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The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

Nowadays, the increase in the capacity of batteries has laid the foundations for a broader diffusion of electric mobility. However, electric mobility is causing a growing electricity demand as well as the need to increase the diffusion of suitable charging stations. Within these last challenges, drawing on the recent literature, this chapter provides a critical and wide-ranging review of papers dealing with the formulation of the problem of the localisation of electric vehicle (EV) charging points. This problem is approached considering the electric charging infrastructure technologies, localisation criteria and related methodologies. This review shows how the ‘electric mobility revolution’ applies the technological innovations provided by the energy supply systems, and the location of these systems within the urban contexts. Since the technological innovations have different options, achieving an international standard of charging systems is still far away. Moreover, as there are several criteria, parameters and methodologies, and some analytical approaches for the localisation of electric vehicle charging points, the formulation of the ‘localisation’ problem should require the application of multi-criteria analysis to be addressed. Finally, the results show that there is no consensus on technologies, criteria, and methodologies to be adopted. Therefore, this wide-ranging analysis of the literature would be useful to support possible benchmarking and systematisation accordingly.

Examines the role of statutory valuations and tax valuations in the public sector, making particular reference to the position in Singapore. Describes the three main statutory valuations in the context of Singapore as development charge levy, property tax assessment and land acquisition compensation, and discusses some of the issues and problems arising from the courts′ interpretation of the relevant laws and the legislation governing them. Details development charge valuation.

The pricing tariffs adopted by the online hosts are full of hidden complexities, such that it is difficult to estimate in advance the cost of online searching. Potential elements of costs are: subscription charges and discount plans, connect time charges, display/print charges, telecommunications charges, session rates, charges for special facilities and charges for special services. The complexities of each of these elements are considered in relation to a number of online hosts. The review offers an insight into the estimation of the costs of online searching.

Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic.

Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India.

These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy.

The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff.

The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage.

Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage.

To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.

This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.

The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.

This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.

Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

The study identifies the challenges of developing the “electric vehicle (EV)” charging infrastructure in India, having an ambitious target of 30% EV adoption by 2030.

First, a systematic literature review determined EV adoption and challenges in the EV charging infrastructure development globally and specifically in India. Secondly, a focussed group study in which 10 domain experts were consulted to identify additional challenges in India's EV adoption involving EV charging infrastructure.

Accordingly, 11 significant challenges of EV charging infrastructure development in India have been identified–seven through the comparative analysis of the literature review and four from the focussed group study. Secondary data provides insight into the situation around developed countries and in developing countries, specifically in India. Finally, the Government of India's measures and priorities to facilitate such a development are emphasised.

The study can help policymakers/researchers understand the gaps and align measures to address the challenges. A focussed group study may have its limitations due to the perception of the experts.

The systematic literature review of 43 articles using comparative analysis and subsequently a focussed group study of experts to verify and add challenges has made the study unique.

This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.

For a number of reasons, I have decided to begin this issue of VINE with an editorial. In the first place, as many of you will already know, at the end of June I gave up my post as Information Officer for Library Automation in order to become an Assistant Librarian in the Applied Sciences Library of University College, Cardiff. Since the Information Officer for Library Automation is ex officio editor of VINE, this means that I will also be handing over responsibility for this publication to my successor, when he or she is appointed. Unfortunately, there will be a hiatus before my successor can take over, so that for a short while, I will continue to edit VINE. However, because of my commitments in Cardiff, I will be unable to provide news of recent developments in U.K. library automation as I formerly did. Thus, in this issue of VINE and the next, I will concentrate on descriptions of automated library systems which I visited whilst on a study tour of North America in April and May of this year. Once my successor takes over, VINE will revert to being a source of up‐to‐date information about automation in specifically U.K. libraries.

This research aims to focus on the problem of optimizing the layout of charging stations.

The authors estimate the total number of charging stations by the number of existing gas stations using clustering by fast search-and-find of density peaks (CFSFDP) algorithm.

This paper reveals the relationship between the distance and the population. Priority should be given to the short-distance and high-frequency travel charging needs in the city, followed by low frequency and travel, which can improve the use efficiency of the whole charging network.

First, the total number of charging stations is estimated by the number of existing gas stations. This method can fully consider the growth potential of charging vehicles instead of the traditional cars. Besides, the cost-benefit method is proposed to explore the marginal coverage distance of the city’s charging network and can be used to reveal the relationship between the distance and the population.