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Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty

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  • Yu, Zhenyu
  • Lu, Fei
  • Zou, Yu
  • Yang, Xudong

Abstract

The widespread use of fluctuating renewable electricity has introduced challenges in balancing power systems. This can be alleviated by using flexible resources on the demand side to provide ancillary services at various timescales (e.g., day-ahead planning or real-time operation). However, practitioners lack a unified framework for measuring energy flexibility in the real-time operation frame. This study considers a control structure for clustered commuter plug-in electric vehicles (PEVs) in an office building with both day-ahead scheduling and real-time dispatch and proposes a generic quantification framework for assessing the capability of PEVs to manage the uncertainties of photovoltaic output and inflexible building load. Our evaluation considered the flexibility shortage in terms of power, energy, and control strategy, and the Monte Carlo method was used for probabilistic analysis. We conducted a sensitivity analysis to identify the key factors determining real-time flexibility. The results suggest that 1.5–2 times the number of PEVs is required to meet the defined level of real-time flexibility rather than planning flexibility. Possible conflicts in flexibility enhancement at different timescales and directions were observed under various day-ahead scheduling strategies; prediction information can be used to mitigate conflicts in scheduling strategies and improve the overall control performance.

Suggested Citation

  • Yu, Zhenyu & Lu, Fei & Zou, Yu & Yang, Xudong, 2022. "Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922007097
    DOI: 10.1016/j.apenergy.2022.119365
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    2. Yusuke Kishita & Yohei Yamaguchi & Yuji Mizuno & Shinichi Fukushige & Yasushi Umeda & Yoshiyuki Shimoda, 2024. "Scenario Analysis of Electricity Demand in the Residential Sector Based on the Diffusion of Energy-Efficient and Energy-Generating Products," Sustainability, MDPI, vol. 16(15), pages 1-15, July.
    3. 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).
    4. Yao, Zhaosheng & Wang, Zhiyuan & Ran, Lun, 2023. "Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities," Applied Energy, Elsevier, vol. 343(C).
    5. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    6. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).

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