scholarly journals A Preliminary Techno-Economic Comparison between DC Electrification and Trains with On-Board Energy Storage Systems

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6702
Author(s):  
Regina Lamedica ◽  
Alessandro Ruvio ◽  
Manuel Tobia ◽  
Guido Guidi Buffarini ◽  
Nicola Carones
Keyword(s):  
Energy Storage ◽  
Numerical Simulations ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Matlab Simulink ◽  
Railway Line ◽  
Different Types ◽  
Economic Comparison

The paper presents a preliminary technical-economic comparison between a 3 kV DC railway and the use of trains with on-board storage systems. Numerical simulations have been carried out on a real railway line, which presents an electrified section at 3 kV DC and a non-electrified section, currently covered by diesel-powered trains. Different types of ESS have been analyzed, implementing the models in Matlab/Simulink environment. A preparatory economic investigation has been carried out.

scholarly journals Data-Driven Model Based Charging Profile Prediction for Energy Storage Systems

2017 ◽  
Author(s):  
Nandha Kumar Kandasamy ◽  
Rudy Tjandra
Keyword(s):  
Energy Storage ◽  
Smart Grid ◽  
Storage Systems ◽  
Distributed Storage ◽  
Data Driven ◽  
Energy Storage Systems ◽  
Storage Devices ◽  
Capacity Loss ◽  
Load Demand ◽  
Different Types

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

H∞ Control Approach to Reduction of Energy Capacity of Energy Storage Systems

2017 ◽  
Vol 137 (8) ◽  
pp. 596-597
Author(s):  
Kenta Koiwa ◽  
Kenta Suzuki ◽  
Kang-Zhi Liu ◽  
Tadanao Zanma ◽  
Masashi Wakaiki ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage Systems ◽  
Energy Capacity ◽  
Energy Storage Systems ◽  
Control Approach

MULTISCALE THERMAL TRANSPORT IN SOLAR THERMOCHEMICAL ENERGY STORAGE SYSTEMS

2018 ◽  
Author(s):  
Like Li ◽  
Kelvin Randhir ◽  
James F. Klausner ◽  
Ren-Wei Mei ◽  
Nick AuYeung
Keyword(s):  
Energy Storage ◽  
Thermal Transport ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Solar Thermochemical
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