scholarly journals A New Approach for Real Time Train Energy Efficiency Optimization

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2660 ◽  
Author(s):  
Agostinho Rocha ◽  
Armando Araújo ◽  
Adriano Carvalho ◽  
João Sepulveda
Keyword(s):  
Energy Efficiency ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Real Time ◽  
Optimization Algorithm ◽  
Normal Operation ◽  
Speed Profile ◽  
Railway Systems ◽  
Optimal Speed ◽  
Advisory System

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

scholarly journals Optimal Speed Profile Determination with Fixed Trip Time in the Electric Train Operation of the Cat Linh-Ha Dong Metro Line based on Pontryagin's Maximum Principle

2020 ◽  
Vol 10 (6) ◽  
pp. 6488-6493
Author(s):  
T. T. T. A. Anh ◽  
N. V. Quyen
Keyword(s):  
Energy Consumption ◽  
Maximum Principle ◽  
Pontryagin’S Maximum Principle ◽  
Speed Profile ◽  
Saving Energy ◽  
Pontryagin's Maximum Principle ◽  
Improve Energy Efficiency ◽  
Optimal Speed ◽  
Trip Time ◽  
Train Operation

The significant energy consumption for railway electric transportation operation poses a great challenge in outlining saving energy solutions. Speed profile optimization based on optimal control theory is one of the most common methods to improve energy efficiency without the railway infrastructure investment costs. The paper proposes an optimization method based on Pontryagin's Maximum Principle (PMP), not only to find optimal switching points in three operation phases: accelerating, coasting, braking, and from these switching points being able to determine the optimal speed profile, but also to ensure fixed-trip time. In order to determine trip time abiding by the scheduled timetables by applying nonlinear programming puts the Lagrange multiplier λ in the objective function regarded as a time constraint condition. The correctness and energy effectiveness of this method have been verified by the simulation results with data collected from the electrified trains of the Cat Linh-Ha Dong metro line in Vietnam. The saving energy levels are compared in three scenarios: electrified train operation tracking the original speed profile (energy consumption of the route: 144.64kWh), train operation tracking the optimal speed profile without fixed-trip time (energy consumption of the route: 129.18kWh), and train operation tracking the optimal speed profile and fixed trip time (energy consumption of the route: 132.99kWh) in an effort to give some useful choices for operating metro lines.

scholarly journals Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1833 ◽  
Author(s):  
Tamás Bányai
Keyword(s):  
Energy Efficiency ◽  
Supply Chain ◽  
Energy Consumption ◽  
Real Time ◽  
Real Time Scheduling ◽  
Improve Energy Efficiency ◽  
Increase Energy Efficiency ◽  
Last Mile ◽  
Time Scheduling ◽  
Last Mile Delivery

Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

scholarly journals On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2126 ◽  
Author(s):  
Lijun Wang ◽  
Jia Yan ◽  
Tao Han ◽  
Dexiang Deng
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Reference Value ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Normal Operation ◽  
Connectivity Probability ◽  
Communication Radius

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

A Variable No-Load Voltage Scheme for Improving Energy Efficiency in DC-Electrified Mass Transit Systems

2014 ◽  
Author(s):  
Álvaro J. López-López ◽  
Lars Abrahamsson ◽  
Ramón R. Pecharromán ◽  
Antonio Fernández-Cardador ◽  
Paloma Cucala ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Mass Transit ◽  
Traffic Density ◽  
Transit Systems ◽  
Load Voltage ◽  
Railway Systems ◽  
Wear And Tear ◽  
High Investment

Railway mass transit systems like subways play a fundamental role in the concept of sustainable cities. In these systems, the amount of passengers strongly fluctuates along the day. Hence, in order to provide a proper service without incurring disproportionate energy consumption, operation at different traffic densities is required. The majority of underground systems are DC-electrified. Standard DC voltages in railway systems are low for historical and safety reasons. In the rush hours, the large number of trains demanding power of the system may lead to overloaded substations and voltage dips. This problem is partially mitigated by means of substation-transformer tap regulation, which allows operators to increase the no-load voltage. High no-load voltage has a beneficial effect at all traffic-density scenarios in terms of transmission losses. However, at the same time it effectively reduces the system’s capacity to absorb regenerated energy, which may lead to inefficient energy consumption figures during off-peak hours. In this paper, the sensitivity of system energy consumption to no-load voltage has been analyzed. Several traffic-density scenarios in a case-study system are explored. As a result, a scheduled no-load voltage scheme is proposed for the operation of the system. This operation strategy improves energy efficiency without incurring a high investment cost. The only costs related to this proposed method are the costs of wear-and-tear in tap-changers. In case there are devices such as energy storage systems installed in the system, there would be additional operation costs related to a simultaneous update of the voltage limits for their operation.

Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

2022 ◽  
Vol 307 ◽  
pp. 118314
Author(s):  
Sihua Yin ◽  
Haidong Yang ◽  
Kangkang Xu ◽  
Chengjiu Zhu ◽  
Shaqing Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
Optimization Analysis ◽  
Efficiency Optimization

scholarly journals Internet of Things Based on Situation-Awareness for Energy Efficiency

2019 ◽  
Vol 12 (1) ◽  
pp. 28-53 ◽  
Author(s):  
Un Hee Schiefelbein ◽  
Diovane Soligo ◽  
Vinícius Maran ◽  
José Palazzo M. De Oliveira ◽  
João Carlos Damasceno Lima ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Environmental Impact ◽  
Real Time ◽  
Situation Awareness ◽  
Electric Energy ◽  
Energy Expenditures ◽  
Electric Energy Consumption

The reduction of electric energy consumption is considered as one of the main challenges in diverse sectors of the economy. To residential customers, the management of energy consumption can bring significant costs reduction and decreased environmental impact. This work presents a solution based on the use of situation-awareness applied in IOT that helps the users to reduce the consumption of electric energy through its own residence. The practical results obtained in the application of this proposal in a real-live scenario confirmed the option of collecting information directly of electrical appliances and inform the user of their energy expenditures in real-time, allowing the knowledge and the management of their expenses.

IOT and Industry 4.0 – Gateway towards improving Energy Efficiency

2017 ◽  
Vol 7 (9) ◽  
pp. 58
Author(s):  
Jait Purohit
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Management ◽  
Manufacturing Industry ◽  
Management Practices ◽  
Data Gathering ◽  
Time Data ◽  
Improve Energy Efficiency ◽  
Day To Day Operations

Energy efficiency (EE) has become an important benchmark in manufacturing industry due the increasing concerns about climate change and tightening of environmental regulations. However, most manufacturing and production industries today are only able to monitor aggregated energy consumption and lack the real-time visibility of EE on the shop floors. The ability to access energy information and effectively analyse such real-time data to extract key indicators is a crucial factor for successful energy management. While enabling real-time online monitoring of Energy Efficiency, it also applies data gathering analysis to detect abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-to-day operations and improve Energy Efficiency by eliminating possible energy wastages on manufacturing shop floors.

scholarly journals Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1998-2009 ◽  
Author(s):  
Francisco Valero ◽  
Francisco Rubio ◽  
Carlos Llopis-Albert
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Random Distribution ◽  
Lower Cost ◽  
Maximum Velocity ◽  
Optimization Techniques ◽  
Sequential Optimization ◽  
Inertial Parameters

SummaryReducing the energy consumed by a car-like mobile robot makes it possible to move at a lower cost, yet it takes more working time. This paper proposes an optimization algorithm for trajectories with optimal times and analyzes the consequences of restricting the energy consumed on the trajectory obtained for a car-like robot. When modeling the dynamic behavior of the vehicle, it is necessary to consider its inertial parameters, the behavior of the motor, and the basic properties of the tire in its interaction with the ground. To obtain collision-free, minimum-time trajectories quadratic sequential optimization techniques are used, where the objective function is the time taken by the robot to move between two given configurations. This is subject to constraints relating to the vehicle and tires as well as the energy consumed, which is the basis for this paper. We work with a real random distribution of consumed energy values following a normal Gaussian distribution in order to analyze its influence on the trajectories obtained by the vehicle. The energy consumed, the time taken, the maximum velocity reached, and the distance traveled are analyzed in order to characterize the properties of the trajectories obtained. The proposed algorithm has been applied to 101 examples, showing that the computational times needed to obtain the solutions are always lower than those required to realize the trajectories. The results obtained allow us to reach conclusions about the energy efficiency of the trajectories.

A multi-period joint energy scheduling algorithm in smart home based on prediction of the residents energy consumption

2015 ◽  
Vol 12 (2) ◽  
pp. 135-148 ◽  
Author(s):  
Jijun Zhao ◽  
Siyuan Gao ◽  
Danping Ren ◽  
Zhihua Li ◽  
Liang Xue
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
User Satisfaction ◽  
Scheduling Algorithm ◽  
Comfort Level ◽  
Fine Tuning ◽  
Prediction Algorithm ◽  
Home Based ◽  
Short Period

In this paper, considering a tradeoff between consumers comfort and energy efficiency, a multi-period joint energy scheduling algorithm (MPJ-ESA) based on prediction of residents energy consumption is proposed, which includes long-period preliminary sch eduling, short-period preliminary scheduling, and real-time fine-tuning scheduling. First, by analyzing historical data of energy consumption, preferred usage profile of consumers is inferred, and the dynamic comfort level is presented. Then the paper uses the wavelet neural networks (WNNs) prediction algorithm to predict the operation of the appliances which are classified into appliances with unschedulable mode and schedulable mode. Based on the energy consumption prediction and dynamic comfort level, home appliances running state are scheduled according to the prediction of renewable energy available amount and real-time pricing (RTP). The simulation results show that scheduling algorithm effectively improves the energy efficiency and enhances user satisfaction with the operation of scheduled appliances and let the consumers comfort and energy efficiency achieve a better tradeoff.

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