A Comparative Study of Recent Optimization Methods for Optimal Sizing of a Green Hybrid Traction Power Supply Substation

Abstract

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.