## 1 Introduction  
Energy management is one of the major challenging tasks in this fast-changing world. The concept of the smart grid has brought a new change in the utilization of technology. However, on the other hand, the massive increase in the demand for production and managing the optimized energy within the grid structure is one of the significant issues with which the R&Ds are currently engaged. Not only within the intelligent grid network but also with the other minor ancillaries attached to the grid structure that accurately measure the energy utilization within the grid. Thus, the increase in Demand for Energy is one of the significant issues that is needed to be addressed [1]. In addition, EV charging stations help the industry optimize its grids' load management. It can also be used to aid the utilities in managing peak demand. On the other hand, the smart grid (SG) also handles the demand response of the industrial and residential load. Thus, the management facility deployed in the SG Network should be strong enough to provide constant optimized power output. It should also be capable of monitoring and rectifying itself in case of any malfunction. Recently, integrating electric vehicles with the smart grid network has been one of the standard techniques used to conserve energy. The SG network [1, 2] is a combination of various distributed systems and functions such as Advanced Metering Infrastructure (AMI), Distribution Automation (DA), and Distributed Management System (DMS), which all integrate to produce the most optimized power flow. Load flow management is also another vital management technique. Especially when the EVs are integrated with the grid structure, the demand for electricity reaches its peak demand. Thus, monitoring the load at various times during the day is necessary, ensuring the overload condition is not reached. Similar load management techniques should be well implemented at various stages of the grid for constant load monitoring. The conventional load management techniques in the SG network are not well suited. Advanced load management techniques such as distributive load response (DLR) and advanced metering infrastructure (AMI) are integrated for the more intelligent operation of the network [3; 4]. Thus, the SG greatly depends on the design, development, and integration of advanced devices and techniques for its efficient performance. Integrating the charging stations with the smart grid contributes to load management techniques and provides ample opportunity for research in this domain. One of the biggest issues in crowded metropolitan areas is the space due to the increased population. There is no extra space for creating the charging infrastructure, and it may become complicated to create one in the future. Therefore, this paper proposes on-demand space-efficient multi-level EV charging station infrastructure for metropolitan cities. We have integrated the novel structure of the infrastructure with the 33-bus power grid for power transaction, load management, and power grid network balance. A novel methodology, that is, a multi-agent system, is deployed on each bus and charging station to collect, share and analyze various power system parameters. The analyzed parameters are used to control and maintain the stability of the power grid. The multi-level charging infrastructure's stress and displacement analysis has been done, and the cost analysis of the proposed infrastructure has been discussed. Developed the analytical model of the proposed method, optimization problem is formulated and is solved using the Genetic algorithm, and simulation has been conducted with varying parameters such as arrival rates, service rates, and the number of charging points available at the charging station. The rest of the paper is organized as follows: Section 2 presents the literature review in which a discussion on existing studies is given. Section 3 provides an overview of the charging stations. Section 4 provides the detail of the proposed approach.