Energy storage systems (ESSs) are enabling technologies for well-established and new applications such as power peak shaving, electric vehicles, integration of renewable energies, etc. This paper presents a review of ESSs for transport and grid applications, covering several aspects as the storage technology, the main applications, and the power converters used to operate some of the energy storage technologies. Special attention is given to the different applications, providing a deep description of the system and addressing the most suitable storage technology. The main objective of this paper is to introduce the subject and to give an updated reference to nonspecialist, academic, and engineers in the field of power electronics.

Energy Storage Systems for Transport and Grid Applications

https://eprints.ncl.ac.uk/file_store/production/197576/5DDBEC49-AF90-4644-892A-3AE82C3777F6.pdf

A systems approach to reduce urban rail energy consumption

An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.

Single-Train Trajectory Optimization

Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.

https://www.mdpi.com/1424-8220/17/6/1457/pdf

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

Traffic operation has a significant impact on energy consumption in metro lines and thus it is important to analyze strategies to minimize it. In lines equipped with Automatic Train Operation systems (ATO), traffic regulation system selects one ATO speed profile on line among a preprogrammed set of optimized speed profiles. Previous works only minimize the energy demanded by the train in pantograph without considering energy savings measured at substations due to regenerative energy in detail. The main objective of this work is to design optimal ATO speed profiles of metro trains taking into account the energy recovered from regenerative brake in order to minimize the net energy at substations. A model of a train with an on-board energy storage device as well as a network model for estimating the energy recovered by the train is presented. Different scenarios are analyzed to assess the achievable energy savings due to possible investments such as installing power inverters or storage devices and energy savings due to the optimal design of ATO speed profiles are estimated. A real line of the Madrid Underground has been considered obtaining comparative results to facilitate an evaluation of the most advantageous scenario and possible investment. A future application would be the optimal design of driving for the newest train signaling system communications-based train control (CBTC). The CBTC improves train operation and control, due to continuous communication with each train. However, its features have not been taken full advantage yet to reduce the energy consumption. The proposed method could be applied to design and execute in real time the most energy-efficient driving according to the traffic and electrical situation of the line.

/pdf/energy-savings-in-metropolitan-railway-substations-through-2jlif7cpx0.pdf

Energy Savings in Metropolitan Railway Substations Through Regenerative Energy Recovery and Optimal Design of ATO Speed Profiles

Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption during a single-train journey by trading-off reductions in energy against increases in running time has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.

Y.V. Bocharnikov

Papers

Optimal driving strategy for traction energy saving on DC suburban railways