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Occupancy-based buildings-to-grid integration framework for smart and connected communities

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  • Dong, Bing
  • Li, Zhaoxuan
  • Taha, Ahmad
  • Gatsis, Nikolaos

Abstract

Buildings-to-grid (BtG) integration simulations are becoming prevalent due to the development of smart buildings and smart grid. Buildings are the major energy consumers of the total electricity production worldwide. There is an urgent need to integrate buildings with smart grid operation to accommodate the needs of flexible load controls due to the increasing of renewable energy resources. In the imminent future, smart buildings can contribute to grid stability by changing their overall demand patterns in response to grid operations. Meanwhile, building thermal energy consumption is also maintained by building operators to satisfy occupants’ thermal comforts. However, explicit large-scale demonstrations based on a simulation platform that integrates building occupancy, building physics, and grid physics at community level have not been explored. This study develops an occupancy behavior driven BtG optimization platform that can simulate, predict and optimize indoor temperature and energy consumption of buildings, generator setpoint and deviation while maintaining acceptable grid frequency. Authors have tested the framework on two standard power networks. The results show that the integrated framework can provide potential cost savings up to 60% comparing with the decoupled operation.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:219:y:2018:i:c:p:123-137
    DOI: 10.1016/j.apenergy.2018.03.007
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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. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    3. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    4. Camarinha-Matos, Luis M., 2016. "Collaborative smart grids – A survey on trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 283-294.
    5. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    6. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    7. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    8. Chen, Xiao & Wang, Qian & Srebric, Jelena, 2016. "Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation," Applied Energy, Elsevier, vol. 164(C), pages 341-351.
    9. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    10. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    11. Goyal, Siddharth & Ingley, Herbert A. & Barooah, Prabir, 2013. "Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance," Applied Energy, Elsevier, vol. 106(C), pages 209-221.
    12. Broeer, Torsten & Fuller, Jason & Tuffner, Francis & Chassin, David & Djilali, Ned, 2014. "Modeling framework and validation of a smart grid and demand response system for wind power integration," Applied Energy, Elsevier, vol. 113(C), pages 199-207.
    13. Labeodan, Timilehin & Aduda, Kennedy & Boxem, Gert & Zeiler, Wim, 2015. "On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction – A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1405-1414.
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