A Novel Edge-Supported Cost-Efficient Resource Management Approach for Smart Grid System

  • Jyotirmaya Mishra
  • Jitendra Sheetlani
  • K. Hemant K. Reddy
  • Diptendu Sinha Roy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


The smart grids, a new-generation power supply system, have the capacity to lowering the cost, can increase service provision tremendously, and make surroundings greener as compared to conventional power supply systems. To interact with the physical world and widen its capabilities, integrated smart grid cyber-physical system (SG-CPS) can be used for computation, communication, and control. To support smart grid (SG), cloud components are employed for storing and processing users’ power demand and control flow information generated at different control components like smart meter (SM), home energy management (HEM), phasor measurement units (PMUs), and soon. But storing smart grid data to cloud and processing incurs unacceptable delays. This paper addresses quality-of-service (QoS) requirements of SGs by integrating fog computing along with cloud computing infrastructure for realizing an Edge Computing integrated Smart Grid (EC-iSG). To that end, this paper presents novel heuristics for resource management of such integrated infrastructure that accounts for parameters such as uplink and downlink communication costs, cost for VM deployment, and cost for communicating among base stations. The results presented demonstrate the efficacy of the proposed methodology.


Smart grid Fog computing Cloud computing Cost optimization HEM PMU 


  1. 1.
    Rajkumar, Ragunathan Raj, et al. “Cyber-physical systems: the next computing revolution.” Proceedings of the 47th Design Automation Conference. ACM, 2010.Google Scholar
  2. 2.
    Jiang, B. (2015). Optimization and Management of Cyber-Physical Systems-Smart Grid and Plug-in Hybrid Electric Vehicles (Doctoral dissertation, Northeastern University Boston).Google Scholar
  3. 3.
    K. Hemant Reddy, D S Roy, D K Mohanta, “Cloud Based Cost Optimization Model for Effective Smart Grid Information Management”. Part 8 (Big Data Analysis and Cyber Physical Systems) of edited volume Cyber-Physical Systems: A Computational Perspective, CRC Press, Taylor & Francis Group, LLC, Florida, USA; Eds. Patnaik L M, Srinivasa K G, Deka G C, Ganesh S.Google Scholar
  4. 4.
    Son, K., Kim, H., Yi, Y., & Krishnamachari, B. (2011). Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks. IEEE journal on selected areas in communications, 29(8), 1525–1536.Google Scholar
  5. 5.
    N. Sung, N.-T. Pham, H. Yoon, S. Lee, and W. Hwang, “Base station association schemes to reduce unnecessary handovers using location awareness in femtocell networks,” Wireless Networks, vol. 19, no. 5, pp. 741–753, 2013. [Online]. Available:
  6. 6.
    Mohanta, D. K., Murthy, C., & Sinha Roy, D. (2016). A brief review of phasor measurement units as sensors for smart grid. Electric Power Components and Systems, 44(4), 411–425.Google Scholar
  7. 7.
    Behera, S., Pattnaik, B. S., Reza, M., & Roy, D. S. (2016). Predicting Consumer Loads for Improved Power Scheduling in Smart Homes. In Computational Intelligence in Data Mining—Volume 2 (pp. 463–473). Springer, New Delhi.Google Scholar
  8. 8.
    Murthy, Cherukuri, K. Ajay Varma, Diptendu Sinha Roy, and Dusmanta Kumar Mohanta. “Reliability evaluation of phasor measurement unit using type-2 fuzzy set theory”; Systems Journal, IEEE 8, no. 4 (2014): 1302–1309.Google Scholar
  9. 9.
    Murthy, Cherukuri, Anadi Mishra, Debashis Ghosh, Diptendu Sinha Roy, and Dusmanta Kumar Mohanta.; “Reliability analysis of phasor measurement unit using hidden Markov Model”; Systems Journal, IEEE 8, no. 4 (2014): 1293–1301.Google Scholar
  10. 10.
    Polaki, S. K., Reza, M., & Roy, D. S. (2015, June). A genetic algorithm for optimal power scheduling for residential energy management. In Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on (pp. 2061–2065). IEEE.Google Scholar
  11. 11.
    Murthy, C., Roy, D. S., & Mohanta, D. K. (2015). Reliability evaluation of phasor measurement unit: A system of systems approach. Electric Power Components and Systems, 43(4), 437–448.Google Scholar
  12. 12.
    Bera, Samaresh, SudipMisra, and Joel JPC Rodrigues. “Cloud computing applications for smart grid: A survey.” IEEE Transactions on Parallel and Distributed Systems 26.5 (2015): 1477–1494.Google Scholar
  13. 13.
    Fang, Xi, et al. “Managing smart grid information in the cloud: opportunities, model, and applications.” IEEE network 26.4 (2012).Google Scholar
  14. 14.
    Kim, Hongseok, et al. “Cloud-based demand response for smart grid: Architecture and distributed algorithms.” Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, 2011.Google Scholar
  15. 15.
    Simmhan, Yogesh, et al. “An informatics approach to demand response optimization in smart grids.” Natural Gas 31 (2011): 60.Google Scholar
  16. 16.
    Nagothu, Kranthimanoj, et al. “Persistent Net-AMI for microgrid infrastructure using cognitive radio on cloud data centers.” IEEE Systems Journal 6.1 (2012): 4–15.Google Scholar
  17. 17.
    Nikolopoulos, Vassilis, et al. “Web-based decision-support system methodology for smart provision of adaptive digital energy services over cloud technologies.” IET software 5.5 (2011): 454–465.Google Scholar
  18. 18.
    Deng, Wei, et al. “Harnessing renewable energy in cloud datacenters: opportunities and challenges.” IEEE Network 28.1 (2014): 48–55.Google Scholar
  19. 19.
    Bonomi, Flavio, et al. “Fog computing and its role in the internet of things.” Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 2012.Google Scholar
  20. 20.
    J. Zhu, D. S. Chan, M. S. Prabhu, P. Natarajan, H. Hu, and F. Bonomi, “Improving web sites performance using edge servers in fog computing architecture,” in Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on. IEEE, 2013, pp. 320–323.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Jyotirmaya Mishra
    • 1
  • Jitendra Sheetlani
    • 2
  • K. Hemant K. Reddy
    • 3
  • Diptendu Sinha Roy
    • 4
  1. 1.Department of Computer Science and EngineeringGandhi Institute of Engineering and TechnologyGunupurIndia
  2. 2.Department of Computer Science and EngineeringSri Satya Sai University of Technology and Medical SciencesSehoreIndia
  3. 3.Department of Computer Science and EngineeringNational Institute of Science and TechnologyBerhampurIndia
  4. 4.Department of Computer Science and EngineeringNational Institute of TechnologyShillongIndia

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