IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3323-d186206.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3323/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3323/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Rosario Miceli, 2013. "Energy Management and Smart Grids," Energies, MDPI, vol. 6(4), pages 1-29, April.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Ma, Yiju & Azuatalam, Donald & Power, Thomas & Chapman, Archie C. & Verbič, Gregor, 2019. "A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks," Applied Energy, Elsevier, vol. 254(C).
    2. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    3. Thijs Klauw & Marco E. T. Gerards & Johann L. Hurink, 2017. "Resource allocation problems in decentralized energy management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 749-773, July.
    4. Simona-Vasilica Oprea & Adela Bâra & Adriana Reveiu, 2018. "Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders," Energies, MDPI, vol. 11(1), pages 1-31, January.
    5. Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    6. Hau, Lee Cheun & Lim, Yun Seng & Liew, Serena Miao San, 2020. "A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system," Applied Energy, Elsevier, vol. 260(C).
    7. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    8. Roberto Casado-Vara & Angel Martín del Rey & Ricardo S. Alonso & Saber Trabelsi & Juan M. Corchado, 2020. "A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study," Mathematics, MDPI, vol. 8(9), pages 1-13, August.
    9. Vašak, Mario & Banjac, Anita & Hure, Nikola & Novak, Hrvoje & Kovačević, Marko, 2023. "Predictive control based assessment of building demand flexibility in fixed time windows," Applied Energy, Elsevier, vol. 329(C).
    10. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    11. Hang Yi & Wenjun Peng & Xiuchun Xiao & Shaojin Feng & Hengde Zhu & Yudong Zhang, 2023. "An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization," Mathematics, MDPI, vol. 11(11), pages 1-15, June.
    12. Joao C. Ferreira & Ana Lucia Martins, 2018. "Building a Community of Users for Open Market Energy," Energies, MDPI, vol. 11(9), pages 1-21, September.
    13. Esmeralda López-Garza & René Fernando Domínguez-Cruz & Fernando Martell-Chávez & Iván Salgado-Tránsito, 2022. "Fuzzy Logic and Linear Programming-Based Power Grid-Enhanced Economical Dispatch for Sustainable and Stable Grid Operation in Eastern Mexico," Energies, MDPI, vol. 15(11), pages 1-18, June.
    14. Zhang, Sheng & Huang, Pei & Sun, Yongjun, 2016. "A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties," Energy, Elsevier, vol. 94(C), pages 654-665.
    15. 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.
    16. Alena Otcenasova & Roman Bodnar & Michal Regula & Marek Hoger & Michal Repak, 2017. "Methodology for Determination of the Number of Equipment Malfunctions Due to Voltage Sags," Energies, MDPI, vol. 10(3), pages 1-26, March.
    17. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    18. Liu, Yinyan & Ma, Jin & Xing, Xinjie & Liu, Xinglu & Wang, Wei, 2022. "A home energy management system incorporating data-driven uncertainty-aware user preference," Applied Energy, Elsevier, vol. 326(C).
    19. Lefeng Cheng & Zhiyi Zhang & Haorong Jiang & Tao Yu & Wenrui Wang & Weifeng Xu & Jinxiu Hua, 2018. "Local Energy Management and Optimization: A Novel Energy Universal Service Bus System Based on Energy Internet Technologies," Energies, MDPI, vol. 11(5), pages 1-38, May.
    20. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).

    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:gam:jeners:v:11:y:2018:i:12:p:3323-:d:186206. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.