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Factor Analysis of the Aggregated Electric Vehicle Load Based on Data Mining

Author

Listed:
  • Qinglai Guo

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

  • Yao Wang

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

  • Hongbin Sun

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

  • Zhengshuo Li

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

  • Shujun Xin

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

  • Boming Zhang

    (State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China)

Abstract

Electric vehicles (EVs) and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU) policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably.

Suggested Citation

  • Qinglai Guo & Yao Wang & Hongbin Sun & Zhengshuo Li & Shujun Xin & Boming Zhang, 2012. "Factor Analysis of the Aggregated Electric Vehicle Load Based on Data Mining," Energies, MDPI, vol. 5(6), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:6:p:2053-2070:d:18481
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    References listed on IDEAS

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    1. Sioshansi, Ramteen & Fagiani, Riccardo & Marano, Vincenzo, 2010. "Cost and emissions impacts of plug-in hybrid vehicles on the Ohio power system," Energy Policy, Elsevier, vol. 38(11), pages 6703-6712, November.
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    Cited by:

    1. Cong Zhang & Haitao Min & Yuanbin Yu & Dai Wang & Justin Luke & Daniel Opila & Samveg Saxena, 2016. "Using CPE Function to Size Capacitor Storage for Electric Vehicles and Quantifying Battery Degradation during Different Driving Cycles," Energies, MDPI, vol. 9(11), pages 1-23, November.
    2. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
    3. Pol Olivella-Rosell & Roberto Villafafila-Robles & Andreas Sumper & Joan Bergas-Jané, 2015. "Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks," Energies, MDPI, vol. 8(5), pages 1-28, May.
    4. Yiqi Lu & Yongpan Li & Da Xie & Enwei Wei & Xianlu Bao & Huafeng Chen & Xiancheng Zhong, 2018. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load," Energies, MDPI, vol. 11(11), pages 1-16, November.

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