Information and engineering sciences | Online ISSN 3068-0115
REVIEWS   (Open Access)

Artificial Intelligence for Critical Power Infrastructure: Challenges and Opportunities

Syed Nurul Islam1*, Anik Biswas2

+ Author Affiliations

Applied IT & Engineering 1 (1) 1-9 https://doi.org/10.25163/engineering.1110397

Submitted: 01 August 2023 Revised: 02 October 2023  Accepted: 06 October 2023  Published: 08 October 2023 


Abstract

AI (artificial intelligence) has become a game-changing force in critical power infrastructure, transforming how energy is generated, utilized, and managed. AI is rooted in hundreds of years of human interest in intelligent machines, and it now provides real-world solutions to the challenges that modern energy systems are facing. AI is very important for making sure that energy is reliable, resilient, and sustainable in the United States, where demand is rising quickly because of population growth and the rapid growth of AI-driven data centers. Applications include predictive maintenance, fault detection, optimizing power flow, and forecasting demand. These lower operational costs make the grid more stable. One of the main contributions is the integration of renewable energy sources (RES). AI makes it possible to accurately predict how much solar and wind energy will be produced, optimize energy storage, and stabilize both distributed and centralized grids. Federal programs like those from the U.S. Department of Energy (DOE) and policies from the Federal Energy Regulatory Commission (FERC) show that the government knows that AI could help speed up the switch to clean energy. At the same time, tech leaders are using AI-powered demand-side management to keep consumption and production in balance in real time. But there are still problems, such as scalability, interoperability, cybersecurity risks, regulatory uncertainty, and a lack of skilled workers. To make sure that everyone can safely and fairly use the technology, these problems need to be fixed. The U.S. case shows that AI can help with both short-term and long-term goals for sustainability, making it a model for changing the way the world uses energy. AI is a key part of the future of energy because it combines advanced analytics with new policy ideas to create power systems that are strong, efficient, and good for the environment.

Keywords: Artificial Intelligence (AI), Power Infrastructure, Renewable Energy Integration, Smart Grid Optimization, Predictive Maintenance

References

Abedinia, O., Lotfi, M., Bagheri, M., Sobhani, B., Shafie-Khah, M., & Catalão, J. P. (2020). Improved EMD-based complex prediction model for wind power forecasting. IEEE Transactions on Sustainable Energy, 11(4), 2790–2802. https://doi.org/10.1109/TSTE.2020.2973741

Adlen, K., & Ridha, K. (2022). Recurrent neural network optimization for wind turbine condition prognosis. Diagnostyka, 23(2), 2022301. https://doi.org/10.29354/diag/149825

Ahmadi, A., Talaei, M., Sadipour, M., Amani, A. M., & Jalili, M. (2022). Deep federated learning-based privacy-preserving wind power forecasting. IEEE Access, 11, 39521–39530. https://doi.org/10.1109/ACCESS.2022.3167065

Akbar, K., Zou, Y., Awais, Q., Baig, M. J. A., & Jamil, M. (2022). A machine learning-based robust state of health (SOH) prediction model for electric vehicle batteries. Electronics, 11(8), 1216. https://doi.org/10.3390/electronics11081216

Al-Yahyai, S., Charabi, Y., & Gastli, A. (2010). Review of the use of numerical weather prediction (NWP) models for wind energy assessment. Renewable and Sustainable Energy Reviews, 14(9), 3192–3198. https://doi.org/10.1016/j.rser.2010.07.001

Bhavsar, S., Pitchumani, R., & Ortega-Vazquez, M. (2021). Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts. Applied Energy, 293, 116964. https://doi.org/10.1016/j.apenergy.2021.116964

Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present, and future. Renewable and Sustainable Energy Reviews, 144, 111051. https://doi.org/10.1016/j.rser.2021.111051

Chen, R., Cao, J., & Zhang, D. (2021). Probabilistic prediction of photovoltaic power using a Bayesian neural network—LSTM model. In Proceedings of the 2021 IEEE 4th International Conference on Renewable Energy and Power Engineering (REPE) (pp. 294–299). Beijing, China. https://doi.org/10.1109/REPE53134.2021.9631933

Clifton, A., Daniels, M., & Lehning, M. (2014). Effect of winds in a mountain pass on turbine performance. Wind Energy, 17(11), 1543–1562. https://doi.org/10.1002/we.1646

Clifton, A., Kilcher, L., Lundquist, J., & Fleming, P. (2013). Using machine learning to predict wind turbine power output. Environmental Research Letters, 8(2), 024009. https://doi.org/10.1088/1748-9326/8/2/024009

Dong, H., Xie, J., & Zhao, X. (2022). Wind farm control technologies: From classical control to reinforcement learning. Progress in Energy, 4(3), 032006. https://doi.org/10.1088/2516-1083/ac7e25

Du, M., Ma, S., & He, Q. (2016). A SCADA data-based anomaly detection method for wind turbines. In Proceedings of the 2016 China International Conference on Electricity Distribution (CICED) (pp. 1–6). Xi’an, China. https://doi.org/10.1109/CICED.2016.7576175

Feng, Z., Liang, M., Zhang, Y., & Hou, S. (2012). Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy, 47, 112–126. https://doi.org/10.1016/j.renene.2012.04.001

Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1), 1–8. https://doi.org/10.1016/j.renene.2011.05.033

Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., Li, W., & Li, K. (2021). Artificial intelligence security: Threats and countermeasures. ACM Computing Surveys, 55(1), 1–36. https://doi.org/10.1145/3464426

Karanki, S. B., Xu, D., Venkatesh, B., & Singh, B. N. (2013). Optimal location of battery energy storage systems in a power distribution network for integrating renewable energy sources. In Proceedings of the 2013 IEEE Energy Conversion Congress and Exposition (pp. 4553–4558). Denver, CO, USA. https://doi.org/10.1109/ECCE.2013.6647279

Karimi, A., Aminifar, F., Fereidunian, A., & Lesani, H. (2019). Energy storage allocation in wind integrated distribution networks: An MILP-based approach. Renewable Energy, 134, 1042–1055. https://doi.org/10.1016/j.renene.2018.12.079

Khan, N., Shahid, Z., Alam, M. M., Bakar Sajak, A. A., Mazliham, M., Khan, T. A., & Ali Rizvi, S. S. (2022). Energy management systems using smart grids: An exhaustive parametric comprehensive analysis of existing trends, significance, opportunities, and challenges. International Transactions on Electrical Energy Systems, 2022, 3358795. https://doi.org/10.1155/2022/3358795

Kolokotsa, D., Kampelis, N., Mavrigiannaki, A., Gentilozzi, M., Paredes, F., Montagnino, F., & Venezia, L. (2019). On the integration of the energy storage in smart grids: Technologies and applications. Energy Storage, 1(1), e50. https://doi.org/10.1002/est2.50

Kulkarni, P. A., Dhoble, A. S., & Padole, P. M. (2019). Deep neural network-based wind speed forecasting and fatigue analysis of a large composite wind turbine blade. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(10), 2794–2812. https://doi.org/10.1177/0954406218798625

Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., & Zheng, M. (2019). Wind power short-term prediction based on LSTM and discrete wavelet transform. Applied Sciences, 9(6), 1108. https://doi.org/10.3390/app9061108

Lu, Y., Sun, L., Zhang, X., Feng, F., Kang, J., & Fu, G. (2018). Condition-based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Applied Ocean Research, 74, 69–79. https://doi.org/10.1016/j.apor.2018.02.005

Ma, Y., Chen, X., Wang, L., & Yang, J. (2021). Study on smart home energy management system based on artificial intelligence. Journal of Sensors, 2021, 9101453. https://doi.org/10.1155/2021/9101453

Margaris, I., Hansen, A. D., Sørensen, P., & Hatziargyriou, N. (2011). Dynamic security issues in autonomous power systems with increasing wind power penetration. Electric Power Systems Research, 81(5), 880–887. https://doi.org/10.1016/j.epsr.2010.11.014

Murat, K., Umit, C., Vinayak, S., & Ozgur, G. (2020). Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools. IEEE Access, 8, 187814–187823. https://doi.org/10.1109/ACCESS.2020.3030674

Nadeem, F., Hussain, S. M. S., Tiwari, P. K., Goswami, A. K., & Ustun, T. S. (2018). Comparative review of energy storage systems, their roles and impacts on future power systems. IEEE Access, 7, 4555–4585. https://doi.org/10.1109/ACCESS.2018.2888497

Pan, G., Zhang, H., Ju, W., Yang, W., Qin, C., Pei, L., Sun, Y., & Wang, R. (2020, November 6–8). A prediction method for ultra short-term wind power prediction basing on long short-term memory network and extreme learning machine. In Proceedings of the 2020 Chinese Automation Congress (CAC) (pp. 7608–7612). Shanghai, China. https://doi.org/10.1109/CAC51589.2020.9327946

Qin, C., & Yu, Y. (2014). Security region based probabilistic small signal stability analysis for power systems with wind power integration. Automation of Electric Power Systems, 38, 43–48


View Dimensions


View Plumx


View Altmetric



4
Save
0
Citation
103
View
0
Share