Novel technologies and strategies for clean transport systems

Roskilly, Anthony Paul; Palacín, Roberto; Yan, Jinyue

doi:10.1016/j.apenergy.2015.09.051

Cited by 64 publications

(14 citation statements)

References 41 publications

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“…As instances can be run concurrently, it is possible to observe system-level effects [3,11,20,23]. These system effects can be seen in terms of decarbonisation.…”

Section: Discussionmentioning

confidence: 99%

“…Designing for urban rail decarbonisation is a systems problem in which it is not just the performance of individual components that defines carbon impact, but also the interaction of multiple components, often within an operational context [3,20,21]. Factors such as duty cycles [22] and local or temporary conditions may have an impact on performance; For example actual data on diesel-based urban rail emissions reveal significant fluctuations due to passenger loading or due to disruption and delay [11].…”

Section: Introductionmentioning

confidence: 99%

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Multi-modelling for Decarbonisation in Urban Rail Systems

et al. 2019

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This paper demonstrates a methodology for flexible, dynamic systems modelling relevant to urban rail decarbonisation. Decarbonisation of urban rail is a vital component of policy and strategy to minimize anthropogenic emissions. Decarbonisation is a systems problem, however, that needs to reflect the interaction between components and processes. Dynamic computer modelling of systems for decarbonisation involves interfacing multiple models together and running them in parallel in order to observe and predict systems-level effects. This is challenging due to the diverse nature of models, achieving parallel model integration and concerns around intellectual property (IP). One solution is the multi-modelling paradigm, which supports integrated, diverse, secure interfacing of models. This paper demonstrates the application of the multi-modelling approach, using the INTO-CPS tool chain. A multi-model was developed comprising key components required for urban rail decarbonisation problems. This multi-model was tested for power consumption in four different scenarios with an example drawn from the Tyne and Wear Metro in Newcastle-upon-Tyne in the United Kingdom. These scenarios compared combinations of decarbonisation intervention (baseline rolling stock versus lightweight, regenerative braking rolling stock and baseline driving style versus energy-efficient defensive driving style), generating different power consumption profiles for each. As such, this serves as a proof of the application of the multi-modelling approach and demonstrates a number of benefits for flexible and rapid systems modelling. This paper fills a knowledge gap by demonstrating a potentially valuable tool for future systems-level decarbonisation challenges in urban rail.

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“…As instances can be run concurrently, it is possible to observe system-level effects [3,11,20,23]. These system effects can be seen in terms of decarbonisation.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-modelling for Decarbonisation in Urban Rail Systems

et al. 2019

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“…Roskilly et al [15] showed that significant reduction in both CO 2 and NO x emissions could be achieved through the life cycle and cost analyses for hybrid propulsion systems.…”

Section: Overview Of Hybrid Shipsmentioning

confidence: 99%

Multi-criteria decision-making for marine propulsion: Hybrid, diesel electric and diesel mechanical systems from cost-environment-risk perspectives

et al. 2018

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The paper introduces a new decision-making process which is used to compare the performance of a ship with either diesel electric hybrid propulsion or conventional propulsion systems. A case study was carried out to compare the performance of both propulsions from cost, environmental and risk perspectives. This paper also overviews the modern approaches of multi-criteria decision-making and highlights some of their shortcomings in particular the fact that these approaches often rely on different criteria such as financial, environmental or risk. This paper aims to overcome this shortcoming by enhancing the process of multi-criteria decision analysis. The key process in this research was to convert all incomparable values into monetary values, thereby enabling the impacts of each criterion to be compared and integrated in a straightforward manner. Results of the case study showed that the use of a hybrid propulsion system could reduce annual operational costs by $ 300,000 (2 % total cost) compared with a diesel electric system and almost $ 1 million (7 %) compared to a diesel *Revised Manuscript with No Changes Marked mechanical propulsion system. In order to investigate the optimal use of the hybrid propulsion system, various operational scenarios were identified and applied to the proposed decision-making process. The results showed that operating the ship in hybrid mode during manoeuvring and berthing is more desirable as the holistic cost can reduce in almost $ 1 million. The advantages of the proposed decision making process was illustrated by comparing the results obtained from a conventional decision-making process using the analytical hierarchical method. It is believed that the research findings not only present general understanding of the possible advantages of hybrid propulsion for stakeholders, but provide them with an insight into the enhanced approach into the multi-criteria decision analysis.

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“…Transportation infrastructure, including airports, is moving toward the use of clean energy technologies and reducing the need for conventional practices that create local sources of pollution and have high environmental impacts [1][2][3]. This includes replacing fossil fuel-based with electricity-based operations and equipment [4]. However, given the significant energy demands associated with existing fossil fuel-based operations, the electric power demand of alternative electric systems must be assessed to evaluate the technical feasibility of electrifying such operations and equipment.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%