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<p>Electric vehicles (EVs) are widely used in the transportation sector, which majorly attracts due to their characteristics of greenhouse gas emissions and environmental friendliness. However, for the effective deployment of EVs, Electric Vehicle Charging Stations (EVCS) must be built quickly and with careful planning. Moreover, mismatch of generation demand, active power losses, compromised voltage profile and voltage stability reduction issues are faced due to the rapid raise of electric load penetration. To address these concerns, a well-planned integration of EVCS with Distributed Generations (DGs) at strategic locations with appropriate capacity becomes essential. Load Flow Analysis is utilized to find power losses for each branch and voltage magnitudes for all buses. The IEEE 69 bus is employed with the Backward-Forward Sweep method for Radial Distribution Systems, and the IEEE 118 bus is a non-radial DS solved with the Newton–Raphson (NR) method. In this research, the accurate determination of optimal locations and capacities for placing EVCS in conjunction with DGs is achieved by utilizing a hybridized optimization technique called Hybrid Moth Flame Optimization (HMFO) which is formed by hybridizing the characteristics of moth and firefly optimization algorithms. The utilization of HMFO results in improved outcomes, including rapid convergence, robustness, and enhanced identification of EVCS deployment locations. Consequently, this optimization approach leads to reduced real and reactive power losses, diminished voltage deviations, increased voltage stability, cost-effectiveness, and accurate determination of EVCS location, and size. The research work illustrates the efficient result values in terms of real power, reactive power loss, AVDI, and AVSI as 55.37&#xa0;kW, 22.39&#xa0;kVAR, 0.02&#xa0;p.u, and 0.87&#xa0;p.u respectively.</p>

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Hybrid Moth Flame Optimization Based Multi-objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generation in Radial and Non-radial DS

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • B. Mohan,
  • M. Sudhakaran,
  • S. Srikiruthika

摘要

Electric vehicles (EVs) are widely used in the transportation sector, which majorly attracts due to their characteristics of greenhouse gas emissions and environmental friendliness. However, for the effective deployment of EVs, Electric Vehicle Charging Stations (EVCS) must be built quickly and with careful planning. Moreover, mismatch of generation demand, active power losses, compromised voltage profile and voltage stability reduction issues are faced due to the rapid raise of electric load penetration. To address these concerns, a well-planned integration of EVCS with Distributed Generations (DGs) at strategic locations with appropriate capacity becomes essential. Load Flow Analysis is utilized to find power losses for each branch and voltage magnitudes for all buses. The IEEE 69 bus is employed with the Backward-Forward Sweep method for Radial Distribution Systems, and the IEEE 118 bus is a non-radial DS solved with the Newton–Raphson (NR) method. In this research, the accurate determination of optimal locations and capacities for placing EVCS in conjunction with DGs is achieved by utilizing a hybridized optimization technique called Hybrid Moth Flame Optimization (HMFO) which is formed by hybridizing the characteristics of moth and firefly optimization algorithms. The utilization of HMFO results in improved outcomes, including rapid convergence, robustness, and enhanced identification of EVCS deployment locations. Consequently, this optimization approach leads to reduced real and reactive power losses, diminished voltage deviations, increased voltage stability, cost-effectiveness, and accurate determination of EVCS location, and size. The research work illustrates the efficient result values in terms of real power, reactive power loss, AVDI, and AVSI as 55.37 kW, 22.39 kVAR, 0.02 p.u, and 0.87 p.u respectively.