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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
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    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.
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    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. 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).
    15. 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.
    16. 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).

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