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Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

Author

Listed:
  • Guodong Liu

    (Power & Energy Systems Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Thomas B. Ollis

    (Power & Energy Systems Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Bailu Xiao

    (Power & Energy Systems Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Xiaohu Zhang

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

  • Kevin Tomsovic

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

Abstract

This paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed multi-objective optimization model optimizes not only the operating cost, including fuel cost, electricity purchasing/selling, storage degradation, voluntary load shedding and the cost associated with customer discomfort as a result of the room temperature deviation from the customer setting point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, we integrate the detailed thermal dynamic model of buildings into the distribution optimal power flow (D-OPF) model for the optimal operation. Thus, the proposed model can directly schedule the heating, ventilation and air-conditioning (HVAC) systems of buildings intelligently so as to to reduce the electricity cost without compromising the comfort of customers. Results of numerical simulation validate the effectiveness of the proposed model and significant savings in electricity cost with network operational constraints satisfied.

Suggested Citation

  • Guodong Liu & Thomas B. Ollis & Bailu Xiao & Xiaohu Zhang & Kevin Tomsovic, 2017. "Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics," Energies, MDPI, vol. 10(10), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1554-:d:114452
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
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    Citations

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    Cited by:

    1. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    2. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim & Yong-Hoon Im, 2018. "Optimal Energy Management of Building Microgrid Networks in Islanded Mode Considering Adjustable Power and Component Outages," Energies, MDPI, vol. 11(9), pages 1-22, September.
    3. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Kevin Tomsovic, 2022. "An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-20, September.
    4. Liu, Guodong & Jiang, Tao & Ollis, Thomas B. & Zhang, Xiaohu & Tomsovic, Kevin, 2019. "Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics," Applied Energy, Elsevier, vol. 239(C), pages 83-95.

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