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Phase Balancing Home Energy Management System Using Model Predictive Control

Author

Listed:
  • Bharath Varsh Rao

    (Electric Energy Systems—Center for Energy, AIT Austrian Institute of Technology, 1210 Vienna, Austria)

  • Friederich Kupzog

    (Electric Energy Systems—Center for Energy, AIT Austrian Institute of Technology, 1210 Vienna, Austria)

  • Martin Kozek

    (Institute of Mechanics and Mechatronics—Faculty of Mechanical and Industrial Engineering, Vienna University of Technology, 1060 Vienna, Austria)

Abstract

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

Suggested Citation

  • Bharath Varsh Rao & Friederich Kupzog & Martin Kozek, 2018. "Phase Balancing Home Energy Management System Using Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3323-:d:186206
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    References listed on IDEAS

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    1. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    2. Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
    3. Killian, M. & Zauner, M. & Kozek, M., 2018. "Comprehensive smart home energy management system using mixed-integer quadratic-programming," Applied Energy, Elsevier, vol. 222(C), pages 662-672.
    4. Rosario Miceli, 2013. "Energy Management and Smart Grids," Energies, MDPI, vol. 6(4), pages 1-29, April.
    5. Yuanyuan Sun & Peixin Li & Shurong Li & Linghan Zhang, 2017. "Contribution Determination for Multiple Unbalanced Sources at the Point of Common Coupling," Energies, MDPI, vol. 10(2), pages 1-17, February.
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    Citations

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    Cited by:

    1. Guanghai Bao & Sikai Ke, 2019. "Load Transfer Device for Solving a Three-Phase Unbalance Problem Under a Low-Voltage Distribution Network," Energies, MDPI, vol. 12(15), pages 1-18, July.
    2. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
    3. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    4. Bharath Varsh Rao & Mark Stefan & Roman Schwalbe & Roman Karl & Friederich Kupzog & Martin Kozek, 2021. "Stratified Control Applied to a Three-Phase Unbalanced Low Voltage Distribution Grid in a Local Peer-to-Peer Energy Community," Energies, MDPI, vol. 14(11), pages 1-19, June.
    5. Miao Li & Yiran Feng & Maojun Zhou & Hailin Mu & Longxi Li & Yajun Wang, 2019. "Economic and Environmental Optimization for Distributed Energy System Integrated with District Energy Network," Energies, MDPI, vol. 12(10), pages 1-19, May.

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