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All Journal International Transactions on Artificial Intelligence (ITALIC)
Nurjanah, Lina
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Computer Science & IT Control & Systems Engineering Library & Information Science

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Assessing the Environmental and Economic Effects of Smart Grid Integration Using SEM Evans, Richard; Oganda, Fitra Putri; Setiawan, Mohamad Agus; Nurjanah, Lina; Sunengsih, Meriyana
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.934

Abstract

The global shift toward renewable energy has intensified the need for intelligent energy management systems capable of addressing variability in power supply and optimizing system-wide performance. Smart grid technologies have emerged as a key enabler in achieving sustainable, efficient, and data-drivenvenergy distribution. This study employs a quantitative approach using Structural Equation Modeling (SEM) via SmartPLS to analyze data collected from stakeholders involved in renewable energy deployment, utility operations, and smart grid implementation. The model evaluates the relationships between smart grid integration, environmental performance, and economic outcomes. The primary aim of this research is to assess how smart grid adoption influences carbon emission reduction, energy efficiency enhancement, and cost optimization within renewable energy ecosystems. The SEM analysis indicates a statistically significant positive effect of smart grid integration on both environmental and economic indicators. Smart grid implementation improves energy efficiency by more than 30%, while operational cost savings reach up to 25% over extended periods. Carbon emission reduction is identified as a key mediating factor within the model, reinforcing the ecological benefits of smart grid adoption. The findings demonstrate that smart grid technologies contribute substantially to both sustainability and economic resilience in renewable energy management. The study provides actionable insights for energy policymakers, grid operators, and industry practitioners, highlighting the vital role of intelligent, data-driven infrastructures in advancing future global energy systems.
Co-Authors Evans, Richard Fitra Putri Oganda Mohamad Agus Setiawan Sunengsih, Meriyana