renewable energy

The increasing demand for reliable, sustainable, and decentralized energy systems has accelerated the adoption of microgrids, particularly in regions with unstable power infrastructure such as Nigeria. However, the effective design and management of microgrids remain challenging due to the variability of renewable energy sources and the lack of intelligent tools that integrate real-time environmental data, predictive analytics, and optimization techniques. This study addresses these challenges by developing an Intelligent Microgrid Management and Optimization System aimed at improving the planning, performance, and operational efficiency of renewable energy-based microgrids. The system was developed as a Python-based desktop application incorporating machine learning, optimization algorithms, and real-time data integration. Support Vector Regression (SVR) models were implemented to predict energy generation from multiple renewable sources using environmental inputs such as solar irradiance, temperature, and wind speed. A genetic algorithm was employed to optimize system component sizing, including solar panels and battery storage, based on cost and reliability objectives. Real-time weather data was retrieved via an API and integrated into a dynamic simulation engine, while a graphical user interface built with PyQt6 provided interactive visualization, monitoring, and control of system operations. The results demonstrate that the developed system achieves high predictive accuracy, with machine learning models attaining strong performance metrics (R² values above 0.88 across energy sources) and low prediction errors. The optimization module effectively identified cost-efficient and reliable microgrid configurations, while the simulation engine maintained stable energy balance and accurate battery state tracking under varying conditions. Overall, the system proved to be computationally efficient, user-friendly, and capable of supporting real-time decision-making, thereby offering a practical and scalable solution for intelligent microgrid design and management.

As the world continues to embrace cleaner and smarter energy solutions, there's a growing need for tools that not only design microgrids but also make them smarter, more responsive, and easier to manage. This project introduces an Intelligent Microgrid Management and Optimization System — a desktop application built with Python — designed to help users plan, optimize, and monitor solar-powered microgrid systems more efficiently. What sets this tool apart is its ability to pull live weather data (like sunlight levels and temperature) using the Open Weather Map API. With this, it can predict how much energy your solar panels might generate and how much power you’ll need, thanks to built-in machine learning models. The system then uses a genetic algorithm to figure out the best combination of solar panel size and battery capacity to meet your energy needs while keeping costs low. The application runs through a simple and responsive user interface (built with PyQt6), offering features like real-time graphs, a weather dashboard, and system control panels. It also supports SCADA-style monitoring, so users can see power generation, battery status, and energy demand in real time. Overall, this tool is designed to be both smart and user-friendly, making it useful not just for engineers and developers, but also for students, researchers, and organizations working on renewable energy solution

Solar photovoltaic (PV) technology is a critical low-carbon solution, but its performance is severely compromised by shading. This study addresses the persistent problem of partial shading, which causes disproportionate power losses and creates thermal stress risks like hot spots. This research aims to quantify the effect of shading on PV panel voltage, current, and power output under controlled laboratory conditions. The methodology employed an experimental approach using an SES TPS- 3720 Solar Energy Trainer. Experiments measured performance under 0% (baseline), 50% (partial), and 100% (full) shading. The study also evaluated the impact of shading material optical properties by testing opaque (wood), semi-opaque (paper), and translucent (plastic film) materials. Measurements were recorded across five irradiance levels using both LED lamp and DC motor loads.Key findings demonstrate a highly non-linear performance degradation. Partial shading covering 50% of the panel area resulted in a 65-70% power loss, far exceeding a proportional reduction.Full shading with opaque (wood) or semi-opaque (paper) materials caused a 100% power loss, eliminating all usable current. Translucent plastic film caused the least degradation (approx. 23% power loss). The results confirm that a material's optical transmittance, not its physical density, is the dominant factor determining shading severity. These findings validate established photovoltaic theory and highlight the critical importance of shadow avoidance in system design. The study reinforces the necessity of mitigation strategies such as bypass diodes and module-level power electronics (MLPE) in shade-prone installations.

This study investigates the possibility of using solar energy as an alternative to the University of Benin's unstable electrical supply from the Benin Distribution Company (BEDC). Using a descriptive survey approach, data was collected from 100 students using a standardized questionnaire and analyzed using mean and standard deviation. The findings show that an irregular power supply disturbs academic activity, but solar energy provides economic and environmental benefits. However, obstacles such as funding and policy constraints exist. The analysis suggests supportive policies, financial incentives, and investments to encourage using solar energy on campuses

Increasing demand for wood and fossil fuel which have limited availability has, over the years, contributed majorly in environmental pollution. The availability of energy for cooking remains a major concern in developing countries and cooking is a daily household activity. The negative environmental effect of wood and fossil fuel necessitates inquest for an alternative energy source that is sustainable. Biogas, over the years of research has shown
favorable characteristics which make it an excellent option as an alternative fuel source. However, more research has to be made into designing and developing devices or appliances that utilize the biogas efficiently. This study details the design and fabrication of a biogas
stove for domestic use with rural communities of developing countries such as Nigeria in mind focusing on characteristics such as efficiency, simplicity and cost-friendliness of the design. The biogas stove consists of the following major components: burner head, mixing
tube, the injector burner support, etc. The Bernoulli’s theorem was used to derive the flow rate of gas as well as key design dimensions to maintain this flow rate. The biogas stove was fabricated using stainless steel for the burner head, mild steel for the mixing chamber and a
brass alloy for the injector component. The material selected were chosen based on considerations given to corrosion, local availability and then cost. The clearance between the cooking pot and the burner head is 45mm, while the clearance between the flame ports is 5mm. The injector is connected to the mixing chamber which tapers down to the throat diameter of 14.7mm which is maintained as the diameter of the mixing chamber. The mixing chamber is connected to the burner head which is a cylindrical component with a top having 32 burner ports each of 3mm diameter drilled into it, from which the gas can be ignited. The result of three water boiling tests places the heating efficiency of the stove at 58.51%