Coordinated Energy Management of the Electric Railway Traction System: Croatian Railways Case Study

A railway energy management system based on hierarchical coordination of electric traction substation energy flows and on-route trains energy consumption is presented in the paper. The railway system is divided into energy-efficient individual trains energy consumption management as a lower level, and the energy-cost-efficient electric traction substation energy flows management as a higher level. The levels are coordinated through parametric hierarchical model predictive control with the main goal of additionally decreasing the operational costs of the overall system. Through interactions with the power grid at the higher level, the system can provide ancillary services and respond to various grid requests. At the same time, lower level trains driving profiles are adjusted to attain the minimum cost of system operation with timetables and on-route constraints respected. The developed algorithm is verified against a detailed real case study scenario with the presented results showing significant cost and energy consumption reduction

Integrating Optimal EV Charging in the Energy Management of Electric Railway Stations

In this paper, an electric railway Energy Management System (EMS) with integration of an Energy Storage System (ESS), Regenerative Braking Energy (RBE), and renewable generation is proposed to minimize the daily operating costs of the railway station while meeting railway and Electric Vehicle (EV) charging demand. Compared to other railway EMS methods, the proposed approach integrates an optimal EV charging policy at the railway station to avoid high power demand due to charging requirements. Specifically, receding horizon control is leveraged to minimize the daily peak power spent on EV charging. The numerical study on an actual railway station in Chur, Switzerland shows that the proposed method that integrates railway demand and optimal EV charging along with ESS, RBE, and renewable generation can significantly reduce the average daily operating cost of the railway station over a large number of different scenarios while ensuring that peak load capacity limits are respected.Comment: to appear in IEEE PowerTech, Belgrade, Serbia, 202

Coordinated Energy Management of the Electric Railway Traction System: Croatian Railways Case Study

A railway energy management system based on hierarchical coordination of electric traction substation energy flows and on-route trains energy consumption is presented in the paper. The railway system is divided into energy-efficient individual trains energy consumption management as a lower level, and the energy-cost-efficient electric traction substation energy flows management as a higher level. The levels are coordinated through parametric hierarchical model predictive control with the main goal of additionally decreasing the operational costs of the overall system. Through interactions with the power grid at the higher level, the system can provide ancillary services and respond to various grid requests. At the same time, lower level trains driving profiles are adjusted to attain the minimum cost of system operation with timetables and on-route constraints respected. The developed algorithm is verified against a detailed real case study scenario with the presented results showing significant cost and energy consumption reduction

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated

Enhancing smart city operation management: integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study

Chance-constrained optimization of storage and PFC capacity for railway electrical smart grids considering uncertain traction load

To foster the utilization of regeneration braking energy and suppress voltage unbalance (VU), a railway electrical smart grid (RESG), intergraded with power flow controller (PFC) and energy storage (ES), is proposed as an important part of next-generation electrified railways. However, under the uncertain traction load, how to design the optimal size of PFC-ES is a challenge during the planning period. Hence, this paper proposes a chance-constrained two-stage programming approach. The first-stage aims to minimising the overall cost of RESG’s devices. The second-stage aims to arrange the energy flow of the PFC-ES with the objective of minimising the expected operation cost under the dynamic VU restriction, and the stochastics characteristics of traction load are transformed into a chance constraint by using a scenario approach. Then, traction power predictions are combined with multivariate Gaussian Mixture Model (multi-GMM) model to generate correlated traction power flow scenarios and to assess VU probabilistic metrics distribution with different confidence levels. Finally, a novel algorithm is designed to select the confidence level and violation probability so that the capacity planning results can ensure the high-efficient and high-quality operation of the RESG. Case studies based on an actual electrified railway demonstrate that the proposed PFC-ES sizing approach can reduce the overall cost by up to 13%