Energy-Optimized Smart Transformers for Renewable-Rich Grids
(This article belongs to the Section Energy and Fuels (ENF))
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Oboma, S. O. and Lambart, E. (2025). Energy-Optimized Smart Transformers for Renewable-Rich Grids. Journal of Engineering Research and Sciences, 4(10), 21–28. https://doi.org/10.55708/js0410003
Sunday Omini Oboma and Edward Lambart. "Energy-Optimized Smart Transformers for Renewable-Rich Grids." Journal of Engineering Research and Sciences 4, no. 10 (October 2025): 21–28. https://doi.org/10.55708/js0410003
S.O. Oboma and E. Lambart, "Energy-Optimized Smart Transformers for Renewable-Rich Grids," Journal of Engineering Research and Sciences, vol. 4, no. 10, pp. 21–28, Oct. 2025, doi: 10.55708/js0410003.
The accelerating and unrestrained use of energy globally raises serious concerns for the future of the planet, primarily due to the environmental devastation caused by fossil fuels. Achieving high energy efficiency in both fuel-driven and renewable energy systems is crucial for future energy optimization. Clean energy production is one of the most effective strategies to mitigate climate change effects. These challenges necessitate a significant shift towards sustainable energy models, specifically smart and renewable energy systems that do not emit greenhouse gases during generation. This paper proposes a novel framework for smart and renewable energy optimization through the design of smart transformers that maximize energy savings without generating harmful radiation. The optimization utilizes a hybrid approach combining Nonlinear Programming (NLP) and an Artificial Intelligence (AI) technique, the Genetic Algorithm (GA), applied to specific transformer design parameters. The validated results demonstrate significant efficiency gains and cost reduction, strengthening the paper's contribution to robust, sustainable energy infrastructure.
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