Charging Spot Research Articles

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Distributed and Multi-Agent Reinforcement Learning Framework for Optimal Electric Vehicle Charging Scheduling

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively.

WEB BASED EV CHARGING STATION FINDER AND SLOT BOOKING

One of the primary challenges is the inadequate availability of EV charging stations, particularly in certain regions or areas with high population density. This scarcity makes it inconvenient for electric vehicle owners to find accessible charging points, leading to range anxiety and reluctance in adopting electric vehicles. This project introduces an innovative solution for optimizing on-road vehicle services and electric vehicle (EV) charging station slot booking systems. Through this integrated platform, users can seamlessly access a range of on-road services, including vehicle maintenance, repair, and emergency assistance, while also reserving slots for EV charging stations. Leveraging advanced technologies, such as real-time GPS tracking and automated scheduling algorithms, the platform facilitates efficient dispatching of service providers to users’ locations, ensuring timely assistance and minimizing downtime for vehicles. Key Words: Electric vehicle charging locator, EV charging point finder, Charging station locator app, Electric car charging station map, EV charger locator service, Electric vehicle charging spot finder, Charging station finder tool, EV charging station search engine.

Forecasting Energy Consumption from EV Station Charging Using RNN, LSTM and GRU Neural Network

The increase in electric vehicles (EVs) has resulted in a substantial escalation in electricity consumption. This increased demand puts more stress on the overall power system. The current study offers a method to predict energy usage patterns by looking closely at when electric vehicles typically need to charge during the day. After that, the collected data were used to create a predictive model using three different deep learning methods: Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). This study employs data pertaining to electric power consumption for EVs charging derived from Kamphaeng Phet Rajabhat University. The practical results show that the proposed model significantly outperforms in predicting power needs at the mentioned charging spots. This is evident in its precise prediction of the total power demands using the algorithm. Among the three types of deep learning structures studied, it's clear that the LSTMs type stands out as the best, achieving the most accurate results. This is supported by a Root Mean Square Error (RMSE) of 0.372 and a Mean Absolute Percentage Error (MAPE) of 11.508%. Additionally, the inquiry facilitates a comprehensive comparison between the dynamics of demand and the parameters of supply. This process yields data that offers valuable insights crucial for the strategic identification of potential electric vehicle charging stations. It also enables the prudent utilization of remaining electrical capacity derived from production processes. These combined efforts converge to ensure the utmost extraction of utility.

Energy management of grid connected PV with efficient inverter based wireless electric vehicle battery charger: A hybrid CSA-QNN technique

A Hybrid CSA-QNN approach is proposed in this manuscript for grid-connected PV with an efficient inverter-based wireless electric vehicle (EV) battery charger. The proposed hybrid method combines the performance of both the circle search algorithm (CSA) and quantum neural networks (QNN), commonly named the CSA-QNN technique. The Circle Search Algorithm helps find the best charging spot by creating a virtual circle, while the Quantum Neural Network optimizes the overall power flow and charging efficiency. Together, these technologies contribute to making wireless charging for EVs more efficient and convenient. The major goal of the manuscript is the design of a wireless EV battery charger with PV integration. Wireless EV charging systems (WEVCS) may be a feasible alternative technology for charging EVs without a plug-in problem. The CSA-QNN method is performed in the MATLAB platform and it is compared to different existing approaches. The CSA-QNN method shows better results than the existing approaches like the Salp Swarm Algorithm (SSA), Wild horse optimizer (WHO), and Particle Swarm optimization (PSO).

Study of the Gyro and Divergence Characteristics of Relativistic Charged Particle Beam Propagation in Earth’s Magnetic Field

Relativistic charged particle beam can be used as a destructive beam weapon in space for debris removal tasks. The trajectories of charged particles are affected by both electric and magnetic forces in the Earth’s magnetic field. In this paper, we firstly analyzed the correlation parameters of the charged particle beam as a weapon when it is propagated in the geomagnetic field. Then, the models were constructed based on COMSOL Multiphysics, and the IGRF model was adopted in the simulation. The gyroradius and the related uncertainty were analyzed by simulation of the charged particle transport in the geomagnetic field at different altitudes. The charged beam spot radius divergency was also simulated. The geomagnetic field can inhibite the diffusion of the electron beam.

Predictors of electric vehicle adoption intent in rideshare drivers relative to commuters

Research examining electric vehicle (EV) adoption intent is growing, but neglects rideshare drivers (e.g., Uber & Lyft), who drive up to 13% of all miles driven in metropolitan areas. This study uses Noppers’ framework on sustainable innovation adoption to compare rideshare drivers to commuters on symbolic and instrumental attribute (e.g., costs, maintenance) perceptions, and their influences on EV adoption intent. It is possible that rideshare drivers have stronger symbolic attribute ratings than commuters because more people see their vehicles for extended periods of time, so what the vehicle signals about drivers may be more salient to drivers; alternatively, because rideshare drivers use their vehicles for work, drivers may perceive vehicles in primarily instrumental terms. We test these competing hypotheses using a survey of 136 rideshare drivers and 378 commuters. Relative to commuters, rideshare drivers rated symbolic attributes higher and EV maintenance costs somewhat more favorably, but significantly fewer had access to a charging spot. A regression model found that both instrumental and symbolic attributes positively predicted EV adoption intent for both groups. Relative to commuters, symbolic attributes were weaker predictors of EV adoption intent among rideshare drivers, whereas instrumental attributes, particularly perceptions that the purchase cost of an EV was lower than that of a gasoline car, were stronger.

Distribution and evolution of surface charges in nanosecond pulsed dielectric barrier discharge under the quiescent air and airflow

The surface charges in nanosecond pulsed dielectric barrier discharge under quiescent air and airflow are detected based on the Pockels effect of electro-optical crystals. In quiescent air, it is found that the surface charge spot propagates and moves in a certain direction due to the combination of the transverse electric field and the thermal accumulation during dozens of consecutive discharge cycles. However, the position of the surface charge spot remains fixed throughout a single discharge cycle (0.83 ms). At the same time, the noticeable decay of surface charges emerges in the above time scales. Furthermore, when the airflow is introduced into the discharge gap, the propagation and movement of surface charges are accelerated. With the increase of airflow velocity, the discharge transforms from a filamentary mode to a diffuse mode, and the distribution of surface charges varies from discrete to uniform. The transition point of the discharge state and charge distribution corresponds to the airflow velocity of 10 m s−1. The airflow accelerates the decay of surface charges, resulting in the shrink and dispersion of surface charges, which is considered to be the fundamental reason for the airflow’s potential to improve discharge uniformity. The inherent mechanism for achieving uniform discharge is revealed in this study.

Designing fast-charge urban electric bus services: An Integer Linear Programming model

Currently, there is serious political support for the decarbonization of transport locally, nationally and even internationally. Public transport operators are focusing on the use of electric buses as an opportunity to reduce greenhouse gas emissions and improve air quality. However, using electric buses requires a functional infrastructure of urban charging points. Fast-charging can be made available thanks to the progress made on the major technological charging devices in recent years. In this study, we consider an optimization problem of the design of an infrastructure for a fast-charge city electric bus service. The decisions which have to be made include determining a mixed fleet of conventional and electric buses, points for electric chargers and power stations, quantities of charging plug devices, a distribution of electric buses between the routes, and matching chargers with power stations. The objective is to maximize the route-weighted total passenger capacity of electric buses. An Integer Linear Programming model has been developed to complement the existing non-linear model. The new model is efficient if the number of possible charging spots is small, which is natural and frequent in practice. Extensive computer experiments demonstrate that our approach delivers near-optimal solutions of the studied problem in ten minutes for real-world instances on a standard PC and it outperforms the earlier approach on every instance.

A hybrid WFS-CGO based approach for optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management

In this document, a new methodology for the optimal allocation of capacitor electric vehicle (EV) charging points on smart grid systems is introduced. To realize a better balance between exploring and exploiting Wing suit Flying Search (WFS), the Chaos Game Optimization (CGO) algorithm is used on WFS performance. Thus, it is named the WFS-CGO approach. Here, along with reactive compensation, allocation of parking and capacitors is presented for congestion management. The proposed approach is used to limit the amount of parking space. The performance of the proposed system is tested on a IEEE 34 bus distribution network. The outcome obtained by the QGDA system is compared to existing procedures like PSO, ALO, ACSO, WFSA, and WFS2ACSO techniques. Under cases 1, 2, and 3, after EV loss, the proposed technique achieves 216.3397, 232.9558, and 265.0174. Additionally, the proposed outcomes portray that an uneven EV charging scenario may cause an important voltage imbalance that goes beyond their allowable limit of 2%.

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

Complex nature of the interaction of AFM tip with the freshly splitted (010) surface of CdWO4 and ZnWO4 single crystals

The discovery of new low-dimensional materials formed by splitting known bulk crystals into separate elementary layers has enabled future realization of still undiscovered potential of long-studied materials. High-resistivity two-dimensional films obtained by splitting ionic layered crystals are of great interest for creating new vertical van der Waals heterostructures. For unveiling the potential of using two-dimensional monocrystalline films of CdWO4 and ZnWO4, it is necessary to gain a more penetrating insight into the processes that occur on the freshly splitted surface of the crystals. In the present study, for the first time we investigated the effect of AFM probe on the (010) surfaces of freshly splitted CdWO4 and ZnWO4 single crystals. It is shown that the observed “protruding areas” on the splitted surfaces of these crystals are of a complex nature, and they are mainly related with the transfer of charges, as well as with the formation of complete bonds on the surface of the broken crystal lattice of examined crystals. The charge spots observed on the splitted surface of the crystals arise as a result of local charging by AFM probe. As a result of charge diffusion, as well as under the influence of ambient medium, the complete dissipation of charges occurs in less than 2 h.

Large-scale magnetic fields enabling fitting of the high-frequency QPOs observed around supermassive black holes

Abstract Recently, it has been argued that the high-frequency quasi-periodic oscillations (QPOs) observed in black hole systems of various scales in mass in cases of supermassive black holes (SMBH) are not consistent with any of the simple physical models, based on frequencies of the geodesic epicyclic motion (Smith et al. 2021, ApJ, 906, 92). We test if such a disease can be simply cured by geodesic models based on epicyclic frequencies modified by the effect of electromagnetic interaction of slightly charged orbiting matter, with large-scale magnetic fields with values observed around SMBHs in active nuclei. Inspired by GRAVITY/ESO observations, we assume a slightly charged hot spot, as the relativistic motion of a plasma in magnetic field leads to charge separation and non-negligible charge density in the orbiting plasma. Its electromagnetic interaction with the large-scale magnetic field around the black hole can be weak enough, allowing for nearly harmonic epicyclical oscillatory motion of the hot spot with frequencies given by modification of those applied in the geodesic model. Even the simplest epicyclic resonance variant of the geodesic model, modified by slight electromagnetic interaction admitted by observations, can fit the QPOs in the case of both stellar-mass and supermassive black holes. We have shown that even a tiny excess of charged particles in the quasi-neutral plasma of the radiating hot spot, allowed by observations, enable an explanation of QPOs observed in active galactic nuclei. We also estimate the effect of the electromagnetic interaction on the shift of the innermost stable circular orbits, implying the degeneracy in the measurements of spins of the black hole candidates.

Integration of multi voltages, multi electric vehicle spots based three phase photovoltaic array charging station to the modern distribution grid with improved electric vehicle charging capability and power quality

This paper proposes the solar photovoltaic (PV) array-based three-phase modern grid integrated multi-voltage and multi electric vehicles (EVs) spots-based charging station (CS). It provides the charging spot of the EVs as per the required voltage level of the EVs battery. The presented CS is multifunctional. Besides, the EV charging capability, it also mitigates the power quality issues due to EVs and nonlinear loads tied at the point of common coupling (PCC). The voltage source converter (VSC) control is developed using the gradient adaptive variable step least mean square (GAVS-LMS) based technique. The presented control extracts the fundamental weight of load current to improve the operation of grid integrated charging stations and the power quality issues. The DC bus voltage is stabilized through the bidirectional converter using the cascaded proportional-integral (PI) control under heavy multi-voltage EV loads. As the charging station is subjected to heavy multi-voltage EV loads, which causes the unstable operation of the CS. Therefore, the small-signal modeling and stability analysis is also carried out in charging/discharging modes. The vehicle to the grid (V2G) and grid to vehicle (G2V) mode transfer are also carried out. These operations are also shown with and without the availability of solar PV generation. The efficacy of the presented CS and its controllers are tested with simulated as well as Opal-RT workbench based experimental results.

Charge Fluctuations on a Flat Interface between Dielectric and Electrolyte or Dense Plasma.

An interface between a dielectric and a medium containing charge carriers with moderate mobility is considered. In equilibrium, stochastic fluxes of positive and negative particles toward the surface have equal average current density j0, and we suppose that the surface absorbs all falling charges. All over the surface, this results in the emergence of oppositely charged spots of various sizes D fluctuating and interacting with each other. Fourier expansion reduces this collection of interacting spots to the ensemble of independently fluctuating charge density waves. An exact solution of the Poisson equation for a single wave on a flat surface was obtained and provided strict proof that a fluctuating electric field is quite strong just above each charge spot but diminishes exponentially with the distance from the plane. The lifetime τ of a charge spot is inversely proportional to the density j0 of the stochastic current while proportional to fluctuation's amplitudes independent of j0. The fluctuation's parameter dependence on the charge spot's size D can vary according to the conducting medium properties.

EV charging station monitoring framework—A case study of Croatia: Performance analysis and anomaly detection under pandemic situation

This paper investigates the pandemic impact on electric vehicle (EV) monitoring framework designed for analysing and tracking of EV charging data in the Croatian electric vehicle charging station grid. The developed framework encompasses multiple features of the EV charging grid behaviour, notably: (i) global predictions of the utilized energy, (ii) number of charging vehicles, and (iii) total time spent while charging. In this paper, we analyse framework performance and key performance indicator (KPI) values during the pre-lockdown period, soft-lockdown period, and post-lockdown lift-off period to show how the pandemic situations and governmental lockdown regulations can impact EV grid performance and EV charging business. In addition to that, we test a daily framework anomaly detection flow that tracks the global predictive performance of the predictive models on the EV grid, to detect if the framework detects this to be a situation that is novel (anomalous) during the soft-lockdown period. Results show that changes in the behaviour during the lockdown period reduce electric demand on EV charging spots, with customers having lower demand and similar behaviour as in other European countries. Furthermore, the soft-lockdown period is detected as a period with more extreme anomalous values for supervised models, and this is shown as an increase in residual anomaly values, flagging 3 times more days as an extreme anomaly than in the normal periods.

Surface Flashover Induced by Metal Contaminants Adhered to Tri-Post Epoxy Insulators under Superimposed Direct and Lightning Impulse Voltages

Metal contaminants can distort the surface electric field of the tri-post epoxy insulator and cause serious surface charge accumulation, significantly reducing the insulation performance of the insulator under the superimposed DC and lightning impulse voltage. In this paper, an experimental platform for charge accumulation and surface flashover of tri-post epoxy insulators under the superimposed DC and lightning impulse voltage was built, by surface point measurement and charge inversion calculation, the surface charge distribution characteristics of tri-post insulators with attached particles was experimentally explored and the influence law of attached metal particles on the charge accumulation was discussed. The results show that metal particles can cause a surge in the surface charge density of the tri-post epoxy insulator, forming bipolar charge spots whose polarity is opposite to that of the adjacent electrodes. The adsorbed metal dust can cause the polarity reversal of adjacent surface charges, forming a large-area unipolar charge spot. Moreover, the flashover voltage of a tri-post insulator under DC superimposed lightning impulse voltage with a clean surface and attached metal particle was measured, and the synergistic induction mechanism of charge spot accumulation and metal particle discharge on the flashover along the face of the tri-post insulator is thereby revealed. Compared with the clean insulators, the surface flashover voltages of tri-post epoxy insulators with metal contaminants adhered decrease under the superimposed voltages of different polarities, but the decline amplitude is greater under the heteropolar composite voltage. When adhered to the middle of the insulator leg, the distribution range of bipolar charge spots is the widest, and the surface flashover voltage decreases sharply, which can drop by 32% compared with the absence of particles. In addition, when the metal dust adsorbed by the tri-post epoxy insulator has a wide distribution range, the impact of metal dust on the flashover voltage is greater than that of the attached metal particles, and its hazard cannot be ignored. The research results can provide a reference for the insulation test method and optimal design of the DC tri-post epoxy insulator.

Automatic control and dispatching of charging currents to a charging station for power-assisted bikes

This work deals with the automatic dispatching of the charging currents in a charging station for power-assisted bikes (ebike). The decision variables such as arduousness index and urgency are determined. The arduousness index is carried out from the GPS ride data. Urgency is calculated using the parking time and ebike batteries state of charge. They are used to determine ebike's charging priorities at the charging station using continue fuzzy logic. Photovoltaic power forecasting is determined over the control horizon using the artificial neural network. On the one hand, the values of the priority, the photovoltaic power forecasting and the storage battery's state of charge are calculated. They allow to control the states of the switches associated with each charging spot and the operating mode of the storage battery (source or load) using discrete fuzzy logic. On the other hand, the interest of the ride's arduousness for a charging station is presented. A comparative study between the charging method integrating the ride arduousness and not is carried out. A case study of the polytech Annecy campus at the University of Savoie Mont Blanc in France is proposed. Results show that: the arduousness index is essential for controlling the charging priority of ebikes at the charging station; Fuzzy logic allows to manage the current dispatching on a charging station; taking into account the ride's arduousness allows to save up to 413.03 (Wh) of profit and 97.90% energy flexibility on the charging station.

Optimal Size of a Smart Ultra-Fast Charging Station

An ever-increasing penetration of electric vehicles (EVs) on the roads inevitably leads to an ever-stringent need for an adequate charging infrastructure. The emerging ultra-fast charging (UFC) technology has the potential to provide a refueling experience similar to that of gasoline vehicles; hence, it has a key role in enabling the adoption of EVs for medium-long distance travels. From the perspective of the UFC station, the differences existing in the EVs currently on the market make the sizing problem more challenging. A suitably conceived charging strategy can help to address these concerns. In this paper, we present a smart charging station concept that, through a modular DC/DC stage design, allows the split of the output power among the different charging ports. We model the issue of finding the optimal charging station as a single-objective optimization problem, where the goal is to find the number of modular shared DC/DC converters, and where the power rate of each module ensures the minimum charging time and charging cost. Simulation results show that the proposed solution could significantly reduce the required installed power. In particular, they prove that with an installed power of 800 kW it is possible to satisfy the needs of a UFC station composed of 10 charging spots.

Imaging and simulations of positive surface and airborne streamers adjacent to dielectric material

This paper presents the imaging and simulations of positive airborne and surface streamers in the air that is adjacent to a dielectric material. The goal of this paper is to investigate the intriguing observation of the occurrence of airborne streamers that originate at a dielectric surface at a distance from the tip of a high-voltage electrode. This effect may be attributed to the accumulation of charges at a spot on the dielectric material during subsequent streamers. Such a positively charged spot causes airborne streamer inception after reaching the critical value of the charge density. This effect is observed in a side-view configuration by high-speed imaging in a time-accumulated and spatially resolved mode. The hypothesis of the occurrence of airborne streamers in certain sequences was complemented by 2D simulations. The surface charge densities were quantitatively evaluated by the simulations. Two types of streamers were simulated: first – without an accumulated surface charge; and second – with a spot of a defined charge density (which triggers the airborne streamer). After reaching the critical value of the charge density, such spots that are charged during subsequent cathode-directed streamer events may trigger a discharge towards the air side. The length of the surface streamer along the dielectric surface is voltage-dependent. The experimental and simulation results extend the insight to streamer mechanism in configurations with charges accumulated on the dielectric surface – including the inception of an airborne streamer.