Advertisement

Burst Traffic Awareness WRR Scheduling Algorithm in Wide Area Network for Smart Grid

  • Xin Tan
  • Xiaohui LiEmail author
  • Zhenxing Liu
  • Yuemin Ding
Conference paper
  • 172 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 312)

Abstract

Smart grid achieves optimal management of the entire power system operation by constant monitoring and rapid demand response (DR) for power supply-demand balance. Constantly monitoring the system state realized by Wide Area Measurement Systems (WAMS) provides a global view of the power grid. With a global view of the grid, Wide Area Control (WAC) generated DR command to improve the stability of power systems. When the regular monitoring data flow and the sudden DR data coexist, the suddenness of the demand response may result in delay or loss of the data packet due to uneven resource allocation when the network communication resources are limited, thereby affecting the accuracy of the power system state estimation. To solve this problem, this paper proposes a burst traffic perception weighted round robin algorithm (BTAWRR). The proposed algorithm defines the weight of the cyclic scheduling according to the periodicity of the monitoring data and the suddenness of the demand response. Then it adopts the iterative cyclic scheduling to adjust the transmission of data packets in time by adaptively sensing the changes of the traffic flow. The simulation results show that the proposed algorithm can effectively reduce the scheduling delay and packet loss rate when the two data coexist, and improve the throughput, which is beneficial to ensure the stability of the smart grid.

Keywords

Burst traffic Scheduling algorithm Weight Monitoring data Demand response 

References

  1. 1.
    Fang, X., Misra, S., Xue, G.L., Yang, D.J.: Smart grid—the new and improved power grid: a survey. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2011)CrossRefGoogle Scholar
  2. 2.
    Xu, S.K., Xie, X.R., Xin, Y.Z.: Present application situation and development tendency of synchronous phasor measurement technology based wide area measurement system. Power Syst. Technol. 29(2), 44–49 (2005)Google Scholar
  3. 3.
    Mahmud, A.S.M.A., Sant, P.: Real-time price savings through price suggestions for the smart grid demand response model. In: 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG), Turkey, pp. 65–69 (2017)Google Scholar
  4. 4.
    Dasgupta, S., Paramasivam, M., Vaidya, U., Ajjarapu, V.: Real-time monitoring of short-term voltage stability using PMU data. IEEE Trans. Power Syst. 28(4), 3702–3711 (2013)CrossRefGoogle Scholar
  5. 5.
    Aravind, M.N., Anju, L.S., Sunitha, R.: Application of compressed sampling to overcome big data issues in synchrophasors. In: 6th International Conference on Power Systems (ICPS), New Delhi, India, pp. 1–5 (2016)Google Scholar
  6. 6.
    Lee, G., Shin, Y.J.: Multiscale PMU data compression based on wide-area event detection. In: 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, pp. 437–442 (2017)Google Scholar
  7. 7.
    Long, D., Li, X.H., Ding, Y.M.: Multicast routing of power grid based on demand response constraints. J. Comput. Appl. 38(4), 1102–1105 (2018)Google Scholar
  8. 8.
    Ding, Y.M., Hong, S.H., Li, X.H.: A demand response energy management scheme for industrial facilities in smart grid. IEEE Trans. Ind. Inform. 10(4), 2257–2269 (2014)CrossRefGoogle Scholar
  9. 9.
    Meliopoulos, A.P.S., Cokkinides, G.J., Wasynczuk, O.: PMU data characterization and application to stability monitoring. In: 2006 IEEE PES Power Systems Conference and Exposition, pp. 151–158. IEEE, Atlanta (2016)Google Scholar
  10. 10.
    Chenine, M., Nordstrom, L.: Investigation of communication delays and data incompleteness in multi-PMU wide area monitoring and control systems. In: 2009 International Conference on Electric Power and Energy Conversion Systems (EPECS), Sharjah, United Arab Emirates, pp. 1–6. IEEE (2009)Google Scholar
  11. 11.
    Rehtanz, C., Beland, J., Benmouyal, G.: Wide area monitoring and control for transmission capability enhancement. CIGRE Technical Brochure, 330 (2007)Google Scholar
  12. 12.
    Ju, P.: Power System Wide Area Measurement Technology. China Machine Press, Beijing (2008)Google Scholar
  13. 13.
    Zivanovic, R., Cairns, C.: Implementation of PMU technology in state estimation: an overview. In: Proceedings of IEEE. AFRICON 1996, Stellenbosch, South Africa, pp. 1006–1011. IEEE (1996)Google Scholar
  14. 14.
    Pan, D., Yang, Y.: FIFO-based multicast scheduling algorithm for virtual output queued packet switches. IEEE Trans. Comput. 54(10), 1283–1297 (2005) CrossRefGoogle Scholar
  15. 15.
    Hahne, E.L., Gallager, R.G.: Round robin scheduling for fair flow control in data communication networks. Massachusetts Institute of Technology, 86 (1986)Google Scholar
  16. 16.
    Katevenis, M., Sidiropoulos, S., Courcoubetis, C.: Weighted round-robin cell multiplexing in a general-purpose ATM switch chip. IEEE J. Sel. Areas Commun. 9(8), 1265–1279 (1991)CrossRefGoogle Scholar
  17. 17.
    Ito, Y., Tasaka, S., Ishibashi, Y.: Variably weighted round robin queueing for core IP routers. In: Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference, Phoenix, AZ, USA, pp. 159–166. IEEE (2002)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  1. 1.School of information Science and EngineeringWuhan University of Science and TechnologyWuhanChina
  2. 2.School of Computer Science and EngineeringTianjin University of TechnologyTianjinChina

Personalised recommendations