Electric Vehicle Charging Stations Research Articles

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

The rise of electric vehicles (EVs) in recent years has underscored the need for well-established Electric Vehicle Charging Station (EVCS) infrastructure. The lack of a reliable network of EVCS is a significant obstacle to the widespread adoption of EVs. Also, one often overlooked aspect of electric vehicles is the importance of using renewable energy sources to recharge their batteries. Without sustainable energy, EVs could produce more greenhouse gas emissions than traditional vehicles. Therefore, it seems essential to design networks of EVCSs that depend on renewable sources to meet their energy needs and ensure convenient access for customers while reducing environmental impact. This study proposes a two-stage stochastic optimization model to determine the optimal locations and capacities for EVCSs and supporting sustainable energy plants. The model considers uncertain factors such as vehicle flows and renewable energy supply. The model combines a hybrid adjusted p-robust and min–max regret approach to address uncertainties to ensure robustness in the network design process. Two approaches including Sample Average Approximation (SAA) and Fuzzy C-means Clustering are employed to solve the model efficiently. Additionally, the Distributionally Robust Optimization (DRO) approach accounts for uncertainty in the probability distribution function of scenarios. The results of these solution approaches are validated through parameter realization, which involves simulating uncertain parameters and verifying the robustness of the solutions obtained from each approach. This validation process is done by comparing the obtained results with the optimal values based on two criteria: the sum of deviation from optimality and the maximum deviation from optimality. By adopting these solution methods, the proposed methodology contributes to developing efficient and sustainable EV infrastructure. This methodology gives investors and city officials robust decision support for establishing EVCS.

Overview and advancement of power system topology addressing pre- and post-event strategies under abnormal operating conditions

The transition towards increased utilization of renewable energy and electric vehicles (EVs), along with the growing use of various other electrical devices, poses challenges to the stable and resilient operation of electric power systems (EPS), especially in the face of natural phenomena associated with climate change. This means that accurate topology and balanced EPS plays a key role to increase the capacity to respond quickly and in a coordinated manner to disaster situations such as cyber-attacks, earthquakes and floods. In this study, a new approach is presented to quickly and accurately detect topology attacks in EPS, thus contributing to making safer and more resilient. The proposed methods provide insights into maintaining uninterrupted electricity service by enabling EPS management through both post- and pre-event operational strategies. This approach is created by identifying faulty points with the obtained topology information and creating microgrid (MG) groups. Machine learning techniques have been integrated into the data intrusion attack detection (DIAD) system, enabling the detection of manipulated or faulty smart meters (SM). Concurrently, a topology identification (TI)-based graph learning algorithm is propounded to determine the exact fault locations before and after the event. For MV region restoration after determining the TI region, a mixed-integer linear programming (MILP) approach is employed to optimize the load restoration process in the MG regions. This approach aims to minimize losses and restore critical loads to their previous state as quickly as possible using flexible and emergency power balancing systems, including grid-support storage systems (GSSs), photovoltaic systems (PVs), electric vehicle charging stations (EVCS), and mobile generators. Moreover, a detailed compilation is presented under the topics of EPS topology, phase identification (PI) and its effect on power system resiliency (PSR), shedding light on the future development of EPS.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.

Load Analysis and Prediction of Electric Vehicle Charging Stations Supported by RF-CNN Algorithm

Load Analysis and Prediction of Electric Vehicle Charging Stations Supported by RF-CNN Algorithm

Spatio‐temporal dynamic navigation for electric vehicle charging using deep reinforcement learning

AbstractThis paper considers the real‐time spatio‐temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path‐based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real‐time dataset comprising of state‐of‐charge and arrival‐departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time‐varying variables. A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. Three case studies are carried out for 24 nodes Sioux‐Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

Heuristics for multi-objective operation of EV charging stations based on Chicken Swarm Optimization

The emissions of greenhouse gasses and high vehicle operating cost are the widespread issues, majorly derived by the large number of conventional fossil-fuel based vehicles. This had led many automobile manufacturers to move towards electric vehicles (EVs). However, EVs significantly impact the power grid because of the energy needed to re-energize their batteries. This study introduces an effective multi-objective function that utilizes Chicken Swarm Optimization (CSO) to perform the optimal operation for the Charging Stations (CSs) within the distribution network. The aim here is to reduce the power losses, the average voltage deviation index (AVDI), voltage stability index (VSI), and the impact of harmonic distortion. The simulations are conducted on 69-bus radial distribution network.

A novel analytical method for optimal management of network congestion caused by electric vehicle charging stations

Congestion management with the emergence of electric vehicle charging stations (EVCSs) can be considered as one of the challenges facing the network operator, whose incorrect management causes the price difference to increase and creates market power. In general, the effect of EVCSs on the congestion rent (CR) of grid lines has been studied in a few studies, which are considered a research gap. In this paper, the share of each EVCS to the variations in line CR and total congestion rent (TCR) is precisely determined. Also, the uncertainty regarding the capacity of the EVCSs is considered to make the problem more real. In addition, the share of other factors as well as marginal, expensive, and cheap units, grid loads, and congested lines are calculated on the CR of grid lines. The proposed method is divided into two parts, in which the share of the five factors to variations in the buses’ locational marginal price (LMP) and the flow of lines is determined in part 1. In part 2, the share of EVCSs to variations in the CR of lines and TCR is computed. The proposed method is implemented using the Kahn-Tucker condition method. This approach is implemented on an IEEE 24-bus system. The analysis of results indicate that the congestion of network lines in the best and worst cases of placing 3 EVCSs in the network increases by 0.0013 % (0.65 $/h) and 0.1 % (48.94 $/h), respectively. Therefore, determining the appropriate placement of the EVCS is of particular importance.

Design of a triple port integrated topology for grid-integrated EV charging stations for three-way power flow

Environmental fluctuations, solar irradiance, and ambient temperature significantly affect photovoltaic (PV) system output. PV systems should be efficient at the Maximum Power Point in various weather climates to maximize their potential power output. The Maximum Power Point Tracking (MPPT) technique is employed to plan a specific location that yields the maximum amount of power. Operating dispersed alternative energy sources connected to the grid in this situation makes energy control an unavoidable task. This research article suggests designing a power electronics converter topology that links sustainable resources and electric vehicles to the power grid. There are four modes of operation for this proposed converter topology: grid-to-vehicle, vehicle-to-grid, renewable-to-vehicle, and renewable-to-grid discussed. The three power electronic converters and their uses are discussed, and their controllers are also designed to maintain the energy balance and stability in all cases. The battery characteristics indicate the operating mode. The work primarily focuses on the converter’s Triple Port Integrated Topology (TPIT) power flow and voltage control. Here, three power converters integrate the TPIT with three systems-the electric grid, renewable energy, and electric vehicles-into one system. The source battery and solar photovoltaic (PV) array cells are integrated using unidirectional and bidirectional DC-DC converters. The future scope of the work is to investigate the potential of adding additional ports for integrating other energy resources, such as hydrogen fuel cells or additional renewable sources, to create a more versatile and robust energy management system for EV charging stations.

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Optimal expansion planning of a self-healing distribution system considering resiliency investment alternatives

This paper proposes a three-stage optimization framework for the expansion planning of a self-healing distribution system that determines the optimal characteristics of distributed generation, energy storage systems, electric vehicle charging stations, and sectionalizing switches for the planning horizon. The main contribution of this model is that the proposed model considers the resilient investment alternatives in the expansion planning exercise to reduce the system's vulnerability against external shocks. The mobile energy storage system commitment in contingent conditions is another contribution of this paper. In the first stage, the optimal location, capacity, and time of installation of the electricity facilities are calculated. Then, the optimal allocation of sectionalizing switches is performed in the second stage. The third stage consists of three levels. In the first and second levels, the optimal normal and contingent operational scheduling are determined, respectively. The system is sectionalized into multi-microgrid systems in contingent conditions. Finally, the resilient investment alternatives for the designed system are evaluated. The proposed model utilizes a self-healing index and resilient expansion planning index to assess the impacts of resilient investment alternatives on the operational scheduling conditions. The proposed model was evaluated using the IEEE 123-bus system. The proposed method reduced the estimated average value of the worst-case energy not supplied by 50.52 % for the 5th year of the planning horizon concerning the no-resiliency investment case. Further, the proposed resilience investment method increased the self-healing index by about 9.32 % concerning the no-resiliency investment case.

Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation

As electric vehicles (EVs) are cross-domain entities with dual attributes related to mobile loads and transportation, their travel modes and charging behaviors are stochastic and uncertain in time and space. Large-scale uncoordinated EV charging may cause problems such as the congestion of charging stations (CSs) and overloading of distribution networks (DNs). To address these challenges and the current research gap, and to optimize the spatial-temporal distribution of EV charging loads, a vehicle-road-network collaborative operation framework is constructed in this paper, and a two-stage spatial-temporal scheduling method for EV charging is proposed. In the first stage, real-time traffic and CSs information are introduced to improve Dijkstra's algorithm, and the value preferences of different types of users are considered to further improve the objective function of the algorithm as well. Thus, a dynamic personalized charging navigation model based on improved Dijkstra's algorithm is proposed to provide real-time guidance allowing users to choose their travel paths and CSs. In the second stage, we consider the charging demands of EV users and the operation status of the DN, and an orderly charging model is established to minimize the peak/valley load difference of the DN. Through comprehensive comparisons of experimental results, the variance in the average utilization rates of CSs and the peak valley difference for the DN are reduced by 80.77 % and 16.91 %, respectively. In addition, the time and economic costs of users can be reduced by 9 min and 7.9 RMB, respectively, thereby reducing travel and toll costs and increasing their willingness to participate in scheduling. The simulation experiment proves that the proposed method achieves multi-agent collaborative optimization of EV users, CSs, and DN.

Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Solar Powered Electric Vehicle Charging Station With Integrated Battery Storage System

ABSTRACTThe shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self‐sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service

The demand for public fast charging stations is increasing with the number of electric vehicles on roads. The charging queues and waiting times get longer, especially during the winter season and on holidays. Priority based service at charging stations can provide shorter delay times to vehicles willing to pay more and lower charging prices for vehicles accepting to wait more. Existing studies use classical feedback control and simulation based control methods to maintain the ratio of high and low priority vehicles’ delay times at the station’s target level. Reinforcement learning has been used successfully for real time control in environments with uncertainties. This study proposes a deep Q-Learning based real time resource allocation model for priority service in fast charging stations (DRL-EXP). Results show that the deep learning approach enables DRL-EXP to provide a more stable and faster response than the existing models. DRL-EXP is also applicable to other priority based service systems that act under uncertainties and require real time control.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Design and evaluation of a standalone electric vehicles charging station for a university campus in Argentina

The increasing popularity of electric vehicles in recent years has led to a growing demand for charging stations. In this context, universities are an ideal setting for their installation, as they have a large number of students, professors, and staff who could benefit from them and, at the same time, it serves as teaching material to raise awareness in the use of renewable energy. This work presents the design and proposal of an electric vehicle charging station for the campus of the Universidad Nacional de Rafaela (UNRaf). The station will be located in an area of the campus where the construction of more buildings and sport facilities is planned. This area will not be connected to the electrical grid and instead, will have an energy storage system to guarantee supply. The station will have the capacity to simultaneously charge 4 bicycles and 2 light electric vehicles, with an average energy demand of 0.786 kWh per hour. Homer Pro software was used for the calculations. The most economically viable option was a 100% renewable solution powered only by solar energy. It is expected to consist of a 15-kW solar system that will produce 22,922 kWh/year and a bank of 30 batteries of 3 kWh plus a single battery of 1 kWh. The installation of the electric vehicle charging station on the UNRaf campus will contribute to promoting the adoption of sustainable transportation, which will help reduce greenhouse gas emissions without using the public power grid.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Systematic site selection solar-powered electric vehicle charging stations: A novel approach to sustainable transportation

This research proposes a new approach to increase the utilization of electric vehicles (EVs) by establishing solar-powered charging stations. Using ArcGIS 10 8.2 software, the optimal locations for the construction of these stations were identified and all technical, economic, environmental, and geological aspects were studied more carefully. Diverging from prior research, the initiative of this research leverages public spaces such as gas stations and eco-lodges to reduce operational costs and enhance station sustainability. This method is globally applicable and sets a precedent for CO2 emission reduction within the transportation sector. Comparative and evaluative analyses of the solar electricity charging infrastructures that support the EVs with regard to the technical and functional parameters are performed. The systematic and innovative concept that has been put forward enables us to identify the places with the highest prospects regarding the construction of Solar-Powered Charging Stations for EVs across the world. As for the case of using this technique in the selected region, it proved that 18 % of the area of Qeshm Island is suitable for constructing solar-based EV-charged stations, and the demand the island will supply by 2040. In the studied region, it was determined that the type of vehicles has a significantly greater impact on energy consumption than the distance traveled by the vehicles. By opting for fuel-efficient vehicles, it is possible to supply approximately 74.96 % of the required energy through solar energy, and these vehicles can even be used for public services such as taxis that cover long distances. The findings indicate that by 2040, the area could transition 11 % of its vehicles to EVs, potentially decreasing CO2 emission by more than 34 tons.