International Journal on Science and Technology

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A Comparative Study of Conventional and Renewable Energy Systems with Advanced Computer Science Applications for Sustainable Power Generation

Author(s) Mr. Harikrishna H, Mr. Aravindh B, Mr. Nikhil kumar, Mr. Chandra Prakash K, Ms. Nyibio Rashedetou Muyah, Dr. I. Bremnavas
Country India
Abstract The Growing Demand for reliable electricity and the urgent need to mitigate climate change have intensified global interest in evaluating both conventional and renewable energy systems for sustainable power generation. This study presents a comprehensive comparative analysis of major energy sources, including coal, natural gas, nuclear, hydropower, wind, solar, and biomass. Each energy system is examined in terms of its operational characteristics, environmental impact, economic feasibility, reliability, and long-term sustainability. While conventional sources such as coal and natural gas provide stable and dispatchable power, they are associated with significant greenhouse gas emissions and environmental concerns. Renewable technologies offer low-carbon alternatives but face challenges related to intermittency, land use, and storage dependency. Nuclear energy occupies a unique position, delivering high-capacity-factor low-carbon electricity while encountering economic and public acceptance constraints. Beyond technological comparison, this review highlights the critical role of advanced computer science applications—including machine learning–based forecasting, smart grid optimization, digital twins, predictive maintenance, and cybersecurity frameworks—in enhancing efficiency, reliability, and data-driven decision-making across modern energy systems. By integrating multidisciplinary insights, the study demonstrates that sustainable power generation requires balanced energy portfolios supported by computational intelligence and system-level optimization rather than reliance on a single energy source.
Keywords Sustainable power generation; Renewable and conventional energy; Lifecycle assessment; Energy system optimization; Artificial intelligence; Smart grids; Digital twins; Decarbonization.
Field Computer Applications
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-20
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