Energy Consumption Of Train Operation Research Articles

The Analysis of Electric Phase Separation and Common Technical Issues of High-speed Railway System in China

The electric railway, as a vital economic transportation artery, offers high efficiency and effectiveness compared to other modes of transportation. The characteristics of efficient transport, comfortable ride and environmental friendliness have effectively driven regional economic construction and industrial economy along the route, which has significantly enhanced cultural and economic exchanges between different regions, becomes the preferred mode of transportation for medium-distance and long-distance travel, and plays an irreplaceable role in China’s current transportation system. Under the increasing demand for passenger flow, the high-speed and high-density tracking operation of trains has become the new normal of high-speed railway operation management. In the background of national science and technology innovation development strategy, how to further shorten the train tracking interval to improve transportation efficiency and further reduce the energy consumption of train operation to improve the energy utilization level of the railway industry is the focus of the current electric railway construction and operation management department. The electric phase separation is a special and intricate part of the traction power supply system in China’s electric railway, and it is an indispensable component. The article describes that some main techniques relating to electrical phase separation are utilized extensively in China’s high-speed railway, by studying the technologies of electric phase separation, a few new methods were given and shared, which will solve the issues of high energy consumption, increased breakdowns, large equipment loss, and the lower speed, to better serve economic and social development and people’s travel.

Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.

An Intelligent Train Operation Method Based on Event-Driven Deep Reinforcement Learning

Train operation control in urban railways is challenging due to its high dynamics, complex environment, and level of comfort and safety. To address these challenges, in this article, the authors propose a new deep reinforcement-based train operation (DRTO) method which includes: 1) A deterministic deep reinforcement learning algorithm, 2) a dynamic incentive system, which is used to ensure safe operation in a multitrain environment, and 3) an event-driven method, which is used to improve the DRTO performance based on an event-driven strategy. To evaluate the performance, we thoroughly compare the proposed method with other operation control solutions on both synthetic and real datasets. Our results demonstrate that DRTO is effective in: 1) Decreasing the energy consumption of train operation, 2) increasing passenger comfort, and 3) achieving a good tradeoff between efficiency and safety. In addition, the effectiveness of the event-driven strategy and the dynamic incentive system is demonstrated in the experiments.

The single train’s punctual and energy-saving operation scheme based on dynamic programming algorithm

Abstract A punctual and energy-saving operation model and some principles to realize energy-saving operation for train are analysed in this paper in order to reduce the energy consumption and obtain the energy-saving speed curve. According to the basic principles of dynamic programming algorithm, the train operation line is discretized. We reduce the space of state variables by using the maximum capacity operation curve and average speed operation curve. And the cost function for energy and time consumption is constructed according to requirements for punctual and energy-saving operation of trains. The simulation results using specific parameters show that the dynamic programming algorithm using reduced state space has high computational efficiency and effectively reduces the energy consumption of train operation.

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the decision maker, and the satisfactory outcomes can be obtained after a limited number of adjustments. The multi-objective particle swarm optimization (MPSO) algorithm is used to optimize the train operation process. An algorithm based on the important relationship between the objective and the preference information of the given reference points is suggested to overcome the shortcomings of the existing algorithms. These methods significantly increase the computational complexity and convergence of the algorithm. An adaptive fuzzy logic system that can simultaneously utilize experience information and field data information is proposed to adjust the consequences of off-line optimization in real time, thereby eliminating the influence of uncertainty on train operation. After optimization and adjustment, the whole running time has been increased by 0.5 s, the energy consumption has been reduced by 12%, the parking accuracy has been increased by 8%, and the comprehensive performance has been enhanced.

Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system.

Evaluating Influence Factors of Energy Consumption for Urban Rail Timetable Using an Optimized Train Control Method

Energy efficiency of urban rail transit system has become more and more significant due to the environmental concern and the rising cost of energy. The energy consumption of train operation is inevitably influenced by several factors. In this paper, the related factors are investigated based on the consideration of the close correlation between the timetable and specific train speed control profiles in practical operation. Evaluation about two typical factors, including the station dwell time and average travelling speed, is carried out using the real time schedule from Batong Line in Beijing Metro. The results illustrate the relationships and provide suggestions in design of the energy-saving timetables.

Dynamic coast control of train movement with genetic algorithm

The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.