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Integration of distributed controllers: Power reference tracking through charging station and building coordination

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  • Wald, Dylan
  • King, Jennifer
  • Bay, Christopher J.
  • Chintala, Rohit
  • Johnson, Kathryn

Abstract

An influx of controllable devices and sensor information can provide both barriers and opportunities for an improved electric grid. When operating by themselves, it has been shown that these devices can provide individual benefits, maintaining some level of convenience, grid stability, and quality of life. However, a clean and sustainable energy future requires the coordinated control of these edge devices on a large scale with state-of-the art control and optimization theory innovation integrated with abundant sensor information. In this work, we take a step toward this vision by proposing a method to explore whether two different types of devices can coordinate to reach consensus on a global objective while continuing to provide their individual benefits. Due to their high impact on the grid and quality of life, this proof-of-concept work focuses on the optimal control of buildings and electric vehicle charging. It is shown that the novel algorithm, termed Network Lasso - Alternating Direction Method of Multipliers - Limited Communication Distributed Model Predictive Control (NALD), successfully achieves the global objective by tracking a power reference from the grid. Simultaneously, the peak electric vehicle charging load is minimized while fully charging each electric vehicle and the internal building temperature is regulated within specified temperature bounds. Results indicate that this can be achieved in the selected example consisting of three charging stations and one large office building.

Suggested Citation

  • Wald, Dylan & King, Jennifer & Bay, Christopher J. & Chintala, Rohit & Johnson, Kathryn, 2022. "Integration of distributed controllers: Power reference tracking through charging station and building coordination," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922002070
    DOI: 10.1016/j.apenergy.2022.118753
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    References listed on IDEAS

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    1. Christopher J. Bay & Rohit Chintala & Bryan P. Rasmussen, 2020. "Steady-State Predictive Optimal Control of Integrated Building Energy Systems Using a Mixed Economic and Occupant Comfort Focused Objective Function," Energies, MDPI, vol. 13(11), pages 1-26, June.
    2. Tuchnitz, Felix & Ebell, Niklas & Schlund, Jonas & Pruckner, Marco, 2021. "Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning," Applied Energy, Elsevier, vol. 285(C).
    3. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
    4. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Coelho, Vitor N. & Weiss Cohen, Miri & Coelho, Igor M. & Liu, Nian & Guimarães, Frederico Gadelha, 2017. "Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids," Applied Energy, Elsevier, vol. 187(C), pages 820-832.
    6. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    7. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
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    Cited by:

    1. Vijayshankar, Sanjana & Chang, Chin-Yao & Utkarsh, Kumar & Wald, Dylan & Ding, Fei & Balamurugan, Sivasathya Pradha & King, Jennifer & Macwan, Richard, 2023. "Assessing the impact of cybersecurity attacks on energy systems," Applied Energy, Elsevier, vol. 345(C).

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