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

Qinglai Guo, Yao Wang, Hongbin Sun, Zhengshuo Li, Shujun Xin and Boming Zhang
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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

Energies, 2012, vol. 5, issue 6, 1-18

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.

Keywords: load model; electric vehicle; linear regression; support vector regression; travel dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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