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A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered

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  • Huang, Pei
  • Lovati, Marco
  • Zhang, Xingxing
  • Bales, Chris

Abstract

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into ‘electricity prosumers’. Existing studies have developed some advanced building side controls that enable renewable energy sharing and that aim to optimize building-cluster-level performance via regulating the energy storage charging/discharging. However, the flexible demand shifting ability of electric vehicles is not considered in these building side controls. For instance, the electric vehicle charging will usually start once they are plugged into charging stations. But, in such charging period the renewable generation may be insufficient to cover the EV charging load, leading to grid electricity imports. Consequently, the building-cluster-level performance is not optimized. Therefore, this study proposes a coordinated control of building prosumers for improving the cluster-level performance, by making use of energy sharing and storage capability of electricity batteries in both buildings and EVs. An EV charging/discharging model is first developed. Then, based on the predicted future 24 h electricity demand and renewable generation data, the coordinated control first considers the whole building cluster as one ‘integrated’ building and optimizes its operation as well as the EV charging/discharging using genetic algorithm. Next, the operation of individual buildings in the future 24 h is coordinated using nonlinear programming. For validation, the developed control has been tested on a real building cluster in Ludvika, Sweden. The study results show that the developed control can increase the cluster-level daily renewable self-consumption rate by 19% and meanwhile reduce the daily electricity bills by 36% compared with the conventional controls.

Suggested Citation

  • Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris, 2020. "A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered," Applied Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304955
    DOI: 10.1016/j.apenergy.2020.114983
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    References listed on IDEAS

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    1. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    2. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris & Hallbeck, Sven & Becker, Anders & Bergqvist, Henrik & Hedberg, Jan & Maturi, Laura, 2019. "Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system," Applied Energy, Elsevier, vol. 255(C).
    3. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    4. Huang, Pei & Sun, Yongjun, 2019. "A clustering based grouping method of nearly zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 235(C), pages 43-55.
    5. Sun, Yongjun & Huang, Gongsheng & Xu, Xinhua & Lai, Alvin Chi-Keung, 2018. "Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls," Applied Energy, Elsevier, vol. 212(C), pages 565-576.
    6. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    7. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    8. van der Kam, Mart & van Sark, Wilfried, 2015. "Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study," Applied Energy, Elsevier, vol. 152(C), pages 20-30.
    9. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    10. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    11. Xylia, Maria & Silveira, Semida, 2017. "On the road to fossil-free public transport: The case of Swedish bus fleets," Energy Policy, Elsevier, vol. 100(C), pages 397-412.
    12. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    13. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    14. Zhang, Bingying & Li, Qiqiang & Wang, Luhao & Feng, Wei, 2018. "Robust optimization for energy transactions in multi-microgrids under uncertainty," Applied Energy, Elsevier, vol. 217(C), pages 346-360.
    15. Zhang, Sheng & Huang, Pei & Sun, Yongjun, 2016. "A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties," Energy, Elsevier, vol. 94(C), pages 654-665.
    16. Dallinger, David & Gerda, Schubert & Wietschel, Martin, 2013. "Integration of intermittent renewable power supply using grid-connected vehicles – A 2030 case study for California and Germany," Applied Energy, Elsevier, vol. 104(C), pages 666-682.
    17. Fan, Cheng & Huang, Gongsheng & Sun, Yongjun, 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level," Energy, Elsevier, vol. 164(C), pages 536-549.
    18. Huang, Pei & Huang, Gongsheng & Sun, Yongjun, 2018. "Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 213(C), pages 486-498.
    19. Luthander, Rasmus & Widén, Joakim & Munkhammar, Joakim & Lingfors, David, 2016. "Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment," Energy, Elsevier, vol. 112(C), pages 221-231.
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    11. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
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