Abstract
The integration of hybrid energy storage systems (HESS) in alternating current (AC) electrified railway systems is attracting widespread interest. However, little attention has been paid to the interaction of optimal size and daily dispatch of HESS within the entire project period. Therefore, a novel bi-level model of railway traction substation energy management (RTSEM) system is developed, which includes a slave level of diurnal HESS dispatch and a master level of HESS sizing. The slave level is formulated as a mixed integer linear programming (MILP) model by coordinating HESS, traction load, regenerative braking energy and renewable energy. As for the master level model, comprehensive cost study within the project period is conducted, with batteries degradation and replacement cost taken into account. Grey wolf optimization technique with embedded CPLEX solver is utilized to solve this RTSEM problem. The proposed model is tested with a real high-speed railway line case in China. The simulation results of several cases with different system elements are presented, and the sensitivity analyses of several parameters are also performed. The obtained results reveal that it shows significant economic-saving potentials with the integration of HESS and renewable energy.
Highlights
The dramatic increase of carbon emissions is driving global climate change and poses risks for human and natural systems [1,2], and a worldwide consensus on reducing atmospheric greenhouse gases (GHGS) has been reached [3,4]
commercial and industrial (C&I) consumers: transformer-capacity-based charge or peak-demand-based charge. The former is related to the capacity of transformers, and the latter depends on the maximum value of the averaged active power consumption in successive 15 min time intervals, during a billing month
This paper proposes a bi-level model combining long-term hybrid energy storage systems (HESS) sizing and short-term diurnal
Summary
The dramatic increase of carbon emissions is driving global climate change and poses risks for human and natural systems [1,2], and a worldwide consensus on reducing atmospheric greenhouse gases (GHGS) has been reached [3,4]. Khayyam et al [23] developed a railway energy management system (REM-S) architecture by coordinating loads, regeneration, storage, and distributed energy resources for optimal energy use It offers the inspiration of applying the research achievements of smart grids to railway systems. In [29] a smart railway station energy management system model was formulated for the utilization of braking energy, and the initial state of charge (SOC) was highlighted in particular as the uncertain factor. It merely concentrated on the reduction of electricity bill of power consumption and paid little attention to the comprehensive cost analysis.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call