• Energy Research

Due to the global pandemic we are experiencing, our lives and social behaviours in urban contexts are deeply changed. Many regulations and rules have been and still are enacted by the world’s governments to minimise the level of risk to which the population is exposed. The majority of them aims at ensuring people social distancing and, in this context, the public transport sector plays a crucial role. Indeed, guaranteeing social distancing on buses or trains, while maintaining good service quality at the same time, represents a very challenging goal. Within this framework, the paper presents a methodology for optimising rail service operations thus satisfying mobility demand while complying with the maximum occupation coefficients allowed by current regulations. In order to show the goodness of the proposed method, it has been applied in the case of a real regional rail line in the south of Italy.

Electrified railway systems are the current focus of energy efficiency studies due to the increasing constraints of environmental sustainability policies. Railway System Operators (RSOs), also encouraged by market deregulation, are thus examining how to update infrastructures and vehicles and/or apply new operating strategies. Given the intrinsic relation between railway systems and their primary feeding grid, each measure applied to a railway system impacts directly on the power grid which supplies the system. Against this background, this paper aims to ascertain the impact of an energy efficiency strategy applied by the RSO system on its primary grid. The energy-efficient strategy is identified as to preserve timetable stability and service quality in a passenger-oriented perspective. Moreover, a method to model the interaction between the systems in terms of power flow exchanges is proposed. The model is used to verify how an eco-driving technique applied to a metro system affects the state variables of the distribution grid. In particular, the primary grid, the traction power substations and the railway system are modelled according to real data. The case study is the Metro Line 1 operating in the city of Naples (Italy). The numerical simulations are carried out with a two-step procedure: first, the power requirements of the rolling stock as a function of the imposed operating service are calculated by OpenTrack ® software; the state variables of the distribution grid are then evaluated by means of the power flow approach.

In recent years the growing interest in environmental issues has prompted researchers to investigate two main areas in the field of rail systems: how to improve performance in order to attract users from other transport modes with greater environmental impacts (such as private cars) and how to reduce energy consumption. On the latter issue, some procedures have been developed for determining suitable ‘green’ driving profiles which are, however, subject to greater travel times. Since precise quantification is critically important, in this paper we propose an approach to determining all operational times analytically, including reserve times. Finally, the methodology is applied in the case of a real metro line for validating the proposal.

Public transport systems can be characterised by a schedule-based or a frequency-based framework according to the kind of service to be operated. In the former case, specific departure and arrival times are set for each run and disclosed to the users; in the latter, instead, it is necessary to maintain a certain headway between two successive runs, rather than a specific timetable structure. This paper focuses on modelling frequency-based systems, which can be described by means of the so-called Merry-Go-Round (MGR) paradigm. The paradigm is first discussed and the related analytical formulation is presented; the role of the terminal station layout is then investigated within this framework. Finally, in order to show the effectiveness of the proposed formulation, it was implemented in the case of a real-scale metro line.

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We propose a model for optimising driving speed profiles on metro lines to reduce traction energy consumption. The model optimises the cruising speed to be maintained on each section between two stations; the functions that link the cruising speed to the travel time on the section and the corresponding energy consumption are built using microscopic railway simulation software. In addition to formulating an optimisation model and its resolution through a gradient algorithm, the problem is also solved by using a simulation model and the corresponding optimisation module, with which stochastic factors may be included in the problem. The results are promising and show that traction energy savings of over 25% compared to non-optimised operations may be achieved.

Sustainable transport frameworks are gaining attention within national and international transportation policies, given the key role that decarbonisation plays in making urban environments people-friendly. Within this context, several shared services and micro-mobility options are being developed, especially as first/last mile facilities, further increasing public transport coverage levels. We present an overview of the environmental impacts of different transport modes and compare them from different perspectives, namely, CO2 emission levels, total costs (also including the user generalised cost) and service life of vehicles involved. The proposed methodology is applied to an urban context, using real trip data and showing the main findings under real conditions.

Energy-saving and energy-recovery strategies represent key factors to achieve operational cost reductions within rail systems’ management tasks. However, in altering service features, they also affect passenger satisfaction. This paper investigates the effect of implementing such measures in the case of rolling stock unavailability. Numerous operational scenarios were explored by analysing different planned headway and rolling stock configurations. The scenarios were simulated with and without the adoption of Energy-Saving Strategies (ESS), both in ordinary and in disruption conditions. Our results show that, in ordinary conditions, the optimal scenarios are those that minimise the planned headway. By contrast, in disrupted conditions, due to greater passenger inconvenience, the use of a time-optimal condition is preferable if a real-time adjustment of ESS is not feasible. However, if the ESS can be updated in real-time, use of ESS is preferable only if the adopted headway is the smallest of those associated with the rolling stock scheme considered.