AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability

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Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learning (March)
Published: Mar 30, 2025
Keywords:
Energy Optimization,
Genetic Algorithms,
Photovoltaic Systems,
Smart Grids

Main Article Content

  T. Sasilatha
Academy of Maritime Education and Training (AMET), Chennai,
  https://orcid.org/0009-0003-5217-2496
  Adolf Asih Suprianto
Politeknik Enjinering Indorama, Purwakarta,
  https://orcid.org/0009-0009-7945-8754
  Hamdani Hamdani
Universitas Mulawarman, Samarinda,
  https://orcid.org/0000-0003-2121-9362

Abstract

The main objective of this research is to improve the efficiency, reliability, and security of the power grid through the integration of artificial intelligence (AI) techniques. The research method involves developing an integrated AI-SGMS framework, including: (1) AI-based Load Forecasting using LSTM and transformer models; (2) Reinforcement Learning for Network Optimization with deep reinforcement learning (DRL) agents; (3) AI-enabled Fault Detection using CNN and autoencoder; (4) AI-driven Intrusion Detection System (IDS) for cybersecurity; and (5) Edge Computing for Decentralized Decision Making. The results show that AI-SGMS is able to optimize energy distribution, improve predictive maintenance, strengthen cybersecurity, and enhance network resilience. The system reduces waste, prevents congestion, detects potential failures, and mitigates cyber threats. Decentralized decision-making ensures rapid response and network resilience. The conclusion of this research is that the application of AI in power grid management, such as AI-SGMS, has the potential to revolutionize energy distribution, reduce operational costs, and support the transition to a sustainable, resilient, and efficient power grid. This research provides a foundation for broader development of AI solutions in power grid management.

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Sasilatha, T., Suprianto, A. A., & Hamdani, H. (2025). AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability. International Journal of Advances in Artificial Intelligence and Machine Learning, 2(1), 27–37. https://doi.org/10.58723/ijaaiml.v2i1.380
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Copyright (c) 2025 T. Sasilatha, Adolf Asih Suprianto, Hamdani Hamdani

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References

Al, Ferdous, A. S. . A., & Gupta, K. D. (2025). An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies. IEEE Access, 1. https://doi.org/10.1109 /ACCESS.2025.3537651

Dawn, S., Ramakrishna, A., Ramesh, M., Das, S. S., Rao, K. D., Islam, M. M., & Ustun, T. S. (2024). Integration of Renewable Energy in Microgrids and Smart Grids in Deregulated Power Systems: A Comparative Exploration. Advanced Energy and Sustainability Research, 5(10), 2400088. https://doi.org/10.1002/aesr.202400088

Dhanushkodi, K., & Thejas, S. (2024). AI Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation. IEEE Access, 12, 173127–173136. https://doi.org/10.1109/ACCESS.2024.3493957

Huang, T., Zhang, Q., Tang, X., Zhao, S., & Lu, X. (2022). A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems. Artificial Intelligence Review, 55(2), 1289–1315. https://doi.org/10.1007/s10462-021-09993-z

Kiasari, M., Ghaffari, M., & Aly, H. H. (2024). A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems. Energies, 17(16). https://doi.org/10.3390/en17164128

Mohanty, A., Ramasamy, A. K., Verayiah, R., Bastia, S., Dash, S. S., Cuce, E., Khan, T. M. Y., & Soudagar, M. E. M. (2024). Power system resilience and strategies for a sustainable infrastructure: A review. Alexandria Engineering Journal, 105, 261–279. https://doi.org/10.1016/j.aej.2024.06.092

Pandiyan, P., Saravanan, S., Usha, K., Kannadasan, R., Alsharif, M. H., & Kim, M. K. (2023). Technological advancements toward smart energy management in smart cities. Energy Reports, 10, 648–677. https://doi.org/10.1016/j.egyr.2023.07.021

Perera, A. T. D., & Kamalaruban, P. (2021). Applications of reinforcement learning in energy systems. Renewable and Sustainable Energy Reviews, 137, 110618. https://doi.org/10.1016/j.rser.2020.110618

Rahman, M. M., Dadon, S. H., He, M., Giesselmann, M., & Hasan, M. M. (2024). An Overview of Power System Flexibility: High Renewable Energy Penetration Scenarios. Energies, 17(24). https://doi.org/10.3390/en17246393

Rajkumar, V. S., Stefanov, A., Presekal, A., Palensky, P., & Torres, J. L. R. (2023). Cyber Attacks on Power Grids: Causes and Propagation of Cascading Failures. IEEE Access, 11, 103154–103176. https://doi.org/10.1109/ACCESS.2023.3317695

Rehmani, M. H., Reisslein, M., Rachedi, A., Erol-Kantarci, M., & Radenkovic, M. (2018). Integrating Renewable Energy Resources into the Smart Grid: Recent Developments in Information and Communication Technologies. IEEE Transactions on Industrial Informatics, 14(7), 2814–2825. https://doi.org/10.1109/TII.2018.2819169

Sharma, N., Acharya, A., Jacob, I., Yamujala, S., Gupta, V., & Bhakar, R. (2021). Major Blackouts of the Decade: Underlying Causes, Recommendations and Arising Challenges. 2021 9th IEEE International Conference on Power Systems (ICPS), 1–6. https://doi.org/10.1109/ICPS52420.2021.9670166