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<p>The rapid growth in electric vehicle (EV) adoption has led to an increased demand for Electric Vehicle Charging Stations (EVCSs), necessitating effective optimization strategies for their placement and sizing in microgrids. This study proposes an Improved Binary Quantum-based Puma Optimizer (IBQP) for the optimal location and sizing of hybrid systems in microgrids with integrated EVCSs and a bus system. The model aims to minimize the system’s total cost while ensuring reliability and sustainability. The optimization problem incorporates both energy generation and charging demands, with an emphasis on renewable energy sources and the integration of a bus system to transport energy efficiently across different parts of the grid. Four different system configurations are evaluated in this work. The first case analyzes the base system with only EVCSs, focusing on their optimal locations and sizes to meet charging demands. The second case integrates Photovoltaic (PV) energy generation alongside EVCSs, optimizing the placement and sizing of both components to maximize energy utilization. The third case extends the system by incorporating Wind power in addition to EVCSs and PV, providing further improvements in renewable energy integration. The fourth case examines the hybrid system combining both PV and Wind power alongside EVCSs and the bus system, aiming to minimize costs and maximize energy sustainability. The IBPO algorithm is implemented to optimize these systems, demonstrating its efficiency and robustness. The IEEE 33-bus distribution system, which is optimized for the location and size of PV and wind energy resources as well as EVCSs, is the system being tested taken into consideration in this research. To examine system performance and energy efficiency, MATLAB was used to run the simulations. The numerical results show a significant reduction in system costs by 15-20% and an improvement in energy sustainability by up to 25% when renewable energy sources are integrated into the micro grid with EVCS and the bus system. This work highlights the effectiveness of the proposed optimization technique in designing cost-efficient and sustainable micro grid systems.</p>

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Improved Binary Quantum-Based Puma Optimizer for Optimal Location and Sizing of Micro Grid with Electric Vehicle Charging Station

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Kaliappan Esakkiappan,
  • Prabaakaran Kandasamy,
  • Raghavendran Raja,
  • Senthil Kumar Rajendran

摘要

The rapid growth in electric vehicle (EV) adoption has led to an increased demand for Electric Vehicle Charging Stations (EVCSs), necessitating effective optimization strategies for their placement and sizing in microgrids. This study proposes an Improved Binary Quantum-based Puma Optimizer (IBQP) for the optimal location and sizing of hybrid systems in microgrids with integrated EVCSs and a bus system. The model aims to minimize the system’s total cost while ensuring reliability and sustainability. The optimization problem incorporates both energy generation and charging demands, with an emphasis on renewable energy sources and the integration of a bus system to transport energy efficiently across different parts of the grid. Four different system configurations are evaluated in this work. The first case analyzes the base system with only EVCSs, focusing on their optimal locations and sizes to meet charging demands. The second case integrates Photovoltaic (PV) energy generation alongside EVCSs, optimizing the placement and sizing of both components to maximize energy utilization. The third case extends the system by incorporating Wind power in addition to EVCSs and PV, providing further improvements in renewable energy integration. The fourth case examines the hybrid system combining both PV and Wind power alongside EVCSs and the bus system, aiming to minimize costs and maximize energy sustainability. The IBPO algorithm is implemented to optimize these systems, demonstrating its efficiency and robustness. The IEEE 33-bus distribution system, which is optimized for the location and size of PV and wind energy resources as well as EVCSs, is the system being tested taken into consideration in this research. To examine system performance and energy efficiency, MATLAB was used to run the simulations. The numerical results show a significant reduction in system costs by 15-20% and an improvement in energy sustainability by up to 25% when renewable energy sources are integrated into the micro grid with EVCS and the bus system. This work highlights the effectiveness of the proposed optimization technique in designing cost-efficient and sustainable micro grid systems.