Utilization Of Regenerative Energy Research Articles

Theoretical study on the dehumidification behaviors of dual-desiccants coated cross-flow heat exchanger with staged adsorption-desorption process

The conventional desiccant coated heat exchangers (DCHEs) suffer from the inefficient utilization of regenerative energy and the inadequate dehumidification capacity of a single desiccant. In this study, a concept of dual-desiccants cross-flow DCHE with staged adsorption-desorption process was proposed to strengthen dehumidification capacity and energy efficiency simultaneously. In order to guide the construction of proposed DCHE, the steady-state dehumidification capacity of single-desiccant (specifically, silica gel, FAM Z01, FAM Z05 and EMM-8) cross-flow DCHE was investigated by thermodynamic analysis. The dual-desiccants cross-flow DCHE was thus constructed, and its numerical model was established to investigate the dehumidification characteristics. Theoretical analysis revealed that single-desiccant cross-flow DCHEs exhibit different optimal relative humidity ranges for dehumidification. The minimum regeneration temperature driving dehumidification cycle increases as the lower supply air humidity is required. Numerical results demonstrated that the staged adsorption process ensures a large dehumidification capacity across a wide humidity range, including low humidity conditions. Meanwhile, the staged desorption process facilitates the cascade utilization of energy and the direct recovery of regenerative waste heat. For dual-desiccants cross-flow DCHE utilizing silica gel and EMM-8, the moisture removal capacity and dehumidification coefficient of performance can reach 0.012 kg/kg and 0.41, respectively.

Metro timetable optimization for improving regenerative braking energy utilization efficiency

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis.

Analysis and Control of Cascaded Energy Storage System for Energy Efficiency and Power Quality Improvement in Electrified Railways

Energy-efficient and grid-friendly railway power system is critical for the sustainable development of electrified railways. In this paper, a cascaded energy storage system (CESS) is investigated for energy efficiency and power quality improvement of the railway power system. First, the detailed operation principles of the CESS for multiple control objectives, including regenerative braking energy (RBE) utilization, reactive power compensation, and negative sequence current suppression, are analyzed. On this basis, a hierarchical power flow control strategy is developed for achieving the power flow management and control of multiple objectives for CESS. Specifically, a multi-objective power flow management strategy is designed in the system layer to allocate the power references for the CESS under sufficient and insufficient capacity scenarios. In the sufficient capacity scenario, the power references are directly obtained by the operation principles. In the insufficient capacity scenario, the power references are allocated through a three-stage optimization method considering the power shaving rate, grid power factor, and grid imbalance degree. Subsequently, the power references from the system layer are tracked in the converter layer to achieve power flow control. Finally, the effectiveness of the proposed hierarchical power flow control strategy is verified through experimental results. The results show that the proposed CESS can achieve superior performance on RBE utilization and power quality improvement under complex operating conditions in electrified railways.

An energy‐efficient timetable optimization method for express/local train with on‐board passenger number considered

AbstractWith the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers’ weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable.

Analysis of voltage unbalance and mitigation of circulating power in bilateral co-phase traction system

The primary aim of this paper is to analyse and mitigate the problems inherent in adopting bilateral co-phase traction network on Indian Railways. The reason for adopting bilateral co-phase traction network is to enhance regenerative braking energy (RBE) utilization in the railway traction network. There are two inherent problems in adopting bilateral co-phase traction configuration. First, it may lead to increase in the Voltage Unbalance factor (VUF) as single-phase loading by trains is effectively doubled across the same phases. Second, and more importantly, it may lead to flow of circulating power, under no load conditions, where power shall flow from leading voltage traction sub-station to lagging voltage traction sub-station. This may offset any advantage due to improved RBE utilization. This paper demonstrates through simulation, using realistic data from Indian Railways, that VUF stays within limits for bi-lateral traction configuration. It is important to mention that improved RBE utilization also plays a role in reducing VUF when there is no feedback of RBE to the grid. For the second problem, this paper takes inspiration from a control scheme existing in literature to adapt it to the proposed scenario. This scheme shows that it is possible to limit the power angle within 0.5˚ and thereby minimising circulating power.

Pareto Optimization of Energy-Saving Timetables Considering the Non-Parallel Operation of Multiple Trains on a Metro Line

In light of reducing train operation energy consumption while maintaining the passenger service level for creating sustainable urban rail transit systems, we address a non-parallel train timetabling problem considering regenerative braking energy utilization and the non-parallel operation of multiple trains on a metro line via a newly proposed multi-objective timetable (MOT) optimization model and an evolutionary algorithm based on NSGA-II. The optimization objectives of the MOT model are to find satisfactory energy-saving timetables on the Pareto frontier by minimizing the total travel time of passengers and minimizing the net energy consumption of trains. An improved multi-objective evolutionary algorithm based on NSGA-II is constructed to generate the optimal arrival and departure times at each station for each train running in a non-parallel operation mode. This study tests the feasibility of the proposed optimization method via an empirical case using the data collected from the Yizhuang Line of the Beijing metro systems in China. The simulation results show that the proposed optimization method satisfies both the energy utilization and passenger service levels along a Pareto front. The MOT improves the overall effectiveness of regenerative braking energy utilization by 29.88% in comparison with the original timetable; it reduces the net operation energy consumption by 44.86% relative to the travel-oriented timetable (TOT); and it reduces the total passenger travel time by 27.18% compared with the energy-oriented timetable (EOT).

Optimization of high-speed train timetable based on regenerative braking energy utilization

Optimization of high-speed train timetable based on regenerative braking energy utilization

Timetable optimization for maximization of regenerative braking energy utilization in traction network of urban rail transit

Timetable optimization is one of the effective solution methods for urban rail transit to achieve energy-saving. Previous studies on timetable optimization only focused on the transfer and utilization of mechanical energy, which did not consider the exchange of energy and line losses in the traction network. Therefore, this paper constructs a cross-substation energy transmission and utilization model taking into account the characteristics of the actual traction power supply systems. An overlap current method is presented to increase the utilization of regenerative braking energy (RBE) of trains and reduce the energy supply of the traction substation by considering the line loss, which is achieved by compensating the current generated by accelerating and braking trains in the substation. The compensation current can be maximized by adjusting the dwell time to achieve energy-saving. The traction power supply systems with highly nonlinear dynamic characteristics are linearized to construct a mixed-integer linear programming (MILP) model, which utilizes the utilization of RBE as the objection and dwell time as the variable. The method of maximum compensation current is verified by numerical examples based on Beijing Batong Line. The results show that the energy-efficient of the method is greatly improved compared with the method of maximizing the overlap time between the accelerating train and the braking train.

Overview of Stability and Coordination Control for MVDC Traction Power Supply System

The traditional 25KV electric railways have various problems such as power quality issues, passing neutral sections, complex storage, and utilization of regenerative braking energy, the medium‐voltage direct current (MVDC) traction power supply system (TPSS) may be a potential alternative electric railway mode and can be freely accessed to the renewable energy and energy storage systems, which is an important development direction of the future low‐carbon RES. This paper first describes the structure and characteristics of the current Railway Electrification System (RES), and then analyzes and summarizes the current research status of the MVDC TPSS topology structure, modeling method, stability criterion, and analysis method and coordination control strategy. Finally, combined with the research status, the development direction of the MVDC TPSS has prospected. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Hierarchical Energy Management of Networked Flexible Traction Substations for Efficient RBE and PV Energy Utilization Within ERs

The particular split-phase traction power system of AC electrified railways (ERs) restricts its regenerative braking energy (RBE) utilization and photovoltaic (PV) penetration. Networked flexible traction substation (FTSS) operation is a promising paradigm to address this restriction. Therefore, this paper proposes a networked FTSS system architecture that ties adjacent FTSSs via unified power flow controllers (UPFC) at sectioning posts (SPs), which matches the technical characteristics of split-phase ERs obtaining the lower capacity requirement. Correspondingly, a real-time hierarchical energy management strategy is designed. The networked FTSS management level optimizes the energy cooperation among FTSSs, i.e., the exchange power at SPs. It aims at maximizing the global RBE and PV energy utilization and shaving the maximum demand of each FTSS. At the individual FTSS management level, each FTSS autonomously regulates the power outputs of the local back-to-back converter and energy storage system to maximize the local RBE and PV energy utilization and improve the local power quality issues. These two energy management levels operate independently with different time scales, making the networked FTSS system have good extensibility and reliability. Finally, the performance of the proposed scheme is verified via hardware-in-the-loop tests.

Integrated energy‐efficient optimization for urban rail transit timetable

AbstractThe energy‐efficient optimization of the train timetable should be carried out on the basis of meeting passenger demand and determining the train operation plan. This paper proposes an integrated energy‐efficient optimization framework for the train timetable. Firstly, two energy‐efficient optimization strategies for this problem are proposed: inter‐station running time allocation and regenerative braking energy utilization. The first strategy aims to reduce the traction energy consumption, realized by selecting the profile based on a given set of speed profiles for each inter‐station sector when ensuring a stable travel time of train routing. The second strategy is intended to fully utilize the regenerative energy feedback from braking trains, realized by setting the departure time of trains based on a small‐range headway adjustment. Then, an integrated energy‐efficient optimization model of the timetable is developed and solved using the genetic algorithm. Finally, a case study of the Guangzhou Metro Line 8 is presented to verify the effectiveness of the method. The results show that the utilization rate of regenerative braking energy in the optimized timetable increases by 8.60%, and the total energy consumption decreases by 9.52%. Regenerative energy utilized roughly doubles during peak hours and increases more than twice as much during off‐peak hours.

Integrated Regenerative Braking Energy Utilization System for Multi-Substations in Electrified Railways

This article proposes an integrated regenerative braking energy utilization system (RBEUS) to improve regenerative braking energy (RBE) utilization in electrified railways. The proposed RBEUS uses a traction substation energy storage system and two sectioning post converters to achieve coordinated RBE utilization in three consecutive traction substations via power-sharing and storage, and the power quality can also be improved. A hierarchically coordinated control strategy is developed based on the operation principle to provide real-time power management and control for the RBEUS. In the system layer, a centralized power management strategy is designed for operation mode management and active power command generation. It uses a sequential quadratic programming-based algorithm to solve the objective function to achieve optimal RBE utilization under different operation modes. In the converter layer, local controllers of the RBEUS enable converters to respond to the active power commands from the system layer and reactive power commands generated by themself for power flow control. The effectiveness of the proposed RBEUS is comprehensively verified by using a hardware-in-the-loop experiment. Besides, a comparison analysis of the proposed RBEUS and literature methods is conducted to testify the superiority of the RBEUS. The feasibility of RBEUS implementation is also discussed from fault protection and economy.

Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud

With the development of urban rail transit, energy conservation has been highly concerned by the worldwide railway industry. The existing energy-saving control methods are hard to meet the requirements of large-scale multi-train intelligent cooperation. Furthermore, in order to optimize the overall energy consumption and make effective utilization of regenerative braking energy (RBE), the micro train driving strategies must be taken into account in the macro train schedule regulation. This article aims to establish a systematic optimization model to describe the train traffic environment and design a deep reinforcement learning (DRL) approach using multi-agent cooperative actor-critic (MACAC) to reschedule multiple trains for energy saving. Due to the high dimensional characteristic of control actions, the action representation technique is applied for generalization. The multiple agents are deployed on the Spark cloud platform to effectively improve the learning efficiency through multi-node parallel computing. The proposed MACAC approach on Spark cloud is capable of dealing with the intelligent learning and computing efficiency of large-scale train operation synergy, and saving energy to the maximum extent on the premise of satisfying various time constraints. The evaluation was carried out through the field data of Beijing Yizhuang subway line. The results show that the proposed MACAC approach can effectively learn to improve the energy efficiency, and the parallel computing method on Spark cloud is suitable for practical applications.

Iteration 최저 소거법을 이용한 직류 전기철도 회생 에너지 활용설비 설계 연구

Recently, various studies are being conducted to establish an eco-friendly energy network in the field of railway transportation to reduce carbon emissions. In particular, if the regenerative energy generated from DC urban railway trains, which is frequently reversed and braked, is used appropriately, railroad operating costs can be reduced, which is economical. In this paper, considering the voltage stabilization aspect and the energy utilization aspect through load flow analysis, an appropriate regenerative energy utilization facility installation method was analyzed. This section introduces the regenerative energy facility design method using the Minimum Elimination Method(MEM) by performing load flow analysis for each substation through TPS(Train Performance Simulation)-based power system analysis. By using MEM, the regenerative energy utilization rate was improved by about 11.9% compared to the existing method.

Bi-hierarchy capacity programming of co-phase TPSS with PV and HESS for minimum life cycle cost

Co-phase traction power supply system (TPSS) with photovoltaic (PV) and hybrid energy storage system (HESS) is a promising way to improve the utilization of regenerative braking energy (RBE) and power quality. However, despite co-phase TPSS with PV and HESS being beneficial from the perspective of technology, how to coordinate the operation strategies of various devices to ensure system economy is a key issue in practical engineering applications. Therefore, this paper proposes a bi-hierarchy capacity programming strategy of co-phase TPSS to minimize life cycle cost. In the upper layer, the integrated lifetime evaluation model of HESS and PFC (HESS-PFC) is proposed for the lower long-term investment cost. Remarkably, the PFC reliability assessment and the HESS degradation process are analyzed. Meanwhile, the capacity of HESS-PFC is obtained, and the lifetime of HESS-PFC is extended. In the lower layer, the power flow of co-phase TPSS is optimized to minimize short-term operating cost under satisfying three-phase voltage unbalance standards. Traction load peak-shaving and valley-filling are achieved by coordinating HESS-PFC and PV. And it is formulated as a mixed integer linear programming model based on efficient linearization methods. Whale optimization algorithm with GUROBI solver embedded is employed to solve this bi-hierarchy model. Finally, case studies show that the proposed model can achieve 23.26% cost reduction and 63.82% PFC lifetime extension, while the voltage unbalance is within an allowable range of 2%.

Optimal planning of hybrid energy storage systems in traction power supply system with railway power conditioner

AbstractThe railway power conditioner (RPC) is a promising technology to improve the regenerative braking energy (RBE) utilization and power quality of the traction power supply system (TPSS). The hybrid energy storage systems (HESS) play a key role in the economic operation of TPSS due to the high cost of the system. The capacity and power of HESS are the critical issues affecting the system performance and cost. Therefore, a hierarchical energy management model is proposed to optimize the HESS sizing and the scheduling of TPSS with RPC. At the master level, a nonlinear mathematical model is built to minimize the HESS cost, which includes capital cost, replacement cost, and recovery cost. At the slave level, considering voltage unbalance as a constraint, a mixed‐integer linear programming model is established to minimize the electricity cost of railway operation. In order to solve the hierarchical nonlinear programming problem, an improved particle Swarm optimization (PSO) algorithm with a GUROBI solver embedded is proposed. Finally, the results show that the 14.08% cost reduction is achieved, and the standard limit value of voltage unbalance is satisfied.

Rail train operation energy-saving optimization based on improved brute-force search

Rail train operation energy consumption mainly focuses on train traction energy consumption. Reducing train traction energy consumption in rail transit operation is significant to developing a green and low-carbon economy and reducing operation costs. The rail train operation energy-saving optimization framework is developed considering the utilization of regenerative braking energy. Firstly, three objectives of punctual arrival, fixed-point parking and minimum energy consumption are provided by train operation strategy analysis. Secondly, the improved brute-force search is developed to solve train operation energy-saving multi-objective problems. The running time, speed, distance, power, and energy consumption of operation intervals are calculated. Finally, Guangzhou Metro Line 7 is taken as an example to verify the effectiveness of the developed optimization model. The results show that the improved brute-force search method effectively finds a more energy-saving turning point under constant interval operation time and has a better energy-saving effect than two other heuristic algorithms.

Electrical characteristics of new three-phase traction power supply system for rail transit

A novel three-phase traction power supply system is proposed to eliminate the adverse effects caused by electric phase separation in catenary and accomplish a unifying manner of traction power supply for rail transit. With the application of two-stage three-phase continuous power supply structure, the electrical characteristics exhibit new features differing from the existing traction system. In this work, the principle for voltage levels determining two-stage network is dissected in accordance with the requirements of traction network and electric locomotive. The equivalent model of three-phase traction system is built for deducing the formula of current distribution and voltage losses. Based on the chain network model of the traction network, a simulation model is established to analyze the electrical characteristics such as traction current distribution, voltage losses, system equivalent impedance, voltage distribution, voltage unbalance and regenerative energy utilization. In a few words, quite a lot traction current of about 99% is undertaken by long-section cable network. The proportion of system voltage losses is small attributed to the two-stage three-phase power supply structure, and the voltage unbalance caused by impedance asymmetry of traction network is less than 1‰. In addition, the utilization rate of regenerative energy for locomotive achieves a significant promotion of over 97%.

Energy-Saving Metro Train Timetable Optimization Method Based on a Dynamic Passenger Flow Distribution

The operation of metro trains with a focus on energy savings can effectively reduce operating costs and carbon emissions. Reducing traction energy consumption and increasing the utilization efficiency of regenerative braking energy are two important energy-saving approaches that are closely related to the metro train interstation running strategy and timetable. Changes in train mass caused by dynamic changes in passenger flow represent one of the important factors affecting the energy consumption and energy-saving operation of metro trains. In this study, the differences in the temporal and spatial distributions of metro line passenger flow were specifically considered, and an energy-saving metro train timetable optimization method focused on the dissipative regenerative braking energy utilization mode was studied. First, a logistic function is used to fit the passenger flow pattern of the origin-destination (OD) station pairs, and the number of passengers getting on and off at each station is derived by establishing the OD dynamic demand matrix for the entire metro line. Then, the passenger load in each station segment is calculated. Next, a timetable optimization model is established to minimize the net energy consumption based on the load difference between station segments and the train motion equation. The interstation running time and dwell time of the metro train are optimized to increase the amount of regenerative braking energy used during the overlap time between the traction and braking actions of adjacent trains in the train operation timetable. A particle swarm optimization and genetic algorithm (PSO-GA) structure is designed to solve the model. The PSO-GA structure has PSO as the main body and integrates the chromosome crossover and mutation operations of the GA into the iterative process to improve the search efficiency of the algorithm. Finally, the proposed method and model are tested based on the actual data of a metro line in Qingdao, China. The goodness of fit of the passenger flow pattern is 0.997. The energy consumption during the study period is reduced by 5169.67 kW h using the optimized timetable. The energy-saving efficiency decreases by 12.18% at a constant OD ratio during the entire travel time and by 20.23% at the same constant load for all station segments. The results of the case analysis prove the effectiveness of the proposed method and model. In addition, the energy-saving timetable can be better optimized by considering the differences in temporal and spatial distributions of dynamic passenger flow.

A Study on the Train Brake Position-Based Control Method for Regenerative Inverters

The use an inverter is one of the representative ways to utilize regenerative braking energy in railway systems. Due to the nature of urban railways that generate a large amount of regenerative energy, the economic advantages are clear. However, in the case of the existing inverter operation method, a method of operating the inverter using the threshold voltage is used, which has a disadvantage in that power cannot be utilized between the no-load voltage and the threshold voltage. Therefore, in this paper, we propose an optimal location selection method and capacity calculation method for installing a regenerative inverter in an urban rail system, and a control method according to the train brake position to increase the regenerative energy utilization rate. First, the inverter capacity and location were selected by selecting the maximum regenerative energy generation for each substation section through the train performance simulation (TPS) based DC power simulation (DCPS). An inverter control method based on train brake position (BP method) is introduced. Finally, PSCAD/EMTDC, a power analysis program, was used to verify the proposed method. As a result, the use of regenerative energy by an inverter increased by about 62.6%, and more energy was saved at nearby substations through the BP method.