International Journal of Energetica
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Statistical Modelling of Outage Events, Available Capacity, and Foreign Exchange Rate with Grid-Connected Power Generation in Nigeria.
Get PDFElectricity is one of the major factors that influence the level of growth of an economy, as well as human development. With applications in diverse spheres of life, it is at the core of human productivity and economic growth. Nations therefore, continually strive to ensure adequate and secured supply. Nigeria’s electric power sector experienced a major event in 2013 with the privatization of successor entities, pursuant to the enactment of the Electric Power Sector Reform Act (EPSRA) 2005. With about ten years of the electricity market in Transitional phase, this research sought to assess the impact of key operational and economic factors on the level of her on-grid electricity supply. Multivariate linear regression, a least squares approximation method was adopted considering four independent variables – Available Capacity (MW), Grid Outage Events (Total and partial), and Foreign Exchange Rate (₦/$). The Statsmodel package of python programming language was used for the 45 months’ data points for each variable. A weak relationship was found with the combined variables explaining 8.1% of the dataset for power generation from the developed model. However, Available Capacity, Grid Outage Events, and Foreign Exchange Rate are not sufficient to determine the growth of grid connected power generation
Effect of Microchannel Aspect Ratio on Laminar Nanofluid Flow and Thermal Performance: A Three-Dimensional Numerical Study
No full textMicrochannel heat sinks have emerged as an effective thermal management solution for compact electronic and energy systems subjected to high heat fluxes. In parallel, nanofluids have been proposed as advanced working fluids capable of enhancing convective heat transfer due to their improved thermophysical properties. In this study, a three-dimensional numerical investigation of laminar nanofluid flow and heat transfer in rectangular microchannels is conducted with a focus on the combined effects of geometric and operating parameters. An Al₂O₃–water nanofluid is employed as the working fluid and modeled as a homogeneous single-phase Newtonian fluid. The governing equations of mass, momentum, and energy conservation are solved using the finite volume method under steady-state conditions. Uniform heat flux is applied to three channel walls, while the remaining wall is assumed adiabatic. The influence of microchannel aspect ratio, hydraulic diameter, and Reynolds number on velocity distribution, temperature fields, and Nusselt number is systematically examined. The results indicate that Reynolds number is the dominant parameter controlling convective heat transfer enhancement, while increasing hydraulic diameter and aspect ratio generally reduce thermal performance under laminar conditions. The findings provide design-oriented insights for efficient rectangular microchannel heat sinks employing nanofluids in advanced thermal management applications
Energy-Efficient Islanding Detection Using CEEMDAN and Neural Network Integration in Photovoltaic Distribution System
Get PDFThis paper proposes an enhanced islanding detection method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a pattern recognition neural network (PANN). Negative sequence voltage data from both islanding and non-islanding scenarios were acquired through MATLAB Simulink simulations, with samples collected at a frequency of 3.84 kHz over a 2.5-second period. The voltage signals were decomposed into intrinsic mode functions (IMFs) using CEEMDAN, after which key features namely normalized max value, standard deviation, and entropy of the IMFs were extracted. The extracted features were used to train the PANN. The model was evaluated using cross-validation and several performance metrics, including accuracy, precision, recall, and F1 score. The proposed model achieved an overall accuracy of 98.6%, with a precision of 100%, a recall of 97%, and an F1 score of 98%. The detection time was found to be 0.2381 seconds, indicating the method's suitability for real-time applications. Furthermore, feature permutation importance analysis highlighted the critical role of certain features in the model's performance. The results demonstrate that the proposed method provides a reliable and efficient solution for islanding detection in grid-connected PV systems, significantly reducing the non-detection zone and ensuring high detection accuracy. This study contributes to developing of advanced detection techniques, enhancing the safety and reliability of modern power systems
Influence of Methanol Solvent and Alkali Catalyst on Biodiesel Production from Cottonseed Oil
Get PDFThis study investigates the influence of methanol solvent and alkali catalyst on biodiesel production from cottonseed oil. Utilizing local cottonseed oil from Ethiopia, the research focuses on optimizing the methanol-to-oil molar ratio and catalyst concentration to maximize biodiesel yield. The transesterification process was conducted with varying methanol-to-oil ratios (5:1, 6:1, 7:1) and sodium hydroxide (NaOH) concentrations (0.5 wt.%, 1 wt.%, 1.5 wt.%). Results indicated that a 6:1 methanol-to-oil ratio and 1 wt.% NaOH at 65 °C yielded an average biodiesel output of 98.25%. Methanol outperformed than ethanol and butanol by producing higher biodiesel yields. Besides, catalyst (NaOH) concentration is crucial for better yield, while deviations led to soap formation or incomplete reactions. In other words, moderate temperatures (55-65 °C) were seen optimal as higher temperatures (eg.,75 °C) caused methanol evaporation, reducing yield. Moreover, methanol's low cost, high reactivity, and ease of recovery, combined with NaOH's efficiency in catalyzing the reaction, were key factors in achieving high yields. This research underscores the importance of precise optimization in biodiesel production, contributing to sustainable energy solutions and promoting the use of regional agricultural resources. Future studies should explore pretreatment of cottonseed oil to further enhance the sustainability and economic viability of biodiesel production
Enhanced Model To Simulate The Performances Of A Steam Power Plant At Different Loads
Get PDFThermal steam power plants are indispensable for global electricity generation. However, assessing their performances under different working loads is crucial for their enhancement. This study aims to develop a comprehensive model using Matlab to simulate the performance of an existing thermal steam power plant (Achouat plant), enhanced with regeneration and re-superheat features. Additionally, an energy analysis is conducted to evaluate its efficiency under both full and partial load conditions. This analysis aims to certify and compare the performance and parameters of different plant components across various operating loads. The obtained results demonstrate that the developed model performs well in modeling the studied plant under various loads. Moreover, the findings affirm that the plant operates most optimally at full load and higher operating loads, showcasing an overall energy efficiency ranging from 33.24% to 37.38% across operating loads from 20% to 100%.
2D Thermal Modeling of a Square Solar Still Glass Cover Using the Poisson Equation and the Finite Difference Method
Get PDFThe aim of this study is to model and solve the two-dimensional Poisson equation using the Finite Difference Method (FDM) in order to approximate the steady-state thermal distribution on a square glass cover of a solar still. The glass surface is represented as a square computational domain subjected to various Dirichlet boundary conditions, enabling the simulation of different thermal loading scenarios. The mathematical formulation is discretized on a uniform mesh, and the resulting linear system is solved efficiently using the Thomas algorithm adapted for block tridiagonal matrices. The numerical results illustrate the influence of boundary temperatures, imposed heat fluxes, and grid resolution on the internal temperature distribution. Although the model is based on conduction-dominated heat transfer and assumes a homogeneous thin glass layer, it provides meaningful insight into the thermal response of solar still glazing. This approach offers a simple and effective framework that can be extended in future work to three-dimensional geometries and to fully coupled radiative–convective heat transfer models
Influence of Vertical Magnetic Field on Heat Transfer and Instabilities of Swirling Cylinder Flows
Get PDFThis study analyzes the influence of a vertical magnetic field on heat transfer and flow stability in swirling motion inside a cylindrical container. The governing magnetohydrodynamic (MHD) equations are solved using the available numerical framework from the original model, extended here to include Lorentz-force effects. Simulations are performed for Hartmann numbers Ha = 0–60 and Reynolds numbers up to Re = 1500. The results show that increasing Ha suppresses velocity fluctuations, reduces the intensity of the secondary vortices by 35–50%, and delays the onset of flow instability. The average Nusselt number decreases by approximately 18% when Ha increases from 0 to 50, indicating magnetic damping of convective transport. These results demonstrate that a vertical magnetic field can effectively stabilize swirling flows and significantly modify their thermal characteristics
Simulation Study of a Hybrid MPPT Controller with Enhanced Walrus Optimization and Levenberg-Marquardt Training
Get PDFThe increasing deployment of photovoltaic (PV) systems demands efficient maximum power point tracking (MPPT) methods to optimize energy extraction under varying environmental conditions. Conventional MPPT algorithms often suffer from slow convergence and instability under fluctuating irradiance and temperature. Here we propose a hybrid MPPT controller combining an enhanced Walrus Optimization algorithm with Vigilante Selection and Juvenile Update (WOVE-NSJ) and Levenberg-Marquardt (LM) training of a feedforward neural network (FNN). This approach improves global search and local fine-tuning, achieving near-ideal tracking efficiencies of 99.98%, reduced average tracking time (0.0914 s), and minimal overshoot with near-instantaneous settling times. Benchmarking against state-of-the-art ANN-based controllers demonstrates superior transient stability and robustness. These findings suggest that the proposed WOVE-NSJ-LM-FNN controller offers a promising solution for real-time, high-performance MPPT in PV systems, enhancing power output and system reliability under dynamic conditions
Integrated Iron Rods Impact on Enhanced Output of Conic Solar Still
Get PDFThis experimental study investigated the performance of modified solar stills (MSS) incorporating structural iron rods in the still basin. The experiment, conducted on September 13, 2024 at the University of El Oued, southeastern Algeria. Three identical conical solar stills: CSS, an MSS0 with 0 cm rod spacing, and an MSS1 with 1 cm rod spacing. Results indicate a negligible impact of the modifications on internal glass temperature, with all stills peaking around 43−43.5°C. However, the presence of iron rods significantly enhanced water temperature and distillation output. Both MSS0 and MSS1 achieved peak water temperatures of approximately 60°C, notably higher than the CSS's peak of 55°C. This thermal advantage translated directly into increased distilled water production. Hourly output data showed MSS0 and MSS1 consistently producing around 93-95 ml during peak hours (12:00-14:00h), compared to the CSS's 88 ml. Cumulatively, MSS0 yielded the highest total output at approximately 740 ml, followed by MSS1 at 720 ml, both substantially exceeding the CSS's 600 ml. These findings highlight the effectiveness of integrating iron rods to improve solar still efficiency, with MSS0 demonstrating marginally superior overall performance
Energy Management of a Photovoltaic–Wind–Battery Energy Storage Microgrid Using Linear Programming and Grey Wolf Optimization Techniques
Get PDFThis study presents a comprehensive optimization analysis of a renewable energy–based hybrid microgrid integrating photovoltaic (PV), wind generation, and battery energy storage systems (BESS). The microgrid energy dispatch problem is formulated through detailed cost models for PV generation, wind power production, and battery charging–discharging operations. Two optimization techniques—Linear Programming (LP) and Grey Wolf Optimization (GWO) are applied to minimize operational and maintenance costs while improving overall system efficiency. The performance of LP and GWO is systematically evaluated through six operational case studies involving different combinations of PV, wind, battery storage, and grid interaction. For the LP-based optimization, the total operating costs are 9,761.02 for Case Study 2 (Wind–Grid), and 5,802.44 for Case Study 4 (Wind–PV–Battery–Grid), 15,668.82 for Case Study 6 (PV–Battery–Grid). A comparative analysis of the results demonstrates that the GWO technique consistently achieves lower operating costs than the LP approach, particularly for the Wind–PV–Battery–Grid configuration, where the minimum cost is $5,802.44. These findings highlight the superior capability of metaheuristic optimization in handling the nonlinear and complex nature of hybrid microgrid energy management problems. Overall, the results provide valuable insights into cost-effective microgrid operation and underscore the potential of advanced optimization techniques for enhancing the economic viability and sustainable integration of renewable energy resources. Results not only reveal the implications for optimizing microgrid operations but also provide indispensable insights for developing cost-effective strategies that emphasize the sustainable integration of renewable energy resources. This study is a valuable resource for researchers and stakeholders seeking to expand the operational efficiency and economic viability of hybrid microgrid systems.