Published September 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

A Systematic Survey on the Application of Artificial Intelligence (ai) Baseline Networks on Grid Computing Techniques - Challenges, Novelty and Prospects

Creators

  • 1. Department of Computer Science, Abubakar Tafawa Balewa University, Bauchi, Nigeria.

Contributors

Contact person:

  • 1. Department of Computer Science, Abubakar Tafawa Balewa University, Bauchi, Nigeria.
  • 2. Department of Computer Science Education, Federal College of Education, Pankshin, Nigeria.
  • 3. Department of Computer Science, Plateau State University, Jos, Nigeria.
  • 4. ICT Unit, Plateau State Poltytechnic, Jos, Nigeria.

Description

Abstract: A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. To increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. To integrate the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has made great strides toward modernizing and expanding it. The study reviews how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

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Additional details

Identifiers

DOI
10.35940/ijsce.D3643.14040924
EISSN
2231-2307

Dates

Accepted
2024-09-15
Manuscript received on 17 August 2024 | Revised Manuscript received on 25 August 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024.

References

  • Abdel-Nasser, M., & Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural computing and applications, 31, 2727-2740. https://doi.org/10.1007/s00521-017-3225-z
  • Ali, S. S., & Choi, B. J. (2020). State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics, 9(6), 1030. https://doi.org/10.3390/electronics9061030
  • D'Oca, S., Corgnati, S. P., & Buso, T. (2014). Smart meters and energy savings in Italy: Determining the effectiveness of persuasive communication in dwellings. Energy Research & Social Science, 3, 131-142. https://doi.org/10.1016/j.erss.2014.07.015
  • Da Silva, F. L., Nishida, C. E., Roijers, D. M., & Costa, A. H. R. (2019). Coordination of electric vehicle charging through multiagent reinforcement learning. IEEE Transactions on Smart Grid, 11(3), 2347-2356. https://doi.org/10.1109/TSG.2019.2952331
  • Dogaru, D. I., & Dumitrache, I. (2019a). Cyber attacks of a power grid analysis using a deep neural network approach. Journal of Control Engineering and Applied Informatics, 21(1), 42-50.
  • Dogaru, D. I., & Dumitrache, I. (2019b). Cyber security of smart grids in the context of big data and machine learning. 2019 22nd International Conference on Control Systems and Computer Science (CSCS), https://doi.org/10.1109/CSCS.2019.00018
  • Erol-Kantarci, M., & Mouftah, H. T. (2013). Smart grid forensic science: applications, challenges, and open issues. IEEE Communications Magazine, 51(1), 68-74. https://doi.org/10.1109/MCOM.2013.6400441
  • Foruzan, E., Soh, L.-K., & Asgarpoor, S. (2018). Reinforcement learning approach for optimal distributed energy management in a microgrid. IEEE Transactions on Power Systems, 33(5), 5749-5758. https://doi.org/10.1109/TPWRS.2018.2823641
  • Goel, S., Hong, Y., Papakonstantinou, V., Kloza, D., Goel, S., & Hong, Y. (2015). Security challenges in smart grid implementation. Smart grid security, 1-39. https://doi.org/10.1007/978-1-4471-6663-4_1
  • Jiang, H., Zhang, J. J., Gao, W., & Wu, Z. (2014). Fault detection, identification, and location in smart grid based on data-driven computational methods. IEEE Transactions on Smart Grid, 5(6), 2947-2956. https://doi.org/10.1109/TSG.2014.2330624
  • Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K.-K. R., & Leung, H. (2019). A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. Ieee Access, 7, 80778-80788. https://doi.org/10.1109/ACCESS.2019.2920326
  • Kumar, N. M., Chand, A. A., Malvoni, M., Prasad, K. A., Mamun, K. A., Islam, F., & Chopra, S. S. (2020). Distributed energy resources and the application of AI, IoT, and blockchain in smart grids. Energies, 13(21), 5739. https://doi.org/10.3390/en13215739
  • Kuzlu, M., Cali, U., Sharma, V., & Güler, Ö. (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.2020.3031477
  • Lee, J., Wang, W., Harrou, F., & Sun, Y. (2020). Wind power prediction using ensemble learning-based models. IEEE Access, 8, 61517-61527. https://doi.org/10.1109/ACCESS.2020.2983234
  • Li, L., Ota, K., & Dong, M. (2017). Everything is the image: CNN-based short-term electrical load forecasting for smart grid. 2017 14th International Symposium on Pervasive Systems, algorithms, and Networks & 2017 11th International Conference on the Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.78
  • Lin, L., Guan, X., Peng, Y., Wang, N., Maharjan, S., & Ohtsuki, T. (2020). Deep reinforcement learning for economic dispatch of a virtual power plant in the internet of energy. IEEE Internet of Things Journal, 7(7), 6288-6301. https://doi.org/10.1109/JIOT.2020.2966232
  • Lu, Z., Lu, X., Wang, W., & Wang, C. (2010). Review and evaluation of security threats on the communication networks in the smart grid. 2010-Milcom 2010 Military Communications Conference, https://doi.org/10.1109/MILCOM.2010.5679551
  • McLaughlin, S., Podkuiko, D., & McDaniel, P. (2010). Energy theft in the advanced metering infrastructure. Critical Information Infrastructures Security: 4th International Workshop, CRITIS 2009, Bonn, Germany, September 30-October 2, 2009. Revised Papers 4, https://doi.org/10.1007/978-3-642-14379-3_15
  • Mendel, J. (2017). Smart grid cyber security challenges: Overview and classification. e-mentor, 68(1), 55-66. https://doi.org/10.15219/em68.1282
  • Najafi, S., Shafie‐khah, M., Siano, P., Wei, W., & Catalão, J. P. (2019). Reinforcement learning method for plug‐in electric vehicle bidding. IET Smart Grid, 2(4), 529-536. https://doi.org/10.1049/iet-stg.2018.0297
  • Nallapaneni, M. K., Chand, A. A., Malvoni, M., Prasad, K. A., Mamun, K. A., Islam, F., & Chopra, S. S. (2020). Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies, 13(21), 5739. https://doi.org/10.3390/en13215739
  • Omitaomu, O. A., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548-568. https://doi.org/10.3390/smartcities4020029
  • Pearson, I. L. (2011). Smart grid cyber security for Europe. Energy Policy, 39(9), 5211-5218. https://doi.org/10.1016/j.enpol.2011.05.043
  • Saboori, H., Mohammadi, M., & Taghe, R. (2011). Virtual power plant (VPP), definition, concept, components and types. 2011 Asia-Pacific Power and Energy Engineering Conference, https://doi.org/10.1109/APPEEC.2011.5749026
  • Sagebiel, J., Müller, J. R., & Rommel, J. (2014). Are consumers willing to pay more for electricity from cooperatives? Results from an online Choice Experiment in Germany. Energy Research & Social Science, 2, 90-101. https://doi.org/10.1016/j.erss.2014.04.003
  • Saputra, Y. M., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Mueck, M. D., & Srikanteswara, S. (2019). Energy demand prediction with federated learning for electric vehicle networks. 2019 IEEE Global Communications Conference (GLOBECOM), https://doi.org/10.1109/GLOBECOM38437.2019.9013587
  • Veldman, E., Geldtmeijer, D. A., Knigge, J. D., & Han Slootweg, J. (2010). Smart grids put into practice: Technological and regulatory aspects. Competition and regulation in Network Industries, 11(3), 287-306. https://doi.org/10.1177/178359171001100303
  • Wan, Z., Li, H., He, H., & Prokhorov, D. (2018). Model-free real-time EV charging scheduling based on deep reinforcement learning. IEEE Transactions on Smart Grid, 10(5), 5246-5257. https://doi.org/10.1109/TSG.2018.2879572
  • Wang, Z., Ogbodo, M., Huang, H., Qiu, C., Hisada, M., & Abdallah, A. B. (2020). AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in a smart grid platform. IEEE Access, 8, 226409-226421. https://doi.org/10.1109/ACCESS.2020.3044612
  • Wei, L., Gao, D., & Luo, C. (2018). False data injection attacks detection with deep belief networks in smart grid. 2018 Chinese Automation Congress (CAC), https://doi.org/10.1109/CAC.2018.8623514
  • Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S. (2020). A multi-agent reinforcement learning-based data-driven method for home energy management. IEEE Transactions on Smart Grid, 11(4), 3201-3211. https://doi.org/10.1109/TSG.2020.2971427
  • Xu, Y., Ahokangas, P., Louis, J.-N., & Pongrácz, E. (2019). Electricity market empowered by artificial intelligence: A platform approach. Energies, 12(21), 4128. https://doi.org/10.3390/en12214128
  • Zhang, C., Li, R., Shi, H., & Li, F. (2020). Deep learning for day‐ahead electricity price forecasting. IET Smart Grid, 3(4), 462-469. https://doi.org/10.1049/iet-stg.2019.0258
  • Zhang, D., Han, X., & Deng, C. (2018). Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE Journal of Power and Energy Systems, 4(3), 362-370. https://doi.org/10.17775/CSEEJPES.2018.00520
  • Zhang, L., Wang, G., & Giannakis, G. B. (2019). Real-time power system state estimation and forecasting via deep unrolled neural networks. IEEE Transactions on Signal Processing, 67(15), 4069-4077. https://doi.org/10.1109/TSP.2019.2926023
  • Sharma, T., & Sharma, R. (2024). Smart Grid Monitoring: Enhancing Reliability and Efficiency in Energy Distribution. In Indian Journal of Data Communication and Networking (Vol. 4, Issue 2, pp. 1–4). https://doi.org/10.54105/ijdcn.d7954.04020224
  • Mathew, A. R. (2019). Cyber-Infrastructure Connections and Smart Gird Security. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 2285–2287). https://doi.org/10.35940/ijeat.f8681.088619
  • Prabhakaran, Prof. R., & Asha, Dr. S. (2019). Enhancing Cyber Security in Power Sector using Machine Learning. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 9, pp. 3382–3386). https://doi.org/10.35940/ijitee.i7860.078919