Utilization Of Regenerative Braking Energy Research Articles

Modeling and SOC estimation of on-board energy storage device for trains under emergency traction

The sudden interruption of train power supply in an extreme environment will seriously threaten the safety of passengers and affect the operational efficiency of the railway system. In this case, the focus of attention becomes a method of running the train to the nearest rescue point based on the limited capacity of the on-board emergency energy storage device. Therefore, this paper reports research on the state of charge (SOC) estimation of train energy storage equipment to optimize the emergency traction strategy and energy utilization rate of trains to the maximum extent. First, with the emergency power supply of on-board lithium titanate batteries, the energy flow model of train emergency power supply is constructed for the first time. Second, based on the second-order equivalent circuit model and considering the utilization of regenerative braking energy, the relationship between battery capacity and open circuit voltage (OCV), temperature, current and SOC are constructed through experimental data, which significantly improves the accuracy and robustness of SOC estimation in complex and variable environments. Then, to effectively eliminate the impact of environmental on the estimation especially in the case of extremely low temperature (−20 °C), online model parameter discrimination and SOC estimation of lithium batteries are realized by combining recursive least squares with an optimal forgetting factor (FRLS) algorithm and a new temperature forgetting factor based adaptive extended Kalman filter (TFFAEKF) algorithm. The experimental results show that the proposed method achieves accurate SOC estimation, and the maximum error is less than 1% at different temperatures.

Multi-objective online driving strategy optimization for energy storage tram considering battery life

Compared with the traditional overhead contact grid or third-rail power supply, energy storage trams equipped with lithium batteries have been developed rapidly because of their advantages of flexible railway laying and high regenerative braking energy utilization. However, trams may face expensive battery replacement costs due to battery degradation. Therefore, this paper proposes a multi-objective optimization method for the tram's driving strategy to reduce operational energy consumption and extend battery life. The method describes the optimization problem as second-order cone programming (SOCP). It uses the interior point algorithm for fast solving, thus obtaining the theoretical optimal speed trajectory in real-time. This feature enables the method to be applied online. To verify the effectiveness and robustness of the proposed method, we conducted simulations under actual operating conditions. The simulation results show that the proposed multi-objective optimization method can extend the battery life by 40 % compared to the single-objective optimization method for energy saving. As a result, the total cost of the whole life cycle is reduced by 6.54 %. Furthermore, this online optimization driving strategy method can effectively deal with unexpected conditions during the actual operation of the tram to ensure the secure operation, punctual arrival, and precise parking of trams. Finally, the effectiveness of the proposed method is further verified by a hardware-in-the-loop (HIL) test.

Power Flow Control-Based Regenerative Braking Energy Utilization in AC Electrified Railways: Review and Future Trends

Power Flow Control-Based Regenerative Braking Energy Utilization in AC Electrified Railways: Review and Future Trends

Power Sharing and Storage-Based Regenerative Braking Energy Utilization for Sectioning Post in Electrified Railways

Power Sharing and Storage-Based Regenerative Braking Energy Utilization for Sectioning Post in Electrified Railways

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.

Regenerative Braking Energy Utilization Analysis in AC/DC Railway Power Supply System With Energy Feedback Systems

In the power supply system with energy feedback system (EFS)s, the energy from EFS (WF) will flow to rectifiers, which is circulation flow (CF). However, the efficiency of CF between substations has been rarely studied. Besides, the relationship between the train operation and regenerative braking energy (RBE) utilizing efficiency is not clear. In this paper, the energy flow structure of the AC/DC railway power supply system including CF is established. In the double-train system, promoting CF when the trains’ distance is long can be beneficial to power utilization efficiency. Chengdu Metro Line 9 is analyzed as the multi-train system based on the iterative AC/DC power flow algorithm. The key to energy saving includes reducing the energy consumption of on-board resistance and the DC traction network loss. Compared with the catenary and rail system, the fourth-rail system can promote system energy efficiency by 23.5% at most. The CF and energy fed to main substations counts for less than 2% and 8% of WF. When the headway time is short, RBE should be avoided feedback to EFS, while the feedback power of EFS should be advocated when the headway time is long. The results can help guide the operation of urban rail to save energy.

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.

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.

A Rolling-Adaptive Peak Clipping Control Strategy Coordinating RBE Recycling and PV Consumption

Electrified railway is one of the major users of electricity in the power system. The access of energy storage system (ESS) effectively solves the problems of peak traction load (TL) shock and regenerative braking energy (RBE) recycling, and the access of photovoltaic (PV) system improves environmental and economic benefits. However, in the current control strategy based on the electrified railway with ESS and PV, there are still problems of less optimal overall peak clipping effect of TL and insufficient penetration of renewable energy sources (RES) due to low utilization and lack of pre-processing capacity of ESS. Therefore, this paper proposes a rolling-adaptive peak clipping (RAPC) control strategy coordinating RBE recycling and PV consumption. Firstly, perform rolling peak clipping through dynamically divided implementation windows to make full use of the available energy in each time period. Secondly, according to the state of charge (SOC) of ESS and the specific working condition of TL, the ESS output is modified accordingly to perform adaptive deep peak clipping maximizing the consumption of PV energy and RBE. The simulation verified that the proposed strategy can improve the overall peak-clipping effect of TL, and achieve full recovery of RBE and complete consumption of PV energy under the same ESS configuration compared to the global optimization method. The results show that the RAPC control strategy can realize optimal peak-clipping and RBE utilization, and increase the PV penetration with limited ESS capacity, thus improving the economic benefits of the traction power supply system (TPSS) and promoting the energy transition of TPSS.

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.

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.

An Energy Storage-Based Railway Power Flow Controller With Partial Compensation Strategy

Focusing on the grid-connection issues and the generated regenerative braking energy (RBE) in the traction power supply system (TPSS), this paper proposes an energy storage-based railway power flow controller (ES-RPFC) with partial compensation strategy (PCS) for grid-connection performance improvement and RBE utilization. The relationship of the primary power factor (PF), the two-phase compensated power, and the converters’ rating is investigated. On this basis, a PCS is developed to reduce the converters’ rating under the premise of satisfying the concerned grid-connection indices. Then, taking the developed PCS into account, a comprehensive power-flow management scheme for ES-RPFC under different operation scenarios is established, and the control strategy of the converter-level controller is given. Finally, the proposal is verified to be feasible and effective by means of both simulation and hardware-in-loop (HIL) experimental results.

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%.

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.

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.

Study on Energy-Saving Optimization of Urban Rail Transit Train Timetable under Regenerative Braking

Energy-saving driving and regenerative braking energy utilization are two main ways to realize energy-saving optimization of urban rail transit train timetables. On the basis of the more mature energy-saving driving achievements of the predecessors, the absorption utilization rate of regenerative braking energy is improved by adjusting the dwell time of trains in the station so that while a train is braking, other trains in the same electric section are just under traction. In this paper, the overlap time between the traction and braking processes of different trains is used as a measure of the proportion of regenerative braking energy that is absorbed. In order to maximize this overlap time, an energy-saving optimization model of urban rail transit train timetable based on regenerative braking technology was established. To facilitate the solution, the nonlinear constraints are converted to linear at the time of model construction in this paper. In the solution, the spatio-temporal local rolling algorithm and the commercial optimization software ILOG CPLEX are used for the solution. The solution results show that the method in this paper can effectively improve the absorption and utilization of regenerative braking energy.