IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v314y2022ics0306261922002070.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922002070
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118753?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    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. 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).

    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. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    2. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    3. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    5. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    6. Zhao, Bo & Chen, Jian & Zhang, Leiqi & Zhang, Xuesong & Qin, Ruwen & Lin, Xiangning, 2018. "Three representative island microgrids in the East China Sea: Key technologies and experiences," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 262-274.
    7. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    8. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    9. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    10. Xiongfeng Deng & Xiyu Zhang, 2022. "Adaptive Fuzzy Tracking Control of Uncertain Nonlinear Multi-Agent Systems with Unknown Control Directions and a Dead-Zone Fault," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    11. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    12. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    13. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    14. Jingpeng Yue & Zhijian Hu & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2019. "A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    15. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    16. Giacomo Segala & Roberto Doriguzzi-Corin & Claudio Peroni & Matteo Gerola & Domenico Siracusa, 2023. "EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation," Energies, MDPI, vol. 16(21), pages 1-28, October.
    17. Samuel M. Muhindo & Roland P. Malhamé & Geza Joos, 2021. "A Novel Mean Field Game-Based Strategy for Charging Electric Vehicles in Solar Powered Parking Lots," Energies, MDPI, vol. 14(24), pages 1-21, December.
    18. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    19. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, April.
    20. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(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:eee:appene:v:314:y:2022:i:c:s0306261922002070. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.