Intelligent Grid Management for Power and Energy Supply and Distribution

Authors

  • Chandan Kumar Panda Biju Patnaik University of Technology, Odisha
  • Chittaranjan Panda Sudhananda Engineering and Research Center, Bhubaneswar

Keywords:

Intelligent grid management, Power and energy supply, Power and energy distribution, Smart grid technologies

Abstract

Modern electrical systems' efficient and reliable distribution of power and energy depend heavily on intelligent grid management. To optimize grid operations, enhanced control and management systems are needed given the increased integration of renewable energy sources and the rising demand for sustainable energy options. In order to effectively monitor, regulate, and optimize power and energy systems, this article suggests an intelligent grid management strategy that integrates smart grid technology, cutting-edge analytics, and control algorithms. The suggested intelligent grid management system makes use of real-time data collection from smart metres, sensors, and other grid equipment to facilitate situational awareness and decision-making. In order to analyze the gathered data and extract insights for grid operation and planning, advanced analytics techniques, including as machine learning and optimization algorithms, are utilized. Utilizingthis information will improve power production, load scheduling, energy storage use, and system stability. In order to encourage energy users to modify their energy consumption habits in response to price signals and grid circumstances, the intelligent grid management system also integrates demand response methods. Demand-side management promotes grid stability and resilience by balancing supply and demand, lowering peak loads. In addition, the suggested approach incorporates distributed energy resources (DERs) into the grid management procedure, including solar panels, wind turbines, and energy storage devices. To maximize their impact on grid performance overall and improve grid resiliency during emergencies, these DERs are coordinated and controlled in a decentralized way. Case studies and simulation results provide as proof of the efficacy of the intelligent grid management strategy. The grid efficiency, energy costs, grid reliability, and integration of renewable energy sources are all improved by the system.

Downloads

Download data is not yet available.

References

Tanya, S., Deepak, K. B., & Chakrabarti, S. (2018). Artificial Intelligence and Its Application in Different Areas. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 189-192). IEEE.

Mishra, S., Bhende, C. N., &Mithulananthan, N. (2009). Fuzzy logic-based automatic generation control of a multi-area power system. Electric Power Systems Research, 79(2), 298-308.

Mansour, D. O., Alomoush, M. I., & Hariri, A. A. (2009). Fuzzy logic controller for damping inter-area oscillations in multi-machine power systems. Energy Conversion and Management, 50(1), 41-47.

Ata, R. (2015). Artificial neural networks: fundamentals, computing, design, and application. Journal of Advanced Computational Intelligence and Intelligent Informatics, 19(1), 1-4.

Mahvash, H., Seifi, A. R., &Noroozian, R. (2015). Mitigation of flicker and power quality improvement in a wind farm connected to a weak grid using DFIGs. In 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 1-6). IEEE.

Shetty, D., Panda, A., Singh, B. N., & Panda, S. (2017). Model-free fuzzy logic controller for STATCOM reactive current control. In 2017 IEEE Power and Energy Society General Meeting (PESGM) (pp. 1-5). IEEE.

Nahak, N., Tripathy, M., Nayak, S., Panda, S., & Nayak, N. (2018). Dual PI controller-based UPFC for power system oscillation damping using improved grey wolf optimizer. International Journal of Electrical Power & Energy Systems, 96, 1-12.

Alajmi, B. N., Ahmed, M. S., & Finney, S. J. (2013). Fuzzy logic-based maximum power point tracking for photovoltaic systems experiencing partial shading conditions. IEEE Transactions on Energy Conversion, 28(4), 893-901.

El Khateb, A., &Ouassaid, M. (2014). Design and implementation of a fuzzy logic-based SEPIC converter for maximum power point tracking. Renewable Energy, 64, 13-21.

Indumathi, R., Gowrishankar, S., & Venkatesh, P. (2012). Design of distribution static compensator (DSTATCOM) for voltage regulation in the presence of non-linear loads. International Journal of Electrical Power & Energy Systems, 43(1), 330-337.

Venkateshkumar, R. (2016). Fuzzy logic-based battery management system for renewable energy systems. In 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-5). IEEE.

Wandhare, R. G., Chaudhari, P. R., &Kolhe, J. P. (2014). Coordinated control of distributed energy resources in a hybrid microgrid. In 2014 International Conference on Energy Efficient Technologies for Sustainability (ICEETS) (pp. 530-535). IEEE.

Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353

Zerk, A.E.; Ouassaid, M.; Zidani, Y. Feasibility Study and Optimization of a Standalone Hybrid System for Remote Area: A Case Study of High Atlas Mountains in Morocco. In Proceedings of the 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, Morocco, 21–27 October 2018; pp. 606–611

1547-2018—IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces|IEEEStandard|IEEE Xplore. Available online: https://ieeexplore.ieee.org/document/8332112 (accessed on 25 January 2022).

IEC 61727:2004|IEC Webstore|Invertor, Smart City, LVDC. Available online: https://webstore.iec.ch/publication/5736 (accessed on 25 January 2022).

Eamani, R. R. ., & Kumar, N. V. . (2023). Design and Analysis of Multiplexer based Approximate Adder for Low Power Applications. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 228–233. https://doi.org/10.17762/ijritcc.v11i3.6341

Ms. Ritika Dhabalia, Ms. Kritika Dhabalia. (2012). An Intelligent Auto-Tracking Vehicle. International Journal of New Practices in Management and Engineering, 1(02), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/5

Downloads

Published

16.07.2023

How to Cite

Panda , C. K. ., & Panda, C. . (2023). Intelligent Grid Management for Power and Energy Supply and Distribution. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 238–245. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3163

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.