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Sustainability modelling and green energy optimisation in microgrid powered distributed FogMicroDataCenters in rural area

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Abstract

Government now-a-days are looking forward to achieve “Energy for All” standards. Countries are committing themselves to reduce carbon emissions. Recently, every IT industry giants, who typically own and operate huge Data Centers (DC) are looking forward to achieve “100% usage of green energy to power their datacenters, contribute to the utility grid in equivalence to their electrical power consumptions”. Electrification and gradual decarbonisation still continue as significant, global concern. Hence it is pretty clear that need for MicroGrid (MG) based facilities is actually a mandatory. But intermittent-characteristic (unreliability/unavailability) of Renewable-Energy (RE) generating sources (like Solar Panels, Wind Turbines) regularly hinder both the stake holders-Government and IT industry from accomplishing such social and economic initiatives. Hence SmartMicroGrid (SMG) connected with utility grid are envisioned to support powering industries inorder to avoid future power outages. Interconnected industry4.0 factories where, actually the smart-management interms of two-way power supply and two-way communication network is foreseen. They are highly dependent on modern, integrated, Information and Communication technologies (IoT sensors, Fog and Cloud computing etc.). In this paper we have focussed on two objectives. Firstly our objective is to identify, optimum renewable-generation-capacities inorder to minimise investment cost of a microgrid. Secondly our objective is to maximize usage of more clean energy for applications running on FogMicroDataCenter (fogMDC), powered by microgrids and controlled by Sofware Defined Networking (SDN). We have used realistic meteorological data of Tamil nadu, India for our proposed work. To the best of our knowledge, this is the first work proposed for two different scenarios, performance modelling and economic modelling aiding MG powered FogMDCs. Proposed economic modelling work is related to finding, the optimum sizing of distributed energy resources and financial cost. Proposed performance modelling is about, the clean energy usages in micro grid powered fogMDC for operations, supported by SDN for efficient distributed Virtual Machine (VM) based resource utilization to guarantee QoS in time sensitive IoT applications.

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References

  1. India is committed for 33 to 35 percent cut in carbon emission intensity by 2030, 2018. [Online]. Available https://www.apnnews.com/pm-modi-says-india-is-committed-for-33-to-35-percent-cut-in-carbon-emission-intensity-by-2030/. Accessed Dec 2018.

  2. Marcacci, S. (2018). Google and apple lead the corporate charge toward 100% renewable energy. [Online]. Available https://www.forbes.com/sites/energyinnovation/2018/04/12/google-and-apple-lead-the-corporate-charge-toward-100-renewable-energy/#50005e691b23. Accessed Dec 2018.

  3. 2018 outlook for energy: A view to 2040, 2018. [Online]. Available https://cdn.exxonmobil.com/~/media/global/files/outlook-for-energy/2018/2018-outlook-for-energy.pdf. Accessed Nov 2018.

  4. Household electrification status. [Online]. Available http://saubhagya.gov.in/. Accessed Jan 2019.

  5. Hassan, H. A. H., Pelov, A., & Nuaymi, L. (2015). Integrating cellular networks, smart grid, and renewable energy: Analysis, architecture, and challenges. IEEE Access, 3, 2755–2770.

    Article  Google Scholar 

  6. Evangelisti, S., Lettieri, P., Clift, R., & Borello, D. (2015). Distributed generation by energy from waste technology: A life cycle perspective. Process Safety and Environment Protection, 93, 161–172.

    Article  Google Scholar 

  7. Zhou, K., Yang, S., & Shao, Z. (2016). Energy internet: The business perspective. Applied Energy, 178, 212–222.

    Article  Google Scholar 

  8. Hernandez, D. (2017). How much data will the Internet of Things (IoT) generate by 2020?. Bangalore: Versa Technology.

    Google Scholar 

  9. Stack, T. (2018). Internet of Things (IoT) data continues to explode exponentially. Who is using that data and how? https://blogs.cisco.com/datacenter/internet-of-things-iot-data-continues-to-explode-exponentially-who-is-using-that-data-and-how. Accessed Oct 2018.

  10. Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015). Fog computing: Platform and applications. In 2015 third IEEE workshop on hot topics in web systems and technologies (HotWeb) (pp. 73–78).

  11. Kim, Y.-J., He, K., Thottan, M., & Deshpande, J. G. (2014) Virtualized and self-configurable utility communications enabled by software-defined networks. In 2014 IEEE international conference on smart grid communications (SmartGridComm) (pp. 416–421).

  12. Jain, R., & Paul, S. (2013). Network virtualization and software defined networking for cloud computing: A survey. IEEE Communications Magazine, 51(11), 24–31.

    Article  Google Scholar 

  13. National Solar Radiation Database (NSRDB). [Online]. Available https://nsrdb.nrel.gov/. Accessed Dec 2018.

  14. Graber, S., Narayanan, T., Alfaro, J., & Palit, D. (2018). Solar microgrids in rural India: Consumers’ willingness to pay for attributes of electricity. Energy for Sustainable Development, 42, 32–43.

    Article  Google Scholar 

  15. Husein, M., & Chung, I.-Y. (2018). Optimal design and financial feasibility of a University campus microgrid considering renewable energy incentives. Applied Energy, 225, 273–289.

    Article  Google Scholar 

  16. Khatib, T., Mohamed, A., & Sopian, K. (2012). Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: Case study of Kuala Terengganu, Malaysia. Energy and Buildings, 47, 321–331.

    Article  Google Scholar 

  17. Kazem, H. A., & Khatib, T. (2013). A novel numerical algorithm for optimal sizing of a photovoltaic/wind/diesel generator/battery microgrid using loss of load probability index. International Journal of Photoenergy, 2013, 718596. https://doi.org/10.1155/2013/718596.

    Article  Google Scholar 

  18. Al-Falahi, M. D. A., Jayasinghe, S. D. G., & Enshaei, H. (2017). A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conversion and Management, 143, 252–274.

    Article  Google Scholar 

  19. Askarzadeh, A. (2018). A memory-based genetic algorithm for optimization of power generation in a microgrid. IEEE Transactions on Sustainable Energy, 9(3), 1081–1089.

    Article  Google Scholar 

  20. Brp, B. R. P., Kandasamy, K., Subramanian, K., & Thangaraj, C. (2015). Energy model of electric sector for Tamil Nadu. International Journal of Applied Engineering Research, 10, 5681–5687.

    Google Scholar 

  21. Gamarra, C., & Guerrero, J. M. (2015). Computational optimization techniques applied to microgrids planning: A review. Renewable and Sustainable Energy Reviews, 48, 413–424.

    Article  Google Scholar 

  22. Gary, M., & Johnson, D. (1979). Computers and intractability: A guide to the theory of NP-completeness. New York: WH Freman and Co.

    Google Scholar 

  23. Al Faruque, M. A., & Vatanparvar, K. (2016). Energy management-as-a-service over fog computing platform. IEEE Internet of Things Journal, 3(2), 161–169.

    Article  Google Scholar 

  24. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., & Leitner, P. (2017). Optimized IoT service placement in the fog. Service-Oriented Computing & Applications, 11(4), 427–443.

    Article  Google Scholar 

  25. Mishra, S. K., Puthal, D., Rodrigues, J. J. P. C., Sahoo, B., & Dutkiewicz, E. (2018). Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications. IEEE Transactions on Industrial Informatics, 14(10), 4497–4506.

    Article  Google Scholar 

  26. Taneja, M., & Davy, A. (2017). Resource aware placement of IoT application modules in fog-cloud computing paradigm. In Proceedings of the IM 2017 - 2017 IFIP/IEEE international Symposium on integrated network and service management (pp. 1222–1228).

  27. Bukhsh, R., Javaid, N., Ali Khan, Z., Ishmanov, F., Afzal, M., & Wadud, Z. (2018). Towards fast response, reduced processing and balanced load in fog-based data-driven smart grid. Energies, 11(12), 3345.

    Article  Google Scholar 

  28. Tu, R., Wang, X., & Yang, Y. (2014). Energy-saving model for SDN data centers. Journal of Supercomputing, 70(3), 1477–1495.

    Article  Google Scholar 

  29. Junior, W., França, A., Dias, K., & de Souza, J. N. (2017). Supporting mobility-aware computational offloading in mobile cloud environment. Journal of Network and Computer Applications, 94, 93–108.

    Article  Google Scholar 

  30. Cardellini, V., Grbac, T.G., Kassler A., Kathiravelu, P., Presti FL, Marotta, A., Nardelli, M., & Veiga, L. (2018). Integrating SDN and NFV with QoS-aware service composition. In I. Ganchev, R. van der Mei, H. van den Berg (Eds.), Autonomous control for a reliable internet of services (pp. 212–240). Cham: Springer.

    Chapter  Google Scholar 

  31. Hannon, C., Yan, J., & Jin, D. (2016). DSSnet: A smart grid modeling platform combining electrical power distribution system simulation and software defined networking emulation. In Proceedings of the 2016 ACM SIGSIM conference on principles of advanced discrete simulation (pp. 131–142).

  32. Wang, J., & Li, D. (2018). Adaptive computing optimization in software-defined network-based industrial internet of things with fog computing. Sensors, 18(8), 2509.

    Article  Google Scholar 

  33. Zhang, Y., Wang, Y., & Wang, X. (2011). Greenware: Greening cloud-scale data centers to maximize the use of renewable energy. In ACM/IFIP/USENIX international conference on distributed systems platforms and open distributed processing (pp. 143–164).

  34. Grigoryan, G., Bahmani, K., Schermerhorn, G., & Liu, Y. (2018). GRASP: A green energy aware SDN platform. In IEEE INFOCOM 2018-IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 784–789).

  35. Palizban, O., & Kauhaniemi, K. (2013). Microgrid control principles in island mode operation. In PowerTech (POWERTECH), 2013 IEEE grenoble (pp. 1–6).

  36. Pan, J., Jain, R., & Paul, S. (2014). A survey of energy efficiency in buildings and microgrids using networking technologies. IEEE Communications Surveys & Tutorials, 16(3), 1709–1731.

    Article  Google Scholar 

  37. Our electric grids are equipped with a ‘tsunami’ of data, but they’re still super vulnerable to storms. [Online]. Available https://www.businessinsider.com.au/smart-electrical-grid-with-big-data-2015-10. Accessed July 2018.

  38. Sanger, D. E. (2018). Russian Hackers Appear to Shift Focus to U.S. Power Grid. [Online]. Available https://www.nytimes.com/2018/07/27/us/politics/russian-hackers-electric-grid-elections-.html. Accessed Jan 2019.

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  1. Vellore Institute of Technology, Vellore, TamilNadu, India

    Padma Priya R & D. Rekha

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Padma Priya R, Rekha, D. Sustainability modelling and green energy optimisation in microgrid powered distributed FogMicroDataCenters in rural area. Wireless Netw 27, 5519–5532 (2021). https://doi.org/10.1007/s11276-019-02207-z

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