Abstract:

The increasing development of traffic in the world has led to an increase in the consumption of electrical energy in electric vehicles. The electric train is one of the most often used electric vehicles. Energy optimization is therefore increasingly important in electrical trains to reduce consumption, the overall cost of electricity, and environmental concerns. As a result, electrical railway energy management needs to be both technically and financially successful. This paper proposes an energy efficiency optimization framework for intelligent railway stations that provide plug-in electric vehicle charging parking capacity use, renewable energy sources (REs), and regenerative braking energy. The structure of the proposed convex programming (CP) model is used for fast and efficient optimization of decision variables, Equipment size, and the cost function of the station for reducing the power purchased from the Grid. Furthermore, by presenting meta-heuristic algorithms, the state variables of the energy storage system (ESS) and Plug-in hybrid electric vehicles (PHEVs) battery have been optimized. The results obtained from the proposed method prove that the operation cost of the station decreases by 61.4 %.