Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty

This paper proposes a robust dynamic control mechanism for bus transit system, taking account of variations in congestion delays and passenger demand, and combines bus holding and operating speed control strategies. By using a prespecified uncertainty set, we propose a state space model for bus motion with delay disturbances and passenger demand uncertainties. According to the Lyapunov function analysis method, we design a robust dynamic control based on the state-feedback scheme as the bus control to achieve the robust stability of the bus transit system, which effectively reduces the bus bunching phenomenon. Furthermore, we formulate a nonlinear optimal control problem to design the robust optimal bus control, which not only reduces the bus bunching, but also improves the schedule adherence and headway regularity of bus service lines. To handle the complexity of the nonlinear optimal control problem with uncertain parameters and disturbances, we reduce it to a convex optimization problem by the minimization of an upper bound on the objective function. The problem is solved in a polynomial time and satisfies the practical real time requirement. Numerical examples are presented to validate the effectiveness of the model and control methods

Issues on simulation of the railway rolling stock operation process – a system and literature review

Railway traffic simulation, taking into account operation and maintenance conditions, is not a new issue in the literature. External effects in such networks (eg. level crossings) were not taken into account in studies. The used models do not take into account sufficiently the process of degradation and recovery of the network. From the technical side, currently carried out simulations are made using similar approaches and techniques as in the initial stage of research. Well-established work in this area could be the basis for evaluation of new solutions. However, the progress in simulation tools during the last years, especially in performance and programming architecture, attempt to create a modern simulation tool. In the paper were presented the main assumptions for the evaluated event-based simulation method, with application to stiff-track transportation network

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

Vision-based pavement marking detection and condition assessment : a case study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as ‘applications’, need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application’s outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: • which of the application functions are influenced by positioning uncertainty; • how positioning uncertainty influences the application output variables; • how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; • what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity

Model predictive energy control of ventilation for underground stations

Smart building systems are opening up new markets, nevertheless the implementation of these novel technologies still lacks suitable and proven whole engineering solutions in complex buildings. This paper presents a detailed approach for the ventilation control of an underground space, as an example of application of the developed solution to a very harsh environment but also highly demanding in terms of energy consumption. The underground spaces are characterized by a particular thermal behavior, because of the continuous and huge thermal exchange they have with the outside, via the openings and the ground surrounding the majority of the building. The main objective of the developed methodology is to reduce energy consumption of ventilation control while maintaining acceptable comfort levels: succeeding in achieving this twofold goal in a real station and the generalization of the approach are the most relevant contributions of the paper. The developed solution is based on a Model-based Predictive Control algorithm used together with a proper monitoring platform. The model predictive control is based on a Bayesian environmental prediction model, which works in cooperation with a weather forecast web service, schedule-based predictions about trains and external fans and an occupancy detection system to appraise the real amount of people. The prediction model develops scenarios useful to allow the controller acting in advance in order to adapt the system to the current and future conditions of use, taking profit of the knowledge of the real ventilation demand. Finally, the proposed control architecture is applied to the Passeig de Gràcia metro station in Barcelona as a case study, validating the usefulness of the proposed approach and obtaining more than 30% of energy savings in the ventilation system, while maintaining the pre-existing comfort levels. The saving percentage values estimated by simulation are confirmed by the direct measures continuously taken on site through energy-meters