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A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers

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  • Elkazaz, Mahmoud
  • Sumner, Mark
  • Naghiyev, Eldar
  • Pholboon, Seksak
  • Davies, Richard
  • Thomas, David

Abstract

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system.

Suggested Citation

  • Elkazaz, Mahmoud & Sumner, Mark & Naghiyev, Eldar & Pholboon, Seksak & Davies, Richard & Thomas, David, 2020. "A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920306309
    DOI: 10.1016/j.apenergy.2020.115118
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    References listed on IDEAS

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    Cited by:

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    8. Elkazaz, Mahmoud & Sumner, Mark & Thomas, David, 2021. "A hierarchical and decentralized energy management system for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 291(C).
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    10. Dinh, Huy Truong & Lee, Kyu-haeng & Kim, Daehee, 2022. "Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization," Applied Energy, Elsevier, vol. 321(C).
    11. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
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    13. Eros D. Escobar & Tatiana Manrique & Idi A. Isaac, 2022. "Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control," Energies, MDPI, vol. 15(21), pages 1-19, October.
    14. Md Masud Rana & Akhlaqur Rahman & Moslem Uddin & Md Rasel Sarkar & SK. A. Shezan & C M F S Reza & Md. Fatin Ishraque & Mohammad Belayet Hossain, 2022. "Efficient Energy Distribution for Smart Household Applications," Energies, MDPI, vol. 15(6), pages 1-19, March.
    15. Henggeler Antunes, Carlos & Alves, Maria João & Soares, Inês, 2022. "A comprehensive and modular set of appliance operation MILP models for demand response optimization," Applied Energy, Elsevier, vol. 320(C).
    16. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
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    18. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).

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