IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v34y2023i8p3237-3264.html

Energy management of smart grid equipped by an Internet of Things (IoT) configuration: A DDFAT methodology

Author

Listed:
  • G. Arunsankar
  • G. Angala Parameswari

Abstract

In this manuscript, an Energy Management System (EMS) with Internet of Things (IoT) framework in distribution system (DS) based on hybrid technique is proposed. The proposed method is the combination of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial Tree (FAT) algorithm; therefore called as DDFAT method. The main intention of proposed method is “to optimize the power control and DS resources by constantly tracking the data into communication framework based on IoT†. In this work, the DS is inter connected to the data acquisition method that is the Internet of things uses single IP address resulting in mesh wireless network devices. The internet of things based communication scheme used to facilitate the growth of demand response (DR) for energy management (EM) DS. The transmitting data is carried out by DDFAT method. In this way, the IoT distribution scheme increases the network flexibility and offers optimal utilization of the accessible resources. Moreover, the DDFAT method is reliable to meet global supply along energy demand. The DDFAT method is implemented in MATLAB Simulink platform under three test cases and its performance is analysed with the existing improved artificial bee colony (IABC), squirrel optimization with gravitational search–assisted neural network (SOGSNN), particle swarm optimization (PSO)–assisted artificial neural network (ANN), Fruit fly Optimization algorithm (FOA), and grasshopper optimization algorithm (GOAPSNN) methods.

Suggested Citation

  • G. Arunsankar & G. Angala Parameswari, 2023. "Energy management of smart grid equipped by an Internet of Things (IoT) configuration: A DDFAT methodology," Energy & Environment, , vol. 34(8), pages 3237-3264, December.
    • Handle: RePEc:sae:engenv:v:34:y:2023:i:8:p:3237-3264
    DOI: 10.1177/0958305X221117519
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X221117519
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0958305X221117519?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bilal Naji Alhasnawi & Basil H. Jasim & Pierluigi Siano & Josep M. Guerrero, 2021. "A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy," Energies, MDPI, vol. 14(11), pages 1-29, May.
    2. Bilal Naji Alhasnawi & Basil H. Jasim & Bishoy E. Sedhom & Eklas Hossain & Josep M. Guerrero, 2021. "A New Decentralized Control Strategy of Microgrids in the Internet of Energy Paradigm," Energies, MDPI, vol. 14(8), pages 1-34, April.
    3. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    4. Bilal Naji Alhasnawi & Basil H. Jasim & Maria Dolores Esteban & Josep M. Guerrero, 2020. "A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances," Sustainability, MDPI, vol. 12(22), pages 1-47, November.
    5. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    6. Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
    7. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    8. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    9. Bingham, Raymond D. & Agelin-Chaab, Martin & Rosen, Marc A., 2019. "Whole building optimization of a residential home with PV and battery storage in The Bahamas," Renewable Energy, Elsevier, vol. 132(C), pages 1088-1103.
    10. Mojtaba Moghimi & Jiannan Liu & Pouya Jamborsalamati & Fida Hasan Md Rafi & Shihanur Rahman & Jahangir Hossain & Sascha Stegen & Junwei Lu, 2018. "Internet of Things Platform for Energy Management in Multi-Microgrid System to Improve Neutral Current Compensation," Energies, MDPI, vol. 11(11), pages 1-22, November.
    11. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    2. Bilal Naji Alhasnawi & Basil H. Jasim & Arshad Naji Alhasnawi & Bishoy E. Sedhom & Ali M. Jasim & Azam Khalili & Vladimír Bureš & Alessandro Burgio & Pierluigi Siano, 2022. "A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA," Energies, MDPI, vol. 15(22), pages 1-29, November.
    3. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    4. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    5. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    6. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    7. Bilal Naji Alhasnawi & Basil H. Jasim & Pierluigi Siano & Josep M. Guerrero, 2021. "A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy," Energies, MDPI, vol. 14(11), pages 1-29, May.
    8. Li, Xiwang & Wen, Jin & Malkawi, Ali, 2016. "An operation optimization and decision framework for a building cluster with distributed energy systems," Applied Energy, Elsevier, vol. 178(C), pages 98-109.
    9. Babak Arbab-Zavar & Emilio J. Palacios-Garcia & Juan C. Vasquez & Josep M. Guerrero, 2021. "Message Queuing Telemetry Transport Communication Infrastructure for Grid-Connected AC Microgrids Management," Energies, MDPI, vol. 14(18), pages 1-31, September.
    10. Maria G. Ioannides & Elias B. Koukoutsis & Anastasios P. Stamelos & Stylianos A. Papazis & Erofili E. Stamataki & Athanasios Papoutsidakis & Vasilios Vikentios & Nikolaos Apostolakis & Michael E. Stam, 2023. "Design and Operation of Internet of Things-Based Monitoring Control System for Induction Machines," Energies, MDPI, vol. 16(7), pages 1-22, March.
    11. Zhang, Hao & Cai, Jie & Fang, Kan & Zhao, Fu & Sutherland, John W., 2017. "Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems," Applied Energy, Elsevier, vol. 205(C), pages 1538-1547.
    12. Bilal Naji Alhasnawi & Basil H. Jasim & Zain-Aldeen S. A. Rahman & Josep M. Guerrero & M. Dolores Esteban, 2021. "A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources," IJERPH, MDPI, vol. 18(15), pages 1-24, July.
    13. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    14. Zurisaddai de la Cruz Severiche Maury & Ana Fernández Vilas & Rebeca P. Díaz Redondo, 2022. "Low-Cost HEM with Arduino and Zigbee Technologies in the Energy Sector in Colombia," Energies, MDPI, vol. 15(10), pages 1-19, May.
    15. Restrepo, Mauricio & Cañizares, Claudio A. & Simpson-Porco, John W. & Su, Peter & Taruc, John, 2021. "Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility," Applied Energy, Elsevier, vol. 290(C).
    16. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    17. Matteo Vaccargiu & Andrea Pinna & Roberto Tonelli & Luisanna Cocco, 2023. "Blockchain in the Energy Sector for SDG Achievement," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    18. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    19. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    20. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:engenv:v:34:y:2023:i:8:p:3237-3264. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.