MODERN TECHNOLOGIES FOR IMPROVING ENERGY EFFICIENCY AND OPTIMAL LOAD MANAGEMENT IN DIGITALIZED ELECTRIC POWER SYSTEMS
Keywords:
digital power systems, smart grid, energy efficiency, load management, artificial intelligence, IoT sensors, big data analytics, demand response, renewable energy integration, energy monitoring.Abstract
This article examines modern approaches to enhancing energy efficiency and optimizing load management in digitalized electric power systems. The integration of smart grid infrastructures, artificial intelligence, digital monitoring devices, and renewable energy sources has fundamentally transformed traditional power systems into highly automated, adaptive, and sustainable models. The study focuses on the technological, organizational, and analytical mechanisms through which digital transformation increases system stability, reliability, and economic efficiency. Special attention is given to demand response programs, Internet of Things (IoT)-based sensor networks, big-data-driven forecasting, and intelligent dispatching methods. The paper also highlights current global trends, challenges, and practical solutions relevant to modern power engineering.
Downloads
References
Gellings, C. W. The Smart Grid: Enabling Energy Efficiency and Demand Response. CRC Press, 2017.
Kirschen, D., Strbac, G. Fundamentals of Power System Economics. John Wiley & Sons, 2018.
Kundur, P. Power System Stability and Control. McGraw-Hill, 1994.
Fang, X., Misra, S., Xue, G., & Yang, D. "Smart Grid — The New and Improved Power Grid: A Survey." IEEE Communications Surveys & Tutorials, 2012.
Stojanovic, N., et al. "Artificial Intelligence Techniques for Load Forecasting in Power Systems." Electric Power Systems Research, 2019.
Lopes, J. A. P., et al. "Integration of Distributed Energy Resources in the Power System." Proceedings of the IEEE, 2011.
Ipakchi, A., & Albuyeh, F. "Grid of the Future: Are We Ready to Transition to Digital Power?" IEEE Power & Energy Magazine, 2009.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.