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.
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References
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)
Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1), 18–28 (2010)
Kelso, J.D.: Buildings energy data book. Department of Energy (2012)
Al Faruque, M.A., Vatanparvar, K.: Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3(2), 161–169 (2016)
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)
Yigit, M., Gungor, V.C., Baktir, S.: Cloud computing for smart grid applications. Comput. Netw. 70, 312–329 (2014)
McKenna, E., Richardson, I., Thomson, M.: Smart meter data: balancing consumer privacy concerns with legitimate applications. Energy Policy 41, 807–814 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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Ullah, R., Javaid, N., Iqbal, Z., Ahmad, I., Jan, A., Jadoon, Y.K. (2019). CRRP Analysis of Cloud Computing in Smart Grid. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_6
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DOI: https://doi.org/10.1007/978-3-319-93659-8_6
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