary-Based Decentralized Primary Control in Islanded Mode
    (Addis Ababa University, 2025-07) Yared Bekele; Getachew Biru (PhD); Tjernberg, Lina Bertling (Prof.) Co-Supervisor
    The accelerating global climate crisis—combined with the uneven distribution of fossil fuel resources, rising oil prices, increasing demand for modern energy systems, and the global shift toward low-carbon economies—has necessitated a fundamental transformation in how energy is produced, distributed, and consumed. Renewable energy sources (RES) such as solar and wind are central to this transition. However, their integration into power systems presents persistent challenges due to intermittency, decentralization, and the limitations of traditional grid infrastructure. Microgrids (MGs) have emerged as a flexible and resilient solution for the decentralized integration of distributed energy resources (DER), playing a key role in advancing intelligent, low-carbon power networks. As the demand for sustainable, secure, and responsive energy systems grows globally, MGs—especially those incorporating DER and hydrogen technologies— are becoming indispensable components of the evolving energy landscape. While MGs hold promise for energy resilience and sustainability, their implementation faces challenges like high initial capital investment for infrastructure, technical complexity in integrating diverse DER, and interoperability issues with legacy grids. These barriers affect stable operation and the broader scalability of MG solutions. Accordingly, this research is directed toward addressing these multifaceted issues to enable more effective localized generation and control, advancing sustainability, energy independence, and resilience. The study begins by developing and evaluating integrated strategies to overcome the technical, economic, and environmental hurdles associated with MG deployment as a first objective. It focuses on the optimized design of a sustainable MG architecture that incorporates solar, wind, and hydrogen-based storage alongside sector coupling mechanisms. Through systemlevel analysis, the research underscores hydrogen’s pivotal role in increasing grid flexibility, compensating for the intermittency of renewable resources, and enhancing long-term energy security. This foundational investigation affirms the value of MGs in supporting decarbonization and improving energy resilience through hydrogen-enabled sector coupling and crosssectoral integration.In regions where utility grids are unreliable—characterized by frequent outages—MGs are often required to operate in islanded mode. While islanded operation enhances energy autonomy and resilience, it also introduces significant technical complexity. Without main grid support, islanded MGs must coordinate energy balance and economic operation across both short-term dynamics and long-term planning. Moreover, islanded MGs dominated by converter-interfaced DERs face considerable challenges in maintaining voltage and frequency stability under dynamic load and generation conditions. These operational challenges highlight the need for robust and well-coordinated control strategies specifically designed for islanded MG environments. In response, the subsequent phase of the thesis develops and evaluates advanced decentralized primary control strategies for islanded MGs. Within this framework, two distinct approaches are pursued: a virtual complex impedance-based method, which enhances transient response, system stability, and power-sharing accuracy by shaping the converter’s output impedance; and a computationally guided optimization framework, which systematically tunes control parameters to better coordinate with the dynamic characteristics of the interfacing filter and electrical network. These strategies are grounded in a detailed analysis of VSCs, including their control architectures and interfacing filters, which are vital for maintaining system stability and ensuring power quality. Building on these strategies, the final phase of the thesis introduces an Adaptive Hybrid PSO-Embedded Genetic Algorithm (AHPEGA) for the neuroevolutionary training of Multilayer Perceptron Controllers (MLPCs) in VSC-based islanded MG. This approach combines the global search efficiency ofGAwith the fine-tuning capabilities of PSO to dynamically optimize both the weights and biases, as well as hyperparameters, of the neural network—resulting in improved convergence and generalization in nonlinear control tasks. Overall, this thesis presents a comprehensive, multi-layered approach to MG control— encompassing sustainable system design and advanced decentralized control strategies— and delivers scalable, sustainable, and computationally guided solutions for resilient operation in islanded mode. These contributions establish a foundation for advancing intelligent control in islanded MGs integrated with renewable resources, thereby supporting the broader transition toward robust, efficient, and sustainable power systems.