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A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

Author

Listed:
  • Bilal Naji Alhasnawi

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

  • Basil H. Jasim

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

  • Maria Dolores Esteban

    (Civil Engineering Department, Hydraulics, Energy and Environment/ProfesorAranguren3, Universidad Politécnca de Madrid (UPM), CP 28040 Madrid, Spain)

  • Josep M. Guerrero

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

Abstract

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9686-:d:448200
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    References listed on IDEAS

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    1. Adia Khalid & Sheraz Aslam & Khursheed Aurangzeb & Syed Irtaza Haider & Mahmood Ashraf & Nadeem Javaid, 2018. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid," Energies, MDPI, vol. 11(12), pages 1-17, December.
    2. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    3. 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.
    4. Ciprian Mihai Coman & Adriana Florescu & Constantin Daniel Oancea, 2020. "Improving the Efficiency and Sustainability of Power Systems Using Distributed Power Factor Correction Methods," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
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    Cited by:

    1. Fabio De Felice & Marta Travaglioni & Antonella Petrillo, 2021. "Innovation Trajectories for a Society 5.0," Data, MDPI, vol. 6(11), pages 1-30, November.
    2. Susie Ruqun WU & Gabriela Shirkey & Ilke Celik & Changliang Shao & Jiquan Chen, 2022. "A Review on the Adoption of AI, BC, and IoT in Sustainability Research," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    3. Junqing Wang & Wenhui Zhao & Lu Qiu & Puyu Yuan, 2021. "Evaluation and Selection of Integrated Energy System Construction Scheme Equipped with Smart Energy Management and Control Platform Using Single-Valued Neutrosophic Numbers," Sustainability, MDPI, vol. 13(5), pages 1-24, March.
    4. Gleydson de Oliveira Cavalcanti & Handson Claudio Dias Pimenta, 2023. "Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review," Energies, MDPI, vol. 16(15), pages 1-29, August.
    5. 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.
    6. 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.
    7. 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.
    8. Joaquín Luque & Benedikt Tepe & Diego Larios & Carlos León & Holger Hesse, 2023. "Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems," Energies, MDPI, vol. 16(14), pages 1-18, July.

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