• Energy Research
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In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

SummaryIn 2008, European Union (EU) announced that from 2012, each international flight taking off and landing in EU would be given an emission permit. Therefore, the period of 2008–2012 can be regarded as a buffer period for global airlines. Although EU formally decides to exclude non‐EU airlines from the EU Emission Trading System on March 4, 2014, it is necessary to investigate the impacts of the policy on airline energy efficiency in this period. Airline energy efficiency is divided into three stages—operations stage, service stage, and sales stage—and Greenhouse gas emission is treated as an undesirable output of service stage. Two models, network range‐adjusted measure model with weak disposability and network range‐adjusted measure model with strong disposability, are established to evaluate the efficiencies of 22 international airlines from 2008 to 2012. The results show that (i) most airlines' efficiencies have decreased in the period, and the EU Emission Trading System is not effective for the efficiency improvement; (ii) the average efficiency of European airlines is almost the same as that of non‐European airlines; and (iii) the model with weak disposability is more reasonable in distinguishing efficiency differences, while strong disposability is a more reasonable way in treating undesirable outputs. Copyright © 2016 John Wiley & Sons, Ltd.

In recent years, many countries have published their timetables to promote electric vehicles. Many researches have focused on the benefits of electric vehicles. Compared with gas vehicles, electric vehicles are more suitable for modern cities, because they are considered to be environment-friendly by the public. Hence we pay attention to the environmental costs of electric vehicles. In this paper, an electric vehicle network is established. To analyze this electric vehicle network, we define environmental costs for the network and propose a stochastic user equilibrium model to describe drivers’ route choice behavior. An algorithm is proposed to solve this model. The model and the algorithm are illustrated through a numerical example. We test the calculation feasibility of the proposed model and the computational efficiency of the proposed algorithm via this numerical example. A comparative analysis is conducted to show the benefits of introducing electric vehicles into traffic networks. With the sensitivity analysis, we also reveal the relationship between people’s environmental awareness, the quantity of electric vehicles and the environmental costs of the overall traffic network.

Geothermal energy piles are an environmentally friendly energy source and an innovative approach to melt snow on the bridge surface and minimize or eliminate the use of deicing salt. However, the application potential of energy piles for bridge deicing or snow melting has not been fully explored for different climates. In this study, the feasibility of using energy piles for bridge deicing in eight cities of the United States was investigated. Temperature response function (G function) method was validated and used to estimate the extracted heat from energy piles installed in the soils with different thermal properties, which was used to heat the bridge deck during snowing. The results of numerical simulation and statistical analysis confirm that the performance of the geothermal deicing system depends on the weather conditions during snowing and thermal properties of soils. The coverage rate of the geothermal system (percentage of snowing time that the geothermal deicing system can keep the bridge surface above 0°C) increases with the increasing air temperature and thermal diffusivities of soils, and decreases with the increasing precipitation rate and wind speed. This deicing method is promising in cities with higher average air temperature and low precipitation rate during snowing.

Under worldwide environmental stress, zero-emission vehicles (ZEV) are rapidly coming to market. However, it is not clear how such vehicles reduce vehicular emissions at a spatially explicit level, which is crucial for developing specific policies. This study proposed a quantitative approach to estimate the effectiveness of ZEVs in reducing emissions to support investment decisions promoting the use of ZEVs. The approach uses existing statewide travel demand and mobile emission models in an integrated framework. Scenarios are designed to measure the emissions reduction effects of ZEVs at different spatial scales (statewide, county, and roadway) and characteristics (densely and sparsely populated counties) and with various levels of market penetration and driving range limits. Results show significant spatial differentiation of the impact of ZEV deployment from county to roadway levels. Offering greater spatial detail and new insights on decision-making processes, this study described an integrated tool for identifying effective strategies for ZEV implementation.

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background.

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

This paper investigates the market potential and environmental benefits of replacing internal combustion engine (ICE) vehicles with battery electric vehicles (BEVs) in the taxi fleet in Nanjing, China. Vehicle trajectory data collected by onboard global positioning system (GPS) units are used to study the travel patterns of taxis. The impacts of charger power, charging infrastructure coverage, and taxi apps on the feasibility of electric taxis are quantified, considering taxi drivers’ recharging behavior and operating activities. It is found that (1) depending on the charger power and coverage, 19% (with AC Level 2 chargers and 20% charger network coverage) to 56% (with DC chargers and 100% charger network coverage) of the ICE vehicles can be replaced by electric taxis without driving pattern changes; (2) by using taxi apps to find nearby passengers and charging stations, drivers could utilize the empty cruising time to charge the battery, which may increase the acceptance of BEVs by up to 82.6% compared to the scenario without taxi apps; and (3) tailpipe emissions in urban areas could be significantly reduced with taxi electrification: a mixed taxi fleet with 46% compressed-natural-gas-powered (CNG) and 54% electricity-powered vehicles can reduce the tailpipe emissions by 48% in comparison with the fleet of 100% CNG taxis.

Extensive effort has been devoted to examining the causal relationship between contributing factors and injury severities. Given the important role of riders’ behaviors in traffic conflicts, this paper aims to analyze the causal effects of traffic conflicts resulting from riders’ behaviors at intersections. The authors collected video data on 152 traffic conflicts caused by riders’ dangerous behaviors in Jiangning District, China. This paper proposes a Bayesian-structural equation modeling (BSEM) approach. Based on the obtained BSEM path coefficient diagram, the factor loadings and path coefficients are analyzed to unveil the potential influence of factors, including personal features, dangerous behavior tendency, temporal and spatial characteristics of dangerous behavior, and the external environment. The results show that compared to human factors, environmental factors have a less direct impact on the severity of traffic conflicts; instead, they have an indirect positive impact on traffic conflicts by affecting behaviors. That is, if riders judge that road conditions are not suitable to conduct dangerous behaviors, they become more cautious in view of current road conditions and time revenue. Furthermore, dangerous cycling behaviors that continue to encroach on the time and space of motorized vehicles are prone to be more dangerous. The dangerous behaviors that continuously encroach on the time and space of motor vehicles (e.g., disobeying traffic signals and riding in a motorway) are significant predictors of serious conflicts. Considering the heterogeneity of riding behavior, these findings could be applied to develop effective education and intervention programs for preventing riders’ high-risk behaviors and improving the traffic environment.