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<p>The increasing use of Electric Vehicles (EVs) poses problems for the stability of the power system, especially in the domains of load control, power loss reduction, and voltage regulation. The Flexible AC Transmission System (FACTS) devices and EV Charging Stations (EVCS) devices are therefore sized and allocated optimally in an enhanced IEEE 33-bus system. This study introduces the CLEA, designed by combining the exploration strength of the Crayfish Optimization Algorithm (COA) and the exploration capability of the Lotus Effect Optimization Algorithm (LEOA) for enhanced optimization performance. To reduce installation costs, enhance voltage profiles, and handle power system emergencies, three different kinds of FACTS devices are used. Three case studies are carried out in MATLAB to evaluate system performance under various scenarios: (1) the base case with only EV integration, (2) the addition of FACTS devices, and (3) the integrated EV and FACTS case. The results demonstrate that the proposed CLEA methodology ensures cost-effective solutions, enhances voltage stability, and minimizes losses in both reactive and active energy sources. In order to increase network resilience and facilitate efficient power distribution, buses 4, 21, 15, 6, 22, 20, and 19 are the ideal locations for FACTS and EVCS devices. In the integrated EV and FACTS case, CLEA achieved a final real power loss of 32.22&#xa0;kW, a significant reduction compared to GWO (57.80&#xa0;kW) and PSO (116.33&#xa0;kW). Furthermore, CLEA resulted in a higher minimum voltage of approximately 0.968 p.u. and a lower maximum voltage deviation of approximately 0.032 p.u. Compared to the other algorithms. This integrated approach provides a scalable and practical solution to manage the increasing demand for EV charging while guaranteeing power distribution systems dependability and stability, offering a significant contribution to sustainable energy systems in the era of electric mobility.</p>

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Intelligent Voltage Stability Enhancement Through Optimal EVCS and FACTS Allocation Using Crayfish-Lotus Effect Algorithm

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • K. S. Gowthaman,
  • K. Dhayalini

摘要

The increasing use of Electric Vehicles (EVs) poses problems for the stability of the power system, especially in the domains of load control, power loss reduction, and voltage regulation. The Flexible AC Transmission System (FACTS) devices and EV Charging Stations (EVCS) devices are therefore sized and allocated optimally in an enhanced IEEE 33-bus system. This study introduces the CLEA, designed by combining the exploration strength of the Crayfish Optimization Algorithm (COA) and the exploration capability of the Lotus Effect Optimization Algorithm (LEOA) for enhanced optimization performance. To reduce installation costs, enhance voltage profiles, and handle power system emergencies, three different kinds of FACTS devices are used. Three case studies are carried out in MATLAB to evaluate system performance under various scenarios: (1) the base case with only EV integration, (2) the addition of FACTS devices, and (3) the integrated EV and FACTS case. The results demonstrate that the proposed CLEA methodology ensures cost-effective solutions, enhances voltage stability, and minimizes losses in both reactive and active energy sources. In order to increase network resilience and facilitate efficient power distribution, buses 4, 21, 15, 6, 22, 20, and 19 are the ideal locations for FACTS and EVCS devices. In the integrated EV and FACTS case, CLEA achieved a final real power loss of 32.22 kW, a significant reduction compared to GWO (57.80 kW) and PSO (116.33 kW). Furthermore, CLEA resulted in a higher minimum voltage of approximately 0.968 p.u. and a lower maximum voltage deviation of approximately 0.032 p.u. Compared to the other algorithms. This integrated approach provides a scalable and practical solution to manage the increasing demand for EV charging while guaranteeing power distribution systems dependability and stability, offering a significant contribution to sustainable energy systems in the era of electric mobility.