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

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Di Liu ◽  
Song-Qing Zhu ◽  
Yun-Rui Bi ◽  
Kun Liu ◽  
You-Xiong Xu
Keyword(s):  
Energy Consumption ◽  
Integer Programming ◽  
Differential Evolution ◽  
Total Energy ◽  
Differential Evolution Algorithm ◽  
Programming Models ◽  
Regenerative Braking ◽  
Total Energy Consumption ◽  
Train Operation ◽  
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.

scholarly journals An Eco-Driving Algorithm for Interoperable Automatic Train Operation

2020 ◽  
Vol 10 (21) ◽  
pp. 7705
Author(s):  
Adrián Fernández-Rodríguez ◽  
Asunción P. Cucala ◽  
Antonio Fernández-Cardador
Keyword(s):  
Energy Consumption ◽  
Differential Evolution ◽  
Minimum Energy ◽  
Differential Evolution Algorithm ◽  
Multiple Time ◽  
Intermediate Target ◽  
Target Time ◽  
Traffic Regulation ◽  
Train Operation ◽  
Automatic Train Operation

The new Automatic Train Operation (ATO) system over the standard European Rail Traffic Management System (ERTMS) will specify the requirements that an automatic train driving system must fulfil in order to be interoperable. The driving is defined by target times located along the journey that are received from the trackside system. Then, the on-board equipment drives the train with the objective of meeting all of the target times. The use of eco-driving methods to calculate the train driving is necessary, as one of the main goals of modern train driving systems is to increase the energy efficiency. This paper presents a simulation-based optimisation algorithm to solve the eco-driving problem constrained by multiple target times. This problem aims to minimize the energy consumption subject to a commercial running time, as the classical eco-driving problem, and also to meet intermediate target times during the journey between stations to enable automatic traffic regulation, especially at junctions. The algorithm proposed combines a Differential Evolution procedure to generate possible solutions with a detailed train simulation model to evaluate them. The use of this algorithm makes possible to find accurate speed profiles that meet the requirements of multiple time objectives. The proposed Differential Evolution algorithm is capable of finding the feasible speed profile with the minimum energy consumption, obtaining a 7.7% of energy variation in the case of a journey with one intermediate target time and 3.1% in the case of two intermediate targets.

Study on Control Strategy for Regenerative Braking of Electric Vehicles

2013 ◽  
Vol 273 ◽  
pp. 669-672
Author(s):  
Zhi Qiang Zhai ◽  
Xin Na Zhang
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Energy Recovery ◽  
Regenerative Braking ◽  
Force Distribution ◽  
Total Energy Consumption ◽  
Braking Force ◽  
Braking Energy Recovery

To improve the efficiency of braking energy recovery,a control strategy for braking force distribution was designed.The front and rear wheels braking force were distributed according to the braking intensity,motor characteristics and relevant regulations.A simulation model was created and analyzed for this control strategy on the basis of software simulationX.The result shows that nearly 26% of total energy consumption are recovered effectively under the UDDS cycle on the premise of security and stability.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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