IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v228y2018icp2510-2525.html
   My bibliography  Save this article

Coordinating the operations of smart buildings in smart grids

Author

Listed:
  • Liu, Yang
  • Yu, Nanpeng
  • Wang, Wei
  • Guan, Xiaohong
  • Xu, Zhanbo
  • Dong, Bing
  • Liu, Ting

Abstract

With big thermal storage capacity and controllable loads such as the heating ventilation and air conditioning systems, buildings have great potential in providing demand response services to the smart grid. However, uncoordinated energy management of a large number of buildings in a distribution feeder can push power distribution systems into the emergency states where operating constraints are not completely satisfied. In this paper, we propose a bi-level building load aggregation methodology to coordinate the operations of heterogeneous smart buildings of a distribution feeder. The proposed methodology not only reduces the electricity costs of buildings but also guarantees that all the distribution operating constraints such as the distribution line thermal limit, phase imbalance, and transformer capacity limit are satisfied.

Suggested Citation

  • Liu, Yang & Yu, Nanpeng & Wang, Wei & Guan, Xiaohong & Xu, Zhanbo & Dong, Bing & Liu, Ting, 2018. "Coordinating the operations of smart buildings in smart grids," Applied Energy, Elsevier, vol. 228(C), pages 2510-2525.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:2510-2525
    DOI: 10.1016/j.apenergy.2018.07.089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918311243
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.07.089?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. Radhakrishnan, Nikitha & Su, Yang & Su, Rong & Poolla, Kameshwar, 2016. "Token based scheduling for energy management in building HVAC systems," Applied Energy, Elsevier, vol. 173(C), pages 67-79.
    2. Godina, Radu & Rodrigues, Eduardo M.G. & Matias, João C.O. & Catalão, João P.S., 2016. "Smart electric vehicle charging scheduler for overloading prevention of an industry client power distribution transformer," Applied Energy, Elsevier, vol. 178(C), pages 29-42.
    3. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    4. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    5. Li, Xiwang & Wen, Jin & Malkawi, Ali, 2016. "An operation optimization and decision framework for a building cluster with distributed energy systems," Applied Energy, Elsevier, vol. 178(C), pages 98-109.
    6. Razmara, M. & Bharati, G.R. & Hanover, Drew & Shahbakhti, M. & Paudyal, S. & Robinett, R.D., 2017. "Building-to-grid predictive power flow control for demand response and demand flexibility programs," Applied Energy, Elsevier, vol. 203(C), pages 128-141.
    7. Goyal, Siddharth & Barooah, Prabir & Middelkoop, Timothy, 2015. "Experimental study of occupancy-based control of HVAC zones," Applied Energy, Elsevier, vol. 140(C), pages 75-84.
    8. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    9. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    10. Marshall L. Fisher, 2004. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 50(12_supple), pages 1861-1871, December.
    11. Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
    12. Behl, Madhur & Smarra, Francesco & Mangharam, Rahul, 2016. "DR-Advisor: A data-driven demand response recommender system," Applied Energy, Elsevier, vol. 170(C), pages 30-46.
    13. Vaghefi, A. & Jafari, M.A. & Bisse, Emmanuel & Lu, Y. & Brouwer, J., 2014. "Modeling and forecasting of cooling and electricity load demand," Applied Energy, Elsevier, vol. 136(C), pages 186-196.
    14. Dong, Bing & Li, Zhaoxuan & Taha, Ahmad & Gatsis, Nikolaos, 2018. "Occupancy-based buildings-to-grid integration framework for smart and connected communities," Applied Energy, Elsevier, vol. 219(C), pages 123-137.
    15. X. Zhao & P. B. Luh & J. Wang, 1999. "Surrogate Gradient Algorithm for Lagrangian Relaxation," Journal of Optimization Theory and Applications, Springer, vol. 100(3), pages 699-712, March.
    16. Marshall L. Fisher, 2004. "Comments on ÜThe Lagrangian Relaxation Method for Solving Integer Programming ProblemsÝ," Management Science, INFORMS, vol. 50(12_supple), pages 1872-1874, December.
    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. Cai, Hanmin & You, Shi & Wu, Jianzhong, 2020. "Agent-based distributed demand response in district heating systems," Applied Energy, Elsevier, vol. 262(C).
    2. Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2021. "Demand response through decentralized optimization in residential areas with wind and photovoltaics," Energy, Elsevier, vol. 223(C).
    3. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    4. Dengiz, Thomas & Jochem, Patrick, 2020. "Decentralized optimization approaches for using the load flexibility of electric heating devices," Energy, Elsevier, vol. 193(C).
    5. 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).
    6. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    7. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
    9. Alvaro Llaria & Jessye Dos Santos & Guillaume Terrasson & Zina Boussaada & Christophe Merlo & Octavian Curea, 2021. "Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management," Energies, MDPI, vol. 14(9), pages 1-37, May.
    10. Laura Canale & Marianna De Monaco & Biagio Di Pietra & Giovanni Puglisi & Giorgio Ficco & Ilaria Bertini & Marco Dell’Isola, 2021. "Estimating the Smart Readiness Indicator in the Italian Residential Building Stock in Different Scenarios," Energies, MDPI, vol. 14(20), pages 1-19, October.
    11. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    12. Shi, Jie & Yu, Nanpeng & Gao, H. Oliver, 2022. "Bidding strategy for wireless charging roads with energy storage in real-time electricity markets," Applied Energy, Elsevier, vol. 327(C).
    13. Yamashita, Daniela Yassuda & Vechiu, Ionel & Gaubert, Jean-Paul, 2020. "A review of hierarchical control for building microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    14. Ma, Jinjin & Yang, Lin & Wang, Donghan & Li, Yiming & Xie, Zuomiao & Lv, Haodong & Woo, Donghyup, 2024. "Digitalization in response to carbon neutrality: Mechanisms, effects and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    15. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    16. Vahab Rostampour & Thom S. Badings & Jacquelien M. A. Scherpen, 2020. "Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation," Energies, MDPI, vol. 13(24), pages 1-19, December.
    17. Mohammadi Rad, Amin & Barforoushi, Taghi, 2020. "Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets," Energy, Elsevier, vol. 192(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. Steeger, Gregory & Rebennack, Steffen, 2017. "Dynamic convexification within nested Benders decomposition using Lagrangian relaxation: An application to the strategic bidding problem," European Journal of Operational Research, Elsevier, vol. 257(2), pages 669-686.
    2. Azadian, Farshid & Murat, Alper & Chinnam, Ratna Babu, 2015. "Integrated production and logistics planning: Contract manufacturing and choice of air/surface transportation," European Journal of Operational Research, Elsevier, vol. 247(1), pages 113-123.
    3. Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Qu, Xiaobo, 2021. "A two-stage stochastic nonlinear integer-programming model for slot allocation of a liner container shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 143-160.
    4. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Yanling Chu & Xiaoju Zhang & Zhongzhen Yang, 2017. "Multiple quay cranes scheduling for double cycling in container terminals," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    6. An, Yu & Zhang, Yu & Zeng, Bo, 2015. "The reliable hub-and-spoke design problem: Models and algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 103-122.
    7. Dollevoet, Twan & van Essen, J. Theresia & Glorie, Kristiaan M., 2018. "Solution methods for the tray optimization problem," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1070-1084.
    8. Alexandre Belloni & Mitchell J. Lovett & William Boulding & Richard Staelin, 2012. "Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers," Marketing Science, INFORMS, vol. 31(4), pages 621-636, July.
    9. Zhizhu Lai & Qun Yue & Zheng Wang & Dongmei Ge & Yulong Chen & Zhihong Zhou, 2022. "The min-p robust optimization approach for facility location problem under uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1134-1160, September.
    10. Yokoya, Daisuke & Duin, Cees W. & Yamada, Takeo, 2011. "A reduction approach to the repeated assignment problem," European Journal of Operational Research, Elsevier, vol. 210(2), pages 185-193, April.
    11. Zhang, Zheng & Wei, Yongqi & Xiong, Youming & Peng, Geng & Wang, Guorong & Lu, Jingsheng & Zhong, Lin & Wang, Jingpeng, 2022. "Influence of the location of drilling fluid loss on wellbore temperature distribution during drilling," Energy, Elsevier, vol. 244(PB).
    12. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    13. Junming Liu & Weiwei Chen & Jingyuan Yang & Hui Xiong & Can Chen, 2022. "Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 769-789, March.
    14. Springborn, Michael R., 2014. "Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 226-242.
    15. Rehman, Obaid Ur & Khan, Shahid A. & Javaid, Nadeem, 2021. "Decoupled building-to-transmission-network for frequency support in PV systems dominated grid," Renewable Energy, Elsevier, vol. 178(C), pages 930-945.
    16. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
    17. Li, Wenjie & Yang, Lixing & Wang, Li & Zhou, Xuesong & Liu, Ronghui & Gao, Ziyou, 2017. "Eco-reliable path finding in time-variant and stochastic networks," Energy, Elsevier, vol. 121(C), pages 372-387.
    18. G. Rius-Sorolla & J. Maheut & Jairo R. Coronado-Hernandez & J. P. Garcia-Sabater, 2020. "Lagrangian relaxation of the generic materials and operations planning model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 105-123, March.
    19. Zheng, Jianfeng & Meng, Qiang & Sun, Zhuo, 2014. "Impact analysis of maritime cabotage legislations on liner hub-and-spoke shipping network design," European Journal of Operational Research, Elsevier, vol. 234(3), pages 874-884.
    20. Vasile BRÄ‚TIAN, 2018. "Portfolio Optimization. Application of the Markowitz Model Using Lagrange and Profitability Forecast," Expert Journal of Economics, Sprint Investify, vol. 6(1), pages 26-34.

    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:228:y:2018:i:c:p:2510-2525. 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.