Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

The concept of Digital Twin (DT) has emerged to enable the benefits of future paradigms such as Industrial Internet of Things and Industry 4.0. The idea is to bring every data source and control interface description related to a product or process available through a single interface, for auto-discovery and automated communication establishment. However, designing the architecture of a DT to serve every future application is an ambitious task. Therefore, prototyping systems for specific applications is required to design the DT incrementally. We developed a novel DT prototype to analyze the requirements of communication in a mission-critical application such as mobile networks supported remote surgery. Such operations require low latency and high levels of security and reliability and therefore are a perfect subject for analysing DT communication and cybersecurity. The system comprised of a robotic arm and HTC Vive Virtual Reality (VR) system connected over a 4G mobile network. More than 70 test users were employed to assess the system. To address the cybersecurity of the system, we incorporated a network manipulation module to test the effect of network outages and attacks; we studied state of the art practices and their utilization within DTs. The capability of the system for actual remote surgery is limited by capabilities of the VR system and insufficient feedback from the robot. However, simulations and research of remote surgeries could be conducted with the system. As a result, we propose ideas for communication establishment and necessary cybersecurity technologies that will help in developing the DT architecture. Furthermore, we concluded that developing the DT requires cross-disciplinary development in several different engineering fields. Each field makes use of its own tools and methods, which do not always fit together perfectly. This is a potentially major obstacle in the realization of Industry 4.0 and similar concepts.

The ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric Vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This work formulates and solves the charging infrastructure-planning problem for Guwahati city, India that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics such as voltage stability, reliability, power loss as well as EV user’s convenience and random road traffic. The placement problem was solved by using a Pareto dominance based hybrid algorithm amalgamating Chicken Swarm Optimization (CSO) and Teaching Learning Based Optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.