Although there is a premise that electric trains are zero-emission, their source of energy (fossil fuel power plants) pollutes the air in a place far from the consuming area (traction power supply substation). On the other hand, the price of generating electricity from fossil fuel resources has risen in the aftermath of their ever-decreasing sources. These two economic-environmental factors have caused Hybrid Renewable Energy Sources (HRESs) to be introduced as an alternative to fossil fuel ones. This paper proposes the concept of Green Hybrid Traction Power Supply Substation (GHTPS), that is, using renewable energy resources to meet a traction substation. To find the best size of HRES components having a minimum Lifecycle cost; an optimization method is essential. For this reason, a comparative study on the application of recent optimization methods is employed to find the optimum size of the proposed grid-connected PV/wind turbine traction substation. The optimization methods are: the Atom search optimization (ASO), Harris Hawks Optimization (HHO), Coyote Optimization Algorithm (COA), Multi-population Ensemble Differential Evolution (MPEDE), Bird Swarm Algorithm (BSA), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and HOMER software. Finally, a sensitivity analysis shows that increasing in the grid electricity price and decreasing the wind turbines investment cost could make renewable energies more economically competitive in the future. Besides Net Present Cost (NPC), Cost of Energy (COE), Payback Time (PT), and various emissions are studied, all of which verify the efficiency of the proposed system.

尽管有一个前提条件，那就是电车是零排放的，但它们的能源（化石燃料发电厂）会污染远离消耗区（牵引电源变电站）的地方的空气。另一方面，由于化石燃料资源发电的价格不断减少，其价格已经上涨。这两个经济环境因素已导致引入混合可再生能源（HRES），以替代化石燃料。本文提出了绿色混合牵引供电变电站（GHTPS）的概念，即利用可再生能源来满足牵引变电站的要求。寻找具有最小生命周期成本的最佳HRES组件尺寸；优化方法至关重要。为此原因，对最新优化方法的应用进行了比较研究，以找到拟议的并网型光伏/风轮机牵引变电站的最佳尺寸。优化方法是：原子搜索优化（ASO），哈里斯·霍克斯优化（HHO），土狼优化算法（COA），多种群集成差分进化（MPEDE），鸟群算法（BSA），蚁狮优化器（ALO） ，灰狼优化器（GWO），人工蜂群（ABC），粒子群优化（PSO），遗传算法（GA）和HOMER软件。最后，敏感性分析表明，提高电网电价和降低风力涡轮机的投资成本可以使可再生能源在未来更具经济竞争力。除了净现值（NPC），能源成本（COE），投资回收期（PT），