Review of state government motor vehicle resources

The LAC was requested to review state motor vehicle resources and make recommendations. It focused on three statewide objectives posed by the committees: (1) Determine if any wasteful duplication exists among state-owned vehicle maintenance facilities. (2) Identify any waste or inefficiency in the use of state owned vehicles. (3) Identify unnecessary or personal use of state-owned vehicles.

Improving energy efficiency in the road freight transport sector: the application of a vehicle approach

Several approaches can be used to analyse performance, energy consumption and CO2 emissions in freight transport. In this paper we define and apply a vehicle-oriented, bottom up survey approach, the so called ‘vehicle approach’, in contrast to a ‘supply chain approach’. The main objective of the approach is to assess the impacts of various freight transport operations on efficiency and energy use. We apply the approach, comparing official statistics on freight transport and energy efficiency in Britain and France. Results on freight intensity, vehicle utilisation, fuel use, fuel efficiency and CO2 intensity are compared for the two countries. The results indicate comparable levels of operational and fuel efficiency in road freight transport operations in the two countries. Issues that can be addressed with the vehicle approach include: the impacts of technology innovations and logistics decisions implemented in freight companies, and the quantification of the effect of policy measures on fuel use at the national level.

Using official data sources to analyse the light goods vehicle fleet and operations in Britain

Report produced as part of the Green Logistics project (EPSRC and Department for Transport funded). Light goods vehicles play a key role in providing goods and services to businesses and other organisations in Britain. In order to better understand the relationship between costs and benefits of LGV operations it is necessary to gain a more detailed appreciation of the roles that these vehicles are fulfilling. This report aims to provide a better understanding of this sector by examining LGV fleet and operations in terms of their characteristics, utilisation and efficiency and purpose. Important potential external impacts of LGVs are also considered.

Internalising the external costs of light goods vehicle transport in Britain

Report produced as part of the Green Logistics project (EPSRC and Department for Transport funded). To what extent do the taxes paid by the light goods vehicles (LGVs) users in Britain cover their allocated infrastructural, environmental and congestion costs? This report is a continuation of a study on the internalisation of the external costs of heavy goods vehicle activity. Research undertaken jointly by the Transport Studies Group at University of Westminster and Logistics Research Centre at Heriot-Watt University has attempted to answer this question using official government transport statistics and monetary valuations for the external costs.

Internalising the external costs of light and heavy goods vehicle transport in London

Report produced as part of the Green Logistics project (EPSRC and Department for Transport funded). This report provides estimates of the total external costs of LGV and HGV operations in London. In 2006, total LGV and HGV activity imposed external costs of approximately £1.75-£1.8 billion using low, medium and high emission cost values. About 27 per cent of these costs were internalised by duties and taxes paid by LGV operators, compared with 26% in the case of HGVs. If congestion costs are excluded, taxes and duties paid by LGV operators are estimated to be 155% of LGVs' allocated infrastructural and environmental costs, compared with 85% in the case of HGVs. When using the medium emission cost values, LGVs accounted for 56% of these external costs in London and HGVs for 44%.

Internalising the external costs of light goods vehicle transport in Britain

To what extent do the taxes paid by the light goods vehicles (LGVs) users in Britain cover their allocated infrastructural, environmental and congestion costs? This report is a continuation of a study on the internalisation of the external costs of heavy goods vehicle activity. Research undertaken jointly by the Transport Studies Group at University of Westminster and Logistics Research Centre at Heriot-Watt University has attempted to answer this question using official government transport statistics and monetary valuations for the external costs.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.