Cooperative control of high-speed trains for headway regulation: A self-triggered model predictive control based approach

The advanced train-to-train and train-to-ground communication technologies equipped in high-speed railways have the potential to allow trains to follow each with a steady headway and improve the safety and performance of the railway systems. A key enabler is a train control system that is able to respond to unforeseen disturbances in the system (e.g., incidents, train delays), and to adjust and coordinate the train headways and speeds. This paper proposes a multi-train cooperative control model based on the dynamic features during train longitude movement to adjust train following headway. In particular, our model simultaneously considers several practical constraints, e.g., train controller output constraints, safe train following distance, as well as communication delays and resources. Then, this control problem is solved through a rolling horizon approach by calculating the Riccati equation with Lagrangian multipliers. Due to the practical communication resource constraints and riding comfort requirement, we also improved the rolling horizon approach into a novel self-triggered model predictive control scheme to overcome these issues. Finally, two case studies are given through simulation experiments. The simulation results are analyzed which demonstrate the effectiveness of the proposed approach

A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Control Hierarchies for Critical Infrastructures in Smart Grid Using Reinforcement Learning and Metaheuristic Optimization

The objective of this work is to develop robust control framework for interdependent smart grid infrastructures comprising two critical infrastructures: 1) power distribution networks that are characterized by high penetration of distributed energy resources (DERs), and 2) DC-rail transportation systems in congested urban areas. The rising integration of DERs into the power grid is causing a paradigm shift in the power distribution network. Consequently, new control challenges for efficient and robust operation of the power grid have surfaced. For instance, the intermittency of renewable energy resources necessitates coordinated control of power flows, voltage regulators, and protection device settings of the online resources in the system. This can be achieved with the help of an active distribution network, equipped with a distribution management system that provides online solutions to control problems, in real-time, by having full or partial observability. Besides, the increasing electrification of other critical infrastructures, such as transportation and communication, necessitates controllers that can accommodate the over-arching control requirements of interdependent critical infrastructures. Present control approaches lack the amalgamation of active distribution management and the flexibility to accommodate other critical infrastructures. In this work two levels of control hierarchies, viz. 1) Primary Controller and 2) Secondary Controller, have been designed for the power distribution system. These controllers can provide active distribution management, which can be expanded for seamless integration of other critical infrastructures. Besides, a real-time simulation-in-the-loop testbed has been developed, so that both transient and steady state performance of the controllers can be evaluated simultaneously. This testbed has been developed using OPAL-RT and DSpace. The effectiveness of these controllers have been tested in three types of active distribution networks: 1) A modified IEEE 5-bus system equipped with a grid-connected microgrid that consists of two DERs, 2) A modified IEEE 13-bus system equipped with an islanded community microgrid (C_-Grid) comprising four DERs, and 3) A modified IEEE-30 bus system comprising five grid-connected distributed generations (DGs). The DERs used for this work are battery energy storage systems and photovoltaic systems. The Primary Controller has been designed for regulating voltage, frequency and current in the system, while maintaining stability of these parameters, in both grid-tied and islanded operating modes. These design approaches consider multiple points of coupling among the DERs, which is lacking in the existing literature that is primarily focused on single point of common coupling. Besides, this work shows a method of incorporating communication latency, which may exist between Primary and Secondary Controller, into the control design. This facilitates performance analysis of the primary controller, when it is subjected to communication latency, and accordingly develop mitigation techniques. The Secondary Controller has been designed using a reinforcement learning technique called Adaptive Critic Design (ACD). ACD can facilitate seamless integration of a power distribution network with other critical infrastructures. The ACD based algorithm functions as a distribution management system where its control objectives are to balance load and generation, to take preventive or corrective measures for mitigating failures and improving system resiliency, to minimize the cost of energy incurred by the loads by dispatching the DERs, and to maintain their state of health. Alongside DERs, the impact of DC-rail transportation on the power distribution network has been investigated here, with an objective of efficiently and economically reducing congestion in power substations. Hybrid energy storage systems, comprising battery, supercapacitor and flywheel, have been used as wayside energy storage technologies for this purpose. These storage technologies reduce congestion by supplying energy during acceleration and coasting of the trains, and replenish their energy by recapturing the regenerative braking energy during deceleration of the same. The trains consume/regenerate energy at a very high rate during acceleration/deceleration, thereby requiring storage technologies with both high energy and power densities. Hence, the three aforementioned storage technologies have been investigated for both standalone and hybrid operations, by considering system performance and resiliency alongside the percentage of energy recovered, without comprising the cost-recuperation over time. This has been achieved using a two-stage method, where the first stage comprises the development of detailed mathematical model of the rail system and the storage technologies. This mathematical model has been optimized using Genetic Algorithm, in order to obtain optimal combinations of type and size of the storage technologies for minimum cost, within the system constraints. In the second stage, a detailed simulation model has been developed by capturing all the dynamics of the transportation network, which could not be entirely represented in the mathematical model. The optimal sizes obtained from the first stage have been used in the second stage to evaluate their performance and accordingly adjust their values. Thus, the mathematical model provides initial values in a large search space, and these values are further tuned based on the results from simulation model

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Energy Management Expert Assistant, a New Concept

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation