The large-scale construction of fast charging stations (FCSs) for electrical vehicles (EVs) is helpful in promoting the EV. It creates a significant challenge for the distribution system operator to determine the optimal planning, especially the siting and sizing of FCSs in the electrical distribution system. Inappropriate planning of fast EV charging stations (EVCSs) cause a negative impact on the distribution system. This paper presented a multiobjective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system. The problem is formulated as a mixed integer non-linear problem (MINLP) to optimize the loss of EV user, network power loss (NPL), FCS development cost and improve the voltage profile of the electrical distribution system. Non-dominated sorting genetic algorithm II (NSGA-II) is used for solving the MINLP. The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.

Current trends suggest that electrical vehicle (EV) is a promising technology for road transportation. There is a substantial increase in the number of EVs due to improved energy efficiency and reduction in environmental impact as compared with internal combustion engine vehicles. The improper planning of fast charging stations (FCSs) and distributed generations (DGs) hurts the distribution system. So the distribution system operator has a significant challenge to identify the optimal location and sizing of FCSs in the distribution power network. This study presents optimal planning of FCSs and DGs with the account of the present and future increase in EV population. A multi-objective optimisation problem is formulated for optimal planning of FCSs and DGs with the objective of minimising the voltage deviation, distribution network power loss, DGs cost and the energy consumption of EV users. This problem is solved for different levels of increase in EV population for different cases. A novel hybrid shuffled frog leap-teaching and learning based optimisation algorithm is proposed and implemented to solve the considered multi-objective problem. The performance of the proposed algorithm is compared with prior-art algorithms in the literature.

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Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.

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