User Charges Research Articles

Feasibility Study of Solar-Powered Electric Vehicle Charging Infrastructure at Selected Petrol Stations in Malaysia

The transition from the conventional internal combustion engine (ICE) to electric vehicles (EV) are expected to result in higher total electricity demand in the country. With the introduction of more EVs into the market, the need for charging stations will grow, subsequently increasing power consumption. This research study aims to assess the potential of solar energy production at petrol stations in central region of Peninsular Malaysia and assess the feasibility of solar-powered petrol stations in meeting the energy demands of electric vehicles. 10 samples of existing petrol stations are selected to install a solar-powered DC charging station as a case study. To study their energy consumption, 90kW DC charger was evaluated. The design of the PV systems for the petrol stations was done by using the PVsyst simulation software. As a result, it is found that 90% of the designed PV systems are able to generate enough energy to meet the estimated annual energy consumption of the 90kW DC charging system. The results also showed that the cost to install the solar-powered charging system for both power ratings can be recouped in less than 2 years. Overall, this research study offers valuable insights for addressing the rising energy consumption resulting from the increased penetration of electric vehicles in the future. Additionally, the implementation of solar-powered charging infrastructure aligns with the country’s renewable energy objectives and efforts to reduce greenhouse gas emissions. However, the assessment of EV penetration and the adoption of charging infrastructure was conducted based on government targets rather than relying on current data regarding the actual numbers and trends of EVs and chargers in Malaysia. The availability of accurate and up-to-date data on the number of EVs, their battery characteristics, chargers, and user charging behaviors in Malaysia would contribute to further enhancing this research work.

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %–26.3 %, and increases Charging pile revenue.

Targeting public spending: Means-testing and user charging

Targeting public spending: Means-testing and user charging

Modeling of organizational behavior framwork for road usage charge management

Organizational behavior plays a crucial role in effective management. However, there is limited research onorganizational behaviors in road usage charging management. This study aims to address this gap by identifying six dimensions of organizational behavior through focus group studies. Questionnaires were administered to 138 professionals in Vietnam, and the data were analyzed using structural equation modeling. The findings revealed six behavioral dimensions: Responsibilities fulfillment (OR1), Progress assurance (OR2), Aligned implementation organization (OR3), Hierarchical and delegated management (OR4), Efficient operation system (OR5), and Transparency and accountability assurance (OR6). Among these dimensions, OR2, OR4, and OR6 significantly impact management performance. Additionally, OR2 mediates the relationship between OR1 and management performance, OR4 mediates between OR3 and OR5, and OR5 indirectly influences management performance through OR6. It is expected that the efficacy of this approach will strengthen the significance of the organizing function and offer a valuable instrument to aid professionals in the field of road usage management.

Electric Vehicle Aggregation in a PV-Battery Charging Station: A Contract-Based Approach

As the proportion of high-power fast charging piles increases, how to more stably aggregate electric vehicles to avoid market risks brought about by user charging behavior is an urgent problem for charging station operators. This is especially so for fast charging station operators. To address this issue, a retail package design method for PV-battery charging stations based on contract theory and Lyapunov optimization is proposed in this paper. The operator can formulate optimal charging and discharging packages under asymmetric information in the day-ahead and real-time stages. Packages with fixed power and prices will be offered to users to reduce the impact of user behavior uncertainty. In addition, the queuing problems of pile allocation and PV-battery energy management are resolved by the Lyapunov drift optimization method. The performance indicators, such as the average power and demand processing capability of in-station equipment, will be optimized while ensuring the stability of the queuing network. The simulation verifies that the proposed method can improve the operator's net income and make the social welfare approach the theoretical value under complete information. Moreover, it shows stronger stability in terms of user aggregation.

How much road deterioration do heavy vehicles cause on Australia’s sealed flexible pavements?

ABSTRACT An evidence-based estimate of the load-related road deterioration due to heavy vehicles on the road network is needed for underpinning heavy vehicle road user charges on Australia’s arterial roads. A network level model for estimating the percentage (%) load-related road deterioration was developed for typical sealed unbound pavements in Australia’s sealed road network. This model was based on a network level roughness deterioration model using observational data from long-term pavement performance (LTPP) and long-term pavement performance maintenance (LTPPM) sites and accelerated loading facility (ALF) experimental data. The model estimates that the % load-related road deterioration varied with changes to the traffic load, pavement strength and an environmental coefficient. This outcome also implies that the roughness deterioration model for sealed granular pavements is likely to be more appropriate for predicting Australia’s sealed granular pavement deterioration than past internationally based roughness deterioration models which were originally based on the deterioration of asphalt bound pavements.

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge.

A Frequency Control Strategy for EV Stations Based on MPC-VSG in Islanded Microgrids

The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multi-objective model predictive control (MPC) algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

Research on energy storage charging piles based on improved genetic algorithm

Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal parameters, as well as the impact of the energy storage system on the stability of the power grid. Secondly, the analysis of the results shows that the energy storage charging piles can not only improve the profit to reduce the user’s electricity cost, but also reduce the impact of electric vehicle charging on the power grid load.

Influence of Parents’ Ability to Pay User Charges on Students’ Participation in Public Day Secondary Schools in Makueni County, Kenya

The study sought to determine the influence of parents’ ability to pay user charges in public day secondary schools in Makueni County, Kenya. The study was guided by human capital theory which was developed by Schultz in 1961. The study employed concurrent research design of mixed methods methodology. Target population was 250 principals, 380 Form 4 class teachers, 250 PA chair persons and 108 area chiefs. The sample size included 50 principals, 76 class teachers, 50 PA chairpersons and 20 chiefs, making a total of 196 research participants. Data was collected using questionnaires, interview schedules and document analysis. Quantitative data was analyzed using descriptive and inferential analysis using the Statistical Package for Social Sciences (SPSS) version 24 and presented using frequency tables and graphs while qualitative data was analyzed thematically and presented using narratives and appropriate verbatim quotes. Descriptive statistics used were mainly mean and standard deviation while inferential statistics used were both correlation and regression analyses. Pearsons’s correlation coefficient was used to determine association /correlation between parents’ ability to pay user charges and students’ participation in public day secondary schools in Makueni County. Bivariate regression analysis was used to show the influence of parents’ ability to pay user charges on students’ participation in public day secondary schools in Makueni County. The study established that parents’ ability to pay user charges positively and significantly influenced students’ participation in public day secondary schools in Makueni County. The study recommended that; government should increase the amount of FDSE allocations channeled to schools to cover user charges imposed, boost scholarships and bursary schemes and also implement feeding programme for the day scholars.

Impact of the User Charging Practice on the Battery Aging in an Electric Vehicle

Impact of the User Charging Practice on the Battery Aging in an Electric Vehicle

Peer-to-Peer Energy Trading With Active Participation of Distributed Generation

Peer-to-peer (P2P) energy trading fosters direct energy exchange between prosumers in a cost-effective manner. However, there are many challenges in implementing the P2P framework on a large scale. The prominent one is maintaining the distribution network operation under prescribed limits. This work proposes a novel P2P energy-sharing framework using a modified Nash bargaining-based cooperative game for all buildings hypothetically aggregated as virtual communities (VCs) based on their location in the distribution network. This framework incorporates the active participation of DGs as well as DSO in the local energy market and has the ability to settle transactions with respect to network constraints using dynamic network usage charges and electricity prices. The hypothetical aggregation or grouping reduces the number of transactions resulting in improved computational efficacy. A shareable battery energy storage system (BESS) is also assumed to be present in each VC. The proposed algorithm is based on cloud computing which is able to derive the equilibrium strategies of all players using a privacy-preserving decentralised approach.

Empowering highway network: Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures, electric vehicles (EVs) have emerged as an effective measure to reduce reliance on fossil fuels, combat climate change, uphold sustainable energy and environmental development, and strive towards carbon peaking and neutrality goals. This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes (DWCLs) and EV charging strategy joint optimization in highway networks. Taking into account established charging resources in highway service areas (HSAs), the nonlinear charging characteristics of EV batteries, and the traffic capacity constraints of DWCLs. The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs. By ensuring that EVs maintain an acceptable state of charge (SoC), the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment, thus reducing the construction cost of wireless charging facilities and user charging expenses. The efficacy and universality of the model are demonstrated using the classical Nguyen–Dupius network as a numerical example and a real-world highway network in Guangdong Province, China. Finally, a sensitivity analysis is conducted to corroborate the stability of the model. The results show that the operating speed of EVs on DWCLs has the largest impact on total cost, while battery capacity has the smallest. This comprehensive study offers vital insights into the strategic deployment of DWCLs, promoting the sustainable and efficient use of EVs in highway networks.

Research on Multi Scenario User Charging Behavior Habit Model Based on LSTM Driven by Historical Data

Under the advocacy of low-carbon and environmental protection, electric vehicles have ushered in rapid development, and the following user charging problems also need to be considered. The consistency of charging stations makes the charging power the same, resulting in different charging efficiencies of different electric vehicles. Based on this, this paper proposes a multi-scenario user charging behavior habit model based on LSTM driven by historical data, which predicts the charging habits of different electric vehicle users, and provides better support for the subsequent multi-charging power allocation strategy. Finally, the data simulation results show that the model and method can more accurately predict the user's charging behavior.

Optimal Scheduling Strategy of Microgrid Based on Reactive Power Compensation of Electric Vehicles

This paper proposes a microgrid optimal scheduling strategy based on the reactive power compensation of electric vehicles to address the issue of interactive fluctuation of voltage and power resulting from a high proportion of new energy integration into the grid. Firstly, for accurate prediction of electric vehicle charging and discharging behavior, the Monte Carlo simulation method was employed for day-ahead prediction. Secondly, an optimization method for reactive power compensation was developed, considering distributed sources such as wind turbines, photovoltaics, and electric vehicles, with the objective of minimizing network loss and power generation costs. Additionally, user-side prices and electric vehicle charging prices were taken into consideration. Finally, utilizing the IEEE14 bus simulation, a comparison was made between the microgrid with and without electric vehicle reactive support capacity. Results demonstrated that, while maintaining a power factor greater than 0.95, the proposed method reduced the average daily generation cost by 0.35%, average daily network loss by 7.0%, average daily bus voltage fluctuation by 48.60%, average electricity price by 2.88%, and EV user charging cost by 21.29%. These findings illustrate that the proposed reactive power compensation optimization method effectively mitigates voltage and power fluctuations resulting from new energy integration, ensuring system safety, reliability, and economic efficiency.

Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model

In response to the increasing number of EVs(electric vehicles) being charged in a disorderly manner, this paper proposes a two-layer deep learning model based on aggregator pricing and EV user charging strategy. Firstly, the relationship linking the power grid, aggregator, and EVs is defined. Secondly, the pricing of the aggregator and the allocation of EV charging stations are divided into two stages. In the first stage, the aggregator sets the tariffs and sends them to the users, and the users receive the charging strategy. In the second stage, the aggregator adjusts the tariff on the basis of the charging strategy provided by the EVs and the actual situation of different charging stations and then releases it. Next, a two-layer deep learning model is built to solve the final pricing and EV charging strategy model, and the concept of humanity is introduced. The upper layer is the charging decision layer, which is solved by deep reinforcement learning. The lower layer is the charging station selection layer, which is solved by deep Q-learning. The method proposed in this paper can effectively improve the profits of the aggregator and reduce the charging cost of EVs.

A Mutually Beneficial Operation Framework for Virtual Power Plants and Electric Vehicle Charging Stations

Virtual power plants (VPPs) and electric vehicle (EV) charging stations (CSs) have been attracting much attention in recent years. However, existing research rarely concerns the cooperation between VPPs and CSs that are managed by different stakeholders. To facilitate the cooperation between VPPs and CSs, this work proposes a cooperative operation framework for a multi-stakeholder VPP-CSs system. In the proposed cooperative framework, day-ahead offering and real-time balancing models are developed to maximize the total benefit of the VPP-CSs system. To support a more flexible operation of the VPP-CSs system with EV energy flexibility, an EV user incentive program is proposed for acquiring EV battery access rights. The conflicting interests of different stakeholders are addressed by a t-value cost allocation method. To alleviate the computational burden in calculating the t-values, a maximum right cost estimation approach is proposed. Case studies confirm that the proposed methods can provide superior performance by increasing 4.6% of VPP profit, increasing 20.7% of CS profit, reducing 16.3% of EV user charging fees, and achieving 99.2% of t-value estimation accuracy.

Impact of electric vechicles on power transmission grids

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by ▪ for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

Public Mixed Funding for Residential Aged Care Facilities Residents' Needs in the Asia-Pacific Region: A Scoping Review.

Due to population aging and sociodemographic change, there is an increasing reliance on residential aged care facilities in the Asia-Pacific region. Most countries have adopted taxation as the primary means to levy capital for funding universal health services and means-testing of benefits may be further incorporated as a policy balance between horizontal equity and fiscal sustainability. It was hypothesized that residential care needs are evaluated by assessments relating to funding; this scoping review seeks to synthesize how such assessments relate to the care needs of residents. Searches were conducted in concordance with a priori protocol for English-language literature published since 2008 in Embase, CINAHL, PubMed, Scopus, JBI, TROVE, and four peak international organizations for studies and reports that describe the assessment of residents' needs in Asia-Pacific countries that used a mixture of taxation and means-testing to publicly fund residential aged care. One paper and 47 reports were included. Australia, New Zealand, and Singapore utilize a taxation and means-tested user charge approach to fund residential aged care needs. The common care needs assessed include health conditions, daily activities, cognition, psychiatric, and behavioral needs. While essential care needs are publicly funded, other holistic care needs, such as spirituality and autonomy-based needs, still need to be covered for meaningful occupation by the residents.

A secure system for electric vehicle battery swap stations using blockchain

As electric vehicles become more popular, battery swap stations are gaining attention as a new type of charging facility. However, the charging process for electric vehicles involves privacy information such as user location and charging mode, which can be easily stolen or leaked, posing security risks and personal privacy concerns for users. Therefore, protecting the privacy of electric vehicle battery swap station users has become an important issue. This paper aims to study a privacy protection system for electric vehicle battery swap stations using blockchain technology. First, the basic principles and application scenarios of blockchain technology are introduced. Second, potential privacy leaks in electric vehicle battery swap stations are analysed, and a privacy protection scheme based on blockchain is proposed, including anonymous identity authentication, zero-knowledge proof, and encrypted communication. Third, a blockchain-based privacy protection system for electric vehicle battery swap stations is designed and implemented, and its performance is experimentally evaluated and compared with traditional privacy protection schemes in terms of security and efficiency. This paper demonstrates that the blockchain-based privacy protection scheme for electric vehicle battery swap stations possesses high levels of security and reliability, effectively safeguarding users' privacy information. Furthermore, this scheme exhibits promising application prospects and potential for widespread adoption. With the continuous development and utilization of blockchain technology, the privacy protection scheme for electric vehicle battery swap stations using blockchain is expected to provide users with more secure, reliable, and convenient charging services.