Abstract
Abstract The world population is growing with more understanding of technical and social changes. Limited energy reserves and exponential demand growth have created a serious concern in all sectors including automobile sector. Electric vehicles (EVs) develop better means of transportation as it suppress to the adverse effects in using conventional ways of transportation. Among all the energy requirements, the automobile sector demands almost 21 %. It is a matter of concern because of limited energy reserves. Concerning all the adverse effects of using conventional ways of transportation, scientists and engineers are working on electric vehicles. Intensive research has been going on for decades to make electric vehicles a substitute for conventional transportation systems with the same reliability. The merit for EV charging infrastructure is an indispensable metric that plays a pivotal role in assessing the efficacy and progress of electric vehicle charging networks. This metric encompasses a range of critical factors, including the density and geographical distribution of charging stations, the charging speed and capacity, the utilization rates, the availability of alternative charging options (e.g., fast charging, slow charging), and the integration of optimization technologies for optimal energy management. Apart from technical advancements, the researchers are working on the challenges related to their mass adoption in current distribution system. For large-scale deployment of EVs, reliable and sustainable charging infrastructure needs to be developed. But the charging station placement problem is a complex problem. Charging stations must be placed in the distribution network in such a way that the negative impacts of the placement of charging stations on the operating parameters of the distribution network are minimal. The various aspects to gain deeper insights into the strengths and weaknesses of existing charging infrastructure, and to propose innovative solutions for further enhancing the efficiency, accessibility, and sustainability of EV charging networks. The location of the charging station is important and must consider the effect of EV charging demand at existing system with overall charging station placement cost. Therefore, the Voltage Reliability Power loss (VRP) Index (that integrates effect of important parameters such as voltage variation, reliability, and power loss) using Discrete Particle Swarm Optimization (DPSO) is minimized including overall charging station cost as an important constraint for this proposed algorithm. This overall charging station cost includes land cost, installation cost, and energy demand cost by electric vehicles. The results are compared with new discrete versions of different PSO techniques, namely, Standard-Discrete Particle Swarm Optimization (S-DPSO) and Passive Congregation Discrete Particle Swarm Optimization (PC-DPSO) optimization techniques.
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