FEASIBILITY STUDY ON HYBRID ENERGY HARVESTING SOLUTION IN RAIL TRANSPORTATION SYSTEM

Abstract

Currently, it is important to investigate environmentally friendly and sustainable energy sources due to ecological issues, such as greenhouse gas emissions and climate change. This thesis represents the feasibility study on hybrid energy harvesting solutions within rail transportation systems, especially the application of solar and wind energy systems. The hybrid energy source is defined as the combination of various types of energy generation equipment. To be more exact, this master’s thesis examines the significance of using solar panels, micro-hydro turbines, and a battery energy storage system (BESS) to supply the energy needs of railway systems in Kazakhstan. It is important to note that the selected train is the Talgo Tulpar passenger train, moving from Astana to Almaty cities. However, the application of small-scale solar and wind energy systems cannot fully support the railway energy network. Therefore, this master’s thesis also considers the integration of solar panels on the sleepers over the railway track. This helps to support the energy network of the railway system. One of the first steps in designing the hybrid energy system is to construct a mathematical model of the system for the rail transportation system. A mathematical model of the proposed hybrid energy system, including analytical calculations and MATLAB simulations, is investigated to assess the performance and energy generation capabilities. To achieve the simulation results, data was collected individually for each energy generation source, also the train’s speed and energy demand profiles were created based on the literature review. Furthermore, the strategic plan for the working principles of the system has been proposed in this master’s thesis. To investigate more accurately the energy network of the proposed system, it has considered three different study cases. By considering one randomly selected day in each season, this master’s thesis demonstrates a seasonal analysis for each study case.