Number Of Public Charging Stations Research Articles

An examination of the effect of external factors on zero-emission vehicle adoption in the United States

This study aims to extend and investigate how external factors (socioeconomic and demographic characteristics, EV-related policy mechanisms, transportation, and climate conditions) influence the actual adoption of battery electric vehicles (BEVs). Using panel data from 49 U.S. states from 2011 to 2020, we estimate a dynamic spatial Durbin model under the space fixed effect to examine the effects of these attributes on BEV adoption in the neighboring states. The results of the analysis suggest that purchase incentives, the number of public charging stations, gasoline prices, and the Hispanic or Latino population, respectively, have positive total effects on state BEV adoption rates in both the short- and long-term. Particularly, the number of public charging stations has positive direct and indirect effects in both the short- and long-term. Gasoline prices have positive spillover effects in both the short- and long-term. The long-term effects of the three characteristics on BEV adoption are greater than their short-term effects. Based on our findings, this study can provide state officials with practical directions and recommendations to help them allocate their resources and implement timely and appropriate regulations, as well as collaborations between states to increase the penetration rates of zero-emission vehicles (ZEVs).

Graded Subsidy Policy-Based Equilibrium Strategy Applied to Investment in Electric Vehicle Chargers: A Case Study in Chengdu

The Chinese government has released plenty of incentive policies to stimulate the diffusion rate of electric vehicles (EV) and the number of public charging stations, such as charging parking lots (PLs). This paper explores cooperative relationships between regional governments and PL owners in the process of investment in EV charging equipment, and a bi-level optimization model is built to examine their hierarchical relationships. Karush-Kuhn-Tucker conditions are utilized to solve the NP-hard problem. Five PLs in Chengdu are selected as examples to examine the validity of this model. The optimal allocation decision of the government and the optimal investment decisions of PL owners are concluded through the calculation results. Compared with non-graded subsidies, the priority of the graded subsidy rules is confirmed. In line with the calculation results, subsidies can promote investors to provide more charging services, and a graded allocation rule can maximize the incentive performance of subsidies.

Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel Geographic Information System (GIS) assisted optimal design method for renewable powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV

The Netherlands is a frontrunner in the field of public charging infrastructure, having one of the highest number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012–2015), a large percentage of the EV fleet were plugin hybrid electric vehicles (PHEV) due to the subsidy scheme at that time. With an increasing number of full electric vehicles (FEVs) on the market and a current subsidy scheme for FEVs only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have an effect on the charging behavior of the complete fleet, and is reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing agent-based model. Results show important effects of this transition on charging infrastructure performance.

A Comprehensive Approach for Maximizing Flexibility Benefits of Electric Vehicles

Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation.

Optimal deployment of public charging stations for plug-in hybrid electric vehicles

This paper develops an equilibrium modeling framework that captures the interactions among availability of public charging opportunities, prices of electricity, and destination and route choices of plug-in hybrid electric vehicles (PHEVs) at regional transportation and power transmission networks coupled by PHEVs. The modeling framework is then applied to determine an optimal allocation of a given number of public charging stations among metropolitan areas in the region to maximize social welfare associated with the coupled networks. The allocation model is formulated as a mathematical program with complementarity constraints, and is solved by an active-set algorithm. Numerical examples are presented to demonstrate the models and offer insights on the equilibrium of the coupled transportation and power networks, and optimally allocating resource for public charging infrastructure.

Modelling the Development of a Regional Charging Infrastructure for Electric Vehicles in Time and Space

This article presents a dynamic spatial model of the development of a charging infrastructure for electric vehicles in the German metropolitan region of Stuttgart. The model consists of several sub-models whose functioning and interactions are explained in detail. The first sub-model simulates the time-spatial development of electric vehicle ownership. The output of this module is used by the second component that determines the resulting demand for charging stations. To quantify this demand, the necessary utilisation of charging stations to allow for the profitability of the infrastructure is calculated. A final processing step simulates the mobility of EVs throughout the Region Stuttgart, and thus allows allocating the need for charging stations in space. We used our model to generate several scenarios of the development of a charging infrastructure in the Region Stuttgart until 2020. The main finding of this work is that the number of public charging stations needed for the region in the long run is quite low. If too many charging stations are installed the infrastructure will be under-utilized and thus cannot be operated economically. The simulation runs show that the installation of public charging infrastructure should be focused on the few biggest urban centres of the region. The scenarios also show that publicly accessible charging stations form only a minor part of the overall number of charging stations. Additionally, it can be seen that the exponential growth of electric vehicle ownership, with very few vehicles at the beginning, but large gains after a few years, requires high flexibility from stakeholders involved in the implementation of charging infrastructure for electric vehicles.