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Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design

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  • Liu, Xiaochen
  • Fu, Zhi
  • Qiu, Siyuan
  • Li, Shaojie
  • Zhang, Tao
  • Liu, Xiaohua
  • Jiang, Yi

Abstract

Demand-side decarbonization and electrification promote a strong coupling between electric vehicles (EVs) and buildings. From a building-centric perspective, this paper established a survey-based simulation platform for charging system design, which supports annual simulations with a 15-min interval. Field survey data of private vehicles in two office buildings located in Beijing (a metropolis) and Ruicheng (a county in China) were used as inputs and for validation. The platform was applied to analyze workplace charging and give suggestions for charging system design. First, the city size greatly influences vehicle behavior and charging demands. This study indicates an unusual double-peak charging demand on weekdays in the county case due to different vehicle behavior from the urban cases. Second, slow charging at workplaces can almost meet the intra-urban travel demand of private EVs. Specifically, chargers with a rated power of 3–4 kW can achieve a guarantee level of EV use of SOC0.999 = 20%. Third, the low solar self-consumption rate of current PV charging systems (generally 18%∼42%) mainly results from significant PV surpluses on weekends and in summer. These findings emphasize building-centric planning of EV-related infrastructures and proper charging strategies for energy interaction between EVs and buildings.

Suggested Citation

  • Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004048
    DOI: 10.1016/j.energy.2023.127010
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    Cited by:

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    3. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(C).
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    5. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    6. Luo, Jianing & Yuan, Yanping & Joybari, Mahmood Mastani & Cao, Xiaoling, 2024. "Development of a prediction-based scheduling control strategy with V2B mode for PV-building-EV integrated systems," Renewable Energy, Elsevier, vol. 224(C).

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