It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power loss, a degradation in voltage profile, and decreased voltage stability margin. To overcome the issues mentioned earlier, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is essential. The connection of an EVCS to the electricity grid will bring new challenges. Distributed generation (DG) sources are incorporated with EVCS to lessen the impact of EV charging load. In this study, charging stations are combined with DG units, which increases the motivation to use EVs. This study proposes an artificial intelligence (AI) approach, the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, to investigate the suitable nodes for EVCS and DGs in a balanced distribution system. The proposed methodology is verified on the IEEE-33 bus and IEEE-69 bus systems. According to the findings, the obtained results are consistent as compared to other existing techniques. These findings are taken into consideration to analyze the reliability of electrical distribution networks. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the electrical system’s reliability.

The burning of fossil fuels and the emission of greenhouse gases motivates policymakers to think about the transition in their approach towards electric vehicles (EVs) from conventional ones. Transportation vehicles’ electrification drives the attention of various researchers and scientists towards the emergence of charging stations (CSs). CS placement is a matter of great concern for large scale penetration of EVs. Old infrastructure causes several challenges in planning the ideal placement of the CS since EVs have not prevailed in recent years. Recently, a lot of studies have been performed on CS placement, which attracts the attention of researchers. Various approaches, objective functions, constraints and range of optimisation techniques are addressed by researchers for optimal placement of CS. This study provides the research outcomes in respect of the placement of CS over the past few years based on objective functions, solution methods, geographic conditions and demand‐side management.

This paper introduces a new hybrid technique for investigating the optimal location of electric vehicle charging stations. Reactive power compensation is also provided along with charging station to deal with power loss issues and retain the distribution network’s reliability. Moreover, vehicle to grid facility has been thoroughly examined in this article. The suggested algorithm is the hybrid of gray wolf optimization (GWO) and particle swarm optimization (PSO). The hybridization of two algorithms combines the desirable attributes of both algorithms and maintains a strong balance between the exploration and exploitation capability. The proposed hybrid approach is applied on IEEE-33 bus and 34-bus distribution systems to minimize the active power loss, maximize the net profit, and improve the reliability of electric grid network. Furthermore, the outcome attained using the anticipated approach is equated with conventional GWO and PSO. For 33-bus system, the proposed technique results in loss reduction of 30.67% and maximizing the net profit by 1142 $ and 1560 $ as compared to GWO and PSO, respectively. Likewise, power losses have been reduced to 27.6% and maximizing the net profit by 1726 $ and 3149 $ as compared to isolated GWO and PSO, respectively, for 34-bus network. The achieved outcomes prove the supremacy of the suggested approach over GWO and PSO to evaluate locations of charging stations’ and reactive power source in radial distribution network.

This study assesses the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), emphasizing its significance for sustainable development. An environmentally friendly EVCS model and a novel metaheuristic optimization technique, the Giza Pyramids Construction Algorithm (GPCA) is employed, to determine the optimal sizing of system components. The objective is to ensure dependability, as determined by the lack of power supply probability (LPSP), while minimizing the levelized cost of energy (LCOE) and total net present cost (TNPC). We assess the efficacy of other algorithms in scaling system components by contrasting the performance of the GPCA with those of the Artificial Hummingbird Algorithm (AHA), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). The study also examines the impact of climatic, financial, and technical factors on system costs, providing insights into the challenges, opportunities, and socio-economic factors influencing the deployment of EV infrastructure in KSA. Findings suggest that the optimal EVCS configuration is a solar photovoltaic/wind turbine (SPV/WT) grid-tied system, resulting in an LCOE of $0.0796/kWh, a TNPC of $102,042, and an annual CO ₂ emission reduction of 18,840 kg. The system achieves a 67.9% renewable penetration rate with 30.8% annual grid electricity purchase and 32.4% sale. Sensitivity analysis on renewable parameters is also conducted, demand charges, grid purchase prices, discount rates, and inflation rates to assess their impact on the system's economic variables. By identifying current constraints and potential strategies, our study aims to inform policy-making and investment decisions for expanding electric vehicle charging infrastructure, with broader implications for developing nations.