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M. Ntombela

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Review Open access Jul 2026

A Comprehensive Review of Electric Vehicle Charging Station Integration and Its Impact on Power System Performance

The rapid growth of Electric Vehicles (EVs) has accelerated the deployment of Electric Vehicle Charging Stations (EVCSs), making their integration into modern power systems increasingly important. While EVCSs support transportation electrification and global decarbonization goals, large-scale integration introduces technical challenges that affect power system operation, reliability, and planning. This review provides a comprehensive assessment of the impact of EVCS integration on power system performance by examining charging technologies, charging stations, charging modes, and the principal components of EVCSs. The review discusses the effects of EV charging on load demand, peak load, voltage profile, voltage stability, active and reactive power losses, transformer loading, and overall grid performance. It further evaluates mitigation strategies, including smart charging, coordinated charging, Demand Response (DR), Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Vehicle-to-Grid (V2G) technology, and Artificial Intelligence (AI)-based energy management. The application of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and advanced optimization algorithms for charging coordination and demand forecasting is also reviewed. Finally, the paper identifies current research challenges and future directions related to charging uncertainty, renewable energy integration, cybersecurity, interoperability, and infrastructure development. The findings demonstrate that intelligent charging strategies combined with renewable energy integration, energy storage, V2G, and AI significantly improve the reliability, efficiency, resilience, and sustainability of future EV-integrated power systems.

M. Ntombela · 0 citations
Conference Jul 2026

Electric Vehicles Integration into the Power Grid Using HGAIPSO Optimization Algorithm

The gradual depletion of conventional fossil-fuel resources, growing environmental concerns, and the increasing complexity associated with modern smart-grid deployment have accelerated the integration of renewable energy distributed generators (REDGs) and electric vehicles (EVs) into electrical power systems. Over recent decades, the global EV industry has experienced considerable growth in both vehicle production and market penetration. This study investigates the ancillary services offered by EVs and evaluates the implications of their large-scale integration into power networks. An optimization-based framework is developed to estimate EV hosting capacity, formulate an enhanced objective function, determine the optimal locations and capacities of charging stations, and evaluate the associated system operating costs. Simulation findings demonstrate that the proposed EV capacity estimation approach effectively represents vehicle charging and discharging behavior. Furthermore, a hybrid genetic algorithm-improved particle swarm optimization (HGAIPSO) technique is introduced to optimize the allocation of EV charging stations while incorporating coordinated charging strategies and renewable distributed generation. The proposed framework mitigates voltage-limit violations, minimizes energy losses and system costs, and improves the overall power-quality performance of the network. The effectiveness of the developed approach is validated through MATLAB-based simulations conducted on the IEEE 118 bus test system.

M. Ntombela, M. Kabeya · 0 citations

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