Optimal energy management in smart grid with internet of things using hybrid technique

被引:2
作者
Nayak, Sabita [1 ]
Kumar, Amit [2 ]
机构
[1] Univ JUT Ranchi, BIT Sindri Dhanbad, Dept Elect & Commun Engn, Jharkhand, India
[2] Univ JUT Ranchi, BIT Sindri Dhanbad, Dept Elect Engn, Jharkhand, India
关键词
Smart home device; smart grid IoT; energy management system; demand response; turbulent flow of water-based optimization (TFWO); FLY OPTIMIZATION ALGORITHM; BIG DATA ANALYTICS; IOT; POWER;
D O I
10.1177/0958305X221120256
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper proposes an energy management system (EMS) in smart grid based on Internet of Things (IoT) configuration using hybrid approach. The proposed approach is joint execution of Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO), hence it is known as M2FWO technique. The main objective of this proposed EMS is to better manage power with the resources of Smart Grid by constantly monitoring data from the IoT-based communication framework. Here, each home device is connected to data acquisition module is utilized to facilitate the demand response (DR) growth for energy management system in smart grid. The framework collects DR from every smart home device and then transmits the data to the centralized server. The transmitting data is enabled by M2FWO method. The smart grid IoT framework enhances the feasibility of these networks makes better use of obtainable resources. The proposed system is in charge to satisfy the total supply with energy requirement. Finally, the proposed model is stimulated on MATLAB/Simulink site, then the efficiency is examined with existing methods.
引用
收藏
页码:3337 / 3364
页数:28
相关论文
共 46 条
  • [1] Ahmad S., 2012, INT J SMART GRID CLE, V1, P15, DOI DOI 10.12720/SGCE.1.1.15-21
  • [2] Fog Computing as a Complementary Approach to Cloud Computing
    Al Yami, Mohammed
    Schaefer, Dirk
    [J]. 2019 INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCIS), 2019, : 152 - 155
  • [3] A Smart Home Energy Management System Using IoT and Big Data Analytics Approach
    Al-Ali, A. R.
    Zualkernan, Imran A.
    Rashid, Mohammed
    Gupta, Ragini
    AliKarar, Mazin
    [J]. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2017, 63 (04) : 426 - 434
  • [4] Smart Meter Driven Segmentation: What Your Consumption Says About You
    Albert, Adrian
    Rajagopal, Ram
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (04) : 4019 - 4030
  • [5] Review of Internet of Things (IoT) in Electric Power and Energy Systems
    Bedi, Guneet
    Venayagamoorthy, Ganesh Kumar
    Singh, Rajendra
    Brooks, Richard R.
    Wang, Kuang-Ching
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (02): : 847 - 870
  • [6] IoT Software Infrastructure for Energy Management and Simulation in Smart Cities
    Brundu, Francesco Gavino
    Patti, Edoardo
    Osello, Anna
    Del Giudice, Matteo
    Rapetti, Niccolo
    Krylovskiy, Alexandr
    Jahn, Marco
    Verda, Vittorio
    Guelpa, Elisa
    Rietto, Laura
    Acquaviva, Andrea
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (02) : 832 - 840
  • [7] Opportunities for enhanced lean construction management using Internet of Things standards
    Dave, Bhargav
    Kubler, Sylvain
    Framling, Kary
    Koskela, Lauri
    [J]. AUTOMATION IN CONSTRUCTION, 2016, 61 : 86 - 97
  • [8] Efficient Energy Management for the Internet of Things in Smart Cities
    Ejaz, Waleed
    Naeem, Muhammad
    Shahid, Adnan
    Anpalagan, Alagan
    Jo, Minho
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (01) : 84 - 91
  • [9] Short-term load forecasting, profile identification, and customer segmentation: A methodology based on periodic time series
    Espinoza, M
    Joye, C
    Belmans, R
    De Moor, B
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (03) : 1622 - 1630
  • [10] Francis SH, 2022, CIRC SYST SIGNAL PR, V41, P1751, DOI 10.1007/s00034-021-01850-2