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Access to reliable electricity remains a challenge for millions in remote African villages, including Lake Ziway's islands in Ethiopia. This study introduces an integrated electricity system for Tulu Gudo Island, combining floating photovoltaics (FPV), pumped-hydro storage (PHS) and diesel generators (DGEs) to overcome energy constraints, land scarcity and sustainability issues. The study assesses electricity demand and solar-PHS potential using LiDAR-based digital elevation model (DEM) data and Geographic Information Systems (GIS). PVsyst and HOMER Pro optimize the system based on net present cost (NPC), cost of energy (COE) and its ability to support a water-energy-food (W-E-F) nexus approach. An optimized configuration with 32.2 KWp FPV and two PHS units (PH: 245KWh (508KWh)) meets Tulu Gudo Island's energy needs through a cycle charging strategy (CCs). This configuration offers economic and environmental sustainability, with an NPC of $154,265 and a COE of $0.140/KWh, while conserving 8760 m³ of water. It integrates successfully with the W-E-F nexus approach, achieving a 7% increase in electricity generation and a 2.4% higher capacity factor compared to conventional setups. The study validates results through comparisons with other simulation tools, ensuring accuracy. This hybrid electricity system has potential applicability in regions with similar conditions worldwide. [ABSTRACT FROM AUTHOR]

All long-term hydropower planning problems require a forecast of the inflow during the planning period. However, it is challenging to accurately forecast inflows for a year or more. Therefore, it is common to use stochastic models considering the uncertainties of the inflow. This paper compares deterministic and stochastic models in a weekly rolling horizon framework considering inflow uncertainty. The stochastic model is tested in both a risk-neutral and a risk-averse version. The rolling horizon framework helps make periodic decisions and update the information in each rolling week, which minimizes the errors in prolonged forecasts. The models aim to utilize the water stored in the rainy season throughout the year with minimum load shedding while storing as much water as possible at the end of the planning horizon. The Conditional Value at Risk ( C V a R ) risk measure is used to develop the risk-averse stochastic model. Three different risk measures are investigated to choose the risk measure that yields the best outcome in the risk-averse problem, and the two best measures are compared to a deterministic and risk-neutral model in a weekly rolling horizon framework. The results show that the risk-neutral and best risk-averse models perform almost equally and are better than the deterministic model. Hence, using a stochastic model would be an improvement to the actual planning performed in the Ethiopian and other African countries' power systems. [ABSTRACT FROM AUTHOR]

In this paper, an optimal generation dispatch of the Ethiopian power system using a hybrid Genetic Algorithm-Hopfield Neural Network (GA-HNN) is presented to reduce recursive blackouts. Reformulation of generation dispatch for a power grid comprising biomass, hydro, solar, waste to energy plant, wind and geothermal have been carried out. Each of these sources requires a mathematical formulation that considers security limits and intermittency of renewables. Modelling and simulation was conducted on MATLAB. According to the simulation results obtained, it can be deduced that GA-HNN based optimal generation dispatch of Ethiopian power system is a key solution in connection to developments needed in the adoption and realization of smarter grids as it concurrently increases its security level and decreases total generation cost. Generally, reducing the number of recursive blackouts, decreasing generation cost, allocating optimal generation level, and reducing computation time are prospects of employing GA-HNN based OGD. [ABSTRACT FROM AUTHOR]

This paper presents Security Constrained Economic Dispatch (SCED) of Renewable Energy Systems (RES) using Hopfield Neural Networks (HNN) to address power mismatch problems of the Ethiopian power grid. The mathematical formulations of SCED for RES comprising biomass, hydro, solar PV, waste to energy plant, wind, and geothermal are presented. Each of these sources requires problem formulation and constraint handling mechanisms considering security limits and credible contingencies. This enables renewable energy systems to provide secure and reliable electric service. Modified IEEE 118 bus system and Ethiopian renewable energy systems were used as case studies. Modelling and simulation were conducted on MATLAB. According to the results obtained, it can be deduced that employing HNN based SCED is a promising step in connection to developments needed in the adoption and realization of smarter grids as it reduces execution time, production cost and the number of blackouts while increasing the security level of a power system of RES. [ABSTRACT FROM AUTHOR]

Background: There is a rich genetic resource base of indigenous chickens in Ethiopia. However, the productivity of indigenous chicken in the country and their generic base has not been explored sufficiently through characterization using multivariate analysis. Objective: The objectives of this study were to understand complex interrelations among morphometric traits of indigenous chickens and predict body weight using principal component (PC) factor analysis. Materials and Methods: A total of 660 (180 males and 480 females) randomly selected chickens of age six months and above were used for the study. Data were collected on body weight and morphometric traits. Results: In factor solution of the PCA with varimax rotation of the transformation matrix, two principal components (PCs) were extracted (PC1 and PC2) explaining 75.76% of the total variation in the original variables. PC1 had the largest share (62.43%) of the total variance and had its loadings on comb length, wattle length, wingspan, comb height, shank length, and keel length while the PC2 shared only 13.33% of the total variance with positive loadings on body length, back length, and neck length. Prediction model based on PC factor scores accounted for 48% of the variation in the body weight and was more valid than the inter-dependent based models (which accounted for 49% of the variation in the body weight) as it removed multi-collinearity which was present as inter-dependent traits were used in the model. Conclusion: According to the findings of this study, body weight can be estimated more accurately from PC factor scores than inter-dependent original morphometric traits (i.e. comb height, comb length, wattle length, neck length, back length, body length, wingspan, shank length, and keel length) and the results obtained could be used by chicken producers and researchers for selection, management purposes and estimating market values of the chickens, since weight is the pivotal point on which animal production thrives. [ABSTRACT FROM AUTHOR]