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|Abstract:||This paper proposes a novel electric vehicle (EV) classification scheme for a photovoltaic (PV)-powered EV charging station (CS) that reduces the effect of intermittency of electricity supply and the cost of energy trading of the CS. Since not all EV drivers would like to be environmentally friendly, all vehicles in the CS are divided into three categories: 1) premium; 2) conservative; and 3) green, according to their charging behavior. Premium and conservative EVs are considered interested only in charging their batteries, with noticeably higher rates of charging for premium EVs. Green vehicles are more environmentally friendly and thus assist the CS to reduce its cost of energy trading by allowing the CS to use their batteries as distributed storage. A different charging scheme is proposed for each type of EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed-integer programming (MIP) technique is used to facilitate the proposed classification scheme. It is shown that the uncertainty in PV generation can be effectively compensated, along with minimization of total cost of energy trading to the CS, by consolidating more green EVs. Real solar and pricing data are used for performance analysis of the system. It is demonstrated that the total cost to the CS reduces considerably as the percentage of green vehicles increases and that the contributions of green EVs in winter are greater than those in summer.|
|Citation:||Tushar, Wayes, Chau Yuen, Shisheng Huang, David B. Smith, and H. Vincent Poor. "Cost minimization of charging stations with photovoltaics: An approach with EV classification." IEEE Transactions on Intelligent Transportation Systems 17, no. 1 (2015): 156-169. doi:10.1109/TITS.2015.2462824|
|Pages:||156 - 169|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||IEEE Transactions on Intelligent Transportation Systems|
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