Cognitive Based Electric Power Management System

Year 2022, Volume: 10 Issue: 1, 85 - 90, 30.01.2022

T. Çetin Akıncı Alfredo A. Martinez-morales

https://doi.org/10.17694/bajece.1060998

Cited By: 4

Abstract

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.

Keywords

Cognitive Management, Electric Power Price Management System, Cognitive Network System, Cognitive Network System, Electric Power Price Management System Cognitive Network System

Supporting Institution

Tübitak

Project Number

1059B192001347

Thanks

This study was supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 2219-International Postdoctoral Research Scholarship Program, with project number 1059B192001347 in the 2020/2 application period. The authors would like to thank TÜBİTAK for their support.

References

• [1] A.Z. Hettinger, E.M. Roth, A.M. Bisantz, “Cognitive Engineering and Health Informatics: Applications and Intersections”, Journal of Biomedical Informatics, Vol.67, pp.21-33. 2017.
• [2] Y. Wang, “The Theoretical Framework of Cognitive Informatics”, International Journal of Cognitive Informatics and Natural Intelligence, Vol.1, No.1, 2007, pp. 1–27.
• [3] V.L. Patel, T.G. Kannampallil, “Cognitive Informatics in Biomedicine and Healthcare”, Journal of Biomedical Informatics, Vol.53, 2015, pp. 3-14.
• [4] M.R. Endsley, R. Hoffman, D. Kaber, E. Roth, “Cognitive Engineering and Decisionmaking: an Overview and Future Course”, Journal of Cognitive Engineering and Decision Making, Vol.1, No.1, 2007, pp.1-21.
• [5] D. A. Norman and S. W. Draper, User Centered System Design: New Perspectives on Human-Computer Interaction, CRC Press, 1986.
• [6] E. Hollnagel, and D.D. Woods, “Cognitive Systems Engineering: New Wine in New Bottles”, International Journal of Man-Machine Studies, 1983, Vol.18, pp.583–591.
• [7] E. Hollnagel, D.D. Woods, Joint Cognitive Systems: Foundations of Cognitive Systems Engineering, CRC Press, 2005, 113-155.
• [8] J. Klaimi, R. Rahim-Amoud, L. Merghem-Boulahia and A. Jrad, Energy Management Algorithms in Smart Grids: State of the Art and Emerging Trends, International Journal of Artificial Intelligence and Applications (IJAIA), Vol.7, No.4, 2016, pp.25-45.
• [9] G. Yuanxiong, M. Pan, Y. Fang, “Optimal Power Management of Residential Customers in the Smart Grid”, IEEE Transactions on Parallel and Distributed Systems, Vol.23, No.9, 2012, pp.1593-1606.
• [10] R. Belkacemi, A. Bababola, Experimental Implementation of Multi-Agent System Algorithm for Distributed Restoration of a Smart Grid System, IEEE SOUTHEASTCON, 2014.
• [11] T. Nagata, Y. Ueda, M. Utatani, A multi-agent approach to smart grid energy management, 10th International Power & Energy Conference (IPEC), pp.327-331, Dec 2012.
• [12] Y. Xue, S. Sirouspour, and A. Emadi, Towards Cognitive Energy Management System of Microgrid in Enabling Transportation Electrification, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).
• [13] B. Bitzer, and E.S. Gebretsadik, Cloud Computing Framework for Smart Grid Applications, 48th International Universities' Power Engineering Conference (UPEC) 2013 48th International Universities', 2013, pp.1-5.
• [14] S. Pierluigi, “Demand response and Smart Grids-A Survey”, Renewable and Sustainable Energy Reviews, Vol.30, 2014, pp.461-478.
• [15] Y. Wang, Using Process Algebra to Describe Human and Software System Behaviors. Brain and Mind:, A Transdisciplinary Journal of Neuroscience and Neurophilosophy, Vol.4, No.2, 2003, pp.199–213.
• [16] Y. Wang, The Theoretical Framework and Cognitive Process of Learning, Proc. 6th IEEE International Conference on Cognitive Informatics (ICCI'07), 2007, pp.470-479.
• [17] L. Ogiela, “Towards Cognitive Economy”, Soft Computing, 2014. Vol.18, No.9, pp.1675-1683.
• [18] D.C. Wyld, Cloud Computing and the Public Sector Around the World, International Journal of Web & Semantic Technology, Vol.1, No.1, 2010, pp.1-20.
• [19] H.F. Atlam, R.J. Walters and G.B. Wills, “Fog Computing and the Internet of Things: A Review”, Big Data Cognitive Computing, Vol.2, No.10, 2018, pp.2-18.
• [20] O. Aizpurúa, R. Galán, A. Jiménez, A New Cognitive-Based Massive Alarm Management System in Electrical Power Administration, Proceedings of the 7th International Caribbean Conference on Devices, Circuits and Systems, Mexico, Apr. 28-30, 2008.
• [21] E.M. Rantanen, J.A. Winkle, T.J. Overbye, Cognitive Task Analysis of Electric Power System Control Center Operations, Human Factors and Ergonomics Society Annual Meeting Proceedings, Vol.52, No.24, pp.1934-1938 · September 2008.
• [22] Ni, J.; Zhang, K.; Lin, X.; Shen, X. Securing Fog Computing for Internet of Things Applications: Challenges and Solutions. IEEE Communacition Surv. Tutor. 2017, 20, 601–628.
• [23] Miller, G. The cognitive revolution: a historical perspective, Trends in Cognitive Sciences, 2003, Vol.7, No.3, pp.141-144.
• [24] Seker, S, Dikun, J. Physical Limits for Self-Contained Systems: An Example of the Human Brain as a Cognitive System. The Journal of Cognitive Systems, 2018, Vol.3, No.2, pp.28-29.
• [25] Militello, L.G., Dominguez, C.O., Lintern, G., Klein, G. The Role of Cognitive Systems Engineering in the Systems Engineering Design Process, System Engineering, 2010, Vol.13, No.3, pp.261-273.
• [26] Alafi, B . A New Cognitive System Modeling with the Combination of Expert and Neural Network Systems. The Journal of Cognitive Systems, 2018, Vol.3, No.2, pp.34-35