Abstract

Traditional car-sharing services are based on the two-way scheme, where the user picks up and returns the vehicle at the same parking station. Some services also allow one-way trips, where the user can return the vehicle in a different station. The one-way scheme is more attractive for users, but may pose a problem in the distribution of the vehicles, due to a possible unbalanced equation between the user’s demand and the availability of vehicles or free slots at the stations. This issue becomes more complex in the case of electric car sharing, where the travel range depends on the state-of-charge of the vehicles. In a previous work, Bruglieri et al. introduced a new approach to relocate the vehicles where cars were moved by personnel of the service operator to keep the system balanced. Such relocation method generates a new challenging pickup and delivery problem called Electric Vehicle Relocation Problem (E-VReP). Consequently, Bruglieri et al. introduced a method to forecast the unbalancing of a car-sharing system. They applied such method to the data yielded by the Milan transport agency, taking into account the location and capacity of charging stations in Milan. Finally, some years later, they introduced the economic sustainability of the relocation approach, introducing a revenue associated to each request, and a cost associated with each operator. In the proposed new model, authors want to add two new features to the E-VReP. The first one is the implementation of a working day with two work shifts. Until now each operator could start working at different times. With this new feature, we set two shifts each day, one in the morning, and one in the afternoon. The second novelty is the possibility for operators to collaborate among them. This means that operators can carpool in an EV in order to reach their destinations faster than if they tried reaching it by bike. In this way, using a Mixed Integer Linear Programming (MILP) formulation of E-VReP, we can estimate the advantages of our relocation approach on verisimilar instances. Outgoing

Abstract

Under EU Directive 2009/28/EC on Renewable Energy each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including an increase in the use of biofuels in transport by 3% by 2010 and ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. Electric vehicles (EVs) do not emit exhaust fumes in the same manner as traditional internal combustion engine (ICE) vehicles. The optimal benefits of EVs can only be truly achieved if EVs are deployed effectively, so that exhaust pipe gaseous emissions are not fully displaced to the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and briefly discussed.

Abstract

The plug-in hybrid electric vehicle allows vehicle propulsion from multiple internal power sources. Electric energy from the grid can be utilized by means of the plug-in connection. An on-line energy management (EM) strategy is proposed to minimize the costs for taking energy from each power source. Especially in a dynamical energy market, an on-line optimization algorithm is desirable since energy prices change over time. By construction, the proposed EM system can operate with, and without prediction information. If predictions are available, an electronic horizon is applied to anticipate on up-coming events and further optimize the strategy. Illustrative examples are given to explain the added value for both solutions. Also the situation where energy is transferred back to the grid is considered. © 2008 IEEE.

Abstract

In 2008, renewable energy accounted for less than 1% of final energy consumption in the Irish transport sector. In order to increase this share to 10% by 2020 as required under EU directive 2009/28/EC, the Irish government has introduced two specific measures: 10% of the transport fleet is to be powered by electricity by 2020, and an obligation on road transport fuel suppliers that biofuels account for a certain portion of their fuel sales. This study forecasts the impact of these existing measures towards meeting the 10% RES-T target by 2020, focussing on private car transport. The methodology presented is derived from a forecast of private car fuel demand based on a technological stock model of Ireland’s fleet. This paper demonstrates the use of this as a tool firstly as an energy forecasting technique and secondly as a method for evaluating the effects of policy measures on the technological composition and consequent renewable energy demand and related CO2emissions of private cars. Technological scenarios examined in this light are electric vehicles, compressed natural gas vehicles and biofuel blending

Abstract

This paper gives information about Renewable Energy Generation and its impacts on E-Mobility. By 2050, the two pillars of modern transformation are E-Mobility and Renewable Energy. This transformation requires adaptation to meet demographic and economic growth without increasing pollution and congestion. The modern world needs affordable, secure and inclusive, sustainable and integrated with customer-centric infrastructure and services. There is a great fundamental change needed in road transportation sector, which is to achieve its objective of a long term transition to a low-carbon economy. Electric Vehicles when charged with the electricity generated from Renewable Energy Sources can reduce future emissions of greenhouse gases and air pollutants from road transport. Electric Vehicles combined with renewable energy paint a different picture from the oversimplification of gas miles versus electric miles.Smart Mobility and Smart Grid technologies make people live more sustainable and efficiently.