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A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy

Author

Listed:
  • Bilal Naji Alhasnawi

    (Electrical Engineering Department, Basrah University, Basrah 61001, Iraq)

  • Basil H. Jasim

    (Electrical Engineering Department, Basrah University, Basrah 61001, Iraq)

  • Pierluigi Siano

    (Department of Management & Innovation Systems, University of Salerno Via Giovanni Paolo II, 132, 84084 Fisciano, Italy)

  • Josep M. Guerrero

    (Center for Research on Microgrid (CROM), Energy Technology Department, University of Aalborg, 9220 Aalborg, Denmark)

Abstract

This paper presents a novel scheduling scheme for the real-time home energy management systems based on Internet of Energy (IoE). The scheme is a multi-agent method that considers two chief purposes including user satisfaction and energy consumption cost. The scheme is designed under environment of microgrid. The user impact in terms of energy cost savings is generally significant in terms of system efficiency. That is why domestic users are involved in the management of domestic appliances. The optimization algorithms are based on an improved version of the rainfall algorithm and the salp swarm algorithm. In this paper, the Time of Use (ToU) model is proposed to define the rates for shoulder-peak and on-peak hours. A two-level communication system connects the microgrid system, implemented in MATLAB, to the cloud server. The local communication level utilizes IP/TCP and MQTT and is used as a protocol for the global communication level. The scheduling controller proposed in this study succeeded the energy saving of 25.3% by using the salp swarm algorithm and saving of 31.335% by using the rainfall algorithm.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3191-:d:565340
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    References listed on IDEAS

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    1. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
    2. Bilal Naji Alhasnawi & Basil H. Jasim & M. Dolores Esteban, 2020. "A New Robust Energy Management and Control Strategy for a Hybrid Microgrid System Based on Green Energy," Sustainability, MDPI, vol. 12(14), pages 1-28, July.
    3. 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.
    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. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    6. Davarzani, Sima & Granell, Ramon & Taylor, Gareth A. & Pisica, Ioana, 2019. "Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
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    Cited by:

    1. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    2. 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.
    3. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. 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.
    5. Senthil Prabu Ramalingam & Prabhakar Karthikeyan Shanmugam, 2022. "Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm," Energies, MDPI, vol. 15(14), pages 1-24, July.
    6. 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.
    7. 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.

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