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Because of the fast expansion of electric vehicles (EVs) in Saudi Arabia, a massive amount of energy will be needed to serve these vehicles. In addition, the transportation sector radiates a considerable amount of toxic gases in the form of SO2 and CO2. The national grid must supply a huge amount of electricity on a regular basis to meet the increasing power demands of EVs. This study thoroughly investigates the technical and economic benefits of an off-grid and grid-connected hybrid energy system with various configurations of a solar, wind turbine and battery energy storage system for the electric vehicle charging load in the Qassim region, Saudi Arabia. The goal is to decrease the cost of energy while reducing the chance of power outages in the system. This is achieved by using a new optimization algorithm called the modified salp swarm optimization algorithm (MSSOA), which is based on an evolutionary algorithm approach. MSSOA is an improved version of SSOA, which addresses its shortcomings. It has two search strategies to enhance its efficiency: first, it uses Levy flight distribution (LFD) to help individuals reach new positions faster, and second, it instructs individuals to spiral around the optimal solution, improving the exploitation phase. The MSSOA's effectiveness is confirmed by comparing its results with those of the conventional salp swarm optimization algorithm and particle swarm optimization (PSO). According to simulation findings, MSSOA has excellent accuracy and robustness. In this region, the SPV/WT/BESS-based EV charging station is the optimal option for EV charging stations. The SPV/WT/BESS design has the lowest LCOE of all feasible configurations in the region under study. The optimum values for the LCOE and TNPC using MSSOA are USD 0.3697/kWh and USD 99,928.34, which are much lower than the optimized values for the LCOE (USD 0.4156) and TNPC (USD 1,12,671.75) using SSOA. Furthermore, a comprehensive techno–economic analysis of optimized hybrid systems is assessed by incorporating the grid-connected option. The grid connected system results in optimized values of the LCOE (USD 0.0732/kWh) and TNPC (USD 1,541,076). The impact of different grid purchase prices on the levelized cost of energy is also studied. Our results will assist the researchers to determine the best technique for the optimization of an optimal energy system. [ABSTRACT FROM AUTHOR]

Deployment of fast‐charging stations for electrified transportation is currently one of the limiting factors in the commercial deployment and paradigm shift of the mobility sector. The rapidly increasing demand for electric vehicles (EVs) brings the demand for accessible and affordable charging infrastructure. The commercial EV fast‐charging load profile depends on the customer's EV charging demand, which poses challenges in determining the overall peak profile. This could lead to unprecedented challenges of harmonic disruption, system losses, supply‐demand balance, and grid reliability. The emergence of bidirectional charging infrastructure and the realization of controlled charging strategies in coordination with the cumulative contribution of renewable energy resources could potentially reduce the challenge of utility grid stability and reliability shortly. Furthermore, the parallel development of smart grid technologies has promising features. This paper aims to provide a comprehensive state‐of‐the‐art review of the major challenges of the utility grid in the swift adoption of fast charging infrastructures available in the open literature, as well as the sustainable implications and potential safety measures. Furthermore, the potential solutions and best practices for the synchronization of electrified transportation with the emerging ecosystem of renewable power systems and various controlled charging strategies are reviewed in detail. [ABSTRACT FROM AUTHOR]