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Renewable energies like solar, wind, etc. have gained a lot of importance in the recent years as they are clean sources that can be brought to use to supply power to charging stations (CS). The growing demand for electric vehicles (EVs) has led to an increasing need for efficient and sustainable charging infrastructure. To optimize the utilization of solar and wind resources, advanced energy management systems are employed in this work. The solar energy system of 25 KW has been integrated with the charging station and its power output and flow across the system has been analyzed that achieves charging of EV clusters. The variable input conditions are studied and power flow management is achieved across the storage systems, grid, and EVs. Wind energy system is further integrated of 11 KW which is operated at its fuel efficiency and then at reduced efficiency to study the impact on the power flow. The control algorithm has been designed with fuzzy interfaced multistage genetic algorithm (F_MS_GA) to achieve efficient energy distribution across the various loading points.

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Solar and Wind Energy-Based Charging Station Designing for EV with Hybrid Storage Systems Having Power Flow Optimization Using F_MS_GA Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Prakash Narayan Tiwari,
  • Kaushal Prasad Tiwari,
  • Anshu Dixit,
  • Aashish Kumar Bohre,
  • Sanjeev Kumar Gupta,
  • Sitaram Pal

摘要

Renewable energies like solar, wind, etc. have gained a lot of importance in the recent years as they are clean sources that can be brought to use to supply power to charging stations (CS). The growing demand for electric vehicles (EVs) has led to an increasing need for efficient and sustainable charging infrastructure. To optimize the utilization of solar and wind resources, advanced energy management systems are employed in this work. The solar energy system of 25 KW has been integrated with the charging station and its power output and flow across the system has been analyzed that achieves charging of EV clusters. The variable input conditions are studied and power flow management is achieved across the storage systems, grid, and EVs. Wind energy system is further integrated of 11 KW which is operated at its fuel efficiency and then at reduced efficiency to study the impact on the power flow. The control algorithm has been designed with fuzzy interfaced multistage genetic algorithm (F_MS_GA) to achieve efficient energy distribution across the various loading points.