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CRRP Analysis of Cloud Computing in Smart Grid

  • Rahim Ullah
  • Nadeem Javaid
  • Zafar Iqbal
  • Iftikhar Ahmad
  • Avais Jan
  • Yasir Khan Jadoon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)

Abstract

With the rapid pace in the evolution and development of technology, the demand of electrical energy is also increasing. Beside the production of energy from traditional and renewable energy sources, the energy management is also required to control the consumption of energy in commercial, industrial and residential houses. Improvement in technologies while reduction in cost has enabled consumers to interconnect the smart devices for reducing cost and energy consumption, this is called internet of things (IoTs). Such increase in the number of smart systems and energy management systems cause a huge amount of data which cannot be processed on traditional system. It requires high computing power and high storage which may be provided by cloud computing. Cloud computing provide resources to customers on demand with low investment and operational cost. The cloud resources are flexible, efficient, scalable and secure. In this paper we simulate the use of cloud computing in smart grid. The datacenters in cloud collect the building’s data, process it and send the results to the building. In this study, we calculate the total response time to each building, the number of requests coming from each building per our, the processing time of each datacenter and the cost of each datacenter (CRRP). The results are useful for energy service providers to select the optimal processing and data storage resources.

Keywords

Virtual Machine (VM) Demand side management Home energy management system Datacenters Cloud computing Renewable energy sources Internet of Things (IoTs) CRRP(Cost Requests per hour Response time and Processing time) 

References

  1. 1.
    Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G.P.: Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inform. 7(4), 529–539 (2011)CrossRefGoogle Scholar
  2. 2.
    Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1), 18–28 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kelso, J.D.: Buildings energy data book. Department of Energy (2012)Google Scholar
  4. 4.
    Al Faruque, M.A., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)CrossRefGoogle Scholar
  5. 5.
    Fang, X., Misra, S., Xue, G., Yang, D.: Smart grid-the new and improved power grid: a survey. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2012)CrossRefGoogle Scholar
  6. 6.
    Yigit, M., Gungor, V.C., Baktir, S.: Cloud computing for smart grid applications. Comput. Netw. 70, 312–329 (2014)CrossRefGoogle Scholar
  7. 7.
    McKenna, E., Richardson, I., Thomson, M.: Smart meter data: balancing consumer privacy concerns with legitimate applications. Energy Policy 41, 807–814 (2012)CrossRefGoogle Scholar
  8. 8.
    Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)Google Scholar
  9. 9.
    Kumar, N., Vasilakos, A.V., Rodrigues, J.J.P.C.: A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities. IEEE Commun. Mag. 55(3), 14–21 (2017)CrossRefGoogle Scholar
  10. 10.
    Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)Google Scholar
  11. 11.
    Reka, S.S., Ramesh, V.: Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming. Perspect. Sci. 8, 169–171 (2016)CrossRefGoogle Scholar
  12. 12.
    Birman, K.P., Lakshmi, L., Van Renesse, R.: White paper running smart grid control software on cloud computing architectures. In: Computational Needs for the Next Generation Electric Grid (2011)Google Scholar
  13. 13.
    Fang, B., Yin, X., Tan, Y., Li, C., Gao, Y., Cao, Y., Li, J.: The contributions of cloud technologies to smart grid. Renew. Sustain. Energy Rev. 59, 1326–1331 (2016)CrossRefGoogle Scholar
  14. 14.
    Moghaddam, M.H.Y., Leon-Garcia, A., Moghaddassian, M.: On the performance of distributed and cloud-based demand response in smart grid. IEEE Trans. Smart Grid (2017)Google Scholar
  15. 15.
    Simmhan, Y., Aman, S., Kumbhare, A., Liu, R., Stevens, S., Zhou, Q., Prasanna, V.: Cloud-based software platform for big data analytics in smart grids. Comput. Sci. Eng. 15(4), 38–47 (2013)CrossRefGoogle Scholar
  16. 16.
    Bera, S., Misra, S., Rodrigues, J.J.P.C.: Cloud computing applications for smart grid: a survey. IEEE Trans. Parallel Distrib. Syst. 26(5), 1477–1494 (2015)CrossRefGoogle Scholar
  17. 17.
    Markovic, D.S., Zivkovic, D., Branovic, I., Popovic, R., Cvetkovic, D.: Smart power grid and cloud computing. Renew. Sustain. Energy Rev. 24, 566–577 (2013)CrossRefGoogle Scholar
  18. 18.
    Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids. Big Data Res. 2(3), 94–101 (2015)CrossRefGoogle Scholar
  19. 19.
    Chen, S.-Y., Lai, C.-F., Huang, Y.-M., Jeng, Y.-L.: Intelligent home-appliance recognition over IoT cloud network. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 639-643. IEEE (2013)Google Scholar
  20. 20.
    Botta, A., De Donato, W., Persico, V., Pescapé, A.: On the integration of cloud computing and internet of things. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 23–30. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Rahim Ullah
    • 1
  • Nadeem Javaid
    • 1
  • Zafar Iqbal
    • 2
  • Iftikhar Ahmad
    • 1
  • Avais Jan
    • 1
  • Yasir Khan Jadoon
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.PMAS Agriculture UniversityRawalpindiPakistan

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