SpringerLink
Log in

An efficient energy management in smart grid based on IOT using ROAWFSA technique

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this manuscript, an energy management system in smart grid based on internet of things (IoT) using hybrid approach is proposed. The proposed hybrid approach is the consolidation of wingsuit flying search algorithm (WFSA) and rain optimization algorithm (ROA) called ROAWFSA approach. The main aim of this work is “to optimally control the power and distribution system resources through continuously monitoring the data depends on the IoT communication scheme”. Here, the distribution system is interlinked with the data acquisition module, which is IoT object through single IP address that results in a mesh wireless network devices. The IoT-based communication framework is used to facilitate the development of demand response for the energy management system in the distribution system. This structure gathers the demand response from load and predicts the data to the centralized server. By using ROAWFSA approach, the transmitting data are activated. By this way, the IoT distribution system increases the network flexibility and gives optimal usage of the obtainable resources. Moreover, the ROAWFSA approach is reliable to meet the global supply and energy demand. The proposed method is executed in MATLAB/Simulink, and its efficiency is compared with existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Spain)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

Data sharing does not apply to this article as no new data have been created or analyzed in this study.

Code availability

Not applicable.

References

  • Agrawal A, Sarviya R (2014) A review of research and development work on solar dryers with heat storage. Int J Sustain Energ 35:583–605

    Google Scholar 

  • Albert A, Rajagopal R (2013) Smart meter driven segmentation: what your consumption says about you. IEEE Trans Power Syst 28:4019–4030

    Google Scholar 

  • Amarnath D, Sujatha S (2018) Internet-of-things-aided energy management in smart grid environment. J Supercomput 76:2302–2314. https://doi.org/10.1007/s11227-018-2492-5

    Article  Google Scholar 

  • Anandakumar H (2019) Optimization, modelling and simulation for evolutionary computation. J Adv Res Dyn Control Syst. https://doi.org/10.5373/JARDCS/V11I9/20193161

    Article  Google Scholar 

  • Arun S, Selvan M (2018) Smart residential energy management system for demand response in buildings with energy storage devices. Front Energy 13:715–730

    Google Scholar 

  • Babar M, Tariq MU, Jan MA (2020a) Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid. Sustain Cities Soc 62:102370

    Google Scholar 

  • Baroudi U, Bin-Yahya M, Alshammari M, Yaqoub U (2018) Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart grid. J Ambient Intell Humaniz Comput 10:1325–1338

    Google Scholar 

  • Cao T, Hwang Y, Radermacher R (2017) Development of an optimization based design framework for microgrid energy systems. Energy 140:340–351

    Google Scholar 

  • Chakraborty N, Mondal A, Mondal S (2017) Intelligent scheduling of thermostatic devices for efficient energy management in smart grid. IEEE Trans Ind Inf 13:2899–2910

    Google Scholar 

  • Chen S, Gooi H, Wang M (2012) Sizing of Energy Storage for Microgrids. IEEE Trans Smart Grid 3:142–151

    Google Scholar 

  • Covic N, Lacevic B (2020) Wingsuit flying search—a novel global optimization algorithm. IEEE Access 8:53883–53900

    Google Scholar 

  • Ding Y, Hong S, Li X (2014) A demand response energy management scheme for industrial facilities in smart grid. IEEE Trans Industr Inf 10:2257–2269

    Google Scholar 

  • Ejaz W, Naeem M, Shahid A, Anpalagan A, Jo M (2017) Efficient energy management for the internet of things in smart cities. IEEE Commun Mag 55:84–91

    Google Scholar 

  • Elkazaz M, Sumner M, Thomas D (2020) Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation. Int J Electr Power Energy Syst 115:105483

    Google Scholar 

  • Erwinski K, Paprocki M, Grzesiak LM, Karwowski K, Wawrzak A (2013) Application of ethernet powerlink for communication in a linux RTAI open CNC system. IEEE Trans Ind Electron 60:628–636

    Google Scholar 

  • Essiet I, Sun Y, Wang Z (2019) Scavenging differential evolution algorithm for smart grid demand side management. Proc Manuf 35:595–600

    Google Scholar 

  • Figueiredo V, Rodrigues F, Vale Z, Gouveia J (2005) An electric energy consumer characterization framework based on data mining techniques. IEEE Trans Power Syst 20:596–602

    Google Scholar 

  • Guan Z, Lu X, Wang N, Wu J, Du X, Guizani M (2020) Towards secure and efficient energy trading in IIoT-enabled energy internet: a blockchain approach. Future Gener Comput Syst 110:686–695

    Google Scholar 

  • Hafeez G, Alimgeer KS, Wadud Z, Khan I, Usman M, Qazi AB, Khan FA (2020) An innovative optimization strategy for efficient energy management with day-ahead demand response signal and energy consumption forecasting in smart grid using artificial neural network. IEEE Access 8:84415–84433

    Google Scholar 

  • Han D, Sun W, Fan X (2018) Dynamic energy management in smart grid: a fast randomized first-order optimization algorithm. Int J Electr Power Energy Syst 94:179–187

    Google Scholar 

  • Huang Y, Wang W, Hou B (2019) A hybrid algorithm for mixed integer nonlinear programming in residential energy management. J Clean Prod 226:940–948

    Google Scholar 

  • Imran A, Hafeez G, Khan I, Usman M, Shafiq Z, Qazi AB, Khalid A, Thoben KD (2020) Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid. IEEE Access 8:139587–139608

    Google Scholar 

  • Javaid N (2017) A new heuristically optimized Home Energy Management controller for smart grid. Sustain Cities Soc 34:211–227

    Google Scholar 

  • Katyara S, Shah MA, Chowdhary BS, Akhtar F, Lashari GA (2018) Monitoring, control and energy management of smart grid system via WSN technology through SCADA applications. Wirel Pers Commun 106:1951–1968

    Google Scholar 

  • Khan ZA, Zafar A, Javaid S, Aslam S, Rahim MH, Javaid N (2019) Hybrid meta-heuristic optimization based home energy management system in smart grid. J Ambient Intell Humaniz Comput 10:4837–4853

    Google Scholar 

  • Laidi M (2012) Study of a solar PV-wind-battery hybrid power system for a remotely located region in the southern Algerian Sahara: case of refrigeration. J Technol Innov Renew Energy. https://doi.org/10.6000/1929-6002.2012.01.01.4

    Article  Google Scholar 

  • Liu Y, Yang C, Jiang L, **e S, Zhang Y (2019) Intelligent edge computing for IoT-based energy management in smart cities. IEEE Network 33:111–117

    Google Scholar 

  • Lu R, Hong S, Yu M (2019) Demand response for home energy management using reinforcement learning and artificial neural network. IEEE Trans Smart Grid 10:6629–6639

    Google Scholar 

  • Martins R, Hesse HC, Jungbauer J, Vorbuchner T, Musilek P (2018) Optimal component sizing for peak shaving in battery energy storage system for industrial applications. Energies 11:2048

    Google Scholar 

  • Marzband M, Ghadimi M, Sumper A, Domínguez-García J (2014) Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Appl Energy 128:164–174

    Google Scholar 

  • Melhem F, Grunder O, Hammoudan Z, Moubayed N (2018) Energy management in electrical smart grid environment using robust optimization algorithm. IEEE Trans Ind Appl 54:2714–2726

    Google Scholar 

  • Moazzeni A, Khamehchi E (2020) Rain optimization algorithm (ROA): a new metaheuristic method for drilling optimization solutions. J Petrol Sci Eng 195:107512

    Google Scholar 

  • Moro J, Duarte L, Ferreira E, Dias J (2013) A home appliance recognition system using the approach of measuring power consumption and power factor on the electrical panel, based on energy meter ICs. Circuits Syst 04:245–251

    Google Scholar 

  • Nguyen D, Le L (2015) Risk-constrained profit maximization for microgrid aggregators with demand response. IEEE Trans Smart Grid 6:135–146

    Google Scholar 

  • Pawar P, Vittal KP (2019) Design and development of advanced smart energy management system integrated with IoT framework in smart grid environment. J Energy Storage 25:100846

    Google Scholar 

  • Rahim S, Javaid N, Khan RD, Nawaz N, Iqbal M (2019) A convex optimization based decentralized real-time energy management model with the optimal integration of microgrid in smart grid. J Clean Prod 236:117688

    Google Scholar 

  • Rajesh P, Naveen C, Venkatesan AK, Sha** FH (2021a) An optimization technique for battery energy storage with wind turbine generator integration in unbalanced radial distribution network. J Energy Storage 43:103160

    Google Scholar 

  • Rajesh P, Sha** FH, Kommula BN (2021b) An efficient integration and control approach to increase the conversion efficiency of high-current low-voltage DC/DC converter. Energy Syst. https://doi.org/10.1007/s12667-021-00452-w

    Article  Google Scholar 

  • Roy K, Mandal K, Mandal A (2019) Energy management of the energy storage-based micro-grid-connected system: an SOGSNN strategy. Soft Comput 24:8481–8494

    Google Scholar 

  • Said O, Tolba A (2021) Accurate performance prediction of IoT communication systems for smart cities: an efficient deep learning based solution. Sustain Cities Soc 69:102830

    Google Scholar 

  • Sha** FH, Rajesh P, Raja MR (2021) An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC. Circuits Syst Signal Process 1–24

  • Sha** FH, Rajesh P (2022) FPGA realization of a reversible data hiding scheme for 5G MIMO-OFDM system by chaotic key generation-based paillier cryptography along with LDPC and its side channel estimation using machine learning technique. J Circuits Syst Comput 31(05):2250093

    Google Scholar 

  • Shekari T, Gholami A, Aminifar F (2019) Optimal energy management in multi-carrier microgrids: an MILP approach. J Mod Power Syst Clean Energy 7:876–886

    Google Scholar 

  • Silva B, Han K (2019) Mutation operator integrated ant colony optimization based domestic appliance scheduling for lucrative demand side management. Future Gener Comput Syst 100:557–568

    Google Scholar 

  • Singh SK, Jeong YS, Park JH (2020) A deep learning-based IoT-oriented infrastructure for secure smart city. Sustain Cities Soc 60:102252

    Google Scholar 

  • Subbaraj P, Rengaraj R, Salivahanan S (2009) Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm. Appl Energy 86:915–921

    Google Scholar 

  • Venkatakrishnan G, Rengaraj R (2017a) Differential evolution with parameter adaptation strategy for an optimal dispatch of residential distributed energy sources. J Comput Theor Nanosci 14:5997–6002

    Google Scholar 

  • Venkatakrishnan GR, Rengaraj R (2017b) Strategy for wind energy development in myanmar– an overview. Int J Adv Eng Res Dev. https://doi.org/10.21090/ijaerd.86529

    Article  Google Scholar 

  • Wang X, Zhang Y, Chen T, Giannakis G (2016) Dynamic energy management for smart-grid-powered coordinated multipoint systems. IEEE J Sel Areas Commun 34:1348–1359

    Google Scholar 

  • Wu X, Hu X, Yin X, Li L, Zeng Z, Pickert V (2019) Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle. J Power Sour 423:358–366

    Google Scholar 

  • Zhao C, He J, Cheng P, Chen J (2017) Consensus-based energy management in smart grid with transmission losses and directed communication. IEEE Trans Smart Grid 8:2049–2061

    Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, Tamilnadu, India

    Giri Rajanbabu Venkatakrishnan & Rengaraj Ramasubbu

  2. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankalathur, Chennai, India

    Rajalakshmi Mohandoss

Authors
  1. Giri Rajanbabu Venkatakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rengaraj Ramasubbu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajalakshmi Mohandoss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giri Rajanbabu Venkatakrishnan.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Consent to participate

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Venkatakrishnan, G.R., Ramasubbu, R. & Mohandoss, R. An efficient energy management in smart grid based on IOT using ROAWFSA technique. Soft Comput 26, 12689–12702 (2022). https://doi.org/10.1007/s00500-022-07266-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07266-7

Keywords

Search

Navigation