Energy Efficiency and Integration of Urban Electrical Transport Systems: EVs and Metro-Trains of Two Real European Lines

Fernández-Rodríguez, Adrián; Fernández-Cardador, Antonio; Cucala, Asunción P.; Falvo, Maria Carmen

doi:10.3390/en12030366

Cited by 30 publications

(38 citation statements)

References 37 publications

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“…The control loop minimizes the difference between an objective speed v obj and the train speed v. At every time step the objective speed is computed in terms of the driving commands and the track speed limits. To do so, the braking curves to the next ceiling speed reduction, v b (s), and to the next stop at a station, v f b (s), are calculated by using Equations (10) and (11).…”

Section: Driving Modelmentioning

confidence: 99%

Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies

et al. 2020

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Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency.

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Section: Driving Modelmentioning

confidence: 99%

Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies

et al. 2020

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“…Fernandez et al investigated two real European metro lines; Italian and Spanish metro line. They stated that most of the regenerative energy was wasted in these and 98% of the regenerated energy can be reused for short interval of trains [9].…”

Section:  Utilization Of Regenerative Energymentioning

confidence: 99%

Raylı Araçlarda Enerji Verimliliği Çalışması

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Çam

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Today, rail vehicles are frequently preferred both in urban and intercity transportation due to their high passenger capacity, speed and increasing environmental awareness. As in every field, energy efficiency studies have become compulsory in these vehicles. Because, they have high energy consumption even in their daily services. For energy efficiency, there are various strategies such as electrification losses reduction, utilization of regenerative braking and improvement of comfort function and efficient driving techniques have been performed in this paper. For this purpose, firstly, the driving of a rail vehicle has been modeled on Matlab considering all vehicle information, track information and operational constraints. Then, four different driving styles have been determined for the efficient use of energy and their effects on travel time and energy consumption have been examined. The study has been tested with the practical data of Ankaray metro line which has eleven stations and is 8.527 km long. According to the results of the paper, coasting control is more effective for long distances and reduction of the maximum speed is more convenient for short distances. Furthermore, it has been demonstrated that thanks to the determined strategies, the vehicle can save up to 11.54-36.37% energy compared to practical driving.

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“…Reinforcement learning (RL) [10,11], which is a vital branch of AI, has potential in the area of intelligent transportation. There are two advantages of RL: First, due to its generality, agents can effectively study many disciplines in a complex environment such as the metro network [12][13][14]; second, an agent with full exploration of the environment can give proper decisions in real-time, which means that RL can be used in optimization problems with real-time requirements. Until now, the train timetable rescheduling (TTR) problem [15][16][17][18] has been repeatedly discussed, however, there is only a small amount of literature using RL as a possible solution.…”

Section: Introductionmentioning

confidence: 99%

Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem

et al. 2019

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Reinforcement learning has potential in the area of intelligent transportation due to its generality and real-time feature. The Q-learning algorithm, which is an early proposed algorithm, has its own merits to solve the train timetable rescheduling (TTR) problem. However, it has shortage in two aspects: Dimensional limits of action and a slow convergence rate. In this paper, a deep deterministic policy gradient (DDPG) algorithm is applied to solve the energy-aimed train timetable rescheduling (ETTR) problem. This algorithm belongs to reinforcement learning, which fulfills real-time requirements of the ETTR problem, and has adaptability on random disturbances. Superior to the Q-learning, DDPG has a continuous state space and action space. After enough training, the learning agent based on DDPG takes proper action by adjusting the cruising speed and the dwelling time continuously for each train in a metro network when random disturbances happen. Although training needs an iteration for thousands of episodes, the policy decision during each testing episode takes a very short time. Models for the metro network, based on a real case of the Shanghai Metro Line 1, are established as a training and testing environment. To validate the energy-saving effect and the real-time feature of the proposed algorithm, four experiments are designed and conducted. Compared with the no action strategy, results show that the proposed algorithm has real-time performance, and saves a significant percentage of energy under random disturbances.

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Energy Efficiency and Integration of Urban Electrical Transport Systems: EVs and Metro-Trains of Two Real European Lines

Cited by 30 publications

References 37 publications

Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies

Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies

Raylı Araçlarda Enerji Verimliliği Çalışması

Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem

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