Electric Vehicle Grid Research Articles

How grid reinforcement costs differ by the income of electric vehicle users

The simultaneous charging of many electric vehicles in future mobility scenarios may lead to peaks and overloads threatening grid stability. The necessary infrastructure investments vary by the number and model type of vehicles driven and the residents’ charging preferences. These attributes significantly depend on socio-economic factors such as income. Using power flow simulations based on real-life driving profiles, we predict massive cost asymmetries with an investment demand up to 33-fold in higher-income compared to lower-income neighborhoods. Many grid operators may redistribute these costs through an across-the-board electricity price increase for all households. In times of rising electricity prices, these unwanted inequitable costing allocations could lead to severe challenges and energy poverty. Policymakers should consider countermeasures like dynamic electricity pricing schemes, income-based electric vehicle subsidies, or improved charging network access to ensure energy equity in future mobility scenarios. Our analysis of the impact of socio-economic factors on electric vehicle grid infrastructure and their quantification contributes to the energy equity discussion.

A Master–Slave Game Model of Electric Vehicle Participation in Electricity Markets under Multiple Incentives

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity.

Techno-economic impact analysis for renewable energy-based hydrogen storage integrated grid electric vehicle charging stations in different potential locations of Malaysia

This study investigates the techno-economic impacts analysis of renewable energy-based hybrid energy storage system integrated grid electric vehicles charging station (EVCS) in Malaysia. Focusing on three potential locations namely Pulau Pinang, Johor Bharu and Kuala Terengganu, the research aims to address the increasing electricity demand from the expanding electric vehicle (EV) infrastructure while mitigating grid instability issues caused by sudden load surges, increased electrical losses, and overload of high voltage devices that leads to power quality issues. Using the HOMER Pro platform, the study models and optimises an EVCS configuration-based hybrid energy storage system that incorporates renewable energy sources (RES) such as photovoltaic (PV), wind turbines (WT), lithium-ion (Li-ion) batteries, hydrogen (H2) tank, fuel cell (FC) and electrolysers considering various geographical and meteorological conditions. The hybrid energy storage configuration offers a long-term energy storage solution, surpassing current batteries' capabilities while providing a stable electricity supply for a sustainable EVCS system. The results demonstrated favourable outcomes, with the total Net Present Cost (NPC) ranging from $1.4 million to $3.4 million across all locations, and the Cost of Energy (COE) ranging from $0.03/kWh to $0.16/kWh. These findings suggest that the optimization methodology is adaptable for implementation in diverse locations with different meteorological conditions. This innovation is beneficial for developing renewable-based EVCS infrastructures, which can support economic growth in Malaysia. In addition, the study emphasizes the necessity of advanced control algorithms to manage power quality issues during peak demand and suggests future field trials to validate the system's real-world performance. Additionally, the optimized EVCS-based hybrid energy storage system contributes to reducing carbon dioxide (CO2) emissions, promoting a cleaner environment and an eco-friendly energy ecosystem.

Simulation analysis of electric vehicle charging station using hybrid sources

This paper described simulation analysis of electric vehicle (EV) charging station using hybrid sources. This paper highlights electric vehicle charging station with photovoltaic panels, batteries, and diesel generator. This study employs a solar, battery, diesel generator set, and grid electric vehicle charging station to provide continuous charging in is landed, grid-linked, and Diesel generator (DG) set connected modes. By utilizing a solar and battery, the charging of battery in electric vehicle application is the primary objective If the storage battery is poor and there is no solar generation, The mode of charging automatically shifted to grid or diesel generator set. Furthermore, the charging station manages the generator voltage and frequency without the need of a mechanical speed governor in conjunction with the storage battery. The demand is nonlinear at unity power factor (UPF). For continuous charging, power used from the grid or the DG set and it is synchronized to the grid/generator voltage by the point of common coupling voltage. To boost charging station operating efficiency, the charging station also performs all power transfer from car to grid, vehicle to house, and vehicle to vehicle.

Impact of Communication System Characteristics on Electric Vehicle Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets.

Optimizing electric vehicle charging schedules and energy management in smart grids using an integrated GA-GRU-RL approach

Introduction: Smart grid technology is a crucial direction for the future development of power systems, with electric vehicles, especially new energy vehicles, serving as important carriers for smart grids. However, the main challenge faced by smart grids is the efficient scheduling of electric vehicle charging and effective energy management within the grid.Methods: To address this issue, we propose a novel approach for intelligent grid electric vehicle charging scheduling and energy management, integrating three powerful technologies: Genetic Algorithm (GA), Gated Recurrent Unit (GRU) neural network, and Reinforcement Learning (RL) algorithm. This integrated approach enables global search, sequence prediction, and intelligent decision-making to optimize electric vehicle charging scheduling and energy management. Firstly, the Genetic Algorithm optimizes electric vehicle charging demands while minimizing peak grid loads. Secondly, the GRU model accurately predicts electric vehicle charging demands and grid load conditions, facilitating the optimization of electric vehicle charging schedules. Lastly, the Reinforcement Learning algorithm focuses on energy management, aiming to minimize grid energy costs while meeting electric vehicle charging demands.Results and discussion: Experimental results demonstrate that the method achieves prediction accuracy and recall rates of 97.56% and 95.17%, respectively, with parameters (M) and triggers (G) at 210.04 M and 115.65G, significantly outperforming traditional models. The approach significantly reduces peak grid loads and energy costs while ensuring the fulfilment of electric vehicle charging demands and promoting the adoption of green energy in smart city environments.

Spatial and Temporal Distribution Characteristics of Electric Vehicle–Grid Interaction Scheduling Using Cluster Optimization

Spatial and Temporal Distribution Characteristics of Electric Vehicle–Grid Interaction Scheduling Using Cluster Optimization

Construction of an economic environment scheduling system based on the multi-objective fuzzy optimization model

Abstract To integrate the combination of electric vehicles and wind power of the transmission system, a wind farm environment multi-objective transmission model considering plug-in electric vehicles was established. The fuzzy optimal scheduling model for the environment and economy target of wind farm power system considering grid interconnection of electric vehicles designed by the author has the following characteristics: A fuzzy multi-objective dynamic scheduling model for the design of wind farm power system considering grid connection of plug-in electric vehicle is established, and the original 10 systems are taken as an example for power system design. The results show that the better model shows that the electric vehicle grid connection can greatly reduce the disadvantage of wind power stochastic uncertainties of system scheduling. The model can be used under different conditions further analyzing the rationality of the model determined by the author. An improved particle swarm optimization algorithm is used to solve this problem. The model takes the environmental and economic benefits into account, which improves the efficiency of the model.

A Comprehensive Review of the Bidirectional Converter Topologies for the Vehicle-to-Grid System

Over the past decade, there has been a great interest in the changeover from cars powered by gasoline to electric vehicles, both within the automotive industry and among customers. The electric vehicle–grid (V2G) technology is a noteworthy innovation that enables the battery of an electric vehicle during idling conditions or parked can function as an energy source that can store or release energy whenever required. This results in energy exchange between the grid and EV batteries. This article reviews various bidirectional converter topologies used in the V2G system. Additionally, it can reduce the cost of charging for electric utilities, thus increasing profits for EV owners. Normally electric grid and the battery of an electric vehicle can be connected through power electronic converters, especially a bidirectional converter, which allows power to flow in both directions. The majority of research work is carried out over the converters for V2G applications and concerns utilizing two conversion stages, such as the AC-DC conversion stage used for correcting the power factor and the DC-DC conversion stage for matching the terminal voltage. Furthermore, a bidirectional conversion can be made for an active power transfer between grid–vehicle (G2V) and V2G effectively. This review explores and examines several topologies of bidirectional converters which make it possible for active power flow between the grid and the vehicle and vice versa. Moreover, different types of charging and discharging systems, such as integrated/non-integrated and on/off board, etc., which have been used for electric vehicle applications, are also discussed. A comparison study is carried out based on several other factors that have been suggested. The utilization of semiconductors in power converters and non-conventional resources in charging and discharging applications are the two improving technologies for electric vehicles.

Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Utilization of Electric Vehicle Grid Integration System for Power Grid Ancillary Services

Electric vehicle grid integration (EVGI) is one of the most important parts of transportation electrification. However, large-scale EV charging/discharging can have an adverse effect on the distribution grid, due to a large amount of load being drawn from or fed back to the power grid. Additionally, the power electronics used in the grid interaction may impose additional complications, such as voltage and frequency deviation, harmonic distortion, etc. With proper control scheme designs for the grid-connected inverters, such complications can be mitigated, and several grid ancillary services, such as voltage and frequency support, reactive power support, and harmonic mitigation, can be facilitated from large-scale EVGI. In this study, a large-scale EVGI system is developed where the vector control implementation of a grid-connected inverter in the d-q reference frame is presented for providing different grid ancillary services using the EVGI system. The EVGI system is operated in different control modes to ensure multiple ancillary services of the power grid. The study is supported by the electromagnetic transient simulation performed in Matlab/Simulink of a large-scale EVGI system. The simulation shows that with the proper control mechanism of grid-connected inverters, EVGI can be used to provide several useful grid ancillary services.

Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation

Increased electric vehicles (EVs) penetration into the distribution network burdens the system during the charging process. But the same turns out to be advantageous if the EVs are used to support the utility based on the requirement. In this work, an attempt is made to efficiently schedule both the grid-to-vehicle and vehicle-to-grid operational modes of EVs with the objective of reducing power loss in the distribution system in presence of distributed generation (DG). The EVs are modeled by considering the dominating factors, such as EV State of Charge (SoC), trip conditions, EV battery capacity, charging/discharging levels. An improvement to the existing grasshopper optimization technique is also suggested in this article, which enhances the exploring capability of the metaheuristic algorithm. The presented methodology is a combination of smart charging method, voltage stability index, and enhanced grasshopper optimization algorithm (EGOA), which decides the size of DGs to be placed in the system. The proposed hybrid approach tries to schedule both the EVs and DGs so as to achieve reduced power loss and improved voltage profile. In order to test the robustness of the proposed strategy, various analyses considering uncertainty in EVs and load conditions are performed.

Techno Economic Analysis of Electric Vehicle Grid Integration Aimed to Provide Network Flexibility Services in Italian Regulatory Framework

The recent forecasts regarding the penetration of electric vehicles (EVs) in the transport market and their impact on national electricity distribution grids has presented new challenges in the fields of both of application and research. In this context, vehicle-to-grid (V2G) technology presents itself as an extremely valid solution in terms of application of the “demand side flexibility” paradigm. In this context, the aim of the paper is to analyze from a technical and economical point of view the use of EVs as new flexibility resources to provide network flexibility services in an Italian framework. Within this scope, a methodology for evaluating the flexibility service that a single EV or an EV fleet can offer, and therefore for estimating the EV storage system charge and discharge profile and determining its economic benefit, is proposed. Some numerical results and observations are reported to highlight possible incentive mechanisms for motivating EV end-users to offer flexibility services.

A PI + Sliding-Mode Controller Based on the Discontinuous Conduction Mode for an Unidirectional Buck–Boost Converter with Electric Vehicle Applications

This paper solves the buck–boost converter operation problem in the discontinuous conduction mode and the feeding a DC bus of a combined battery/solar-powered electric vehicle grid. Since the sun’s radiation has a very important effect on the performance of photovoltaic solar modules due to its continuous variation, the main task of the system under study is the regulation of the output voltage from an MPPT system located at the output of the panels in order to obtain a DC bus voltage that is fixed to 24 V. This is ensured via a double-loop scheme, where the current inner loop relies on sliding-mode control; meanwhile, the outer voltage loop considers a proportional–integral action. Additionally, the current loop implements an adaptive hysteresis logic in order to operate at a fixed frequency. The closed-loop system’s performance is checked via numerical results with respect to step changes in the load, input voltage, and output voltage reference variations.

An optimized energy management system for vehicle to vehicle power transfer using micro grid charging station integrated Gridable Electric Vehicles

World nations are promoting Gridable Electric Vehicles (GEVs’) in the context of increasing environmental pollution. GEVs’ include Plug-in Hybrid Electric vehicles (PHEVs’) and Battery Electric vehicles (EVs). The increased penetration of GEVs’ into the utility grid for charging, can lead to grid instability and regulation issues. The charging scenarios would be un-coordinated in countries like India, resulting in unstable load demand on the grid. The solution to this is the adoption of an aggregator regulated Micro Grid charging station (MGCS). The MGCS would facilitate energy flow to acceptor GEV clients from donor GEV clients through Vehicle to Vehicle (V2V) power transfer, thereby reducing the load on the utility grid. The MGCS would be supported by a photovoltaic system. This paper proposes the utilization of GEVs’ in supporting the MGCS through the regulation of an on-board Intelligent Energy Management System (IEMS). The IEMS performs communication and price negotiation with the MGCS aggregator; decides the energy level that the donor GEV clients can provide considering its future journey and criticality; and regulate the on board converter to facilitate slow, regular and fast charging of the acceptor GEV clients.

Distorted Stability Space and Instability Triggering Mechanism of EV Aggregation Delays in the Secondary Frequency Regulation of Electrical Grid-Electric Vehicle System

Aggregated electric vehicles (EVs) are emerging as promising resources for the frequency regulation of the electrical grid, but the aggregation dynamics caused by various aggregation delays make unknown threats against the frequency stability. To address this stability uncertainty, a general aggregation-delay model is established for the electrical grid-electric vehicle system, which integrates heterogeneous-delay effect and reveals the sequential impact process of aggregation delays during the frequency regulation. To precisely visualize the aggregation delay influence, the maximum aggregation delay interval is discretized by the Chebyshev nodes, which formulates a delay distorted matrix reducing the infinite operator dimension. With this matrix, the damping ratio and the sensitivity indexes are then extracted constructing a complete workflow to characterize the asymptotic stability for EV aggregation delays. By the proposed approach, the distorted stability space and uniform target eigenvalue are accurately revealed in a typical electrical grid-electric vehicle system. It is also demonstrated that the aggregation delays infect the frequency stability through three unstable modes in a low-frequency oscillation form.

Electric Vehicle–Grid Integration with Voltage Regulation in Radial Distribution Networks

In this paper, a vehicle–grid integration (VGI) control strategy for radial power distribution networks is presented. The control schemes are designed at both microgrid level and distribution level. At the VGI microgrid level, the available power capacity for electric vehicle (EV) charging is optimally allocated for charging electric vehicles to meet charging requirements. At the distribution grid level, a distributed voltage compensation algorithm is designed to recover voltage violation when it happens at a distribution node. The voltage compensation is achieved through a negotiation between the grid-level agent and VGI microgrid agents using the alternating direction method of multipliers. In each negotiation round, individual agents pursue their own objectives. The computation can be carried out in parallel for each agent. The presented VGI control schemes are simulated and verified in a modified IEEE 37 bus distribution system. The simulation results are presented to show the effectiveness of the VGI control algorithms and the effect of algorithm parameters on the convergence of agent negotiation.

Self-Sustainable off grid Electric Vehicle EV Charging Station with Integration of Renewable Sources

Electric Vehicles (EV) are the world’s future transport systems. With the rise in pollutions and its effects on the environment, there has been a large scale movetowards electrical vehicles. But the plug point availability for charging is the serious problem faced by the mostof Electric Vehicle consumers. Therefore, there is a definite need to move from the GRID based/connected charging stations to standalone off-grid stations for charging the Electric Vehicles. The objective of this paper is to arrive at the best configuration or mix of the renewable resources and energy storage systems along with conventional Diesel Generator set which together works in offgrid for Electric Vehicle charging. As aconclusion, by utilizing self-sustainable off-grid power generation technology, the availability of EV charging stations in remote localities at affordable price can be made and mainly it reduces burden on the existing electrical infrastructure.

Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: The case of South Korea

With the world seeking ways to cope with climate change, the interest in and demand for electric vehicles are increasing as part of the efforts to resolve the issue of fine dust, especially in South Korea. The Korean government has consistently announced plans to promote electric vehicles as a means of transportation by providing benefits such as subsidies for electric vehicle purchase and expansion of charging infrastructure. Meanwhile, as electric vehicles continue to grow in number, the energy industry has become attentive to its role as a resource for power generation through vehicle to grid technology. This study analyzes electric vehicle consumer preferences using the discrete choice experiment (DCE) and found that there exists a clear nested structure in Korean consumers’ choice of vehicle. The study also estimates the amount of vehicle to grid electricity supply in the power market and calculates not only national but also individual economic benefit of electric vehicle owners participating in vehicle to grid services based on the estimated amount of electricity supplied. The results of scenario analysis indicate that the estimated electric vehicle supply in Korea will be about 560,000 units cumulatively and that the vehicle to grid electric vehicle power supply scale will reach 1.81 GW by 2030. The estimation shows that the economic benefit of vehicle to grid at the national power market level is 50.9 billion KRW per year, while the economic benefit at an individual level (per vehicle) is 104,151 KRW.

Smart EV Charging: A Global Review of Promising Practices

The electrification of transport in Europe is in the early stages of a market transformation that has the potential to significantly cut emissions in both the transportation and energy sectors, while generating wider benefits for society. The research underpinning this study finds that the greatest value from integrating electric vehicles (EVs) into the power grid can be generated by charging them when and where it is most beneficial for the power system, while ensuring consumers’ mobility needs are met at an affordable cost. An emerging body of research on electric vehicle grid integration focuses on modeling the cost of integration under various scenarios, but few studies look at the existing promising practices that are based on policy tools in use today. The authors of this study conducted a qualitative review of policies for EV grid integration in the EU and U.S. markets. We found that, in order to unlock the environmental and economic opportunities associated with market uptake, three policy strategies are most effective: cost-reflective pricing, intelligent technology, and integrated infrastructure planning. The study also explores the implications of these practices for policymakers and regulators in the EU (A short version of this paper was presented at the 32nd Electric Vehicles Symposium in Lyon, France, in May 2019).