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This paper investigates the economic viability of plug-in hybrid electric vehicles (PHEV) in Shanghai, China based on a real-world in-use PHEV dataset. To quantify PHEV drivers’ gross profit compared with internal combustion engine vehicle (ICEV) owners, a total cost of ownership (TCO) model is adopted taking account of vehicle retail price, tax credits, subsidies, insurance, maintenance, energy prices, and resale value. The impact of the determinants for gross profit are examined in relation to vehicle distance traveled (VDT) electrically, gasoline price, electricity price, and car-buying cost. It is found that: (1) only 10% of the deployment of PHEVs (i.e., BYD Qin) is economically viable if the benefit from a free license plate is exempt; (2) the 100-kilometer gross profit of PHEVs increases linearly with the electric driving distance, while the saving of energy cost per kilometer decreases with the total VDT; (3) PHEVs’ profit could be significantly improved by reducing the car-buying cost—a decrease of 10% in car-buying cost makes 80% of the PHEV deployment feasible; and (4) if switching the daytime charges to off-peak hours, 50% of the PHEV deployment will become feasible.

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.

Algorithms for current automatic train operation (ATO) focus mainly on reducing the mechanical energy of motion for a single train within an existing timetable. However, the reuse of regenerative energy is another factor that contributes to energy consumption and conservation in multitrain networks. To improve regenerative energy receptivity and energy savings in a bidirectional metro transit network, this study formulated a coordinated train control algorithm that was based on genetic algorithm techniques. The energy saving potential of different station departure time intervals between two opposing trains (synchronization time) was tested. Simulation on the Visual C++ platform demonstrated that the algorithm could provide an optimal train speed profile with better energy performance while also satisfying operational constraints. Different synchronization times have different optimization ratios. This research was another step to facilitate the development of an ATO control algorithm that considers overall energy consumption. Increased knowledge of the influence of synchronization time at stations on energy consumption in regenerative multitrain networks will also aid in the design of more energy-efficient timetables.

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

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.

Abstract Electric taxis have the potential to improve urban air quality and save driver’s energy expenditure. Although battery electric vehicles (BEVs) have drawbacks such as the limited range and charging inconvenience, technological progress has been presenting promising potential for electric taxis. Many cities around the world including New York City, USA are taking initiatives to replace gasoline taxis with plug-in electric vehicles. This paper extracts ten variables from the trip data of the New York City yellow taxis to represent their spatial-temporal travel patterns in terms of driver-shift, travel demand and dwell, and examines the implications of these driving patterns on the BEV taxi feasibility. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs of a given driving range during a year. It is found that the currently deployed 280 public charging stations in New York City are far from sufficient to support a large BEV taxi fleet. However, adding merely 372 new charging stations at various locations where taxis frequently dwell can potentially make BEVs with 200- and 300-mile ranges feasible for more than half of the taxi fleet. The results also show that taxis with certain characteristics are more suitable for switching to BEV-200 or BEV-300, such as fewer daily shifts, fewer drivers assigned to the taxi, shorter daily driving distance, fewer daily dwells but longer dwelling time, and higher likelihood to dwell at the borough of Manhattan.

Abstract This paper presents an optimization framework to determine the government incentive schemes to promote battery electric vehicle (BEV) taxis. The impacts of drivers’ operating behaviors, charger network coverage, BEV range, vehicle costs, and energy prices are taken into account. A two-stage optimization model is proposed, which describes the interplay between the government subsidy scheme and taxi drivers’ acceptance of BEVs. To quantify drivers’ acceptance, a data-driven microsimulation model is used to simulate driving and charging activities based on GPS trajectory data collected from conventional gasoline taxis in Changsha, China. The optimal government subsidy scheme is solved using the genetic algorithm. The key findings include: (1) detour for charging is inevitable for BEV taxis and would cause significant disruption in operational activities, especially for small-range BEVs (e.g. 150 km). (2) Subsidizing on vehicle purchase is necessary, and the subsidy intensity is expected to maintain at the current level to achieve an electrification goal of more than 50%. The government should provide financial support for public charging exclusive of vehicle purchase. (3) Different taxi drivers might prefer different BEV ranges, thereby they should be allowed to select from diversified BEV models, instead of deploying a single vehicle model for the entire taxi fleet.

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.