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Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.
Rocznik
Tom
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art. no. e145569
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari, India
autor
- Department of Electrical and Electronics Engineering, Amrita College of Engineering and Technology, Nagercoil, India
autor
- Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi-626004, India
autor
- Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Anna University, India
Bibliografia
- [1] Q. Li and M. Zhou, “The future-oriented grid-smart grid,” J. Comput., vol. 6, no. 1, pp. 98–105, 2011, doi: 10.4304/jcp.6.1.98-105.
- [2] P. Agrawal, “Overview of DOE microgrid activities,” in Proc. Symp. Microgrid, 2006, pp. 1–32.
- [3] S. Rahman and Rinaldy, “An efficient load model for analyzing demand side management impacts,” IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1219–1226, 1993, doi: 10.1109/59.260874.
- [4] I. Cohen and C.C. Wang, “An optimization method for load management scheduling,” IEEE Trans. Power Syst., vol. 3, no. 2, pp. 612–618, 1988, doi: 10.1109/59.192913.
- [5] M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, New York: Wiley–IEEE Press, 2002, pp. 1–509.
- [6] Z.N. Popovic and D.S. Popovic, “Direct load control as a market-based program in deregulated power industries,” in 2003 IEEE Bologna Power Tech Conference Proceedings, Italy, 2003, p. 4, doi: 10.1109/PTC.2003.1304491.
- [7] Z. Zhang, J. Wang, T. Ding, and X. Wang, “A two-layer model for microgrid real-time dispatch based on energy storage system charging/discharging hidden costs,” IEEE Trans. Sustain. Energy, vol. 8, no. 1, pp. 33–42, 2017, doi: 10.1109/TSTE.2016.2577040.
- [8] H. Yang et al., “Operational planning of electric vehicles for balancing wind power and load fluctuations in a microgrid,” IEEE Trans. Sustain. Energy, vol. 8, no. 2, pp. 592–604, 2017, doi: 10.1109/TSTE.2016.2613941.
- [9] Rabiee, M. Sadeghi, J. Aghaeic, and A. Heidari, “Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties,” Renew. Sustain. Energy Rev., vol. 57, pp. 721–739, 2016, doi: 10.1016/j.rser.2015.12.041.
- [10] I. Essiet, Y. Sun, and Z. Wang, “Scavenging differential evolution algorithm for smart grid demand side management,” Procedia Manuf., vol. 35, pp. 595–600, 2019, doi: 10.1016/j.promfg.2019.05.084.
- [11] S. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings, “Agent-based control for decentralized demand side management in the smart grid,” in Proc. The 10th International Conference on Autonomous Agents and Multiagent Systems, Taiwan, 2011, pp. 5–12.
- [12] S. Bu and F.R. Yu, “A game-theoretical scheme in the smart grid with demand-side management: Towards a smart cyber-physical power infrastructure,” IEEE Trans. Emerg. Top. Comput., vol. 1, no. 1, pp. 22–32, 2013, doi: 10.1109/TETC.2013.2273457.
- [13] Z.M. Fadlullah, D.M. Quan, N. Kato, and I. Stojmenovic, “GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid,” IEEE Syst. J., vol. 8, no. 2, pp. 588–597, 2014, doi: 10.1109/JSYST.2013.2260934.
- [14] T. Logenthiran, D. Srinivasan, and T.Z. Shun, “Demand side management in smart grid using heuristic optimization,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1244–1252, 2012, doi: 10.1109/TSG.2012.2195686.
- [15] Z. Zhu, J. Tang, S. Lambotharan, W.H. Chin and Z. Fan, “An integer linear programming-based optimization for home demand-side management in smart grid,” in Proc. 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), USA, 2012, pp. 1–5, doi: 10.1109/ISGT.2012.6175785.
- [16] Q. Zhu, Z. Han, and T. Ba¸sar, “A differential game approach to distributed demand side management in smart grid,” in Proc. 2012 IEEE International Conference on Communications (ICC), Canada, 2012, pp. 3345–3350, doi: 10.1109/ICC.2012.6364562.
- [17] C. Bharathi, D. Rekha, and V. Vijayakumar, “Genetic algorithm-based demand side management for smart grid,” Wireless Pers. Commun., vol. 93, no. 2, pp. 481–502, 2017, doi: 10.1007/s11277-017-3959-z.
- [18] Z. Cao et al., “Optimal cloud computing resource allocation for demand side management in smart grid,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1943–1955, 2016, doi: 10.1109/TSG.2015.2512712.
- [19] S.A. Hashmi, C.F. Ali, and S. Zafar, “Internet of things and cloud computing-based energy management system for demand side management in smart grid,” Int. J. Energy Res., vol. 45, no. 1, pp. 1007–1022, 2021, doi: 10.1002/er.6141.
- [20] R.S. Liu and Y.F. Hsu, “A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional energy trading,” Int. J. Electr. Power Energy Syst., vol. 97, pp. 396–407, 2018, doi: 10.1016/j.ijepes.2017.11.023.
- [21] W. Kamrat, “Selected information technology tools supporting maintenance and operation management electrical grids,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 5, p. e138089, 2021, doi: 10.24425/bpasts.2021.138089.
- [22] W. Jarzyna, “A survey of the synchronization process of synchronous generators and power electronic converters,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 6, pp. 1069–1083, 2019, doi: 10.24425/bpasts.2019.131565.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-881dc462-5b2d-44c3-a078-546e824afbff