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<p>The increasing popularity of electric cars, EVs, and fast charging stations is putting significant strain on local electrical systems due to the high demand. Renewable energies offer a viable approach for addressing electricity shortages and managing load spikes. This paper presents a novel approach to address the additional electricity burden by optimizing the management of resources. The approach focuses on distribution systems that are integrated with fast electric vehicle charging stations. The proposed methodology utilizes a multi-objective hypergraph particle swarm optimization (MOHGPSO) algorithm to optimize various objectives, including energy cost minimization, power loss minimization, and pollution emission reduction. Additionally, the study incorporates EV scheduling using the Least Laxity First rule at the charging station to optimize charging processes efficiently. Furthermore, weak bus identification is performed using the Power Transfer Distribution Factor method, facilitating targeted improvements to grid infrastructure. To increase the sustainability and resilience of the grid system, the study incorporates wind, photovoltaic, and micro-turbine in the distribution grid. The effectiveness and practical applicability of the proposed approach are demonstrated through comprehensive simulations and case studies. The findings indicate that the new MOHGPSO method effectively decreases energy cost, pollution emission, and power loss compared to MOPSO. For example, the MOHGPSO approach reduced the daily energy cost by 57.11% compared with MOPSO. In addition, the voltage profile in MOHGPSO is also enhanced to 0.9454 p.u.</p>

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A Multi-objective Approach for Fast EV Charging Stations Integrated with Grid and Renewable Energy Sources

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Shreya Upadhyay,
  • Annapurna Bhargava,
  • Rajive Tiwari

摘要

The increasing popularity of electric cars, EVs, and fast charging stations is putting significant strain on local electrical systems due to the high demand. Renewable energies offer a viable approach for addressing electricity shortages and managing load spikes. This paper presents a novel approach to address the additional electricity burden by optimizing the management of resources. The approach focuses on distribution systems that are integrated with fast electric vehicle charging stations. The proposed methodology utilizes a multi-objective hypergraph particle swarm optimization (MOHGPSO) algorithm to optimize various objectives, including energy cost minimization, power loss minimization, and pollution emission reduction. Additionally, the study incorporates EV scheduling using the Least Laxity First rule at the charging station to optimize charging processes efficiently. Furthermore, weak bus identification is performed using the Power Transfer Distribution Factor method, facilitating targeted improvements to grid infrastructure. To increase the sustainability and resilience of the grid system, the study incorporates wind, photovoltaic, and micro-turbine in the distribution grid. The effectiveness and practical applicability of the proposed approach are demonstrated through comprehensive simulations and case studies. The findings indicate that the new MOHGPSO method effectively decreases energy cost, pollution emission, and power loss compared to MOPSO. For example, the MOHGPSO approach reduced the daily energy cost by 57.11% compared with MOPSO. In addition, the voltage profile in MOHGPSO is also enhanced to 0.9454 p.u.