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Research Article |

Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

Author(s): Vijaya Kumar Begari*, Dr. V.C. Veera Reddy and Dr. P. Sujatha

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 4

Publisher : FOREX Publication

Published : 02 December 2023

e-ISSN : 2347-470X

Page(s) : 1110-1119

DOI: https://doi.org/10.37391/ijeer.110431

Abstract

Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability. The control agents of Distributed Generations (DGs) are optimized using GWO algorithm based on the deviations in frequencies of DGs. Particle Swarm Optimization based Multi Agent System (PSO-MAS) and Conventional Multi Agent System performance are compared with the suggested technique's implementation on IEEE 30 bus system and testing in MATLAB/Simulink environment. The outcomes showed that the suggested approach is successfully improving the stability of an interconnected power system.

Keywords: Particle Swarm Optimization based Multi Agent System (PSO-MAS), Conventional Multi Agent System (C-MAS), MATLAB, Distributed Generations (DGs).




Vijaya Kumar Begari*, Research Scholar, Electrical & Electronics Engineering, J.N.T.U.A Anantapuramu, India; Email: vijay.begari@gmail.com

Dr. V.C. Veera Reddy, Professor, Electrical & Electronics Engineering, S, P, M, V, V Tirupati, India; Email: veerareddyvc@gmail.com

Dr. P. Sujatha, Professor, Electrical & Electronics Engineering, J.N.T.U.A Anantapuramu, India; Email: psujatha1993@gmail.com

    [1] Otuoze AO, Mustafa MW, Larik RM (2018) Smart grids security challenges: Classification bysources of threats. J Electr Syst Inf Technol 5: 468–483.
    [2] Gungor VC, Sahin D, Kocak T, et al. (2011) Smart grid technologies: Communication technologies and standards. IEEE Trans Ind Inf 7: 529–539. 3.
    [3] Brinkerink M, Deane P, Collins S, et al. (2018) Developing a global interconnected power system model. Global Energy Interconnect 1: 330–343.
    [4] Takeda S, Sakurai S, Yamamoto Y, et al. (2016) Limitation of fusion power plant installation on future power grids under the effect of renewable and nuclear power sources. Fusion Eng Des 109–111: 1754–1758.
    [5] Liu W, Li N, Jiang Z, et al. (2018) Smart Micro-grid system with Wind/PV/Battery. Energy Procedia 152: 1212–1217.
    [6] Hirsch A, Parag Y, Guerrero J (2018) Microgrids: A review of technologies, key drivers, and outstanding issues. Renewable Sustainable Energy Rev 90: 402–411.
    [7] Adefarati T, Bansal RC (2019) Economic and environmental analysis of a cogeneration power system with the incorporation of renewable energy resources. Energy Procedia 158: 803–808.
    [8] Rafik M, Bahnasse A, Khiat A, et al. (2019) Towards a smart energy sharing in micro smart grid adopting SDN approach. Proc Comput Sci 151: 717–724.
    [9] Sha A, Aiello M (2018) Topological considerations on decentralized energy exchange in the smart grid. Proc Comput Sci 130: 720–727. [7] A. J. del Real, A. Arce, and C. Bordons, “Combined environmental and economic dispatch of smart grids using distributed model predictive control,” International Journal of Electrical Power Energy Systems, vol. 54, pp. 65 – 76, 2014.
    [10] Q. Hu, F. Li, and C. fei Chen, “A smart home test bed for undergraduate education to bridge the curriculum gap from traditional power systems to modernized smart grids,” Education, IEEE Transactions on, vol. 58, no. 1, pp. 32–38, Feb 2015.
    [11] S. Chanda and A. De, “A multi-objective solution algorithm for optimum utilization of smart grid infrastructure towards social welfare,” International Journal of Electrical Power Energy Systems, vol. 58, pp. 307 – 318, 2014.
    [12] M. Goulden, B. Bedwell, S. Rennick-Egglestone, T. Rodden, and A. Spence, “Smart grids, smart users? the role of the user in demand side management,” Energy Research Social Science, vol. 2, pp. 21 – 29, 2014.
    [13] J. Evora, J. J. Hernandez, and M. Hernandez, “A MOPSO technique for direct load control in smart grid,” Expert Systems with Applications, vol. 42, no. 21, pp. 7456 – 7465, 2015.
    [14] B. Karimi, V. Namboodiri, and M. Jadliwala, “Scalable meter data collection in smart grids through message concatenation,” Smart Grid, IEEE Transactions on, vol. 6, no. 4, pp. 1697–1706, July 2015.
    [15] J. Liang, G. K. Venayagamoorthy, and R. G. Harley, “Wide-area measurement based dynamic stochastic optimal power flow control for smart grids with high variability and uncertainty,” Smart Grid, IEEE Transactions on, vol. 3, no. 1, pp. 59–69, March 2012.
    [16] A. N. Milioudis, G. T. Andreou, and D. P. Labridis, “Enhanced protection scheme for smart grids using power line communications techniques x2014;part ii: Location of high impedance fault position,” Smart Grid, IEEE Transactions on, vol. 3, no. 4, pp. 1631–1640, Dec 2012.
    [17] F. N. Claessen, B. Claessens, M. P. F. Hommelberg, A.Molderink, V. Bakker, H. A. Toersche, and M. A. van den Broek, “Comparative analysis of tertiary control systems for smart grids using the flex street model,” Renewable Energy, vol. 69, pp. 260 – 270, 2014.
    [18] J. Xi, Y. Yu, G. Liu, and Y. Zhong, “Guaranteed-cost consensus for singular multi-agent systems with switching topologies,” Circuits and Systems I: Regular Papers, IEEE Transactions on, vol. 61, no. 5, pp. 1531–1542, May 2014.
    [19] D. Juneja, A. Singh, R. Singh, and S. Mukherjee, “A thorough insight into theoretical and practical developments in multiagent systems,” International Journal of Ambient Computing and Intelligence (IJACI), vol. 8, no. 1, pp. 23–49, 2017.
    [20] J. Lu, F. Chen, and G. Chen, “Nonsmooth leader- following formation control of nonidentical multi-agent systems with directed communication topologies,” Automatica, vol. 64, pp. 112 – 120, 2016.
    [21] Z. Miao, Y. Wang, and R. Fierro, “Collision-free consensus in multi-agent networks: A monotone systems perspective,” Automatica, vol. 64, pp. 217 – 225, 2016.
    [22] M. Chennoufi, F. Bendella, and M. Bouzid, “Multi-agent simulation collision avoidance of complex system: application to evacuation crowd behavior,” International Journal of Ambient Computing and Intelligence (IJACI), vol. 9, no. 1, pp. 43–59, 2018.
    [23] X. Wu, Y. Tang, J. Cao, and W. Zhang, “Distributed consensus of stochastic delayed multi-agent systems under asynchronous switching,” Cybernetics, IEEE Transactions on, vol. PP, no. 99, pp. 1–1, 2015.
    [24] Z.-L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Trans. Power Syst.,vol. 18, no. 3, pp. 1187–1195, Aug. 2003.
    [25] S Mirjalili, S M Mirjalili, A Lewis. Grey Wolf Optimizer. Advances in Engineering Software. 2014; 69(1): 46-61.

Vijaya Kumar Begari, Dr. V.C. Veera Reddy and Dr. P. Sujatha (2023), Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System. IJEER 11(4), 1110-1119. DOI: 10.37391/ijeer.110431.