This paper proposes an enhanced genetic algorithm model, named EGA-TEP, to solve the transmission expansion planning (TEP) problem of electric power system networks. Heuristic information is integrated into the evolutionary process of metaheuristic to improve the expansion plans (solutions). This heuristic information is translated in the form of sensitivity indices, based on the circuit loading/overloading and observed load shedding, considering both the intact network and the “N-1” contingency operating conditions (security criteria). In addition, an iterative process of evolutionary runs (ERs) is adopted as the basis for designing the EGA-TEP. These contributions make the optimization tool more robust and ready to handle different types of systems. The efficiency of the proposed EGA-TEP tool is consistently evaluated through performance statistical indices. Results obtained with systems with different characteristics and dimensions are presented and widely discussed.

本文提出了一种改进的遗传算法模型，称为EGA-TEP，以解决电力系统网络的传输扩展规划（TEP）问题。启发式信息被集成到元启发式的进化过程中，以改进扩展计划（解决方案）。考虑到完整的网络和“ N-1”应急运行条件（安全标准），基于电路负载/过载和观察到的负载减少，以启发性指数的形式转换此启发式信息。此外，采用进化运行的迭代过程（ER）作为设计EGA-TEP的基础。这些贡献使优化工具更加强大，可以随时处理不同类型的系统。建议的EGA-TEP工具的效率通过性能统计指标进行持续评估。介绍并广泛讨论了使用具有不同特征和尺寸的系统获得的结果。