Charging Cost Research Articles

Optimization Research on the Impact of Charging Load and Energy Efficiency of Pure Electric Vehicles

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.

Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.

ESTIMATION OF OIL SPILLAGE AND SALVAGE REVENUE IN KOKORI OIL FIELD USING NUMERICAL METHODS AND PYTHON ALGORITHM

The study aimed to estimate oil spillage in the Kokori Oil Field, discovered in 1958, using a linear model. The field has 23 oil wells and produced 383 million barrels of oil. A linear model was developed using Python programming, comparing it with the trapezoidal method and salvage revenue. Results showed a linear relationship between oil spill quantity, duration, and mitigation measures. This study provides a valuable model for estimating oil spillage in the Kokori oil field, emphasizing the importance of accurate estimation for environmental and economic purposes. The study presents a comprehensive model for estimating oil spillage in the Kokori oil field, emphasizing the significance of accurate estimation for environmental and economic purposes. The correlation coefficient value supports the model's sufficiency, and the calculated salvage revenue indicates a commendable projected value for the Kokori oil field at its end of use. Salvage revenue is the estimated value of an asset at the end of its useful life, which is crucial in determining the cost of goods sold and depreciation charge. It lowers the asset's cost, influences its usable life, resale value, and replacement cost. Salvage revenue is calculated by subtracting revenue generated without mitigation measures from revenue realized with mitigation measures.

Self-reported public fast charging infrastructure demand: What do existing and potential electric vehicle adopters want and where?

Electric vehicles (EVs) promise emissions reduction, but adoption rates must increase to meet 2030 targets. Since public fast charging infrastructure (PFCI) is considered important to increase EV uptake, this research aims to assess its demand across existing and potential EV adopters in Ireland. Online mapping tools were used to capture 1635 self-reported locations where users prefer PFCI, along with other related preferences from 545 respondents. The results were analysed using a mixed-method approach including exploratory factor analysis (EFA), Geographic Information System (GIS), two-step cluster analysis, and qualitative analysis. The results indicate that expanding PFCI is critical for increasing EV uptake, with 81.4% of potential EV adopters showing a significant impact on uptake. Major deterrents identified include insufficient availability of public fast chargers followed by high charging costs. The qualitative analysis highlights existing EV users’ demand for improved end-user experiences, such as unified payment systems. Most preferred locations for PFCI installation include travel routes, followed by shopping centres/ supermarkets. Results also indicate that a reliable second-hand market would be key to increasing EV uptake. The findings from this study can serve policymakers in effectively rolling out PFCI, while the methodology employed can be replicated in other comparable study areas.

Health screening disparities in people living with HIV; A nationwide organized screening setting

BackgroundWe identified the uptake of people living with HIV (PLWH) using health-screening services and compared the screening rate with the general population, to identify factors associated with the use of screening services by PLWH. MethodsThis population-based study used data from the Korean National Health Insurance, a single mandatory health insurance system covering all residents. The uptake rates for screening for general health, gastric cancer, and colorectal cancer, which were provided by a national health screening program with free of charge or minimal cost in Korea from 2010–2020. Factors associated with general health, gastric cancer, and colorectal cancer screening were also explored. ResultsScreening uptake rates for general health, gastric cancer, and colorectal cancer in PLWH increased from 2010–2019 but decreased in 2020. The standardized screening ratio showed lower screening rates for general health, gastric cancer, and colorectal cancer in PLWH with 0.80 (95 % confidence interval (CI) = 0.77–0.83), 0.64 (95 % CI = 0.61–0.67), and 0.67 (95 % CI = 0.64–0.71) in 2019. Increased age, Charlson Comorbidity Index, and years since HIV diagnosis were associated with increased screening uptake in PLWH. Otherwise, PLWH recipients of medical aid programs received less for each screening compared to PLWH who paid insurance premiums higher than 50 %. An increased medication possession ratio of highly active antiretroviral therapy was associated with decreased general health and gastric cancer screening uptake but increased colorectal cancer screening uptake. ConclusionsSignificant disparities in health-screening uptake were observed in PLWH compared to the general population in a nationwide organized screening setting without cost barriers.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Techno-economic analysis of power-to-heat-to-power plants: Mapping optimal combinations of thermal energy storage and power cycles

To enable the widespread exploitation of intermittent, low-cost, and non-dispatchable renewable energy technologies, energy storage plays a key role in providing the required flexibility. This study introduces maps of optimal combination of Thermal Energy Storage (TES) and power cycles, supporting decision-making in power-to-heat-to-power applications. These maps span a wide temperature range from 200 to 1200 °C and are proposed for different charging costs, installed capacities, and storage durations. For thermal-to-electricity reconversion, this study explores power blocks including steam Rankine cycle, supercritical CO2 (sCO2) Brayton cycle, Organic Rankine Cycle (ORC), and combined gas turbine with Rankine and sCO2. Results highlight that, in a grid-based plant with a 50 EUR/MWh charging cost, the most cost-effective pairing involves sCO2 cycles with recompression and intercooling, with particle TES at 600–800 °C. Air packed-bed suits scenarios where TES contributes significantly to capital costs or involves low charging costs. Molten salt TES is the optimal choice when the design temperatures align with salt temperature limitations. Particle TES proves cost-effective across a broad temperature range and scales (10–200 MW). For solar-based systems, the integration of molten salt TES with simple sCO2 recuperated cycles demonstrates market potential for southern European locations.

Open Access Publishing Metrics, Cost, and Impact in Health Professions Education Journals

Publishing in health professions education (HPE) journals is an integral component of academic discourse and career progression. Research in this field is shifting to an open access (OA) publishing model. To identify the characteristics and publishing models of HPE journals and explore potential associations between publication costs and journal metrics. This cross-sectional study was conducted between September 20, 2023, and February 14, 2024, using the World Bank purchasing power parity (PPP) index to analyze relative costs of article processing charges (APCs). Data on journal characteristics, impact metrics, APCs, and waiver or discount were extracted from the National Library of Medicine, Scimago, Scopus, journal websites, and email correspondence with editorial staff of journals. All HPE journals indexed in PubMed, written in or translated into English, and with HPE as a core component of their mission were included in the analysis. Two-year impact factor, H-index, cite score, Scientific Journal Ranking, and APC. Among the 51 journals included, 27 (53%) adopted OA-only and 24 (47%) adopted hybrid publishing models. The median (IQR) APC for all journals was $2820.00 ($928.00-$3300.00). Associations were observed between impact factor and APC (β coefficient, $386.84; 95% CI, $226.84-$546.84; P < .001) and between cite score and APC (β coefficient, $282.40; 95% CI, $148.12-$416.61; P < .001). Of 20 journal websites with information regarding fee waivers or discounts, 7 journals (35%) confirmed fee waiver or discount. The PPP index analysis of the top 39 countries publishing HPE research showed that the financial burden of meeting the median APC for publication was 1.94 to 10.26 times higher for authors from lower-income countries than for authors from the US. Results of this cross-sectional study suggest that adoption by HPE journals of an OA publishing model was high but access to APC waivers or discounts was limited. These factors create barriers to equitable OA practices, necessitating concerted efforts, such as increasing transparency of publishing costs, implementing economic impact analysis, expanding waivers to eligible authors, and applying holistic impact factor scoring.

Is Migraine Surgery Financially Viable for the Hospital and Surgeon? A Single-Surgeon, Ten-Year Study at an Academic Institution.

Data exists to demonstrate that migraine surgery is cost-effective for the long-term treatment of headaches, but no data exists regarding its financial viability for healthcare institutions. The goals of the current study are to demonstrate that (1) migraine surgery is financially viable for an academic institution with a net-positive profitability and (2) migraine surgery is a sought-after therapy that can draw patients into the healthcare system. A retrospective chart review of migraine and bilateral breast reduction (BBR) consults seen in clinic between 05/01/2011-05/30/2020 was conducted. Patient age, sex, payor information, and distance from the hospital was collected. Profit margin, percentage profit, collection percentage, and conversion rate to surgery from clinic was calculated. 407 new migraine consults and 119 new BBR consults were included from which, 150 underwent migraine surgeries and 117 underwent BBR respectively. Migraine surgery group came from a further distance for the initial consult (P-value<0.001) and for surgery (P-value<0.001). There were also more migraine surgery patients from out-of-state (P-value=0.012). Migraine clinic patients had a larger ratio of private insurers (P-value<0.001). Migraine surgery patients had a greater total charge (P-value<0.001), estimated reimbursement (P-value=0.001), and total cost (P-value<0.001). This study provides evidence that migraine surgery is financially viable for hospitals and while performing similarly to bilateral breast reduction in profit margin, it brought in more patients from outside the catchment area of our institution. These findings may encourage hospitals to recruit migraine surgeons to improve care for this underserved patient population.

Optimal Energy Storage Allocation for Combined Wind-PV-EVs-ES System Based on Improved Triangulation Topology Aggregation Optimizer

To determine the ES allocation based on a specific number of EVs connected to a combined WPESS, this paper develops an ESS allocation model that considers the impact of EV charging behavior on LSD, ES allocation cost, new energy utilization rate, and self-power rate. First, several scenarios are generated using Monte Carlo sampling (MCS), and a typical day is selected through Backward Reduction (BR). Next, the Monte Carlo method is employed to generate conventional EV charging curves and optimize EV charging behavior by considering LSD and user charging costs. Subsequently, an ES capacity allocation model is developed, considering system costs, new energy utilization rate, and self-power rate. Finally, an improved triangulation topology aggregation optimizer (TTAO) is proposed, incorporating the logistic map, Golden Sine Algorithm (Gold-SA) strategy, and lens inverse imaging learning strategy. These enhancements improve the algorithm’s ability to identify global optimal solutions and facilitate its escape from local optima, significantly enhancing the optimization effectiveness of TTAO. The analysis of the calculation example indicates that after optimizing the charging behavior of EVs, the average daily cost is reduced by 204.94, the self-power rate increases by 2.25%, and the utilization rate of new energy sources rises by 2.50%, all while maintaining the same ES capacity.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Optimizing electric vehicle charging scheduling using enhanced multi-agent neural networks with dynamic pricing

Electric vehicles have gained widespread recognition as an environmentally conscious mode of transportation, primarily due to significant technological advancements and reduced emissions. When seamlessly integrated into the smart grid infrastructure, these vehicles can serve as flexible energy consumers and potential energy storage units. However, the efficient management of charging stations due to dynamic load demand & pricing poses a significant challenge, aiming to reduce wait times for electric vehicle owners and reduce electricity costs. To overcome this challenge, this study proposes a solution based on dynamic pricing policy and the adaptive Markov decision process. The approach utilizes a reinforcement learning-enabled enhanced multi-agent neural network (EMANN) to optimize charging schedules. This adaptive Markov decision process achieved optimal scheduling by minimizing charging costs during off-peak hours while completing charging in the shortest possible time. Furthermore, a constrained policy is employed to train the enhanced neural network using reinforcement learning, allowing the network to learn and solve adaptive Markov decision processes directly. To evaluate the effectiveness of this solution, the study conducted a numerical experiment using real-time data from Tesla electric vehicles. Combined with queuing models, the EMANN yielded impressive results, including a peak savings of 19.05% and an average profit of $376.1 per kilowatt-hour, achieved within a network convergence time of 512 s. The proposed algorithm for scheduling electric vehicles has been proven to be efficient in addressing the complexities associated with managing electric vehicle charging stations.

Optimal charging of electric vehicles in smart stations and its effects on the distribution network using meerkat optimization algorithm

The proliferation of electric vehicles (EVs) presents a significant challenge and opportunity for the energy sector. This study proposes a novel approach for optimizing EV charging within smart stations, considering its impact on the distribution network. Leveraging the Meerkat Optimization Algorithm (MOA), we address the complex optimization problem of balancing EV charging demands with grid constraints. Navigating distribution grid energy management complexities, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model tailored for grid operations, featuring meticulous formulations for energy management. The model optimizes battery usage, EV energy management, compensator utilization, and distributed generation dispatch. Through extensive simulations, we demonstrate the approach's effectiveness in minimizing charging costs, reducing grid congestion, and enhancing overall system performance. The multi-objective function minimizes energy losses, power purchases, load curtailment, distributed generation, and battery/EV expenses over 24 h. Simulations validate a significant reduction in the distribution grid's operating cost. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring grid reliability and efficiency. Incorporating electric vehicles (EVs) into the system yields significant improvements across performance indicators compared to scenarios without EVs. Results indicate a 19 % reduction in the objective function value, with a notable 74 % decrease in energy purchase and a 60 % reduction in energy losses. Additionally, load shedding decreases by approximately 75 %, while voltage deviation decreases by around 44 %. Importantly, no PV or WD curtailment is observed with EV integration, showcasing its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.

Enabling EV Charging at varied locations using Smart Meters

Electric vehicles primarily run on stored electrical energy, which needs to be replenished at regular intervals of time. Commercially, EVs are charged at charging stations, however they can be charged anywhere whether it be at home, public charging stations or even at workplaces. The commercial charging stations are highly costly and require huge setup. The main aim of this paper is to enable charging at any commercial and non-commercial establishment and optimize the cost of charging and grid usage by using an IOT (Internet of Things) device for bill generation and grid usage analysis.The charge enabling process is based on a QR based activation of the device. The user scans the QR code and registers his details, this enables the charge cycle. The bill is generated based on the start time and the end time of the charge cycle and the energy consumed in that cycle and a text message is sent to the consumer. The effect of charging load is also analyzed and hence an appropriate action is taken to optimize the peak load or demand. Keywords: Internet of Things (IoT), Electric Vehicles (EVs), Smart Energymeter, QR-based activation

Uncertain scheduling potential of charging stations under multi-attribute uncertain charging decisions of electric vehicles

A method for analyzing the uncertain adjustability of charging stations (CSs) under the influence of multi-attribute group decision-makings of electric vehicles (EVs) is proposed. Firstly, considering physical constraints of parameters such as charging power and energy stored, a model for characterizing the schedulability of a single EV under different charging modes including rated power charging, adjustable charging, and flexible charging and discharging is constructed. Secondly, since the charging mode selection of EVs is a comprehensive decision considering charging cost, charging time and other attributes, and individual decision-making is often affected by group users, an EV charging mode selection probability model is developed based on the multi-attribute group decision-making theory. Further, scheduling potential boundary parameters of CSs affected by the scheduling potential of all EVs in the station are derived based on the Minkowski sum theory, and their probability characteristics are calculated by combining the charging mode selection probability of single EVs and the schedulability under each mode. Finally, case studies prove the superiority of the proposed theory.

A two-sided equilibrium model of Vehicle-to-Vehicle charging platform

A novel mobile charging service utilizing vehicle-to-vehicle (V2V) charging technology has been proposed as a complement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study develops a two-sided equilibrium model for a V2V charging platform, where the demand-side charging vehicles (CVs) choose between charging piles and the V2V platform, and the supple-side discharging vehicles (DVs) decide whether to provide discharging services. The V2V platform matches CVs with DVs, receiving compensation from CV owners and reimbursing DV owners. To explore the strategic interactions between the V2V platform and fixed CI operators, we construct an analytical bi-level model that examines different operation regimes, including competitive and cooperative scenarios, i.e., non-cooperative game and Nash Bargaining game. The lower-level model is a two-sided equilibrium model that quantifies charging and discharging choices of EV owners, while the upper-level model optimizes the pricing decisions of the V2V platform and fixed CI operator. Numerical results indicate that the introduction of the V2V platform can reduce the overall charging costs, thus encourage the use of public charging services. Furthermore, it is suggested that the V2V platform be managed by independent enterprises, rather than existing fixed CI operators, to promote the EVs at relatively low charging costs.

Reinforcement learning-based charging cluster determination algorithm for optimal charger placement in wireless rechargeable sensor networks

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.