In recent times, the limited availability of fossil fuels and growing concerns regarding the emission of greenhouse gases (GHGs) have directly impacted the shift from conventional automobiles to electric vehicles (EVs). Additionally, there have been notable advancements in new energy research, which have significantly improved the viability of EVs. Consequently, EVs have gained widespread recognition and have been rapidly adopted in many countries worldwide. However, the rapid growth of EVs has given rise to several challenges, such as insufficient charging infrastructure, unequal distribution, high costs, and a lack of charging stations, which have become increasingly significant. The limited availability of charging facilities is hindering the widespread adoption of EVs. However, as more people embrace EVs, there has been a growth in the installation of electric vehicle charging stations (EVCSs) in public locations. Recent research has focused on identifying the ideal locations for EVCSs in order to assist the electrification of transport systems and meet the growing demand for EVs. A well-developed EVCS infrastructure can help address some key issues facing EVs, such as pricing and range limitations. Researchers have used various methodologies, objective functions, and constraints to formulate the problem of identifying the best sites for EVCSs. Current research is focused on determining the best locations for EVCSs. This endeavor intends to ease the transition to electrified transport networks while also addressing the growing demand for EVs. This review article explores various optimization techniques to achieve optimal solutions while considering the impact of EV charging load on the distribution system (DS), environmental implications, and economic impact. The research used a standard IEEE 33-bus radial distribution system (RDS) with a full variety of potential energy sources to improve understanding of the subject. The use of the bald eagle search algorithm (BESA) and cuckoo search algorithm (CSA) aided in the best identification of energy source locations and their relative capacities. In addition, the examination of EV charging techniques, including both the traditional charging technique (TCT) and the innovative charging technique (ICT), is being undertaken to assess the effectiveness of these approaches. The study included ten separate scenarios, each of which was thoroughly evaluated to demonstrate the individual and synergistic usefulness of various energy sources in mitigating the effects of EV charging on the DS. The collected data from the empirical inquiry was aggregated and thoroughly analyzed.

This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.

This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective function is formulated to minimize the total power losses of the system subjected to equality and inequality constraints. Loss sensitivity factor (LSF) and Voltage Stability Index (VSI) are used to predetermine the optimal location of DG and DSTATCOM, respectively. Recently developed nature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM. In the present work, five different cases have been considered during DG and DSTATCOM placement to access the performance of the proposed technique. To check the feasibility, the proposed method is tested on IEEE 12-bus, 34-bus, and 69-bus radial distribution system and the results were compared with other existing techniques.

The high penetration of renewable sources owing to less environmental pollution creates challenges for the grid operators. Virtual Power Plant is a novel concept that will integrate the small distributed energy resources and will act as a single conventional power plant in the electricity market. As a core energy management system in VPP, the energy should be dispatched optimally for achieving the maximum profit. Therefore, smart energy management is developed in this article of VPP with PLC and IoT in a unified market environment that integrates the DA and RT market. The cost characteristics for the interruptible load, battery storage system are modelled individually. The proposed scheme can efficiently handle the energy demand for the VPP domain. Four different scenarios are considered with different loading conditions for validation of the concept of smart energy management. The profitability for each scenario is shown with the experimental results.

Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system’s performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system’s net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS.