Key points are not available for this paper at this time.
One of the challenges of big cities is Green House Gases (GHG) emissions due to driving cars with Internal Conventional Engines (ICE), which are dependent on fossil fuels. Plug-In Electric Vehicles (PEVs) have been developing to cope with the challenges. Flexible fuels, convenience, safe charging, high performance and cost saving are also considered as significant benefits of the technologies. In spite of aforementioned advantages, inappropriate place and size of aggregated PEVs increase loss and voltage degradation. Therefore, optimal planning of Charging Stations (CSs), which consider loss and voltage, is presented in this paper. Time-based programs of Demand Response (DR) are also employed for improving loss and voltage. Time-Of-Use (TOU), Critical Peak Pricing (CPP) and Real Time Pricing (RTP) are applied on the problem. Candidate place of CSs is optimally found by traffic network along with distribution network. Genetic Algorithm (GA) is used to solve the problem. Simulation carries out a 33 bus radial distribution network. Results reveal optimal size of CSs as well as DR programs effect to minimize loss and voltage drop in distribution network. Results demonstrate CSs with more capacity farther power supply may increase loss and voltage degradation. Thus, close candidate places are sized by CSs with more capacity. Results also approve integration of CSs to grid may cause %33 loss and %0.3-%2.3 voltage degradation, which DR with different programs (CPP, TOU and RTP) can improve %6, %8 and %10.5 loss and %0.25 voltage drop in sequence.
Pazouki et al. (Sun,) studied this question.