Charging Policy Research Articles

Dynamic evaluation of environmental-economic performance of vehicle emission reduction policy from the perspective of the loss-aversion effect

With increasing intensification of vehicle pollutants and CO2 emissions, it has become difficult to realize the co-control of pollution reduction and carbon reduction. Considering the limitations of the single-congestion charging policy, this study first analyzed the dual effect of charging on vehicle travel using the loss-aversion effect and constructed a decision algorithm of vehicle trips based on this effect. In the second step, the strategy of public publicity and public transport priority policy (PTPP) were introduced to establish a vehicle energy conservation and emission reduction (ECER) management model based on the mode of “charging + public publicity + public transport priority” (CPP). Finally, the following conclusions were drawn based on a dynamic simulation: (1) The single-charging mode can induce side effects, leading to a rebound in the number of vehicle trips. (2) Increasing the investment in publicity funds and expanding the scope of publicity forms can significantly raise the public's awareness of environmental protection and have a restraining effect on vehicle trips. (3) Compared with the single-charging policy, the CPP mode has multiple performance levels for the environment and economy. Moreover, the CO, CO2 emissions, and economic costs decreased by 45.67%, 31.93%, and 45%, respectively.

Performance evaluation and optimization of design parameters for electric vehicle-sharing platforms by considering vehicle dynamics

Global adoption of electric vehicles (EVs) faces many challenges such as range anxiety, high cost of EVs, and inadequate charging infrastructure. EV-sharing platforms resolve such concerns by setting up an optimal configuration for charging infrastructure and optimizing the charging decisions for depleted EVs. These platforms manage the vehicles’ flow to different charging stations and decide when and to what energy level the depleted vehicles should be recharged. Station-based platforms are one of the mainstream vehicle sharing systems where the customer picks-up and drops-off the vehicle at the designated stations. If a vehicle’s battery energy level falls below a threshold after completing the customer trip, it is charged either partially or fully at the charging station. This study addresses various operational and strategic decisions (such as the number of chargers, vehicle repositioning, and partial charging policy) for a one-way station-based EV-sharing platform using a stylized three-stage analytical framework. We use vehicle dynamics to model the EV powertrain and regenerative braking under different traffic conditions and simulate them using AVL CRUISE™. We model the platform operations using an open queuing network and provide a mixed-integer non-linear optimization program using inputs from the queuing network and vehicle dynamics simulation. We also provide a bound-based heuristic to solve this NP-hard optimization problem. We generate various managerial insights for an efficient implementation of the partial charging policy for EV-sharing platforms. The increase in the partial charging probability (the fraction of depleted vehicles charged partially) reduces the effective charging demand, resulting in fewer chargers and a higher profit. On the other hand, if we increase the target battery energy level for partial charging, the platform’s profit decreases due to higher effective charging demand dominating the benefits of lower charging frequency of vehicles.

Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System

Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.

Laxity Differentiated Pricing and Deadline Differentiated Threshold Scheduling for a Public Electric Vehicle Charging Station

In this article, we study the online pricing and charging scheduling problem for a public electric vehicle (EV) charging station under stochastic electricity prices and renewable generation. We formulate the sequential decision making problem as a partially observable Markov decision process with continuous state and action spaces, with an objective of profit or social welfare maximization. The joint pricing and charging problem is challenging due to the curse of dimensionality (in both the system state and action spaces) and the unknown dynamics of system uncertainties. We propose a novel laxity differentiated pricing (LDP) scheme to tradeoff between electricity cost (associated with EV charging) and opportunity cost (associated with parking infrastructure occupancy). Shown to be optimal under arbitrary system dynamics, the characterized deadline-differentiated threshold charging (DTC) policy is integrated into a model-free soft actor critic (SAC) algorithm to reduce the action dimensionality. Numerical results demonstrate that the proposed SAC + LDP + DTC approach significantly outperforms alternative methods with various pricing and charging schemes.

Impact of trucks registered in other provinces on ETC differentiated charging in provincial expressway network

To evaluate the effect of policy on province-wide toll discount for electronic toll collection (ETC) trucks, and to clarify the influence of trucks registered in other provinces on the toll discount flowed back to the local enterprises, we use structured query language server (SQL Server) database technology to process the toll collection data on expressway network, and propose an algorithm of recovery rate for the explanatory variables of toll preference, and verify the feasibility of the algorithm is by a case study. The results show that the recovery rate of the study case is 43.38% when there is an 85% discount on ETC truck toll in 2019, and 52.77% of the 56.62% toll discount outflowed from the case studied is generated by the trucks registered in other provinces according to a conservative estimate. Among them, the proportion of the toll discount of trucks registered in other provinces with 6 axles accounts for 77.29%, which is the root of toll discount loss. Although the traffic number across the province is not large, the toll discount is rather large owing to the long distance and heavy load. The recovery or loss of toll discount concentrates in the trucks with 2 axles, 4 axles and 6 axles, and trucks with 6 axles accounting for the largest proportion. This paper provides a reliable analysis basis for the accuracy of the differential charging policy on expressway, and promotes the rapid development of transportation economy.

Optimal Charging Oriented Sensor Placement and Flexible Scheduling in Rechargeable WSNs

The recent breakthroughs in Wireless Power Transfer (WPT) facilitate supporting rechargeable sensors to enrich a series of energy-consuming applications. However, most charging scheduling schemes in rechargeable wireless sensor networks (WSNs) focus on sensing tasks instead of charging utility, which leaves a considerably high performance gap in the optimal result. Moreover, the charging scheduling is usually non-flexible, in which a full or nothing charging policy suffers from relatively low charging coverage as well as low efficiency. In this article, we focus on how to efficiently improve charging utility when introducing charging-oriented sensor placement and flexible scheduling policy. We formulate a general maximization optimization problem under a general routing constraint, which generates great difficulty. We utilize area partition and charging discretization methods to transform into the scope of maximizing a submodular function problem. Thus, a constant approximation algorithm is delivered to construct a near optimal charging tour. We analyze the performance loss from the discretization to guarantee that the output of the proposed algorithm has more than (1-ɛ)(1-1/ e )/4 of the optimal solution, where ɛ is an arbitrarily small positive parameter (0 < ɛ < 1). Both simulations and field experiments are conducted to evaluate the performance of our proposed algorithm.

Pravovi aspekty polityky dekarbonizatsii transportnoho sektoru ekonomiky krain-chleniv YeS

The legal aspects of the policy of decarbonisation of the transport sector of the economy of the Member States of the European Union are examined. The main international act to combat global warming (the Paris climate agreement) is examined. The European Green Deal, introduced under the terms of the 2015 Paris Climate Agreement to limit global annual temperature rise to 1.5°C, is taken into account, which is a prerequisite for achieving climate neutrality. It is noted that the European Green Agreement aims to increase the share of multimodal transport services; production and use of alternative fuels in all modes of transport; reducing air pollution in cities caused by the transport sector. The key policies of the member states of the European Union in the field of decarbonisation of the transport sector are considered: the policy of promoting the increase of the average load of vehicles through their shared use; congestion charge policy; the policy of promoting bicycle and pedestrian zones; city-wide ban on gasoline/diesel vehicles; the policy of reducing emission norms. The Fit for 55 package is studied — a set of legislative proposals to reduce greenhouse gas emissions in the EU by at least 55 % by 2030. Ukraine’s course on decarbonisation is considered. It is determined that by signing and ratifying the Paris Agreement, Ukraine has taken a course towards decarbonisation, it is also determined that climate change issues are regulated by the Association Agreement between Ukraine and the EU. The impact of the National Transport Strategy of Ukraine for the period up to 2030 on the process of decarbonisation of the transport sector of the Ukrainian economy is analysed. Using the example of the policies of the Member States of the European Union, a number of initiatives are proposed that will enable better integration of electric transport in the future. Significantly reducing greenhouse gas emissions into the atmosphere and achieving climate neutrality are found to have long-term positive effects on human health and well-being, protection of biodiversity and natural resources.

Allocation strategy for time‐sensitive tasks in mobile edge computing: An observable perspective

SummaryWith the development of computing technology and the popularization of application in 5G network, mobile‐edge computing (MEC), which can effectively reduce time delay and save energy consumption, has attracted extensive attention from the academic community. How to make a suitable allocation decision for tasks becomes one of the critical issues in MEC systems. In order to yield the most benefit to a newly arriving task, by placing a decision making module (DMM) in a mobile device, a MEC system architecture is presented. Based on the net benefit of a newly arriving task, the DMM makes a decision of dropping the task, allocating the task to the local execution system, or offloading the task to the MEC server via the transmission system. From the observable perspective of task individuals, a pure threshold strategy is proposed to show that a Nash equilibrium is always allowed. By constructing a two‐dimensional continuous‐time Markov chain, a social optimal threshold strategy is proposed. Numerical results show that threshold under the pure threshold strategy is always greater than that under the social optimal threshold strategy. For this, a charging policy is presented to coincide the pure threshold strategy with the social optimal threshold strategy.

Public Acceptability of Environmentally Linked Congestion and Parking Charging Policies in Greek Urban Centers

Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

Electrification of a bus system with fast charging stations: Impact of battery degradation on design decisions

In this paper, we evaluate the cost of the electrification of an existing bus network. We propose a family of bi-objective mathematical models to demonstrate the trade-off between strategic (i.e., battery sizing and the locations of charging stations) and operational decisions (i.e., battery degradation). The proposed mathematical models investigate different charging policies and measure their impacts on overall cost. Battery degradation is estimated by a tailored and linearized semi-empirical approach and is explicitly incorporated in the proposed mixed-integer linear models. The impact of different charging policies on reducing the overall costs is evaluated for a bus network in Rotterdam. The results show that allowing for flexibility in the loss of energy levels at each bus cycle results in savings up to 17% in battery aging.

Optimal pricing and budget decisions in public health systems with delay sensitive patients

Abstract The congestion of public hospitals for elective treatment in some countries and regions, such as Canada and Hong Kong where the free health policy is implemented, is a serious issue. The main reason is the excessive demand generated by the provision of free service. In response, the government can set appropriate service price and budget for public hospitals to moderate such demand. This is often referred to as the charging policy, implemented in countries such as China. A Stackelberg game is established for a health system consisting of a government, a public health provider and delay sensitive patients. The results show that when the customers' waiting cost is low (e.g., the market demand, the patients delay sensitivity, or the unit capacity cost is low), the free health policy outperforms the charging policy; otherwise, the charging policy is better. Moreover, we find that the equilibrium waiting time and the equilibrium price decrease with the market demand when the funder attaches more importance to patients’ welfare than the budget surplus and the total budget is sufficient.

Impact of charging and reward-penalty policies on household recycling: A case study in China.

With the drastically growing amount of household waste in China, increasing the recovery rate of household recyclable waste (HRW) is important for creating an environmentally friendly and green future. The waste charging policy, pay as you throw (PAYT), is widely used in developed countries, but it is not applicable in China with some unique features of waste recycling. Particularly, residents in China can sell HRW to formal recyclers, or throw waste to a waste sit under the supervision of classification superintendents (CSs). In this study, we propose an improved pay as a site throws (PAST) policy. And two complementary policies, the reward policy (RP) and reward-penalty policy (RPP), are considered to increase the feasibility of PAST. An integrated HRW recycling system model is developed, which combines the system dynamics (SD) approach and game theory to explore the impacts of these policies on the proportion of classified waste, recovery rates, recycling prices, profits, and social profit. Results show that (1) the PAST policy effectively promotes the waste classification proportion and HRW recovery rate. (2) An RP or RPP should be implemented along with the PAST policy to compensate for CSs' losses. (3) These polices should be applied for CSs in the early stage which can reduce the HRW recycling price, combat the likelihood of residents selling HRW, and promote waste classification. (4) When they are applied for recycling companies in the late stage more social profit is obtained. This study provides more targeted policies for China's HRW recycling measures. It also provides insights for HRW policy making in other developing countries.

On optimal charging scheduling for electric vehicles with wind power generation

We consider the scheduling of battery charging of electric vehicles (EVs) integrated with renewable power generation. The increasing adoption of EVs and the development of renewable energies contribute importance to this research. The optimization of charging scheduling is challenging because of the large action space, the multi-stage decision making, and the high uncertainty. To solve this problem is time-consuming when the scale of the system is large. It is urgent to develop a practical and efficient method to properly schedule the charging of EVs. The contribution of this work is threefold. First, we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation. An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds. Second, the scenario when the supply of the renewable power generation is deficient is investigated. We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first (mLLLP) rule. Third, we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations. We test the proposed algorithm by numerical experiments. The results show that it performs better than the other existing rule-based methods.

How Consumers Choose the Green Electricity Product Instead of Conventional Electricity? A Welfare Analysis Approach

The aim of this article is to analyze theoretically whether changes in an access charge pricing policy can promote green electricity use. The model considers two hypothetical firms: One firm offers green electricity product, and the other firm provides conventional electricity. Each firm pays an access charge to the natural monopoly in order to have access to the transportation network. Through using a vertical differentiation model, we try to analyze the link between the access charges paid by the two firms and the qualities offered. We also attempt to explain why the consumers do not often choose green electricity. Finally, we suggest a new policy that can lead the consumers to choose the green electricity product instead of conventional electricity. Our model shows that changes in the access charge pricing policies can constitute a relevant component in the reforms of the electricity sector and can have impacts on the consumption of the green electricity. A differentiating access charge policy could allow a better penetration of the green electricity product and an increase in the social welfare when consumers have greater preferences in environmental qualities. Otherwise, their preservation at a uniform level is desirable in order to maintain the competition. JEL Classifications: B21, C72, L13, Q21, P28

Power output optimization of electric vehicles smart charging hubs using deep reinforcement learning

Since most branches of the distribution grid may already be close to their maximum capacity, smart management when charging electric vehicles (EVs) is becoming more and more crucial. In fact, office buildings might not be able to handle several transactions at the same time, especially considering the next generation of fast chargers which are very power expensive. Thus, an efficient charging policy needs to be found. This paper proposes the scheduling of real-time EVs charging through deep reinforcement learning (DRL) techniques. DRL has been chosen because it can adaptively learn from interacting with the surrounding environment. The focus of the optimization is to ensure the completion of the charging transactions in a timely manner, while shifting the load from the times of peak demand. The novelty of the proposed approach lies in its innovative framework: pools of electric vehicles with different characteristics are categorized using a clustering algorithm, a tree-based classifier has been developed to sort new instances of EVs, and a multilayer perceptron artificial deep neural network has been trained to predict the expected duration of each charging session. These features are used as inputs to the DRL agent, and are mapped into actions that adjust the maximum power associated to each charging station. The model has been compared to a traditional charging algorithm and increasingly challenging scenarios have been considered. Results have shown that the developed algorithm fails less than the baseline, with a reduction of the load due to EVs charging of 80% during peak times.

Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect

This study deals with a fundamental electric bus charging scheduling (EBCS) problem for a single public transport route by considering the nonlinear electric bus (EB) charging profile and battery degradation effect under the partial charging policy, which allows EBs to be charged any length of time and make good use of dwell times between consecutive trips. Given a group of trip tasks for an EB fleet and charger type, the problem is to minimize the total cost for a public transport operator of providing peak-hour bus services for a focal single public transport route by simultaneously determining the EB-to-trip assignment and EB charging schedule with charger type choice subject to the necessary EB operational constraints. We first build a mixed-integer nonlinear and nonconvex programming (MINL&NCP) model for the EBCS problem. To effectively solve the MINL&NCP model to global optimality, we subsequently develop two mixed-integer linear programming (MILP) models by means of linearization and approximation techniques. To accelerate the solution efficiency, we further create three families of valid inequalities depending on the unique features of the problem. A real case study based on the No.171 bus route in Singapore is conducted to demonstrate the performance of the developed models. Extensive numerical experiments are carried out to seek valuable managerial insights for public transport operators.

Enduring impacts of cathedral choral training on choristers’ lives

ABSTRACT Investigations of the English Cathedral Choral tradition have ranged from studies of the pedagogical practices and environmental affordances and constraints that support choral excellence to the introduction of female voices into a male-dominated musical practice. Studies of choral participation have investigated the health and wellbeing outcomes of choral participation across the lifespan, and the psychological benefits and life skills of teamwork and leadership that develop in choral settings. These studies have focused on perceptions of current participation. Less is known of the enduring impacts of early choral participation across the lifespan. This study investigates two former cathedral choristers’ experience through extended interview and narrative case studies. Both choristers reported participation in the cathedral choir as significant and life-changing, continuing to impact them many years later. Chorister habits of thought and practice including attention to detail, care of others, assuming leadership, and self-discipline were reported to endure in life beyond the choir. Being thrust into the midst of a politically charged policy change introducing female cathedral choristers created life-forming experiences for girls, in what was retrospectively described as a first act of feminism. This research provides insights into the ways in which extra-curricular ensembles and vocal tuition shape possible musical selves.

The Heterogeneous Relationship between Pollution Charges and Enterprise Green Technology Innovation, Based on the Data of Chinese Industrial Enterprises

Enterprises’ green technology innovation is critical to achieving the “win-win” of enterprise competitiveness and environmental protection. The impact of environmental regulation on green technology innovation by enterprises has been widely considered, but the conclusion has not yet been determined, and needs to be studied in detail. To this end, we studied the impact of pollution charge policy on different types of green technology innovation by industrial enterprises in China. We found that (1) the impact of pollution charges on most types of green technology innovation by enterprises has increased significantly over time; (2) the pollution charge policy has a certain inhibition effect on the end-of-pipe technology innovation, but can promote the process improvement of reducing industrial wastewater emissions; (3) there is a U-shaped relationship between the pollution charges and some green technological innovation (e.g., emission intensity of SO2, industrial wastewater emission intensity, and industrial wastewater removal intensity), which is dynamically adjusted over time; and (4) the larger the enterprise’s solid assets, the faster the asset depreciation will inhibit the enterprise from adopting the green process innovation strategy.

US public charge policy and Latinx immigrants’ thoughts about health and healthcare utilization

ABSTRACT Objectives We examined Latinx immigrants’ perceptions of US policy related to restrictions on immigrants’ use of public resources and their thoughts about the influence of these on immigrants’ healthcare utilization. Design A series of 16 focus group discussions with Latinx immigrant men and women (documented and undocumented) (N = 130) were conducted between May and July 2017 across four US cities. Results Four central themes emerged: participants attributed the limited resources available for affordable healthcare for many uninsured US immigrants (both documented and undocumented) to the US government’s view of immigrants as burdens on public resources and its subsequent unwillingness to dedicate funds for their care; participants expressed concerns, some unfounded, about negative immigration ramifications arising from diagnosis with health conditions perceived to be serious and/or expensive to treat; participants noted that some immigrants avoided using health programs and services to which they were entitled because of immigration concerns; finally, participants described how access to information on immigration laws and healthcare resources, and conversely, misinformation about these, influenced healthcare utilization. Conclusions Participants were acutely aware of the image of immigrants as public charges or potential burdens on government resources that underlies US immigration policy. In some cases, participants came to inaccurate and potentially harmful conclusions about the substance of laws and regulations based on their beliefs about the government’s rejection of immigrants who may burden public resources. This underscores the importance of ensuring that immigrants have access to information on immigration-related laws and regulations and on healthcare resources available to them. Participants noted that access to information also fostered resilience to widespread misinformation. Importantly, however, participants’ beliefs had some basis in US immigration policy discourse. Law and policy makers should reconsider legislation and political commentary that frame self-reliance, the guiding principle of US immigration policy, in terms of immigrants’ use of publicly funded healthcare resources.

Differentiated Toll Optimization on Goods Vehicles Based on Prediction of Demand for Large-Scale Network

The area rate of the toll road network is designed to influence the regional road network traffic flow configuration and an important factor of goods vehicle path selection, transportation, and other related departments in formulating the related standard. The toll rate is usually combined with the local passenger and cargo loading conditions. Adjusting and optimizing the charge policy, optimization of processes and methods is still facing many difficulties. In this paper, the model parameters and toll status of various goods vehicles are considered, and an optimization algorithm of toll rates for different vehicles in a large-scale road network is proposed. By constructing a traffic demand prediction model based on multiple truck classification, this paper analyzes the distribution status of network trucks and the final charging under the influence of different rates. At the bottom of the algorithm is an analytical model composed of nonlinear equations whose dimensions scale linearly with the scale of the road network. The scale of the road network is independent of the path set and link attribute. The model is verified and the parameters are calibrated by a small network. Finally, the Road network is selected for empirical analysis in Xinjiang Autonomous Region. A case study shows that the analytical structure information provided by the analytic network model enables the algorithm to quickly identify high quality solutions, and the algorithm has better simulation accuracy and premium rate design advantages.