In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno–economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. The study aims to maximize the technical, financial, and social benefits while ensuring that all security constraints are met. To assess the financial viability of the proposed model over a 10-year horizon, a detailed economic analysis comprising installation cost, operation, and maintenance cost is conducted. To make the model more realistic, various practical parameters, such as the inflation rate and interest rate, are incorporated during the financial analysis. Additionally, to highlight the societal benefits of the approach, the study quantifies the long-term carbon emissions and the corresponding cost of emissions. The proposed framework is tested on both a 33-bus distribution network and a 108-bus Indian distribution network. Various planning scenarios are explored, with different configurations of the EVCE, solar-based DG, and DSTATCOM, to assist power system planners in selecting the most suitable strategy.

Electric vehicles (EVs) are significantly increasing the burden on the electricity sector, resulting in power loss, a poor voltage profile, and voltage stability difficulties. To address this, a solid planning framework for efficient deployment of EV charging stations and solar energy resources in the distribution network is being established in the present work. A practical probabilistic model of EV is developed using Probability Distribution Function (PDF) considering uncertainty parameters i.e.trip distance, trip end time, types of battery and battery capacity extracted from real National Household Travel Survey (NHTS) datasheet. The Teaching Learning Based Optimization (TLBO) algorithm is used to solve the problem of optimum joint deployment of EV charging station and solar energy, minimizing power loss reduction index (PLRI) and voltage deviation index(VDI) while considering system constraints. Further the voltage stability of the system is also analyzed with the incorporation of devices in the network. In this paper, the optimization problem is solved using MATLAB (version R2018a) software, and the IEEE 69 bus system is employed as a test system. The simulation outcomes show that optimal deployment of EV charging stations with photovoltaic (PV) units improves voltage profile, the penetration level of EV charging station and also voltage stability index (VSI) of the system, but reduces total real loss. The technique is compared with three different algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), in terms of convergence characteristics.

In the present work, a strategic multi-scenario EV ultra-fast charging station (CS) planning framework is designed to provide advantages to charging station owners, Distribution Network Operators, and EV owners. Locations of CSs are identified using zonal division and the Voltage Stability Index strategy. The number of chargers is determined using the Harris Hawk Optimization (HHO) technique while minimizing the installation, operational costs of CS, and energy loss costs considering all the power system security constraints. To ensure a realistic planning model, uncertainties in EV charging behavior and electricity prices are managed through the 2m-Point Estimate Method. This method produces multiple scenarios of uncertain parameters, which effectively represent the actual dataset, thereby facilitating comprehensive multi-scenario planning. This study incorporates annual EV and system load growth in a long-term planning model of ten years, ensuring the distribution network meets future demand for sustainable transportation infrastructure. The proposed research work is tested on a 33-bus distribution network and a 51-bus real Indian distribution network. To evaluate the financial and environmental benefits of the planning, a cost-benefit analysis in terms of the Return-on-Investment index and a carbon emission analysis are performed, respectively. Furthermore, to prove the efficacy of the HHO technique, the results are compared with several existing algorithms.

In recent days, due to the increasing number of electric vehicle charging stations (EVCSs) and additional power consumption by domestic, commercial, and industrial consumers, the overall power system performance suffers, which further degrades voltage profile, reduces stability, increases losses, and may also create a voltage collapse problem. Therefore, it is crucial to predetermine a maximum loadability limit for voltage collapse analysis and a practical allowable extra load for safe and secure operation, keeping the bus voltage within the security limits. To mitigate the problems, unique and innovative formulae such as the maximum load multiplier (MLM) and practical load multiplier (PLM) have been developed to consider line resistance. The determination of actual permissible extra load for a bus enables quick assessment of bus-wise suitable capacities and the number of EVs that can be charged simultaneously in the charging station. The planning engineers can easily settle on the extra load demand by domestic, commercial, and industrial consumers, while maintaining the voltage security constraint. The proposed technique is simple, non-iterative, computationally inexpensive, and applicable to both transmission and distribution systems. The proposed work is tested on a 57-bus transmission system and 69-bus radial distribution system, and the obtained results from the developed formulae are verified by comparing with conventional iterative methods.