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Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper. [ABSTRACT FROM AUTHOR]

• Design an optimal charging and discharging strategy to minimise the load variance under the uncertain scenario. • Design a priority-based charging/discharging scheduling by satisfying the different optimization constraints. • Develop a new index based on the real and reactive power loss reduction to provide reliable and quality supply to the consumer. • Formualte a multi-objective problem to solve the simultaneous load variance minimization and to maximise the quality of supply by minimizing the power loss index. • Maiden application of most valuable player algorithm to solve the multi-objective problem. In this paper, a priority-based Vehicle to Grid (V2G) optimization scheduling, considering their interaction with the distribution systems, has been proposed to minimise the load-variance of the load profile. In addition, two new indices, active power loss reduction index (PLR) and reactive power loss reduction index (QLR), have also been mathematically formulated. The developed indices, based on system's active and reactive power, guarantees the best quality of electrical supply to the load terminals. The Most Valuable Player (MVP) algorithm along with various Electrical Vehicle (EV) constraints, has been considered in this work to obtain the minimum values of load variance, PLR and QLR. Further, the results, as achieved through the proposed method, are compared with the other existing approaches to reveal its effectiveness and accuracy. The statistical analysis also supports the superiority of the MVP algorithm. Furthermore, parametric case studies are also done to declare the optimal results. It is shown that the proposed method with the developed indices provide the best quality of electrical supply to the load terminals. Finally, an optimisation model combining 'load-variance', ' PLR ', and ' QLR ' as a multi-objective planning problem has been presented. This developed model can obtain the non-dominated solutions to reach the final EVs planning schedule, thus achieving a more realistic and better solution than the existing approaches. The results are validated on a standard IEEE 69-bus radial distribution system. [ABSTRACT FROM AUTHOR]

With the ever-increasing penetration of electric vehicles (EVs), extreme fast charging stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are also installed for reducing the peak charging power. However, integrating the XFCS with a high-capacity power converter into the power distribution network (PDN) is difficult and uneconomical due to the restrictions regarding urban planning and high investment in PDN expansion. Considering the fluctuation in the EV charging demand and the limited capacity of the power converter, a collaborative policy for real-time EV charging power allocation and BES discharging power control is proposed based on Markov Decision Process (MDP), which is solved by the constraint deep deterministic policy gradient (CDDPG). The proposed model makes it possible to integrate the XFCS with reduced capacity power converter into the PDN with a minimal negative impact on the quality of service (QoS) of EV owners. Finally, the experimental evaluation with real-word data sets demonstrates that the proposed approach is more effective than benchmark methods in dynamically allocating charging power for XFCS. • The charging process and satisfaction for extreme fast charging are analyzed. • An MDP-based power allocation policy is proposed to enhance charging satisfaction. • A constraint DDPG algorithm is proposed to avoid excessive extra service time. • A cooperation policy for XFCS and battery energy storages is proposed. [ABSTRACT FROM AUTHOR]