Journal of Technology Innovations and Energy

A review of the current situation and challenges facing Egyptian renewable energy technology

2024-09-16T15:57:32+00:00

Egypt is pivotal in the global energy transportation. It has committed to the Paris Agreement and presented its recognition of the necessity for international cooperation in tackling climate change. Despite these commitments, Egypt faces significant challenges in its energy sector. With the high domestic energy demands, the nation's crude oil reserve is projected to be depleted within the next 15 years. It is crucial to prioritize and focus on renewable energy technology and its utilization in the present and future frameworks of Egypt's energy by addressing these issues to maintain its strategic position in global energy markets. The study aims to examine Egypt's current energy landscape and assess its potential for adopting renewable energy sources. To meet the nation's electricity demands the study explores both the current state and future possibilities for renewable and sustainable energy in Egypt, taking into account plans from both the private and public sectors. The adoption of such strategies will guide the authorities of Egypt towards achieving the nation's renewable energy ambitions, including the 2035 energy strategy, with the ultimate goal of becoming a net-zero emissions nation. Egypt's renewable energy sector is largely underdeveloped and faces numerous challenges, despite it has abundant and varied resources. These challenges include barriers in manufacturing, political hurdles, economic constraints, and technological limitations. This study suggests several strategies for Egypt to optimize its utilization of renewable resources. The suggestions are made considering the existing energy policies and identifying potential obstacles in the deployment of renewable energy systems. This study would assist Egypt and other nations in establishing a clear path toward achieving the global objective of net zero emissions in the coming decades.

AI Innovations, Renewable Energy, and Social Change: A Pathway to Global Sustainability

2024-09-26T08:21:26+00:00

Artificial Intelligence (AI) has emerged as a transformative force in the renewable energy sector, significantly improving energy generation, distribution, and consumption efficiency. Through advanced algorithms and machine learning, AI optimizes energy systems by predicting demand, managing supply, and enhancing the integration of renewable sources like solar and wind power. Beyond its technological contributions, AI plays a crucial role in promoting social change by advancing renewable energy solutions. It fosters global sustainability not only by reducing carbon emissions but also by increasing energy accessibility, especially in remote or underserved regions. This expansion of energy access has the potential to improve living standards, stimulate economic development, and create employment opportunities in green industries. Furthermore, AI supports a just energy transition by helping to ensure that the benefits of renewable energy are distributed equitably across societies, reducing the socio-economic disparities often associated with traditional energy systems. As AI continues to evolve, it holds great promise for further innovations in smart grids, energy storage solutions, and predictive maintenance, which will be vital in enhancing system reliability and sustainability. However, challenges remain, such as the need for large-scale data, addressing ethical concerns related to AI deployment, and ensuring that technological advancements do not widen existing inequalities. This review examines the integration of AI technologies into renewable energy systems, evaluates the socio-environmental implications, and highlights the challenges and opportunities that lie ahead in leveraging AI for both environmental sustainability and social progress.

A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

2024-09-21T17:49:14+00:00

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.

Comparative Analysis of Autoclaved Aerated Concrete (AAC) vs. Traditional Building Materials for Energy-Efficient Green Building

2024-08-24T17:20:21+00:00

This study evaluates a product that generates less pollution than traditional construction materials, focusing on its entire lifecycle from production to operational use. It highlights reductions in energy consumption and economic savings, emphasizing the environmental benefits of new materials. The research includes a case study of a five-story apartment, where autoclaved materials resulted in approximately 10% energy savings. During production, pressed bricks required 62 gigajoules to construct 100 square meters of wall, compared to 3.6 gigajoules for autoclaved blocks, indicating that pressed bricks consume 15.5 times more energy. Transportation also showed differences due to the lower weight of autoclaved blocks, with pressed bricks consuming 1.8 gigajoules of energy compared to 0.45 gigajoules for autoclaved materials. In implementation, the labor and time required for autoclaved materials were half that needed for brick walls in Iran. A high correlation (R²=0.92) was found between thermal conductivity and density for AAC. The production of pressed bricks, which demands very high temperatures, leads to a fivefold increase in fuel consumption. Additionally, because autoclaved blocks require less material per square meter, there is a tenfold increase in fuel consumption per square meter. The study underscores the substantial benefits of adopting autoclaved aerated concrete in construction, both in terms of environmental impact and energy efficiency, highlighting its potential for more sustainable and cost-effective building practices.

Repowering of a wind energy production field - case study of SIDI-DAOUD field in northeastern Tunisia

2024-07-23T19:04:10+00:00

Every energy production system, whether conventional or renewable, reaches its final limits after a period of operation predefined by the feasibility study for such a project. Then we'll have to think about renewal to maintain a certain level of reliability and availability as a factor of operational safety. This project seeks of studying the repowering of two 1st phases of wind farm in Sidi-Daoued in north Tunisia, which have reached the end of their life (20 years); it aims to identify three new configurations and to simulate different scenarios to determine which types of turbines are the most optimal for a repowering project. The three types of wind turbine selected were Vestas 4200kw, Nordex Acciona and Siemens-Gamesa, each with a power output of 4500kw. These are the best-known types of wind turbine in the world, and the heights of the hubs are very similar, at around 105m, so that a comparison can be made between the three models. Therefore, the average speeds are around 7.8 m/s2.The final number of turbines to be installed on site is around 13 turbines for the three scenarios, but the result favours the second scenario with Siemens-GAMESA wind turbines, where the annual production can exceed 210.1 GWH with a capacity factor equal to 41 percent.