Advertisement

Smart residential electricity distribution system (SREDS) for demand response under smart grid environment

  • Selvan Manickavasagam ParvathyEmail author
Original Research
  • 15 Downloads

Abstract

The present research work attempts to develop a Smart Residential Electricity Distribution System (SREDS) for enabling Smart Residences (SR) to participate in Demand Response (DR) schemes of Power Distribution Company (PDC) with either Flat Rate tariff, Time of Use tariff or Real Time Pricing. SREDS includes a Smart Energy Meter (SEM) enabled with Internet of Things, an Intelligent Residential Load Management System (IRLMS) with Wi-Fi connectivity, distributed Wi-Fi enabled Smart Load Nodes (SLN) and Smart Battery Charing/Discharging Controller (SBCDC). The SEM receives the key parameters such as electricity pricing, maximum demand limit, etc. as per the DR schemes from the PDC through Meter Data Management System (MDMS) using Internet. IRLMS receives the required operational timings of the loads as per the desire and comfort of the user either through the user interface or from the SLN connected to the appliances participating in DR. If the SR is equipped with battery storage system, then the battery units are interfaced with the IRLMS through SBCDC. The present status of the battery is communicated to IRLMS by SBCDC. The communication between SEM, IRLMS, SLNs and SBCDC is established through Home Area Network using Wi-Fi. The IRLMS processes the PDC parameters and load requirements, and executes an optimization algorithm to find the best possible scheduling of appliances in order to get minimized electricity bill. The SEM communicates back the energy consumption data to the PDC through MDMS.

Keywords

Smart grid Demand response Residential load management system Smart load node 

Notes

Funding

Funding was provided by the Ministry of Electronics and Information Technology, Government of India under Visvesvaraya Young Faculty Research Fellowship being implemented by Digital India Corporation (Formerly Media Lab Asia) [Grant No. PhD-MLA-4(16)/2014].

References

  1. 1.
    Fang X, Misra S, Xue G, Yang D (2012) Smart grid the new and improved power grid: a survey. IEEE Commun Surv Tutor 14(4):944–980CrossRefGoogle Scholar
  2. 2.
    Collotta M, Pau G (2015) A novel energy management approach for smart homes using bluetooth low energy. IEEE J Sel Areas Commun 33(12):2988–2996CrossRefGoogle Scholar
  3. 3.
    Wang P, Ye F, Chen X (2018) A smart home gateway platform for data collection and awareness. IEEE Commun Mag 56(9):87–93CrossRefGoogle Scholar
  4. 4.
    Balijepalli VSKM, Pradhan V, Khaparde SA, Shereef RM (2011) Review of demand response under smart grid paradigm. In: Proceedings of ISGT2011-India, pp 236–243Google Scholar
  5. 5.
    Costanzo GT, Zhu G, Anjos MF, Savard G (2012) A system architecture for autonomous demand side load management in smart buildings. IEEE Trans Smart Grid 3(4):2157–2165CrossRefGoogle Scholar
  6. 6.
    Pipattanasomporn M, Kuzlu M, Rahman S (2012) An algorithm for intelligent home energy management and demand response analysis. IEEE Trans Smart Grid 3(4):2166–2173CrossRefGoogle Scholar
  7. 7.
    Adika CO, Wang L (2014) Autonomous appliance scheduling for household energy management. IEEE Trans Smart Grid 5(2):673–682CrossRefGoogle Scholar
  8. 8.
    Wang C, Zhou Y, Jiao B, Wang Y, Liu W, Wang D (2015) Robust optimization for load scheduling of a smart home with photovoltaic system. Energy Convers Manag 102:247–257CrossRefGoogle Scholar
  9. 9.
    Mohsenian-Rad A-H, Wong VWS, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331CrossRefGoogle Scholar
  10. 10.
    Wang Y, Saad W, Han Z, Poor HV, Basar T (2014) A game-theoretic approach to energy trading in the smart grid. IEEE Trans Smart Grid 5(3):1439–1450CrossRefGoogle Scholar
  11. 11.
    Chen H, Li Y, Louie RHY, Vucetic B (2014) Autonomous demand side management based on energy consumption scheduling and instantaneous load billing: an aggregative game approach. IEEE Trans Smart Grid 5(4):1744–1754CrossRefGoogle Scholar
  12. 12.
    Chai B, Chen J, Yang Z, Zhang Y (2014) Demand response management with multiple utility companies: a two-level game approach. IEEE Trans Smart Grid 5(2):722–731CrossRefGoogle Scholar
  13. 13.
    Maharjan S, Zhu Q, Zhang Y, Gjessing S, Basar T (2013) Dependable demand response management in the smart grid: a stackelberg game approach. IEEE Trans Smart Grid 4(1):120–132CrossRefGoogle Scholar
  14. 14.
    Nekouei E, Alpcan T, Chattopadhyay D (2015) Game-theoretic frameworks for demand response in electricity markets. IEEE Trans Smart Grid 6(2):748–758CrossRefGoogle Scholar
  15. 15.
    Tushar W, Zhang JA, Smith DB, Poor HV, Thiebaux S (2014) Prioritizing consumers in smart grid: a game theoretic approach. IEEE Trans Smart Grid 5(3):1429–1438CrossRefGoogle Scholar
  16. 16.
    Deng R, Yang Z, Chen J, Asr NR, Chow M-Y (2014) Residential energy consumption scheduling: a coupled-constraint game approach. IEEE Trans Smart Grid 5(3):1340–1350CrossRefGoogle Scholar
  17. 17.
    La QD, Chan YWE, Soong B-H (2016) Power management of intelligent buildings facilitated by smart grid: a market approach. IEEE Trans Smart Grid 7(3):1389–1400CrossRefGoogle Scholar
  18. 18.
    Arun SL, Selvan MP (2017) Dynamic demand response in smart buildings using an intelligent residential load management system. IET Gener Transm Distrib 11(17):4348–4357CrossRefGoogle Scholar

Copyright information

© CSI Publications 2019

Authors and Affiliations

  1. 1.National Institute of Technology TiruchirappalliTiruchirappalliIndia

Personalised recommendations