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The Multiobjective Based Large-Scale Electric Vehicle Charging Behaviours Analysis

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  • Yimin Zhou
  • Zhifei Li
  • Xinyu Wu

Abstract

In the paper, the effect of the charging behaviours of electric vehicles (EVs) on the grid load is discussed. The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. A nonlinear stochastic programming model with the maximized comprehensive index is developed to analyze the charging schemes, and a heuristic searching algorithm is used for the optimal parameters configuration. With the comparison of the evaluation criteria, the multiobjective strategy is more appropriate than the single-objective strategy for the charging, i.e., electricity price. Furthermore, considering the characteristics of the normal batteries and charging piles, user behaviour and EV scale, a Monte Carlo simulation process is designed to simulate the large-scale EVs traveling behaviours in long-term periods. The obtained simulation results can provide prediction for the analysis of the energy demand growth tendency of the future EVs regulation. As a precedent of open-source simulation system, this paper provides a stand-alone strategy and architecture to regulate the EV charging behaviours without the unified monitoring or management of the grid.

Suggested Citation

  • Yimin Zhou & Zhifei Li & Xinyu Wu, 2018. "The Multiobjective Based Large-Scale Electric Vehicle Charging Behaviours Analysis," Complexity, Hindawi, vol. 2018, pages 1-16, October.
    • Handle: RePEc:hin:complx:1968435
    DOI: 10.1155/2018/1968435
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    Cited by:

    1. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    2. Wu, Ji & Su, Hao & Meng, Jinhao & Lin, Mingqiang, 2023. "Electric vehicle charging scheduling considering infrastructure constraints," Energy, Elsevier, vol. 278(PA).
    3. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    4. Williams, B. & Bishop, D. & Hooper, G. & Chase, J.G., 2024. "Driving change: Electric vehicle charging behavior and peak loading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

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