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As renewable energy sources and electric car systems are rapidly integrated into the distribution network, it is crucial to guarantee the quality, efficiency, and dependability of the electric power distribution system today. The problem of reconfiguring the power grid with the objective of minimizing losses and optimizing the application of sustainable energy, as well as the charging/discharging processes of electric vehicles, is an essential issue. The Simulated Annealing (SA) and Genetic Algorithm (GA) are two algorithms with different strengths, and their combination can enhance the convergence speed and avoid local optima in optimization problems. Therefore, the research suggests employing a combined approach of the SA and GA algorithms, with parameter enhancements tailored for the reconfiguration of the distribution network, considering renewable energy sources and electric vehicles. The findings were applied and verified on the IEEE 33-bus test platform across different scenarios, affirming the precision and dependability of the method put forward.

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Enhancements and Integration of Simulated Annealing and Genetic Algorithm for the Problem of Reconfiguring Distributed Electric Power Networks Considering Distributed Generation and Electric Vehicles

  • 会议论文

  • 发表日期：

    2025年

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  • Le Tien Phong,
  • Nguyen Tung Linh,
  • Trinh Trong Chuong,
  • Truong Viet Anh

摘要

As renewable energy sources and electric car systems are rapidly integrated into the distribution network, it is crucial to guarantee the quality, efficiency, and dependability of the electric power distribution system today. The problem of reconfiguring the power grid with the objective of minimizing losses and optimizing the application of sustainable energy, as well as the charging/discharging processes of electric vehicles, is an essential issue. The Simulated Annealing (SA) and Genetic Algorithm (GA) are two algorithms with different strengths, and their combination can enhance the convergence speed and avoid local optima in optimization problems. Therefore, the research suggests employing a combined approach of the SA and GA algorithms, with parameter enhancements tailored for the reconfiguration of the distribution network, considering renewable energy sources and electric vehicles. The findings were applied and verified on the IEEE 33-bus test platform across different scenarios, affirming the precision and dependability of the method put forward.