Cost Minimization of Charging Stations with Photovoltaics: An Approach\n with EV Classification

This paper proposes a novel electric vehicle (EV) classification scheme for a\nphotovoltaic (PV) powered EV charging station (CS) that reduces the effect of\nintermittency of electricity supply as well as reducing the cost of energy\ntrading of the CS. Since not all EV drivers would like to be environmentally\nfriendly, all vehicles in the CS are divided into three categories: 1) premium,\n2) conservative, and 3) green, according to their charging behavior. Premium\nand conservative EVs are considered to be interested only in charging their\nbatteries, with noticeably higher rate of charging for premium EVs. Green\nvehicles are more environmentally friendly, and thus assist the CS to reduce\nits cost of energy trading by allowing the CS to use their batteries as\ndistributed storage. A different charging scheme is proposed for each type of\nEV, which is adopted by the CS to encourage more EVs to be green. A basic mixed\ninteger programming (MIP) technique is used to facilitate the proposed\nclassification scheme. It is shown that the uncertainty in PV generation can be\neffectively compensated, along with minimization of total cost of energy\ntrading to the CS, by consolidating more green EVs. Real solar and pricing data\nare used for performance analysis of the system. It is demonstrated that the\ntotal cost to the CS reduces considerably as the percentage of green vehicles\nincreases, and also that the contributions of green EVs in winter are greater\nthan those in summer.\n