Edge Computing for Smart Grid: An Overview on Architectures and Solutions

  • Farzad SamieEmail author
  • Lars Bauer
  • Jörg Henkel
Part of the Power Systems book series (POWSYS)


Internet of Things (IoT) has made small objects and things to be networked and interconnected, and even connected to the Internet in order to offer advanced control and monitoring services. Smart embedded devices along with intelligent decision-making ability will increase the efficiency of services in different domains including smart grid. Similar to other IoT domain, smart grid consist of a massive number of sensors and data sources which continuously collect high-resolution data. Managing the large volume of data has been identified as one of the major challenges in IoT. To address this issue, Edge Computing envisions to process the data at the edge of the IoT network close to the embedded devices where the data is collected. This chapter aims to investigate the edge computing solutions for the smart grid. An edge computing model for the smart grid information processing, with a focus on smart home, is presented in this chapter. The advantages of this model in terms of self-supporting and privacy are discussed. Moreover, we present a use-case for smart home automation where the operating mode of home appliances are determined dynamically to respect the limited power budget of home while maximizing the user’s satisfaction and utility.


  1. 1.
    Apthorpe, N., Reisman, D., Sundaresan, S., Narayanan, A., Feamster, N.: Spying on the smart home: Privacy attacks and defenses on encrypted IoT traffic (2017). arXiv:1708.05044
  2. 2.
    Belley, C., Gaboury, S., Bouchard, B., Bouzouane, A.: A new system for assistance and guidance in smart homes based on electrical devices identification. In: International Conference on PErvasive Technologies Related to Assistive Environments (PETRA’14), p. 11. ACM (2014)Google Scholar
  3. 3.
    Bera, S., Misra, S., Rodrigues, J.J.: Cloud computing applications for smart grid: a survey. IEEE Trans. Parallel Distrib. Syst. 26(5), 1477–1494 (2015)CrossRefGoogle Scholar
  4. 4.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)Google Scholar
  5. 5.
    Bortolotti, D., Mangia, M., Bartolini, A., Rovatti, R., Setti, G., Benini, L.: Energy-aware bio-signal compressed sensing reconstruction on the WBSN-gateway. IEEE Trans. Emerg. Topics Comput. (2016)Google Scholar
  6. 6.
    Chen, S., Xu, H., Liu, D., Hu, B., Wang, H.: A vision of IoT: applications, challenges, and opportunities with china perspective. IEEE Internet Things J. 1(4), 349–359 (2014)CrossRefGoogle Scholar
  7. 7.
    Dahoumane, T., Haddadi, M.: Smart home control system based on Raspberry Pi and ZigBee. In: International Conference in Artificial Intelligence in Renewable Energetic Systems, pp. 34–42. Springer (2017)Google Scholar
  8. 8.
    Dusparic, I., Taylor, A., Marinescu, A., Golpayegani, F., Clarke, S.: Residential demand response: experimental evaluation and comparison of self-organizing techniques. Renew. Sustain. Energy Rev. 80, 1528–1536 (2017)CrossRefGoogle Scholar
  9. 9.
    Erol-Kantarci, M., Mouftah, H.T.: Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun. Surv. Tutor. 17(1), 179–197 (2015)CrossRefGoogle Scholar
  10. 10.
    Fan, Z.: A distributed demand response algorithm and its application to phev charging in smart grids. IEEE Trans. Smart Grid 3(3), 1280–1290 (2012)CrossRefGoogle Scholar
  11. 11.
    Fang, X., Yang, D., Xue, G.: Online strategizing distributed renewable energy resource access in islanded microgrids. In: IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1–6. IEEE (2011)Google Scholar
  12. 12.
    Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag 8(1) (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Folkens, J.: Building a gateway to the Internet of Things (2015). Accessed 5 June 2018
  14. 14.
    Greveler, U., Glösekötterz, P., Justusy, B., Loehr, D.: Multimedia content identification through smart meter power usage profiles. In: Proceedings of the International Conference on Information and Knowledge Engineering (IKE), p. 1 (2012)Google Scholar
  15. 15.
    Gartner says 8.4 billion connected “things” will be in use in 2017. (2017). Published: 7 Feb 2017, Accessed 1 Aug 2017
  16. 16.
    Gungor, V.C., Lu, B., Hancke, G.P.: Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans. Ind. Electron. 57(10), 3557–3564 (2010)CrossRefGoogle Scholar
  17. 17.
    Hamid, O., Barbarosou, M., Papageorgas, P., Prekas, K., Salame, C.: Automatic recognition of electric loads analyzing the characteristic parameters of the consumed electric power through a non-intrusive monitoring methodology. Energy Procedia 119, 742–751 (2017)CrossRefGoogle Scholar
  18. 18.
    Henkel, J., Pagani, S., Amrouch, H., Bauer, L., Samie, F.: Ultra-low power and dependability for IoT devices (invited paper for IoT technologies). In: Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 954–959. IEEE (2017)Google Scholar
  19. 19.
    Hu, P., Dhelim, S., Ning, H., Qiu, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. (2017)Google Scholar
  20. 20.
    Lazarescu, M.T.: Internet of things low-cost long-term environmental monitoring with reusable wireless sensor network platform. In: Internet of Things, pp. 169–196. Springer (2014)Google Scholar
  21. 21.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. smart grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  22. 22.
    Lu, R., Hong, S.H., Zhang, X.: A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach. Appl. Energy 220, 220–230 (2018)CrossRefGoogle Scholar
  23. 23.
    Maitre, J., Glon, G., Gaboury, S., Bouchard, B., Bouzouane, A.: Efficient appliances recognition in smart homes based on active and reactive power, fast fourier transform and decision trees. In: AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments (2015)Google Scholar
  24. 24.
    McDaniel, P., McLaughlin, S.: Security and privacy challenges in the smart grid. IEEE Sec. Priv. 7(3) (2009)CrossRefGoogle Scholar
  25. 25.
    Mekki, K., Bajic, E., Chaxel, F., Meyer, F.: A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express (2018)Google Scholar
  26. 26.
    Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of things: vision, applications and research challenges. Ad hoc Netw. 10(7), 1497–1516 (2012)CrossRefGoogle Scholar
  27. 27.
    Multitech: Introduction to LoRa (2015). Accessed 11 May 2018
  28. 28.
    O’Neill, D., Levorato, M., Goldsmith, A., Mitra, U.: Residential demand response using reinforcement learning. In: First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 409–414. IEEE (2010)Google Scholar
  29. 29.
    Peters, M., Ketter, W., Saar-Tsechansky, M., Collins, J.: A reinforcement learning approach to autonomous decision-making in smart electricity markets. Mach. Learn. 92(1), 5–39 (2013)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Qiu, T., Zheng, K., Song, H., Han, M., Kantarci, B.: A local-optimization emergency scheduling scheme with self-recovery for a smart grid. IEEE Trans. Ind. Inf. 13(6), 3195–3205 (2017)CrossRefGoogle Scholar
  31. 31.
    Raghunath, M., Anil Kumar, G.: Integrated smart home management and security system based on wireless video streaming using internet of things. IJMCA 5(2), 029–034 (2017)Google Scholar
  32. 32.
    Ramachandran, B., Srivastava, S.K., Edrington, C.S., Cartes, D.A.: An intelligent auction scheme for smart grid market using a hybrid immune algorithm. IEEE Trans. Ind. Electron. 58(10), 4603–4612 (2011)CrossRefGoogle Scholar
  33. 33.
    Raphael, C.: Why edge computing is crucial for the IoT (2016). Published on 12 Nov 2015, Accessed 6 July 2016
  34. 34.
    Reddy, P.P., Veloso, M.M.: Learned behaviors of multiple autonomous agents in smart grid markets. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI) (2011)Google Scholar
  35. 35.
    Reddy, P.P., Veloso, M.M.: Strategy learning for autonomous agents in smart grid markets. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 22, p. 1446 (2011)Google Scholar
  36. 36.
    Samie, F.: Resource management for edge computing in internet of things (IoT). Ph.D. thesis, Karlsruhe Institute of Technology (KIT) (2018). Scholar
  37. 37.
    Samie, F., Bauer, L., Henkel, J.: IoT technologies for embedded computing: A survey. In: Proceedings of International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS’16), p. 8. ACM (2016)Google Scholar
  38. 38.
    Samie, F., Tsoutsouras, V., Xydis, S., Bauer, L., Soudris, D., Henkel, J.: Distributed QoS management for internet of things under resource constraints. In: Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), p. 9. ACM (2016)Google Scholar
  39. 39.
    Shanthamallu, U.S., Spanias, A., Tepedelenlioglu, C., Stanley, M.: A brief survey of machine learning methods and their sensor and IoT applications. In: 8th International Conference on Information, Intelligence, Systems & Applications (IISA’17), pp. 1–8. IEEE (2017)Google Scholar
  40. 40.
    Sharma, N., Sharma, P., Irwin, D., Shenoy, P.: Predicting solar generation from weather forecasts using machine learning. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 528–533. IEEE (2011)Google Scholar
  41. 41.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  42. 42.
    Simões, M.G., Roche, R., Kyriakides, E., Suryanarayanan, S., Blunier, B., McBee, K., Nguyen, P., Ribeiro, P., Miraoui, A.: Comparison of smart grid technologies and progress in the USA and Europe. In: Smart Grid Applications and Developments, pp. 221–238. Springer (2014)Google Scholar
  43. 43.
    Smith, P.: Comparing low-power wireless technologies. Tech Zone, Digikey Online Magazine, Digi-Key Corporation (2011)Google Scholar
  44. 44.
    Tan, Z., Yang, P., Nehorai, A.: An optimal and distributed demand response strategy with electric vehicles in the smart grid. IEEE Trans. Smart Grid 5(2), 861–869 (2014)CrossRefGoogle Scholar
  45. 45.
    Thapa, R., Jiao, L., Oommen, B.J., Yazidi, A.: A learning automaton-based scheme for scheduling domestic shiftable loads in smart grids. IEEE Access 6, 5348–5361 (2018)CrossRefGoogle Scholar
  46. 46.
    Weber, R.H.: Internet of things: privacy issues revisited. Comput. Law Sec. Rev. 31(5), 618–627 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations