Author(s)

Junxing Zhu ¹, Baoyi Wang ¹, Chenxu Wang ¹, Shaomin Zhang ¹

Affiliation(s)

¹ School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China

Corresponding Author

Shaomin Zhang

ABSTRACT

With the increase of plug-in electric vehicles (PEV), the uncontrolled charging of them may pose a wide pressure on the operation of regional distribution network. In order to reduce adverse impacts of PEVs, an intelligent charging strategy for a cluster of PEVs is proposed. Considering several constraints such as the charger’s maximum charging power, a multi-objective optimization scheduling model is proposed with the objectives of minimizing the total charging cost and minimizing load variance basing on dynamic time-of-use (TOU) price. The Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ) is adopted to solve the optimization problem, and the MATLAB calculation results prove the feasibility and effectiveness of the proposed strategy. Factors such as the number of PEVs, the TOU price and the length of time-window are also analyzed to further study PEV charging load’s characteristics.

KEYWORDS

Plug-in electric vehicle; coordinated charging; time-of-use price; multi-objective optimization; impact analysis

CITE THIS PAPER

Shaomin, Z., Chenxu, W.,Baoyi, W., Junxing Z. (2017) Multi-objective Optimization Charging Strategy for Plug-in Electric Vehicles Based on Dynamic Time-of-use Price. Journal of Electrotechnology, Electrical Engineering and Management (2017) Vol.2, Num.1: 28-34.

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