Abstract
In this paper, a dual-stage modeling and optimization framework has been developed to obtain an optimal combination and size of wayside energy storage systems (WESSs) for application in DC rail transportation. Energy storage technologies may consist of a standalone battery, a standalone supercapacitor, a standalone flywheel, or a combination of these. Results from the dual-stage modeling and optimization process have been utilized for deducing an application-specific composition of type and size of the WESSs. These applications consist of different percentages of energy saving due to regenerative braking, voltage regulation, peak demand reduction, estimated payback period, and system resiliency. In the first stage, sizes of the ESSs have been estimated using developed detailed mathematical models, and optimized using the Genetic Algorithm (GA). In the second stage, the respective sizes of ESSs are simulated by developing an all-inclusive model of the transit system, ESS and ESS management system (EMS) in MATLAB/Simulink. The mathematical modeling provides initial recommendations for the sizes from a large search space. However, the dynamic simulation contributes to the optimization by highlighting the transit system constraints and practical limitations of ESSs, which impose bounds on the maximum energy that can be captured from decelerating trains.
Highlights
DC rail transportation systems, in urban areas around the world, consume around 14.7 to 1600 GWh of energy annually [1,2,3]
We focus on the retrieval of regenerative braking energy using wayside energy storage systems (WESSs)
The mathematical model is optimized for saving different percentages of regenerative energy
Summary
DC rail transportation systems, in urban areas around the world, consume around 14.7 to 1600 GWh of energy annually [1,2,3]. In some major electric rail transportation systems around the world, such as New York City Transit (NYCT), trains run with a minimum headway of 60 s and the regenerative braking occurs within a span of 30 s with a peak power of about 3 MW [1,9] Such systems would require storage technologies with high power density that implies fast charging capability along with a large number of life-cycles. This stage is vital to (a) validate the fidelity of the mathematical model and (b) accurately capture the dynamics of rail transportation system along with ESSs, whose emulation by mathematical equations is complex to develop This stage uses the optimal sizes from the first stage as guiding parameters to perform the simulations for both single and 24 h cycle of trains. The simulation results infer application specific choices of type, size, and combination of ESSs
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