Edge Computing for Smart Grids: Analyzing the role of edge computing in optimizing energy distribution in smart grid systems

Authors

  • Dr. Hatice Özbay Associate Professor of Electrical and Electronics Engineering, Istanbul Technical University, Turkey Author

Keywords:

Smart Grids

Abstract

Edge computing has emerged as a promising technology for optimizing energy distribution in smart grid systems. This paper analyzes the role of edge computing in enhancing the efficiency, reliability, and scalability of smart grids. We discuss the key challenges and opportunities in integrating edge computing with smart grids and propose a framework for leveraging edge computing for real-time energy management. Through simulations and case studies, we demonstrate the potential benefits of edge computing in reducing energy wastage, improving grid stability, and enabling decentralized energy trading. Our findings suggest that edge computing can significantly enhance the performance of smart grid systems, paving the way for a more sustainable and resilient energy infrastructure.

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References

Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices." Asian Journal of Research in Computer Science 16.4 (2023): 239-256.

Shahane, Vishal. "Investigating the Efficacy of Machine Learning Models for Automated Failure Detection and Root Cause Analysis in Cloud Service Infrastructure." African Journal of Artificial Intelligence and Sustainable Development2.2 (2022): 26-51.

Muthusubramanian, Muthukrishnan, and Jawaharbabu Jeyaraman. "Data Engineering Innovations: Exploring the Intersection with Cloud Computing, Machine Learning, and AI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 76-84.

Tillu, Ravish, Bhargav Kumar Konidena, and Vathsala Periyasamy. "Navigating Regulatory Complexity: Leveraging AI/ML for Accurate Reporting." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 149-166.

Sharma, Kapil Kumar, Manish Tomar, and Anish Tadimarri. "AI-driven marketing: Transforming sales processes for success in the digital age." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 250-260.

Abouelyazid, Mahmoud. "Natural Language Processing for Automated Customer Support in E-Commerce: Advanced Techniques for Intent Recognition and Response Generation." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 195-232.

Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.

Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.

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Published

30-06-2023

How to Cite

[1]
Dr. Hatice Özbay, “Edge Computing for Smart Grids: Analyzing the role of edge computing in optimizing energy distribution in smart grid systems”, Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, pp. 273–284, Jun. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/51

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