User Charges Research Articles

Reducing Carbon Emissions of EV Charging via User Behavior and Carbon Intensity Analysis

The increased adoption of electric vehicles (EVs) has the potential to significantly reduce carbon emissions by decreasing reliance on fossil fuels. However, as more EVs populate our roads, there will be a heightened demand for electricity from the grid, leading to increased carbon emissions due to EV charging. Currently, most EV owners do not align their charging behavior with periods of low carbon intensity in the power grid. To reduce the carbon emissions resulting from EV charging, it is essential to alter the charging behavior of EV users. To tackle this problem, we propose a solution that leverages user charging behavior data with carbon intensity data to identify optimal charging times for EVs, resulting in reduced carbon emissions. Our simulation results using 10,000 EVs demonstrate that the proposed carbon-aware EV charging algorithm can reduce 9.3% of carbon emissions by optimizing EV charging times.

Improving access to healthcare in Ireland: an implementation failure.

There are significant barriers to accessing health and social care services in Ireland including high user charges, long waits and limited availability of some services. While a number of reform proposals have committed to improving access to health care, implementation of these proposals has been limited. The aim of this paper is to identify and discuss policy implementation failures concerned with improving access to health and social care services in Ireland. Four potential reasons for the repeated failure to implement stated reform proposals are identified including a failure to identify and address the practicalities of implementation, competing health care demands, the political cycle and stakeholder resistance. While there has been a shift in Irish health care policy documents in the last 10 years with increasing emphasis on ensuring access to health care based on need rather than ability to pay, a repeated failure to implement the proposed reforms raises questions as to whether there is a real commitment to improving access to health care.

Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications

Capacity estimation plays a vital role in ensuring the health and safety management of lithium-ion battery-based electric-drive systems. This research focuses on developing a transferable data-driven framework for accurately estimating the capacity of lithium-ion batteries with the same chemistry but different capacities in field applications. The proposed approach leverages universal information from a laboratory dataset and utilizes a pre-trained network designed for small-capacity batteries with constant-current discharging profiles. By applying this framework, capacity estimation for large-capacity batteries under drive cycles can be efficiently achieved with improved performance. In addition, the incremental capacity analysis is employed on two datasets, selecting a robust voltage interval for health indicator extraction with physical interpretations and uncertainty awareness of different fast charging protocols. The feature extraction and dimension increase processes are automated, utilizing the last short charging sequences in wide voltage intervals while considering the uncertainty related to various user charging habits. Results demonstrate that the proposed strategy significantly enhances both robustness and accuracy. When compared to conventional methods, the proposed method exhibits an average root mean square error improvement of 68.40% and 65.89% in the best and worst cases, respectively. The robustness of the proposed strategy is further verified through 30 randomized health indicator verifications. This research showcases the potential of transferable deep learning in improving capacity estimation by leveraging universal information for field applications. The findings emphasize the importance of sharing knowledge across different capacities of lithium-ion batteries, enabling more effective and accurate capacity estimation techniques.

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.

Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning

This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations.

Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment

Battery health evaluation is vital for ensuring the security and reliability of lithium-ion batteries. However, the currently proposed methods generally require high-quality input data for feature extraction in online applications. To overcome this obstacle, this paper proposes a rapid online health estimation method only based on partial constant-voltage (CV) charging segment. Firstly, through primary analysis of battery test data, the evolution of CV charging current is confirmed to be correlated with battery capacity. Subsequently, the current evolution constant of CV charging phase is mathematically formulated and quantitatively characterized using a novel health indicator (HI). Besides, charging time and charging capacity are also extracted as HIs to comprehensively capture the CV charging behavior and enhance the robustness of data-driven models. Considering the user's charging habits, an optimized CV segment is determined, enabling a significant reduction in data size and coverage. Finally, three data-driven methods are employed to construct health estimation models by using the extracted HIs, and the best performance is achieved by Gaussian process regression with MAE and RMSE lower than 0.8% and 1%, respectively. Remarkably, the proposed method demonstrates superiority in dealing with sparse sampling, and satisfactory results with 2.9% error under the sparsity of 10 s are obtained.

Development of a multi criteria model for assisting EV user charging decisions

Development of a multi criteria model for assisting EV user charging decisions

A numerical evaluation of a latent heat thermal energy storage system in the presence of various types of nanoparticles

Due to the high demand for using phase change materials (PCMs) in thermal systems and to the goal of improving the thermal efficiency of solar systems, herein, the heat storage performance of nano PCM (nPCM) was numerically scrutinized. For this purpose, a vertical double-tube heat exchanger containing paraffin and five different nanoparticles was simulated. By originating scientific results, 31 radial fins were considered on the tube side of the heat exchanger. RT35 paraffin wax as PCM includes a melting point close to the ambient temperature, making it easy to investigate domestic usage's charging and discharging processes. It was found that nanoparticles be able to decrease the duration of melting and solidification processes. Adding 5 vol% of Cu, CuO, TiO2, Carbon nanotube (CNT), and Graphene nanoplatelet (GNP) to the PCM caused 6.6%, 10.2%, 10.7%, 33.4%, and 56.4% of the melting time reduction, while time reductions of 20.3%, 13.4%, 13.2%, 43.1%, and 69.7% for solidification were observed, respectively. The results showed that thermal conductivity, density, and heat capacity properties affect the melting process, whereas thermal conductivity was the most critical parameter in the solidification process. Thus, despite the better performance of CuO and TiO2 compared to Cu in the melting process, it diminished the solidification time greater than the others. Also, the presence of nanoparticles was more significant in the solidification process. Moreover, the heat transfer coefficient of GNP-PCM was the highest among others (66.63 W/m2.K for 5 vol%), while it was 24.68 W/m2.K for 5 vol% of Cu-PCM. This comprehensive study yielded beneficial results for preparing more efficient thermal energy storage systems.

Multi-microgrids distributed peer-to-peer energy trading in distribution system considering uncertainty risk

The cooperative energy trading model for multi-microgrids in the distribution network systems under uncertainty is investigated in this paper, which permits peer-to-peer (P2P) energy trading under the system network constraints. The model is formulated as a two-stage problem, i.e. cooperative welfare maximization problem based on the conditional value at risk (CVaR) (P1) and market clearing problem based on the Nash Bargaining theory (P2). In the first stage, the CVaR is used to quantify the risk of uncertainty of source and load, and a stochastic optimization model is formed to identify energy scheduling and reactive power control. The bi-level nested alternating direction method of multiplier based on consistent communication (Bi-CADMM) is offered as a privacy-preserving technique for solving P1. Then, the second stage determines the mutual payments of the microgrids (MGs) and network usage charges for the distribution systems operator (DSO) according to the negotiation coefficient in the form of centralized solving technology. Case studies on a modified IEEE-33 system with 4-MGs show that the Bi-CADMM has strong convergence performance and can guarantee the sub-optimal and feasible solution compared with the centralized technology first. The final results also show that the decision-makers with different preferences can determine the degree of conservatism of the scheduling through the risk factor to deal with the uncertainty, and the suggested energy trading model is beneficial in terms of lowering participants' costs and improving voltage security.

A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning

Reinforcement learning (RL) is popularly used for the development of an orderly charging strategy for electric vehicles (EVs). However, a new environment (e.g., charging areas and times) will cause EV users' driving behaviors and electricity prices to change, which leads to the trained RL-based charging strategy is not suitable. Besides, developing a new RL-based charging strategy for the new environment will cost too much time and data samples. In this paper, a deep transfer reinforcement learning (DTRL)-based charging method for EVs is proposed to realize the transfer of trained RL-based charging strategy to the new environment. Firstly, we formulate the uncertainty problem of EV charging behaviors as a Markov Decision Process (MDP) with an unknown state transfer function. Furthermore, an RL-based charging strategy based on deep deterministic policy gradient (DDPG) is well-trained by using massive driving and environmental data samples. Finally, an EV charging method based on transfer learning (TL) and DDPG is proposed to perform the knowledge transfer on the trained RL-based charging strategy to the new environment. The proposed method is verified by numerous simulations. The results show that the proposed approach can reduce the outliers to meet the user charging demands and shorten the EV charging strategy development time in the new environment.

Emissions and Total Cost of Ownership for Diesel and Battery Electric Freight Pickup and Delivery Trucks in New Zealand: Implications for Transition

Road freight transport contributes to a large portion of greenhouse gas (GHG) emissions. Transitioning diesel to battery electric (BE) trucks is an attractive sustainability solution. To evaluate the BE transition in New Zealand (NZ), this study analysed the life-cycle GHG emissions and total cost of ownership (TCO) of diesel and BE trucks based on real industry data. The freight pickup and delivery (PUD) operations were simulated by a discrete-event simulation (DES) model. Spreadsheet models were constructed for life-cycle assessment (LCA) and TCO for a truck operational lifetime of 10 years (first owner), this being the typical usage of a tier-one freight company in New Zealand (NZ). The whole-of-life emissions from the diesel and BE trucks are 717,641 kg and 62,466 kg CO2e, respectively. For the use phase (first owner), the emissions are 686,754 kg and 8714 kg CO2e, respectively; i.e., the BE is 1.27% of the diesel truck. The TCO results are 528,124 NZ dollars (NZD) and 529,573 NZD (as of 2022), respectively. The battery price and road user charge are the most sensitive variables for the BE truck. BE truck transitions are explored for freight companies, customers, and the government. For the purchase of BE trucks, the break-even point is about 9.5 years, and straight-line depreciation increases freight costs by 8.3%. Government subsidy options are evaluated. The cost of emission credits on the emissions trading scheme (ETS) is not expected to drive the transition. An integrated model is created for DES freight logistics, LCA emissions, and TCO costs supported by real industry data. This allows a close examination of the transition economics.

Dynamic Charge Acceptance Compared to Electrochemical Impedance Spectroscopy Parameters: Dependencies on Additives, State of Charge, and Prior Usage

The goal of this work was to predict the dynamic charge acceptance (DCA) for cells using different additives on the negative electrode from the evaluation of small-signal measurements by electrochemical impedance spectroscopy (EIS). Thereby, various operating points were evaluated, such as state of charge (SoC) and prior usage (charge or discharge). The 2V test cells under investigation utilized plates of enhanced flooded 3P2N battery cells (EFB). They contained three positive and two negative electrodes. The latter varied in their additive composition. In total, eight different negative electrodes were investigated, five including specially synthesized amorphous carbon as an additive, two with unknown additive mixes, and one including a commercially available carbon black. The best parameters for predicting the DCA were found within the first semicircle of the negative half-cell spectra measured during a superimposed charging current.

Thinking Beyond the Gas Tax: A Case Study Analysis of a Road User Charge System for Texas

Surface transportation funding in the U.S. is heavily reliant on fuel taxes that are becoming less effective as fuel efficiency increases and alternative fuel vehicles become more common. This report examines the benefits and challenges of using road user charges (RUCs) of various types to address this issue, before analyzing the potential revenue from a hypothetical RUC on four urban corridors within Texas. Common issues for RUCs are discussed, which include privacy, interstate coordination, and program administration. A range of possible options for RUCs are discussed, followed by an analysis of four corridors in the following counties: Bexar, Collin, Harris, and Lubbock. Data from TTI’s Urban Mobility Report on VMT were utilized along with vehicle registration data from TTI’s TRENDS model to estimate an average miles per gallon for each county based on the local fleet. Three scenarios were then developed including a revenue recovery scenario, a variable pricing scenario, and a carbon tax scenario. This report concludes with a discussion of the scenario results. Specific challenges and barriers for each scenario are discussed along with a discussion of the data sources and privacy trade-offs.

EV-Station-Grid Coordination Optimization Strategy Considering Psychological Preferences

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.

Evaluation of Factors influencing Health Care Seeking Behaviours among Caretakers of Children below 5 Years in Bugala Island, Kalangala District

Childhood illnesses present a major public health challenges for a developing country like Uganda which is aggravated by a suboptimal health seeking behaviour by the parents or guardians. Appropriate medical care could prevent a significant number of child deaths and complications. The aim of this research was to determine the factors influencing health care seeking behaviours among caretakers of children below 5 years at Bugala Island, Kalangala district. A cross-sectional descriptive study design quantitative in nature was conducted in rural area of Bugala Island using simple random sampling, a total of 50 participants were recruited into the study. Results indicated that more than 60% had poor health seeking behaviours associated with increased number of children. Furthermore, poor means of transport, high costs of transport and user charges as well as others being delayed by traditional healers. Level of education was found to have high influence in early health care seeking behaviours. This study recommends that an intensive awareness campaigns through multimedia approach stressing on female literacy, improving on socio-economic status of people along with counselling on early health care seeking practices especially for under 5 years children should be encouraged. Also government should consider Bugala Island and Kalangala district in general as special area with unique challenges and put a health unit at village levels because of long distances from the town in order to reduce mortality and morbidity of under 5 years Children. A study on attitudes of health workers in relation to early health care seeking behaviours needs also to be carried out. Keywords: Caretakers, Bugala Island, child deaths, health care

Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

This paper mainly studies the multiclass stochastic user equilibrium problem considering the market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing electric vehicles and gasoline vehicles (GVs). In order to analyze the random charging and path choice behaviors of BEV users and extract the differences in travel behaviors between BEV and GV users, an improved logit-based model, multilabel algorithm, and queuing theory are applied. The influencing factors of charging possibility mainly include the initial state of charge (SOC), the SOC at the beginning of charging, and the psychologically acceptable safe SOC threshold arriving at the destination. Diversity choices of user paths and charging locations will result in changes in queuing traffic and differences in queuing time. Conversely, different stations have different queuing dwell times, which will also affect the routing and charging locations for BEVs with RCB. The path-based method of successive averages (MSA) is adopted to solve the model. Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified.

Privacy-Preserving User-Centric Authentication Protocol for IoT-Enabled Vehicular Charging System Using Decentralized Identity

There has been a shift towards the use of Electric Vehicles (EV) in recent years. Though EVs offer many advantages, there are concerns on the cyber security of its components and the privacy of its users. When users charge their EVs at a charging station, they need to reveal their personal details. An attacker can compromise the users' privacy by identifying and tracking where users charge their EVs. Hence, there is a need to protect EVs from cyber-attacks and preserve its users' privacy. In this paper, we address the problem of privacy preservation of users while charging their EVs in a 5G-enabled vehicular charging system. We propose a user-centric authentication protocol for EV charging based on Decentralized Identifier (DID) and blockchain. We use Verifiable Credential (VC) together with DID which provides Zero-Knowledge Proof (ZKP) about the user. Users have complete control over their identities resulting in user-centric authentication. At the same time, a third party can verify the user's legitimacy before providing services. Hence, our protocol makes the charging service available in a secure way, preserving the privacy of the user.

Profit improvement strategy of electric vehicle charging stations sharing carbon trading revenue

With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profit improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on the fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profit optimization of the dynamic charging price model by particle swarm optimization algorithm. Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods. The dynamic charging price method is superior, and under the power purchase price of 0.5 yuan/kWh, it can lower EV user charging costs by 16.25%, improve CS profits by 30.09%, and reduce carbon emissions by 74.68%.

"How Did Local Governments Recover Their Financial Condition? Lessons from Citizen Participation"

This study offers lessons for overcoming future economic crises by examining whether democratic management helped local governments recover their financial condition. During the Great Recession, local governments were forced to make painful decisions regarding increased taxes and user charges, and cuts in public services. Several case studies provide evidence that citizen participation allows localities to increase property taxes as well as cut public services. However, broader, systematic empirical research is lacking. This study found that local governments with participatory budgeting were more likely to improve their financial condition during the Great Recession. The results allow us to identify the role of citizens in fiscal decision-making, offering lessons on how local governments can overcome possible fiscal crises in the future.

Distribution System Management Model Based on the Cooperative Concept of Unifying the Multi-Owned Networks

The growth of microgrids to fulfil electricity demand could lead to arguments or conflicts between the private microgrid system owners and the existing national distribution authority, especially in overlapping sales areas. The core issue identified in this paper is a distribution system management model based on the cooperative concept of unifying the multi-owned networks as a single system to ensure the natural monopoly function. This model can avoid complex wheeling charges across multi-owned grids within overlapping sales areas, in which all network users pay at the expense of the merged system charge. This paper proposes the model and shows numerical examples using a suitable distribution network pricing model to recover existing costs and plan for future system expansion. The proposed model and charging algorithm were tested on a modified IEEE 13 bus to examine their impacts. The result demonstrates that the model resolves the issues with the overlapping sales area and creates a fair scenario for the network users regarding the usage charges. Thailand is chosen as the primary reference area, as it is a country with an actual sample case for the argument.