Abstract
The smart girds (SGs) are used to accommodate the growing demand of electric systems and monitor the power consumption with bidirectional communication and power flows. Smart buildings as key partners of the smart grid for the energy transition. Smart grids co-ordinate the needs and capabilities of all generators, grid operators, end-users and electricity market stakeholders to operate all parts of the system as efficiently as possible, minimising costs and environmental impacts while maximising system reliability, resilience and stability. The users demand for energy varies dynamically in different time slots. The power grids needs ideal load balancing for supply and demand of electricity between end-users and utility providers. The main characteristics of the SGs are its heterogeneous architecture that includes reduce the costly impact of blackouts, help measure and reduce energy consumption, reduce their carbon footprint and provides the power quality for the range of needs. The cloud-fog based computing model is used to achieve the objective of load balancing in the SG. The cloud layer provides on-demand delivery of resources. The fog layer is the extension of the cloud that lies between the cloud and end-user layer. The fog layer minimizes the latency, enhances the reliability of cloud facilities and reduced the load on the cloud because fog is an edge computing and it analyzing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure. The end-users required electricity through the Macrogrids (MGs) and Utilities installed on fog and cloud layer respectively. The cloud-fog computing framework uses different algorithms for load balancing objective. In this paper, three algorithms are used such as Round Robin (RR), throttled and Hybrid Genetic Algorithm using Bin Packing Technique for load balancing.
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References
Fang, X., Misra, S., Xue, G., Yang, D.: SG-the new and improved power grid: a survey. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2012)
Mohsenian-Rad, A.H., leon-Garcia, A.: Coordination of cloud computing and smart power grids, in SG Communications (SmartGridComm), In: 2010 First IEEE International Conference on 2010 (2010)
Quinn, E.L.: Smart metering and privacy: existing laws and competing policies, Colorado Public Utilities Commission, Technical Report (2009)
Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58, 154–160 (2017)
Javaid, N., Ahmed, A., Iqbal, S., Ashraf, M.: Day ahead real time pricing and critical peak pricing based power scheduling for smart homes with different duty cycles. Energies 11(6), 1464 (2018). ISSN: 1996-
Hassan Rahim, M., Khalid, A., Javaid, N., Ashraf, M., Aurangzeb, K., Saud Altamrah, A.: Exploiting game theoretic based coordination among appliances in smart homes for efficient energy utilization. Energies 11(6), 1426 (2018). ISSN: 1996-1073
Luo, F., Zhao, J., Yang Dong, Z., Chen, Y., Xu, Y., Zhang, X., Po Wong, K.: Cloud-based information infrastructure for next-generation power grid: conception, architecture, and applications. IEEE Trans. SG 7(4), 1896–1912 (2016)
Okay, F.Y., Ozdemir, S.: A fog computing based smartgrid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)
Zhang, Y., Chen, M.: Cloud Based 5G Wireless Networks. Springer Briefs in computer science. (2016, Nov 9)
Sidorov, V., Ng, W.K.: A confidentiality-preserving search technique for encrypted relational cloud databases. In: 2016 IEEE Second International Conference on Big Data Computing Service and Applications(BigDataService), Mar 29, pp. 244–251
Yigit, M., Gungor, V.C., Baktir, S.: Cloud computing for smart grid applications in Computer Networks 70, 312–29 (2014), Sep 9
Fang, X., Misra, S., Xue, G., Yang, D.: Managing smart grid information in the cloud: opportunities, model, and applications, IEEE Netw 26(4) (2012)
Tuballa, M.L., Abundo, M.L.: A review of the development of Smart Grid technologies. Renew. Sustain. Energy Rev. 59, 710–725 (2016)
Barik, R.K., Dubey, H., Samaddar, A.B., Gupta, R.D., Ray, P.K.: FogGIS: fog computing for geospatial big data analytics. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), pp. 613–618 (2016)
Dubey, H., Monteiro, A., Constant, N., Abtahi, M., Borthakur, D., Mahler, L., Sun, Y., Yang, Q., Akbar, U., Mankodiya, K.: Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications. arXiv:1706.08012 (2017)
Byun.J, Kim, Y., Hwang, Z., Park, S.: An intelligent cloud-based energy management system using machine to machine communications in future energy environments. In: IEEE International Conference on Consumer Electronics (ICCE) (2012)
Chen, S.Y., Lai, C.F., Huang, Y.M., Jeng, Y.L.: Intelligent home-appliance recognition over IoT cloud network. In: Proceedings of 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 639–643 (2013)
Hassan Rahim, M., Khalid, A., Javaid, N., Alhussein, M., Aurangzeb, K., Ali Khan, Z.: Energy efficient smart buildings using coordination among appliances generating large data. IEEE Access, vol. PP, no. 99, pp. 1-1, ISSN: 2169-3536
Ye, X., Yin, Y., Lan, L.: Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment. IEEE Access 5, 16006–20 (2017)
Nadeem, Z., Javaid, N., Waqar Malik, A., Iqbal, S.: Scheduling appliances with GA, TLBO, FA, OSR and their hybrids using chance constrained optimization for smart homes. Energies 11(4), 888 (2018). ISSN: 1996-
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Zubair, M., Javaid, N., Ismail, M., Zakria, M., Asad Zaheer, M., Saeed, F. (2019). Integration of Cloud-Fog Based Platform for Load Balancing Using Hybrid Genetic Algorithm Using Bin Packing Technique. In: Xhafa, F., Leu, FY., Ficco, M., Yang, CT. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-02607-3_25
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DOI: https://doi.org/10.1007/978-3-030-02607-3_25
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