Advertisement

A Novel Smart Grid Model for Efficient Resources

  • Aidong Xu
  • Wenjin Hou
  • Yunan Zhang
  • Yixin Jiang
  • Wenxin Lei
  • Hong WenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

It is a key issue to efficiently manage resources in the smart grid (SG) network that is a dynamic distributed grid, in which the production, storage and users of electricity will work together under specific control. Therefore, in such network an important challenge is how to achieve unified control of distributed equipment on coordinating generators and users distributed in different geographical locations. This paper proposes an innovation model that is the edge computing nodes can be used to collect, compute, and store data while the edge computing nodes are being connected via peer-to-peer. By this way, the peered edge devices can communicate with each other after data processing. The experimental results of the proposed model show that there is a significant improvement to energy resources management due to the abilities of real time control. As results, the economic cost is decreased while the utilization of renewable energy is increased.

Keywords

Smart grid Edge computing Peer-to-peer 

Notes

Acknowledgments

This work was supported by National major R&D program (2018YFB0904900 and 2018YFB0904905).

References

  1. 1.
    Brown, R.E.: Impact of smart grid on distribution system design. In: IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–4 (2008)Google Scholar
  2. 2.
    Rahimi, F., Ipakchi, A.: Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 1(1), 82–88 (2010)CrossRefGoogle Scholar
  3. 3.
    Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, pp. 1–6 (2016)Google Scholar
  4. 4.
    Zahoor, S., Javaid, N., Khan, A., Ruqia, B., Muhammad, F.J., Zahid, M.: A cloud-fog-based smart grid model for efficient resource utilization. In: 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, pp. 1154–1160 (2018)Google Scholar
  5. 5.
    Xie, Y.P., Wen, H., Wu, B., Jiang, Y.X., Meng, J.X.: A modified hierarchical attribute-based encryption access control method for mobile cloud computing. IEEE Trans. Cloud Comput. 7(2), 383–391 (2019)CrossRefGoogle Scholar
  6. 6.
    Xie, Y.P., Jiang, Y.X., Wu, J.S., Wen, H., et al.: Three-layers secure access control for cloud-based smart grids. In: IEEE VTC 2015-Fall, September 2015Google Scholar
  7. 7.
    Xie, Y.P., Jiang, Y.X., Liao, R.F., Wen, H., et al.: A hierarchical key management system applied in cloud-based smart grid. In: IEEE ICCC 2015, November 2015Google Scholar
  8. 8.
    Xie, Y.P., Jiang, Y.X., Liao, R.F., Wen, H., et al.: User privacy protection for cloud computing based smart grid. In: IEEE ICCC 2015, November 2015Google Scholar
  9. 9.
    Han, Q.Y., Wen, H., Ma, T., Wu, B.: Self-nominating trust model based on hierarchical fuzzy systems for peer-to-peer networks. In: 2014 IEEE/CIC International Conference on Communications in China, Shanghai, pp. 199–204 (2014)Google Scholar
  10. 10.
    Han, Q.Y., Wen, H., Ren, M.Y., et al.: A topological potential weighted community-based recommendation trust model for P2P networks. Peer-to-Peer Netw. Appl. 8(6), 1048–1058 (2015)CrossRefGoogle Scholar
  11. 11.
    Han, Q.Y., Wen, H., Wu, J.S., Ren, M.Y.: Rumor spreading and security monitoring in complex networks. In: The 4th International Conference on Computational Social Networks (CSoNet), pp. 48–59 (2015)Google Scholar
  12. 12.
    Han, Q.Y., Wen, H., Feng, G., et al.: Self-nominating trust model based on hierarchical fuzzy systems for peer-to-peer networks. Peer-to-Peer Netw. Appl. 4(7), 1020–1030 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhan, M., Wu, J., Zhang, Z.Z., Wen, H., Wu, J.J.: Low-complexity error correction for ISO/IEC/IEEE 21451-5 sensor and actuator networks. IEEE Sens. J. 15, 2622–2630 (2015)CrossRefGoogle Scholar
  14. 14.
    Xiao, J., Wen, H., Wu, B., et al.: Joint design on DCN placement and survivable cloud service provision over all-optical mesh networks. IEEE Trans. Commun. 62(1), 235–245 (2014)CrossRefGoogle Scholar
  15. 15.
    Fu, S., Wu, B., Wen, H., et al.: Transmission scheduling and game theoretical power allocation for interference coordination in CoMP. IEEE Trans. Wireless Commun. 13(1), 112–123 (2014)CrossRefGoogle Scholar
  16. 16.
    Zhan, M., Wu, J., Wen, H.: Reduced memory decoding schemes for turbo decoding based on storing the index of the state metric. IET Commun. 8(12), 2095–2105 (2014)CrossRefGoogle Scholar
  17. 17.
    Wen, H., Li, S., Zhu, X., Zhou, L.: A framework of the PHY-layer approach to defense against security threats in cognitive radio networks. IEEE Netw. 27(3), 34–39 (2013)CrossRefGoogle Scholar
  18. 18.
    Hu, L., Wen, H., Wu, B., Pan, F., Liao, R.F., Song, H.H., Tang, J., Wang, X.: Cooperative jamming for physical layer security enhancement in internet of things. IEEE Internet Things J. 5(1), 219–228 (2018)CrossRefGoogle Scholar
  19. 19.
    Xie, F.Y., Wen, H., Li, Y.S., et al.: Optimized coherent integration-based radio frequency fingerprinting in internet of things. IEEE Internet Things J. 5(5), 3967–3977 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Aidong Xu
    • 1
  • Wenjin Hou
    • 2
  • Yunan Zhang
    • 1
  • Yixin Jiang
    • 1
  • Wenxin Lei
    • 2
  • Hong Wen
    • 2
    Email author
  1. 1.EPRI, China Southern Power Grid Co., Ltd.GuangzhouChina
  2. 2.National Key Lab of CommunicationUniversity of Electronic Science, and Technology of ChinaChengduChina

Personalised recommendations